Benedikt Schwab

Automated Driving: Analysis of Standard-Setting Dynamics and Development of a Pedestrian Simulation Model

TECHNICALUNIVERSITYOFMUNICH TUM School of Management

Thesis Master of Science November 30, 2017

AUTOMATEDDRIVING Analysis of Standard-Setting Dynamics and Development of a Pedestrian Simulation Model

benedikt schwab

Technical University of Munich Schöller Chair in Technology and Innovation Management Prof. Dr. Joachim Henkel M. Sc. Lisa Teubner

Technical University of Munich Chair of Computational Modeling and Simulation Prof. Dr.-Ing. André Borrmann Dr. rer. nat. Peter Kielar

ABSTRACT

Automated driving has the potential to disrupt the automotive in- dustry. Realizing this technology will involve various industries and will lead to the emergence of uniform approaches, which will be adopted by multiple stakeholders from different areas. This includes standardization areas, such as communication, digital mapping, and minimum quality requirements. The intention of this work is to systematically analyze the standard- ization dynamics in the areas relevant to automated driving. Further- more, the industry’s key actors are identified and their positioning regarding standards organizations and consortia is reviewed. Hereby, the specification of tests to ensure properly functioning systems con- stitutes one of the most essential standardization topics. The verifica- tion of automated driving systems will include standardized scenario simulations, which model rural and urban traffic situations. Since particularly pedestrians are exposed to malfunctioning auto- mated driving systems, a realistic simulation of pedestrian behavior is crucial for the testing of such systems. Therefore, the second part of this work aims at the implementation of a model, which describes pedestrian behavior when interacting with cars in urban crossing sce- narios. The driving simulator Virtual Test Drive and the pedestrian simulation framework MomenTUMv2 are used for this purpose. ZUSAMMENFASSUNG

Automatisiertes Fahren hat das Potential die Automobilindustrie dis- ruptiv zu verändern. Die Realisierung dieser Technologie involviert unterschiedliche Industrien und wird zu einer Entwicklung von ein- heitlichen Lösungsansätzen führen, die von zahlreichen Stakeholdern aus unterschiedlichen Bereichen übernommen werden. Dazu gehören Standardisierungsbereiche wie Kommunikation, digitale Kartierung und qualitative Mindestanforderungen. Ziel dieser Arbeit ist es, die Standardisierungsdynamiken in den für das automatisierte Fahren relevanten Bereichen systematisch zu analysieren. Darüber hinaus werden die Schlüsselakteure der Bran- che identifiziert und ihre Positionierung in Bezug auf Standardisie- rungsorganisationen und Konsortien inspiziert. Eines der wichtigs- ten Standardisierungsthemen ist dabei die Spezifikation von Tests zur Gewährleistung ordnungsgemäß funktionierender Systeme. Die Funktionalität automatisierter Fahrsysteme wird durch die Simulati- on von standardisierten Szenarien verifiziert werden, die ländliche und städtische Verkehrssituationen abbilden. Da insbesondere Fußgänger der Fehlfunktion von automatisierten Fahrsystemen ausgesetzt sind, ist eine realistische Simulation des Fußgängerverhaltens von entscheidender Bedeutung für das Testen solcher Systeme. Der zweite Teil dieser Arbeit zielt daher auf die Im- plementierung eines Modells ab, welches Fußgängerverhalten bei der Interaktion mit Autos in städtischen Kreuzungsszenarien beschreibt. Hierfür werden der Fahrzeugsimulator Virtual Test Drive und das Fuß- gängersimulationsframework MomenTUMv2 verwendet. ACKNOWLEDGMENTS

Lisa Teubner, Dr. Peter Kielar, Christoph Sippl, Prof. Dr. Joachim Henkel, Prof. Dr. André Borrmann. Sarah, Mike. My parents, my sister, Lisa. I wish to express my sincere gratitude to you.

CONTENTS

1 introduction1 1.1 Objectives ...... 2 1.2 Outline ...... 2 i standard-setting dynamics 2 standardization theory5 2.1 Types of Standards ...... 6 2.1.1 Quality Standards ...... 7 2.1.2 Compatibility Standards ...... 8 2.2 Market Extent ...... 8 2.3 Standardization Processes ...... 10 2.3.1 Unsponsored Standards ...... 11 2.3.2 Sponsored Standards ...... 13 2.3.3 Voluntary Standards ...... 15 2.3.4 Mandated Standards ...... 16 2.4 Control and Positioning ...... 16 2.5 Product Scope ...... 17 3 compatibility standardization dynamics 19 3.1 Terminilogy ...... 19 3.2 Sensors and Actuators ...... 21 3.3 Car2X Communication ...... 24 3.3.1 Automated Driving Use Cases ...... 24 3.3.2 IEEE 802.11p / ITS-G5 ...... 25 3.3.3 Mobile Broadband ...... 27 3.4 Navigation and Mapping ...... 30 4 quality standardization dynamics 33 4.1 Technical Challenges ...... 33 4.2 Germany ...... 34 4.3 European Union ...... 35 4.4 EU-US-Japan Trilateral Cooperation ...... 36 ii pedestrian behavior model 5 literature review 41 5.1 Scales of Modelling ...... 41 5.2 Pedestrian Behavioral Levels ...... 42 5.3 Related Models ...... 42 5.3.1 Feng et al. 2013 ...... 42 5.3.2 Hashimoto et al. 2016 ...... 43 5.3.3 Anvari et al. 2015 ...... 44 5.3.4 Zeng et al. 2014, 2017 ...... 45 5.3.5 Overview of Models ...... 46 6 pedestrian behaviour model 47 x contents

6.1 Strategic Level ...... 47 6.2 Tactical Level ...... 48 6.3 Operational Level ...... 49 6.3.1 Driving Force ...... 50 6.3.2 Conflicting Pedestrian ...... 50 6.3.3 Conflicting Car ...... 52 6.3.4 Crosswalk Boundary ...... 53 7 implementation 55 7.1 Simulation Setup ...... 55 7.2 Virtual Test Drive ...... 56 7.3 Intermediary Process ...... 57 7.4 MomenTUMv2 ...... 58 7.4.1 Car Manager ...... 60 7.4.2 Additional Areas ...... 60 7.4.3 Geometry ...... 61 7.4.4 Visualization ...... 62 7.4.5 Tactical Model ...... 62 7.4.6 Operational Model ...... 63 8 dataset 65 8.1 Ko-PER Intersection Dataset ...... 65 8.2 Dataset Preparation ...... 66

iii concluding discussion 9 discussion 71 9.1 Pedestrian Simulation Model ...... 71 9.1.1 Tactical Model ...... 71 9.1.2 Operational Model ...... 74 9.2 Standardization Potentials ...... 79 10 conclusion and outlook 83

iv appendix a companies per industry 87 a.1 Automotive ...... 87 a.2 Telecommunication ...... 89 a.3 Navigation and Mapping ...... 90 b model parameters 91

bibliography 93 LISTOFFIGURES

Figure 2.1 Statistics on the Standards War Betamax–VHS ...... 5 Figure 2.2 Network Effect ...... 10 Figure 2.3 Standards Reinforcement Mechanism ...... 13 Figure 3.1 Replacing the Human Driver with Technology ...... 19 Figure 3.2 Protocol Stack of DSRC and C-ITS ...... 26 Figure 3.3 Building Blocks of the NDS Specification ...... 32 Figure 4.1 Generic V Model ...... 34 Figure 4.2 Central Issues of the PEGASUS Project ...... 35 Figure 4.3 Organization Chart of the Trilateral Cooperation ...... 38 Figure 5.1 Distributed Simulation Setup ...... 41 Figure 5.2 Pedestrians on a Square Lattice ...... 43 Figure 5.3 DBN of the Pedestrian Behavior Model by Hashimoto et al...... 43 Figure 5.4 Social Force Model for Shared Spaces by Anvari et al...... 44 Figure 5.5 Signalized Crosswalk of Zeng et al.’s Model ...... 45 Figure 6.1 Exemplary Intersection with Origin and Destination Areas . . . . . 47 Figure 6.2 Exemplary Pedestrian Crossing with Navigation Graph ...... 48 Figure 6.3 Potentially Colliding Pedestrians i and j ...... 50 Figure 6.4 Tangential Force Exerted from Pedestrian j on Pedestrian i ..... 51 Figure 6.5 Angle ϕi,j between Pedestrian i and j ...... 52 Figure 6.6 Car k Exerting Repulsive Forces on Pedestrian i and j ...... 53 Figure 6.7 Social Forces of Pedestrian i and j on the Crosswalk ...... 53 Figure 7.1 Distributed Simulation Setup ...... 55 Figure 7.2 Road Network and Scenario Creation with VTD ...... 56 Figure 7.3 Structure of MomenTUMv2 ...... 59 Figure 7.4 Layers and Layer Groups of AutoCAD ...... 60 Figure 7.5 Possible Intersections of Two Rays ...... 61 #„ Figure 7.6 Ellipse with Pedestrian at Point x and Normal Vector n ...... 62 Figure 7.7 Segment Splitting ...... 62 Figure 7.8 3D View of MomenTUMv2’s Visualization Tool ...... 63 Figure 8.1 Public Crossing in Aschaffenburg ...... 65 Figure 8.2 Ko-PER Intersection Drawn in AutoCAD ...... 66 Figure 8.3 Trajectories of the Ko-PER Dataset ...... 67 Figure 9.1 Trajectories of Pedestrians Simulated with µs = 0.2 ...... 72 Figure 9.2 Trajectories of Pedestrians Simulated with µs = 0.3 ...... 73 Figure 9.3 Ko-PER Sequence 1b...... 74 Figure 9.4 Simulated Pedestrians with Different Pedestrian Interaction Forces 75 Figure 9.5 Pedestrians Before Collision ...... 75 Figure 9.6 Pedestrian Trajectories of the Ko-PER Dataset ...... 76 Figure 9.7 Trajectories of Simulated Pedestrians Crossing the Street ...... 76 Figure 9.8 Strength of Social Forces Exerted by a Crosswalk on a Pedestrian . 77 Figure 9.9 Pedestrian Crossing without Cars Located Nearby ...... 77 Figure 9.10 Repulsive Effect of a Starting Car ...... 78 Figure 9.11 Strength of Social Forces Exerted on a Pedestrian by a Car . . . . . 79 Figure 9.12 Database Concept for Testing Highly Automated Driving Systems 80

LISTOFTABLES

Table 2.1 Types of Standards ...... 7 Table 2.2 Standard Properties Regarding Market Extent ...... 9 Table 2.3 Standardization Processes ...... 11 Table 2.4 Control and Positioning Regarding Standards ...... 17 Table 2.5 Strategic Positioning in the Standards War Betamax–VHS ...... 17 Table 2.6 Standard Properties Regarding Product Scope ...... 18 Table 3.1 Organizations Involved in Defining and Standardizing the Termi- nology ...... 20 Table 3.2 Automated Driving Levels ...... 21 Table 3.3 Protocols for Inner Car Communication ...... 22 Table 3.4 Promoting and Adopting Members of the OPEN Alliance SIG .... 23 Table 3.5 Firm Members of the Working Group IEEE P802.11 and the Car-2- Car Communication Consortium ...... 26 Table 3.6 SDOs cooperating within 3GPP ...... 27 Table 3.7 3GPP Releases ...... 27 Table 3.8 Member Firms of th SDO Cooperating within the 3GPP and 5GAA . 29 Table 3.9 Overview of Consortia Coordinated by the OADF ...... 31 Table 4.1 Members of the PEGASUS Project ...... 35 Table 4.2 Partners of the European Platform ERTICO and the Consortium VRA 36 Table 4.3 Selection of Identified Standardization Needs by VRA ...... 37 Table 5.1 Pedestrian Behavioral Levels ...... 42 Table 5.2 Overview of Pedestrian Behavior Models ...... 46 Table 7.1 Simulation Data Protocol ...... 57 Table 7.2 Overview of the MomenTUMv2 Project ...... 58 Table 8.1 Number of Objects in Ko-PER Dataset ...... 66 Table A.1 Largest Vehicle Manufacturers with Brands ...... 87 Table A.2 Largest OEM Parts Suppliers ...... 88 Table A.3 Largest Telecommunication Equipment Makers ...... 89 Table A.4 Largest Telecommunication Service Providers ...... 89 Table A.5 Map Data Providers ...... 90 Table B.1 Parameters Used for Simulations ...... 91 LISTINGS

Listing 7.1 XML Configuration of the Tactical Model ...... 63 Listing 7.2 XML Configuration of the Perception Model ...... 64 Listing 7.3 XML Configuration of the Walking Model ...... 64

ACRONYMS

3D Three-Dimensional

3GPP 3rd Generation Partnership Project

3G Third Generation

4G Fourth Generation

5G 5th Generation Mobile Networks

5GAA 5G Automotive Association

ADASIS Advanced Driver Assistance Systems Interface Specifications

ADTF Automotive Data and Time-Triggered Framework

ANSI American National Standards Institute

ARIB Association of Radio Industries and Businesses

ATIS Alliance for Telecommunications Industry Solutions

BASt Bundesanstalt für Straßenwesen

BTS Base Transceiver Station

C2B Car-to-Backend

C2C Car-to-Car

C2I Car-to-Infrastructure

C2X Car-to-Everything

CAD Computer-Aided Design

CAN Controller Area Network

CCSA China Communications Standards Association xiv acronyms

CD Compact Disk

CEN Comité Européen de Normalisation

C-ITS Cooperative Intelligent Transportation System

CSMA/CA Carrier Sense Multiple Access/ Collision Avoidance

CSV Comma-Separated Values

D2D Device-to-Device

DBN Dynamic Bayesian Network

DIN Deutsches Institut für Normung

DSK Dvorak Simplified Keyboard

DSRC Dedicated Short Range Communication

ESO European Standardization Organization

ETSI European Telecommunications Standards Institute

EU European Union

GAAP Generally Accepted Accounting Principles

GUI Graphical User Interface

HDMI High-Definition Multimedia Interface

HTML Hypertext Markup Language

IEEE Institute of Electrical and Electronics Engineers

IETF Internet Engineering Task Force

IFRS International Financial Reporting Standards

ISO International Organization for Standardization

ITS Intelligent Transportation Systems

JSON JavaScript Object Notation

LAN Local Area Networking

LIN Local Interconnect Network

LTE Long Term Evolution

MOST Media Oriented Systems Transport

NDS Navigation Data Standard

NHTSA National Highway Traffic Safety Administration acronyms xv

NSB National Standards Body

OADF Open AutoDrive Forum

OD Origin Destination

OEM Original Equipment Manufacturer

OPEN One-Pair Ether-Net

OSI Open Systems Interconnection

PC Personal Computer

PDF Portable Document Format

RDB Runtime Data Bus

SAE Society of Automotive Engineers

SCP Simulation Control Protocol

SDO Standards Developing Organization

SIG Special Interest Group

SMB Small and Medium-sized Businesses

SSO Standards Setting Organization

TSDSI Telecommunications Standards Development Society

TTA Telecommunications Technology Association

TTC Telecommunication Technology Committee

UE User Equipment

USA United States of America

USB Universal Serial Bus

US United States

VANET Vehicular Ad Hoc Networks

VCR Video Cassette Recording

VHS Video Home System

VRA Vehicle and Road Automation

VTD Virtual Test Drive

W3C World Wide Web Consortium

WLAN Wireless Local Area Networking

XML Extensible Markup Language

INTRODUCTION 1

The introduction of more highly automated driving systems, especially with the option of automated collision prevention, may be socially and ethically mandated if it can unlock existing potential for damage limitation.

Ethics Commission on Automated and Connected Driving [Eth17, p. 11]

The safety of all road users can be improved by deploying highly automated driving systems. In order to investigate ethical guidelines regarding automated driving, an Ethics Commission was appointed by a German Federal Ministry and involved experts representing var- ied fields, such as jurisprudence, philosophy, and social sciences. This Commission concluded, that the introduction of such systems may be even ethically mandated, if a positive balance of risks is achieved [Eth17, p. 7]. This raises the central question of how collision preven- tion can be ensured, when automated driving systems are confronted with real traffic situations. Clearly, the testing and the safety verifica- tion of such systems are of fundamental importance, as their deploy- ment is only justifiable, if these systems are less harmful than human drivers [Eth17, p. 10]. However, the testing procedures have to be capable of ensuring safety for a vast number of traffic scenarios and traffic states. This is eminently challenging for urban scenarios, as the number of influ- encing factors and decision-taking participants increases even more compared to highway and rural situations. Hereby, the simulation of a vehicle’s environment constitutes an approach to test the perfor- mance of a system by exposing it to a high number of controlled sce- narios. Since pedestrians are particularly vulnerable to inaccurately functioning driving systems, a realistic pedestrian simulation can be of significant importance in the testing of automated driving systems. The safety verification of automated driving systems in a multitude of scenarios is clearly not a manufacturer-specific concern, but rather a macrosocial question, as all road users are exposed to the risks involved. Since the German basic law grants every person the right to life and physical integrity (Article 2 Grundgesetz), the state has the duty to ensure that the driving systems of each manufacturer fulfill safety requirements. Therefore, testing methods and procedures have to be standardized and the compliance with such standards has to be audited. Suitable testing methods are currently under development and first standardization initiatives have been formed. 2 introduction

1.1 objectives

The objective of this thesis is to investigate current standardization approaches and dynamics in the field of automated driving. This in- cludes not only dynamics for compatibility standardization, but also standard-setting dynamics for the assurance of the product’s qual- ity. The second objective of this work is to develop a pedestrian sim- ulation model, which describes the interaction of pedestrians with cars. Thereby, the pedestrian behavior model shall be implemented within the pedestrian simulation framework MomenTUMv2, whereas the cars shall be simulated by the driving simulator Virtual Test Drive.

