Autonomous transportation for a Swedish production facility Mapping the technological and regulatory hurdles
Karl Fredriksson
Industrial Design Engineering, master's level 2021
Luleå University of Technology Department of Social Sciences, Technology and Arts
Autonomous transportation for a Swedish production facility - Mapping the technological and regulatory hurdles
Karl Fredriksson 2021
SUPERVISOR: Magnus Stenberg REVIEWER: Maili Schönning EXAMINER: Lena Abrahamsson
MSc in INDUSTRIAL DESIGN ENGINEERING Department of Social Sciences, Technology and Arts
CIVILINGENJÖR I TEKNISK DESIGN Master of Science Thesis in Industrial Design Engineering
Autonomous transportation for a Swedish production facility Mapping the technological and regulatory hurdles
© Karl Fredriksson
As per request, the identity of the client has been withheld
Published and distributed by Luleå University of Technology SE-971 87 Luleå, Sweden Telephone: + 46 (0) 920 49 00 00
Printed in Luleå Sweden by Luleå University of Technology Reproservice Luleå, 2016
Abstract
The technology of autonomous vehicles has the potential to provide a significant number of safety, efficiency and environmental benefits to those who are able to harness it. As such, it is only natural that the company which is the subject of this project should want to explore this field, since the company prides itself on being at the cutting edge of both environmental sustainability and technological advancement. This inquiry was therefore launched in order to amass a sufficient knowledge base to enable management to make informed decisions about the possible future implementation of autonomous trucks, specifically to handle the logistics flow between their production facility in Skellefteå, Sweden and the nearby harbour. The first step to achieving this objective consisted of an exploration of the state of autonomous vehicle technology as well as the regulatory framework in Sweden for operating such systems on public roadways. Information was gathered from a vast array of sources, including academic literature, official reports from various authorities, journalistic publications as well as interviews with individuals with competence or experience within this field. While the regulatory situation in Sweden at the moment offers no legal way to operate autonomous vehicles on public roads, it is possible to be granted permission to perform trials of this technology under certain conditions. An investigation was undertaken to determine whether this might be a viable option for the company’s case. As such, hazard analysis was performed on the proposed route in Skellefteå. The method for this was based off of methodology gained from sources who had previously executed safety cases for trials of autonomous technology. A list of potential hazards relevant to the operation of autonomous vehicles was composed, together with variables with which to measure their severity. The relevance and appropriate scope of these hazards and variables was then verified by discussing it with sources with competence in this field. The route was then travelled in order to observe the prevalence of the aforementioned variables. The information was completed and verified through various reports gathered from the Swedish Transport Administration and the Swedish Meteorological and Hydrological Institute. The result of the inquiry was that the autonomous technology on the market today is not sufficiently advanced to handle the specified application with an adequate level of safety. The route is also of limited use in establishing trials for testing of autonomous vehicles. While there are uses for autonomous transportation technology, great breakthroughs are needed before the technology reaches the level needed to handle such complex challenges as would be encountered on the proposed application.
KEYWORDS: AUTONOMOUS VEHICLES, AUTONOMOUS TRUCKS, ARTIFICIAL INTELLIGENCE, INQUIRY, TRAFFIC SAFETY
Sammanfattning Självkörande fordon är en teknologi som visar potential för betydande fördelar inom säkerhet, effektivitet och miljömässigt för dem som kan tygla den. Det framstår därför som naturligt att uppdragsgivaren till detta projekt skulle vara intresserad av denna teknik, då företaget är känt för att vara vid både miljö- och teknikfrågornas framkant. Därför lanserades detta utredningsarbete för att sammanställa tillräcklig kunskap för att kunna ta informerade beslut om en potentiell implementering av ett autonomt transportsystem från deras fabrik i Skellefteå till Skelleftehamn. Denna utredning började med att kartlägga hur autonoma fordonstekniken ser ut idag, samt de regulatoriska möjligheterna att driva autonoma system på allmän