Situation Assessment at Intersections for Driver Assistance and Automated Vehicle Control

von der Fakult¨at fur¨ Naturwissenschaften der Technischen Universit¨at Chemnitz genehmigte Dissertation zur Erlangung des akademischen Grades

doctor rerum naturalium

(Dr. rer. nat.)

vorgelegt von Thomas Streubel, M.Sc. geboren am 6. Februar 1985 in Schlema eingereicht am 24.11.2015

Gutachter: Prof. Dr. Karl Heinz Hoﬀmann Prof. Dr. Josef Krems

Tag der Verteidigung: 20. Januar 2016 2 Bibliographische Beschreibung

Streubel, Thomas Situation Assessment at Intersections for Driver Assistance and Automated Vehicle Control Dissertation (in englischer Sprache) Technische Universit¨at Chemnitz, Fakult¨at fur¨ Naturwissenschaften, 2015 120 Seiten mit 44 Abbildungen und 63 Literaturzitaten

Referat

Die Entwicklung von Fahrerassistenz und automatisiertem Fahren ist in vollem Gan- ge und entwickelt sich zunehmend in Richtung urbanen Verkehrsraum. Hier stellen besonders komplexe Verkehrssituationen sowohl fur¨ den Fahrer als auch fur¨ As- sistenzsysteme eine Herausforderung dar. Zur Bewältigung dieser Situationen sind neue Systemansätze notwendig, die eine Situationsanalyse und -bewertung beinhal- ten. Dieser Prozess der Situationseinschätzung ist der Schlussel¨ zum Erkennen von kritischen Situationen und daraus abgeleiteten Warnungs- und Eingriffsstrategien. Diese Arbeit stellt einen Systemansatz vor, welcher den Prozess der Situationsein- schätzung abbildet mit einem Fokus auf die Prädiktion der Fahrerintention. Das Systemdesign basiert dabei auf dem Situation Awareness Model von Endsley. Der Prädiktionsalgorithmus ist mit Hilfe von Hidden Markov Modellen umgesetzt. Zur Bestimmung der Modellparameter wurde eine existierende Datenbasis genutzt und zur Bestimmung von relevanten Variablen fur¨ die Prädiktion der Fahrtrichtung wäh- rend der Kreuzungsannäherung analysiert. Dabei wurden Daten zur Fahrdynamik ausgewählt anstelle von Fahrereingaben um die Prädiktion später auf externe Fahr- zeuge mittels Sensorinformationen zu erweitern. Es wurden hohe Prädiktionsraten bei zeitlichen Abständen von mehreren Sekunden bis zum Kreuzungseintritt erzielt. Die Prädiktion wurde in das System zur Situationseinschätzung integriert. Weiterhin beinhaltet das System eine statische Kreuzungsmodellierung. Dabei werden digita- le Kartendaten genutzt um eine Repräsentation der Kreuzung und ihrer statischen Attribute zu erzeugen und die der Kreuzungsform entsprechenden Prädiktionsmo- delle auszuwählen. Das Gesamtsystem ist als Matlab Tool mit einer Schnittstelle zum CAN Bus implementiert. Weiterhin wurde eine Fahrstudie zum naturlichen¨ Fahrverhalten durchgefuhrt¨ um mögliche Unterschiede und Gemeinsamkeiten bei der Annäherung an Kreuzungen in Abhängigkeit der Form und Regulierung zu identifizieren. Hierbei wurde die Distanz zur Kreuzung und die Geschwindigkeit bei Fahrereingaben im Bezug zur folgenden Kreuzung gemessen (Gaspedalverlassen, Bremspedalbetätigung, Blinkeraktivierung). Die Ergebnisse der Studie wurden genutzt um die Notwendigkeit verschiedener Prädiktionsmodelle in Abhängigkeit von Form der Kreuzung zu bestimmen. Das System läuft in Echtzeit und wurde im realen Straßenverkehr getestet.

Schlusselw¨ ¨orter: Situationsanalyse, Situationsbewertung an Kreuzungen, Kreu- zungsassistenz, Fahrverhaltenanalyse, Fahrerintentionserkennung, Fahrtrichtungs- pr¨adiktion, Realfahrstudie, Verkehrskreuzungen

3 Danksagung

Ich danke Prof. Dr. Karl Heinz Hoffmann fur¨ seine Rolle als Doktorvater und als treuer Begleiter meines wissenschaftlichen Werdegangs. Unsere konstruktiven und offenen Diskussionen haben mir stets neue Perspektiven eröffnet und mich motiviert eigene und andere Ergebnisse kritisch zu hinterfragen. Des Weiteren möchte ich Prof. Dr. Josef Krems fur¨ seine Bereitschaft danken diese Arbeit begutachtet zu haben. Mein Dank gilt auch den Mitarbeitern der Abteilungen Advanced Technology und Active Safety Technology der Adam Opel AG, die mich auf dem Weg zu dieser Arbeit begleitet haben. Ich danke allen Kollegen, insbesondere Stefan Berger, Bernd Buchs,¨ Frank Bonarens, Gerald Schmidt, Marco Moebus, Uwe Hahne, Christian Jerusalem und Christoph Schmidt fur¨ ihre Unterstutzung¨ und die motivierenden Gespräche. Weiterhin gilt mein Dank meinen Doktorandenkol- legen Hagen Stubing,¨ Jonas Firl, Rami Zarife, Lena Rittger, Robert Murmann, Falko Kuster,¨ Jens Heine, Carsten Buttner,¨ Tobias Ruckelt,¨ Jens Ferdinand, Tir- za Jung, Bernhard Wandtner und all den anderen die noch auf dem Weg sind - ihr packt das! Die Arbeit mit euch hat mir sehr viel Spaß gemacht. In den gemeinsamen Gesprächen erhielt ich immer wieder Impulse und konnte mich konstruktiv Austauschen. Besonders die Bereitschaft einander zu unterstutzen¨ u.a. bei Probandenversuchen war eine große Hilfe und nicht selbstverständlich. Hier sei besonders Lena fur¨ die gute Zusammenarbeit und die Erkenntnisse in der Verkehrspsychologie sowie Falko fur¨ den intensiven Austausch bei technischen und individuellen Belangen gedankt. Neben der gemeinsamen Arbeit sind auch einige Freundschaften entstanden, die meine Doktorandenzeit bereichert haben und hoffentlich auch bei räumlicher Trennung die Zukunft uberdauern.¨ Besonders unsere Doktorandenstammtische waren kleine Highlights, die mir viel Freude und Kraft gegeben haben. Auch den ehemaligen Studenten Dennis Abel, Igor Achieser, Hussam Al Hussein und Sascha Gröger, deren Betreuung ich ubernehmen¨ durfte, sei herzlich fur¨ ihre gute Arbeit gedankt. Weiterhin möchte ich der Arbeitsgruppe am Lehrstuhl von Prof. Hoffmann danken, insbesondere Janett Prehl, Kim Schmidt und Frank Boldt fur¨ den wissenschaftlichen Austausch und die gemeinsame Lehre. Besonders die gegenseitige Unterstutzung¨ beim Korrekturlesen von Papern auch außerhalb der ublichen¨ Ar- beitszeiten war sehr hilfreich. Ein ganz besonderes Dankeschön geht an meine Familie und Freunde, die mich in meiner Arbeit unterstutzt¨ haben und mich stets ermutigten meine Ziele voller Tatendrang zu verfolgen und schließlich mit dieser Arbeit meine akademische Ausbildung zu krönen. Hierfur¨ danke ich besonders meinen Eltern, meiner Oma und meinem Bruder Frank mit Familie, sowie meinem Freund Steffen besonders fur¨ die stets unkomplizierte Unterbringung in Chemnitz, Jutta fur¨ den Zuspruch diese Arbeit anzugehen und Sandra fur¨ den Glauben an meine Fähigkeiten.

Special thanks go to my American brother Kyle and a very special friend Emily for the support, when I struggled with the English language.

4 Abstract

The development of driver assistance and automated vehicle control is in process and finds its way more and more into urban traffic environments. Here, the complexity of traffic situations is highly challenging and requires system approaches to com- prehend such situations. The key element is the process of situation assessment to identify critical situations in advance and derive adequate warning and intervention strategies. This thesis introduces a system approach to establish a situation assessment process with the focus on the prediction of the driver intention. The system design is based on the Situation Awareness model by Endsley. Further, a prediction algorithm is created using Hidden Markov Models. To define the parameters of the models, an existing database is used and previously analyzed to identify reasonable variables that indicate an intended driving direction while approaching the intersection. Here, vehicle dynamics are used instead of driver inputs to enable a further extension of the prediction, i.e. to predict the driving intention of other vehicles detected by sensors. High prediction rates at temporal distances of several seconds before entering the intersection are accomplished. The prediction is integrated in a system for situation assessment including an intersection model. A Matlab tool is created with an interface to the vehicle CAN bus and the intersection modeling which uses digital map data to establish a representation of the intersection. To identify differences and similarities in the process of approaching an intersection dependent on the intersection shape and regulation, a naturalistic driving study is conducted. Here, the distance to the intersection and velocity is observed on driver inputs related to the upcoming intersection (leaving the gas pedal, pushing the brake, using the turn signal). The findings are used to determine separate prediction models dependent on shape and regulation of the upcoming intersection. The system runs in real-time and is tested in a real traffic environment.

Keywords: situation assessment, Advanced Driver Assistance Systems for intersections, driver intention estimation, prediction of driving direction, driving behavior analysis, naturalistic driving study, traﬃc intersections

5 6 Contents

List of Figures9

Acronyms 11

1 Introduction 13 1.1 Motivation ...... 18 1.2 Outline ...... 22

2 Fundamentals 23 2.1 Traﬃc Intersections ...... 23 2.2 Situation Assessment ...... 25 2.3 Prediction of Driver Intention ...... 28 2.3.1 Methods Overview ...... 29 2.3.2 Hidden Markov Models ...... 31 2.4 Localization ...... 34

3 Driving Behavior 39 3.1 Data Analysis ...... 39 3.1.1 Data selection and processing ...... 40 3.1.2 Results ...... 42 3.1.3 Conclusion ...... 53 3.2 Naturalistic Driving Study ...... 55 3.2.1 Background ...... 55 3.2.2 Methods ...... 56 3.2.3 Results ...... 60 3.2.4 Discussion and Conclusion ...... 71

4 Prediction Algorithm 75 4.1 Framework ...... 75 4.2 Input data ...... 79 4.3 Evaluation ...... 82 4.4 Validation ...... 90 4.5 Conclusion ...... 95

5 System Approach 97 5.1 Sensing ...... 97 5.2 Situation analysis ...... 98 5.3 Prediction ...... 99 5.3.1 Implementation ...... 100 5.3.2 Graphical User Interface (GUI) ...... 103 5.3.3 Testing and Outlook ...... 104

6 Conclusion and Outlook 107

Bibliography 109

7 8 List of Figures

1.1 World Urbanization Prospects [1]...... 13 1.2 Overview of a selection of ADAS introduced in this chapter . . . . . 15 1.3 Diﬀerent vehicle sensors to establish a surround view [9]...... 17

2.1 Distribution of intersection angles in two analyzed areas ...... 25 2.2 Situation Awareness model by Endsley and system approach . . . . 26 2.3 Example of a intersection scenario with conﬂict (left) and without conﬂict (right) dependent on the driving direction of the blue vehicle 29 2.4 Trellis structure of a HMM ...... 32 2.5 Schema of the Baum-Welch algorithm ...... 34 2.6 24-satellite constellation required for a full coverage [43]...... 36

3.1 Intersection at simTD test site in Friedberg, Germany [46]...... 40 3.2 Velocity for approaching the intersection on the main road ...... 43 3.3 Velocity for approaching the intersection from the side roads . . . . 44 3.4 Velocity on the main road according to the direction of travel . . . . 45 3.5 Average acceleration for approaching the intersection on the main road 46 3.6 Average acceleration for approaching the intersection from the side roads...... 47 3.7 Frequency of turn signal usage dependent on DTI for main road sequences ...... 48 3.8 Cumulative temporal distribution of turn signal activation on main road ...... 49 3.9 Cumulative temporal distribution of turn signal activation on side roads 50 3.10 Frequency of shifting dependent on DTI for side road sequences . . . 51 3.11 Probability density of the DTI when shifting on the main road . . . 51 3.12 Average yaw rate for left and right turning sequences on the main road 53 3.13 Test vehicle with GPS antenna on the roof ...... 57 3.14 OSM map [17] including relevant intersections (indices and driving direction) ...... 58 3.15 DTI on driver inputs at X-intersections according to direction . . . . 62 3.16 Velocity on driver inputs at X-intersections according to direction . . 63 3.17 DTI on driver inputs turning left at intersections of different shape . 64 3.18 Velocity on driver inputs turning left at intersections of different shape 65 3.19 DTI on driver inputs turning right at intersections of different shape 66 3.20 Velocity on driver inputs turning right at intersections of different shape 67 3.21 DTI on driver inputs going straight at intersections with different shape 68 3.22 Velocity on driver inputs going straight at intersections of different shape ...... 69 3.23 DTI on driver inputs at signposted intersections ...... 70 3.24 Velocity on driver inputs at signposted intersections ...... 71

4.1 Vehicle state vector as input of the prediction framework ...... 79 4.2 Recognition rate varying the cluster distance method ...... 85

9 List of Figures

4.3 Recognition rate varying the number of symbols ...... 86 4.4 Recognition rate varying the size of the learning dataset ...... 87 4.5 Recognition rate varying number of hidden states ...... 89 4.6 Recognition rate varying number of mixtures in GMMs ...... 90 4.7 Validation of prediction rate varying number of hidden states . . . . 93

5.1 Interface for intersection modeling (schematic) ...... 99 5.2 Activity diagram of the prediction tool ...... 101 5.3 Orientation coordinate system in the vehicle ...... 102 5.4 Prediction tool GUI ...... 104

10 Acronyms

ADAS Advanced Driver Assistance Systems ADMA Automotive Dynamic Motion Analyzer ANOVA analysis of variance CAN Controller Area Network DTI distance to intersection EU European Union FCA Forward Collision Alert GMM Gaussian Mixture Model GNSS Global Navigation Satellite System GPS Global Positioning Service HMM Hidden Markov Model LDW Lane Departure Warning OSM Open Street Map PRT Perception Reaction Time SA Situation Awareness SBSA Side Blind Spot Alert TTI time to intersection USB Universal Serial Bus V2I Vehicle-to-Infrastructure V2V Vehicle-to-Vehicle

11 12 1 Introduction

Mobility is a basic need in modern societies. In this context, road traffic plays a major role due to individuality and flexibility. Though, the increasing number of vehicles combined with ongoing urbanization (see Figure 1.1) leads to great challenges in traffic management and vehicle development. Many metropolitan areas worldwide suffer from huge traffic jams in rush hours. Also, the increasing complexity of traffic situations due to high traffic density is demanding on the drivers abilities and leads to an increase of accidents. This is not only a threat to the safety of passengers and other traffic participants, but also another obstruction that can result in a total gridlock. Here, intersections are danger areas in particular because of their function as traffic nodes.

Figure 1.1: World Urbanization Prospects [1]

The avoidance of traffic accidents is a central goal in the field of active safety in vehicle development. Here, Advanced Driver Assistance Systems (ADAS) are developed to assist the driver in uncertain situations. While a central issue is safety, there are also aspects of driving comfort and improving efficiency by reducing fuel consumption and emissions. On a low level, the assistance is accomplished by pre- senting information to the driver to positively influence the upcoming decision. This is mainly used for comfort features, e.g. the shift indication to improve efficiency and induce an ecological driving behavior. Traffic Light Assist (TLA) is another example aiming for the same goal [2]. Here, information and recommendations are provided

13 1 Introduction to the driver for how to approach the upcoming traffic light efficiently, i.e. to either avoid a full stop by reducing speed at an early stage (red to green phase change) or to induce coasting when passing on green is impossible (green to red phase change). The information is provided by the traffic light and sent to the vehicle by Vehicle- to-X (V2X) communication technology. So, the driver can be informed even before the traffic light is visible. The system also includes a safety aspect, since red light violations were significantly reduced with the assistant system.

In more critical situations, there are systems issuing warnings in an auditive, visual or haptic manner to draw attention to the situation and evoke an adequate reaction. Here, existing features are Lane Departure Warning (LDW), Side Blind Spot Alert (SBSA) and Forward Collision Alert (FCA). The ﬁrst two are for lateral assistance. While the LDW steps in when the driver is unintentionally leaving the lane, the SBSA indicates a vehicle driving in the blind spot of the driver. The FCA is a longitudinal feature that warns when a collision with a vehicle or other detected object ahead is imminent.

On the highest level of assistance, ADAS intervene or take over control of the driving task. The basic control systems are again divided into lateral and longitudinal control and are called Lane Keep Assist (LKA) and Active Cruise Control (ACC), respectively. Depending on the design of the system, the LKA is supporting the driver in staying within the lane by a steering impulse to the lane center when crossing the lane marks unintentionally (without using the turn signal). A further advancement of this system is Lane Centering Control (LCC) where a steady control keeps the vehicle in the center of the lane to avoid leaving the lane at all. The ACC is an enhancement of the known Cruise Control (CC) which keeps a desired speed determined by the driver. With the help of sensor systems in the front (usually radar or lidar) vehicles ahead are detected and the ACC reduces the velocity to keep a safety distance to a vehicle driving in the same lane up front.

For critical situations, there are features for brake control such as an Emergency Brake Assist (EBA) which increases the driver’s initiated brake up to a full braking. Autonomous Emergency Braking (AEB) systems are a further development. These initiate a braking when obstacles ahead are detected by sensor systems and a collision is imminent. Figure 1.2 gives an overview of the introduced systems categorized in safety and comfort systems. The level of assistance indicates the inﬂuence of the system between giving information up to taking control. In the near future, there will be a combination of lateral and longitudinal control towards automated driving applications [3] where the driver will steadily shift from having control to supervising the control system.

14 Advanced Driver Assistance Systems (ADAS)

Safety Systems Comfort

low

Levelassistance of Lateral Longitudinal shift indication LDW FCA SBSA (TLA) Traffic sign LKA AEB recognition LCC ACC CC

high in progress

Figure 1.2: Overview of a selection of ADAS introduced in this chapter

On one side, technological enhancements stimulate the further development of new ADAS but also governmental requirements push systems into vehicles of different types. For example, the European Commission introduced safety standards in 2009 (Regulation No 661/2009 [4]) enacting deadlines for equipping new cars and vehicle types with certain driver assistance systems. The regulation states that Lane Departure Warning Systems (LDWS) and Advanced Emergency Braking Systems (AEBS) become mandatory in new trucks and buses by November 2015. Further implementation regulations were issued for specification of the requirements of the systems (Commission Regulation No 351/2012 [5] for LDWS and No 347/2012 [6] for AEBS). The introduction of AEBS applies in the first instance only to utility vehicles (admissible total weight above 8 tons) braked by compressed air. The regulation No 347/2012 further states that in the “collision warning phase [...] the system shall provide the driver with appropriate warnings”. If there is no reaction, it will be followed by the “emergency braking phase” in which for approaching moving objects the “subject vehicle shall not impact with the moving target” and for stationary targets the “speed reduction of [the] subject vehicle shall be not less than 10 km/h”. This refers to approval level 1 while in the level 2 a speed reduction of at least 20 km/h is required. There are some critical voices requesting even higher standards that match the state of the art especially to avoid accidents with stationary objects at all [7]. Finally, all new types and later all new utility vehicles must be equipped with braking assistance systems by Nov. 2016 and Nov. 2018, respectively. So far, the regulations affect only trucks and buses, since scenarios where heavy trucks hit the tail end of a traffic

15 1 Introduction jam or buses leave the road appear occasionally on highways and lead to terrible accidents with high numbers of casualties. The development and implementation of ADAS for passenger cars is especially influenced by the New Car Assessment Programs (NCAPs). These are regional in- stitutions that perform standardized crash tests and safety assessments to provide independent and objective safety classifications of different cars for the customer. For this, the passive and active safety of a vehicle is evaluated according to a defined point system (1-5 stars). On one side, the manufactures are interested in developing their vehicles to achieve five star ratings (maximum) as an additional sales pitch. On the other side, the customer can easily compare different vehicles in regards to safety without a detailed look into the specifications of all systems. Recently, the Euro NCAP agreed on a roadmap to raise the requirements for high ratings and defined “the detailed targets and points for the years 2016 up to 2020.” [8] The focus here is clearly on active safety and accident avoidance. Cars that have passive safety features only will not be rated above 3 stars anymore. So, ADAS will most likely be further implemented in new cars. Euro NCAP especially awards crash avoidance systems but will further demand improvements in their robustness, i.e. reduction of false positive activations. The realization of active safety systems was enabled by the enhancements of environmental sensor systems for in-vehicle usage. New cars are equipped with many different sensors such as infrared for short range (parking applications), radar and lidar for mid- and long-range and cameras combined with image processing units. The latter are preferred for lateral features especially LKA and LCC, since cameras can detect lane markings. Cameras are also utilized for pedestrian detection. Radar and lidar sensors are mainly used to detect static or dynamic objects in the line of sight and for the blind spot detection. For redundancy in safety features, multiple sensors are applied and combinations of them can be used. While lidar is less vulnerable to noise and provides better lateral resolution, the disadvantage of high signal absorption in bad weather conditions prevails. Therefore, radar sensors are dominating over lidar which also reduces the price due to the high number of units produced. Additionally, vehicles can share information via Vehicle-to-Vehicle (V2V) or Vehicle-to-Infrastructure (V2I) communication links becoming aware of other vehicles or traffic situations out of sight. A digital map is also considered a sensor in the broader sense providing precise information of the road infrastructure enabling traffic sign features and lane recommendations for navigation systems. It can also provide curvature information of the road ahead. Combined with a precise localization on lane level, it is utilized for path planning especially in inner-city road infrastructure. All these sensors and information systems are providing great amounts of environmental data. This raises issues like data management and software optimization. However, merging data from different sources can establish a view of the vehicle’s surroundings (see Figure 1.3). This is called data fusion and is

16 a subject of present research. A detailed representation of the cars surrounding is required for highly automated driving.

Figure 1.3: Diﬀerent vehicle sensors to establish a surround view [9]

Technically, automated driving is operable and car manufactures are ready to in- troduce solutions for highway traffic since it is basically a combination of LCC and ACC [10]. However, the regulations and laws do not yet allow autonomous vehicles in most parts of the world. Usually, there are only special permits given for small test fleets and scientific research. While the longitudinal control can be operated automatically by ACC, the lateral control is still considered critical and must be continuously controlled by the driver. Currently, LCC is only assisting the driver while the hands have to remain on the steering wheel. Also, the driver has to be able to override the systems at all times. So far, the ECE Regulation 79 (vehicle steering) approves a continuous control of the steering by a system only to a maximum velocity of 12 km/h (10 km/h limit with 20% deviation) [11]. This was enacted recently to enable automated parking assistance features (hands-free). Besides, a system was introduced called Traffic Jam Assist (TJA) that basically controls the vehicle in stop and go traffic up to 60 km/h. However, the system is required to be disabled when the driver is inactive. The driver’s activity is monitored practically by sensors in the steering system, so at least one hand has to remain on the steering wheel. For automated control, multiple sensor systems are necessary for redundancy in the obstacle detection and actuators for steering control. The latter is usually an electronic steering or also called steer-by-wire system. Beside the hardware, algo-

17 1 Introduction rithms are necessary to process the sensor data and conclude an appropriate course of action. In this process, a situation analysis is the key aspect. Here, a human driver is still predominant over a system due to the capability of abstraction, and therefore, the ability to handle situations never experienced before. Another fact in favor of a driver is the ability to learn and the expectation of behavior of other traffic participants. For example, when a driver sees a vehicle ahead with activated turn signal to the right, he/she is expecting the vehicle to turn and hereby reduce the speed. Based on this expectation, the driver will most likely reduce the speed and keep an additional safe distance. To establish system approaches with similar functionality such as behavior prediction and situation assessment is a major focus in research. So far, systems are able to operate under a specific range of conditions or in predefined scenarios. This is sufficient for simple driving tasks in a controlled environment, such as following a vehicle ahead or passing an obstacle on one-way roads. This covers most scenarios typically found in highway traffic. The variety and complexity of situations is particularly relevant in urban traffic. Here, ADAS are just evolving and automated driving is still the object of research. The innumerable mass of scenarios are most challenging for rule based system approaches. Still, the complex scenarios are very demanding on the cognitive abilities of the driver leading to an increase in the error rate. This illustrates the high demand on assistance. Here, intersections are of especially high interest in the urban environment as a basic attribute in street networks of cities.

1.1 Motivation

The development of situation assessment is necessary to enable driver assistance especially in an urban environment. Also, it is essential for the realization of fully automated driving which will be a major step in vehicle enhancement. The idea of autonomous road traffic in cities is appealing for multiple reasons. Amongst others, automated vehicles have the potential to reduce traffic accidents and accordingly keep road fatalities to a minimum. This reduction will not only save lives, which is definitely the major drive for ADAS development, but will also prevent injuries and reduce accident related economic costs. Besides, the resulting traffic synchronization will increase the traffic flow and reduce congestions. Here, intersections are particularly fragile. The overall traffic efficiency will improve, reducing the individual fuel consumption (per vehicle) and emission accordingly. Traffic optimization is one object of research in the field of intelligent transportation systems. So, an intersection with automated vehicle handling was introduced in [12] using a Cooperative Vehicle Intersection Control (CVIC) algorithm which showed a time reduction of 33% and a reduction of fuel consumption of 44% compared to conventional intersections. Furthermore, the vehicles had almost no stop delay

18 1.1 Motivation

(reduction of 99%). Emissions are also an important issue that need to be handled concerning the threat of smog in downtown areas. As long as there are no emission free modes of transportation widely spread, the emissions especially in urban areas ought to be minimized. Autonomous vehicles could contribute to this goal. The European Commission states similar goals in their “Roadmap to a Single Euro- pean Transport Area” in 2011 [13]. “Cities suﬀer most from congestion, poor air quality and noise exposure.