1.2 outline

This thesis is structured in three parts. Part i involves the analysis of standard-setting dynamics regarding the topic of automated driving. Therefore, the theory on the different types of standards and their emergence processes are explained in Chapter 2. The theory is then applied by observing the compatibility standardization dynamics in the field of automated driving in Chapter 3. Furthermore, Chapter 4 discusses the dynamics of quality standardization efforts to ensure properly functioning driving systems. Part ii addresses the development of a pedestrian simulation model. In order to achieve this objective, the literature on pedestrian behav- ior models for intersection scenarios is reviewed in Chapter 5. Subse- quently, Chapter 6 comprises the explanation of the model’s theoret- ical foundation, whereas its implementation details are described in Chapter 7. The Ko-PER dataset, which contains recorded trajectories of pedestrians and cars, is introduced in Chapter 8. Part iii concludes the thesis by discussing the conducted work in Chapter 9. Finally, Chapter 10 provides a conclusion with an outlook for future work. Part I

STANDARD-SETTINGDYNAMICS

STANDARDIZATIONTHEORY 2

Standards have played a central role in some of the most important innovations of recent years. A well-known standards wars started in the late 1970s and was about the prevailing format on the video cas- sette market. In 1975 Sony introduced the Betamax format, which was considered to be technological more advanced to its competitor. Even though JVC’s Video Home System (VHS) format was introduced a Betamax vs. VHS year later, it succeeded with a 95 % market share eleven years after its market entrance, as shown in Figure 2.1. The standards strategy

40 Betamax VHS

20

0 1.0 Annual Production [mil]

0.5 Share [%]

0.0 1976 1978 1980 1982 1984 1986 1988

Figure 2.1: Annual production and market share between Betamax and VHS. Based on the statistics by Cusumano et al. [Cus+92, p. 30]. of JVC proved to be superior, as the firm offered quasi open standard specifications at moderate license fees and actively created an alliance with other producing firms [Cus+92]. Standards take different forms and are present across most indus- tries. Further products, in which standardization plays a central role, include CDs, DVDs, PCs, and PDFs. However, standards are also present in non high-tech products, such as razor blades, electrical voltages, typewriter keyboards, and railway networks [Gri95, pp. 1 sq.]. Standardization entails consumer benefits in most cases. This par- ticularly applies to network markets, whereby the telecommunication market constitutes a typical example. The utility and therefore the value of a product increases, the more other consumers are partici- pating on the market with compatible products. In contrast to the Value of network telephone market, in which the value is completely driven by the standards number of users, other products deliver an intrinsic value which is 6 standardization theory

not based on the network. This includes PDF format for example, as it provides value without others consumers using it. However, the for- mat PDF gains value, when more consumers adopt it due to the facili- tated exchange of digital documents among consumers. Since manu- facturers will often prefer industry standards compared to solutions with smaller market shares, an industry standard enables a wide va- riety of complementary products. For example, firms will more likely develop software for platforms with high market share, which leads to a higher number of potential customers. This in turn increases the value of the original platform [Lem02, pp. 1896 sq.]. Non-network markets can also benefit from standardization. Stan- dardizing product parts can entail a competitive effect, which in turn Value of increases the quality and decreases the price. By contrast, minimum non-network standards are not facilitating competition, but promoting social wel- standards fare due to the avoidance of imperfectly informed consumers de- ciding arationally. For example, minimum license standards for doc- tors, lawyers, assure consumers not to hire unqualified help [Lem02, p. 1897]. Several different definitions of the term “standardization” have deVries’s definition been provided by institutions and researchers. deVries collected, an- alyzed and compared existing definitions of organizations, dictionar- ies, experts, international and national standardization organizations. Based on the compared definitions and the practical use of the term “standardization”, deVries derived the following definition:

Standardization is the activity of establishing and record- ing, a limited set of solutions, to actual or potential match- ing problems1,2, directed at benefits for the party or parties involved, balancing their needs, and intending and expect- ing that these solutions will be repeatedly or continuously used, during a certain period, by a substantial number of the parties for whom they are meant. (deVries [deV97, p. 79], 1997)

2.1 types of standards

According to the definition, standards restrict the variety of a poten- tial product to a limited set of solutions. Thereby, some standards precisely specify product features, whereas other standards only de- scribe a loose set of characteristics.

1 Matching problems. Problem of determining one or more features of different interre- lated entities in a way that they harmonize with one other or of determining one or more features of an entity because of its relation(s) with one or more other entities.2 2 Entity. Any concrete or abstract thing that exists, did exist, or might exist, including associations among these things. Example: A person, object, event, idea, process, etc. 2.1 types of standards7

Different types of standards can be identified and are listed in Table 2.1. In general, standards can be categorized into two major groups. Quality standards are concerned with features and characteristics of the product itself. Compatibility standards describe the link to other products and services.

Table 2.1: Types of standards [Gri95, p. 22].

Category Type Examples

Quality Minimum attributes Measurement and grades Packaging, weight and measures Public regulation Health and safety, trade descriptions Product characteristics Style and tastes Fashion, breakfast cereals, brands Production economies Raw materials, automobiles Compatibility Complementary products VCR tapes, software, auto parts Complementary services Support Maintenance, servicing Knowledge User training, experience Direct networks Telephones, railways, LANs

2.1.1 Quality Standards

Quality standards can in turn be further subdivided into product char- acteristics standards and minimum attributes standards. The latter de- scribe measurement requirements for products and minimum quality specifications, such as the European Union (EU) energy label, which defines energy efficiency levels [EC17]. Further examples include the Minimum attributes European emission standards [Eur12a] and the EU’s quality standard for cucumbers, which was revoked in 2009, but is in use nevertheless [Eur88; Han13]. Standards defining minimum attributes of products and services are often incorporated into legal standards or required by government regulations to protect consumers. Consumer protec- tion is achieved, for example, by means of health and safety standards and facilitate product purchases by reducing searching and transac- tion costs [Gri95, p. 22]. On the contrary, product characteristics standards are loosely defined and describe bundles of features which are similar within a product group. Brands can indicate a consistent quality and communicate a Product certain image to the consumer. A firm particularly focused on deliv- characteristics ering a special usability experience, will communicate via its brand and image that all products will meet such quality characteristics. 8 standardization theory

Production economies explain why groups of products and services, generally share a set of features. For example, mobile phones have a common set of product characteristics, as all of them contain a mi- crophone, a speaker, and have almost the same product size. Further examples are cars sharing components and potato crisps having the same thickness. The main reason why product features have common characteristics is the economical advantage of reducing the develop- ment and manufacturing costs [Gri95, p. 22].

2.1.2 Compatibility Standards

Compatibility standards define the interface requirements to connect a product to its complementary product or create a network of prod- ucts [Gri95, pp. 22 sq.]. The demand for the good car also drives the Complementary demand for it’s complementary good fuel. For this, the fuel has to products and fulfill certain product characteristics and therefore conform to com- services patibility standards, so that the engine works properly. Complemen- tary services also require compatibility standards. In order to offer repair services for a car of a special brand, a repair shop might re- quire a specific set of training and tools. Another example constitutes the prescription of a drug, which requires complementary knowledge and training. If core products complement each other and thereby create a di- rect network, interface standards are required to ensure compatibil- ity. For example, the interface standards for mobile phones specify Direct networks how and when radio signals are sent. Furthermore, railway routes require the same electrification systems and the same railway gauge— the distance between rails—to be compatible with each other [Puf92]. The value of the network increases by its number of users or railway routes. Not all standards can be attributed to a single category. A car com- ponent, such as a headlamp or car seats, has similar product charac- teristics and has to be compatible to communicate with the control systems of a car. In this case, the supply side benefits from produc- tion economies and the demand side benefits or requires compatibil- ity [Gri95, p. 23].

2.2 market extent

Standards can not only be differentiated by their type, but also by their appearance on the market. Table 2.2 gives an overview of differ- ent properties classifying a standard with respect to its appearance on the market. The dimension group refers to the group, which adopts the stan- dard. Thus, multi-firm standards are adopted by different firms which produce similar products [Gri95, p. 24]. For example, “ISO/IEC 7810” 2.2 market extent9

Table 2.2: Standard properties regarding market extent.

Dimension Property Example

Group Multi-firm ISO/IEC 7810:2003 Multi-product IEEE 802.11 Multi-generation power plugs and sockets Fragmentation Monolithic QWERTY-Keyboard Fragmented Bank cards is a series of standards that specify the physical conditions of iden- tification cards [Int03]. Since multiple banks adopt this standard for Multi-firm their banking cards, the standard falls under this category. On the contrary, a corporation can standardize a modular system for their products to achieve economies of scales. A standard of such a mod- ular system, which is only shared to suppliers but not to competing firms, does not fall under this category. Standards that are adopted across product lines within a firm are defined as multi-product standards [Gri95, p. 24]. Most products of a product line “mobile phone” will be equipped with a Wireless Lo- cal Area Networking (WLAN) chipset that conforms to the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standard [Ins17a]. Multi-product However, a company might adopt national or regional safety stan- dards only for a market-specific derivative of a product, and not across the whole product line. For example, the United States (US) Code of Federal Regulations defines the standard, that convex mir- rors require the safety warning “Objects in Mirror Are Closer Than They Appear” [492]. Multi-generation standards describe standards that are adopted over several generations of a product. This category includes standards Multi-generation for alternating current power plugs and sockets, and the standard specifying the Universal Serial Bus (USB) data exchange. Another property of a standard constitutes rivalry standards on the market. A monolithic standard represents a standard that dominates Monolithic the industry [Gri95, p. 24]. For example, most Latin-script keyboards conform to the “QWERTY” layout and Blu-rays have won the format war over HD-DVDs [Dav85;CS 09]. The bank card market inhabits different standards, such as Master- Card and Visa, and is therefore denoted as fragmented. As markets Fragmented develop over time, a single standard can prevail out of a market with multiple standards. Often, standardization is fragmented internation- ally, when countries or regions prefer a certain standard [Gri95, p. 24]. This raises the question of why single standards often tend to diffuse and prevail in the market. 10 standardization theory

Network Effect Theory

The benefits of standardization in network and non-network markets have already been discussed at the beginning of Chapter 2. The under- lying assumption is referred to as network effect. Katz and Shapiro describe several sources of positive consumption externalities, which increase the consumer’s utility of a product. Such sources include more readily available product information and the effect, that mar- ket share can signal product quality [KS85, p. 424]. Furthermore, Katz and Shapiro distinguish between direct and in- Direct and indirect direct network effects. Direct network effects describe situations in network externalities which the utility of a good clearly depends on the number of other users [KS85, p. 424]. Indirect network effects, in turn, describe ef- fects where consumers benefit from other users due to interdepen- dencies of complementary goods [Bec06, p. 43]. The latter includes the hardware-software paradigm: A consumer benefits from other users purchasing similar hardware, since the variability and amount of software developed for the hardware will increase [KS85, p. 424].

ui ui

Standard A

Standard B bin

ai

Expected network size [Ne] Expected network size [Ne] Figure 2.2: Network effects depending on the expected network size.

The relation of a network effect, shown in Figure 2.2, is typically expressed as an utility function of consumer i:

e ui(n) = ai + bi N , bi > 0. (2.1)

It is assumed that each consumer buys at most one unit. The utility e consists of a stand-alone valuation ai and a second term bi N , which represents the expected size of the network. Thereby, N is usually measured in number of compatible units sold and Ne denotes the expected number. The consumer i individually evaluates the stand- alone valuation and network valuation [MR96, pp. 185 sq.].

2.3 standardization processes

Standards can emerge in various ways. Research on this topic has pro- posed several categories to distinguish between standardization pro- cesses. Standardization processes can generally be divided into two 2.3 standardization processes 11

main categories: De jure standards are formally negotiated and medi- ated by official standards bodies. Standards which are determined by market forces are referred to as de facto standards [Gri95, p. 25;FS 88, pp. 2 sq.]. However, a more detailed categorization was proposed by David and Greenstein and is listed in Table 2.3. According to the definition

Table 2.3: Standardization processes by David and Greenstein [DG90, p. 4].

Group Standardization Definition Process

De facto Unsponsored Unsponsored standards are sets of specifications without an identified originator having a proprietary interest and without a sponsoring agency. Sponsored Sponsored standards emerge from one or multiple entities, such as suppliers, users or private cooperative ventures, holding a proprietary interest and creating incentives for others to adopt. De jure Voluntary Voluntary standards are developed from standards-writing organizations, but do not have the force of law behind them. Mandated Mandated standards emerge from government agencies, which have regulative authority.

of David and Greenstein, unsponsored and sponsored standardiza- tion processes fall under the category of de facto processes. Voluntary and mandated standardization processes are generally known as de jure processes [DG90, p. 4].

2.3.1 Unsponsored Standards

This type of standardization process has been subject to research which focuses on the economic processes affecting the formation of unsponsored standards. The category describes situations where no agent has proprietary interest and no entity, such as firms and users, are large enough to influence other entities due to pricing and tech- nology decisions. A prominent and much cited example for unsponsored standard- ization is the emergence of the QWERTY keyboard layout. Its begin- QWERTY keyboard ning can be traced back to the year 1867, when Christopher Latham layout Sholes invented a primitive writing machine. The machine’s technical problems prevented an immediate commercial introduction. In order to resolve issues of clashing and jamming typebars, the key order- ing was rearranged from an alphabetical order to the QWERTY stan- dard. Moreover, the letters of the brand name “TYPE WRITER” were 12 standardization theory

placed in one row of the keyboard to impress customers. A series of uncoordinated decisions followed, which involved typewriter manu- facturers, typing schools, early typists and employers. The interaction of those events lead to the widespread adoption of the QWERTY key- board layout, despite the fact that it is ergonomically inferior to other alternatives, such as the Dvorak Simplified Keyboard (DSK)[Dav85]. Another example for an unsponsored standardization process con- stitute nuclear power reactors and the used material therefor. In order to control the energy level of the neutrons in the reactor, a material, named moderator, is used. Further, a coolant material is utilized to Nuclear power transfer the produced heat from the reactor core. The three types reactors of reactors are distinguished by the used material: light water (H2O), heavy water (D2O) and gas graphite—gas as coolant and graphite as moderator [Cow90, p. 545]. Despite the serious doubts concerning the economic and technical superiority of the light water technology from the beginning, a series of circumstances lead to the adoption of that technology. Cowan identified three main events attributing the rise: In the late 1940s the US Navy chose light water for their propul- sion program and subsequently researched the technology. Second, the explosion of the Soviet bomb was the cause to rush a civilian US power project, before physicists were ready to make a choice between the technologies. The third event were the decisions of the US and the European governments to subsidize the light water technology [Cow90, pp. 543,566].

Lock-In by Historical Events A central question of standardization research is whether a market containing multiple competing standards will stabilize or will lock in to a single standard. Arthur has been one of the first contributing to the economics of standards in 1983, and 1989 respectively. Arthur compares the competition of unsponsored technologies with increas- Increasing returns ing returns. This describes the phenomenon that complex technolo- gies often show increasing returns to adoption. The more adoption a technology achieves, the more experience is gained, which in turn improves the technology [Art89, p. 116]. Due to the US Navy’s early adoption and development efforts into the light water, the technology had a head start, when a demand for civilian nuclear power genera- tion emerged [Cow90, p. 541]. Arthur shows that small historical events during early diffusion periods can cause the economy to gradually lock in towards one tech- nology. The increasing returns technology does not necessarily have to be superior to the competing technology and, according to Arthur, such small historical events limit the predictability of future market shares. The dependence on early random and historic events is also referred to as path-dependence [Art89, p. 128]. 2.3 standardization processes 13

Bandwagon Coordination Problem The coordination problem is the following: If a majority adopts a certain standard, the benefits of conforming to it surpass the private costs of adoption. However, if the fraction adopting the standard does not exceed a critical amount, the consumer’s benefits may not justify the private costs. Thus, each agent anticipates the decision of others Anticipation of other and if an important agent publicly commits to a certain standard, agents others will follow due to knowing that they will be at least compatible with the leader [FS88, p. 2]. The ones with the largest private gains will switch first, later followed by the ones with the largest network gains. This dynamic process is often referred to as bandwagon effect and is depicted in Figure 2.3 [DG90, p. 9]. Larger installed base

More complements produced Further adoptions Greater credibility of standard

Reinforces value to users

Figure 2.3: Standards reinforcement mechanism [Gri95, p. 27].

Further, Farrell and Saloner describe the situation of “excess inertia” where agents are unanimously in favour for a technology, but are only moderately willing to start the change. Due to insufficient motivation Excess inertia and incentives, no agent wants to start the bandwagon process [FS85, p. 72].

2.3.2 Sponsored Standards

David and Greenstein further defines the sponsored standardization process, which also belongs to the group of de facto processes. It clearly differs from the unsponsored processes, since the agents de- veloping the standards act with proprietary interest. In order to en- Anticipation of able compatibility to complementary goods, sponsoring agents will rivalry reactions manipulate technical standards and pursue price-setting strategies. Therefore, sponsoring agents anticipate possible reactions of rivals, as this influences the adoption rates of alternative standards. Research on sponsored standardization processes includes the analysis of the strategic behavior [DG90, pp. 12 sq.]. A prominent example for a sponsored standardization process con- stitutes the Bluetooth standard. In the early 1990s, multiple firms in- vestigated the possibility to wirelessly connect computers, laptops, mobile phones and other electronics devices with each other.At that time two major mobile phone manufacturers, Nokia and Ericsson, were developing short-range radio technologies and it became clear that a standard was required to avoid potential market fragmentation. 14 standardization theory

Both companies independently approached Intel as potential partner and then agreed to form an alliance, with a limited number of promot- Bluetooth ers developing the standard. Further, it was decided that firms could join the alliance as “adopters” and thus receive access to the intellec- tual property at no cost. However, only promoting members are able to influence and develop the technical specification, adopters have no rights to influence the development process. The five firms, Nokia, Er- icsson, Intel, IBM and Toshiba founded the Bluetooth Special Interest Group (SIG) in 1998. Each firm contributed knowledge from their in- dustry and over 2000 adopter firms joined by the of April 2001 [Kei02, pp. 207 sq.].

Alliances In order to establish a de facto standard on the market, a majority of agents have to adopt it. An alliance can facilitate the establish- ment on the market, since it addresses two problems in the process of standardization. First, a technological solution has to be found. Thereby, a small group of standard developers enables a fast deci- sion process and complementary knowledge within that group pro- motes a mutual understanding for each other’s needs. In the case of 1 Nokia, Ericsson Bluetooth, the mobile phone,1 the semiconducter,2 and the Personal 2 Intel Computer (PC)3 industry were represented in the promoters group. 3 IBM, Toshiba Later four additional firms4 became members of the promoter group, 4 Microsoft, 3Com, which in turn diversified the interests. It was reported that the larger Motorola, Lucent number of promoting firms slowed down the compromising and de- cision process [Kei02, pp. 210 sq.]. The second problem addressed by an alliance constitutes the stan- dard diffusion in the market. Alliances can facilitate the triggering of the bandwagon effect, since their members provide a larger installed base, which increases the utility due to network externalities. Fur- thermore, the credibility of a standard is enhanced due to an alliance [Gri95, p. 42].

Strategies Schilling identified three strategical areas for a firm to manage and promote the standard diffusion in the market. Usually firms protect innovation by means of patents, copyrights, secrecy and other mecha- nisms. However, the protection of proprietary technology inhibits the Liberal licensing adoption. Thus, a free diffusion and liberal licensing of related tech- nology may constitute a strategy to quickly build the installed base. On the other hand, license fees for adopting a standard can pose an income stream which could finance the standard development [Sch99, pp. 269 sq.]. Proprietary and open standards are further discussed in Section 2.4. 2.3 standardization processes 15

Another option to encourage diffusion is to create contractual ar- rangements with complementary goods providers, and distributors. Such contracts can induce adoption due to price discounts, special ser- vices or advertising assistance. Bundling is also a successful method Complementary to increase the installed base. Thereby, standards piggyback on com- products plementary goods, such as the operating system MS-DOS, which was originally bundled with IBM [Sch99, pp. 270 sq.]. Aggressive promotion and marketing can further facilitate the de- ployment. This includes, for example, influencing the perceived in- Promotion and stalled base by advertising, penetration pricing, and educating con- marketing sumers [Sch99, pp. 271 sq.].

2.3.3 Voluntary Standards

The negotiations and agreements upon de jure standards take place in standards organizations. Here, voluntary standards denote the cir- cumstance that their application is not mandated by law. Such or- ganizations were either founded by private initiatives or are public agencies created by governments [DG90, p. 24].