väg I Sverige. Informationen samlades från en mängd olika källor, inklusive akademisk litteratur, rapporter från officiella källor, journalistiska källor samt från intervjuer med personer som besitter kompentens och erfarenhet av ämnet. Emedan den regulatoriska situationen i Sverige för stunden inte medger något lagligt sätt att operera självkörande fordon på allmän väg så finns det möjlighet att få tillstånd att utföra försöksverksamhet med sådana fordon så länge vissa villkor uppfylls. En utredning genomfördes för att fastslå om sådan verksamhet skulle kunna vara relevant i företagets fall. I och med detta så utfördes en riskanalys på den föreslagna rutten i Skellefteå. Metoden för dess utförande baserades på metodologi som hämtades från källor som tidigare hade utfört säkerhetsbevisningar för försöksverksamhet på autonoma fordon. En lista av möjliga risker framtogs, tillsammans med mätpunkter vilka skulle kunna användas för att fastslå deras betydelse för autonom fordonteknologi. Dessa riskers relevans och lämpligheten av dess omfattning diskuterades därefter med källor med kompetens inom området. Sedan besöktes rutten för att observationer om mätpunkternas förekomst kunde utföras. Informationen kompletterades och verifierades därefter med information från ett antal rapporter från Trafikverket och Sveriges Meteorologiska och Hydrologiska Institut. Det man kommit fram till är att det idag inte finns något autonomt fordonssystem som är tillräckligt avancerat att klara rutten mellan fabriken och hamnen med god nog säkerhet. Rutten är dessutom av begränsat värde vad det gäller att testa sådana system. Även om det finns autonoma system i operation i dagsläget så ligger dock tekniken långt under den nivå som skulle behövas för att ta sig an de utmaningar som skulle uppstå I det föreslagna användningsområdet.
NYCKELORD: SJÄLVKÖRANDE FORDON, SJÄLVKÖRANDE LASTBILAR, ARTIFICIELL INTELLIGENS, UTREDNINGSARBETE, TRAFIKSÄKERHET
Content 1 Introduction 1 1.1 Background 1 1.2 Stakeholders 1 1.3 Objective and aims 2 1.4 Project scope 2 1.5 Thesis outline 3 2 Context 5 2.1 Benfits of autonomous transportation 5 2.2 The client: Mission and philosophy 7 3 Method 9 3.1 Process 9 3.2 Project planning 10 3.3 context immersion 10 3.4 Literature review 11 3.5 Hazard analysis 12 3.6 Interviews 14 4 Current state of autonomous vehicles 2 4.1 Classification of autonomous vehicles 2 4.2 Decision making hierarchy of self-driving cars 3 4.3 Artificial Intelligence and machine learning 4 4.4 Sensors used in autonomous vehicles 6 4.5 Technology readiness levels 7 4.6 Testing 8 4.7 Junction classification 9 4.8 Autonomous trucking systems 10 5 Regulations: current state and looking to the future 15 5.1 Permission to perform AV tests 15 5.2 Application for permission 16 5.3 state regulation: looking to the future 17 5.4 Licensing as a model for certification 19 6 Hazard analysis of the proposed route 20 6.1 The proposed route: scope and limitations 20 6.2 Lane changes and obstacle avoidance 22 6.3 Junctions 24 6.4 Non-protected road users 26 6.5 Weather and sun 29 6.6 Communication 33 6.7 Dangerous goods 34 7 Discussion 35 7.1 Av trials at the production facility 35 7.2 The future of autonomous transportation 39 8 Conclusion and recommendations 42
9 References 43
List of appendices
Interview template Swedish Transport Agency 1 page Interview Dr. Missy Cummings 1 page Interview template Einride 1 page Interview template Scania 1 page Autonomous vehicle hazard assessment 3 pages Rules and infrastructure of the junctions of road 372 1page
1 Introduction
The production facility of the client is set to become the first large-scale lithium- ion battery production facility in Europe. In order to transport incoming materials and outgoing products as safely, efficiently and economically as possible, the client has requested an inquiry into the possibilities afforded through the use of autonomous trucks. This is a technology which is poised to become central to all logistics operations in the coming decades, with significant benefits to the safety, efficiency, environmental impact and costs. However, as of today, the technology is still at an early stage of its developmental lifespan and is sure to be subject to momentous advancements and innovation in the coming years.
The central tenets by which the client base all their operations are boldness, passion and excellence. By boldly exploring uncharted technologies, passionately embracing the changes they present and, with excellence, efficiently extracting the benefits thereof, this investigation is a reflection of the many factors which make the client one of the most exciting and cutting edge innovators in the world.