Urban transport is responsible for about a quarter of CO2 emissions from transport, and 69% of road accidents occur in cities.” There is also mentioned a “60% emission reduction target” (of the emission in 2010 reached by 2050). Besides, the high number of 25.700 road deaths within the European Union (EU) in 2014 is to be reduced [14]. The goal is to halve the fatalities of 2010 by 2020 (see quote below). Looking further in the future, the EU is even more ambitious. While ADAS already reduced traffic accidents considerably, one future goal is to zero the fatalities occurring in road traffic. There are different initiatives aiming for the so called “Vision Zero” with different approaches and it is also on the agenda of the European Commission [13]. “By 2050, move close to zero fatalities in road transport. In line with this goal, the EU aims at halving road casualties by 2020.” Other goals mentioned there are the break of oil dependency and reduction of emissions. Today, accidents occur increasingly in urban areas also due to the urbanization previously illustrated in Figure 1.1. The German traffic accident statistic indicates that over 74% of the recorded 2.4 million accidents happened on urban roads in 2014 [15]. This is a raise of 2.5% compared to the year before, while the overall amount of accidents slightly decreased. Personal injuries occurred in about one of ten accidents in urban areas (11.7%). Thereby, more than a quarter of a million people were injured and 983 lost their life in urban traffic. The considerable increase of the casualties of about 5% compared to the year 2013 is alarming, since it almost reaches the level of 2011 reversing the declines of the past years. This shows certainly the necessity for further developments in safety systems for urban traffic environments. Table 1.1 gives an overview of the ratio of urban accidents and casualties compared to the overall numbers for the past years. The German traffic accident statistic also provides more detailed information cat- egorizing the accidents (see Table 1.2)[15]. In the following, the focus will be on accidents with personal injuries in urban traffic investigating their appearance related to intersections. The numbers presented are from the year 2014. The kind of accident criteria describes the entire course of events and there are ten different kinds listed in the statistics. Here, a “Collision with another vehicle which turns

19 1 Introduction

Table 1.1: Statistic of traﬃc accidents in Germany over the last years [15] 2011 2012 2013 2014 Traﬃc accidents overall (registered by police) 2,361,457 2,401,843 2,414,011 2,406,685 on urban roads 1,743,065 1,751,166 1,746,474 1,789,278 73.8% 72.9% 72.3% 74.3% with personal injuries 306,266 299,637 291,105 302,435 on urban roads 210,427 206,696 199,650 209,618 68.7% 69.0% 68.6% 69.3% Casualties overall 396,374 387,978 377,481 392,912 on urban roads 255,405 251,371 242,498 254,454 64.4% 64.8% 64.2% 64.8% Fatalities 4,009 3,600 3,339 3,377 on urban roads 1,115 1,062 977 983 27.8% 29.5% 29.3% 29.1%

into or crosses a road” appears most likely on urban roads leading to 33% of the accidents and to one third of the casualties. 216 people died in this kind of accident.

Another category is the type of accident which describes the conflict situation leading to an accident. In contrast to the kind of accident, the type is indicating the initial conflict leading to the collision instead of describing the collision itself. This is especially relevant for the accident analysis and for potential actions to resolve conflicts in hazard spots. Here, seven types of accidents are distinguished of which two

Table 1.2: Relevant categories of accidents in German accident statistics [15] with thereby thereof Accidents on urban roads injuries casualties fatalities 209,618 254,454 983 Kind of accident collision with another vehicle which 69,124 84,814 216 turns into or crosses a road 33.0% 33.3% 22,0% Type of accident accident caused by 34,146 42,146 105 turning oﬀ the road 16.3% 16.6% 10.7% accident caused by 55,194 67.333 166 turning into a road or by crossing it 26.3% 33.3% 16.9% Characterization of accident site intersection 50,425 64,717 198 24.1% 25.4% 20.1% road branch 49,434 59,314 185 23.6% 23.3% 18.8%

20 1.1 Motivation are relevant for intersections. First, there is the type “Accident caused by turning oﬀ the road” which is described in [15] as:

“The accident was caused by a conflict between a vehicle turning off and another road user approaching from the same or opposite direction (incl. pedestrians) at crossings, junctions and entries to premise or car parks. Whoever follows the priority turn of a main road is not considered as turning off.”

This type occurred in 16.3% of the accidents in urban environment with personal injuries. While the proportion of the casualties is similar, the fatality ratio is lower with 10.7%. This is also the case for the second type called “Accident caused by turning into a road or by crossing it” which is deﬁned as:

“The accident was caused by a conﬂict between a road user turning into a road or crossing it and having to give way and a vehicle having the right of way at crossings, junctions, or exits from premises and car parks.”

This type of accident has the highest appearance on urban roads with a rate of 26.3%. As mentioned before, the fatality ratio here is significantly lower with 16.9%. So, considering the types of accidents, 42.6% are related to turning on or off an urban road leading to a similar ratio of casualties. The lower fatality ratio is most likely related to the overall lower speeds in inner-city traffic but also the reduction of speed when turning. Also, the high standards in passive safety might be a reason.

In the statistics the accident site is also characterized. Here, the two relevant sites are intersections and road branches. The latter is related to junctions where one road meets a continuous road and ends there. A typical example is a T- or Y-junction. There is no indication of the traﬃc control for both types. As expected from the type of accident criteria, almost half of the accidents with injuries in urban environments happened at intersections and road branches about equally shared. Table 1.2 gives an overview of the categorized accidents and casualties that occurred on urban roads.

The statistics also include data about misbehavior of the driver causing an accident. Here, the “violation of right of way” is of interest. This was the cause of an accident with injuries in 17.6% of the recorded violations. Another typical misbehavior that occurs at intersections is “mistakes made when turning” and led in 10.1% of the cases to an accident. Considering the numbers of casualties from Table 1.2 resulting from these mistakes, a further development of ADAS for intersection scenarios has the potential to reduce these comparatively high numbers of accidents and injured persons. Here, the situation assessment plays a key role to establish these assistance features that aim to reduce the risk of accidents.

21 1 Introduction

1.2 Outline

The following chapter“Fundamentals”includes basics about traffic intersections with a short analysis of typical intersection types as well as the concept of situation assessment in the context of Situation Awareness (SA) by humans. Further, an overview of prediction methods for the driver intention is given and the used method is briefly introduced. The basic understanding of the localization with satellite systems is also covered at the end of the chapter which is required for the determination of distances relative to the intersection. Chapter3 includes an analysis of driving data at a particular intersection and a natural driving study. The first was performed to retrieve a better understanding of the approaching behavior at an intersection depending on the driving direction. This led to the identification of parameters for the prediction algorithm, which is introduced in chapter 4. The driving study was conducted to obtain more detailed information about the preparatory behavior especially about when and where the driver inputs apply. Also, typical intersection shapes were varied to adapt the prediction models to particular intersection types. The retrieved data was also used to train the prediction models and test the in-vehicle application in chapter 5. In“Prediction Algorithm”the prediction algorithm for the intended driving direction at intersections is introduced. The prediction is evaluated with different parameter setups and tested with the database analyzed in the first part of chapter 3. Further- more, different designs are tested for the link between the input data and discrete states of the prediction model. The prediction algorithm is finally integrated in a real-time system for situation assessment and implemented in a vehicle environment which is described in chapter 5 “System Approach”. The choice of models for the system are influenced by the results from the natural driving study from the second part of chapter 3. The system was also tested in a vehicle driving in a real traffic environment. The last chapter “Conclusion and Outlook” summarizes the results and gives a brief outlook.

22 2 Fundamentals

2.1 Traﬃc Intersections

Intersections are a common element of traffic infrastructure and are fundamental components in urban environments. An intersection is defined as a structure where multiple roads meet or cross. The intersection area is the space that is used by vehicles approaching from different directions. Especially, the limited space used by multiple traffic participants leads to an increase in collision risks and is therefore of interest in the research on traffic safety in urban environments. Typical shapes are three-way and four-way intersections. Three-way intersections occur for example, when a secondary road merges into a main road forming a T-junction or when a road splits in two directions forming a Y-junction. The shape does not necessarily reflect the priority of the roads. Four-way intersections are usually X-shaped. More complex intersections with more than four branches exist, but are not prevalent. Also, traffic circles could be considered as a special intersection type. Further details on the frequency of occurrence and typical intersection angles are introduced below in this section. Another characteristic is the traffic regulation at an intersection. Traffic lights are commonly used in areas with high traffic volume. A traffic light changes frequently the accessibility of the intersection area for different road users reducing or even avoiding conflicts. However, this regulation limits the flow capacity resulting in a typical bottleneck in urban traffic. An explicit static regulation is realizewed with yield or stop signs and corresponding right of way signs on the main road. In Ger- many, these traffic signs are always combined to indicate a right of way route, which is not necessarily straight. Vehicles coming from a side branch according to this route will face a yield or even stop sign. Constructs like all-way stop, commonly known in the USA, are not existing in Germany. In case of an unregulated intersection, i.e. without the presence of traffic lights or signposts, the “priority to the right” rule applies (see 8 “Vorfahrt” in StVO [16]). Drivers approaching the intersection § yield to vehicles coming from the next branch right of them. When the rare situation occurs that vehicles from each direction reach the intersection simultaneously, the drivers need to agree on who will go first. The shape and regulation of inner-city intersections and their frequency of occurrence were subject of an analysis1. For the development of route guidance systems,

1performed within the framework of an internship by Igor Achieser

23 2 Fundamentals different map databases are established with extensive road data. One of them is Open Street Map (OSM)[17]. Here, Global Positioning Service (GPS) tracks are provided by users (crowdsourcing) to establish an open source database of the road infrastructure. This database was easily accessible and therefore used to retrieve information about intersections in a certain area. A software tool was implemented in Java to search for intersections in a predefined area selected by a bounding box. This rectangular box was positioned to enclose most of a city area. The shape is categorized between three-way and four-way intersections and traffic circles. Further, the intersection angles were evaluated. For the traffic regulation, only traffic lights are included in the data. The analysis was performed for several urban areas in particular for Ruesselsheim and Chemnitz, both located in Germany. Since the bounding box was rectangular, the focus was to include the urban area of the cities. So, for Chemnitz the box included only an area of about 58 km2 around the city center. The results for Ruesselsheim were validated with the official data provided by the responsible department of the city administration. The results are shown in Table 2.1.

Ruesselsheim, Germany Chemnitz, Germany official statistics OSM analysis OSM analysis overall intersections 767 846 1,453 Regulation traffic lights 32 21 131 Shape traffic circle 3 3 2 three-way intersections 579 652 1,013 four-way intersections 180 188 433

Table 2.1: Intersections Ruesselsheim, Germany

According to the official count, there are 767 intersections on public roads in Rues- selsheim, Germany. The OSM tool identified about 10% additional junctions. This has multiple reasons. One is that roads with branches to parking lots or driveways were counted if they are not tagged correctly in the database. Besides, roads with separated lanes in each direction are treated as independent segments in the OSM data handling. This way, a big four-way intersection appears as four intersections in the data. Although, this was partially resolved by implementing a range detection around each intersection and merging those that are located within close range. Fur- ther, the database is not containing information whether a road is private or public. This is also leading to an offset displayed by the high difference in the number of three-way intersections. There is also an offset in the detection of traffic lights. Here, the reason is basically the lack of information in the database. Open Street Map is based on user information and community support. Hereby, traffic lights have to be tagged manually

24 2.2 Situation Assessment which is optional for submitting road data. However, traffic lights are rather few compared to the overall number of intersections in an urban environment. Since they are usually installed on intersections with high traffic volume, they only appear on the main routes through a city road network. Overall, the tool gives a rough overview of the typical intersection shapes. The numbers show a ratio between three-way intersections to four-way intersections of about 3:1 for Ruesselsheim and 2:1 for Chemnitz, respectively. Traffic circles are very rare in both cities. Further, the intersection angles were retrieved from the database. Hereby, the angle between each two branches of an intersection is extracted which leads to angles between 0◦ and 180◦. This results in three intersection angles for three-way intersections and six for a four-way intersection. So, the latter are slightly overrated. The distributions for both city areas are shown in Figure 2.1.

50 Ruesselsheim Chemnitz 40

Relative frequency [%] 10

0 10 30 50 70 90 110 130 150 170

Intersection angle [◦]

Figure 2.1: Distribution of intersection angles in two analyzed areas

The histogram indicates that the intersection angles are similarly distributed in both observed urban environments. Obviously, the predominant intersection angle is close to perpendicular (around 90◦) with about 46%. The nearly straight angles typical for T-junctions are also prevalent with about 30%. Conclusively, the X-intersections and T-junctions with nearly perpendicular intersection angles seem to be the predominant intersection shapes in urban environments.

2.2 Situation Assessment

The driving task requires the perception of relevant objects in a traffic situation by the driver. Further, the objects such as other vehicles, traffic signs etc. have to be identified and understood in their context. Also, presumptions of the objects are made. For example, in a situation approaching an intersection, the driver is

25 2 Fundamentals spotting a vehicle coming from the right. Now, assuming the “priority to the right rule” applies and the driver is aware of that, he/she will expect the other vehicle to enter the intersection. The driver will adapt his/her actions to yield and let the other vehicle pass the intersection first. A widely used concept to describe this process and the state of recognizing a situation was introduced by Endsley in the field of human factors [18]. Originally, it was applied to describe the process of perception, interpretation and prediction of situations for pilots operating airplanes. The concept is called Situation Awareness (SA) and is defined by Endsley as “the perception of elements in the environment within a volume of time and space, the comprehension of their meaning, and the projection of their status in the near future.” [18] So, while SA is “a state of knowledge”, situation assessment is understood as “the process of achieving, acquiring, or maintaining SA (situation awareness)”. This conception relates to the cognitive process by a human to describe the state of the environment based on the sensory perception. The SA is structured in three hierarchical levels: perception, comprehension and projection (see top of Figure 2.2). In the first step, the state and the attributes of relevant objects is perceived. The next step is the processing of the information to form an overall picture of the situation. Here, the meaning of the single elements is retrieved and their significance. Finally, anticipations are made of future states of objects, especially when identified as dynamic objects in the comprehension level.

Situation Awareness model by Endsley

Perception Comprehension Projection

Transferring to system

Sensing Situation analysis Prediction

Focus

Figure 2.2: Situation Awareness model by Endsley and system approach

The Situation Awareness model is complemented by theories of cognitive psychol- ogy in order to describe the human process obtaining Situation Awareness [19]. This relates especially to the creation of a representation of a situation and the consequential choice of action. Here, situation assessment as process to establish Situation Awareness is described as a process of understanding (Verstehensprozess). This leads to a situation model containing all relevant elements and representing

26 2.2 Situation Assessment the entire situation. This model is the basic for any action taken. However, these actions change the situation which leads to an update of the situation model. This interaction is explained in detail in [19]. The central resource for this process is the working memory. There is no consistent definition of the term situation in the driving context [20]. Three different terms are defined in [21] - traffic situation, driving situation and driver situation. Here, a traffic situation is described as objective, spatial and temporal constellation of all traffic related measures of the surrounding of traffic participants. This includes basically all relevant objects even those unperceived by the driver, for example because of line-of-sight obstructions. A driving situation is a subset of the traffic situation which is in principle perceivable by the driver. Finally, the driver situation is the actual assessed situation the driver is aware of. Further definitions for situations in the driving context are assembled in [20]. In conclusion, the situation perceived by a person is always a subjective representation of the environment. It consists inherently of an interpretation of the reality based on the sensory inputs. The human information processing filters the relevant information and constructs a reasonable image. This established representation is the foundation for selected actions. So, the action is based on the definition of the situation by an individual. This interrelation between interpretation of a situation and resulting actions is similarly described in the field of sociology as basis for the Thomas theorem [22]: If men define situations as real, they are real in their consequences. This thesis focuses on a system approach for the process of situation assessment. The transfer of the Situation Awareness model as concept for a technical system is presented in Figure 2.2 (at the bottom). The human perception is operationalized by the sensor system of a vehicle. Meanwhile, the sensing is highly advanced and covers the whole vehicle surrounding (see Figure 1.3). Furthermore, this is extended by the recently developed Vehicle-to-X (V2X) communication technology and the electronic horizon retrieved from digital maps. So, a representation of the vehicle’s environment is established way beyond the range and capacity of the human perception. In contrast, a human driver is still predominant on the comprehension level due to the abstraction abilities. Indeed, the sensors are capable of recognizing objects and identifying other vehicles or pedestrians, but it is still a great challenge to merge this to a holistic representation of the situation and especially derive reasonable intervention strategies. Existing approaches for the data fusion and environmental modeling require multiple processing units and are yet only realized for automated driving in test vehicles for research. A comprehensive logical system is required to handle every possible situation which is most challenging in urban traffic environments. Human long term memory and ability to adopt experiences to unknown situations remain unmatched. This applies

27 2 Fundamentals also to the projection (SA level 3). The extensive range of experience and its rapid application to any situation is unreproducible by a system yet. However, for dynamic objects a limited prediction could be achieved by extrapolation of the current trajectory. Further approaches for a prediction especially of the driver intention is subject of present research. So, it was a key aspect in a recent research project for urban traﬃc called UR:BAN (Urban Space: User oriented assistance systems and network management) [23]. The following section presents the state of the art in this matter and introduces diﬀerent methods for prediction driving behavior.

2.3 Prediction of Driver Intention

For situation assessment it is essential to predict the driver intention. This way, a possible conflict can be detected in advance and warning or even intervention strategies can take action. In the dynamic process of driving, an analysis of the current driving situation is just insufficient in complex scenarios. So, it is called for an estimation of the further process of a situation. Hereby, an existing realization is for example an Emergency Brake Assist (EBA). The sensors detect obstacles in the path. Further, the distance to the obstacle is determined and a time to collision (TTC) is calculated under the assumption of constant velocity. So, the path is extrapolated. Once a threshold is reached warnings are issued or further interventions are initialized. This logical and rule- based procedure is a constructive concept for implementing features applicable to simple and clear situations. However, this is harder to apply in complex scenarios, e.g. in an urban traffic environment especially at intersections. Here, multiple factors influence the situation and its progression. It is hardly feasible to consider all possible situations that may arise and provide adequate actions to react. A decisive role for the development of the situation is played by the driver intention. This is illustrated in Figure 2.3. A critical situation only arises, if the driver in the blue vehicle is intending to turn left (scenario in the left picture). In contrast, the noncritical situation is displayed in the right picture where both vehicles cross the intersection straightly. So, the driver intention is crucial for the progress of a situation and its criticality. This, in turn, determines the need of a warning or intervention. So, several research teams work on the recognition of driver intention using different methods. This leads to intelligent system approaches, which will increase the performance and acceptance of assistant systems. Further, the prediction is essential for the development towards higher automation. Vehicles that are able to take over the driving task partially or fully must be capable to estimate the movement of other vehicles. This is especially relevant in the transition phase where automated and manual operated vehicles will drive in the same environment. The system requires a prediction performance similar

28 2.3 Prediction of Driver Intention

Figure 2.3: Example of a intersection scenario with conflict (left) and without conflict (right) dependent on the driving direction of the blue vehicle to the human skill described in the process of situation assessment in the section before. In the following, various methods are introduced to achieve a prediction of driving behavior in different scenarios. The method utilized in this thesis is described in more detail after that including the mathematical structure.

2.3.1 Methods Overview

The prediction of the driving behavior is divide into an estimation of the trajectory of the vehicle or a maneuver based prediction according to the use case. The trajectory prediction is highly relevant in situations when the dynamic objects such as vehicles come close towards each other, e.g. in an intersection area or on highways passing each other. A maneuver based prediction is aiming rather for an early estimation of the further progress on a scenario level. This means determining if a driver is about to perform an overtaking maneuver on a highway or where the driver intends to go on an intersection. While the trajectory prediction is limited to a small time horizon, the prediction of a maneuver covers a larger time span. However, the latter comes with a high uncertainty where in the future the vehicle will be located exactly. A trajectory prediction can be established with several methods. The straight forward approach is to simply extrapolate the present state of the vehicle based on an underlying motion model. Here, an exact dynamic model including all physical attributes of the motion is usually undesired and lacks real-time capability. There- fore, kinematic models are utilized of which the bicycle model is the most common one. It approximates a vehicle as having only two tires and front wheel-drive. It is a simple approach still taking some dynamic characteristics of a vehicle into account. Other simple models are based on assumptions such as the Constant Turn Rate and Velocity (CTRV) and Constant Turn Rate and Acceleration (CTRA) model which are capable to depict a vehicle related lateral motion by considering yaw in the state

29 2 Fundamentals vector of the vehicle. The prediction is established by extrapolation of the motion based on the chosen model in case the present state is known. This is shown by Ammoun et al. in [24] using a bicycle model. To model the increasing uncertainty of the progression of the trajectory, Gaussian noise is used here. Other model based approaches were introduced using a mathematical driver model called Intelligent Driver Model (IDM) [25] or an elastic band approach in a potential ﬁeld [26]. The limitation of these approaches are inherent in the motion model utilized and due to the extrapolation only reasonable within a time span of 1 s.

For a long range trajectory prediction, dynamic and environmental constraints are taken into account by retrieving a dataset of realistic trajectories. This is accomplished by recording real driving data or simulating realistic driving data. The latter can be realized by using Monte Carlo Simulation [27, 28]. The dataset is clustered obtaining representative trajectories usually linked to certain maneuvers. The prediction is performed using classiﬁcation algorithms, i.e. a part of the currently driven trajectory is compared to the clustered trajectories and matched using a certain metric. So, possible criteria are the mean Euclidean distance between trajectories or a method called the Longest Common Subsequence (LCS) and Quaternion-based Ro- tationally Invariant LCS (QRLCS). The last two mentioned were used in [29] to ﬁnd a set of potential future trajectories based on weights considering road constraints. It was further applied in scenarios including an interaction with a preceding vehicle [30].

The maneuver based prediction aims to recognize a sequence of actions the driver is about to execute. Typical scenarios are lane changes on multi-lane roads and turns at intersections. The uncertainty of the driver’s intention calls for probabilistic methods. Bayesian networks are utilized to infer the driver intention in lane changing scenarios [31, 32] as well as in intersection approaches to determine the turning intention [33]. Both scenarios are covered in an exploratory approach [34]. A generic classification of driving situations using Bayesian networks is established in [20]. Another method are neuronal networks to model the driver behavior [35, 36]. In [36], a multilayer perceptron is used with situation specific learning in scenarios with traffic lights and preceding vehicles. Here, the prediction horizon is 3 s. While Bayesian and neuronal networks are an efficient method to model uncertain systems, the dynamic aspects of a driving situation are not inherently implemented.

This is resolved using dynamic Bayesian models and in particular Hidden Markov Models (HMMs). These are a capable method to model dynamic stochastic pro- cesses. There have been approaches to use HMMs for driver behavior recognition [37]. In this case, a system was introduced to predict the turning intention at a T-shaped intersection using only the steering angle. The Hidden Markov Model was modiﬁed to process continuous data. Also, traﬃc light violation behavior was estimated with this method [38]. Another approach was using Hidden Markov Models to

30 2.3 Prediction of Driver Intention determine the driver intended direction (left or right) at an intersection [39]. Again, the steering angle was used as single sensor input. Since the steering is the last action of many when turning at an intersection, better results can be accomplished involving additional vehicle information such as velocity. In [40], HMMs were used for estimation vehicle states and maneuver prediction. In this thesis, Hidden Markov Models are utilized in a similar way, but in addition the it is further investigated how the performance of the prediction can be improved by a larger learning database and what optimal model parameters are.

2.3.2 Hidden Markov Models

A Hidden Markov Model (HMM) is a stochastic model used to describe a dynamic process. It combines two Markov Chains of which one is hidden, giving the method its name. The hidden chain represents the state of the system and is not observable.

N is the number of possible states S = (S1, ..., SN ) and determines the system’s de- gree of freedom. In each time step (discrete), a hidden state qt is adopted dependent only on the previous hidden state qt 1 (ﬁrst-order Markov Chain). This restriction − is the Markov attribute which is a simpliﬁcation indicating the model character. It is also referred to as memory free process. Various applications show that this is a reasonable approximation in this driving context. Mathematically, a HMM is described by the transition matrix A, the observation matrix B and the starting distribution π. The way the system changes between hidden states in discrete time steps t is determined by the transition matrix A:

A = aij aij = P (qt = Sj qt = Si) { } ⇒ +1 | So, A is a N N matrix. The initialization of the sequence is given by the prob- × abilistic distribution of the ﬁrst state πi = P (q1 = Si). This vector is of length N and its entries sum-up to 1. So, the hidden state sequence q = (q1, ..., qT ) of length T is created.