Organizations

Standards Developing Organizations (SDOs) are bodies which are con- stituted on a national, regional or international level in order to de- velop and to certify compliance with other standards. SDOs approach the process of standardization by means of a detailed, structured, and consensus based process [Haw+17, pp. 4 sq.]. Each country usually has one National Standards Body (NSB), which is a SDO recognized from the respective country. For example, the National Standards American National Standards Institute (ANSI) and the Deutsches In- Body stitut für Normung (DIN) are NSBs of the US and Germany. In order to achieve global harmonization of standards, coordination is organized by means of the International Organization for Standardization (ISO) [Int15, p. 1]. However, there also exist SDOs, which act on a regional level, such as the EU. This includes the Comité Européen de Normalisation (CEN), a European Standardization Organization (ESO), which is officially recognized by the EU and brings the European NSBs together [Com17]. European Furthermore, the European Telecommunications Standards Institute Standardization Organization (ETSI) is also recognized by the EU, whereas the Alliance for Telecom- munications Industry Solutions (ATIS) is its North American counter- part. The telecommunication standards development is globally co- ordinated by the 3rd Generation Partnership Project (3GPP), which unites seven standards organizations including ETSI and ATIS [Pen15, pp. 25 sqq.; 3rd17c]. National and international standards bodies can be differentiated by the type of their conflicts. The conflicts on the national level involve economic interests of firms, whereas the eco- 16 standardization theory

nomic and political policies of countries or regions are disputed on an international level [DG90, p. 24]. Furthermore, consortia and forums are subsumed under the term Consortia and Standards Setting Organization (SSO). Similar to SDOs, SSOs also set forums standards, but they do not necessarily conform to the rules of SDO and are not coordinated within the SDO system [Haw+17, pp. 4 sq.]. The World Wide Web Consortium (W3C) and the Internet Engineering Task Force (IETF) belong to this category and both emerged from aca- demic backgrounds [Sim14, p. 106]. However, the given definitions are not unambiguous within the literature.

2.3.4 Mandated Standards

The reasons why governments have particular interests in standard- setting are diverse. In order to achieve national or supranational goals, National and government mandate the adoption of standards [DG90, p. 29]. An ex- supranational goals ample for this constitute food standards due to public health or envi- ronmental reasons. Another example of mandated minimum quality standards are security standards regarding the approving of vehicles [Wis15, pp. 4 sqq.]. Moreover, the innovation and industrial competi- tiveness might be fostered by mandated standard-setting. Further, market coordination problems and network externalities can lead to situations in which an intervention by the government can resolve externality problems. An argument for governmental in- tervention are the network effects gained by mandated and broad adoption [DG90, p. 29]. If standards are referred to in laws and reg- ulations, their application can become mandatory. On the one hand, standards can be distinguished by the authorities introducing such laws and regulations. On the other hand, standards can be differenti- ated with respect to the entities who have to apply them. An example for standardization is the compilation of financial re- ports for companies. Since public companies are globally traded on financial markets, a uniformed financial reporting standard facilitates International transparency and efficiency. The IFRS Foundation is an international Financial Reporting and not-for-profit SDO known for its International Financial Reporting Standards Standards (IFRS). Most national jurisdictions mandate domestic pub- lic companies to file their financial report according to IFRS. However, some countries only permit reporting under IFRS and the US mandates domestic public companies to report according to Generally Accepted Accounting Principles (GAAP), which is US specific [IFR17].

2.4 control and positioning

The dimensions access and leadership, which are listed in Table 2.4, re- late to a firm’s strategic decisions concerning a standard. Firms are able to access open standards and the adoption of such is not accom- 2.5 product scope 17

Table 2.4: Control and positioning of firms with regard to standards [Gri95, p. 23].

Dimension Property Example

Access Open HTML Proprietary HDMI Leadership Lead Sony (Betamax), JVC (VHS) Follow Sanyo (Betamax), Matsushita (VHS)

panied with restrictions. An example for an open standard is HTML5 which is developed by the W3C [Wor14]. Firms, SDOs, or other entities hold property rights over the proprietary standards. If a firm wants Open and to adopt a proprietary standard it may have to pay royalties. Such proprietary standards are usually protected by means of patents, copyrights, and firm-specific knowledge. For example, adopters of HDMI have to pay annual fees and royalties per units sold. In addition test equipment services are offered by the HDMI licensing administrator [HDM17]. Another aspect relates to the positioning of a firm with respect to a standard. One can distinguish between leading firms which actively Leading and contribute and develop a standard.On the contrary, firms which sim- following ply adopt the standard are only following the technological develop- ment of it. Table 2.5 lists the strategic positioning of the firms which were in- volved in the standard war between Betamax and VHS. As discussed at the beginning of this chapter, Sony and JVC introduced their own Video Cassette Recording (VCR) format. Sanyo and Matsushita were manufacturing companies, which followed the Betamax and VHS for- mat standard [Cus+92].

Proprietary Open Lead Sony (Betamax) JVC (VHS) Follow Sanyo (Betamax) Matsushita (VHS)

Table 2.5: Strategic positioning in the standards war Betamax–VHS [Gri95, p. 32].

2.5 product scope

The category “product scope” addresses the different dimensions re- garding the ways a product is determined by a standard. An overview of the dimensions and their properties is given in Table 2.6. Thereby, the degree refers to the proportion of a product, which is covered by the standard. The higher the standardization degree, Degree the less freedom do firms have in designing their product or service. 18 standardization theory

Table 2.6: Standard characteristics regarding product scope [Gri95, p. 23].

Dimension Property

Degree Significance of standard features Level Functional layer(s) standardization Means Built-in Gateway, converter

The requirements and test methods of Diesel fuel are standardized by the DIN EN 590 and leave very little room for product differenti- ation [Deu17]. In contrast, a standard describing a modular product platform will only define the interfaces between parts. Nevertheless, the standard enables a variety of different parts to mix-and-match. The dimension level refers to the standardization depth: For ex- Level ample, a compatibility standard can only specify the interface itself. Thereby, the system is treated as a black box and no requirements are laid down on the inner of the black box. If the compatibility standard defines certain response times, this may affect the inner components of a system [Gri95, p. 24]. The dimension means addresses the question of how compatibility Means is achieved. The standard’s technology might either be built-in and thus the architecture of the product or service conforms to the stan- dard anyway. If this is not the case, an adapter can be utilized to support a certain standard [Gri95, p. 24]. COMPATIBILITYSTANDARDIZATIONDYNAMICS 3

Standardization will not only be beneficial for automated driving, but also necessary in some areas. In the following, current standardiza- tion efforts with regard to their influencing firms are discussed. Automated driving means that the human driver is replaced by technology, as shown in Figure 3.1. Therefore, the development tasks Structure can be structured according to the tasks a human carries out during driving. The first four areas represented in Figure 3.1 are discussed

Eyes Sensors

Reflexes/coordination Actuator control of movement

Vehicle to X Ears communication (not mandatory)

Maps/environmental Memory models

Decision making Machine learning capabilities algorithms

Figure 3.1: Replacing the human driver with technology [Rol16, p. 8].

in the following of this chapter, whereas standardization dynamics in terms of ensuring safe decision making is analyzed in Chapter 4. However, the term “automated driving” and its standardization ef- forts are examined first.

3.1 terminilogy

In recent years, three major organizations, which are listed in Ta- ble 3.1, have proposed terminology definitions and standards. They describe and structure the capabilities of automated driving systems into several levels. The Bundesanstalt für Straßenwesen (BASt) and the National Highway Traffic Safety Administration (NHTSA) are or- ganizations, which are subordinated to the German government and US government, respectively. Hereby, the BASt’s mission is to pro- 20 compatibility standardization dynamics

Table 3.1: Organizations which proposed terminology definitions and standards regarding auto- mated driving.

Abbr. Organization Description

BASt Bundesanstalt für The research institute of the German Ministry of Transport and Digital Straßenwesen Infrastructure investigated the legal consequence of increased vehicle automation in 2012. The institute compiled a set of five automation levels including partial-, high- and full automation [Gas+12, p. 9]. NHTSA National Highway This agency of the US Department of Transportation develops, sets, Traffic Safety and enforces federal motor vehicle safety standards. The agency Administration provided a description and definition of automation levels [Nat13, pp. 4 sq.]. SAE Society of Automotive The US-based SDO developed the standard J3016, which was originally Engineers published in 2014 and revised in 2016 [SAE16a].

vide technical-scientific research and advice for politics, but standard- setting is not included [Bun17]. Contrary to the BASt, the NHTSA has the mission to increase traffic safety by developing and setting stan- Organizations dards. Furthermore, it has the mandate to enforce standards due to the Highway Safety Act of 1970 [U.S17]. The Society of Automo- tive Engineers (SAE) is a globally operating society, bringing together engineers and scientists from different industries. Moreover, the so- ciety develops standards, such as the standard J3016, which defines five levels of automated driving. Despite the initial introduction of an own definition, the NHTSA announced to adopt the SAE standard in 2016 [SAE16b]. Table 3.2 provides a summary of the proposed levels from each organization. Generally, each classification starts at complete manual driving and moves towards fully automated driving without the re- quirement of human interaction. The key attributes differentiating the automation levels are based on the question of whether the human or the system is in charge of monitoring the driving environment. SAE levels 0–2 contain a gradual responsibility transition from the human driver to the system for steering, accelerating, and decelerating the vehicle. However, the human driver has always to monitor the envi- Levels of automation ronment and to perform the remaining driving tasks. In contrast, the human is not obligated to monitor the environment any more, when a level 3 system is active. Nevertheless, the human has to serve as fallback driver, when the system requests to. Level 4 systems are ca- pable of driving and monitoring the environment. Furthermore, the human driver is not required as a fallback in certain environments un- der certain conditions, such as motorways for example. At SAE level 5 the system can perform the complete driving task from start to end during all conditions [SAE16a, pp. 22-24]. The definitions of SAE levels 0–3 are very similar to the correspond- ing level definitions of the BASt and the NHTSA. However, the BASt does not include an automated driving system according to SAE level 3.2 sensors and actuators 21

Table 3.2: SAE’s automated driving levels and their attributes with the corresponding levels of NHTSA and BASt [SAE14; Smi13].

SAE Name Execution of Monitoring Fallback of System Corr. Corr. level steering and of driving dynamic capability NHTSA BASt acceleration/ environ- driving task level level deceleration ment

Driver monitors the driving environment 0 No Driving Driver Driver Driver n/a 0 Driver Automation only 1 Driver Driver & Driver Driver Some 1 Assisted Assistance System driving modes 2 Partial System Driver Driver Some 2 Partially Driving driving auto- Assistance modes mated

Automated driving system monitors the driving environment 3 Conditional System System Driver Some 3 Highly Driving driving auto- Automation modes mated 4 High Driving System System System Some 4 Fully au- Automation driving tomated modes 5 Full Driving System System System All driving 4 Automation modes

5, which performs the driving task under all conditions and envi- ronments. Therefore, an automated driving vehicle without steering wheel is not represented in BASt’s terminology [Gas+12, p. 9]. Whereas level 4 of NHTSA is defined as automated driving for an entire trip without requiring the human as a fallback, the SAE subdivides this in level 4 and 5 depending on whether the system is capable to drive automated only in certain environments or always.

3.2 sensors and actuators

The environment of an automated vehicle is detected by its sensors. A vehicle’s actuators have to perform the steering and acceleration tasks of the advanced driving system. In recent years, the internal car communication has increased due to more information-based application and is expected to further in- crease due to automated driving applications. Therefore, several com- munication protocol standards have been introduced to enable com- patibility between automotive components. However, the complexity increased also, since nine networking protocols have been specified 22 compatibility standardization dynamics

[Boa14, p. 36], whereas a selected list is given in Table 3.3. Controller

Table 3.3: Protocols for inner car communication.

Protocol Intro. Attributes [Ixi14, p. 15]

CAN 1983 ISO 11898 Two wires, reliable, inexpensive, relatively low bandwidth of 1 Mbit s−1, shared medium LIN 2001 ISO 17987 Single wire, lower cost than CAN, lower bandwidth than CAN − MOST 2001 MOST Ring architecture of up to 50 Mbit s 1, relatively high Cooperation bandwidth, high cost Flexray 2005 ISO 17458 Shared serial bus, higher bandwidth of up to 10 Mbit s−1, higher cost, shared medium

Area Network (CAN) was originally developed by Bosch in 1983 and was specified as an ISO standard in 1993 [Vec16, p. 1]. The system is used for chassis, powertrain, and body electronics. Local Interconnect Network (LIN) and Flexray were both originally developed in consor- Status quo tia and were transferred to ISO standards. LIN is used for the commu- nication establishment to body electronics, such as mirrors or power seats, and the applications of Flexray include the communication for active suspension systems and adaptive cruise control. The protocol Media Oriented Systems Transport (MOST) is only used for camera and video applications and is maintained by a partnership referred to as MOST Cooperation [MOS17]. Each of those communication pro- tocols is capable of different bandwidths and involves different costs. Therefore, multiple communication systems are deployed in a single car for different purposes. This leads to wiring harnesses being the third heaviest component in cars [Ixi14, p. 7]. However, the traditional networking protocols represent serious challenges for the increasing throughput demands of automated driv- ing [Boa14]. In order to resolve the complexity of multiple propri- etary networking standards, new specifications for the application of Automotive Ethernet in cars are developed [Ixi14, p. 8]. Ethernet is part of the Ethernet IEEE 802 standard family and is known for its application in the com- puter industry for over 20 years [Hea17]. It operates on the physical and data link layer of the Open Systems Interconnection (OSI) model and is in widespread use. Despite its market diffusion, Ethernet was not adopted due to not meeting several requirements for automotive application. Electromagnetic interference requirements were not ful- filled, since Ethernet was susceptible to noise. Furthermore, very low latencies under 10 µs for fast reacting sensors were not supported. Moreover, Ethernet did not support bandwidth allocation for differ- ent streams and clock synchronization between devices [Ixi14, p. 8]. 3.2 sensors and actuators 23

Broadcom initially developed the BroadR-Reach standard, which allowed for longer distances of copper wiring and met the electro- magnetic interference requirements of the automotive industry [Ixi14, p. 11]. This Ethernet standard operates on the physical layer and uti- lizes an unshielded single twisted pair cable. In order to establish OPEN Alliance SIG wide scale adoption of Ethernet connectivity, the One-Pair Ether-Net (OPEN) Alliance SIG was formed in 2011 [Bro+11]. Similar to the found- ing story of the Bluetooth SIG, which was discussed in Section 2.3.2, the founding members of the OPEN Alliance consisted of players from different industries. Thereby, the initial members included companies from the semiconducter industry, such as Broadcom, NXP Semicon- ductors, and Freescale Semiconducters. Furthermore, the automotive industry was represented by BMW and Hyundai, whereas Harman represents an automotive supplier focusing on audio and infotain- ment solutions [Bro+11].

Table 3.4: Promoting and adopting members of the OPEN Alliance SIG.

Organization Manufacturer Supplier

OPEN Alliance [OPE17] Promoters BMW, Daimler, General Motors, Bosch, Continental Hyundai, Jaguar, Land Rover, Re- nault, Toyota, Volkswagen Adopters BAIC, Citroën, Fiat Chrysler, Ford, Aisin, Delphi, Denso, Honda, Hyundai, Mazda, Mitsubishi Hyundai Mobis, Lear, Motors, Nissan, Peugeot, Tata Panasonic, Schaeffler, Sumitomo, Valeo, Yazaki

Table 3.4 lists the promoting members, who have the right to influ- ence strategic decisions by participating in the Steering Committee. Both, promoting and adopting members, have the right to contribute to the technical committees of the OPEN alliance [OPE17]. Table 3.4 was compiled by analyzing the memberships of the 20 largest auto- motive manufacturers and suppliers, whereas Table A.1 and Table A.2 provide a complete list of them. Clearly, the majority of automotive manufacturers are members of the alliance. The European manufacturers are participating in this al- liance without exception, whereas only three Chinese manufacturers and the Japanese manufacturer Suzuki are not members. However, this is somewhat different for the suppliers, since only half of the suppliers listed in Table A.2 are part of the alliance. The OPEN Alliance and its standardization efforts belongs to the group of sponsored standardization processes. Here, Hyundai and BMW constitute the leaders of this technology, as they are the first to deploy automotive Ethernet in their cars [Ixi14, p. 4]. Since this standard ensures the communication compatibility between devices, 24 compatibility standardization dynamics

a cooperation on a unified protocol is vital. Furthermore, the stan- dards for vehicle networking is highly fragmented and it is estimated that this technology has the potential to reduce 80 % of the costs and 30 % of the cabling weight [Ixi14, p. 9].

3.3 car2x communication

As cars will be able to direct theirselves through more and more com- plex traffic scenarios, they will benefit from communicating with their environment. This includes the communication from Car-to-Car (C2C) C2C, C2I, C2B and from Car-to-Infrastructure (C2I) elements, such as traffic lights and traffic signs. Furthermore, the term “Car-to-Backend (C2B)” de- scribes the communication to backend systems, which could be traf- fic control centres or vendor specific information servers. C2C, C2I and C2B are often subsumed under the term “Car-to-Everything (C2X)” [Fuc+15, p. 526]. Automated driving does not necessarily require the communica- tion to the car’s environment [5G 15, p. 14]. However, C2X communi- cation enables the car to increase its scope of perception beyond its own sensed environment. It allows to receive, aggregate, and share sensed real-time information about traffic events from and with other traffic participants [Cac+15, p. 89]. Since cooperative behavior and communication between different systems requires compatible pro- tocols, efforts are undertaken to agree on joint standards. Use cases in which communication requirements play a central role and stan- dardization efforts towards the enabling of such are discussed in the following.

3.3.1 Automated Driving Use Cases

Overtaking maneuvers on the road are part of the driving routine and performed multiple times during a trip. Overtaking on unidirec- tional roads is less risky compared to two-way roads, since oncom- ing vehicles are always possible and approaching quickly. Therefore, the safety of automated overtaking maneuvers is increased by coop- Automated erative behavior of the involved vehicles. A vehicle might need to overtaking increase the gap in front, so that the overtaking vehicle can quickly merge from the opposed lane into the gap. Fast coordination is par- ticularly important if an oncoming vehicle is approaching. It is esti- mated that such use cases require an end-to-end latency of 10 ms and a reliability, defined as maximum tolerable packet loss rate, of 10−5 [5G 15, p. 30]. The second use case describes situations which are close to vehicle collisions and require communication to resolve the situation in order Cooperative collision to avoid harmful outcomes. When normal traffic control mechanisms avoidance fail and the planned trajectories lead to a potential collision, the co- 3.3 car2x communication 25

operation between vehicles can prevent and resolve critical situations [Haf+13, p. 2]. In such scenarios the vehicles have to exchange their trajectories, assess them, and agree upon a joint strategy to overcome the situation. The trajectory handshake has to be completed within 100 ms with a reliability of 10−5. It is estimated that the exchanged status information update should be received within 10 ms and with a probability of 99.9 % [5G 15, p. 30]. Closely spaced vehicle chains on highways are described as high- density platooning. This driving strategy has the potential to reduce fuel consumption as a result of decreased air drag and to increase the capacity of roads due to smoother traffic flow [Och+16, p. 1]. Form- High-density ing and maintaining platoons requires the constant and real-time ex- platooning change of kinematic state information to keep the inter-vehicle dis- tances constantly low. Similar to the other use cases, end-to-end la- tency of 10 ms and a reliability 10−5 is estimated to be required [5G 15, p. 30].