1.1 BACKGROUND In 2016, the client was founded with the goal to create sustainable lithium ion battery production within Europe in order to facilitate the transition towards renewable energy. This mission started with an research and development centre in Västerås, and will continue with the creation of the production facility in Skellefteå. The initial construction on the production facility began in June 2018. The facility is projected to have a capacity of 16GWh/year.
From the production facility to the industrial harbour of Skellefteå there is a distance of approximately 10 km by existing roads. Once production commences at the facility, an estimated 12000 shipping containers will have to make a round-trip of these destinations every year, and thereafter increasing to a projected 40000. As a company at the forefront of both technology and environmental sustainability, the client is eager to explore the possibilities of using autonomous trucks to automate these transports. The information gathered in this report will be used to help inform their future implementation.
What follows is a master thesis project for the Luleå University of Technology (course code A7009A).
1.2 STAKEHOLDERS Primary Stakeholders
The client Management - need to factor in the results of the project in their strategy Logistics team 1
- the results of project will have a direct effect on their decision making IT team - will eventually be responsible to amass sufficient competence to run any of the recommended systems Operators - a final human point of contact before the autonomous system takes over: this transition is a point of interest Maintenance - will be responsible for maintaining the vehicles Drivers - will likely initially be necessary to supervise the systems operation
Skellefteå harbour Management - apart from having some responsibility for the systems success: may gain experience from the systems implementation to use in other parts of their operation Operators - a final human point of contact before the system runs autonomously
Secondary Stakeholders
Other road users - have an interest in the system operating safely and efficiently Transportstyrelsen - will need to make an informed decision on the extent the system is legally allowed to operate
1.3 OBJECTIVE AND AIMS The project objective is to amass a sufficient knowledge base to enable the client to make informed decisions about the future implementation of autonomous trucks at their production facility.
The aims are: ● To surmise existing research of autonomous trucking technology so as to be easily understood by management. ● To perform a thorough investigation in the current regulatory status for autonomous trucking, as well as a prognosis for how it may advance in the future, as well as recommendations for steps the client may take in order to bring along the necessary changes sooner. ● To present report on the feasibility of utilizing autonomous trucking to handle the logistics flow of the production facility, and the possible timescale of when such technology might be implemented.
1.4 PROJECT SCOPE Due to time and resource constraints, this project will not delve into any primary research and development of any particular autonomous trucking system: only
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secondary research and development of more general concepts for the systems implementation will be undertaken.
The type of transportation used will be battery driven heavy lorries. The client has already entered into negotiations with a supplier. As such, only the autonomous system will be investigated; specific trucks will not be considered, neither will alternate forms of transport.
The logistics flow which will be investigated are the products and materials being transported from the facility in Skellefteå to Skellefteå harbour, and vice versa. Other flows will not be considered.
1.5 THESIS OUTLINE What follows is a brief description of the contents of each chapter in order to provide a simple way to find the desired information.
1. Introduction A look at the circumstances which gave rise to the project, as well as establishing the objectives, aims and stakeholders.
2. Context A review of the reasons that autonomous transportation might be beneficial to the logistics of the production facility. This subject is explored from the perspective of autonomous technology as well as the perspective of the client as a company.
3. Method An explanation of what was done, why it was done and how the information was verified.
4. Current state of autonomous vehicles An exploration of the technology of autonomous vehicles, including an overview of how the technology works, highlighting some of the challenges which must still be faced, as well as an examination of the main actors on the market.
5. Regulations: current state and looking to the future A summary of the possibilities of operating autonomous vehicles on public roads currently llowed through Swedish regulation, as well as an exploration of how the future might look
6. Hazard analysis of the proposed route An examination of the most significant hazards encountered on the route between the production facility and the harbour, from the perspective of an autonomous transportation system.
7. Discussion An analysis of the possibilities and realities of operating autonomous vehicles of the proposed route, followed by an exploration of what the future may hold and in regards to autonomous vehicles.
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8. Conclusions and recommendations Presenting the most important findings from the discussion chapter in a clear and concise way.
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2 Context
The context in which this project finds its relevancy can be examined by considering one central question: why should the client want to explore autonomous transportation technology? The answer to this question may be framed in two distinct perspectives: first, what are the benefits of autonomous transportation, and second, how do these fit in with the client as a company? In this section both these questions will be answered.