While the states are unobservable, the observation sequence O = (O1, ..., OT ) results by emitting a certain symbol R = (R1, ..., RM ) each time step. Here, M is the number of discriminable symbols each connected with the states S by the probabilistic distribution in the observation matrix B. Thus, the probability of an observation

Rj at the time t while in state Si is determined by the observation matrix B which is of size N M. ×

B = bij bij = P (Ot = Rj qt = Si) { } ⇒ | In a HMM, the state space is always discrete, while the observation space can also be continuous. This is established by incorporating a Gaussian Mixture Model (GMM). For further references see the implementation in section 4.2.

31 2 Fundamentals

So, a discrete Hidden Markov Model λ(A, B, π) is deﬁned by the above explained parameters. Graphically, the process can be displayed in a Trellis structure as shown in Figure 2.4.

퐴 퐴 퐴 퐴

qt-1 qt qt+1 퐵 퐵 퐵

Ot-1 Ot Ot+1

Figure 2.4: Trellis structure of a HMM

There are three essential problems working with HMMs. The first is to evaluate how well the model represents a certain set of data (sequence). Another one is to decode an observation sequence to retrieve the optimal state sequences given a model. The last is to learn the model parameters with a set of training data sequences. There are algorithms for each of these problems. Two of them are used in this thesis and therefore are briefly introduced in the following. Further details are given in a tutorial by Rabiner [41]. The evaluation and the learning procedures are answering the following questions. Evaluation: What is the probability for an existing observation sequence O being created by a given HMM λ? Here, the probability P (O λ) is calculated. | Learning: What are the optimal model parameters so the probability for an existing observation sequence O is maximized? Here, the model parameters of the HMM λ(A, B, π) are determined so P (O λ) is maximal. | The algorithm solving the first problem is called forward algorithm. Although, the probability P (O λ) can be calculated with a brute force approach, this algorithm is | much more efficient due to the inductive procedure. Initially, a forward variable is defined as the probability of an observation sub-sequence of length t adopting the system state Si at the end of the sub-sequence given the HMM λ.

αt(i) := P (O O ...Ot, qt = Si λ) 1 2 | So, the initialization of the forward variables is easily retrieved from the starting distribution π and the observation probability for O1.

α (i) = P (O , q = Si λ) = πibi(O ) 1 i N 1 1 1 | 1 ≤ ≤ Though, there are N forward variables to be calculated for the initial time step. The induction formula is given by:

32 2.3 Prediction of Driver Intention

" N # X αt (i) = αt(h)ahi bi(Ot ) 1 i N +1 +1 ≤ ≤ h=1

This algorithm is utilizing the simple fact that each state can only be reached from one of N possible states in the time step before. So, there are only N T forward · variables to be calculated where T is the length of the observation sequence O. The sum over the last alphas (t = T ) is the wanted probability, since each alpha represents the probability of the observation sequence ending in one of N possible states.

" N # X P (O λ) = αT (i) 1 i N | ≤ ≤ i=1

This algorithm is superior over the brute force approach where the probability of all possible state sequences is calculated. Since there are N T sequences, the calculation time is rising exponentially the longer the observation sequence is. This is ineﬃcient and unfeasible even with decent computing capacity. However, the computing cost for the forward algorithm is in the order of N 2T .

The second problem is to retrieve optimal model parameters with a given set of data. There is no known analytical algorithm for this problem. A common numerical solu- tion gives the Baum-Welch algorithm, which is a type of Expectation-Maximization

(EM) algorithm (see Figure 2.5). First, initial values λ0 are determined for the HMM parameters. This is established either by random choice or manually in case there is a priori knowledge of the model parameters available. Given the initial parameters, the expected frequencies of occurrence of system states and the frequencies of transitions between them are calculated taking into consideration the observation sequence. This is called the expectation step. Further, in the maximization step the model parameters are adapted according to the previously retrieved frequencies using the principle of counting events. For example, in case the ﬁrst state S1 was expected to be reached 100 times and there were 20 expected transitions to the second state S2 the new parameter in the transition matrix A for a12 is set to 0.2. The new model parameters λ¯ are used again to calculate new frequencies of states and translations in another iteration. Baum and Welch have proven that the probability that the given observation sequence was created by the new model is the same or higher than with the previous parameters. Furthermore, this algorithm converges quickly to a local optimum. However, the global optimum remains uncertain while the parameter space is complex with multiple local optima. Therefore, it is recommended to run the algorithm several times with diﬀerent starting conditions varying the initial values of the HMM parameters. So, they should not only be randomized but also scattered evenly through the probability space. In some applications ex-

33 2 Fundamentals

„Calculate expected state transition and observation values“

휆0 Expectation-Step

푃(푂|휆 ) ≥ 푃(푂|휆)

Maximization-Step

„Adaption of model parameters“

Figure 2.5: Schema of the Baum-Welch algorithm pert knowledge can be applied to approximate some parameters to fit in a certain pattern. Besides, the learning procedure requires a certain amount of training data to retrieve meaningful parameter values. Here, the choices of the number of hidden states N and number of symbols M correlates with the size of the required dataset for learning. This means more states and symbols let the model parameters grow or more precisely the matrices A and B increase in size requiring more information to determine the parameters in these matrices. So, the amount of learning data needs to fit the number of parameters. Otherwise, typical problems might occur working with learning procedures. In case of a small dataset for learning, the model recognizes only the learned sequences but is unable to identify similar patterns. So the intended classification of a wider group usually fails, since small differences compared to the learning sequences lead to low probability rates. Also, the quality of the data is critical for the performance of the model. When the learning dataset is very homogeneous, sequences with high noise rates result in a poor recognition rates. Conclusively, it seems reasonable to evaluate the modeling according to the problem by varying the model parameters to find an optimal set.

2.4 Localization

For a precise localization today, a Global Navigation Satellite System (GNSS) is used. It is applicable for navigation of airplanes, ships and ground vehicles. This section gives a short overview based on [42]. This book is recommended for further details. The principles of localization are followed by a calculation method to transfer GPS coordinates into metric coordinates. This is needed for the localization of vehicles relative to objects and within the road network. It is used in this thesis to determine the distance to an intersection. Today, GPS is the most commonly one in use which is administrated by the United States Department of Defense. Since 2000 when the Selective Availability was turned

34 2.4 Localization off, the precision raised from about 100 meters up to 3.5 meters today [43] (hori- zontally) for civilian applications (Standard Positioning Service). This enabled an effective use of GPS for navigation systems especially for vehicles. Also, the low price and development in GPS receivers spread the availability further, so most modern smartphones are equipped with a receiving unit. Other than GPS, there are also efforts in other Countries to establish their own GNSS, e.g. GLONASS (Russia), BeiDou (China) and Galileo (European Union). The latter two are still under construction, i.e. more satellites will be deployed in the orbit. However, GLONASS is fully operating and several localization units (such as smartphones) include chips capable of receiving and processing data from GLONASS. Speaking about localization services, usually the name GPS is used as substitute for any satellite based system.

The existing GNSSs require a minimum of 24 satellites permanently online orbiting the planet in a certain constellation (see Figure 2.6). These transmit radio signals (GPS frequencies between 1.1 - 1.6 GHz) including their position and an exact time stamp when the signal leaves the satellite. The localization is based on the principle of time of ﬂight (TOF) between the satellite and the receiver. Mathematically, only 3 satellites are required for an accurate localization in 3-dimensional space. However, the clock in the receiver is not synchronized with the GPS time. Since the communication is unidirectional, this leads to a systematic error in the calculation of the distance also called pseudo-ranging. This is overcome by considering a fourth satellite signal to determine the time for the receiver (set of linear equations with 4 variables). Additional satellite signals can improve the localization further.

Other effects influencing the precision are signal delays mainly in the ionosphere but also in the troposphere, shadowing (signal damping) and multipath (signal scattering due to reflections). While for the signal delays corrections are available by sending a second signal with a different frequency, the shadowing and multipath is tackled by a reference signal from a ground station. These correction signals are generated on the ground and further distributed via satellite (SBAS: Satellite Based Augmentation System). The system for North America is called WAAS (Wide Area Augmentation System) while in Europe the EGNOS (European Geostationary Navigation Overlay System) is established.

To determine the coordinates of a position on earth using the positioning explained above, a unified reference system is required (global coordinate system). The World Geodetic System 1984 (WGS 84) is used with GPS and approximates the globe to a rotationally symmetric ellipsoid [44]. So, the positioning retrieved from a GPS device is in polar coordinates. The zero meridian passes Greenwich (GB) and the reference for the latitude is the equator. For simplification, the earth could be approximated as a sphere to calculate distances on the surface. However, the ellipsoid in the WGS84 model is defined by the semimajor axis R (equatorial radius) and the flattening f.

35 2 Fundamentals

Figure 2.6: 24-satellite constellation required for a full coverage [43]

R = 6, 378, 137.0 m f = 1/298.257223563 ( 3.35%) ≈ Using these two parameters, the ellipsoid is clearly deﬁned. The semiminor axis r (polar distance) results to

r = (1 f)R = 6, 356, 752.3142 m − and the numerical eccentricity en to

r r2 en = 1 = 0.0818191908426 − R2 For the calculation of the distance between two points on the surface, the polar coordinates have to be transformed into a metric coordinate system on the surface

[44]. First, two radii of curvature are determined one applying to the latitude (RN ) and the other to the longitude (RE). These radii are depending on the latitude (lat) position on the ellipsoid.

R(1 e2) RN (lat) = − 2 3 (1 e2 sin lat) 2 − n R RE(lat) = 2 1 (1 e2 sin lat) 2 − n Further, this is necessary to retrieve scaling factors to convert two points given their GPS position (lat, long) into points in metric coordinates. Hereby, one point is a reference point (lat0, long0) where the origin of the metric coordinate system is placed. The other point is determined relatively to the reference point. Since

36 2.4 Localization the above radii refer to the surface of the ellipsoid, there is an oﬀset if one is on an elevated position on earth. However, the GPS receiver provides a height with regards to the mean sea level (msl). This is a typical reference surface of a geoid characterized by a constant gravity. The deviation between the ellipsoid and the mean sea level is called undulation. This error is avoided by most GPS receivers calculating the undulation and providing the height in relation to the msl (hmsl).

π SFN = (RN (lat ) + hmsl) 0 · 180

π SFE = (RE(lat ) + hmsl) cos(lat ) 0 · 180 · 0

These scaling factors have a unit of meter per decimal degrees (m/◦) and are ﬁnally used to calculate the lateral and longitudinal distance between reference point and actual position in metric coordinates.

X = (lat lat ) SFN − 0 ·

Y = (long long ) SFE − 0 · Finally, the overall distance results to:

p d = X2 + Y 2

37 38 3 Driving Behavior

Driving behavior is a fundamental element for situation assessment. As long as the driver is fully in charge of the vehicle, he/she plays an active role in road traffic. Hereby, the driver’s behavior has a direct influence on the development of traffic situations. A decisive role is the intention of the driver. As presented in section 2.2, on maneuver level, the driver’s action is a consequence of the perception and assessment of a situation with the prior goal of following a certain route. In intersection situations, this is reflected in choosing the optimal direction to reach the destination. Today, navigation systems support the driver in the routing task while driving in unknown areas. However, the execution of maneuvers on intersections is still performed by the driver. The driving behavior prior to entering the intersection leads to the corresponding driving direction. So, the examination of the preparatory behavior at intersections is of interest to determine how this is linked to the maneuver executed at the intersection when driving in one of multiple directions. In a first step, driving data of intersection approaches was analyzed to identify differences in the preparatory behavior driving in different directions. The database contained data of only one intersection. The accordant analysis is presented in this chapter. Further, the data was used to evaluate the prediction algorithm introduced in chapter 4. For an extension of the analysis and a generalization, a naturalistic driving study was conducted to examine the behavior at different intersection types, i.e. with different regulation and shape. Still, the focus was on differences dependent on driving direction. However, another aspect was to find similarities when passing intersections of different shape in the same direction. This is examined to reduce complexity in the prediction framework. In case no similarities are found, each direction at each different intersection would require a new model. The data collected during the study was also used for the implementation of the prediction application. For a detailed explanation see chapter 5.

3.1 Data Analysis

This data analysis was performed in a first step to identify differences in the driving behavior when approaching an intersection. The main results were published in [45] and presented at the Automotive meets Electronics conference 2014. The focus was to find variables and according time frames where differences appear dependent on

39 3 Driving Behavior the direction of travel. Also, we wanted to establish a qualitative description of the process of actions taken by the driver. The research questions of interest were: How does the preparatory behavior of drivers differ between the intended direction? At which distance (spatial and temporal) does the driving behavior change when turning compared to going straight? The database used here, was established within the project simTD (Safe and Intel- ligent Mobility Test Field Germany). This was a large-scaled field test for Vehicle- to-Vehicle (V2V) communication. For the analysis, data was used from two days of testing on the project’s test ground in Friedberg, Germany. There, a major intersection was installed which had to be passed frequently during the test (see Figure 3.1). The dataset consisted of driving data of approximately 30 vehicles of different brands. The drivers had no special training. The test was not related to this analysis and the drivers were driving without any instruction for which direction to choose at the intersection. The existing traffic light was deactivated and the main road was in west-east direction. Vehicles coming from south or north had to yield.

Intersection area

Figure 3.1: Intersection at simTD test site in Friedberg, Germany [46]

3.1.1 Data selection and processing

Since the test vehicles were of different brands, the data has been preprocessed and harmonized by a defined simTD protocol. This protocol managed the recorded signals and assigned standard identification numbers to all variables. The datasets were stored in multiple TXT files by vehicle. Due to differences in the data metrics and sensors between the brands, some datasets were incomplete or asynchronous and

40 3.1 Data Analysis were discarded accordingly. Also, the frequency of recording was not homogenous over all datasets. For the comparison of diﬀerent datasets, this was overcome by interpolation and extrapolation to retrieve all relevant data at a time interval of 100 ms. Multiple ﬁnal plausibility checks excluded datasets with unrealistic values for speed, acceleration and yaw rate.

For each vehicle the route driven was retraced using GPS data. This was used to determine the distance to intersection (DTI) for each time step. The precision was approximately 3 m according to the data specification of the used GPS systems. Unfortunately, the positioning of the intersection markings was not measured. It was impossible to be carried out later, because the simTD project was already finished and the intersection had been removed before this analysis was conducted. Thus, to use the intersection as reference system, its boundaries were retrieved from map data using Google Earth. The intersection area was defined as the space which is passed by vehicles approaching from different directions (cp. Figure 3.1). The center of the intersection served as reference point. Accordingly, all GPS coordinates were transformed into metric coordinates to easily retrieve the distance to intersection (cf. section 2.4). Here, the DTI refers to the distance between the GPS data in the database and the map-based GPS points of the intersection area. So a DTI of zero indicates an entering into the intersection. However, the position of the GPS antenna on the vehicles was unknown and therefore remained unconsidered.

The preprocessed database was now searched for intersection crossings. This was realized using a search algorithm ﬁnding points within the intersection area. Since the approach of the intersection was of interest, sequences were extracted from 100 m distance to the intersection up to the exit of the intersection area. The sequences were labeled according to the direction of origin (north, east, south, west) and the executed maneuver (turning left, turning right, going straight). The latter was established by tracking the coordinates where the vehicle entered and left the intersection. Overall, 2492 sequences were extracted and used for the evaluation (see Table 3.1). Since the direction of travel was chosen by the driver, there are high variations of sequences of diﬀerent types. For this reason, the data analysis has a descriptive character.

Table 3.1: Number of approaches according to origin and driving direction driving direction origin left right straight east 6 267 659 west 143 171 482 north 18 102 58 south 61 401 124

41 3 Driving Behavior

In [47], the velocity, yaw rate and steering wheel angle have been identiﬁed as suitable prediction variables for the complexity of intersection situations. So, the variables of interest were chosen to be velocity, acceleration and yaw rate to describe the vehicle dynamics and further the gear, turn signal, steering wheel angle and steering wheel angle velocity as driver inputs. For the analysis of the gear usage, all sequences of vehicles with automatic transmission were excluded, since the human shifting behavior was of interest. The turn signal status was either retrieved through the turn indicator lever position or the blinker signal (the status of the actual turn indicator light).

3.1.2 Results

In the publication [45], the approaches from east and west are grouped to main road sequences, which are considered to be similar independent of the origin. For the side road (approach from north and south), this is done accordingly. However, the geometry of the intersection is not symmetrical (cp. Figure 3.1). While the branch to the south connects perpendicularly, the branch to the north meets at an angle of approximately 70◦. This is expected to influence the turning behavior on the main road as well as the approaches from the side roads. Therefore, turning on the main road and the side road approaches are investigated in further detail. The groups are only clustered to main road or side road sequences when similarities appear. For approaches on the main road, the analysis concentrates on turning maneuvers, since going straight on main roads implies a simple driving task without significant variable changes. Further, it is distinguished between turning left, turning right and going straight when approaching from the side road. Here, differences dependent on the direction of travel are not expected, since the prior concern should be to yield at the intersection. All sequences start at a DTI of 100 meters. Here, the dynamic variables (velocity, acceleration, yaw rate) are examined in their progress over the entire distance while the state variables (gear, turn signal, begin of turning) are of interest when changing dependent on the DTI and time to intersection (TTI). The latter refers to the real time before the vehicle entered the intersection which is retrieved from the data and not the estimated value commonly calculated through DTI divided by the velocity.

Velocity proﬁle

The velocity sequences are averaged as a function of DTI according to the origin (east, west, north, south) and the direction of travel (left, right, straight). First, the turning sequences on the main road are examined comparing the driving origin to determine the expected inﬂuence of the geometry. For going straight on the main road (coming from east or west) there are no diﬀerences as expected. The comparisons of the origin for turning on the main road are displayed in Figure 3.2.

42 3.1 Data Analysis

60 60

50 50

40 40

30 30

20 20

10 east left 10 east right Average velocity [km/h] west left west right 0 0 100 80 60 40 20 0 100 80 60 40 20 0 Distance to intersection [m] Distance to intersection [m]

(a) turning left (b) turning right

Figure 3.2: Velocity for approaching the intersection on the main road

For turning right, the averaged velocity profiles are very similar (cp. Sub- figure 3.2(b)). The mean velocity is close to 50 km/h (sd = 10 km/h) at a distance of 100 meters to the intersection and decreases down to 33 km/h on average for approaches from east and to 28 km/h for approaches from west, accordingly. In both cases the standard deviation when entering the intersection is about 5 km/h. The slight differences can be explained by the geometry of the intersection. The branch to the north allows turning right while coming from the east at a higher velocity due to the smaller angle of intersection compared to coming from the other direction where the angle is perpendicular. However, the similarities prevail and therefore both are combined to “turning right on the main road” to compare the profiles according to the direction of travel.

This is not applicable for turning left on the main road. Here, the averaged sequences differ noticeably (cp. Sub-figure 3.2(a)). While approaches from the east start at a comparatively high velocity of about 58 km/h (sd = 12 km/h) and decline to about 27 km/h (sd = 6 km/h), vehicles coming from the west approach at an average velocity of about 46 km/h (sd = 8 km/h) and drop in speed to about 21 km/h (sd = 9 km/h). The differences are again explainable with the geometry similar to the right turns. This time, coming from the west and turning left requires a reduced speed due to the greater turning angle compared to the other direction. Another effect is that the traffic was not controlled and sequences are included where oncoming traffic interfered. With this in mind, the biggest weakness of the comparison is the great difference in the number of available sequences (see Table 3.1). There are only six left turning sequences coming from east calling the informative value into question. Therefore, only the left turning sequences when approaching from the west are used for “turning left on the main road”.

43 3 Driving Behavior

The velocity profiles of both side road branches differ significantly according to the origin (cp. sub-figures in Figure 3.3). Especially, the first part of the sequences are related to the preceding curves before the intersection. While the north branch has a drawn-out curve passed with an average velocity around 30 km/h, there is a sharp perpendicular curve before entering the intersection from the south at a distance of about 40 m. This curve is also passed at around 30 km/h. In the close range, the approach is similar again and the velocity is reduced down to around 10 km/h. Here, the sequences are included where the driver had to stop, because of cross traffic. This is why the mean velocities are rather low.

60 60

50 50

40 40

30 30

20 20 north left south left 10 north right 10 south right Average velocity [km/h] north straight south straight 0 0 100 80 60 40 20 0 100 80 60 40 20 0 Distance to intersection [m] Distance to intersection [m]

(a) coming from north (b) coming from south

Figure 3.3: Velocity for approaching the intersection from the side roads

Comparing the sequences within the sub-figures, it stands out that the velocity profiles are alike independent of the direction of travel. The drop in velocity at close range seems to be a reaction to the upcoming intersection and in this case the yield sign. The similar progress indicates that the reaction to the yield sign is dominant in the approach behavior independent of the intended driving direction. However, there are differences in the main road approaches according to the direction of travel. The velocity sequences are grouped as mentioned above and averaged over the DTI. So, the velocity profiles for driving on the main road are shown in Figure 3.4. The shades indicate the standard deviation, which was left out for going straight. These sequences show a constant velocity around 50 km/h with a standard deviation of about 11 km/h over the entire distance. This was also the speed limit on the test ground. The high deviation shows that there was a wide range of accepted speeds for the drivers. The average velocity when turning right is similar compared to going straight for the first part of the approach. It decreases monotonically coming closer to the intersection. Also, the standard deviation is decreasing showing that the speed range

44 3.1 Data Analysis

20 main left 10 main right Average velocity [km/h] main straight 0 100 80 60 40 20 0 Distance to intersection [m]

Figure 3.4: Velocity on the main road according to the direction of travel adjusted by the drivers is smaller for a turning event. However, the velocity does not differ distinctly until about 20 meters before entering the intersection from the average straight profile. Here, the decline is proceeding stronger down to an average speed of approximately 31 km/h (sd = 5 km/h) when entering the intersection. The average velocity for turning left starts slightly lower compared to turning right. Both graphs decrease parallel up to about 30 meters before the intersection. There, the velocity is reduced considerably down to about 21 km/h (sd = 9 km/h) on average when entering the intersection area. The high deviation is explained by the traffic interference which is not excluded from the data.

Deceleration process

The deceleration process can already be estimated from the velocity proﬁles. Unfor- tunately, the brake pedal signal was either unavailable in the database or in other cases unreliable because of many missing data. Therefore, the acceleration values were evaluated to estimate the usage of the brake. The average deceleration is close to zero at a DTI of 100 meters for all main road sequences. This value is nearly constant over the entire approaching phase when going straight and there appears to be no braking as expected. For all maneuvers, the deceleration is shown in Figure 3.5. As mentioned above, turning left at the main road only includes approaches from the west. The graphs are similar for the ﬁrst half of the approaching distance. There is a slight deceleration averaging up to 0.5 m/s2 for both turning maneuvers which is obtained presumably by leaving the gas pedal and caused by the engine brake, the aerodynamic resistance and the rolling friction. This is commonly known as coasting. An actual braking is above this value and appears subsequently after coasting for both turning maneuvers. However, the average gradient of the deceleration is greater for left turning sequences than for

45 3 Driving Behavior right turning sequences. The right-turning vehicles decelerate continuously up to 1.8 m/s2 (sd = 0.9 m/s2) when entering the intersection. The mean acceleration for left turns shows a minimum at a distance to intersection of about 13 meters (see Figure 3.5). Here, the average deceleration is about 1.9 m/s2 (sd = 1.0 m/s2) which is marginally more than for turning right. Since the left-turning vehicles have to yield to the oncoming traﬃc, this extremum indicates that the drivers decelerate in advance before they turn to avoid oncoming vehicles foresightedly. Also, the sequences where oncoming traﬃc was present were not extracted from the analysis.

1 ] 2

1 −

2 main left − main right main straight Average acceleration [m/s 3 −100 80 60 40 20 0 Distance to intersection [m]

Figure 3.5: Average acceleration for approaching the intersection on the main road

The deceleration process on both side roads (north and south) was highly influenced by their geometry, especially through the sharp curves on both sides before the intersection. This was already indicated by the velocity profiles. Further, there appeared to be no difference dependent on the direction of travel at the intersection. So, the acceleration profiles were combined for all directions (see Figure 3.6). For the approaches from the north, the first deceleration is a reaction to the curve before the intersection. The actual braking seems to start around a DTI of 36 m when the acceleration drops rapidly in Sub-figure 3.6(a). The standard deviation indicated by the dotted lines is comparatively high especially at the minimum of the acceleration of -1.8m/s2 (sd = 0.9 m/s2) at a distance of about 16 meters before entering the intersection. The acceleration profile for approaches from the south branch start out with a deceleration of about 1.0m/s2 increasing to 1.6m/s2 over the first 20 meters and remaining there for another 30 meters. This braking with a high variance (see Sub-figure 3.6(b)) reflects the approach of the sharp left curve before the intersection coming from the south branch. The curve is passed without braking indicated by the shape of an arch in the graph. Obviously, there is even an acceleration out of the turn in some sequences with a maximum of averaging about 0.3m/s2 at a distance of 30 meters. Afterwards, the actual braking as a reaction on the intersection starts

46 3.1 Data Analysis and reaches the minimum acceleration of -1.7m/s2 (sd = 0.9 m/s2) at a distance of approximately 10 meters to the intersection.

1 1 ] 2

0 0

1 1 − −

2 2 − − north all south all Average acceleration [m/s 3 3 −100 80 60 40 20 0 −100 80 60 40 20 0 Distance to intersection [m] Distance to intersection [m]

(a) coming from north (b) coming from south

Figure 3.6: Average acceleration for approaching the intersection from the side roads

Conclusively, the intensity of the deceleration does not exceed 2.0 m/s2 on average. However, the standard deviations are high especially for the side road approaches. This reflects high variations in the driving behavior. Besides, the vehicles have to yield and therefore in some sequences come to a complete stop when there was cross traffic present. The traffic situation is included in the data and cannot be reconstructed.