3.3.2 IEEE 802.11p / ITS-G5

The SDO IEEE develops and specifies the IEEE 802.11 standard, which is commonly known as WLAN standard [Ins17a]. Many electrical de- vices, including laptops, mobile phones and tablets, have adopted this standard and incorporated the technology. Due to the chipset’s support for high data rates and low production cost, IEEE 802.11 repre- sents a universal solution for diverse applications. The standard has been subject to further developments fixing technology issues and adding more functionality. The development of new 802.11 standards is organized in multiple task groups [CP+13]. In November 2004 the “Task Group p” was founded to develop enhancements to the 802.11 standard in order to support Intelligent Transportation Systems (ITS) applications [Ins17a]. The developed ver- sion 802.11p of the standard was affirmed per votes in April 2010 and IEEE 802.11p then integrated into the IEEE 802.11-2012 under the name Vehicular Ad Hoc Networks (VANET)[Ins17a; IEE12, p. ix]. A European version named ITS-G5 was derived and adapted to European requirements, ITS-G5 whereas G5 refers to the 5.9 GHz frequency band [Fes14, p. 167]. Fur- thermore, ITS-G5 was specified by the SDO ETSI under the name ETSI EN 302 663 [Eur12b]. The IEEE 802.11p standard comprises an important feature for the application of the discussed use cases: It allows to establish a direct communication between source and destination endpoint without re- lying on the coverage of a infrastructure network. This entails two Device-to- major advantages: First, no or poor network coverage does not in- Device (D2D) communication hibit C2C and C2I communication. Second, D2D communication signif- icantly decreases the end-to-end latency, since the data is not routed through the network infrastructure. 26 compatibility standardization dynamics

However, the standard entails also several limitations. Due to the utilized medium access strategy Carrier Sense Multiple Access/ Col- lision Avoidance (CSMA/CA), the collision probability increases, as the load of the network increases [Eic07, p. 5]. This means that dense sce- narios, such as crowded intersections in cities, can lead to degraded data throughput and increased delays. Furthermore, the standard’s probabilistic nature leads to the limitation that latency, bandwidth, and reliability can not be guaranteed [5G 15, pp. 39,49].

Figure 3.2: Protocol stack and related core standards of DSRC in USA (left) and C-ITS in Europe (right) [Fes15, pp. 411 sq.].

As depicted in Figure 3.2, the IEEE developed a series of 1609 stan- dards named Dedicated Short Range Communication (DSRC) in the US. Further, the ETSI and CEN proposed another series of standards for Cooperative Intelligent Transportation Systems (C-ITSs) in Europe. This includes the GeoNetworking protocol (EN 302 636), which uses geographical information to address the area in which a package should be distributed [CP+13, p. 70].

Table 3.5: Firm members of the working group IEEE P802.11 and the Car-2-Car Communication Consortium.

Automotive Telecommunication

Organization Manufacturer Supplier Vendor Operator

IEEE P802.11 General Motors Panasonic Ericsson, Huawei, AT&T, Deutsche [Ins17b] Nokia, Samsung, Telekom, Nippon, ZTE Orange Car-2-Car Audi, BMW, Ford, Honda, Bosch, Huawei Communication Hyundai, Jaguar, Land Continental, Consortium Rover, MAN, Opel, PSA Delphi, Denso, [CAR17] Peugeot Citroën, Groupe Valeo Renault, Toyota, Volkswagen

Table 3.5 gives an overview of the member firms, which participate in the standardization process of IEEE 802.11. Only one major vehi- cle manufacturer and one supplier are members of the IEEE P802.11 IEEE P802.11 working group. In contrast, all telecommunication equipment ven- dors are present, whereas a complete list is provided in Table A.3. Furthermore, only a limited number of the 20 largest network opera- 3.3 car2x communication 27

tors are part of the SDO’s decision process. However, this suggests that the automotive industry is not directly influencing the specification of the WLAN standard. The objective of the Car-2-Car Communication Consortium is to develop and contribute to the C-ITS standard in Europe. Furthermore, Car-2-Car it aims at globally harmonizing C2C communication standards and Communication Consortium achieves European harmonization by contributing to ETSI’s technical committee for ITS [CAR17]. As listed in Table 3.5, worldwide vehicle manufacturers are participating in this consortium, whereas Western manufacturers are more represented. This relation is also obtained for the suppliers, as the Japene supplier Denso constitutes the only non-Western exception.

3.3.3 Mobile Broadband

Cellular networks enable mobile phones to access the internet via Base Transceiver Stations (BTSs), whereas the protocol standardization plays an essential role, as mobile phones of various vendors have to be compatible with BTSs of different vendors. Thereby, the standardiza- tion process is globally coordinated by the organization 3GPP, which 3GPP unites seven organization partners [3rd17c]. The partners are telecom- munication SDOs operating on a national or supranational level and are listed in Table 3.6.

Table 3.6: SDOs cooperating within 3GPP [3rd17c].

Organization Abbreviation Country

Association of Radio Industries and Businesses ARIB Japan Alliance for Telecommunications Industry Solutions ATIS USA China Communications Standards Association CCSA China European Telecommunications Standards Institute ETSI Europe Telecommunications Standards Development Society TSDSI India Telecommunications Technology Association TTA Korea Table 3.7: Timeline Telecommunication Technology Committee TTC Japan of 3GPP releases [3rd17a]. The original aim of the 3GPP was to enhance the development of Rel. Date Third Generation (3G) mobile communications systems, when it was founded in 1998. However, after completing 3G, it continued specify- 10 Jun. 2011 ing next generations of mobile communication and is currently work- 11 Mar. 2013 ing on 5th Generation Mobile Networks (5G)[Pen15, p. 33]. The de- 12 Mar. 2015 velopment process is structured in releases which are approximately 13 Mar. 2016 published every year or every two years. For example, Release 10 in- 14 Jun. 2017 troduced Long Term Evolution (LTE)-Advanced and is the first speci- 15 Sep. 2018 exp. fication which qualifies for Fourth Generation (4G) networks [3rd17c]. 28 compatibility standardization dynamics

Long Term Evolution with Proximity Services Until Release 12, the standardization development was mainly fo- cused on the communication between BTS and User Equipments (UEs), such as mobile phones or cars. However, a feature named “proximity services” was published with Release 12, enabling direct D2D commu- nication. This feature is of particular interest for C2C and C2I commu- nication, as it standardizes a direct communication between devices and bypasses the infrastructure. The proximity service feature consists of two functions: The dis- covery function enables a UE to discover other UEs that are nearby via the LTE air interface and the second function comprises the D2D communication. Proximity refers not only to the physical distance, but is also dependent on channel conditions, delay and throughput D2D communication [Lin+14, p. 3; 3rd17b, p. 8]. The resource allocation can be managed in a scheduled mode, where a BTS determines the radio resources, and an autonomous mode, in which the UE selects the radio resource from a pool. However, the proximity service standard also comprises vari- ous limitations including collision risks during resource allocation in autonomous mode, security mechanisms, and slow connection setup procedures. The latter inhibits low delay C2C and C2I applications as discussed Section 3.3.1 [5G 15, pp. 45 sq.].

Fifth Generation

The 5G of communication systems is developed to address the com- munication demands of 2020 and beyond. It can be envisioned as service-led consolidating of 3G, 4G, WLAN providing greater coverage and reliability. To achieve a true generation shift, a bandwidth of over −1 5G targets 1 Gbit s and a latency of 1 ms are required. If these objectives are achieved, 5G could enable automated driving including the use cases, which were discussed in Section 3.3.1 [GSM14, pp. 9 sq.]. However, the targeted goal of sub-1 ms latency is estimated to be particularly challenging and might therefore be relaxed. Since 5G is currently un- der development, multiple requirements have been proposed by re- search studies and field tests to achieve and to enhance C2X commu- nications [GSM14; NGM15].

Organizational Overview Table 3.8 lists the players of the telecommunication and automotive industry, whereas complete lists are provided in Appendix A. If an au- tomotive manufacturer has a membership in one of the national and supranational SDOs, which are united by the 3GPP, then the manufac- turer predominately chooses to participate in his regional SDO. The exceptions are Hyundai, Mitsubishi, and Toyota, who are not only members of the Japanese and Korean SDOs, but also of the ETSI. A fur- ther exception constitutes the North American manufacturing market, 3.3 car2x communication 29

Table 3.8: Member firms of the SDOs cooperating within the 3GPP and 5GAA.

Automotive Telecommunication

Organization Manufacturer Supplier Vendor Operator

ARIB (Japan) Honda, Mitsubishi, Nissan, Denso, Ericsson, Huawei, KDDI, Nippon, [Ass17] Toyota Panasonic, Nokia, Samsung Softbank Sumitomo

ATIS (USA) Ericsson, Huawei, AT&T, Verizon [All17] Nokia, Samsung

CCSA (China) Mitsubishi Denso Ericsson, Huawei, China Mobile [Chi17] Nokia, Samsung, ZTE

ETSI (Europe) Audi, Daimler, Groupe Bosch, Ericsson, Huawei, AT&T, China [Eur17b] PSA, Hyundai, Mitsubishi, Continental, Nokia, Samsung, Telecom, Renault, Toyota, Denso, Magna, ZTE Deutsche Volkswagen Panasonic, Telekom, Etisalat, Valeo Nippon, Orange, Telecom Italia, Telefónica, Telstra, Verizon, Vodafone

TSDSI (India) Tata Ericsson, Huawei, Vodafone [Tel17b] Nokia, Samsung

TTA (Korea) Hyundai Hyundai Mobis Ericsson, Huawei, [Tel17c] Nokia, Samsung

TTC (Japan) Mitsubishi, Toyota Denso, Ericsson, Huawei, KDDI, Nippon, [Tel17a] Panasonic, Nokia Softbank Sumitomo

5GAA Audi, BAIC, BMW, Ford, Bosch, Ericsson, Huawei, KDDI, Orange, [5G 17] Jaguar, Land Rover, SAIC, Continental, Nokia, Samsung, Softbank, Volkswagen Denso, ZTE Telefónica, Telstra, Panasonic, Verizon, Vodafone Valeo

since neither Ford nor General Motors are members of the listed SDOs. Moreover, no automotive manufacturer and supplier is member of the ATIS. Similar to the IEEE 802.11p in Table 3.5, no Chinese automobile manufacturer is a member of the SDOs. Furthermore, the 5G Automo- tive Association (5GAA)’s target is to bring the telecommunication and automotive industry together. They follow the mission to harmonize use cases and ITS applications by promoting vehicle communication within 5G [5G 17]. Clearly, the IEEE 802.11p standard is in direct competition to the cellular network standards, which are harmonized by 3GPP. Hereby, 5G benefits from building upon existing LTE infrastructure, but is es- timated to arrive after 2020. A key enabler for C2X applications is already standardized and specified in LTE-Advanced. In contrast, the 30 compatibility standardization dynamics

802.11p standard has been tested for around ten years, but entails technical limitations nevertheless [Cor16, p. 12]. This market com- prises high network effects, as the utility of adopting one of the stan- dards is directly dependent on the network size. Furthermore, the adoption of either one of the standards could be exposed to excess in- ertia, since the involved firms do not want to risk potentially wrong investments.

3.4 navigation and mapping

Digital map data will play a central role in realizing automated driv- ing. The main focus of maps has been to provide navigation services and point of interest services. However, the maps have to overcome three fundamental challenges. First, vehicles have to precisely localize themselves by matching the sensed information with world reference Challenges map data. A vehicle does not only require the information on which lane it moves, but also needs to acquire the distance to the roadside. Second, a vehicle has to receive information about the environment, which is not in direct sight. This includes traffic conditions, ice on the road or accidents that lie 10 km ahead. Third, to achieve broad market acceptance an enhanced driving experience has to be deliv- ered. Thus, road conditions and changes thereof will influence the automated driving behavior [RR15, p. 51]. The challenges will require high-definition maps with real-time functionality. Since the content of maps and their environment changes High-definition over time, frequent map updates will be inevitable. Furthermore, ve- maps hicles will send sensed information, such as traffic incidents or mod- ified road sections, back to the map platform. The aggregation of up-to-date vehicle positions will enable real-time traffic estimations and the map will enhance its data by the vehicles’ sensors [SH16]. If vehicles obtain information about traffic flows, road construc- tions, accidents, weather, lane closures, etc. and send it to back to the map, the user’s utility of the map increases by the number of users. This relation suggests that users will benefit from direct net- Network effects work effects, as described in Section 2.2. The network effects might be dependent on the region, since a user’s utility of a high-definition map is only enhanced by those users whose movement patterns are located in the same region. In 2015, the Open AutoDrive Forum (OADF) was founded to coordi- OADF nate and align consortia on the topic of autonomous driving. Cur- rently multiple consortia are represented by OADF, each of which concerned with different aspects of maps and navigation [Nav17c]. Table 3.9 lists the companies and organization, which are members of the OADF and their consortia. The ADASIS Forum was established in 2001 aims at standardizing the interface between navigation sys- tems and advanced driving assistance systems. This includes the tra- 3.4 navigation and mapping 31

Table 3.9: Overview of consortia coordinated by the OADF.

Automotive

Organization Manufacturer Supplier Map data provider

Open AutoDrive Audi, BMW, Daimler, Ford, Aisin, Bosch, Denso, Baidu, Google, Forum (OADF)[Nav17c] Hyundai, Jaguar, Land Panasonic HERE, NavInfo, Rover, Mitsubishi, Opel TomTom, Zenrin

Advanced Driver BMW, Daimler, Ford, Aisin, Bosch, Baidu, HERE, Assistance Systems Honda, Hyundai, Jaguar, Continental, Denso, NavInfo, TomTom, Interface Land Rover, Nissan, Opel, Magna, Panasonic, Zenrin Specifications (ADASIS) Renault, Toyota, Volkswa- Valeo [ADA17] gen Navigation Data BMW, Daimler, Hyundai, Aisin, Bosch, Denso, Baidu, HERE, Standard (NDS)[Nav17b] Nissan, Renault, Volkswa- Panasonic NavInfo, TomTom, gen Zenrin

jectory of the road ahead, road gradients, traffic lights. The forum ADASIS brings together vehicle manufacturers, navigation and assistance sys- tems provider and map data providers. It was originally launched by Navteq, a firm later bought by HERE, and was formally reorganized to be coordinated by ERTICO. The forum is driven by the industry and open to public and private organizations [ADA17]. The second member of the OADF is the NDS Association, which pur- sues the target of standardizing map formats. Thus, it allows the map providers to store the data in their own proprietary format. Third par- ties can access the map due to the standardized NDS access format. NDS Association The standard’s architecture is modular, as depicted in Figure 3.3, and supports inter alia incremental updating [Nav17b]. High-definition mapping is estimated to create a $ 10–20 billion market by 2030 [Rol16, p. 10]. An early market entrance of map data providers might be essential, as additional users contribute data to the offered real-time maps and therefore trigger network effects. A list of relevant map data providers in various continents is given in Table A.5. Apart from Google, each map data provider is represented in all forums and associations. The vehicle manufacturer members are predominately from Europe, USA, and Japan. This is similar for automotive suppliers, whereas Aisin constitutes an exception. Vehicle manufacturers can avoid strong dependencies on specific map data providers by promoting and adopting a standard. 32 compatibility standardization dynamics

Figure 3.3: Building blocks of the NDS specifications [Emd16]. QUALITYSTANDARDIZATIONDYNAMICS 4

A major issue of automated driving systems constitutes the assurance of safety, since system failures can have lethal consequences. Thus, governments have a vital interest in regulating such systems in order to protect their citizens. This can be achieved by setting the legisla- tive framework for manufacturers and mandating safety standards [Gas+12]. However, this additionally involves a technical challenge, Safety standards as methods and procedures have to be developed to test highly auto- mated driving systems under a variety of conditions. National and supranational legislation are also in competition to attract businesses and firms. Hereby, locational advantages can be re- alized by loosening the legislative restrictions. As large manufactur- Competition ing firms and suppliers are operating in various markets throughout the world, investing and testing decisions are influenced by regula- tory frameworks. Clearly, standards ensuring properly functioning systems belong to the category of minimum quality standards. Fur- thermore, the law enforcement of national and supranational legisla- tion could lead to geographically fragmented standards.

4.1 technical challenges

As software has been present in vehicles since 1976 and the amount of it is ever increasing, standards for software development processes have already been developed [Pre+07, pp. 1 sq.]. A prominent ex- ISO 26262 ample constitutes the ISO 26262 standard, which defines functional safety requirements for electrical and electronic systems with respect to road vehicles [Hil12]. The V software development model, which is illustrated in Fig- ure 4.1, has long been applied in the field of automotive software engineering and is also referenced in the ISO 26262 standard. The left side starts at specifying the requirements until the actual implemen- tation. Thereby, each of these steps is broken down into subsystems, V model which are then treated in parallel. The right side validates and verifies the functionality of each level by testing [KW16, pp. 1 sq.]. Koopman and Wagner have identified multiple areas, in which the testing of automated driving—level 4 of NHTSA—vehicles according to this model represents a major challenge. An obvious challenge are the complex requirements. The V model assumes that the require- ments are known, complete, and specified in an unambiguous man- ner. Due to the abundance of different traffic scenarios combined with rare events, it seems rather impossible to compile a requirements doc- 34 quality standardization dynamics

REQUIREMENTS VALIDATION & TRACEABILITY ACCEPTANCE SPECIFICATION TEST Review Review VERIFICATION & SYSTEM SYSTEM TRACEABILITY INTEGRATION SPECIFICATION & TEST Review Review VERIFICATION & SUBSYSTEM/ SUBSYSTEM/ TRACEABILITY COMPONENT COMPONENT SPECIFICATION TEST Review Review VERIFICATION & PROGRAM TRACEABILITY PROGRAM SPECIFICATION TEST Review Review VERIFICATION & MODULE TRACEABILITY UNIT TEST SPECIFICATION Review Review

SOURCE CODE Review

Figure 4.1: Generic V model by Koopman and Wagner [KW16, p. 2].

ument, which handles all of these exceptions [KW16, p. 3]. Another identified area is the challenge of realizing a safety concept from fail- Challenges safe to fail-operational [Mar+17, pp. 412 sq.]. Fail-safety describes the capability of a system to bring itself into a fail-safe state to avoid hazards. In order to achieve fail-operational systems, three systems run in parallel and thus enable a redundancy if one of the systems fails. Therefore, a fail-operational system remains operational, but de- graded if a subsystem fails [KW16]. The ISO 26262 working group is currently working on an enhanced version [Mar+17, p. 413].