2.1 BENFITS OF AUTONOMOUS TRANSPORTATION Although there have been experiments and attempts at developing autonomous vehicles (AV) for the road since at least the late 1930s, work on implementing the concept specifically on trucking is a newer development (Wetmore, 2003). Some of the earliest attempts at finding real world applications of the technology took place within the mining industry. One of the very first was the Komatsu’s Autonomous Haulage System, where testing of a fleet of 5 Ultra Class trucks was commenced in 2005, in the Codelco Mine Radomiro Tomic in Chile (Moore, 2018). This application was well chosen as the mining industry is one of those with the most widespread use of AV to date. There are several characteristics of AV which make them suitable for this work, as well as other potential advantages which the technology could produce, many of which are beneficial to the potential applications envisioned at the clients facilities.
One of the benefits which are most attractive when talking about logistical transportation applications of AV is the increase in efficiency that the technology may bring. One of the more obvious areas where AV would be more efficient would be the decrease in labour costs. When estimating the generalized transport cost for road freight transports in the EU, the Joint Research Centre arrived at the conclusion that the drivers wages accounted for the largest single component of the GTC: on average 42.1% of the costs (Persyn et al., 2020). If those costs could be mitigated through autonomous technology the impact would understandably be significant; even more so in Sweden, which currently holds some of the highest labour costs in EU and the entire world (Eurostat, 2020).
Another gain in efficiency would be seen in the reliability and scheduling components of logistical operations. By eliminating human errors and needs, such as those of taking breaks to eat, sleep, and go to the bathroom, the logistical system may see great improvements in both time and fuel efficiency (Fagnant & Kockelman, 2015). Such improvements have already been observed in the cases where AV technology has been implemented in closed off facilities (Van Meldert & De Boeck, 2016).
More than the benefits to the individual users of the technology, advancements in 5
efficiency bring further systematic benefits to the greater world. One self-evident such benefit is the ecological impact of operating at greater fuel efficiency. However, there are other, less obvious advantages such a system may bring. Due to the high degree of control possible over an AV motion planning algorithm, simulations suggest that the amount of road wear caused by trucking could be significantly lowered if appropriate algorithms were used (Chen et al., 2019). Analysis has also revealed that AV technology would likely help prevent congestion and increase the throughput of the roadways on which they operate. Due to more predictable and controlled stopping and acceleration patterns, autonomous vehicles could increase the stability of the traffic stream as well as helping to alleviate the effects of shockwave congestion (Talebpour & Mahmassani, 2016).
Another area where AV could make theoretical advancements is road safety. Advancements in automation, with technologies such as collision warnings, automatic emergency braking and lane departure warnings have already had an impact on road safety, to the extent that insurance companies in Europe already offer premiums on vehicles with such new features (Bartz, 2017). Any autonomous system would combine advanced forms of these technologies with other advantages, such as faster-than-human reaction times and probabilistically calculated behaviour algorithms for consistent and optimal decision making (Paden et al., 2016). In addition to this, AV lack some of the failings human drivers are plagued by. In the US, for example, 94% of accidents are due to driver error. 31% of accidents involve a driver who at the time was legally intoxicated, 10% of accidents stem from the driver being distracted and 7% are due to drivers falling asleep (Singh, 2018). Naturally, AV systems do not suffer from these deficiencies, and so it is projected that they could have a significant impact on lowering accident rates (Paden et al., 2016).
Almost all of the benefits previously discussed have one thing in common: they scale with the relative proportion of AV on the roadways. This is due in part to the increased predictability of AV in comparison to human drives, but also to the possibilities of Vehicle to Vehicle (V2V) or Vehicle to Infrastructure (V2I) communication, allowing the autonomous systems to make more informed route and motion planning decisions. The benefits to both road wear and congestion, for example, increase substantially with the proportion of AV in circulation (Chen et al., 2019; Talebpour & Mahmassani, 2016). The increased predictability, as well as connectivity, would also make the road safety of the vehicles easier to secure. The reduced congestion would have knock-on effects for fuel efficiency of all travellers (Fagnant & Kockelman, 2015). Furthermore, there are reports and projections of driver shortages in both the European and US trucking industries which self driving trucks would undeniably help alleviate (IRU Report Forecasts Alarming Jump in Driver Shortage in Europe, 2020; Huff, 2020).