Turn indicator usage

Of the 1169 turning sequences, there were only 938 cases where turn signal information was available from the turn signal lever or one of the four blinker signals. It is unclear, whether the missing turn signal data is caused by a missing driver input or a failure in the data recording. However, the large gap suggests that problems in transferring the data into the uniﬁed simTD protocol caused the missing data. In the available data, there were 18 misuses or rather missing turn signal activations; 10 times when turning left and in 8 right turning sequences. So, the majority of test drivers used the turn signal correctly considering the available data. The beginning of the turn indication varied intensely and was obviously inﬂuenced by the lane structure of the intersection. There was a left turning lane and a combined right turning and straight lane each on the main road (cp. Figure 3.1). The bus lane was used for turning left approaching from east. So, when turning left the turn signal was already used to show the intention of changing into the left turning lane. For turning right, it depends on which lane the driver is in when approaching the intersection. So, the turn signal indicates either a lane change to the right or

47 3 Driving Behavior the actual turning intention. This was not distinguishable, because a lane level localization was unrealizable with the GPS system used.

40 indicator right indicator left 30

20 Frequency 10

0 100 80 60 40 20 0 Distance to intersection [m]

Figure 3.7: Frequency of turn signal usage dependent on DTI for main road sequences

The DTI when starting the turn signal is displayed in Figure 3.7 and shows the spatial distribution of the activation of the turn signal on the main road. The left turning sequences have early turn signals, which is a sign of the assumed lane change. The mean distance when starting the turn signal is about 80 m (sd = 23 m) when turning left. For right turns, the turn signal is spread widely over the examined approaching distance. Here, the mean DTI is 51 m (sd = 28 m). The high standard deviations and the distribution displayed in Figure 3.7 indicate that the usage of the turn signal especially for right turns seems spatially randomized and there is no pattern observable. However, when analyzing the temporal distance (time to intersection), i.e. the actual time before entering the intersection and starting the turn signal, there is a connection observable (see Figure 3.8). The graph shows the cumulative relative frequency for a better comparison of the two directions because of the high diﬀerences in the amount of data for each direction. About half of the drivers start the indication 7.9 s before entering the intersection when turning left and 4.1 s for right turns accordingly. The graph shows also that about 2 s before the intersection is reached, 80% of right-turning vehicles and all of the left-turning ones have activated the turn indicator respectively.

The shape of the graph in Figure 3.8 indicates that the activation of the turn signal is related to the velocity rather than the distance to the intersection. This was tested for right turns using an approximation of the distribution of the velocity when the turn signal is activated. The results show that the velocity at turn signal activation turning right on the main road is nearly Gaussian distributed with a mean velocity of 45 km/h and a standard deviation of 8.7 km/h.

48 3.1 Data Analysis

1 indicator left 0.8 indicator right

0.6

0.4

0.2

Cumulative relative frequency 0 20 15 10 5 0 Time to intersection [s]

Figure 3.8: Cumulative temporal distribution of turn signal activation on main road

For the side road, there was only one lane coming from north but an extra left turning lane coming from south. Accordingly, drivers approaching from south might have activated the turn signal to indicate the lane change. Due to the diﬀerences in the shape of the side roads depending on the branch, the turn signal activation is examined separately. In contrast to main road approaches, the turn signal activation is localized in a smaller interval for the side roads. For turning right, there are normal distributions to approximate the distance when the turn signal is activated while the sample size for turning left approaching on the side roads is too small. It stands out that despite the diﬀerences in shape of the side road branches and the previous curves before entering the intersection, the turn signal usage for right turns is similar approaching from either side, north or south. So, the turn signal is on average activated at a DTI of 23 m (sd = 13 m) coming from north and of 24 m (sd = 10 m) coming from south respectively.

The temporal distance is also similar and is displayed in Figure 3.9. There were only a few sequences turning left approaching from the north and therefore are not comparable. All other graphs show strong similarities even between left and right when approaching from the south. The majority of the drivers seem to activate the turn signal at similar temporal distances. For turning right when coming from the north, half have activated the turn signal before 4.7 s. For the approaches from the south, most activations appear at a TTI around 3.8 s when turning left and around 4.4 s when turning right respectively. All turning right sequences combined also showed a nearly Gaussian distributed velocity at turn signal activation with a mean value of 25 km/h and a standard deviation of 10 km/h.

49 3 Driving Behavior

1 1 indicator left indicator left 0.8 indicator right 0.8 indicator right

0.6 0.6

0.4 0.4

0.2 0.2

Cumulative relative frequency 0 0 20 15 10 5 0 20 15 10 5 0 Time to intersection [s] Time to intersection [s]

(a) coming from north (b) coming from south

Figure 3.9: Cumulative temporal distribution of turn signal activation on side roads

Gear shift behavior

For the gearshift usage, the point of reference was the last manual gear change before entering the intersection. Vehicles with automatic transmission were ignored, since only human behavior was relevant. Unfortunately, the majority of vehicles in the test were automatic. To filter for manual gear changes, the presence of the signal for the clutch pedal was sought. Still, there were missing gear signals in the remaining data. Overall, only 271 relevant sequences were extracted for the evaluation. Since the observed behavior was similar in the approaches on both side road branches, the approaching sequences are grouped together for the evaluation of the gear shift behavior. Especially, the last part of the approach where the gear shift is expected, is dominated by the preparation to yield to the cross traffic. For the main road sequences only the turning scenarios are analyzed, since shifting is unnecessary when driving with nearly constant speed. In the following, the gear selection was examined and the DTI where gear changes appeared. For all approaches on the side road, the average DTI when shifting was 22 m (sd = 20 m). The allocation of the DTI is presented in Figure 3.10. Thereby, downshifting is predominant as expected. There were mostly gear changes from third as well as from second gear into first gear (each with 32%) which is the most common gear when entering the intersection. This indicated multiple full stop scenarios on the side road. Another downshift from third into second gear appeared in 27% of the cases. Here, drivers probably reach the intersection with no cross traffic present and therefore can pass without stopping. The intersection approaches on the main road were differentiated between left and right turns. There were only a few sequences available. Therefore, the distribution of the DTI when shifting is presented in Figure 3.11 as a probability density func-

50 3.1 Data Analysis

20 side road all

10 Frequency 5

0 100 80 60 40 20 0 Distance to intersection [m]

Figure 3.10: Frequency of shifting dependent on DTI for side road sequences

tion approximated from the data. Qualitatively, the gearshift appears closer to the intersection when turning right compared to turning left. This corresponds to the ﬁndings in the deceleration process (cp. Figure 3.5).

2 10− 3 · main left main right 2

Probability 1

100 80 60 40 20 0 Distance to intersection [m]

Figure 3.11: Probability density of the DTI when shifting on the main road

For both directions, the gear was most often shifted from third into second (44% left turning, 42% right turning). When turning left on the main road, a downshift into first gear was observed in 28% of the cases. This seems to indicate a situation with oncoming traffic that forces the driver to stop or nearly stop before turning. Driving in either direction, the intersection was predominantly entered in second gear. However, the gear choice may not be solely dependent on the driver’s preference but also on the motorization of the vehicle and the transmission ratio which differed in this analysis due to the different cars used for the test.

51 3 Driving Behavior

Turning behavior

The analysis of the turning behavior intends to identify the starting point of a turning maneuver. This point was determined by the yaw rate, since the steering wheel angle was not evaluable due to missing and implausible data and the extreme noise in the steering wheel angle velocity. The turning behavior was only examined for approaches of the intersection on the main road. This was because the curves on the side roads before the intersection distort the examination and also due to an insufficient amount of data especially for left turns. Since a vehicle is slightly yawing even when going straight, turning is not indicated by a yaw rate other than zero. Therefore, to determine a threshold for a turning maneuver, the going straight sequences were selected (TTI [0, 10]) and the standard ∈ deviation of the yaw rate was calculated as a function of time. The extremum of this function was chosen to be the threshold. In this case, the amount was 0.56◦/s. When the yaw rate first exceeds the absolute value of this threshold while the vehicle is within a DTI of 50 meters, this is declared the starting point of the turning maneuver. Here, turning left implies a positive yaw value, while turning right is negative. The mean yaw rate profiles for left turns and right turns on the main road are presented in Figure 3.12. In the figure, the dotted lines represent the standard deviation. The starting point of the turn in the average curve is indicated by the vertical gray line. Obviously, the left turn starts on average at a larger distance to the intersection compared to the right turn. Also, the deviation is greater for turning left. This difference appears because of the larger curve radius when turning left, which is why the yawing is starting earlier. The intersection on the test area was relatively big with multiple lanes. So, while the driver execute a right turn driving similar paths, the left turn can vary more between the drivers. It depends on the individual preferences to which extend a corner cutting is accepted. This leads to a wider range of driven trajectories indicated by the high standard deviation for left turns. The evaluation of the sequences individually confirmed the results from the average profiles. Here, the DTI and TTI at the starting point of turning was determined in each sequence according to the process mentioned above. The beginning of a left turn is 1.8 s (sd = 0.7 s) before entering the intersection and at a DTI of about 12 m (sd = 8 m). For right turns, the yawing is initiated latter at an average TTI of 0.7 s (sd = 0.4 s) and an average distance of 6 m (sd = 4 m) to the intersection. The values indicate that a turning event initialized by the steering input of the driver and consequently the yawing of the vehicle is distinguishable shortly before entering the intersection. The steering is the final step of action clearly displaying the turning intention. In terms of a warning or intervention strategy in case of a collision risk, the time before entering the intersection area seems critically low.

52 3.1 Data Analysis

20 main left 20 main right /s] ◦ 10 10

0 0

10 10 − −

Average yaw rate [ 20 20 − − 50 40 30 20 10 0 50 40 30 20 10 0 Distance to intersection [m] Distance to intersection [m]

(a) turning left (b) turning right

Figure 3.12: Average yaw rate for left and right turning sequences on the main road

So, the location where the turn begins is clearly defined and is dependent on the geometry of the intersection. The velocity as well as the acceleration at this point seem normally distributed by approximation for both directions. The average speed is 27 13 km/h for left turns and 35 6 km/h for right turns accordingly. The ± ± higher variation in the velocity when turning left is explained by the interference of oncoming traffic included in the data. The acceleration is more alike and the drivers seem to brake in both maneuvers when starting to turn. For left turns, the average acceleration is 1.4 m/s2 (sd = 1.2 m/s2). In contrast, the average − acceleration when the right turns start is 1.6 m/s2 (sd = 0.9 m/s2). Again the left − turn sequences have higher variations which relates to traffic interference. In addition, the velocity at the beginning of the turn correlates with the distance to intersection for turning right (r = 0.60) as well as for turning left (r = 0.86) maneuvers. This means, drivers that start steering earlier with a tendency to cut the corner are faster than others who tend to turn later driving a sharper curve (smaller curve radius). This is reasonable regarding the physical perspective of a turning maneuver. The maximum speed for driving stabilized in a curve is direct proportional to the curve radius. A fast corner cutting turn is referred to as sporty driving style.

3.1.3 Conclusion

The approaching behavior on the main road diﬀered considerably according to the driving direction. All diﬀerences are shortly summarized in the following boxes.

53 3 Driving Behavior

Turning left Turning right

average velocity at intersection average velocity at intersection 22 km/h 32 km/h

extremum in deceleration monotonous progress of deceleration

early turn signal activation (turning temporal distance when turn signal lane) . all before TTI of 3 s activation app. normally distributed

gearshift further away gearshift closer

begin of turning further out begin of turning closer to intersection

While the velocity is reduced with a higher intensity and also to a lower amount when turning left, the right turn sequences show rather gradually braking. However, turning left required yielding to oncoming traffic which was not excluded from the data since information about the surroundings was unavailable. This is also an explanation for the minimum in the average acceleration for left turning sequences. The findings in the gear shifting behavior correspond to the velocity and acceleration profiles and indicate that the shifting is executed earlier when turning left compared to turning right. Also, the yawing starts even further before entering the intersection due to the bigger curve radius when turning left. On the side road, the preparation to yield to possible cross traffic was the predominant goal. Differences according to the direction of travel were unobservable. Though, the shape of the road before entering the intersection was a factor. Here, the differences influenced the deceleration process and the velocity respectively. Coming from north, there was a stretched curve leading to the intersection while from the south, there was a sharp left turn followed by a straight segment merging to the intersection. This especially influenced the velocity shortly before the intersection and also the visibility of the upcoming intersection. Although, the drivers passed through the intersection multiple times, eventually learning its shape and position. However, the intensity of the deceleration was similar for approaches from both sides. Also, the usage of the turn signal was in corresponding temporal intervals before the intersection especially for turning right. The gear changes were combined for all side road approaches due to a lack of data and indicated the majority of gear shifts roughly in-between a DTI of 5 to 25 meters. Also, the first gear as the predominant choice shows that several drivers had to yield to cross traffic and therefore come to a complete stop. Overall, the data for the side road can be utilized to develop a compliance behavior prediction for intersection approaches with yield signs.

54 3.2 Naturalistic Driving Study

3.2 Naturalistic Driving Study

While in the first analysis the driving behavior was examined at one particular intersection, hereupon an exploratory naturalistic driving study was conducted. The main goal was still to find different behavior patterns dependent on the direction of travel at an intersection. Here, the focus was on driver inputs, e.g. brake pedal usage and as previously mentioned the activation of the turn signal. The goal was to find differences as a basis for the prediction algorithm introduced in chapter 4. Besides, the influence of differences in the characteristics of the intersection were of interest to determine possible similarities. This could potentially reduce the number of necessary prediction models for different intersection types and reduce the complexity of the approach. The test drives were performed on public roads in Ruesselsheim, Germany, with no influence on the traffic conditions. Parts of this study were published in [48] and presented at the ITS World Congress 2015 in Bordeaux. This publication includes only the analysis of braking behavior and the usage of the turning indicator with a smaller data sample (16 probands). The usage of the brake when approaching an intersection without traffic present is an indication of a reaction to the upcoming intersection. Here, the previous action of leaving the gas pedal was also determined to examine the approaching behavior in more detail. This provides a closer look at the transition from leaving the gas pedal to pushing the brake (including a possible coasting phase). Also, the study was later extended by 15 further probands driving the same route. This was also used to validate the first results. Turning right at an intersection without traffic signs (priority to the right) does not require yielding to any other approaching vehicle. Here, deceleration if at all is only necessary for a smooth turning maneuver and depends on the geometry of the intersection and the approaching speed. On the opposite, turning left or going straight at the same intersection (assuming it is X-shaped) requires looking out for other vehicles. It is assumed that this increase in complexity of the driving task leads to initiation of a deceleration process at an earlier stage further away for the intersection. Comparing intersection layouts, it is expected that X-intersections are perceived as more complex than T-junctions and therefore, the onset of braking is at a greater distance to intersection (DTI). When approaching intersections with yield and stop signs, there are no differences expected in brake onset varying the driving direction. However, data in these situations might show a typical stopping behavior. This could be used to create prediction models for non-compliant behavior.

3.2.1 Background

The observation of driving behavior in naturalistic intersection situations is crucial to develop Advanced Driver Assistance Systems (ADAS) to reduce the high numbers

55 3 Driving Behavior of accidents occurring at urban intersections (see section 1.1). Research has shown that driving behavior when approaching urban intersections depends inter alia on surrounding traffic [49], driving styles and driver states [50] and initial speed [51]. The previous data analysis also indicated differences related to the direction of travel (see section 3.1). Also, the individual driver type plays a role especially for the operated speed [52]. An overview of the recent development in the research of driving behavior at intersections is given in [53]. In this paper, the driving behavior is grouped in prediction-based behavior and safety-based behavior analysis. The latter concentrates on safety issues when approaching an intersection, e.g. Perception Reaction Time (PRT), dilemma zone and gap acceptance. Hereby, the dilemma zone is a focus of research at signalized intersections where the driver behavior that leads to red light violations is examined. Depending on the velocity driven and the traffic light pre-emption, there is a dilemma zone. In case of a phase change from green to amber, this is defined as the area where the driver is neither able to safely stop before the traffic light nor to pass the intersection before the change to red. An overview of the research on this topic is provided by Zhang et al. [54]. The prediction-based behavior analysis focuses on the evaluation of data from human factors and vehicle dynamics to create systems for maneuver predictions; mainly turning at the intersection. This prediction is fundamental for the development of ADAS in an urban environment (cp. section 2.3), e.g. for a Left Turn Assist (LTA).

3.2.2 Methods

Participants

Overall, 31 drivers (ﬁve female) between the age of 22 and 48 participated in the study. The mean age was 29.5 years (sd = 5.5 years). The mean driving mileage of the past year was about 17,300 km (sd = 8,600 km) with approximately 25% in an urban environment, 22% on rural roads and 53% on highways. All drivers were in possession of a driver’s license for several years.

Test vehicle

In the study, an Opel Zaﬁra Tourer was used with 130 hp (96 kW) and a 6 gear automatic transmission. The vehicle is equipped with an Automotive Dynamic Mo- tion Analyzer (ADMA) including a GSM modem and a GPS receiver for global positioning with diﬀerential GPS correction. Further, there is an external GPS antenna on the roof (see Figure 3.13) for improved signal reception. This system has a localization precision between 1.8 m down to 2 cm using real time kinematic (RTK) [44]. The latter requires a registered chip in the GSM module and a stabled mo- bile network connection. Additionally, there is a separately installed GPS mouse

56 3.2 Naturalistic Driving Study as backup system. This device has an accuracy of around 3 meters (depending on shadowing effects and multipath scattering). The entire data logging was performed with a CAN box by Vector which was connected to the CAN bus of the vehicle. This box was connected via USB to a laptop to retrieve the vehicle’s state (velocity, acceleration, yaw rate) and the driver inputs such as gas pedal position, brake pedal position, steering wheel angle and turn signal lever status. Furthermore, there were two USB cameras installed to record the scene in front of the vehicle and the driver. The video sequences were only used post-hoc for the identification of surrounding traffic and the validation of dynamic driving data.

Figure 3.13: Test vehicle with GPS antenna on the roof

Route and intersections

The route was on urban roads in Ruesselsheim, Germany. Overall, the drivers passed 20 relevant intersections and two of them twice from different directions leading to 22 relevant intersection approaches. For the evaluation, 15 of them were considered and subdivided according to the present traffic regulation and the shape of the intersection (see Figure 3.14). The others were discarded because of irrelevant intersection types, undesired anomalies in the geometry or high traffic volume. The latter was an undesired and uncontrollable influence especially through preceding vehicles. The intersection approach indices and variations are shown in Table 3.2. The speed limit was 30 km/h along the entire route. All intersections had single-lane approach roads. The area was mostly a residential estate with moderate traffic during the time the study was conducted. Surrounding and geometry of the evaluated intersections were similar. The intersection angles were nearly perpendicular in all cases.

57 3 Driving Behavior

21 22

4 6 16 17 18 15

8 9 14

13 10

Figure 3.14: OSM map [17] including relevant intersections (indices and driving direction)

Design

The study was conducted in a partial within design with three factors: intersection shape, regulation and driving direction. The intersection shape is varied between T-junction and X-intersection, while the traffic regulation differs in priority to the right, yield and stop. Each type of intersection was passed in all possible directions (going straight, turning left or right) except for the one with a stop sign, which was passed going straight. At a stop sign differences in the approaching behavior dependent on the driving direction are not expected due to the prior task of stopping at the intersection. Also going straight at a T-junction with a left branch was not investigated since it is considered to be similar to driving down a normal road.

58 3.2 Naturalistic Driving Study

Table 3.2: Overview of analyzed intersections according to type Intersection type Direction of travel regulation shape left right straight priority right T-junction (branch right) – 15 13 16 17 | priority right T-junction (branch left) – 6 - | priority right T-junction 20 10 12 priority right X-shape (crossroad) 21 9 14 yield X-shape (crossroad) 18 22 4 stop X-shape (crossroad) - - 8

All drivers included in a comparison analysis have passed the same intersections. Unfortunately, there was a construction site on the route during one day of testing which led to missing data at several intersections for 2 probands. Furthermore, the route was driven only once in the first execution of the study. So, in case of obvious traffic interference, e.g. a preceding vehicle, the intersection approach sequences was discarded, since surrounding traffic is influencing the approaching behavior [49]. This was necessary since the traffic conditions were uncontrolled due to driving on public roads. In case of missing information for driver inputs of one proband at just one intersection approach, the mean value of all other drivers was inserted. In the extension of the study, this was avoided by repeating the approach of the intersection afterwards if necessary and driving the entire route twice. Leaving the accelerator pedal (AccPdlLeave) and pushing the brake pedal (BrPdl- Push) are indications of a reaction to the upcoming intersection. Therefore, the DTI and velocity are monitored at the point when the accelerator pedal is released and the point when the brake pedal is pushed. Also, the turn signal usage was evaluated by tracking the turn switch activation (TrnSwAct). Here, again DTI and velocity were of interest when the turn signal is activated. These are the dependent variables of the analysis. While the DTI provides a spatial location of the reaction related to the intersection, the combination of DTI and velocity also reveals a temporal component by calculating the TTI. An interesting question is whether the drivers show the same reaction at an intersection rather spatially or temporally correlated. The DTI was not measured directly but determined through the GPS points retrieved from the measurements according to the procedure described in section 2.4. Other than in the previous data analysis, here the intersection points were obtained previously by a test drive with the ADMA system.

Procedure

The route was set with the navigation app Osmand which was installed on a smartphone. This was positioned at the windshield. The app uses Open Street Map (OSM) data [17] and was necessary to guide the driver along the predeﬁned

59 3 Driving Behavior route in a standardized setup. The instruction was to drive in a normal manner without endangering anybody and to follow the route indicated by the navigation app, which also had a voice output for the turning events. The study was conducted under real traffic conditions. One lap took about 20 minutes. The instructor was the front passenger and supervised the procedure by recording traffic events (e.g. crossing pedestrians). In the back seat, there was a technical supervisor who verified the recording procedure on the laptop. In a short questionnaire directly after the ride, the participants weighted in a five point Likert scale their driving effort, driving performance, driving efficiency and how safe and how natural they were driving. There was also a place for comments to stressful and critical situations. This was conducted to determine a possible influence of the chosen route of the behavior.

3.2.3 Results

As mentioned before, the analysis was conducted according to a repeated measures design. Hence, the only sequences included were those in which a driver experienced all factor combinations relevant to the respective analysis. Because of missing data (reason explained in “Design”), the set of considered test drivers differs slightly between each analysis but is consistent within. The exact number of test drives is reported accordingly. This was performed in a few exceptional cases to avoid an overall data reduction. The driving data was converted from CANape into Matlab. The detection of the driver inputs was realized with Matlab scripts and in case of discrepancy checked manually. This data was transferred to Excel. The statistical analysis was performed using the Real Statistics add-in [55]. The following basics about statistical analysis were retrieved from the Real Statistics website and the reference book by Bortz [56]. All datasets were tested for normal distribution with the Shapiro-Wilk test (α = 0.01). Another pre-condition required for a repeated measures analysis of variance (ANOVA) is sphericity of the dataset. This means the correlations between variances of different samples are homogeneous. If this requirement is not met, a correction factor is used to reduce the degrees of freedom (df) for the test. Commonly the correction factors by Greenhouse-Geisser GG which tend to be to low (conservative) or by Huyhn-Feldt HF (slightly to high) are used to adjust the df values. In the results, the declaration of the correction and the df adjustments is left out for the benefit of comparability and confirmability (integer df) of the results. However, the correction was calculated and it was checked whether significances still occurred. If this was not the case, it is explicitly mentioned. As post-hoc tests, t-tests for pairwise samples were used at a significance level of 5% and with Bonferroni alpha correction. The effect size is specified by Cohen’s d. Small effects are around 0.2 while large effects are around 0.8. The graphs follow loosely a color coding for

60 3.2 Naturalistic Driving Study the driving directions - turning left (red), turning right (blue) and going straight (green). The error bars of the displayed values represent the confidence interval on a confidence level of 95%. From the questionnaire emerged that the drivers rated their driving behavior in the test drive very natural with a mean of 4.2 (sd = 0.7) on the five point Likert scale. Also, the driving performance and safety was assessed prevalently high with average values of 3.8 and 3.9 (both sd = 0.8) respectively. The driving effort was moderate in the middle of the scale with 2.6 (sd = 1.0). Most comments referred to parked cars in combination with the narrow roads, which was a problem for the evaluation in some places and a reason for the elimination of some intersections. Interestingly, the subjective evaluation of the safety of the ride correlated with the average mileage of the past year (r = 0.57). Thus, the drivers with more driving practice felt safer on the ride.