4.2 germany

The German automotive industry takes the view that standardized procedures for experimenting and testing of automated driving sys- tems are required [PEG17]. Therefore, the Federal Ministry for Eco- nomic Affairs and Energy of Germany has initiated a project named PEGASUS in 2016. The project’s mission is to create standardized procedures for testing and developing automated driving systems. Furthermore, this project aims at establishing quality criteria, meth- ods, and scenarios to allow for a rapid implementation of automated driving and is structured into four sub-projects, which are shown in Figure 4.2. Table 4.1 lists all involved partners in the PEGASUS project. Thereby, the project brings several stakeholders together, who are part of the automated driving development process [PEG17]. Each German car manufacturer and the two largest German suppliers are members of the project. Furthermore, the company TÜV SÜD, which is a test lab providing services to certify the conformance of standards, is also 4.3 european union 35

Scenario Analysis & Implementation Reflection of Results Testing Quality Measures Process & Embedding

▪ What human capacity ▪ Which tools, methods ▪ How can complete-ness ▪ Is the concept does the application and processes are of relevant test runs be sustainable? require? necessary? ensured? ▪ How does the process ▪ What about technical ▪ What do the criteria of embedding work? capacity? and measures for these test runs look like? ▪ Is it sufficiently accepted? ▪ What can be tested in labs or in simulation? ▪ Which criteria and What must be tested on measures can be test grounds, what deducted from it? must be tested on the road?

Figure 4.2: Central issues of the PEGASUS project [Zlo+17, p. 4].

Table 4.1: Members of the PEGASUS project [PEG17].

Category Firms

Manufacturers Audi, BMW, Daimler, Opel, Volkswagen Supplier Automotive Distance Control, Bosch, Continen- tal Test Lab TÜV Süd SMBs fka, iMAR, IPG, QTronic, TraceTronic, VIRES Scientific institutes DLR, TU Darmstadt

involved in the project. Moreover, scientific research expertise is con- tributed by a university and a research institute. Clearly, the development of such safety procedures comprises ini- tial upfront costs, whereas competencies of different fields are re- quired. A project initiated by the government increases the credi- bility, as the political framework plays a central role for automated driving. Furthermore, the subsidies provide an incentive for partner- ing, whereas only German firms and German research institutes are represented in this project. The question arises whether a geographi- cally limited list of firms constitutes a beneficial factor. For example, the initial promoting group of the Bluetooth alliance comprised five firms with their headquarters in four different firms. However, the sit- uations are clearly different, since the development and adoption of Bluetooth was not highly dependent on the decisions of authorities.

4.3 european union

The EU has established several initiatives to facilitate advancements in the transporting industry. The cooperation platform “ERTICO” was founded in 1991 and comprises over a hundred partners, whereas a selection is listed in Table 4.2. ERTICO’s partners include public authorities, research institutes, universities, and also users, such as associations [ERT17]. Thereby, ERTICO targets at implementing and 36 quality standardization dynamics

Table 4.2: Partners of the European platform ERTICO and the consortium VRA.

Automotive

Organization Manufacturer Supplier Other

ERTICO BMW, Fiat, Ford, Aisin, Bosch, HERE, TomTom, [ERT17] Honda, Jaguar, MINI, Continental, Denso, Ericsson, Huawei, Renault, Toyota, Panasonic, Valeo Deutsche Telekom, Volkswagen Telecom Italia, Vodafone VRA [Veh17] Jaguar, Land Rover, Denso HERE Renault

deploying enabling technologies and aims at evaluating related tech- ERTICO nologies for ITS systems [ERT17]. ERTICO provides a platform to coordinate the standardization requirements of the automotive indus- try. The compiled requirements are then passed onto SDOs, such as the SAE, IEEE, and ISO to facilitate a global harmonization [Eur14]. ERTICO coordinates inter alia the consortium Vehicle and Road Automation (VRA), which is funded by the European Commission. VRA’s mission is to identify deployment requirements for several ar- VRA eas. This includes regulatory and legal aspects, as well as standard- ization and certification needs [Veh17]. Furthermore, the VRA consor- tium facilitates the cooperation between other EU projects and con- tributes to the trilateral cooperation between the EU, USA, and Japan, which is discussed in more detail in Section 4.4 [Arr16]. Despite VRA’s potential influence by contributing compiled requirements to the US and Japan, only a minority of manufacturers and suppliers are mem- bers of the consortium.

4.4 eu-us-japan trilateral cooperation

International cooperation on information sharing regarding Intelli- gent Transportation Systems (ITS) has a long history. This exchange was originated by the European Commission, the US Department of Transport and the Road Bureau of Ministry of Land, Infrastructure, Members Transport and Tourism of Japan. The cooperation consists of a series of bilateral and trilateral agreements, whereas the exchange activities are officially authorized among each other [Eur17a, pp. 28 sq.]. Figure 4.3 illustrates the organization chart. Bilateral and trilateral working groups have been established on different topics of interest and are coordinated and steered by superordinated groups [Eur17a, pp. 28 sq.]. The task groups cooperate on the harmonization of com- Organization patibility as well as quality standards. Here, the working group 4.4 eu-us-japan trilateral cooperation 37

Table 4.3: Selection of identified standardization requirements by VRA [Veh13, pp. 40-42].

Generic architecture Minimum performance and operational requirement stan- dardization of infrastructural elements. This also include quality standards from a single component level to the com- plete vehicle. Cybersecurity Standardization regarding cybersecurity could reduce the risk of malicious attacks. For example, this includes firewall standards and security certifications between communicat- ing elements. Scenarios representing the Standardized minimum set of scenarios, which represent the real world real world, minimize costs, and ensure vendor interoperabil- ity. Such standards are critical for development and valida- tion of automated driving systems. Roadworthiness testing Testing standards not only address the need for develop- ment, but also for market deployment. Such standards in- clude system performance tests and fail-safe operation tests.

“Standards Harmonization” works on communication protocols and infrastructure message standards. Furthermore, a trilateral automation group was founded by the approval of the steering committee in 2012. Their mission includes the information exchange of the results obtained by regional research programs. The identification of standardization and harmonization needs in order to internationally develop and deploy automated driv- ing is also part of the work group’s mission. The involved parties have agreed on eight areas, which are of shared interest, whereas only six of them are depicted in Figure 4.3 [Eur17a, p. 29]. The area “road- worthiness testing” addresses the necessity of defining tests and test environments for the use on public roads. To achieve this goal, safety levels and a methodology to validate such levels is researched in this group. Moreover, this area also includes the topic on system valida- tion by means of standardized test environments [iMo13, p. 20]. The Chinese automobile market constitutes the largest automobile market in the world showing double-digit growth rates in recent years and 28 million newly registered vehicles in 2016 [PwC16; Org17]. In contrast, roughly 18 million vehicles have been sold in USA in 2016, whereas 17.5 million vehicles have been sold in the EU. Japan rep- Global effect resents the fourth largest automobile market with 5 million vehicles registered annually [Org17]. Since the members of this trilateral coop- eration do not only account for the largest markets apart from China, but also provide a large proportion of the key vehicle manufactur- ers, a coordination on this level affects around 40 % of sold vehicles 38 quality standardization dynamics

Steering Group Government Only Co-Chair: Takumi Yamamoto, MLIT/Ken Leonard, ITS-JPO US-DOT /Colette Maloney, EC DG CONNECT

Coordinating Group ：EU-US WG Co-Chair: Hideyuki Kanoshima, MLIT/Brian Cronin, ITS-JPO US-DOT ：US-Japan WG /Wolfgang Hoefs, EC DG CONNECT ：Trilateral WG Working Group Driver Safety Sustainabi Standards Evaluation Probe Distraction Applicatio lity Harmoniza Tools and Automation Data & HMI ns Applications tion Methods （Japan: US-Japan Trilateral WG Trilateral WG observer）

Harmonization Task Group Sub-Group

HTG1 & HTG3 HTG2 HTG4/5 Accessible Evaluation Roadworthiness ITS Security and Safety Message Infrastructure Message Transport testing and Communications Harmonization ‐US Standards Protocols BSM and EU CAM certification

HTG6 HTG7 HTG8 Human Factor Digital Legal Issue ITS Security Policy for (Candidate New Work (Candidate New Work Infrastructure Cooperative ITS Item) Gap & Overlap Item) Probe Data Environments Analysis Standards

Figure 4.3: Structure of the trilateral cooperation as of 2015 [Zlo+17, p. 4].

worldwide. Therefore, safety standards authorized and enforced by these markets clearly have the potential to attract followers. Part II

PEDESTRIANBEHAVIORMODEL

LITERATUREREVIEW 5

Over the last decades several models have been developed to simulate the dynamics of pedestrian behavior. Thereby, different modelling techniques have been applied, which are reviewed in the following. Moreover, models specifically proposed for intersection situations are discussed.

5.1 scales of modelling

Pedestrian behavior models can be differentiated according to their spatial resolution. Figure 5.1 illustrates three different scales of mod- elling pedestrian behavior. The macroscopic approach is based on

Figure 5.1: Macroscopic (left), mesoscopic (middle), and microscopic (right) modelling approach.

aggregating pedestrians to traffic flows, rather than modelling sin- gle pedestrians. Modelling traffic flows is often achieved by means Macroscopic of fluid or continuum mechanics theories [Pap+09, p. 243]. Since the approach objective of this thesis is to model pedestrians interacting with cars, macroscopic models are not further considered and discussed. Mesoscopic models describe pedestrians as individuals, which are not able to move continuously in space. The movement is restricted to discretized positions on a two-dimensional grid, as shown in Fig- ure 5.1. This type of modelling has the advantage of increased com- Mesoscopic approach putational efficiency, due to the limited number of cells and states on the grid. However, the discretization of space introduces an accuracy problem for cells, which are partially blocked by an obstacle. Allow- ing pedestrians to move onto partially blocked cells leads to pedes- trians moving into obstacles. On the contrary, if pedestrians do not move onto such cells, their moving space is more limited compared to the reality [Bie+16, p. 352]. 42 literature review

Microscopic models, depicted on the right of Figure 5.1, describe pedestrians as individuals, which are able to continuously move in Microscopic space. This type of modelling enables pedestrian movements with approach high spatial resolution and thus allows for more detailed simulation results. However, the increased resolution comes at the cost of higher computational complexity [Bie+16, pp. 352 sq.]. Simulating pedestrian behavior does not necessarily mean that the deployed model follows one of these approaches. Techniques to cou- ple models of different scales have been proposed by Biedermann et al. [Bie+14; Bie+16].

5.2 pedestrian behavioral levels

Hoogendoorn et al. proposed a concept to distinguish pedestrian behavior into three levels, which are listed in Table 5.1 [Hoo+01; HB04]. Each of those levels exchanges information with the level

Table 5.1: Pedestrian behavioral levels according to Hoogendoorn and Bovy [Hoo+01;HB 04].

Strategic level On the strategic level pedestrians choose what activities to perform and thus identify a destination. Tactical level In order to reach the identified destination, a route has to be planned on the tactical level. Operational level Finally, the actual movement of the pedestrian along the planned route takes place on the operational level.

above and/or below [HB04]. This behavioral categorization has found wide application in research [Pap+09, p. 252; Rob+09, p. 37].

5.3 related models

Some developed models describe situations at intersections, whereas others also model the interaction between pedestrians and cars. A selection of models which are related to the objective of this thesis are discussed in the following.

5.3.1 Feng et al. 2013

In order to simulate the crossing of pedestrians at signalized streets, Feng et al. based their model on a cellular automata, as depicted in Figure 5.2. Thereby, the space of the crossing is divided into cells with a fixed width of 0.6 m, which approximately corresponds to a pedestrian possibly carrying a bag. The pedestrians are divided into two categories: up-walkers and down-walkers. The movement into an adjacent cell is modelled with dynamic transition probabilities, 5.3 related models 43

which are influenced by three parameters. First, the benefit parame- ter describes the pedestrians’ general direction to reach the destina- tion, which is achieved by increasing the transition probabilities into the destination’s direction. Second, the attraction parameter models the attraction to other pedestrians who move into the same direction. Third, the occupancy parameter restricts the movement to cells occu- pied by another pedestrian [Fen+13]. Since the pedestrians’ positions are discrete, this model falls under the category of mesoscopic models. This in turn introduces the limi- tations of the mesoscopic approach, as discussed in Section 5.1. Fur- thermore, Feng et al. do not take cars or other vehicles into account, Figure 5.2: Pedes- which could partially block cells on the crossing space. trians on a square lattice. 5.3.2 Hashimoto et al. 2016

Hashimoto et al. developed a probabilistic behavior model for signal- ized crossing situations, which is based on a Dynamic Bayesian Net- work (DBN). Figure 5.3 depicts the directed acyclic graph of the DBN. Each node represents a random variable, which can have discrete or

St−1 Decision St Signal

Dt−1 Dt

Mt−1 Mt Motion

Spt−1 Spt Speed Drt−1 Drt Direction Pt−1 Pt Position

Zt−1 Zt Observation

Figure 5.3: DBN of the pedestrian behavior model by Hashimoto et al. El- lipses denote continuous random variables and rectangles repre- sent discrete random variables [Has+16, p. 169].

continuous values. The edges of the graph represent the conditional dependencies between the nodes. Hashimoto et al. introduced several random variables, starting with the traffic signal St ∈ {PG, PFG, PR}. These values represent the three phases of Japanese traffic signals. St obviously influences the Random variables pedestrian’s decision Dt ∈ {cross, wait}. Mt models the different mo- tion types, such as standing, walking, and running. Furthermore, Spt denotes the pedestrian moving speed; Drt the pedestrian’s moving direction and Pt the pedestrian’s position. The random variable Zt de- scribes the measured position of the pedestrian [Has+16, pp. 168 sq.]. This behavior model is particularly targeted for vehicles at sig- nalized intersections to assess the risk of a possible collision with 44 literature review

a pedestrian. The different behavioral levels are closely linked to each other, as they are part of a single DBN. Furthermore, this type of model requires a dataset to estimate the conditional probabilities. Introducing a new contextual random variable, such as the overall traffic volume at the crossing, requires the contextual information to be included in the dataset. For example, less traffic might increase the probability of pedestrians crossing during red signal phases.

5.3.3 Anvari et al. 2015

Anvari et al. developed a behavior model for vehicle-pedestrian inter- actions in shared space environments. Shared spaces describe streets or places, which are used by pedestrians and vehicles together. Such spaces are designed by reducing the distinction between pedestrian areas and streets [Anv+15, p. 85]. Anvari et al.’s shared space model describes not only the behavior of pedestrians, but also for cars. The model is structured into three layers, whereas the first is responsible for the trajectory and the plan- ning. In order to reach the final destination, intermediate destinations Strategic and are identified by a flood-fill algorithm. Furthermore, the trajectories tactical behavior to the different destinations are planned by using the shortest path level [Anv+15]. Thus, this layer undertakes the tasks of the tactical and strategical behavioral level according to Hoogendoorn et al. A modified version of Helbing and Molnár’s social force model [HM95] is utilized for Anvari et al.’s second layer [Anv+15]. Thereby, the pedestrian’s operational behavior is modelled as sum of several Operational behavior forces, which affect the pedestrian. Anvari et al. argues that pedes- level trians and cars move with identical priorities in shared spaces and therefore the social force approach is also applied to cars, as shown in Figure 5.5 [Anv+15, p. 91]. Compared to the original model of Hel-

#„ Fγ1 #„ Fγ2 #„ #„ F #„ #„ Fαn α1#„ Fγ3 Fγn Fα2

#„ Fα3

Figure 5.4: Modified social force model for shared spaces by Anvari et al.

bing and Molnár [HM95], Anvari et al. has introduced forces to model the influence of pedestrians on cars and vice versa. For example, a re- 5.3 related models 45

pulsive force exerted by cars on pedestrians is added to describe the circumstance that pedestrians avoid potential collisions. The third layer defines several rules, such as constraints for steer- ing angles and movement speeds for cars. Maximum steering angles, no lateral movements, speed limits, and speed reductions for curved trajectories are modelled by means of constraints. Furthermore, rules Rule-based for conflict avoidance are introduced to describe car-pedestrian and constraints car-car interactions. This includes a left hand driving preference, for shared spaces in the UK [Anv+15, pp. 98 sq.].

5.3.4 Zeng et al. 2014, 2017

Zeng et al. have presented models to describe pedestrian behavior at signalized crossings [Zen+14b; Zen+17]. Each of these microscopic models is based on the concept of social forces and differs in the scope of modelling. The model, shown in Figure 5.5, was published

N2 Desired exit ~ N1 position Pexit 3

2 Desired entering position

1 Waiting area F1 F2

Figure 5.5: Signalized crosswalk of Zeng et al.’s model.

in 2014 and considers only a single signalized crosswalk [Zen+14b]. It consists of four Origin Destination (OD) zones (F1, F2, N1, N2) and is structured into three stages, which are depicted as circled numbers. Strategic behavior In order to model entering and exiting positions, Zeng et al. con- level ducted an empirical analysis to estimate the position distributions. The identification of the desired entering and exiting positions is part of the strategic behavioral level. The go decision after a green signal phase is modelled as a Weibull distribution. However, the main focus is on the second stage, the actual road crossing [Zen+14b, p. 145]. The operational level of Zeng et al.’s model is based on the so- cial force approach, whereas additional forces were introduced. This includes an attractive force directed towards the middle of the cross- walk and a repulsive force from cars. Zeng et al.’s model, which was published in 2017, describes pedes- trian behavior at a complete signalized intersection, rather than a sin- gle crosswalk. Therefore, the OD zones are placed at the beginnings 46 literature review

and endings of the sideways. In order to route towards the destina- tion, a finely-meshed cell-based network is introduced, which serves as the basis for a navigation graph. Each cell comprises a node and is interlinked to the adjacent cells, whereas the edge costs correspond to the Euclidean distance. A shortest path algorithm is applied for the tactical level, in which cells can be blocked by other pedestrians. However, the model does not address pedestrians, who intentionally take a shortcut, since the navigation graph is only available for side- walks and crosswalks. The operational level is similar to the model published in 2014 [Zen+17].

5.3.5 Overview of Models

Table 5.2 provides an overview of the previously discussed models.

Table 5.2: Overview of pedestrian behavior models.

Authors Scale Approach Situation

Feng et al. [Fen+13] Mesoscopic Cellular automata Pedestrians crossing the street Hashimoto et al. [Has+16] Microscopic Bayesian Network Signalized intersections Anvari et al. [Anv+15] Microscopic Pedestrians: Social force, Shared space environments Cars: Rule-based social force Zeng et al. [Zen+14b] Microscopic Social force Signalized crosswalk Zeng et al. [Zen+17] Microscopic Social force Signalized intersection PEDESTRIANBEHAVIOURMODEL 6

In order to simulate the behavior of pedestrians, a theoretical model is required. In the following, the theory of the developed model is described, whereas the chapter is structured into the three behav- ioral levels introduced by Hoogendoorn et al. [Hoo+01;HB 04], as discussed in Section 5.2.

6.1 strategic level

According to Hoogendoorn et al. [Hoo+01], pedestrians choose their next activities and identify a destination on the strategic level. It is assumed that the modelled pedestrians want to reach a destination outside of the intersection area. This could include a shop, their home, and the railway station. Since the scope of the model is limited to the intersection area itself, pedestrians will only traverse the intersection. Destination It is assumed, that no intermediary goals, such as stores or benches, identification are present in the intersection scenario. As depicted in Figure 6.1, origin and destination areas are placed at the beginnings and endings of sidewalks. A Markov chain approach

Footpath Destination

Crosswalk

Sidewalk Origin

Figure 6.1: Exemplary intersection with origin and destination areas.

is utilized to model the destination selection of pedestrians. Thereby, each origin and destination is represented as node in a graph and the selection of a destination is described by transition probabilities. The probabilities can be expressed in a transition matrix, which is 48 pedestrian behaviour model

commonly referred to as origin-destination matrix in pedestrian dy- namics research [BR11].