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2.2 THE CLIENT: MISSION AND PHILOSOPHY As we as a species continue to press on into the first tentative decades of the third millennium, it is becoming abundantly clear that if the future is to be one to look forward to, significant changes need to be made in the way we use the resources of our planet. Lithium ion batteries are a key part of this cleaner future, helping to reduce the amount of fossil fuels being consumed, as well as helping to ease the transition to sustainable energy sources (Sasmal et al., 2016). However, efficient use of this technology is not without its obstacles. First, the materials needed to create lithium ion batteries are obtained through mining, oftentimes with a devastating environmental and humanitarian impact (Katwala, 2018). Second, the manufacturing capacity for the batteries is almost entirely based in eastern Asia. This results in less sustainable end products due to the inefficiency of transporting them halfway around the world, as well as causing the supply to be unacceptably insecure. The client proposes to solve these issues through a simple central aim: to create local European manufacturing capacity for lithium ion batteries through the cleanest means possible. The company will accomplish these goals through implementing their revolutionary recycling process for batteries in a production facility in Skellefteå, Sweden.1
The production facility is currently being built on a 25 hectare plot of land south east of the city of Skellefteå, Sweden. With a projected capacity of 40 GWh, enough to power 600 000 electric vehicles, the plant will be at the cutting edge of modern manufacturing, with advanced automation technologies, a full recycling plant and utilizing 100% clean energy. The facility has a target of using 50% recycled materials in new cells by 2030, seen as a step on the road towards the ideal vision of making batteries a closed material cycle. The industrial harbour of Skellefteå is located 11 km from the facility. The most direct route runs on the 372, a major roadway with a maximum speed of 80km/h. Projections show that at full capacity the facility will need to perform tens of thousands of return journeys through this route each year, carrying standard 40ft shipping containers each way. 1 (see fig. 1)
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Fig, 1: The projected volume for the logistics flow between the production facility and Skellefteå harbour until 20261
On the road to these goals, it is not surprising that the client may wish to look towards autonomous transport as a solution to their logistics flow. As a company at the forefront of technological advancement, it is logical to look towards the technology being touted as the future of the transportation industry. Since a significant portion of their manufacturing process is to be highly automated, there is a certain logic to extending this methodology to their logistics flow in order to maximize the time and cost effectiveness. Further, the environmental benefits promised by AV are certainly in line with their overall mission of creating a cleaner future. In short, autonomous transportation is predicted to be a central part of the same generation of manufacturing and industry which the client is spearheading, and so it is only natural that the client should want to integrate it into their system as early as possible1
8 1A. BRUNDIN, THE CLIENT, PERSONAL COMMUNICATION, 30-10-2020
3 Method
As an inquiry, this project in essence consisted of the gathering and structuring of information in a way which will be useful for management to make decisions going forward. This section will describe the different stages of this process.
3.1 PROCESS The first phase of the project consisted of planning, followed closely by information gathering: first through studying existing literature and research, and then through interviewing persons of interest. When an overview of the available technologies had been acquired, the investigation moved to acquiring and analysing knowledge specifically relevant to the project aims. During such a process it is essential to use the information acquired during the previous stages of the project, as well as to perform further, deeper investigation into areas of study particular to the requirements of the production facility. As such, the project employed the iterative process, with a project cycle which can be seen in fig. 2. The cycle was iterated throughout the project: the work began with an exploration of autonomous vehicle technology through academic literature. After this the focus was changed to exploring the regulatory situation of autonomous vehicles in Swecen. To accomplish this, a return to the literature research phase was necessary in order to gather information specific to this subject, and so the cycle was recommenced. This cyclical process was repeated in a similar fashion for all of the chapters included in this report.
Research and Planning and literature review data collection
Evaluation Analysis and refocus
Fig. 2: The project circle
This cycle was originally based on the structure of Bellgran’s “ramverk” model for the development of production systems, as seen in table 1. However, the process was repurposed, rearranged and simplified to more closely fit the project, which in its essence is more of an inquiry than a development project. As such, the category of “development” was substituted by “analysis” and “realisation” by evaluation. “Context” and “performance” were given the much broader remit of “research” in general, and the planning phase was mainly concerned with strategizing what empirical and other data needed to be collected to inform the analysis stage.
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Table 1 “Ramverk” for the development of production systems (Bellgran & Säfsten, 2005 Context and performance Planning Development Realisation Leadership and Contextual impact direction Preparatory Physical System performance Structured process Specialised Operational
3.2 PROJECT PLANNING The project planning was performed on two distinct levels: the project as a whole and on a weekly basis.