Priority to the right intersections

For intersections without traffic signs, the priority to the right rule applies on Ger- man roads (cp. section 2.1). For this type, the study distinguished between two shapes (T-junction vs. X-intersection) and all possible driving directions (left vs. right vs. straight). There are three possible forms of T-junctions depending on the direction from which the intersection is approached. Accordingly, there are only two driving directions possible. Thereby, going straight at a T-junction with a left branch was ignored, since it bears comparison with driving on a straight road if there is no traffic present. Due to the missing third driving direction for T-junctions, a combined evaluation in terms of a two-way analysis of variance (ANOVA) (factor 1 shape, factor 2 driving direction) is not applicable. In [48], this was evaded by grouping the three forms of a T-junction and choosing representatives for each direction. Here, there are more T-junctions included in the analysis to find differences and similarities between different orientations and to compare all of them with the X-shape. So, the X-intersections are examined in detail according to the driving direction first. Here, differences in the approaching phase are of interest to infer the intended driving direction from different behavior. Further, the results are compared with the according variations of the T-shape where the same driving direction is possible. Thus, the influence of the different shapes is evaluated. In this part, there were 28 complete datasets for the evaluation. Unfortunately, sequences of three drivers were discarded due to the construction site. All others passed all intersections with priority to the right regulation without interference.

X-intersection The approaching behavior was compared between the three X- shaped intersections (see Table 3.2) according to the driving direction (left, right,

61 3 Driving Behavior straight) which was the independent factor. A one-way repeated measures ANOVA was performed for the DTI and velocity each for the events of leaving the gas pedal (AccPdlLeave) and pushing the brake pedal (BrPdlPush). For the turn switch activation (TrnSwAct), two paired two-sample t-test were suﬃcient (each for DTI and velocity), since the turn signal is only activated when turning left or right.

50 left right 40 straight

30 Distance to intersection [m] 20 AccPdlLeave TrnSwAct BrPdlPush

Figure 3.15: DTI on driver inputs at X-intersections according to direction

There are significant effects for the distance to intersection (DTI) when leaving the gas pedal (F (2, 54) = 17.48, p < .001) as well as for the DTI at the onset of braking (F (2, 54) = 31.23, p < .001). Also, the turn signal activation is significantly distinguishable between left and right turns (t(27) = 4.18, p < .001, d = 0.79). This effect is considerably strong which is indicated by the high Cohen d value. It was not detected in [48] with a smaller sample size. The results are shown in Figure 3.15 with all mean values and the confidence intervals. It can be seen clearly that when turning right leaving the gas pedal as well as pushing the brake pedal is executed closer to the intersection in comparison to turning left or going straight. This was also confirmed by post-hoc t-tests with a Bonferroni alpha correction (α = 0.0167). Turning right was significantly different from both other driving directions (all p < .001). The turn signal is also activated closer to the intersection when turning right compared to turning left. The turn signal was forgotten in two approaches for turning left and three times for right turns. Data was still available since these probands repeated the approaches using the turn signal in one of two runs. The alignment of the driver inputs (x-axis) in Figure 3.15 was chosen to display the chronology of events. It shows that on average the turn signal is activated during the coasting phase between leaving the gas pedal and pushing the brake. However, a detailed look into the sequences for each proband individually creates a different picture. Here, only 11 probands (38.3%) activated the turn signal while coasting when turning left and 8 (28.6%) when turning right respectively. The same number of drivers turning left (11/38.3%) and even half of them turning right (14/50%) are

62 3.2 Naturalistic Driving Study starting the turn signal within 2 seconds before leaving the gas pedal. The rest (6/21.4% each) activated the turn signal after braking. This indicates that the turn signal by itself is not an adequate parameter for an early prediction of a turning event.

35 left 30 right straight

Velocity [km/h] 25

20 AccPdlLeave TrnSwAct BrPdlPush

Figure 3.16: Velocity on driver inputs at X-intersections according to direction

In addition to the DTI, the velocity was examined at the aforementioned driver inputs and also compared according to the driving direction at the different X- intersection approaches. There is also a significant effect for all three events - leaving the gas pedal (F (2, 54) = 23.83, p < .001), pushing the brake pedal (F (2, 54) = 18.39, p < .001) and activating the turn switch (t(27) = 7.2, p < .001, d = 1.36). The results are shown in Figure 3.16. Here, turning left differs significantly from both other driving directions (all p < .001). The velocity is considerably high in all cases and even higher than the speed limit on the route. This could relate to the characteristics of the intersection (No. 21) and the road before which was slightly wider and the intersection was well visible.

T-intersections For the comparison of intersections of diﬀerent shapes, the approaches at T-junctions and X-intersections are compared when driving in the same direction. This means intersections where the same driving direction is possible are compared with each other (column-wise comparison according to Table 3.2). Note that there are multiple intersections of the same shape for T-junctions (cp. Ta- ble 3.2). Again, the DTI and velocity for leaving the gas pedal, pushing the brake and activating the turn signal are examined. In the following, to distinguish between the three diﬀerent orientations of T-junctions, they are labeled T-oriented (T), T-junction with left branch (- ) and T-junction with right branch ( -). | |

Turning left is compared between the X-intersection and two T-junctions of diﬀerent shape, one T-oriented (No. 20) and another with a left branch (No. 6).

63 3 Driving Behavior

A repeated measures ANOVA was performed for both distance to intersection and velocity at driver inputs to examine differences in the approaching behavior. The distance to intersection (DTI) shows significant effects for all three driver inputs. The mean values with confidence intervals are shown in Figure 3.17. The DTI when leaving the gas pedal is significantly different (F (2, 54) = 9.91, p < .01) as well as when pushing the brake (F (2, 54) = 12.42, p < .001). While there are no significant differences between the X-intersection and the T-oriented junction, only the behavior at the T-junction with left branch differs significantly from the other two shapes in leaving the gas pedal (both p < .01) as well as in pushing the brake (- vs. T p < .001 and - vs. X p < .01). Here, the drivers leave the gas pedal and | | also brake closer to the intersection.

50 X-shape T-oriented 40 branch left

30 Distance to intersection [m] 20 AccPdlLeave TrnSwAct BrPdlPush

Figure 3.17: DTI on driver inputs turning left at intersections of diﬀerent shape

The turn switch activation also differs significantly (F (2, 54) = 11.81, p < .001). Here, the turn signal is activated closer to the intersection when turning left at the T- junction with left branch compared to the T-oriented intersection (p < .01, d = 0.60) and also compared to the X-intersection (p < .001). In addition to the already reported missing turn signals for X-intersections, one driver forgot to indicate the turn at intersection No. 6 (branch left) and two at intersection No. 20 (T-oriented). The results for the velocity are shown in Figure 3.18. There are significant differences in the velocity at all driver inputs. The gas pedal was released at different velocities (F (2, 54) = 71.68, p < .001) turning left. At the X-intersection the velocity was higher compared to the T-oriented intersection (p < .0167, d = 0.5) as well as compared to the T-junction with left branch (p < .001, d = 0.92). There is also a clear difference between both T-junctions (p < .001, d = 0.77). The velocity when activating the turn signal also shows a significant effect (F (2, 54) = 7.14, p < .01). Here, the drivers were driving slower when using the turn switch to turn left at the T- junction with left branch as compared to both other intersections (both p < .001). It stands out that there is a high variance in the velocity at the T-oriented intersection.

64 3.2 Naturalistic Driving Study

The usage of the brake pedal shows only diﬀerences between the X-intersection and T-junction with left branch (p < .001, d = 0.92).

35 X-shape 30 T-oriented branch left

Velocity [km/h] 25

20 AccPdlLeave TrnSwAct BrPdlPush

Figure 3.18: Velocity on driver inputs turning left at intersections of diﬀerent shape

The steps in the velocities between the different intersections at leaving the gas pedal and switching the turn lever are also visible in the DTI shown above (cp. Figure 3.17). This corresponds to the closer the activation the lower the velocity. It could be assumed that the temporal distance to the intersection is still similar in all cases even though there are differences in the spatial distance indicated by the DTI. A short look into the time to intersection (TTI) values calculated though dividing the distance to intersection by the velocity confirm the assumption for leaving the gas pedal. Here, at all intersections the average TTI is between 5.3 s (branch left) and 5.8 s (X-shape). However, the turn signal activation and also the brake are both still activated significantly closer to the intersection temporally. Since the average distance between gas pedal release and brake pedal push is comparable for all three intersection shapes, the coasting phase seems similar and only slightly shifted towards the intersection for the T-junction with left branch. The coasting phase is spatially averaging around 15 meters which corresponds to a time of about 1.6 s considering the driven speed.

Turning right is compared between the X-intersection and three T-junctions of which two are T-oriented (No. 10 and No. 12) and one with a right branch. The reason for choosing two intersections of the same shape is to determine if the differences found are explained exclusively by the differences in shape or if there are other factors which were not considered but did influence the approaching behavior. There are significant differences in the DTI when leaving the gas pedal (F (3, 81) = 10.74, p < .001) and for pushing the brake pedal (F (3, 81) = 27.61, p < .001). The distance at turn signal activation indicates no significant effect. Figure 3.19 shows the mean values and confidence intervals. It stands out that at intersection No. 10 the drivers left the gas pedal and also braked further away from the intersection entrance

65 3 Driving Behavior point. All results from the comparative t-tests (Bonferroni adjusted α = 0.0083) are shown in Table 3.3. Here, the values above the main diagonal belong to leaving the gas pedal, while the values below are the comparison between the intersections for using the brake pedal.

Table 3.3: Results of post-hoc tests for turning right at intersections of diﬀerent shape X-shape T-oriented T-oriented branch right vs. (10) (12) X-shape p < .001 T-oriented (10) p < .001 p < .001 p < .001 AccPdlLeave T-oriented (12) p < .001 p < .001 branch right p < .001 p < .0083 BrPdlPush

The results indicate differences for one of the T-oriented intersections (No. 10) even compared to the intersection of the same shape (No. 12). A further in-depth analysis of the video material provided an indication for this conspicuous finding. The drivers left the gas pedal and pushed the brake prematurely at intersection No. 10 when there were parked cars at the side close to the intersection entrance point. The parked cars narrowed the road down leaving only space for one vehicle to pass. Consequently, the driver inputs in these sequences could be a reaction to the parked cars rather than the upcoming intersection distorting the actual results. This influence on the driving behavior was not considered in the execution of the study and was not a factor. Also, it was not influenceable when driving in public traffic.

60 X-shape 50 T-oriented (10) T-oriented (12) 40 branch right

30 Distance to intersection [m] 20 AccPdlLeave TrnSwAct BrPdlPush

Figure 3.19: DTI on driver inputs turning right at intersections of diﬀerent shape

However, while the T-junction with right branch showed no significant differences to the X-intersection, both T-oriented intersections differ significantly in the DTI when braking. In summary, the T-oriented intersection No. 10 stands out, because of interference through parked cars and the different shape which led to differences

66 3.2 Naturalistic Driving Study in the braking behavior, while the turn signal was activated in the same spatial distance to the intersection at all four intersections. Although, a high number of drivers missed using the turn signal especially at the T-junction with right branch. Overall, there were 9 turn signal violations at T-junctions in addition to the 3 missed signals turning right at the X-intersection. Two probands even forgot to activate the turn signal multiple times at different T-junctions. Three drivers forgot it twice at the same intersection leading to missing data (reduction of samples for TrnSwAct). The examination of the velocity for turning right showed significant differences for all three driver inputs, leaving the gas pedal (F (3, 81) = 5.05, p < .01), pushing the brake pedal (F (3, 81) = 8.86, p < .001) as well as activating the turn signal (F (3, 69) = 6.95, p < .001)1. For leaving the gas pedal and using the brake the velocity driven was only significantly higher for intersection No. 12 (all p < .0083). The velocity when using the turn signal differs between intersection No. 10 and the other T-oriented intersection No. 12 (p < .001). Another significant effect is between the T-oriented intersection No. 12 and the X-intersection (p < .001). While at one T-oriented intersection (No. 10) the velocity is rather lower, the drivers were going faster at the other T-oriented intersection (No. 12) on average. The results are displayed in Figure 3.20.

35 X-shape T-oriented (10) 30 T-oriented (12) branch right

Velocity [km/h] 25

20 AccPdlLeave TrnSwAct BrPdlPush

Figure 3.20: Velocity on driver inputs turning right at intersections of diﬀerent shape

A look in the time to intersection (TTI) shows again, that there is an interrelation between the DTI and the corresponding velocity at the point of driver inputs. Ignoring intersection No. 10 which is already identified as an exception, the temporal distance for leaving the gas pedal is between 4.7 s (T-oriented No. 12) and 5.3 s (T-junction branch right). In between, there is the X-intersection with 4.7 s. A corresponding test shows no significant differences for the three intersections (F (2, 54) = 2.60, p = 0.08). For the turn signal activation, there is also no significant difference in the temporal distance for all four approaches (F (3, 69) = 0.90, p = 0.44). This is simultaneously to the spatial distance where

1 smaller p with df adjustment GG = 0.55,F (1.7, 38.0) = 6.95, p < .05

67 3 Driving Behavior there was also no difference (see Figure 3.19). Though, the differences found in the velocity when starting the turn signal are having no effect on the temporal distance. For braking, besides the high TTI of 5 s at intersection No. 10, there is only one significant difference between T-oriented intersection No. 12 and the X-intersection (t(27) = 3.61, p < .0083, d = 0.68). Consequently, the braking behavior when turning right seems to be different at T-oriented intersections which is already indicated by the comparisons of the DTI and the velocity.

Going straight at an unregulated intersection (priority to the right rule) requires a deceleration to yield to potentially approaching vehicles from the right. Hence, there are only two different shapes compared here, the X-intersection and three T- junctions with right branch. Again, intersections of the same shape are included in the comparison to identify other possible factors influencing the driving behavior. Here, only leaving the gas pedal and pushing the brake is examined, since the turn signal remains untouched when passing the intersection going straight. As before, the DTI and velocity are compared at both driver inputs conducting multiple ANOVAs. The DTI shows significant differences when leaving the gas pedal (F (3, 81) = 54.76, p < .001) as well as when initializing the brake (F (3, 81) = 62.69, p < .001). Figure 3.21 shows clearly, that both driver inputs are executed further away when approaching the X-intersection. This is also confirmed by corresponding t-tests. Ad- ditionally, one of the T-junctions with right branch (No. 17) also stands out when leaving the gas pedal (all p < .001) and when using the brake (all p < .001). The other two T-junctions with right branch (No. 13 and No. 16) show no significant differences in the DTI.

60 X-shape 50 branch right (13) branch right (16) 40 branch right (17)

30 Distance to intersection [m] 20 AccPdlLeave BrPdlPush

Figure 3.21: DTI on driver inputs going straight at intersections with diﬀerent shape

A detailed analysis of the sequences showed that the premature driver inputs at intersection No. 17 appear consistently and are not linked to any eye-catching events or objects traceable in the present video material. An additional inspection of the route revealed diﬀerences in the surrounding of the intersections. On the right

68 3.2 Naturalistic Driving Study parcel before intersection No. 17, there was a high hedge which appeared to be an obstruction of sight. Comparing the surroundings with the other T-junctions showed that the visibility constraint present at intersection No. 17 is considerably more severe. Consequently, the drivers can only see oncoming traffic from the right branch when closer to the intersection. This requires a lower speed to maintain the ability to yield. The data indicates that this it is realized by the preceding release of the gas pedal and premature braking. For the velocity, there are significant differences neither when leaving the gas pedal nor when pushing the brake (cp. Figure 3.22). At the former, the drivers are keeping a speed slightly above 30 km/h. Since the coasting phase is rather short, the velocity declines marginally before the braking starts. With an average 15 meters, coasting is the longest at X-intersections. The temporal distance for the driver inputs correspond to the spatial distance. The approaching behavior shows significant differences between the two compared shapes, X-intersection and T-junction with right branch.

35 X-shape branch right (13) 30 branch right (16) branch right (17)

Velocity [km/h] 25

20 AccPdlLeave BrPdlPush

Figure 3.22: Velocity on driver inputs going straight at intersections of diﬀerent shape

Signposted intersections

In the study, the drivers passed three intersections with a traﬃc sign and one of those was passed twice from diﬀerent directions (see yield intersection in the center of Figure 3.14). Each direction was examined for intersections with a yield sign, while there was only one intersection with stop sign which was passed going straight. The approaches of the yield intersections were compared according to the driving direction (left, right, straight) representing the independent variable. The dependent variables again were the distance to intersection (DTI) and velocity when leaving the gas pedal (AccPdlLeave), when pushing the brake pedal (BrPdlPush) and when activating the turn switch (TrnSwAct). Due to a construction site on one day of

69 3 Driving Behavior testing, there was missing data for three test drivers. Though, overall there were 28 samples for the comparison of the approaches on yield intersections. Due to high traffic interference in the approaches to the stop intersection, multiple sequences had to be discarded. Therefore, the stop intersection is only qualitatively compared to the yield intersection approaches. The mean values are included in the graphs of the yield intersection. The distance to intersection (DTI) when leaving the gas pedal at yield intersections shows significant effects (F (2, 54) = 16.91, p < .001) as well as when using the brake (F (2, 54) = 25.06, p < .001). In both cases, the driver inputs occur closer to the intersection when turning left compared to turning right and going straight (all p < .001). However, there is no effect between turning right and going straight. The paired-sample t-test comparing the DTI at turn signal activation shows a significant difference (t(25) = 4.40, p < .001, d = 0.88). Here, three sequences were disregarded due to missing turn signals. The turn signal is also initiated closer to the intersection when turning left. These effects remained undiscovered in the previous analysis [48] with smaller sample size, although the tendency began to show there. The mean distances with confidence intervals are displayed in Figure 3.23. Qualitatively, the DTI at driver inputs approaching the stop intersection is similar to turning right and going straight at yield intersections.

50 yield left 40 yield right yield straight 30 stop straight

20 Distance to intersection [m] 10 AccPdlLeave TrnSwAct BrPdlPush

Figure 3.23: DTI on driver inputs at signposted intersections

The comparison of the velocity results in significant effects in all examined driver inputs at yield intersection approaches similar to the DTI. Here, the velocity when leaving the gas pedal (F (2, 54) = 17.73, p < .001) as well as when starting to brake (F (2, 54) = 15.04, p < .001) is significantly lower when turning left (all p < .001). There appears to be no difference in the velocity between turning right and going straight at yield intersection. Although, the variance in the sequences going straight is obviously lower. These results correspond to the DTI at driver inputs. However, the examination of the temporal distance (calculated TTI) also indicate a difference for the left turn sequences which is about 1 s below the TTI of the other two direc-

70 3.2 Naturalistic Driving Study tions. While the coasting phase appears to be of similar spatial (app. 10 m) and temporal (app. 1.2 s) length on average, the initiation of the deceleration process just starts with an oﬀset when at the yield intersection turning left. Since the turn signal activation occurs considerably closer to the intersection when turning left, the velocity here is also signiﬁcantly lower than when turning right (t(25) = 2.84, p < .01, d = 0.57). The means are displayed in Figure 3.24 including the velocities for AccPdlLeave and BrPdlPush at the stop intersection which is slightly above the velocities for going straight at the yield intersection. Interestingly, the velocity when leaving the gas pedal in the turning left sequences is below 30 km/h in average.

yield left 30 yield right yield straight 25 stop straight Velocity [km/h] 20

15 AccPdlLeave TrnSwAct BrPdlPush

Figure 3.24: Velocity on driver inputs at signposted intersections

During the execution of the study, several parked cars along the route were observable. Unfortunately, due to the residential area, parked cars at the side of the road were unavoidable. In the approach of intersection No. 18 (yield left), the concen- tration of parked cars was especially high and were present for all test drives. This was reaﬃrmed in an additional examination of the video material. The obstruction narrowed the road down to a width only passable by one vehicle along most of the latter part of the approach.

3.2.4 Discussion and Conclusion

The results indicate that there are differences in the preparatory behavior in dependency of the direction of travel as well as the shape of the intersection. The former is shown in the analysis of the X-intersection approaches. There, the deceleration process in form of leaving the gas pedal followed by pushing the brake was initiated spatial and temporal closer when turning right. In the comparison of different shapes (X-intersection vs. T-junction), differences as well as similarities occurred. So, the behavior was alike when turning left at the X-intersection compared to the T-oriented intersection. This arose from the similarities in the DTI

71 3 Driving Behavior and in the velocity when leaving the gas pedal and even clearer when using the brake. Similar commonalities exist between the X-intersection and the T-junction with branch right when turning right. Here, the similar behavior is also indicated by the spatial adjacency and related velocity at the point of braking. Contrariwise, there are noticeable differences in the comparisons of the shape of unregulated intersections (priority to the right). In the following cases these differences are assumed to trace back to the variation of intersection shapes. So, the deceleration process turning left at the T-junction with branch left is executed considerably closer to the intersection compared to at the X-intersection or T-oriented intersection. This can be explained through the missing factor of possible traffic from the right at this shape of intersection. The driver only has to yield to oncoming vehicles that are early visible and where excluded in the study by discarding sequences with traffic present. This way the left turning maneuver is executed without interference, in contrast to turning left at a X-intersection or a T-oriented intersection where the driver has to prepare to yield to a potentially approaching vehicle from the right. When turning right, differences appeared at onset of braking between the T-oriented intersection and both other shapes in the comparison (X-intersection and T-junction with branch right). Here, the brake was pushed earlier at the T-oriented intersection (No. 12). This could be related to the fact that when approaching a T-oriented intersection visually the road ends looking ahead, which brings a certain lack of clarity about the shape and geometry of the upcoming intersection. The comparison of driving straight at the X-intersection compared to the T-junction with branch left showed also differences in the behavior. It is assumed that the later brake response in the T-junction layout might be related to the higher complexity of the X-intersection. Additionally, the analyzed X-intersection had a slight misalignment, which might have additionally increased complexity in comparison to the T-junction. All results related to differences in the shape are displayed in Table 3.4. The same color refers to similarities while “D” stands for “different from driving the same direction at intersections of other shape.”

Table 3.4: Overview of results from comparison according to shape Direction of travel Intersection shape left right straight X-intersection (crossroad) D T-junction (branch right) – D | T-junction (branch left) – D - | T-oriented junction D

Some differences in the approaching behavior are connected to previously unconsidered influencing factors such as parked cars alongside the road. Parked cars are perceived as relevant objects in traffic scenes and can be potential hazards [57], e.g. a person within the parked car opens the door or the driver pulls out of the park-

72 3.2 Naturalistic Driving Study ing lot. Thus, parked cars influence the driving behavior as previously examined in the results of turning right at the T-oriented junction No. 10 with priority to the right regulation. In the yield intersection approaches, the unexpected differences for turning left and the suspicious low velocity when leaving the gas pedal are also indications for the influence of the parked cars present there. However, the reactions seem contrary, since at the yield intersection turning left the drivers decelerated later but were already driving slower, while at the T-oriented junction the deceleration starts earlier and there were no differences in the velocity compared to the other approaches. It is expected that the length of the narrow zone emerging from the obstruction (parked cars) was a factor here. In the approach towards the yield intersection turning left, there were multiple parked cars present alongside, while at intersection No. 10 (T-oriented turning right) only single parked cars led to an obstruction. In general, it stands out that the intervals (error bars) for the spatial distance when leaving the gas pedal are obviously wider than for activating the brake. A further look into the data shows the reason. There is a more individual preference leaving the gas pedal depending on personal manner of driving related to coasting. Otherwise, the onset of braking is located in a more fixed spatial frame for all drivers. So, using the engine brake and aerodynamic resistance in the coasting phase for reducing the velocity when approaching an intersection is believed to be an individual driving characteristic. However, the spatial and even the temporal distance where the drivers start to brake is of high conformity: there is a relatively small distance interval were all drivers feel the necessity to decelerate as preparation to pass the intersection. In contrast, the turn signal usage showed high variations in the DTI and velocity indicated by the error bars in the graphs. This reveals an individual preference of operating the turn switch. There are sequences in the analysis where the turn signal was activated before leaving the gas pedal as well as such later before pushing the brake pedal and also after the brake. At X-intersections, the turn signal is activated spatially in average between leaving the gas pedal and pushing the brake. The operation of the turn switch at T-junctions is equally distributed to the three zones previously described. The only exception is turning right at T-junction No. 10 (T-oriented) where the turn signal occurs after the brake. Here, the influence of the parked cars close to the intersection plays a superior role. Apart from that, a late activation of the turn signal was also observed at yield intersections even to a point directly before entering the intersection. Also, the high number of missing turn signals was eye-catching. Overall, the turn switch remained untouched in 21 turning sequences. The data show that one proband stands out as exceedingly oblivious missing to indicate eight turning maneuvers. This is extraordinary in consideration of the partial test environment created by such a study. There was no obvious reason to explain this incident. It is expected that the average absence of turn signals is even higher in an unobserved driving surrounding. This uncertainty and the high

73 3 Driving Behavior variance in the location of the turn signal illustrates, that it is not an overall reliable parameter to indicate a turn maneuver. Another anomaly in the approaching behavior was examined at intersection No. 17 (T-junction with branch right). Here, a visual constraint was present in form of a high hedge that reduced the visibility of possible approaching vehicles from the right branch for the driver. When choosing a route for a study concerning intersection crossings, a detailed characterization of the line of sights is an important requirement since small differences can influence the driving behavior. This is a general challenge of naturalistic driving studies conducted in a real traffic surrounding beside the uncontrolled traffic that might interfere with the proband. The major advantage why natural driving studies are of interest is the observation and examination of driving behavior in a realistic driving environment. Therefore, the results are of high external validity, since the data was retrieved in a natural driving environment.