6.2 tactical level

Provided the case that a destination was identified on the strategic behavioral level, the route choice to reach the destination is modelled on the behavioral tactical level [HB04, p. 172]. Pedestrians have dedicated areas to reach their destination at an intersection. As shown in Figure 6.1, the dedicated areas include side- walks, crosswalks, and footpaths. In an ordinary situation, pedestri- ans generally prefer these dedicated areas to navigate towards their destination. However, there also exist situations in which a pedestrian will cross the road without taking a detour via a crosswalk. It is as- sumed, that pedestrians have different risk tolerances and that they perceive a situation differently. Therefore, one pedestrian might tol- erate the risk of diagonally crossing an intersection, whereas another Situation evaluation one avoids this risk. This may be due to the preference of saving time or due to a higher individual risk tolerance. Figure 6.2 shows a crosswalk overlaid by an undirected navigation graph in orange. A graph consists of nodes, which are referred to as #„ nk, and edges denoted as ek↔l. Furthermore, nk denotes the position of nk and d(ek↔l) describes the Euclidean distance of ek↔l. In this exemplary situation, a pedestrian originates in the green area and identifies the blue area as destination. Subsequently, the pedestrian

n1 n2 n3

e1,5 e3,5

n4 n5

Figure 6.2: Exemplary pedestrian crossing with navigation graph in orange. The green area denotes the origin of a pedestrian and the blue area illustrates the destination.

has to navigate through the situation to reach the destination. The set A denotes the different types of areas, so that

{crosswalk, sidewalk, street, . . . } ∈ A.(6.1)

In order to model the path finding, each edge ek↔l has a cost ck↔l, which takes the risks of the area a ∈ A into account. Therefore, the variable da(ek↔l) denotes the length of ek↔l, which is placed on area 6.3 operational level 49

a. The cost is then modelled by the following formula, whereas pi,a denotes the perceived cost function of pedestrian i regarding area a:

ci,k↔l = ∑ pi,a(da(ek↔l)) (6.2) ∀a∈A A navigation solely based on the distance would be realized by the perceived cost function pi,a(da) = da, ∀a. It is assumed, that edge sec- tions, which are located on areas dedicated to pedestrians, constitute a lower perceived cost. This is due to a lower exposure to risk, which includes the risk of a car accident, for example. Subsequently, pedes- trian i asses the cost of taking the edge ek↔l by the following formula:

 dasi, if a = crosswalk, sidewalk, and footpath pi,a(da) = (6.3) da, otherwise

Here, the parameter si ∈ [0, 1] denotes the strength of preferring areas, which are dedicated for pedestrians. If s1 = 0, pedestrian 1 will take any detour, to avoid the risk of walking on an area, which is not dedicated for pedestrian purposes. It is assumed that si is normally 2 distributed, so that si ∼ N (µs, σs ).

6.3 operational level

As discussed in Section 5.2, the operational level addresses the phys- ical movement along the planned route. To describe the operational behavior, a set of variables is intro- #„ duced. The position of pedestrian i is denoted as x and the ve- #„ p,i locity is indicated as vp,i. Further, the desired velocity of pedestrian d #„d i is indicated as vp,i, whereas the next target is denoted as xp,i. Sim- Variables ilarly, the car k is also described by position and velocity variables, but with the index c, k. Since the space is modelled as planar, each #„ #„ #„ #„ 2×1 vector xp,i, vp,i, xc,k, vc,k ∈ R is in the two-dimensional Euclidean #„ d space. The variables vp,i = k vp,ik and the desired velocity vp,i ∈ R are scalars. The time is discretized and thus, described as tk = k∆t, whereas k denotes the simulation step and ∆t the duration of a time step. The velocity of pedestrian i at time tk can be obtained by: #„ #„ #„ vp,i(tk) = vp,i(tk−1) + ap,i(tk)∆t (6.4) #„ Here, ap,i denotes the acceleration of pedestrian i. Subsequently, the position of simulation step k can be computed by means of the fol- lowing formula: #„ #„ #„ xp,i(tk) = xp,i(tk−1) + vp,i(tk)∆t (6.5) The velocity change of pedestrian i is then modelled by means of so- cial forces. These social forces describe the pedestrian’s motivation 50 pedestrian behaviour model

to act and are influenced by environmental effects, such as other pedestrians or obstacles. However, social forces do not describe forces, which are exerted on the body of pedestrians [HM95, p. 51]. The forces can stem from different effects and are modelled to follow the superposition principle. The resulting force of the model is obtained by the following formula: #„ #„ #„ #„ #„ #„ dri ped car cro Fp,i(tk) = mp,i ap,i(tk) = Fp,i + Fp,i + Fp,i + Fp,i (6.6)

Here, mp,i denotes the mass of pedestrian i. However, multiple models do not explicitly include the pedestrian’s mass and therefore, simplify it by assuming it to be 1 kg [Anv+15, p. 89; Zen+14b, p. 146; Zen+17, p. 42]. Clearly, including or excluding the modelling of pedestrian masses has an impact on several parameters.

6.3.1 Driving Force

In order to reach the next target, the pedestrian will steer towards it. Therefore, the desired direction of pedestrian i can be expressed as:

#„d #„ #„ x − xp,i d = p,i dp,i #„d #„ (6.7) k xp,i − xp,ik

Helbing and Molnár introduced the driving force, which attracts the pedestrian towards the next target [HM95]. In the case of no other disturbing influences, the pedestrian will move in the desired direc- tion and will approach the desired velocity with the relaxation time τp. The driving force is modelled by: #„ #„ dri 1  d d #„  Fp,i = vp,i dp,i − vp,i (6.8) τp

6.3.2 Conflicting Pedestrian

#„ vp,j A pedestrian influences the motion of other pedestrians, due to desir- ing a private sphere and avoiding collisions [HM95, p. 51]. Zeng et al. assume that the movement of other pedestrians creates an elliptical #„ force field. Further, it is assumed that this force is only exerted on c ↔ i j a pedestrian if the conflicting pedestrian is in visual range [Zen+17, p. 43]. #„ The visual perception of pedestrians is modelled as a cone directed vp,i towards the heading of a pedestrian. The sight cone is described by an opening angle γv and a distance rv, which represents the visual range and is depicted in Figure 6.4 [Zen+17, pp. 43 sq.]. Furthermore, Figure 6.3: Pedes- a situation in which pedestrian i perceives pedestrian j is illustrated trians i and j poten- tially colliding at in Figure 6.3. If both keep their current velocities constant, a collision #„ #„ point ci↔j. will occur at point ci↔j. 6.3 operational level 51

The time to conflict describes the duration until pedestrian i reaches #„ the collision point ci↔j and is computed by: Time to conflict #„ #„ k ci↔j − xp,ik TTCi,i↔j = #„ (6.9) k vp,ik

Subsequently, TTCj,i↔j describes the duration until pedestrian j ar- rives at the collision point [Zen+17, pp. 43 sq.]. Furthermore, the rel- ative time to conflict measures the timing difference between the ar- rival of pedestrian i and j. The relative time to conflict is given by: Relative time to conflict

  TTCi,i↔j − TTCj,i↔j , if collision point exists RTTCi↔j = (6.10) +∞, otherwise

The modelling of the force field is shown in Figure 6.4, whereas the #„ped force f j→i is exerted from pedestrian j onto pedestrian i. The nor-

γ v #„ xp,j

#„ #„ v p bj→i ,j ∆ v t r

#„ vp,i #„ped f j→i

Figure 6.4: Repulsive force from conflicting pedestrian j exerted onto pedes- trian i. The ivory cone illustrates the vision of pedestrian i with opening angle γv and vision range rv. #„ mal vector nj→i describes the perpendicular to the ellipse at the posi- #„ #„ped tion xp,i of pedestrian i. As depicted in Figure 6.4, the force f j→i = ped #„ fj→i nj→i is perpendicular to the ellipse [Zen+17, p. 44]. Thereby, the semi-minor axis of the force field is described by the following for- mula: q #„ #„ 1 #„
2 #„ 2 b → = k d → k + k d → − v ∆tk − k v ∆tk j i 2 j i j i j j (6.11) #„ #„ #„ dj→i = xp,i − xp,j. 52 pedestrian behaviour model

#„ped The force f j→i is described by the following formulas: #„ ped r r #„ f j→i = Aβ exp(−Bβbj→i − BβαRTTCi↔j)ω(ϕi,j) nj→i (6.12) #„ np #„ ped = ped Fp,i ∑ f j→i (6.13) j=1 j6=i

#„ped The force Fp,i expresses the superposition of np individual conflict- r r ing pedestrians and the parameters Aβ, Bβ, Bβα have to be estimated by calibrating the model. The angular dependence of pedestrian interactions was proposed #„ by Johansson et al. [Joh+07] and the concept was incorporated into di,j Zeng et al.’s operational model [Zen+17]. It describes the effect that the interaction is assumed to be stronger, if a pedestrian is directly in front compared to being on the side. Therefore, let ϕi,j be the angle #„ #„ #„ between vp,i and di→j, as depicted in Figure 6.5. The variable ω(ϕi,j) vp,i ϕi,j describes the angular effect in Equation 6.12 and is calculated by:   1 + cos(ϕi,j) ω(ϕ ) = q(ϕ ) λ + (1 − λ ) (6.14) i,j i,j α α 2

Figure 6.5: Angle The parameter λα ∈ [0, 1] denotes the strength of the effect [Zen+17, ϕ between pedes- i,j q( ) trian i and j. p. 44]. The function ϕi,j controls, that the effect only occurs when the pedestrian is in front and is denoted as:  1, if |ϕi,j| ≤ π/2 q(ϕi,j) = (6.15) 0, otherwise

However, an opening angle of γv ≤ π leads to |ϕi,j| ≤ π/2, since only pedestrians located in the visual cone are considered anyway.

6.3.3 Conflicting Car

If a car k is in the pedestrian’s visual range, a repulsive social force from the car is generated. Such a situation with two pedestrians i and j is shown in Figure 6.6. The force from k onto pedestrian i is formulated by:  #„ #„ #„ #„ #„ #„ car Ac exp(−Bck xp,i − bp,ik) nk→i, if vp,i · ni→k > 0 f k→i = (6.16) 0, otherwise #„ Here, b is the closest point on the car’s boundary to pedestrian p,i #„ i. Subsequently, the vector n describes the normalized direction #„ #„ k→i from bp,i to the xp,i. The model parameters Ac, Bc denote the interac- tion strengths and have to be estimated [Zen+17, p. 48]. 6.3 operational level 53

#„ vc,k

#„ #„ x #„ car p,i car f k→i f k→j Figure 6.6: Car k exerting repulsive forces exerted on pedestrian i and j.

In the case of multiple cars, it is assumed that the interaction forces follow the superposition principle:

#„ np #„ car car Fp,i = ∑ f k→i (6.17) k=1

6.3.4 Crosswalk Boundary

Zeng et al. observed an effect that pedestrians tend to walk inside the boundary of crosswalks [Zen+14b, p. 146]. This situation is depicted in Figure 6.7. The reaction of pedestrian i to a crosswalk is mathemat-

#„ xp,j #„ cros Fp,j #„ bp,j #„ cros Fp,i #„ x p,i #„ bp,i

Figure 6.7: Social forces of pedestrian i and j on the crosswalk. The blue lines denote the boundaries of the crosswalk.

ically formulated by:

 #„ #„ #„ #„ Ar exp(−Br k x − b k) n , if inside crosswalk cros = b b p,i p,i b→i Fp,i #„ #„ #„  a a Ab exp(−Bbk xp,i − bp,ik) ni→b, if outside crosswalk (6.18) #„ In this case, bp,i denotes the closest point on the crosswalk’s bound- ary to pedestrian i. If a pedestrian is outside the crosswalk, a force to- wards the crosswalk will attract the pedestrian towards the boundary #„ r r a a ni→b. The model parameters Ab, Bb, Ab, Bb describe the strengths of the forces and have to be determined by calibration [Zen+17, pp. 48 sq.].

IMPLEMENTATION 7

The simulation of the pedestrians and cars is performed in a dis- tributed manner. In the following, the utilized simulation software and the communication software is explained. Moreover, the imple- mentation of the model and the further development of the commu- nication software are discussed.

7.1 simulation setup

The simulation consists of two simulating processes and an intermedi- ary process, whereas the setup is schematically shown in Figure 7.1. The software Virtual Test Drive (VTD) is a driving vehicle simulator

SCP, RDB ActiveMQ

Cars Cars Virtual Test Drive Intermediary Process MomenTUMv2

Pedestrians Pedestrians

Figure 7.1: Setup of the distributed simulation.

and is discussed in more detail in Section 7.2. Then, the information of the simulated cars is sent via the Runtime Data Bus (RDB) proto- col to the intermediary process. This includes information about the VTD cars, such as positions, velocities, and directions. The task of the intermediary process is to coordinate the simula- tion procedure. Initialization, starting and stopping the simulation requires coordination of the involved simulators. Moreover, the sim- Intermediary process ulators have to be synchronized during runtime. The second simulation software is MomenTUMv2, which is a pedes- trian simulation framework. The pedestrian model, which was dis- cussed in Chapter 6, was implemented by utilizing the provided func- tionality of the framework. During runtime, the framework receives MomenTUMv2 current information of the cars from the intermediary process and sends the information of the simulated pedestrians to the intermedi- ary. The concept of the simulation setup was part of a previous thesis [Sch17, pp. 5 sqq.]. However, the functionality of the intermediary process was further developed during the course of this thesis. This 56 implementation

particularly includes the exchange of car information from VTD to- wards MomenTUMv2.

7.2 virtual test drive

The software “VTD” is a tool-chain for simulating driving applications in the automotive sector. In this sector, VTD is used for developing and testing driver assistance systems with automated driving functional- ity [VIR17b]. The tool-chain supports several steps in the simulation procedure. VTD comprises a builder to create road networks and 3D graphics, which is shown on the left of Figure 7.2. The created road networks can be exported as an open format “OpenDRIVE”.

Figure 7.2: Creation of the road network (left) and setting the scenario (right) with VTD.

The image on the right shows an editor, where the scenario can be set up. Cars can be placed on the previously designed map and their Editor attributes can be manipulated by means of this editor. Furthermore, triggers can be set to start lane changes, speed changes, and receive Simulation Control Protocol (SCP) signals for example [VIR17b]. SCP and RDB refer to the protocols, which enable the communica- tion between VTD and other elements. Thereby, VTD is used to support Protocols various steps in the automotive engineering process. Software in-the- loop describes the testing of software models within a simulation, whereas hardware in-the-loop means the testing of hardware compo- nents within a simulation. Testing software models in combination with a driver is subsequently named driver in-the-loop testing. VTD supports the environment simulation for each type of testing. Clearly, to test a component by means of simulating the environment, the in- terface format has to cover the objects, which are perceived by other components [VIR14]. 7.3 intermediary process 57

7.3 intermediary process

The intermediary process was originally implemented as part of a thesis about developing a proof-of-concept. The concept showed that MomenTUMv2 could contribute simulated pedestrians to traffic sce- narios in VTD [Sch17]. Therefore, the first step was to implement the sending of pedestrian information from MomenTUMv2 to VTD. This was achieved by utilizing the tool “Automotive Data and Time- Triggered Framework (ADTF)”. It is provided with a toolbox, which Implementation manages the communication to VTD via the protocols SCP and RDB. Since the behavioral model of pedestrians is dependent on the vehi- cles, the simulated cars have to be sent from VTD to MomenTUMv2. To realize the bidirectional exchange of simulated objects, the net- work protocol was adjusted and enhanced, as shown in Table 7.1. The ADTF-toolbox allows to obtain prepared information from VTD’s net- work protocols by inheriting callback functions. Thus, an up-to-date Enhanced protocol list of cars with attributes is stored at the intermediary process. How- ever, the interface to MomenTUMv2 requires a protocol, which sup- ports the structured information exchange of pedestrians, cars, and potentially other dynamic objects, such as traffic lights.

Table 7.1: Serialization of simulated objects for network transmission.

JavaScript Object Notation (JSON) representation

Pedestrians Cars Type Unit

[{ "id" : "1", [{ "id" : "1", integer "type" : "pedestrian", "type" : "car", string "timeStep" : "5", integer "time" : "2.0", "time" : "2.0", double s "x":"10.7", "x":"5.31", double m "y":"8.1", "y":"8.03", double m "xHeading":"0.65", "xHeading":"1.0", double m "yHeading":"0.76", "yHeading":"0.0", double m "xVelocity":"0.3", "xVelocity":"5.4", double m s−1 "yVelocity":"0.2", "yVelocity":"0.0", double m s−1 "bodyRadius":".23"}, "sizeLength":"4.9", double m "sizeWidth":"1.9", double m "sizeHeight":"1.4"}, double m { "id" : "2", { "id" : "1", integer "type" : "pedestrian", "type" : "car", string "timeStep" : "5", integer "time" : "2.0", "time" : "2.0", double s "x":"5.31", "x":"5.31", double m "y":"8.03", "y":"8.03", double m ...}] ...}] 58 implementation

7.4 momentumv2

In order to simulate pedestrians in various environments, theories which describe the behavior of pedestrians are required. Many dif- ferent theories and models describing pedestrian behavior have been proposed and developed. However, the various theories can be clas- sified into different categories. This includes the categorization into strategic, tactical and operational behavior levels, which was intro- duced by Hoogendoorn et al. [Hoo+01] and was discussed in Sec- tion 5.2. To implement and simulate different behavior models Kielar et al. developed the modular and generic framework MomenTUMv2 for pedestrian dynamics research [Kie+16]. The framework facilitates the implementation of new models by providing an already existing set of functionality, such as models to generate or absorb pedestrians. Moreover, MomenTUMv2 comprises visualization tools, map genera- MomenTUMv2 tion tools, and methods for analyzing the simulation results. Thereby, framework MomenTUMv2 follows a modular approach by specifiying informa- tion interfaces between the models. This enables the exchange of sin- gle models and therefore facilitates the comparison between imple- mented behavior models [Kie+16, pp. 10 sq.]. The framework is based on an agent-based approach to simulate multiple pedestrians. Thus, pedestrians are simulated as individual entities. Figure 7.3 illustrates the structure of MomenTUMv2, whereas the behavior models are depicted in gray. The framework follows Hoogendoorn et al. concept by differing between strategic, tactical, and operational models [Kie+16, p. 12]. MomenTUMv2 is implemented in Java and comprises multiple packages, which are listed in Table 7.2

Table 7.2: Overview of the MomenTUMv2 project [11 sqq. Kie+16].