For the project as a whole, a Gantt chart was created. (see fig. 3) As a tool, it is regarded by most as an effective method of visualizing the work which must be done for a project to reach its completion. It was useful as a simple means to get an overview of how the project was progressing in relation to external and internal, hard and soft deadlines.
Fig. 3: The Gantt chart for the project
On a weekly basis, the planning took the form of a system of brief reports. These consisted of a list of the work completed since the last session, the deviations from previous planning, and the planned activity due to be done before the next week. These reports served as important documentation of the progress made, as well as an opportunity to analyse, critique and adjust the direction of the project in light of new information. They were also presented to the internal thesis supervisor before each biweekly review session, and served a dual function as a form of mini agenda for each meeting.
3.3 CONTEXT IMMERSION This project had two areas where it was important to build up some form of contextual immersion. The first of these was the philosophy and mission of the client. This was an important part of the project as it helped in defining the perspective from which the client motivated their desire to explore the possibilities afforded by autonomous transport. The process started off with an introduction to the company at their offices in Stockholm, which was followed up with onboarding sessions. Contact was also held with the external supervisor through biweekly video conference meetings, where both parties were able to update each other on developments, as well as to comment on the path the project was taking. In order to stay informed and in touch with the general goings on and culture of the company, the weekly logistics department meetings were also attended through video conference software.
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The next step of context immersion was to gain an insight into the industry and technology of autonomous trucking. Ideally, the preferred way to gain exposure to this field would have been through visiting facilities where this technology was either being tried or had already been implemented. However, this proved impossible due to the circumstances brought on by the COVID-19 pandemic. Instead, the required knowledge was gained through researching the topic on the internet. The first step was to read through the Wikipedia entries on the subject, starting with the entry titled “Autonomous Vehicles”, then moving on towards “Autonomous Trucking”, and so forth, progressing to more specialized subjects. The point of this was to get an overview of the subject, gain familiarity with all the terms and a decent grasp on the history of the technology. While Wikipedia was never used as a direct source for any information presented in this report, it proved immensely useful in order to build up a general understanding of the field of research. Whenever a subject was deemed of specific interest for the project it served as a stepping stone to direct the research, after which verifiable sources could be found to confirm or deny the information, such as academic papers and interviews with authorities on the subject. Once this source of information had been covered, news articles and the websites and press releases of the companies involved in the field served as sources of information. To stay abreast of new developments, as well as staying immersed in the field, Google news was searched on a weekly or biweekly basis throughout the project. Certain key words were used, such as “autonomous vehicles”, “autonomous trucking” and the names of select companies, such as TuSimple and Waymo.
3.4 LITERATURE REVIEW The literature review was performed with two main purposes. The first was to amass a base of academically verifiable information on autonomous trucking technology, in order to present the client with a summary in line with the project aims. This was done through querying databases such as SCOPUS. The key words used ranged from the more generalised, such as “autonomous vehicles”, on to more specific topics such as “LIDAR”. There was an effort to choose the literature with the most citations, as well as the newest literature. There was also a focus on searching for survey articles, as these often proved useful to gain an overview of a specific subject, as well as a way to discover other useful articles through the references. Often, when such an article could not be found in the databases, it could easily be acquired by contacting the author directly. In most cases, the response was positive and the article was sent over email in a matter of days.
The second purpose was to research specific topics which were deemed relevant to the subsequent portions of the project, including the regulatory survey and especially the hazard analysis. The subjects which required further analysis were in part those directly related to the capabilities of AV technology, such as clarifying exactly under which circumstances the sensors were able to operate. Such cases required the return to the academic databases in a way which may be regarded as a classic example of the iterative process at work. Othertimes, the knowledge needed was on subjects peripherally related, such as the different classifications of dangerous goods or Swedish standards in road design. This was often sequestered from reports acquired from the websites of various government agencies, such as
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the Swedish Transport Administration and the Swedish Civil Contingencies Agency. Another source of the reports were through the interviews conducted with various individuals. Oftentimes, they could supply reports of particular interest, or otherwise give helpful leads as to where one might look for the necessary information.