74 4 Prediction Algorithm

The prediction of the driver intention is of high relevance for the development of driver assistance as well as automated driving. Though, critical situations may arise dependent on the maneuver the driver intends to execute. In intersection scenarios, the driving direction is such a crucial factor (see Figure 2.3 in section 2.3). Here, the system approach concentrates on the prediction of the ego driver intention. However, the algorithm is designed with the thought of adapting the system to predict the driver intention of other vehicles using sensor data. Since the driver is in full charge of the vehicle control at present, only the turn signal is an indication of a turning scenario. For the prediction of the ego vehicle, it is easily retrievable but not always reliable (cf. subsection 3.2.4). It is also used for indication of lane changing or passing maneuvers that may occur before the intersection (cf. section 3.1.2). Also, a misuse is possible or rather a missing deactivation after the last utilization. However, a solid prediction should be established by taking into account other operational input of the driver. The analysis in section 3.1 concludes that the vehicle dynamics as a result of the driver’s operation are indications for his or her intended direction of travel. However, the prediction algorithm introduced here is based on probabilistic methods. In general, these methods inherently hold a trade-off between the temporal horizon of the prediction and its validity. For establishing a solid prediction, a framework is created and evaluated. Several parameters and the dataset for learning is varied to find an optimal and robust design and gain experience about the performance level of the approach. The evaluation is conducted offline using the database introduced in section 3.1. The overall concept of the framework and some test results were published in [58]. Further, the framework is adapted for a real-time application. This is implemented to run in a test vehicle. Though, the application is predicting the driving direction live in a real driving environment. The realization in a test vehicle was published in [59] and is introduced in the next chapter.

4.1 Framework

First of all, a prediction framework is established to determine the driving direction when approaching an intersection. The prediction is methodologically based on Hidden Markov Models (HMMs) which are introduced in subsection 2.3.2. Further, a four-way intersection is considered as standard intersection. Thus, there are three

75 4 Prediction Algorithm possible driving directions. The driver can turn left, turn right or go straight. The possibility of a U-turn remains unconsidered. The framework consists of three different HMMs each for one possible driving direction (λleft, λright, λstraight). This is similar to other approaches (cp. [40]). Within the models, the observable symbols are the measurable driving data while the hidden states are an abstract construct. They could be imagined as indefinite states in the decision process of the driver which are not observable. However, a detailed definition or classification of the hidden states is unnecessary for the implementation, since the prediction results from the assignment of a sequence to a model representing a driving direction. Therefore, the hidden states are not contemplated in detail and besides are different for each model. The transition matrix A is chosen to be a dense matrix, i.e. all hidden states are directly connected. In this case, more parameters need to be estimated (cp. subsection 2.3.2). Although, one could argue that the decision process briefly described by the hidden states is temporal linear inducing a left-right-model as in [39], which would lead to a sparse matrix. However, to allow the decision to change during the intersection approach, a dense matrix is chosen. This also increases the robustness with respect to noisy driving data. This means in detail, if there were zeros in the transition matrix, errors in the driving data would result in implausible hidden state sequences. The consequences are errors in the forward algorithm routine, i.e. a sequence appears unrecognized by one or more models. So, the framework could exclude one or more driving directions during the approaching process without revision. In the worst case scenario, all models could crash leaving no result to report. This would be an undesirable progress in the prediction, since one scenario will definitively occur. A stop before the intersection is unconsidered. The size of the observation matrix B is depending on the number of hidden states N and the number of observable symbols M. The connection of the driving data and the set of symbols in the models is explained in detail in the following subsection “Input data”. The starting distribution π is of less importance in this application, since the progress is more of interest than the starting state. Also, in an ergodic model where each state is reached from any other with a certain probability, the starting state loses relevance the longer the sequence evolves. All parameters are determined by a learning process using the Baum-Welch algorithm (see Figure 2.5). This requires a dataset of driving sequences. Logically, each model learns with the according sequences related to the driving direction. The learning algorithm is run several times with different starting conditions due to its characteristic of leading to a local optimum. Here, ten times was chosen as a compromise between run-time and performance. The parameter set best describing the learning dataset is selected. The required driving data contains intersection crossings including the information of the actual driving direction. The database

76 4.1 Framework retrieved from the field test and evaluated in section 3.1 will be utilized. As mentioned in the introduction of the method, the quality and size of the dataset for learning the parameters is decisive for the performance of the prediction. Especially, the proportion between degrees of freedom of the model and amount of data for learning is relevant for the generalization of the system. This refers to a memory effect which means a stochastic model is only capable to recognize the dataset utilized for learning without the ability to find corresponding patterns in other data. Therefore, several variations are tested in the evaluation to find a balance between number of parameters and size of dataset. The remaining data is used for estimating the performance of the established prediction framework.

This structure has several advantages compared to one HMM with the driving direction as hidden states. First, the parameters of the separate models can be retrieved by the Baum-Welch algorithm. Also, the probabilities for a sequence belonging to one of the models can be compared to determine the validity of the prediction. Be- sides, an advantage of the overall framework is the potential to retrieve a prediction outcome at any time during the approaching of the intersection. Since the models inherently contain patterns of the approaching progress retrieved from the learning dataset, even a small sub-sequence can be evaluated and recognized. So, the point of the prediction is not ﬁxed spatially or temporally as it is in other approaches with diﬀerent methods (cf. subsection 2.3.1). This enables a driving prediction even in an early state, way before entering the intersection, which is highly desirable in terms of planning a warning and intervention strategy. However, the validity of the prediction is questionable at high distances but increases the further the vehicle closes in on the intersection. This corresponds to the behavior analysis, where differences observable in the driving dynamics become distinctive in close range of the intersection.

Using the framework for prediction, a sequence of driving data serves as input. The data consists of a set of predeﬁned dynamics structured in an overall discrete time interval. This means, there are values for each attribute at all contained time steps. This sequence is evaluated by each HMM using the forward algorithm. The resulting probabilities (P (O λ ),P (O λ ),P (O λ )) are compared, and the | left | right | straight sequence is assigned to the direction according to the model that it best relates to (highest probability). Since the value of the probability (P (O λ)) decreases with | increasing length of the sequence, the forward algorithm runs with the logarithm of the probability. This is also called the log-likelihood. Also, working with the logarithm of the probability prevents problems with the precision of the ﬂoating- point numbers in a computer system. So, even small probabilities are representable without the risk of truncations to zero. Conclusively, the framework is designed to run dynamically multiple times during the intersection approach.

77 4 Prediction Algorithm

The code is implemented in Matlab. For working with HMMs, an existing toolbox was used [60]. Here, the forward algorithm as well as the Baum-Welch algorithm are included. The simTD data introduced and analyzed in section 3.1 serves as database. The dynamic driving data available is chosen as input in particular the velocity, acceleration and yaw rate. Here, the main road sequences are included only. On the one hand, this is necessary because of the traffic interference included in the data. Also, coming from the side branches (north, south) the approaching behavior was dominated by the intention to yield and therefore to slow down or stop the vehicle in case of cross traffic. So, there were no significant differences in the approaching behavior according to the driving direction (see Figure 3.3). On the other hand, the prediction is part of the situation assessment to identify potential conflicts in the intersection area. This risk of a conflict declines, in case the driver complies with the formality to yield. Also, the lack of dynamic information of a stopping vehicle reduces the ability to predict the driver intention. Here, the turn signal might be the best indication. The advantage of using the vehicle dynamics rather than driver inputs is the adaptability of the framework to predict the driving direction of other vehicles as well. This could be accomplished by sensor information. So, while from the perspective of an ego-vehicle the driver intention of others remains unknown, the framework could estimate the behavior based on sensor information. So, velocity, acceleration and yaw rate were selected in main road sequences since they showed a distinguishable progression in the approaching process. In addition to the driving data, the distance to intersection (DTI) is included as observation in the HMMs. The distance is determined by utilizing the recorded GPS data of the vehicles and the retrieved GPS position of the intersection from map data. For the calculation, the method described in section 2.4 was used. While the sequence of the vehicle dynamics describes the process, including the DTI inherently encodes a spatial dependency to observe a certain state in the vehicle dynamics. This increases the robustness of the prediction in case of interference, e.g. by a preceding vehicle, since this changes the process but not necessarily the outcome. For example, when a slow preceding vehicle forces the driver to brake way before the intersection, although the intention is to go straight, the process seems to indicate a turning intention while in combination with the distance this assumption is weakened. As explained in subsection 3.1.1, the driving data is preprocessed resulting in intersection approach sequences over a distance of 100 meters before entering the intersection. The analysis showed that there are hardly differences in the driving behavior or rather vehicle dynamics at this distance. So, a prediction before that point seems unreasonable. Also, the dynamic driving data was interpolated to a sampling rate of 100 ms. All sequences are label according to the the direction of travel (left, right, straight). Unfortunately, the number of sequences for the three maneuvers is highly diverging causing some difficulties in the testing of the frame-

78 4.2 Input data work. Overall, the utilized database consists of 146 turning left, 411 turning right and over 1000 going straight sequences. In the following, the integration of the recorded data as input in the HMMs is described. Further, various parameter constellations are evaluated by determining the recognition rate for the learning dataset. Afterwards, the models were tested with the remaining dataset.

4.2 Input data

The four variables distance to intersection (DTI), velocity v, acceleration a and yaw rate ψ are merged in a state vector of the vehicle (~s). This state is retrieved at discrete time steps t and updated frequently serving as input in the framework (see Figure 4.1). In the data the sampling rate is 10 Hz. The sequence of the state vector represents the observation in the HMMs. Figure 4.1 illustrates the input of vehicle data into the framework.

푠 ego 푡 = DTI, 푣, 푎, 휓

휆left 휆right 휆straight

Figure 4.1: Vehicle state vector as input of the prediction framework

Corresponding to the structure of a discrete HMM as introduced in subsection 2.3.2, the observation in each time step is chosen from a set of countable symbols. The driving data retrieved from the Controller Area Network (CAN) bus is within a discrete interval with a certain resolution. This depends on the number of bits reserved for the information deﬁned in the data length code (DLC) of a CAN message. Such a message contains signals with the information encoded. For the vehicle dynamics the resolution is ﬁne using a DLC up to 8 bytes. This is implemented to accomplish high precision in the measured data. However, it also leads to 264 distinguishable values for just one variable. This illustrates that it is inapplicable to assign a symbol to every combination of measured value. On the one hand, the B matrix would have an extreme size requiring an enormous amount of data for the learning of the parameters. For example, if all four variables have a detail level of 264, the number of symbols would be 2256. On the other hand, in the learning data each of the symbols would need to appear at least once which is unrealistic. So, B would become a

79 4 Prediction Algorithm sparse matrix leading to errors in case a sequence for testing shows symbols missing in the learning dataset.

One way to overcome this discrepancy is to reduce the parameter space by clustering the learning dataset. The resulting cluster centroids represent the symbols in the HMM. Here, the entire dataset chosen for learning consists of multiple vehicle state vectors spread out in four dimensions, one for each considered variable (DT I, v, a, ψ). These points are grouped using k-means clustering [61]. This algorithm is comparable to the Expectation-Maximization (EM) algorithm which is similar to the learning process of the HMM described in subsection 2.3.2. It is available as Matlab function. The number of cluster centers are defined in advance which fits the requirement of the problem addressed here, since the number of symbols is a varied parameter. The algorithm starts by randomly setting up centroids. Afterwards, the distance between each point and all cluster centers is calculated. The points are assigned to the centroids closest to them. This is depending on the distance measure which by default is the Euclidean distance. In the evaluation another distance measure is introduced and tested. The assigned points are used to determine new cluster centroids. Again, all distances are calculated and the points are assigned to the nearest centroid. This algorithm is iterative and converts to a local optimum. Therefore, several runs with different randomized starting values are recommended. Here, the number of runs was set to 10. The procedure is based on the least squares method minimizing the variances between points and their assigned centroid. Mathematically, this is expressed by minimizing the residual sum of squares (RSS)[62].

K X X 2 RSS = (~s ~µ(ck)) − k=1 ~s ck ∈

Here, K is the number of cluster centroids and ck is the k-th cluster. The algorithm is terminated either when no more changes in the centroids position appear between iterations or after a predeﬁned number of iterations. The assigning step in the algorithm is used later in the evaluation to determine which symbol the vehicle state vector (~s) belongs to. This method provides fast results to retrieve symbols in the HMM. However, it is susceptible to outliers in the data. Since all points are included, outliers displace the centroids. This eﬀect is reduced the more cluster centroids are used.

Also, using an Euclidean distance is problematic because of the different measures and span in the measured variables. This refers to the different intervals of the variables. While the DTI with the widest span is between 0 and 100, the acceleration values are within a smaller interval of about [-9, 6]. So, differences in the DTI outweigh differences in the acceleration just because of the intervals. Therefore, the data is scaled. Since the DTI is representing the widest interval, the dynamic vehicle

80 4.2 Input data data is expanded to ﬁt in intervals 100 units wide. An alternative would be a scale invariant distance measure. Another approach to connect the hidden states with the observed measurements is similar to clustering. Instead of ﬁxed centroids, the mapping can be established using multivariate Gaussian distributions. So, a hidden state is not emitting a discrete symbol (represented by the cluster centroid) based on a discrete distribution than rather a continuous vector (vehicle state vector) determined by a probability density function. This function is estimated with a mixture of Gaussian distributions. This link is established by integrating a Gaussian Mixture Model (GMM) for each hidden state into the established model. So, it represents the distribution of the measured data when the system is in one of the N hidden states. The observation matrix B from the discrete HMM is replaced by N GMMs λbi(i = 1, ..., N). A Gaussian Mixture Model is an approximation of an unknown distributed set of dim random variables using the weighted sum of M mixtures of Gaussian distributions. The following brief introduction is based on [63]. The model is characterized by the vectors ~µ of size dim containing the means for each mixture, the covariance matrices C of size dim dim and the mixture weights ωj(j = 1, ..., M). Here, × the set of random variables corresponds to the 4-dimensional vehicle state vector ~s (dim = 4). Accordingly, the density of the mixed distribution for one hidden state is given by:

M X p(~s λb) = ωj g(~s ~µj,Cj) | · | j=1

The mixture weights determine the influence of each component Gaussian density g(~s ~µj,Cj) which is defined as: | 1 1 g(~s ~µ ,C ) = exp (~s ~µ ) C 1(~s ~µ ) j j d/2 1/2 j 0 j− j | (2π) Cj −2 − − | | PM The sum of the weights satisfies the constraint j=1 ωj = 1. So, the result is an actual probability density. Depending on the number of mixtures for the modeling, the number of mean vectors and covariance matrices and accordingly the number of parameters increases. These are determined similarly to the learning process of a HMM by the Expectation-Maximization (EM) algorithm. For the implementation in the HMM, the GMM parameters are stored in single variables. So, the vector µ becomes a 3-dimensional array of size d M N, the × × covariance matrices C becomes a 4-dimensional array of size dim dim M N × × × and the weights ω are stored in a M N matrix. This indicates that the number × of parameters that are necessary just for the matching of the hidden states to the vehicle state vector including the four measurements (dim = 4) is 21 N M. × × Focusing on the necessary parameters, this corresponds to a k-means clustering

81 4 Prediction Algorithm with 21 M centroids. This should be considered when comparing both methods × in the evaluation.

4.3 Evaluation

In the evaluation phase, various parameter setups are implemented and the according HMMs learn with a specified dataset. For a start, the setups are evaluated by the recognition of the sequences used for learning. Hence, the performance can be estimated and assumptions are made, that are verified in the following validation phase. There, an offline prediction is simulated by evaluating the sequences drawn from the rest of the database that was excluded from the learning dataset. The learning dataset was chosen randomly but kept consistent over all tests except the one varying the size of the dataset. So, the comparability of different variations is independent of the dataset for parameter learning which is known to have great influence on the results. This influence is obvious when comparing the results here with the results in [58] where a different random learning dataset was used. The following parameters are varied in the evaluation with discrete Hidden Markov Models: the distance measure for clustering, the number of symbols (M) which is equivalent to the number of cluster centroids, the size of the dataset for learning (LS) and the number of hidden states (N). All these variations have a noticeable effect on the recognition of the learned sequences. The size of the dataset for learning is consistent between the HMMs of a setup. So, all three prediction models receive the same number of sequences for the learning process. The number of symbols has an effect on the size of the observation matrix and inherently on the amount of information extracted from the measured data. The number of hidden states is also influencing the size of the observation matrix B and also the transition matrix A. Since it represents an abstract inherent state of the process, the variation is necessary to find a suitable number best describing this process. In a similar implementation, the choice of this parameter seems to be based on experiences with the method and is set to 5 [40]. However, not all combinations of variations are considered to reduce the complexity of the evaluation. Instead, a standard setup is introduced with 5 hidden states, 16 symbols and 146 sequences for learning and the evaluation is executed subsequently. The distance measure for clustering is varied between Euclidean and Cityblock. The latter refers to the sum over the absolute distance along each dimension. It reduces the computing time but is also susceptible to the scale effect mentioned previously. The standard value for the number of symbols was chosen to have two occurrences for each dimension, i.e. the combination of two different characteristics in the measured variable. For example, two characteristics of the velocity could be labeled slow and fast. All four variables are combined in the vehicle state vector

82 4.3 Evaluation which has 4 dimensions accordingly. The cluster centroids represent a combination of these characteristics in the 4-dimensional variable space. Thus, there are 24 (16) combinations of two characteristics in four variables. For the variation, the number of symbols is increased to have 3 and 4 characteristics for each measured variable leading to 34 (81) and 44 (256) symbols respectively. The entire learning dataset is clustered together. So, the symbols represent the same area in the vehicle state space for all three HMMs.

The number of available sequences were unbalanced for the different driving directions. There were only 146 left turning scenarios compared to 411 sequences with right turns. Most of the drivers went straight (over 1000 sequences). A smaller dataset for learning lacks the diversity necessary for generalization, i.e. the model adapts to the learning dataset explicitly. This should lead to a memory effect with high recognition rate on one side but poor performance in the prediction of unknown sequences. So, the maximum size for the dataset is 146 sequences since there are no more left turns and the size shall be consistent between all three models. For the variation, this size is reduced to half (73 sequences) and quarter (36 sequences) of the standard value to observe this effect. Finally, the number of hidden states was varied between 2 and 10.

As described in subsection 2.3.2, learning the parameters is an iterative procedure defined by the Baum-Welch algorithm. It determines the model parameters and converges towards a local optimum. Therefore, the learning is performed several times with randomized starting values. This makes the comparison of different parameter sets complicated, since the results may relate to a better optimum found in the learning process rather than the differences in the parameters. So, the log-likelihood of the learning process is stated for the established models. This represents the probability that the complete learning dataset (LS) used for a HMM is associated with this model (P (LS λ)). This gives an estimation of the performance with the par- | ticular parameter set. However, the probability changes for the variation of hidden states, number of sequences in the dataset for learning and number of symbols.

After the learning, the evaluation is performed by sampling a sequence time step by time step. This means parts of the sequence is presented to all three HMMs and in each time step a estimated driving direction is retrieved by comparing the log-likelihood of each HMM. This process is running until the end of the sequence is reached. The last prediction result is compared with the actual direction driven in the sequence. In case of a match, the sequence counts as correctly predicted. Then, the actual time before the vehicle entered the intersection is determined where the correct result ﬁrst appeared without any changes in between using backtracking. This value is called prediction time to intersection (TTI).

83 4 Prediction Algorithm

Distance measure for clustering (Cityblock vs. Euclidean) The quality of the clustering is inﬂuencing the information transferred into the model. For k-means clustering, a distance measure determined the allocation of the points to a centroid. Since this allocation is necessary for the prediction also (to determine which symbol to assign to the measured vehicle state vector), a second distance measure is evaluated besides the Euclidean distance. For the Cityblock distance measure, the sum of the absolute values of the entries in the distance vector between two points is calculated instead of the magnitude of the vector itself. So, the calculation requires slightly less computing time. Also, the centroid is the component-wise median of the cluster points when using the Cityblock measure instead of the mean of the points when using the Euclidean distance.

The same learning dataset was used and the HMMs were parameterized with the standard parameters. The learning results using the Euclidean distance (LL = 1.77 104, LL = 1.38 104, LL = 1.26 104) were slightly left − · right − · straight − · better than using the Cityblock distance measure (LL = 1.83 104, LL = left − · right 1.44 104, LL = 1.30 104). Here, the log-likelihood (LL) of each model − · straight − · expresses the probability of the match between the learning dataset overall and the created model.

The evaluation results are illustrated in Figure 4.2. The bars represent the number of correctly recognized sequences from the learning dataset. The diﬀerent color shades indicate increments in the prediction TTI. These are from dark to light: >3 s, >2 s, >1 s and <1 s. The turning left sequences are recognized all but one for both variants. Although, the prediction TTI is better using the Cityblock option. Also, there are certainly more sequences for right turns recognized in the Cityblock section. However, the high bar in dark magenta indicates that most of the rights turns were recognized early using the Euclidean option. The sequences for going straight have a low recognition rate for both which is surprising. The analysis of the data in section 3.1 suggests that going straight is a simple progress. A closer look into some sequences shows that the high deviations in the velocity and acceleration are a result of speed reductions through coasting. Obviously, the other two models for turning events pick up on this process leading to false predictions. The setup with Euclidean distance recognizes slightly more sequences going straight.

Overall, the variation of the distance measure for clustering showed balanced differences in the performance. A high prediction rate for each direction is mostly desired. The Euclidean distance shows slightly better results for the going straight event and also indicates a higher prediction TTI for turning right sequences. Therefore, it is chosen the preferred distance measure in the k-means clustering for the further evaluation. The chosen measures are too similar to find significant differences. Con- sequently, there is no test performed on the rest of the data.

84 4.3 Evaluation

146

120

90 Cityblock 60 Euclidean

30 Recognized sequences

left right straight

Figure 4.2: Recognition rate varying the cluster distance method

Variation of cluster centroids The centroids in the clustering are corresponding to the symbols in the HMM. Therefore, a higher number potentially leads to more transitions in the hidden states. Also, more information is retrieved from the dataset. However, the increase in parameters requires more learning data. The number of centroids is increased in two steps from the standard value 16 up to 81 (LL = 3.91 104, LL = 3.17 104, LL = 2.91 104) and further to left − · right − · straight − · 256 (LL = 5.60 104, LL = 4.56 104, LL = 4.11 104). The dis- left − · right − · straight − · tance measure in the k-means clustering was chosen to be Euclidean. The framework for 16 centroids is adopted from the comparison above. Thus, the evaluation results are identical with the Euclidean setup above. It is only included for comparison reasons. The log-likelihood is decreasing due to smaller probabilities in the B matrix when the number of symbols rises. Comparing the models within a setup, it seems the going straight models (λstraight) are better adapted to the learning dataset than the other two. However, the reason for the higher LLstraight in all setups is that the straight sequences are shorter, i.e. less time steps because of the higher velocity and missing deceleration. Since the log-likelihood is the multiplication of probabilities along the sequences, the less time steps there are the larger is the probability.

The results are displayed in Figure 4.3. The color coding follows the same scheme as in the previous comparison (from dark to light: >3 s, >2 s, >1 s and <1 s). The higher number of centroids allows a more accurate partitioning of the observation space containing the vehicle state vectors. This leads to the improvements that are clearly visible. The left turns are again highly recognized. Obviously, the sequences contained in this dataset are homogenous and considerably distinguishable from the other two directions. However, with 81 and 256 symbols, the number of sequences with high prediction TTI increase further for left turning sequences. For the right turns, the model with 81 symbols recognizes two sequences more than the model with 256 symbols. A further increase in symbols will probably not increase the

85 4 Prediction Algorithm recognition rate any further. The recognition rate for straight sequences increases for each step up to 86% with 256 symbols.

146

120

90 16 symbols 81 symbols 60 256 symbols 30 Recognized sequences

left right straight

Figure 4.3: Recognition rate varying the number of symbols

Conclusively, the increase in cluster centroids and HMM symbols accordingly improved the recognition rate and also the average prediction TTI. However, the testing below will show if the size of the learning dataset is still suﬃcient or if the improvement only applies to the recognition of the learned data instead of the prediction of unknown sequences. Also, the required parameters increase by a factor of about 3 using 256 symbols instead of 81. This should be considered in the following variation, especially since the recognition improvements are relatively small.

Variation of learning dataset size The size of the dataset used for learning is influencing the performance of the prediction. It is expected that a reduction of sequences for learning will improve the recognition on the cost of the prediction of unknown sequences. Also, the number of symbols was set to 256 increasing the size of the observation matrix B to 5 256. The already evaluated setup with 146 learning × sequences (LS146) is included for comparison reasons. Besides, two variations are evaluated with half of the learning dataset (LS73) and a quarter of the standard learning dataset (LS36). The learning procedure returned the the models for the 50% dataset (LL = 2.63 104, LL = 2.31 104, LL = 1.90 104) and for left − · right − · straight − · the 25% dataset (LL = 1.16 104, LL = 1.04 104, LL = 1.00 104). left − · right − · straight − · The probabilities differ for each setup because the length of all learning sequences is of course different varying the dataset. However, the size of the dataset is kept consistent over all three scenarios because of the clustering which is performed with the entire set. An unbalanced number of sequences would influence the cluster centroids. This would lead to an over representation of the direction with more sequences for learning. The results of the comparison are displayed in Figure 4.4. Also, the y-axis represents a relative recognition rate for a better comparability. In all three setups, left turns

86 4.3 Evaluation were recognized nearly perfectly. This again indicates a high homogeneity in the left sequences and patterns that are clearly distinguishable. Apparently, there are no anomalies in the data for left turns which is remarkable since the traﬃc interference is included in the data. 100

60 LS36 LS73 40 LS146

20 Recognition rate [%]

left right straight

Figure 4.4: Recognition rate varying the size of the learning dataset

The best recognition results were achieved with the smallest learning dataset as expected. The further test with unknown sequences will prove if this high recognition will also result in high prediction rates. While for left turns the recognition remains stabilized, the rates reduce the larger the size of the dataset. This eﬀect is stronger for the straight sequences than for the right turning ones. Again, this indicates that there are outliers in the going straight dataset.