Package Description

Configuration This packages manages the configuring of all classes by processing the XML file. Data The data packages includes classes and interfaces representing inter alia pedestrians, their states, and different areas. Infrastructure This package provides mechanisms which are essential for the simulation procedure, such as the time manager, and exception handling. Model This package contains the all models structured by model type. Simulator Manages and coordinates the simulation procedure. Third Party This folder contains third-party libraries. Tools This package includes layout tools, such as the AutoCAD plugin for exporting. Utility Supports the other packages by providing mathematical algorithms, such as graph algorithms, and geometrical computations. Visualization Contains Graphical User Interface (GUI) for visualization. 7.4 momentumv2 59 Loop Cycle Handling Handling Termination Process Writers Process Process Network Process Post-Processings · Control messages · Control · Handle waiting output writers · Data · Model output writers · Network output writer · All models · Network Strategic Process Analyses Process ] and extended by a network interface, depicted in green Strategic Models 9 Operational Models · Time-based analysis · Unit-based analysis · destination choice models · standing models · Walking models · Walking · Standing models · Destination choice models , p. 16 Behavior Models Thread Handling Order Handling Tactical Models Tactical Process Models Process Update Handling · Routing models · Queuing models models · Searching · Participating models developed by Kielar et al. [ Kie+ 2 MomenTUMv Support Models Additional Models Process Absorbers Process · Query models models · Perception · Meta models · Pedestrian absobers ]. 21 , p. 17 [ Sch Con fi g.xml Start : Structure of the framework Processings Con fi guration 3 . Pre-Processings 7 Process Generators Process · All models · Network · Layout · Con fi gurations · Network · Pedestrian generators · Pedestrian seeds · Car generators Figure 60 implementation

To implement the models, discussed in Section 6.2 and Section 6.3, several functionalities were added to multiple packages.

7.4.1 Car Manager

The information of the cars is received over the network interface. To further utilize this information and access the cars in the models, two steps are carried out. First, the information is deserialized and pro- Deserialization and cessed. This is performed by means of a generator in the model pack- data processing age, which connects to the network and then receives the simulation messages in JSON format during each simulation cycle. Furthermore, a CSV file import was implemented to rerun pedestrian simulations with the same traffic. Second, for storing and accessing the cars, a car manager class was Information added to the data package. Further, static and operational states and management the class representing the car were implemented.

7.4.2 Additional Areas

As it was discussed in Chapter 6, the tactical model requires infor- mation about areas, which are dedicated for pedestrian#„ use, such as cros crosswalks and sidewalks. Furthermore, the force Fp,i of the op- erational model requires information about the boundaries of the crosswalks. Figure 7.4 shows the drawing of a scenario by means

Figure 7.4: AutoCAD with overview of layers and layer groups.

of the CAD-tool AutoCAD [Aut17a]. The created scenario can then be exported to an XML file for MomenTUMv2 by utilizing a plugin for AutoCAD, which is part of the tool package [Kie+16]. The plu- 7.4 momentumv2 61

gin was expanded to also export polygons representing crosswalks or sidewalks. In order to export polygons placed on the layers “Cross- walk” or “Sidewalk”, the layers have to be added to the layer group “TaggedAreas”. Subsequently, the plugin will export the polygons on the layer “Crosswalk” as Extensible Markup Language (XML) nodes “TaggedArea” with type “Crosswalk”.

7.4.3 Geometry

Since several geometrical operations are performed by the tactical and operational model, functionality was added to the utility package. As discussed in Section 6.3.2, a pedestrian influences other pedestri- ans by walking in their visual range. In order to compute social forces from potential collisions with other pedestrians, it is necessary to find the conflict point. Therefore, the movement of each pedestrian can be #„ geometrically represented by the means of a ray. A ray comprises an d #„ initial point x representing the position of a pedestrian and a direc- #„ #„ x tion d representing the velocity vector. As depicted in Figure 7.5, two rays can have different relations to each other. The geometrical op-

a) b) c) d) Figure 7.5: Possible intersections of two rays.

erations utilized in MomenTUMv2 are mostly based on the collision detection library dyn4j [Bit17]. Since the calculation of intersections between rays was not part of dyn4j’s ray class, this functionality was added to MomenTUMv2’s wrapper class. Once the potential collision point is determined, the magnitude of #„ped the force k f j→i k is calculable, as discussed in Section 6.3.2. However, the direction of this force is perpendicular to the elliptic force field. #„ #„ As illustrated in Figure 7.6, the normal vector n at point x , which represents the pedestrian, has to be obtained. Therefore, an ellipse Ellipses class was added to the utility package, which is based on the Geo- Regression library [Abe17]. This library comprises the functionality #„ of algorithmtically estimating the closest point x on the ellipse to #„ another point y . This enables the obtainment of normal vectors for points that are either placed on the ellipse or not. The car is geometrically represented as a rectangle and thus, a class, which wraps the rectangle class of the dyn4j library, was introduced 62 implementation

#„ #„ y n #„ #„ b #„ x f 1 f 2

#„ #„ Figure 7.6: Ellipse with focal points f , f and minor axis b [Are+15, p. 780]. #„ 1 2 #„ The point x denotes the position of a pedestrian and n denotes the normal vector on the ellipse.

Rectangles [Bit17]. Similarly, to the#„ interaction force between pedestrians,#„ the repulsive force of cars f car requires the calculation of the point ( b ) k→i #„ p,i on the rectangle, which is closest to another ( xp,i). The edges have to be analyzed for the graph routing in the tactical model. Depending on the underlying areas of an edge, it’s weight is estimated. Figure 7.7 shows an edge of a graph with two polygons

n1 n2

Figure 7.7: Splitting an original segment by the means of polygons.

representing different area types. To determine the distances of each area type, the functionality was added to obtain a list of subsegments, which result by splitting the original segment with a polygon.

7.4.4 Visualization

Different data can be written to output files. This includes the posi- tions of pedestrians and data for analyzing, such as xt-densities. To in- terpret the simulated scenarios, the visualization package contains a GUI, which is reads the layout file and output files [Kie+16, p. 13]. The visualizer was expanded to support the visualization of cross- walks, sidewalks, and footways. As shown in Figure 7.8, the function- ality of visualizing the movement of cars was also added.

7.4.5 Tactical Model

The tactical model was implemented by utilizing the preexisting func- tionality of the framework. Since the functionality of graphs and a Di- jksta algorithm were already implemented [Kie+16], the realization of the tactical model was achieved by only implementing the calculation 7.4 momentumv2 63

Figure 7.8: 3D view of MomenTUMv2’s visualizer with sidewalks and crosswalks in light blue. The cars are represented as dark blue boxes.

of the edge weights. Therefore, the Euclidean distances of the edges and the pedestrian dedicated shares thereof are processed during the pre-processing phase. Then, the weights are updated according to the individual factor si for each routing phase. Listing 7.1 shows an ex- emplary configuration of the tactical model with the parameters µs and σs.

Listing 7.1: XML configuration of the tactical model.

1 2 3 4 5 6

7.4.6 Operational Model

The forces of the operational model, which were discussed in Sec- tion 6.3, depend on the pedestrian’s perception. If a car or another γ rv pedestrian is in the visual range of the model, a repulsive force is ex- v erted. Therefore, a perception model was implemented, which exam- ines whether the object of interest is located within the visual radius rv and within the visual angle γv. Listing 7.2 gives an exemplary con- figuration of the perception model. 64 implementation

Listing 7.2: XML configuration of the perception model.

1 2 3 4 5 6

The implementation of the operational model is achieved by con- forming to the interfaces and processing the provided information, such as car data, pedestrian areas, and obstacle lists. The operational behavior model is called for each pedestrian and for each simulation step. Thus, the walking state, which consists of position, velocity, and heading information is updated during each cycle. As explained in Section 6.3, the acceleration of each pedestrian is modelled by the means of social forces. To improve readability, each social force is implemented in a dedicated function. Listing 7.3 shows the exemplary configuration of the operational model. Furthermore, a list of parameters is also given in Table B.1. To compare the impact of social forces, the model supports the replace- ment of pedestrian interaction force with the version proposed by Helbing et al. [Hel+00]. Therefore, the first part of the configuration consists of the parameters required for social force model of Helbing et al. [Hel+00] and the second part comprises the parameters of the operational model, which was discussed in Section 6.3.

Listing 7.3: XML configuration of the walking model.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 DATASET 8

In order to compare the implemented behaviour model to the reality, a real-world dataset is required. As the pedestrian model is addition- Requirements ally influenced by cars, such a dataset should contain the positions and directions of both. Another requirement constitutes the modelled intersection situation.

8.1 ko-per intersection dataset

The research initiative “Ko-FAS” aimed at increasing traffic safety by the means of cooperative systems and ran from 2009 to 2013. The ini- tiative consisted of three sub-projects, whereas “Ko-PER” was one of them. In order to provide a full all-round vision at crossings with Ko-FAS, Ko-PER poor visibility, infrastructure was equipped with multiple sensors. The goals of Ko-PER included the early provision of information to the driver about potential hazardous situations. Furthermore, advi- sory warnings should be provided by the system [Wer14, p. 13]. The sensors were installed at a signalized crossing in Aschaffen- burg, Germany and is shown in Figure 8.1. Four laserscanners record-

Figure 8.1: Public crossing in Aschaffenburg [Sch+14, p. 123; Str+14, p. 1].

ing with 12.5 Hz and eight cameras operating with 25 Hz were de- ployed to record three scenes. The provided data of the first scene comprises information for each object, such as position, velocities, ac- celeration, sizes, and angles. For the second and third scene, only Sensors and reference data for two cars was extracted. In order to reduce the file recorded data sizes, the first sequence was divided into four sub-sequences, whereas a list of tracked objects is given in Table 8.1. Due to the division of the dataset, the same pedestrians are recorded across sub-sequences. The 66 dataset

Table 8.1: Objects in the first sequence, which was recorded for 6:28 min [Str+14].

Sequence Cars Trucks Pedestrians Bikes Duration [s]

1a 63 1 10 0 96 1b 63 3 13 3 96 1c 81 5 7 3 96 1d 83 3 8 4 97

complete dataset is provided by the Universität Ulm and is available 5 http://www.uni- at their webpage. 5 ulm.de/in/mrm/ Since the dataset comprises only a limited number of pedestrians, forschung/ it might not be sufficient for statistical significance. However, this datensaetze.html dataset qualifies for qualitatively comparing the dynamics of real pedestrian to the implemented model. Furthermore, the accuracy of the Ko-PER’s dataset is beneficial, as the implemented operational model describes movement patterns of pedestrians.

8.2 dataset preparation

To simulate pedestrian behavior for this crossing with MomenTUMv2, the dataset had to be prepared. Therefore, the layout of the intersec- tion was redrawn in AutoCAD, as depicted in Figure 8.2.

Figure 8.2: Ko-PER intersection drawn in AutoCAD. The blue polygons de- note obstacle and the orange areas represent crosswalks and side- walks. Furthermore, the generator and absorber fields are col- ored green and red.

In order to compare and simulate pedestrian behavior with simi- lar environment, the trajectory data of pedestrians, cars, and trucks 8.2 dataset preparation 67 were extracted from the dataset. This was achieved by the means of the provided DataSetViewer, which is a Matlab program to view and process the dataset [Str+14]. The data preparation included reindex- ing of the objects and recalculation of time reference points as well as position reference points. The resulting pedestrian trajectories of the four sub-sequences are visualized in Figure 8.3.

(a) Sequence 1a: 10 pedestrian trajectories (b) Sequence 1b: 13 pedestrian trajectories

(c) Sequence 1c: 7 pedestrian trajectories (d) Sequence 1d: 8 pedestrian trajectories

Figure 8.3: Trajectories of the Ko-PER dataset [Str+14].

Part III

CONCLUDINGDISCUSSION

DISCUSSION 9

The pedestrian behavior model is qualitatively assessed and com- pared to the Ko-PER dataset in the following. Potential standardiza- tion areas and their accompanied attributes are discussed afterwards.

9.1 pedestrian simulation model

In order to simulate the pedestrian behavior, the parameters µs and 2 σs of the tactical model have to be estimated first. However, the Ko- PER dataset does not provide a sufficient quantity to estimate the two parameters, since it only consists of trajectories of 24 individual pedestrians. Furthermore, 15 pedestrians do not face an ambiguous decision of whether to route over the street or over sidewalks/cross- walks. This is due to the fact, that their trajectories start and end on sidewalks and their shortest routes is located on side- and crosswalks anyway. Thus, the parameters are discussed by means of an exem- plary scenario, in which the routing is influenced by their perceived risk tolerance.

9.1.1 Tactical Model

The parameters describe the normal distribution of the factor, which models the decreased risk of a pedestrian taking a sidewalk or cross- walk, as stated in Equation 6.3. Therefore, an exemplary scenario is

(a) σs = 0.1 : pa = 59.9 %, pb = 49.1 %, pc = 0 % (b) σs = 0.2 : pa = 55 %, pb = 41.6 %, pc = 3.4 %

Figure 9.1: Trajectories of pedestrians simulated with µs = 0.2.

simulated in which the routing is influenced by the factor si, which is 2 normally distributed by N (µs, σs ). 72 discussion

(c) σs = 0.3 : pa = 53.3 %, pb = 35.5 %, pc = 11.2 % (d) σs = 0.4 : pa = 52.5 %, pb = 29.4 %, pc = 18.1 %

Figure 9.1: (Continued) Trajectories of pedestrians simulated with µs = 0.2.

In this scenario pedestrians start at the south-west and have to cross the street once to reach their destination, which is located in the east of the layout. As depicted in Figure 9.2d, the pedestrians have three major routes to reach the destination:

(a) si < 0.225: routing over the three crosswalks constitutes the lowest risk and longest distance.

(b) 0.225 ≤ si < 0.565: routing over the sideways and crossing the street once.

(c) 0.565 ≤ si: routing over the sidewalk and diagonally crossing the street constitutes the highest risk.

Figure 9.1 and Figure 9.2 show the simulated trajectories for µs = 0.2 and µs = 0.3, respectively. Thereby, the variable pi denotes the probability of a pedestrian taking the route i ∈ {a, b, c}. Figure 9.1a and Figure 9.2a show a very low probability of 0 % and 0.4 %, that pedestrians take the diagonal route c. For the following discussion of the operational model in Section 9.1.2, it is assumed that the majority of pedestrians would take the route b and roughly a third of pedestrians choose the route over the three crosswalks. This assumptions suggests the parameters µs = 0.3 and σs = 0.2. Clearly, the tactical model and its assumed parameters are not ad- dressing complete pedestrian behavior, which can be found in real traffic scenarios. First, the tactical behavioral level does not model traffic light phases. Therefore, pedestrians are not waiting in front of Traffic lights crosswalk in case of a red traffic light phase. Zeng et al. model the waiting by adjusting the driving force of the operational level. The driving force is influenced by a binary stop or go decision [Zen+17, pp. 46 sq.]. However, it could be beneficial to model this pedestrian behavior on the tactical behavioral level, since this not only conforms to Hoogendoorn et al.’s categorization [Hoo+01] but also conforms 9.1 pedestrian simulation model 73

(a) σs = 0.1 : pa = 22.7 %, pb = 76.9 %, pc = 0.4 % (b) σs = 0.2 : pa = 35.4 %, pb = 55.4 %, pc = 9.3 %

(c) σs = 0.3 : pa = 40.1 %, pb = 41.0 %, pc = 18.9 % (d) σs = 0.4 : pa = 42.6 %, pb = 32.1 %, pc = 25.4 %

Figure 9.2: Trajectories of pedestrians simulated with µs = 0.3. to MomenTUMv2’s framework approach [Kie17, pp. 162 sq.]. Depend- ing on a pedestrian’s context and his perception, a tactical model is chosen. These models include routing models, searching models, par- ticipating and queuing models. To model the pedestrian behavior dy- namics of waiting in front of red signal phases, a waiting or queuing model needs to be implemented. The second oversimplification constitutes the routing based on the Euclidean distance and a normally distributed perceived cost on ar- eas, which are dedicated to pedestrians. A pedestrian crossing in unsignalized situations involves more influencing factors. This in- Additional cludes locations where pedestrians are more likely to cross the street influencing factors and the interaction between driver and jaywalkers [Zhe+15;ZE 17]. However, the topic of pedestrian-vehicle interactions outside of cross- walks comprises another research topic. Nevertheless, the tactical model describes routing over areas, which are preferred by pedestrians, such as crosswalks and sidewalks. Fur- thermore, a pedestrian is modelled to individually evaluate the situa- tion and potential routes. 74 discussion

9.1.2 Operational Model

In the following, the modelled operational behavior is qualitatively compared to the pedestrians, which were tracked and recorded in the Ko-PER dataset. Therefore, a selection of scenarios is structured according to the model’s social forces, which were explained in Sec- tion 6.3. The utilized parameters are the result of Zeng et al.’s calibration. Zeng et al. collected trajectory data on an intersection by the means of a quadrocopter for a period of one hour. The trajectories were semi- automatically extracted. Then, Zeng et al. calibrated the social force Zeng et al.’s by applying a genetic algorithm to minimize relative errors. Thereby, calibration the set of parameters are optimized for three different fitness func- tions. The first fitness function minimizes the relative distance errors, which denote the distance between observed and modelled trajec- tory points. Further, the second minimizes the relative angular errors, which denote the angle between the next observed position and the next simulated position. The third fitness function describes the mean of the relative distance error and relative angular error [Zen+17, p. 51]. To equally address the importance of the angular and distance error, the parameter set of the third fitness function was utilized for the further discussion and is listed in Table B.1.

Pedestrian Interaction The pedestrian interaction force models the desired private sphere and the avoidance of collisions. Figure 9.3 shows a situation where

Figure 9.3: Sequence 1b of the Ko-PER dataset with trajectories [Str+14].

two groups of two pedestrians walk towards south-west and one pedestrian is approaching the crosswalk from the other side. Further- more, a social group four pedestrians is waiting at the southern cor- ner of the intersection for a green traffic light phase. When comparing the situation from the dataset to the simulated results, shown in Figure 9.4, several differences have to be pointed 9.1 pedestrian simulation model 75 out. The implemented model does not describe the forming of social groups and their dynamics. However, as shown in Figure 9.3, social groups impact the trajectories, since pedestrians form different group patterns [Gor+16]. The Ko-PER trajectories show a higher spatial de- gree of dispersion, as the pedestrians within a group walk side by side.

(a) Pedestrian interaction force proposed by Zeng et (b) Pedestrian interaction force by Helbing et al. al. [Zen+17] [Hel+00] with modifications by Köster et al. [Kö+13] to improve robustness

Figure 9.4: Simulated situation, which is similar to the sequence 1b of the Ko-PER dataset.

In Zeng et al.’s model pedestrians are represented as points. Fur- ther, it is assumed, that the repulsive interaction force is only exerted, if the velocity vectors form a collision point [Zen+17]. However, these assumptions do not cover all situations accurately. An exemplary sit- uation is depicted in Figure 9.5. Since the body radius is neglected in the model and the velocity vectors are parallel, this situation will not entail a repulsive force on either of the two pedestrians. Therefore, the repulsive behavior between pedestrians is not addressed in all sit- uations, whereas the situation depicted in Figure 9.4a illustrates one of them. Figure 9.5: Pedes- Figure 9.4b illustrates the same scenario, whereas the interaction trians right before force is replaced by Helbing et al.’s version of it [Hel+00, p. 3]. In a collision. contrast, this force takes the body radius into account and does not model potential collisions by means of intersecting velocity vectors. As depicted in Figure 9.4b, the pedestrians perform evasive manoeu- vres and therefore the green bodies of the pedestrian do not collide.

Crosswalk Interaction The crosswalk interaction force models the tendency of pedestrians to walk inside of crosswalks. This relation is also supported by the pedestrians, who are crossing the north-western street in the Ko-PER dataset, as shown in Figure 9.6. Apart from two pedestrians, the ma- jority of the all pedestrian trajectories is located within the crosswalk. 76 discussion

(a) Sequence 1a (b) Sequence 1b (c) Sequence 1c (d) Sequence 1d

Figure 9.6: Pedestrian trajectories of the Ko-PER dataset.

One pedestrian in Figure 9.6d is taking a shortcut towards his destina- tion in north. The second exception constitutes the pedestrian, who is almost walking on the boundary of the crosswalk in Figure 9.6c. Fur- thermore, the trajectories suggest that pedestrians tend to enter and leave the crosswalk area with slight shortcuts to lessen the walking distance. In order to simulate the pedestrian behavior at crosswalks, pedes- trians walk from the northern origins towards the south by crossing the same street as in Figure 9.6. Thereby, Figure 9.7a depicts the gen-

(a) Routing graph (b) Trajectories of simulated pedestrians crossing the street

Figure 9.7: Trajectories of simulated pedestrians crossing the street.

erated graph by which pedestrians route towards their destination. The driving force of a pedestrian is directed towards the next routed node. Figure 9.7b shows a simulated scenario with five pedestrians crossing the street. Here, the crosswalk force attracts the pedestrian towards the middle of the crosswalk and therefore the pedestrians’ trajectories slightly deviate from the edges of the graph. Figure 9.8 shows a plot of the different social forces for the selected pedestrian (red) in Figure 9.7b. Since the selected pedestrian walks diagonally 9.1 pedestrian simulation model 77

0.5 Driving Force Pedestrian Interaction Force

0.4 Crosswalk Force

0.3

0.2

0.1 magnitude of social force [m/s^2] magnitude of social force

0.0

20 22 24 26 28 time [s]

Figure 9.8: Magnitude of social forces, which are exerted on the red pedes- trian in Figure 9.7b. on the crosswalk, the magnitude of the crosswalk forces are stronger when entering and leaving. The reason for the spike at 19.2 s is the dis- tance jump from the one boundary to the other. This distance change impacts the social force, as stated in Equation 6.18. The comparison between the dataset trajectories and the simulation trajectories suggests that real pedestrians show more path variability. As mentioned before, one reason for this are social group dynamics. However, another reason is the graph, which shows a limited number of edges traversing the crosswalk. Modelling a more realistic crossing behavior could involve a graph generation, which produces edges representing the short entering and leaving shortcuts.

Car Interaction This interaction force models the repulsive effect of cars on pedestri- ans. In order to compare the modelled effect, a pedestrian crossing

Figure 9.9: Pedestrian crossing the street without cars located nearby. the street is simulated with and without nearby cars. The trajectory of a pedestrian in absence of other cars is shown in Figure 9.9. In con- trast, Figure 9.10 shows a pedestrian and his trajectory with nearby 78 discussion

cars. The model parameters, such as desired velocity, route choice, origin and destination are identical over the two simulations.

(a) Simulated situation at 72 s (b) Simulated situation at 75.5 s

(c) Simulated situation at 79 s (d) Simulated situation at 80 s

Figure 9.10: Repulsive effect of a starting car.

In this scenario, the cars are waiting until 79 s and then start moving towards south-east. The pedestrian is heading towards south-west and his originally planned trajectory is near the simulated cars. Since the pedestrians are in his visual range and rather close, the pedestrian alters his trajectory compared to Figure 9.9. As shown in Figure 9.10, the pedestrian keeps a certain distance between him and the car by walking closer to the middle of the crosswalk. Figure 9.11 shows a graph of the forces exerted on the pedestrian during traversing the crosswalk. The repulsive force increases as the pedestrian approaches the vehicles and the sudden drops are caused, since a certain car is not in the field of vision any longer. Furthermore, the strength of the driving force also increases, so that the pedes- trian reaches his next node on the routing graph. Here, the question arises of whether the perception of a pedestrian is realistically mod- elled by assuming a sight cone. Clearly, this assumption neglects a 9.2 standardization potentials 79

0.6 Driving Force Pedestrian Interaction Force 0.5 Car Interaction Force Crosswalk Force

0.4

0.3

0.2 magnitude of social force [m/s^2] magnitude of social force 0.1

0.0

70 72 74 76 78 80 82 time

Figure 9.11: Magnitude of social forces, which are exerted on the pedestrian in Figure 9.10.

pedestrian’s auditory perception of vehicles. Nevertheless, the imple- mented repulsive effect of cars suggest a realistic pedestrian behavior.

9.2 standardization potentials

As discussed in Chapter 4, the performance of an automated driving system can be tested by virtually exposing the system to controlled traffic scenarios. In order to validate and safeguard automated driv- ing systems, standards are necessary to ensure system performance and failsafe operation. This standardization need was formulated by the VRA consortium [Veh13, p. 41] and the development of such standardized testing methods constitutes the goal of the PEGASUS project, which was discussed in Section 4.2 [PEG17]. The standardization areas for testing automated driving systems can be generally categorized into compatibility and quality standards. In order to simulate different scenarios in various situations, a lan- guage is required to describe the logical map data, which contains road networks, infrastructural elements, road surfaces, buildings, and so on. A map standard clearly entails network effects for the involved adopters. Developers of simulation software, cartographers, testing Static content facilities benefit from an industry-wide standard, as it facilitates the description exchange of digitally represented maps. Since the testing of driver standardization assistance systems is not new, an already existing standard could be adjusted for the requirements of testing automated driving sys- tems. The likelihood of adapting an existing map standard depends on the sunk investments and switching costs of complementary prod- ucts and knowledge. The OpenDRIVE standard is directed towards this demand and is developed by the firm VIRES Simulationstechnolo- gie GmbH, which also develops the driving simulator VTD [VIR15]. Clearly, a widespread adoption of the OpenDRIVE standard stimu- Influence of the driver behavior in the controllability assessment

Database concept80 discussion

The database conceptlates consists the demand of different for complementary database entities products, following such the as dataVIRES processing’s driv- from (raw) measurementing simulator. data over abstracted scenario clusters (logical scenarios) to test specifications for the signMap-off standardsprocess, see comprise Figure 2 the. The static basic content idea is of to a simulation,use data from whereas differ- the description of the dynamic content includes the parameterization ent sources (see section Data sources for the database), group the scenarios in this data to log- of the utilized models, initial positions, and scenario conditions, such ical scenarios and to daserive weather. the test The specifications PEGASUS projectfor the hassign further-off process identified based requirementson the logical scenarios. Figure 2 depictsfor a the unified related language database to entities describe and the their dynamic connecting content data processing of scenar- chain,Dynamic which content is describedios. in The section requirements Data processing include chain the for compatibility the database with. test specifica- description tion databases, as well as the instantiation of specific scenarios from standardization In this data processingthe the database coverage [VIR of 16the, p.scenario35]. VIRES informationis again (y addressing-axis) increases this demandwith the different database entities:by developing While the an raw open measurement standard named data providesOpenSCENARIO only selective[VIR info17ar-]. As VIRES is also a member of the PEGASUS project, they are in mation on possible scenario characteristics for the logical scenarios, the condensation of this the strategic position to directly collect standardization needs, which information in the parameterare discussed space of within the logical the PEGASUS scenarios enhances project. Since the knowledge stakeholders of poss fromi- ble parameter combinationsseveral within areas, a such logical as manufacturing,scenario. Deducing automotive the test specifications suppliers, research, infor- mation on exposure, potentialand test labs,severity are and represented controllability in the are PEGASUS added for project, the parameter a mutual space un- derstanding between those industries is gained. Moreover, the PEGA- improving the information coverage of the scenarios. SUS project does not only provide valuable technical input for VIRES, but also offers access to potential adopters, which are necessary to es- At the same time as the coverage of scenario information increases, the data volume is re- tablish a de facto standard in the market. VIRES can benefit from the duced (x-axis), especiallyPEGASUS between project, raw data as this and project logical has scenarios. the potential Raw datato trigger contains a band- also information that is of wagonsecondary effect importance for the OpenSCENARIO for the logical scenariosstandard and due the toparameter the provision space. However, if it is necessaryof a larger to assess installed detailed base. information Clearly, the it networkis always externalitiespossible to trace comprise back between test specification,the benefit logical ofscenario a simplified and raw exchange data. of simulator-agnostic scenario descriptions.

Requirement definition and filtering criteria

0 Use case definition 7 Data processing chain Test specification high • Definition of functional deduction scope of HAD • Definition of scenario 6 • Select scenarios based Test filter Scenario searching and on functional scope clustering • Add information on … specifications 5 • Exposure Scenario • Scenario clustering • Severity characterization • Combined scenarios • Controllability 4 for sections of the Parameter Calculation of scenario with frequencies • Cut to scenario snippets parameter space 3 affiliation • User specific retrace on space by data owner 2 Generation of deduced of likelihoods > 95% single scenarios 1 Data transformation signal • Calculation of Generation of common • Calculation of indicators for the Logical environment and traffic affiliation likelihoods of C0 C1 C2 • Format checks • Raw data enrichment scenarios description situations to a specific scenario C / S E/ S4 S3 E / S / C • Indexing with deduced signals scenario over time • Harmonization of signal • Assignment of access ID1 ID2 Σ E1 E3 E2 names rights 1 - Parameter min TTC (s) • Transformation in space common data format scenario1

1 - scenario2

Frequency Frequency

Likelihood min TTC (s) Szenario ID TTC 0 - speed Likelihood 0 Time (s)

lane position Deducedsignals 0 - Coverage Coverage of scenario information Time (s)

0 Post processing of Scenariovariable Time (s) individual scenarios

Zeit (s) e.g. for individual case assessment, function Data processing chain development, etc.

Raw data External data low low Data volume reduction high Figure 9.12: Database testing concept to validate highly automated driving systems with relevant Figuretraffic 2: Datab scenariosase concept [Pü+17 with, p. 3different]. database entities and data processing chain

Figure 9.12 shows a concept to test highly automated vehicles by means of a database validation-3- framework, which is developed by the PEGASUS project. Thereby, an essential challenge constitutes the 9.2 standardization potentials 81 identification of relevant traffic scenarios and model parametrizations, by which automated driving systems shall be tested. In order to en- sure the safety of automated driving systems from all manufacturers, the simulation scenarios have to be specified and could constitute a part of the vehicle’s sign off process. Specifying a catalog of simula- Standardized test tion scenarios belongs to the group of minimum attributes standards scenarios and and aims at covering all necessary traffic situations and eventuali- criticality metrics ties. Step 7 in Figure 9.12 represents the deduction of the simulated test cases. The performance evaluation of an automated driving sys- tem requires metrics, which estimate the criticality of the situations. Thus, the passing or failing of test cases requires the standardization of values that may not be exceeded or fallen short of. However, the standardization of test catalogs and criticality metrics depends on the country’s traffic regulations and its culture.

CONCLUSIONANDOUTLOOK 10

Pedestrians and the protection of them will play an essential role in re- alizing and establishing automated driving. According to the Ethics Commission, the deployment of automated driving systems is only justifiable, if such systems are capable of handling critical situations more reliable than human drivers and therefore improve the safety for all road users [Eth17, p. 10]. Thus, the safety verification of auto- mated driving systems constitutes not only a technical development challenge, but is also subject to standardization efforts. Since auto- mated driving systems of all vehicle manufacturers pose a potential risk to other road users, standardized testing procedures are under development to ensure vendor-agnostic safety on the roads. The intention of this work was to investigate the different standard- setting dynamics related to the area of automated driving. Therefore, the key players of the involved industries were identified and their memberships in SDOs, consortia, and SIGs analyzed. Compatibility standardization fields, such as terminology, digital mapping, internal car and C2X communication were portrayed. Furthermore, initiatives of the EU and of Germany to develop quality standards for ensur- ing safety of automated driving systems were discussed. However, the conducted work is more directed towards creating an overview, rather than describing an in-depth view of standardization dynamics. In order to acquire an in-depth perspective of a certain standardiza- tion process within a standards body, a qualitative research could be carried out as a next step. Therefore, interviews of stakeholders in dif- ferent positions, meeting protocols, technical solution proposals, and mailing lists could contribute to a content analysis. In order to verify the safety of automated driving systems from different vendors, standardized simulation tests have the potential to play a significant role, as they enable the efficient testing of vast numbers of traffic scenarios under controlled environments. Hereby, pedestrian behavior simulation is particularly crucial for urban sce- narios, since pedestrians are directly exposed to malfunctioning driv- ing systems. Therefore, the second part of this thesis consisted of developing a pedestrian behavior model, which describes the inter- action between pedestrians and cars. The pedestrian behavior model was qualitatively assessed by comparing the simulated behavior re- sults to recorded pedestrian behavior of the Ko-PER dataset. Future improvements could involve the description of social group behavior as well as a more differentiated contextual situation awareness.

Part IV

APPENDIX

COMPANIESPERINDUSTRY A a.1 automotive

Table A.1: 20 largest vehicle manufacturers with their brands by produced units in 2016 [Org].

Country Manufacturer Units 2015 Brands Source

Asia China BAIC Group 1 169 894 BJ, New Energy Vehicle, Senova, [BAI17, p. 10] Wevan Changan Automobile 1 540 133 Changan [Cho17] Dongfeng 1 209 296 Dongfeng, Liuzhou, Venucia [Don17, p. 50] SAIC 2 260 579 MG, Maxus, Roewe [SAI17, p. 10] India Tata Motors 1 009 369 Jaguar, Land Rover, Tata, Tata [Tat17, p. 2] Daewoo Japan Honda 4 543 838 Acura, Honda [Hon17, p. 5] Mazda 1 540 576 Mazda [Maz17] Mitsubishi Motors 1 218 853 Mitsubishi [Mit17] Suzuki 3 034 081 Suzuki [Suz17] Toyota 10 083 831 Daihatsu, Hino, Lexus, Toyota [Toy17, p. 2] Nissan 5 170 074 Datsun, Infiniti, Nissan [Nis17, p. 5] South Korea Hyundai 7 988 479 Genesis, Hyundai, Kia [Hyu17]

Europe France Groupe PSA 2 982 035 Citroën, DS Automobiles, Opel, [SAI17] Peugeot, Vauxhall Renault 3 032 652 Alpine, Dacia, LADA, Renault, [Gro17, p. 12] Renault Samsung Motors Italy Fiat Chrysler 4 865 233 Abarth, Alfa Romeo, Chrysler, [Fia17, pp. 40 sq.] Dodge, Fiat, Jeep, Lancia, Ram Germany BMW Group 2 279 503 BMW, MINI, Rolls-Royce [Bay17, p. 4] Daimler 2 134 645 Mercedes, smart [Dai17, p. 93] Volkswagen Group 9 872 424 Audi, Bentley, MAN, Porsche, [Vol17, p. 2] SEAT, Scania, Volkswagen, ŠKODA

North America USA Ford 6 396 369 Ford, Lincoln [For17] General Motors 7 485 587 Buick, Cadillac, Chevrolet, [Gen17] GMC, Holden, Wing 88 companies per industry

Table A.2: 20 largest OEM parts supplier by sales of automotive original equipment parts in 2016 [Aut17b].

Continent Country Company OEM part sales ($ in millions)

Asia China Yanfeng Automotive Trim Systems Co. 12 991 Japan Aisin Seiki Co. 31 389 Denso Corp. 36 184 JTEKT Corp. 10 778 Panasonic Automotive Systems Co. 11 988 Sumitomo Electric Industries 12 835 Yazaki Corp. 15 600 South Korea Hyundai Mobis 27 207

Europe France Faurecia 20 700 Valeo SA 17 384 Germany Continental AG 32 680 Mahle GmbH 12 173 Robert Bosch GmbH 46 500 Schaeffler AG 10 883 Thyssenkrupp AG 10 986 ZF Friedrichshafen AG 38 465

North America Canada Magna International Inc. 36 445 USA Adient 16 837 Delphi Automotive 16 661 Lear Corp. 18 558 A.2 telecommunication 89 a.2 telecommunication

Table A.3: Telecommunication equipment makers by global market share in 2015 [KDB15].

Continent Country Company Market share

Asia China Huawei 24 % ZTE 7 % South Korea Samsung 3 %

Europe Sweden Ericsson 33 % Finland Nokia Corporation 29 %

Table A.4: 20 largest telecommunication service companies by revenue [For].

Continent Country Company Revenue ($ in billions)

Asia China China Mobile 106.8 China Telecom 53.0 Japan KDDI 43.2 Nippon Telegraph & Tel 105.0 Softbank 82.1 Saudi Arabia Saudi Telecom 13.8 Singapore SingTel 11.9 United Arab Emirates Etisalat 14.3

Australia Australia Telstra 18.6

Europe France Orange 45.3 Vivendi 12.0 Germany Deutsche Telekom 80.9 Italia Telecom Italia 21.0 Spain Telefónica 57.6 United Kingdom BT Group 31.7 Vodafone 60.8

North America Canada BCE 16.4 USA AT&T 163.8 Verizon Communications 126.0

South America Mexico América Móvil 52.2 90 companies per industry

a.3 navigation and mapping

Table A.5: List of map data provider.

Continent Country Company Source

Asia China Baidu [Bai17] NavInfo [Nav17a] Japan Zenrin [ZEN17] Europe Netherlands HERE [HER17] TomTom [Tom17] North America USA Google, Waymo [Bou17] MODELPARAMETERS B

Table B.1: Parameters used for simulations, if not stated otherwise. The operational parameters were estimated by Zeng et al. [Zen+14b; Zen+17].

Model Var. Range Unit Value Description

Tactical

µs 0.3 Mean of perceived cost factor

σs 0.2 Standard deviation of perceived cost factor

Operational

Visual rv m 30 Visual range

γv ° 120 Visual opening angle

Driving τp [0.5, 4.5] s 0.5 Relaxation time vd [0.9, 1.1) m s−1 2.5 % Desired velocity distribution [1.1, 1.3) m s−1 2.5 % [1.3, 1.5) m s−1 5.5 % [1.5, 1.7) m s−1 22 % [1.7, 1.9) m s−1 39 % [1.9, 2.1) m s−1 17 % [2.1, 2.3) m s−1 8 % [2.3, 2.5) m s−1 3 % [2.5, 2.7) m s−1 1 % [2.7, 2.9) m s−1 1 %

Conflicting pedestrian λα [0, 1] Strength of angular effect r −2 Aβ [0.1, 2.0] m s 1.28 Interaction strength for repulsive force r −1 Bβ [0, 16] m 1.42 Interaction range for relative distance r −1 Bβα [0.1, 0.8] m 0.51 Interaction range for relative conflict time −2 Car Ac [0.1, 1.6] m s 0.86 Interaction strength −1 Bc [0.1, 0.8] m 0.36 Interaction range r −2 Crosswalk Ab [0.1, 0.6] m s 0.35 Interaction strength for repulsive force r −1 Bb [0.1, 5.0] m 2.65 Interaction range for repulsive force a −2 Ab [0.1, 0.45] m s 0.25 Interaction strength for attractive force a −1 Bb [0.1, 1.7] m 0.46 Interaction range for attractive force

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