Variation of the number of hidden states The hidden states represent abstract states in the decision making process which are not defined in detail. Their number influences the size of the transition matrix A as well as the size of the observation matrix B. So, more hidden states require a larger number of parameters to be learned. Since the time development of the hidden states sequence is the key functionality to find the patterns in the intersection approaching process, their number is relevant for the performance. The standard value of 5 was chosen based on an existing approach [40]. Thus, the number of states is varied widely between 2 and 10 to examine their influence in detail. Since the setup with 81 symbols showed decent results, it is chosen for this variation, especially to keep the number of required parameters manageable in perspective of the further growth with more hidden states.

Also, the large dataset for learning is used (LS146). Accordingly, the setup with 5 hidden states was previously generated and is included for a better comparison. The results of the learning process are displayed in form of the log-likelihood of the according learning dataset in Table 4.1. From left to right the probabilities increase, which still indicates that the lengths of the sequences are shorter from left to right in the table. The increase in the log-likelihood with a higher number of hidden

87 4 Prediction Algorithm states N implies a better representation of the learning dataset with more hidden states. However, this refers to the overall dataset rather than each single sequence. It appears since more hidden states allow a stronger preference in the allocation to the symbols. This means the probabilities in the B matrix increases for some symbols that are frequently reached, which leads to a higher overall probability for the dataset. The evaluation will show if the recognition rate is also rising with increase in the number of hidden states.

Table 4.1: Log-likelihood of learning dataset with diﬀerent number of hidden states

N LLleft LLright LLstraight 2 4.91 104 4.01 104 3.69 104 − · − · − · 3 4.50 104 3.69 104 3.33 104 − · − · − · 4 4.21 104 3.42 104 3.22 104 − · − · − · 5 3.91 104 3.17 104 2.91 104 − · − · − · 6 3.70 104 3.03 104 2.79 104 − · − · − · 7 3.61 104 2.93 104 2.62 104 − · − · − · 8 3.43 104 2.88 104 2.62 104 − · − · − · 9 3.28 104 2.71 104 2.43 104 − · − · − · 10 3.10 104 2.63 104 2.34 104 − · − · − ·

The results of the evaluation are presented in Figure 4.5. The recognition rates of left turning sequences are still high even with fewer hidden states. Also, the mean prediction TTI is above 6 s for all cases. Rights turns are also recognized early (mean prediction TTI above 4 s) and at high rates greater than 97%. So, between one and four out of the 146 sequences were identified mistakenly in the different variations of the number of hidden states. The main influence of the variation appears in the straight sequences. Here, the recognition rate fluctuates below 80% for the first variations between 2 and 5 hidden states. With a further increase the recognition rate rises to 88% with a decent mean prediction TTI of 5.7 s. This is the best performance in the variation, since the recognition rate drops again with 9 and 10 hidden states respectively. The validation of the results will show, if the variances especially in the straight sequences is reduced to the variation of hidden states. It could also be related to variances in the quality of the learning performance, i.e. in some cases a better local optimum was found than in others.

88 4.3 Evaluation

100 N = 2 80 N = 3 N = 4 60 N = 5 N = 6 40 N = 7 N = 8 20 N = 9 Recognition rate [%] N = 10

left right straight

Figure 4.5: Recognition rate varying number of hidden states

Gaussian Mixture Model Using GMMs as link between the measured data and the HMM was explained in detail in section 4.2. For each hidden state, a GMM is established with M mixtures of Gaussian distributions. The number of mixtures determines the shape of the distribution function representing the probability of a vehicle state vector while in a certain hidden state of the HMM. This variable is varied between 2 and 4. The more mixtures are used the better representation but this requires also a greater amount of parameters to be learned. The other parameters are set to 5 hidden states (standard parameter) and half of the learning dataset (LS73). The latter was chosen to reserve sequences for the validation of the left turning model. The advantage of using GMMs is that previous clustering and scaling is unnecessary. It is expected that the information transfer into the model is more eﬃcient this way. The results of the evaluation are shown in Figure 4.6. The recognition rates are very high for all three driving directions. The color shades refer to the prediction TTI in the same way as before (from dark to light: >3 s, >2 s, >1 s and <1 s). All turning left sequences were correctly recognized by all three variations. However, the model with 2 Gaussian mixtures performed slightly better identifying some sequences earlier than in the other models. The recognition of right turn sequences is similarly high. Only one sequences was missed by the model with 2 mixtures. The straight sequences have also a high recognition rate. However, the light shades indicate that a high amount of sequences is correctly identiﬁed in the last second before entering the intersection. The high recognition rates are promising. The validation will show if this also applies to high prediction rates in the unused dataset. Also, it stands out that the prediction TTI is lower in average.

89 4 Prediction Algorithm

2 mixtures 40 3 mixtures 4 mixtures 20 Recognized sequences

left right straight

Figure 4.6: Recognition rate varying number of mixtures in GMMs

4.4 Validation

In the validation of the framework, the different setups evaluated previously are tested with the dataset which remained unused for the learning process. The evaluation led to the following assumptions. A higher number of centroids results in a more accurate sampling of the data which improves the prediction. However, this requires more model parameters and increases the computing time. A decrease in the size of the learning dataset resulted in high recognition rates. However, a smaller dataset is corresponding to less information input into the model. The prediction of these models is expected to decrease in performance due to a memorizing effect. Also, the optimum number of hidden states for the HMMs with 256 symbols and the given modeling approach seems to be 8. The variation of the distance measure for the clustering was not validated since the two variations differed hardly and both require scaling. For all further models, the Euclidean distance was used as standard measure for the distance between points in the vehicle state space. Also, the clustering required a consistent learning dataset for all three HMMs. Otherwise, the cluster centroids would have been shifted to the scenario with the largest dataset for learning. This restriction and the unbalanced number of available sequences impeded the validation of the turning left scenario since only 146 sequences were available here. The problem of consistent size of the learning dataset for each model applies not necessarily to the approach using GMMs. This is addressed further below. The remaining dataset which was excluded in the learning process is tested with the established models. Unfortunately, the sequences for left turns was completely used for the learning in most variations except the comparing of different sizes of the learning dataset. So, there were no sequences left for the validation. However, the evaluation showed that the left turns were mainly recognized by all different variations. This indicates that in this dataset the sequences are homogenous and

90 4.4 Validation clearly distinguishable compared to sequences of the other two directions. The validation was performed analogically to the recognition, i.e. the sequences are partially presented to each model and the highest log-likelihood determines the prediction outcome. The prediction TTI represents the time were the correct direction ﬁrst appeared without further changes. The results of the variations of number of symbols and size of learning dataset are displayed in Table 4.2. It starts with the standard parameters and shows all evaluated parameter setups. The varied parameter is emphasized with bold letters. The mean prediction TTI is averaged over all correct identiﬁed sequences. Further, only the early and late prediction TTI slots are stated in relation to the correct predicted sequence.

Table 4.2: Prediction results of diﬀerent variations left right straight Varying number of symbols 16 symbols / 5 hidden states / LS146 rel. correct prediction - 79% 75% mean prediction TTI - 7.5s 5.5s prediction TTI >3s - 94% 80% prediction TTI <1s - 2.3% 2.2% 81 symbols / 5 hidden states / LS146 rel. correct prediction - 95% 85% mean prediction TTI - 6.1s 4.3s prediction TTI >3s - 80% 68% prediction TTI <1s - 3.1% 26% 256 symbols / 5 hidden states / LS146 rel. correct prediction - 82% 70% mean prediction TTI - 7.8s 3.9s prediction TTI >3s - 94% 57% prediction TTI <1s - 0% 20% Varying size of learning dataset 256 symbols / 5 hidden states / LS73 rel. correct prediction 100% 69% 66% mean prediction TTI 8.3s 4.3s 6.9s prediction TTI >3s 90% 56% 98% prediction TTI <1s 0% 18% 0.6% 256 symbols / 5 hidden states / LS36 rel. correct prediction 100% 38% 59% mean prediction TTI 6.9s 7.9s 4.7s prediction TTI >3s 89% 100% 65% prediction TTI <1s 0.9% 0% 3.4%

The increase in the number of symbols did not necessarily lead to a better performance. However, the results with 16 symbols show that there is a minimum not

91 4 Prediction Algorithm to be fallen below to ensure the distinguishability between the different intersection approaching phases. More symbols improve the information transfer from the data into the model, but require more data for a solid estimation of the model parameters. The highest prediction rates are already established using 81 symbols. Here, 95% of the right turning sequences and 85% of the straight sequences are correctly identified. Obviously, the size of the learning dataset is too small for 256 symbols, since the prediction rate drops for both right turns and straight sequences. Interestingly, the majority of the right turns (94%) is correctly predicted with both setups using 16 symbols and 256 symbols. This indicates that a high number of right turning sequences is similar and distinguishable in an early stage of the intersection approach. The dataset with straight sequences seems to be more diverse leading to errors in the prediction. As expected, the prediction rate drops harshly when reducing the number of learning sequences for the right turning and going straight scenario. However, left sequences are estimated extremely accurate with 100% prediction rate with both half and quarter learning sequences. This leads to the conclusion that the turning left sequences are overall very similar, i.e. the entire 146 sequences. This leads to a highly adapted left turning model which is extremely specialized. At this point, this adaptability is especially impressive considering that the sequences included traffic interference which is influencing left turns in particular. Contrariwise, the straight sequences seem very diverse and are numerously included in the dataset. So, the according HMM has difficulties to adapt to all straight sequences equally well. Also, there were anomalies in some sequences that are comparable to turning behavior, such as reducing speed during the intersection approach. Therefore, certain not typical straight sequences are identified mistakenly as turning sequences. However, the variation of the size of the learning dataset shows clearly the previously introduced memory effect at least in the right turning and straight scenario. While the recognition rate rose in the evaluation with smaller dataset, the prediction rate drops below

70% with the half of the usual learning dataset (LS73) and even further below 40% for right turns and 60% for going straight with the smallest learning dataset tested

(LS36). So, the models were adapted to the dataset for learning particularly which resulted in a decrease of detection of similar sequences. This is illustrated by the poor performance of the turning right model and especially in the prediction TTI. All sequences that were correctly predicted, were identiﬁed at an early stage and therefore must be similar to the learning sequences. In the variation of hidden states, the evaluation showed best results using 8 hidden states with 81 symbols and the largest learning dataset (LS143). The results of the validation conﬁrm this as an optimal value in this setup (see Figure 4.7). There is a clear maximum in the performance, since the prediction rate drops again using more hidden states. Still, it has to be considered that for each variation, the learning process was performed leading to local optima of parameters. Also, the performance

92 4.4 Validation is dependent on the learning dataset and diﬀerent setups might improve with other data in the learning process.

100 N = 2 80 N = 3 N = 4 60 N = 5 N = 6 40 N = 7 N = 8

Prediction rate [%] 20 N = 9 N = 10

left right straight

Figure 4.7: Validation of prediction rate varying number of hidden states

Using 8 hidden states, 93% of the right turns and 94% of the straight sequences are identified correctly. In this case, the mean prediction TTI is 6.9 s and 5.5 s for left turning and straight scenarios respectively. Comparing all setups, the prediction TTI varies intensely in the variation of hidden states. However, better performance in the correct prediction is not necessarily leading to a decrease of the mean prediction time as it could be assumed. The best setup for turning left is 5 hidden states (95%). While this framework detects 5 more left turning sequences, 91 straight sequences are missed compared to the framework with 8 hidden states. The variation of the mixtures of Gaussian distributions utilizing GMMs shows similar results for all three variants (see Table 4.3). Here, 5 hidden states and half the learning dataset were used. Compared to the clustering with half the learning data, the prediction rate is solid above 80% even for right turning and going straight. However, the mean prediction TTI is rather low and a high number of sequences is correctly identified less than a second before entering the intersection. The left turns are detected almost completely and at an early stage of the approaching stage. The prediction rate of the right turns is slightly below the rate of the straight sequences. In both cases, about half of the correct predicted sequences is identified more than 3 s before reaching the intersection. Furthermore, in more than 10% of the turning right and going straight sequences late prediction appears. However, the required parameters with the three setups compares to clustering with 42 to 84 centroids. Taking this into consideration the results are better than using clustering. Also, the GMM approach requires no scaling and since the clustering is obsolete, the consistency of the learning dataset for each model is unnecessary. This reduces the computing time and eliminates the information loss due to the sharp discretization.

93 4 Prediction Algorithm

Table 4.3: Prediction results for variation of mixtures in GMM left right straight Varying number of mixtures 2 mixtures / 5 hidden states / LS73 rel. correct prediction 99% 86% 89% mean prediction TTI 8.6s 3.6s 3.6s prediction TTI >3s 90% 48% 50% prediction TTI <1s 1.4% 24% 11% 3 mixtures / 5 hidden states / LS73 rel. correct prediction 100% 83% 86% mean prediction TTI 8.8s 3.4s 3.6s prediction TTI >3s 92% 46% 50% prediction TTI <1s 2.7% 24% 16% 4 mixtures / 5 hidden states / LS73 rel. correct prediction 100% 83% 86% mean prediction TTI 8.8s 3.9s 3.3s prediction TTI >3s 92% 49% 45% prediction TTI <1s 2.7% 17% 16%

In Table 4.4 a confusion matrix is displayed showing the results with 3 mixtures, 5 hidden states and a randomized 20% learning dataset. This means one-ﬁfth of the data available for each driving direction is used as learning data. The remaining 80% are tested and the prediction performances illustrated in the confusion matrix.

Table 4.4: Confusion matrix using Gaussian mixtures (3 mixtures, 5 hidden states) Predicted direction Direction driven left right straight left 116 1 0 right 2 329 6 straight 2 72 806 mean prediction time 4.9s 4.2s 4.8s

The prediction rates are high for all three scenarios. Only one left turn is mistakenly estimated as a right turn. This is impressive considering the small amount of sequences for learning the left turn model (26 sequences). Right turns were correctly predicted in 97% of the sequences. The few errors identified right turns as left turns in 2 sequences and as going straight in 6 sequences respectively. Also, 92% of the straight sequences are predicted as such. Here, a high number of 72 straight sequences is identified as right turns by mistake. This confirms what was already assumed before that there are several straight sequences with reduction of speed in the approach which are therefore expected to be right turns. However, the prediction TTI is between 4 s and 5 s in average. Comparing with the clustering method,

94 4.5 Conclusion these are rather low. The validation of the variation of mixtures already showed that there are multiple sequences detected shortly before entering the intersection.

4.5 Conclusion

A prediction framework was established to predict the driving direction at a four-way intersection using vehicle dynamics and the distance to intersection as input data. The framework was tested with different variations of parameter sets to examine the influence of each parameter. To use the measured vehicle data as input to the framework two different approaches were introduced. On the one hand, the vehicle state vectors are clustered to find representatives that serve as symbols in the Hidden Markov Models (HMMs). On the other hand, an alternative is to use Gaussian Mixture Models (GMMs) to estimate the distribution of vehicle state vectors emitted by a hidden state. Using the clustering method, the best results were established with 8 hidden states,

81 cluster centroids and a learning dataset of 143 sequences (LS143). The variation of the cluster centroids illustrated the trade-oﬀ between available learning data and required parameters. Further, a memory eﬀect was shown in the variation of the size of the learning dataset for right turns and straight scenarios. Fewer learning data was better recognized by the model but showed poor results identifying unknown sequences. The approach with GMM showed solid prediction rates but lower prediction TTI values. However, its advantage is the variation of the learning dataset for each HMM. This also leads to the capability of an online learning approach, i.e. new sequences that are estimated can be used for learning the according model after the driving direction is determined.

95 96 5 System Approach

The prediction algorithm is embedded in a system designed for situation assessment based on the approach illustrated in Figure 2.2. Here, the focus is on situation analysis and prediction. Still, the sensing requirement and current innovations are shortly introduced. The main part is the implementation of a real-time application running the prediction algorithm in a test vehicle.

5.1 Sensing

For the detection of objects, sensors are necessary corresponding to the human senses. In an urban environment, there are considerably more traffic participants present that might become relevant for the situation assessment. In the process, line-of-sight obstructions impede the perception additionally. Here, common sensor systems such as lidar, radar and cameras are also affected due to their dependency on a line of sight to an object. Recently developed communication technologies overcome this constraint, providing information about the static and dynamic surrounding beyond the range of the human perception. This advantage is used on one side to provide this information to the driver and on the other side to enable new features to assist the driver in an urban environment. This environmental modeling is the basis for those applications. Comprehensive databases are available with road data in form of a digital map. So far, map data has already been used for navigation applications. Further development includes a more precise representation of the traffic network on at least a lane level. This provides detailed information about the accessible space for the vehicle. In this connection, back end solutions are developed to store the static environmental data (road infrastructure) as well as changeable infrastructural information (traffic signs, construction sites). The back end is kept up-to-date by a communication link to vehicles, i.e. sensor information from vehicles is retrieved and processed into the digital map database (personal information, IAA, Frankfurt, 2015). This advancement is capable to provide road information in a more dynamic way including temporary constraints and traffic signs. However, a constant communication link between vehicle and back end is required. An essential challenge is the localization of the ego vehicle within the digital map. The quality of the position information provided by GNSS is decreasing in urban environments due to multipath scattering

97 5 System Approach and shadowing in urban canyons. So far, a lane level localization is not realized with common GPS receiver units. In addition to the communication with service providers, the Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication technology will be introduced in new vehicles shortly (personal information, ITS World Congress, Bordeaux, 2015). This offers new approaches especially for urban applications. Through communication between vehicles, information is retrieved of other vehicles without the constraint of line of sight. After the introduction, the benefit will rise with a increase in market penetration. Traffic light assistance was realized in a development stage using V2I communication. The combined information of dynamic objects and the static traffic environment merges into a environmental representation of a part of the traffic situation including considerably more information than contained in the driver situation perceived by a human driver.

5.2 Situation analysis

The situation analysis corresponds to the comprehension level in the Situation Awareness model (see section 2.2). So, it contains the classification of all relevant objects within a certain range of the ego vehicle. The minimum requirement for a system is to include all objects of the driving situation. This is the representation a driver could possibly establish with the human senses. However, as in the previous section explained, there is information available about the environment as well as about objects that are located at further distance. This foresight is called electronic- horizon and significantly improves the situation analysis. Here, the focus is on the static traffic environment especially road attributes. Details about the characteristics of the intersection, such as shape and regulation, are necessary to establish an intersection model which can be visualized according to Figure 2.3. The static information about the road network and intersections in particular are provided by digital maps. The company HERE (former NAVTEQ) is a map provider with an extensive database on road information. They also offer a framework for map-based driver assistance applications called ADASRP (Advanced Driver Assistance Systems Research Platform). The data includes a variety of road attributes, but also speed limits and traffic signs. The structure of the data is specified in the ADASIS (Advanced Driver Assistance Systems Interface Specification) protocol. Therein, the road network is organized in knots called stubs connected by so called links. The combination of several links is called path. The distance to traffic signs or stubs are indicated by an offset starting at the beginning of a path. Also, the vehicle position along the path is stated in this way. The positioning is established correspondingly to navigation systems. A GPS receiver is used to locate the position of the vehicle in the map. Further, an electronic horizon is

98 5.3 Prediction

OpenDrive database

Intersection GPS receiver Interface model

ADASRP

Figure 5.1: Interface for intersection modeling (schematic) established that contains all the data including the road attributes and geometry of the upcoming road segments to a certain predefined distance of path. The intersection modeling is designed in a modular setup to remain independent of the data source. Therefore, an interface is created that can process ADASIS data as well as data in OpenDrive format (see Figure 5.1). The latter is an open data format where the road network data is stored in a structural pattern similar to the XML (eXtensible Markup Language) format. It is especially used in simulation applications. However, there was no data available for the region. So, some intersections were measured and manually encoded in XML files for testing the system. The advantage of the interface is, when using other road data sources the interface is reworked instead of the entire application. The data source for modeling is selectable within the interface. The ADASIS data is provided either by running ADASRP on the same platform or by receiving the data via CAN bus from a source within the test bed. In both cases, the actual GPS location is required for map matching. In the manually established OpenDrive database, the position of the intersection is listed. The interface delivers the actual position to compare both coordinates and preselects the closest intersection. Whether the vehicle is approaching this intersection directly is determined by the heading also provided by the GPS unit. When approaching the intersection and closing in under a DTI of about 110 m the modeling starts and a simplified graphical output is generated in the application (see Figure 5.4).

5.3 Prediction

The prediction algorithm introduced in chapter 4 is adapted for a real time application of the prediction of the driving direction. The test ﬁeld is identical with the area of the study in section 3.2. The data collected in the study included the vehicle dynamics next to the evaluated driver inputs. Thus, the intersection approaches are

99 5 System Approach used to learn the Hidden Markov Models in the prediction framework. The study included intersections of different shape and regulation. This system concentrates on unregulated intersections, i.e. intersection with priority to the right rule. At yield and stop intersections, the behavior is dominated by the concern to give right of way which leads to a reduction of speed independent of the intended driving direction. However, the different shapes (X-intersection and T-junction) are considered in the system. Also, the conclusions of the behavior analysis are applied by merging shapes where similar driving behavior was examined. According to Table 3.4, two combined prediction models are created. For left turns, XT one HMM for X-intersections and T-oriented junctions (λleft ) is created and another T l one for T-junctions with left branch (λleft). Another combined model is established XT r for right turns at X-intersections and T-junctions with right branch (λright). The T other HMM for turning right refers to T-oriented junctions (λright). Going straight differed significantly between both examined intersections leading to separate HMMs X T r (λstraight, λstraight). All six HMMs are learned with the according sequences provided by the data collection in the study. The Gaussian mixture approach with 3 mixtures and 5 hidden states is used as prediction framework in the application. On the one side, this gives the opportunity to implement an online learning feature and makes previous clustering and scaling of data obsolete. On the other side, this setup showed reasonable results as shown in Table 4.4.

5.3.1 Implementation

The intersection type is encoded in the generated XML ﬁles for the intersections to be tested. This information is provided by the intersection model through the interface. The application runs on a laptop connected to the vehicle CAN bus via a Vector CANbox. So, the relevant vehicle dynamics (velocity, acceleration, yaw rate) are retrieved over this interface using the Vector tool CANape. The prediction algorithm is embedded in a Matlab/Simulink tool with a graphical user interface (GUI). The tool is connected to the CANape software through a Matlab speciﬁc interface. In case of using OpenDrive, the distance to intersection is calculated as explained in section 2.4 using the actual GPS coordinates from the CAN bus and the GPS coordinates of the intersection provided by the interface to the intersection modeling. This is obsolete when ADASIS data is available, since the distance is already included there. All data is merged to a time-discrete array (sequence) with a resolution of 100 ms using interpolation. The activity diagram in Figure 5.2 illustrates the progression in the application. After initialization, the entire system runs permanently in a loop until it is canceled. Here, the measured OpenDrive data was used as source for the intersection modeling. The interface to the intersection model provides the information about the

100 5.3 Prediction

start

calculate distance clear buﬀer to intersection d

calculate angle training to intersection φ

optional

d < 110 determine actual ∧ false φ ∼= heading direction driven

true

intersection modeling false

φ = heading true ∼

retrieve CAN bus data from buﬀer

calculate distance prediction to intersection of direction

Figure 5.2: Activity diagram of the prediction tool

closest intersection by comparing the actual GPS data of the vehicle with the GPS coordinates of the intersections in the database. This distance d to the nearest intersection is tracked once the application is initialized. Further, the direction to the intersection from the position of the vehicle is determined by calculating the heading angle towards the intersection φ. This angle is determined in the same orientation system as the actual heading provided by the GPS unit (north 0◦ counting clock-

101 5 System Approach wise). The calculation is performed using the scalar product between a unit vector pointing north and the vector ~r from the vehicle to the intersection (see Figure 5.3).

~r ~ey φ = arccos · ~r | | In case the vector towards the intersection has a negative value in x-direction, the resulting angle is increased by 180◦ to follow the orientation regulation. This angle is compared to the actual heading of the vehicle in the orientation coordinate system (see Figure 5.3). This is performed to determine if the vehicle is approaching the intersection rather than driving on a parallel road. In case the angles match nearly and the distance d drops below 110 m, the intersection modeling is initialized. The prediction is supposed to start at a DTI of 100 m, since the sequences for learning were limited to this distance. Since the modeling consumes time and the vehicle is in motion, a small oﬀset of 10 m is added.

0° Intersection X

φ 270° 90° vehicle heading

180°

Figure 5.3: Orientation coordinate system in the vehicle

The intersection modeling is followed by the actual prediction. Right after the initialization of the application, CANape was started in the background to collect constantly the relevant vehicle dynamic data from the CAN bus and store it in a buffer. Now, this buffer with CAN data is readout and merged to a sequence combined with already retrieved data of this intersection approach. Further, the DTI is updated using the newest position available. Finally, the prediction is executed in the same way as in the prediction framework (cf. section 4.3). The sequence is presented to the according models for each possible direction (three or two options depending of intersection type). Then, the log-likelihoods are calculated using the forward algorithm. The direction with the highest log-likelihood is the prediction result. Further, a validity is determined based on the differences between the two

102 5.3 Prediction highest log-likelihood outcomes. This is realized through the ratio between the difference and the overall log-likelihood of the best result. Under the assumption, that loglik is the sorted vector (descending) with the log-likelihood values, the validity is determined by:

loglik(2) loglik(1) val = min 1; − loglik(1)

The validity is small if the distance between the two best results is tight and it is ceiled at 1 for differences between the two log-likelihoods greater than the absolute maximum log-likelihood. So, the prediction outcome is estimated by the relation between the single results, since the absolute value is depending on the length of the sequence presented. The prediction result as well as the validity is presented in the GUI of the tool (see Figure 5.4). One prediction cycle runs approximately 1 s. However, a code optimization is expected to improve this run-time. The cycle rewinds as long as the vehicle approaches the intersection. This is tracked by the comparison of the heading with the orientation towards the intersection. Tracking the changes in the heading and comparing the measured values before and after passing the intersection, enables the determination of the actual driving direction. As soon as the vehicle passes the intersection, the prediction is terminated. The GUI indicates if the final prediction was correct by coloring the arrow displayed in Figure 5.4 green. Before tracing the next intersection approach, there is an optional training process implemented. Since the driving direction was determined, the entire sequences can be used to train the according model and further improve the model adaption. This is applicable even though the prediction failed to estimate the correct direction. The sequence is passed to the model which matches the actual direction driven independent of the prediction. This also enables personalization in the models in case a driver detection is realized. At last, the buffers are cleared and used variables are deleted before the tool resets to the idle mode, in which the next intersection is determined and the process starts again.

5.3.2 Graphical User Interface (GUI)

The tool is simple to use with a customized Matlab GUI (see Figure 5.4). The application is started by pressing the “Start Prediction” button. In the initialization, the user is requested to choose a data source for the road information. The options are ADASIS, OpenDrive or Oﬄine. The latter allows testing of the tool with already stored sequences. The tool runs according to the activity diagram in Figure 5.2 and can be interrupted at any stage with the “Stop” button. From there, the tool can be restarted or closed with “Exit”.

103 5 System Approach

Figure 5.4: Prediction tool GUI

After initializing the tool, the closest intersection is displayed in written form. This information is included only in the XML ﬁles using OpenDrive data. Further, the DTI is shown. In a test drive, the previously mentioned conditions are checked after the data source is determined. When the vehicle closes in on an intersection, the modeling is executed and visualized schematically in the GUI. The prediction outcome is indicated by an arrow as well as in written form. The validity is indicated in percent.

The ﬁeld “number of runs” shows how many prediction cycles were performed. There are also some status notiﬁcation for debugging. Errors are displayed in the “Run” area while the “State” indicates the mode in which the tool is currently in. When the intersection is passed, the tool freezes shortly to indicate if the prediction was correct (arrow turns green) and to display the actual driving direction. Afterwards, the user is asked to decide if the sequence shall be used for training the according model or if the data is discarded.

5.3.3 Testing and Outlook

The tool showed a stable performance and was tested at several intersections in the test area. The prediction results were comparable with the outcome of the evaluation of the prediction framework in section 4.1. Further, the integrated training feature was tested and is assumed to lead to further improvements. However, the computation time is in some cases interfering with the next intersection approach, which appears especially in urban areas where intersections are close together. An

104 5.3 Prediction alternative could be to store the sequences and perform the training afterwards or implement a parallel process for the training. The prediction tool is restricted to certain intersections when using OpenDrive data, since there was no database available. This can be overcome by running the ADASRP on the laptop retrieving ADASIS data. Also, further training data is expected to improve the prediction performance especially in the early stage of the intersection approach. So far, the turn signal remains unconsidered in the prediction even though it is the only externally perceivable information of a possible turning intention. However, as the naturalistic driving study revealed, the turn signal was not activated several times. Also, in the evaluation of the prediction in chapter 4, the correct driving direction was estimated before the activation of the turn signal. Still, it seems reasonable to include the turn signal to increase the validity of the prediction. So far, the influence of other traffic participants is unconsidered. In the tested traffic environment, the visibility constraints through line-of-sight obstructions impeded the perception of other vehicles approaching from other direction to a point shortly before entering the intersection. Still, preceding vehicles influence the approaching behavior. This could be considered through an additional parameter in the HMMs or through extending the prediction framework by another model (λfollowing).

105 106 6 Conclusion and Outlook

In this thesis, a system is presented which allows an assessment of intersection scenarios. The focus was on a situation analysis including a representation of the static environment at the intersection and the prediction of the driving direction while approaching it. The latter was realized with a prediction framework using Hidden Markov Models (HMMs). To determine suitable input variables for the prediction, a thorough analysis of driving behavior at intersections was performed. For this purpose, an existing database was used. This database was adapted and utilized for the determination of the model parameters in the prediction framework. The analysis of the vehicle dynamics indicated obvious differences in the intersection approach according to the driving direction. Also, the conducted naturalistic driving study revealed differences and also similarities in the occurrence of driver behavior at intersections of different shape. Here, the most common shapes which are T-junctions and X-intersections with nearly perpendicular crossing angles were examined. A self-provided analysis tool showed that this are the predominant intersection shapes in urban road networks. In the study, the influence of preceding vehicles was eliminated by discarding corresponding data. Thus, preceding vehicles are unconsidered in the prediction framework. Also, other factors like line of sight obstructions and parked vehicles showed influence on the driving behavior which is excluded in the prediction. In this system approach the turn signal remained unconsidered. This was decided because of the high variations in the turn signal activation as well as due to multiple misses which were observed in the driving study. Besides, the framework is designed to be applicable to predict the driving direction also of other vehicles using sensor systems. Since a camera based detection of turn signals is complicated at intersections, the prediction is established independent of this information. However, for the ego vehicle the turn signal could serve as additional source for the prediction. An in-depth evaluation of the prediction framework identified optimal parameters and showed results with high prediction rates at several seconds before reaching the intersection. A setup was created using Gaussian Mixture Models as link between input variables and the hidden states in the HMMs. This allowed the use of variable sizes of datasets for learning and therefore enables an online learning approach. Finally, a real-time assessment system was implemented and integrated in a vehicle environment. This system provides a situation assessment including an intersection model and the prediction for the ego vehicle. It runs stable and was tested in real

107 6 Conclusion and Outlook traffic scenarios. The prediction results worked surprisingly well and were robust even in cases with traffic interference. The system displays continuously an estimate of the driving direction and a corresponding validity while approaching an intersection. It is also capable of using the recorded driving data as training input to further optimize the inherent prediction models during the test drive or afterwards. This increases the performance and can alternatively be used to personalize the prediction for different drivers. The system introduced here can be used as basic platform for the development of Advanced Driver Assistance Systems (ADAS) for intersection scenarios. The inherent situation assessment is also relevant for the realization of automated vehicle control at intersections. Since the prediction is based on a stochastic model, the validation of such a system will require new approaches. Especially the implemented online learning process creates variations in the system outputs. These variations have the potential to predict situations correctly which were previously not identified, which increases the prediction performance. However, it cannot be excluded that this overall increase of performance comes with false predictions of situations that were previously identified correctly. This variation is relevant for the design of the intervention strategy. While false positive activations in a warning feature are only annoying, they might impair the situation in a control system. Conclusively, the early prediction established here can be used in a foresightful manner to reduce the risk of critical situations before they occur.

108 Bibliography

[1] United Nations, Department of Economic and Social Affairs, Population Divi- sion, Ed., World Urbanization Prospects: The 2014 Revision, Highlights, 2014. [2] L. Rittger, “Driving Behaviour and Driver Assistance at Traffic Light Intersec- tions,” Ph.D. dissertation, Universität Wurzburg,¨ 2015. [3] J. Kim, R. R. Rajkumar, and M. Jochim, “Towards Dependable Autonomous Driving Vehicles: A System-level Approach,” SIGBED Rev., vol. 10, no. 1, pp. 29–32, Feb. 2013. [Online]. Available: http: //doi.acm.org/10.1145/2492385.2492390 [4] REGULATION (EC) No 661/2009 concerning type-approval requirements for the general safety of motor vehicles, their trailers and systems, components and separate technical units intended therefor, Official Journal of the European Union, European Parliament and Council of the European Union, Std., July 2009. [5] COMMISSION REGULATION (EU) No 351/2012, implementing Regulation (EC) No 661/2009 of the European Parliament and of the Council as regards type-approval requirements for the installation of lane departure warning systems in motor vehicles, Official Journal of the European Union, European Com- mission, Std., April 2012. [6] COMMISSION REGULATION (EU) No 347/2012, implementing Regulation (EC) No 661/2009 of the European Parliament and of the Council with respect to type-approval requirements for certain categories of motor vehicles with regard to advanced emergency braking systems, Official Journal of the European Union, European Commission, Std., April 2012. [7] E. Petersen, Ed., Zu Fahrerassistenzsystemen FAS fur¨ Straßen- und speziell Nutzfahrzeuge, vol. 16, Landesverkehrswacht Niedersachsen eV, Nov. 2010, aktualisiert Jan. 2013. Accessed 06-06-2015. [On- line]. Available: http://www.landesverkehrswacht.de/fileadmin/downloads/ Wissensblaetter/Wissensblatt16FAS.pdf [8] Euro NCAP Ratings Group, “Euro ncap rating review 2015,” European New Car Assessment Programme, Tech. Rep. Version 1.0, March 2015. [9] Bosch AG. Bosch surround sensors. Accessed 06-10-2015. [On- line]. Available: http://auto2015.bosch.com.cn/web/images/technology press/ new20150404 06 w800.jpg

109 Bibliography

[10] N. Doll and S. Maaß, “Deutschland will Nummer 1 bei ”Geis- terautos” werden,” Die Welt (online), 2015, accessed 06-15- 2015. [Online]. Available: http://www.welt.de/wirtschaft/article139930665/ Deutschland-will-Nummer-1-bei-Geisterautos-werden.html [11] Uniform Provisions Concerning the Approval of: Vehicles with regard to Steer- ing Equipment, United Nations, Std. Regulation No. 79-01, Rev. 2, Feb 2014. [12] J. Lee and B. Park, “Development and Evaluation of a Cooperative Vehicle Intersection Control Algorithm Under the Connected Vehicles Environment,” IEEE Transactions on Intelligent Transportation Systems, vol. 13, no. 1, pp. 81–90, March 2012. [13] WHITE PAPER 2011 Roadmap to a Single European Transport Area - Towards a competitive and resource efficient transport system, European Commission, Std., March 2011. [14] J. Adamowicz and J. Salsby, “How safe are your roads? Commission road safety statistics show small improvement for 2014,” European Commission - Press release, March 2015. [15] Statistisches Bundesamt Deutschland, “Verkehr - Verkehrsunfälle 2014,” Destatis, Tech. Rep. Fachserie 8 Reihe 7, 2015. [16] Strassenverkehrsordnung (StVO) der Bundesrepublik Deutschland vom 6. März 2013 (BGBl. I S. 367), “ 8 Vorfahrt,” accessed 09-14-2015. [Online]. § Available: http://www.gesetze-im-internet.de/stvo 2013/ 8.html [17] “OpenStreetMap,” accessed 04-15-2014. [Online]. Available: www. openstreetmap.org [18] M. R. Endsley, “Toward a Theory of Situation Awareness in Dynamic Systems,” Human Factors: The Journal of the Human Factors and Ergonomics Society, vol. 37, no. 1, pp. 32–64, 1995. [19] M. Baumann and J. F. Krems, “Situation awareness and driving,” in Modelling Driver Behaviour in Automotive Environments: Critical Issues in Driver In- teractions with Intelligent Transport Systems, P. C. Cacciabue, Ed. Springer, 2007, pp. 253–265. [20] J. H. Schneider, “Modellierung und Erkennung von Fahrsituationen und Fahrmanövern fur¨ sicherheitsrelevante Fahrerassistenzsysteme,” Ph.D. dissertation, TU Chemnitz, 2009. [21] G. Reichart, “Menschliche Zuverlässigkeit beim Fuhren¨ von Kraftfahrzeugen,” Ph.D. dissertation, TU Munchen,¨ 2000. [22] W. Thomas and D. Thomas, Child in America: Behavior problems and programs. New York, NY, USA: Knopf, 1928. [23] UR:BAN-Konsortium. (2013) Urbaner Raum: Benutzergerechte Assisten- zsysteme und Netzmanagement. Accessed 11-15-2015. [Online]. Available: http://urban-online.org/

110 Bibliography

[24] S. Ammoun and F. Nashashibi, “Real time trajectory prediction for collision risk estimation between vehicles,” IEEE International Conference on Intelligent Computer Communication and Processing, pp. 417–422, 2009. [25] M. Liebner, M. Baumann, F. Klanner, and C. Stiller, “Driver intent inference at urban intersections using the intelligent driver model,” in IEEE Intelligent Vehicles Symposium, Jun. 2012, pp. 1162–1167. [26] T. Brandt, “A Predictive Potential Field Concept for Shared Vehicle Guidance,” Ph.D. dissertation, Universität Paderborn, 2007. [27] A. E. Broadhurst, S. Baker, and T. Kanade, “Monte Carlo road safety reason- ing,” in IEEE Intelligent Vehicles Symposium, Jun. 2005, pp. 319–324. [28] S. Danielsson, L. Petersson, and A. Eidehall,“Monte Carlo based Threat Assess- ment: Analysis and Improvements,” in IEEE Intelligent Vehicles Symposium, Jun. 2007, pp. 233–238. [29] C. Hermes, C. Wöhler, K. Schenk, and F. Kummert, “Long-term Vehicle Motion Prediction,” in IEEE Intelligent Vehicles Symposium, 2009, pp. 652–657. [30]E.K äfer, C. Hermes, C. Wohler, F. Kummert, H. Ritter, and C. Wöhler, “Recognition and Prediction of Situations in Urban Traffic Scenarios,” in IEEE International Conference on Pattern Recognition, Aug. 2010, pp. 4234–4237. [31] R. Schubert, K. Schulze, and G. Wanielik, “Situation Assessment for Auto- matic Lane-Change Maneuvers,” IEEE Transactions on Intelligent Transporta- tion Systems, vol. 11, no. 3, pp. 607–616, 2010. [32] D. Kasper, G. Weidl, T. Dang, G. Breuel, A. Tamke, A. Wedel, and W. Rosen- stiel, “Object-Oriented Bayesian Networks for Detection of Lane Change Ma- neuvers,” IEEE Intelligent Transportation Systems Magazine, vol. 4, no. 3, pp. 19–31, 2012. [33] F. Klanner, “Entwicklung eines kommunikationsbasierten Querverkehrsassisten- ten im Fahrzeug,” Ph.D. dissertation, TU Darmstadt, 2008. [34] A. Gerdes,“Driving Manoeuvre Recognition,”in World Congress and Exhibition on Intelligent Transport Systems and Services, London, 2006, pp. 1–8. [35] U. Handmann, I. M. Leefken, and W. von Seelen, “Scene Interpretation and Behavior Planning for Driver Assistance,” Enhanced and Synthetic Vision 2000 at AEROSENSE 2000, vol. 4023, pp. 201–212, Jun. 2000. [36] M. G. Ortiz, J. Schmudderich,¨ F. Kummert, A. Geppperth, and A. Gep- perth, “Situation-specific learning for ego-vehicle behavior prediction systems,” in IEEE Conference on Intelligent Transportation Systems, 2011, pp. 1237– 1242. [37] Q. Tran and J. Firl, “A Probabilistic Discriminative Approach for Situation Recognition in Traffic Scenarios,” in IEEE Intelligent Vehicles Symposium, Jun. 2012, pp. 147–152.

111 Bibliography

[38] G. S. Aoude, V. R. Desaraju, L. H. Stephens, and J. P. How, “Behavior Clas- sification Algorithms at Intersections and Validation Using Naturalistic Data,” in Proc. of the IEEE Intelligent Vehicles Symposium, Jun. 2011, pp. 601–606. [39] H. Berndt and K. Dietmayer, “Driver intention inference with vehicle onboard sensors,” in IEEE Conference on Vehicular Electronics and Safety, 2009, pp. 102–107. [40] V. Gadepally, A. Kurt, A. Krishnamurthy, and U. Ozg¨ uner,¨ “Driver/Vehicle State Estimation and Detection,” in 14th International IEEE Conference on Intelligent Transportation Systems, Oct. 2011, pp. 582–587. [41] L. Rabiner, “A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition,” Proceedings of the IEEE, vol. 77, no. 2, pp. 257–286, Feb. 1989. [42] B. Hofmann-Wellenhof, H. Lichtenegger, and E. Wasle, GNSS Global Navigation Satellite Systems - GPS, GLONASS, Galileo & more. Springer, 2007. [43] National Coordination Office for Space-Based Positioning, Navigation, and Timing, “Official U.S. Government information about the Global Positioning System (GPS) and related topics,” 2015, accessed: 09-03-2015. [Online]. Available: http://www.gps.gov/ [44] Technische Dokumentation - ADMA Version 2x.6.2, GeneSys Elektronik GmbH, Jun 2012. [45] T. Streubel and K. H. Hoffmann, “Fahrverhaltenanalyse an Kreuzungen auf Basis von Fahrdaten,” in Automotive meets Electronics, 2014, pp. 31–36. [46] GeoBasis-DE/BKG, “simTD test area Friedberg, Germany.” [47] M. J. Henning, M. Jentsch, P. Lindner, J. F. Krems, B. Spanner-Ulmer, and G. Wanielik, “Prädiktion der Komplexität von Kreuzungssituationen durch Fahrverhaltensanalyse,” Mensch, Technik, Organisation - Vernetzung im Produktentstehungs- und -herstellungsprozess, pp. 291–294, 2011. [48] T. Streubel, L. Rittger, K. H. Hoffmann, and J. F. Krems, “Naturalistic driving behavior at inner-city intersections,” in 22nd ITS World Congress, 2015. [49] T. Sato and M. Akamatsu, “Analysis of drivers’ preparatory behaviour before turning at intersections,” IET Intelligent Transport Systems, vol. 3, no. 4, pp. 379–389, 2009. [50] Z. R. Doerzaph, “Intersection Stopping Behavior as Influenced by Driver State: Implications for Intersection Decision Support Systems,” Master’s thesis, Vir- ginia State University, 2004. [51] R. Haas, V. Inman, A. Dixson, and D. Warren, “Use of Intelligent Transporta- tion System Data to Determine Driver Deceleration and Acceleration Behavior,” Transportation Research Record: Journal of the Transportation Research Board, vol. 1899, pp. 3–10, 2004.

112 Bibliography

[52] B. S. Liu, “Association of intersection approach speed with driver characteristics, vehicle type and traffic conditions comparing urban and suburban areas,” Accident Analysis and Prevention, vol. 39, no. 499, pp. 216–223, 2007. [53] M. S. Shirazi and B. Morris, “Observing Behaviors at Intersections: A Review of Recent Studies & Developments,” in IEEE Intelligent Vehicles Symposium, 2015. [54] Y. Zhang, C. Fu, and L. Hu, “Yellow light dilemma zone researches: a review,” Journal of Traffic and Transportation Engineering (English Edition), vol. 1, no. 5, pp. 338–352, Oct. 2014. [55] C. Zaiontz. (2015) Real Statistics Using Excel. Accessed 06-10-2015. [Online]. Available: www.real-statistics.com [56] J. Bortz, Statistik fur¨ Human- und Sozialwissenschaftler, ser. 6. Springer Ver- lag, 2005. [57] D. Rösler, “Fahrrelevanz von Elementen des Strassenverkehrs - Beschreibung, Erfassung und Anwendung,” Ph.D. dissertation, TU Chemnitz, 2010. [58] T. Streubel and K. H. Hoffmann, “Prediction of Driver Intended Path at Inter- sections,” in IEEE Intelligent Vehicle Symposium, 2014, pp. 134–139. [59] T. Streubel and K. H. Hoffmann, “Realisierung eines Fahrtrichtungsprädiktors fur¨ Kreuzungen,” in Automotive meets Electronics, 2015, pp. 32–36. [60] K. Murphy, “Bayes Net Toolbox for Matlab,” version 1.0.7, Oct. 2007, downloaded Oct. 2012. [Online]. Available: https://code.google.com/p/bnt/ [61] J. Hartigan, Clustering Algorithms, ser. Wiley Series in Probability and Math- ematical Statistics. Books on Demand, 1975. [62] C. D. Manning, P. Raghavan, and H. Schutze,¨ Introduction to Information Retrieval. Cambridge, UK: Cambridge University Press, 2008. [Online]. Available: http://nlp.stanford.edu/IR-book/information-retrieval-book.html [63] D. A. Reynolds, “Gaussian Mixture Models,” in Encyclopedia of Biometrics, S. Z. Li and A. K. Jain, Eds. Springer US, 2009, pp. 659–663.

113 114 Selbstst¨andigkeitserkl¨arung

Ich erkläre, dass ich die vorliegende Arbeit selbstständig und nur unter der Verwendung der angegebenen Literatur und Hilfsmittel angefertigt habe. Ich versichere, nicht bereits fruher¨ oder gleichzeitig bei anderen Hochschulen oder an dieser Universität ein Promotionsverfahren beantragt zu haben. Diese Dissertation ist als solche noch nicht veröffentlicht. Ich erkenne die Promotionsordnung der Fakultät fur¨ Naturwissenschaften der Technischen Universität Chemnitz vom 31. Januar 2011 an.

Chemnitz, 21. Januar 2016

Thomas Streubel

115 116 Curriculum Vitae

Name Thomas Streubel Geburtsdatum 6. Februar 1985 Geburtsort Schlema Kontakt [email protected]

10/2005 - 09/2009 Technische Universit¨at Chemnitz Bachelorstudium Computational Science

10/2006 - Fakult¨at fur¨ Naturwissenschaften 07/2007 Tutorent¨atigkeit

10/2008 - Volkswagen AG, Wolfsburg 06/2009 Konzernforschung - Abt. Integrierte Sicherheit und Licht Praktikum: Entwicklung von Software fur¨ Lichtapplikationen Bachelorarbeit:“Realisierung einer aktiven, pr¨aventiven Bremsleuchte fur¨ Personenkraftwagen”

10/2009 - 11/2011 Technische Universit¨at Chemnitz Masterstudium Computational Science

11/2010 - Fakult¨at fur¨ Maschinenbau 03/2011 Hilfswissenschaftliche Mitarbeit Implementierung nutzerspeziﬁscher Bedienelemente in LabVIEW

04/2011 - Adam Opel AG, Russelsheim¨ 09/2011 Vorausentwicklung - Abt. Advanced Active Safety Masterarbeit:“Artiﬁcial Potential Fields as a Concept of Environment Modeling for Forward Directed Driver Assistance Systems”

02/2012 - 10/2015 Adam Opel AG, Russelsheim¨ Industriepromotion

SS13 & Technische Universit¨at Chemnitz SS14 & Fakult¨at fur¨ Naturwissenschaften SS15 Unterstutzung¨ Lehre in Simulation Naturwissenschaftlicher Prozesse

11/2015 - 01/2016 Technische Universit¨at Chemnitz Fakult¨at fur¨ Naturwissenschaften Wissenschaftlicher Mitarbeiter

117 118 Publications

T. Streubel and R. Zarife, Verfahren fur¨ einen adaptiven Rechtsabbiegeassistenten, Gebrauchsmuster, Jan. 2013.

T. Streubel, “Fahrassistenzsystem, Fahrzeug mit einem Fahrassistenzsystem und Verfahren zum Betrieb eines Fahrerassistenzsystems.” Patent DE102013013747 A1, Aug. 2013.

T. Streubel, M. Moebus and K. H. Hoﬀmann, “Generische Umfeldmodellierung - Autonome Fahrzeugsteuerung durch eine Risikokarte,” in Proc. of VDI - Elektronik im Fahrzeug, Baden-Baden, Oct. 2013. (Auto-Electronic Excellence Award)

T. Streubel and K. H. Hoﬀmann, “Fahrverhaltenanalyse an Kreuzungen auf Basis von Fahrzeugdaten,” in Proc. of Automotive meets Electronics 2014, Dortmund, Feb. 2014. (Best Paper Award)

T. Streubel and K. H. Hoffmann, “Autonomous Vehicle Control through Dynamic Traffic Scenarios Based on Artificial Potential Fields,” presentation at DPG Spring Meeting, Dresden, Apr. 2014.

T. Streubel and K. H. Hoﬀmann, “Prediction of Driver Intended Path at Intersections,” in Proc. of IEEE Intelligent Vehicles Symposium, Dearborn, USA, Jun. 2014.

T. Streubel, “Fahrverhaltensanalyse zur besseren Fahrerassistenz,” Fachmagazin Mechatronik, I.G.T. Verlag, Munchen,¨ Jun. 2014.

T. Streubel, “Driver assistance system, motor vehicle having a driver assistance system, and a method for operating a driver assistance system.” U.S. Patent US20150057835 A1, Aug. 2014.

T. Streubel and K. H. Hoﬀmann, “Realisierung eines Fahrtrichtungspr¨adiktors fur¨ Kreuzungen,” in Proc. of Automotive meets Electronics 2015, Dortmund, Feb. 2015.

T. Streubel, L. Rittger, K. H. Hoﬀmann and J. F. Krems, “Naturalistic driving behavior at inner-city intersections,” in Proc. of ITS World Congress, Bordeaux, France, Oct. 2015.

119 120