3.5 HAZARD ANALYSIS A central part of the project was the hazard analysis performed on the route between the production facility and Skellefteå harbour by county road 372. The initial purpose of this work was to gather information on the route in order to assess the viability of implementing an AV transportation system or testing such a system on the route. The assessment would also serve as a useful starting point from which future hazard and risk analyses may be launched, as well as a framework upon which to discuss the strengths and weaknesses of the autonomous systems currently on the market. The information may also prove useful when developing those infrastructural elements of the route which the client still have some control over, as well as when making decisions when implementing any non- autonomous transportation system.
The initial step in producing the hazard assessment was to create a plan. This consisted of listing all of the relevant potential hazards which lay along the route, the reasons why they were of particular interest in regards to autonomous vehicles and the data which should be collected in order to assess the hazards. This was then used to plan a trip to visit the actual route in Skellefteå. The purposes of this visit was to collect any data which must be amassed in person on the ground, to get a feeling for the route itself in order to make qualitative statements about its features, as well as to observe the route in an attempt to discover any hazards which may have been missed when producing the initial plan.
The main focus of the trip to Skellefteå was to gather information on the junctions. This was done by driving along the route from start to finish, and stopping at each junction to collect the relevant data. This method was based off of the reports of safety cases performed in preparation for previous trials of autonomous technology.1 Some of the aspects of the route which were investigated were the traffic rules and laws decided the right of way at each junction as well as what infrastructure and signage existed in order to signal or enforce these. This information was recorded on a spread sheet. Pictures were also taken of each junction, both to refer to later as well as to document any peculiarities and hazards present at specific junctions. The points where the environment broke the line of sight for the vehicles of the 372 was observed and assessed. Unless it was completely clear that these would not be an issue for the visibility on the 372, the coordinates of these would be noted on google maps, the exact locations of them determined through scrutiny of the aerial photographs provided on the software (see fig. 4). This was done in order for further analysis to be performed out of the field.
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Fig. 4 The route and the coordinates collected during the hazard assessment
While the junctions of the route were being investigated, observations were made concerning other aspects of the route. The locations of bus stops, pedestrian crossings, train crossings, bicycle paths and points of concentration of pedestrian traffic were recorded on google maps. Where deemed potentially useful, these features were photographed for future reference. Finally, separate drives were performed to gather information about the lane widths, the amount of lanes, speed limits and the road quality. However, difficulties were encountered when attempting to stop and record these safely. Instead, their approximate location was memorised and verified by other means at a later date.
The data collected on the trip was augmented and completed with data from other sources. One resource which was relied upon heavily was the Swedish Transport Administration. Since the 372 is a county road, its upkeep and infrastructure lies directly under their responsibility. As such, their database Lastkajen contained vast amounts of information in the form of maps, detailing different infrastructure elements and regulations, such as the number of lanes, road width and speed limits. These were then analysed using mapping software such as ArcGIS, Google Earth and the Swedish Transport Administration’s own map-generating software, Nationella VägDataBasen (NVDB). In addition, the Swedish Transport Administration has recently produced their own hazard assessment report on road 372 (Swedish Traffic Agency & Ramström, 2019). The work is still on-going, but the report was developed in order to take decisions about the infrastructural changes needed to raise the safety of the road. This was deemed necessary due to the numerous accidents on the road in the past decade; the road had at times been 13
known as one of Sweden’s 100 most dangerous (Swedish Traffic Agency & Ramström, 2019). This report proved extremely useful both in order to validate and verify the information already gathered, as well as to contribute information and statistics on hazards and accidents which could not realistically be gathered through the previously described hazard assessment methods.
Data concerning weather was sourced from the Swedish Meteorological and Hydrological Institute (SMHI). The results from their measuring stations are open to the public, and as such can be accessed freely. The data for some the categories relevant to this report stretched as far back in time as the late 1960s. However, in order to arrive at comparable datasets, only data from 1989 and onwards was used as this was the oldest data available in one of the categories. The measuring stations chosen were those closest to the route which actively recorded the required data: Kusmark D for snow depth and precipitation, and Skellefteå Flygplats for visibility and temperature.
3.6 INTERVIEWS A large part of the project was accomplished through the information gathered by interviewing people of interest within the AV industry. These interviews were at the most semi-structured, with templates developed with possible lines of questioning, but where the great majority of the information was gathered through improvised follow up questions. Often, these interviews were improvised from start to finish, with a brief description of the client’s case as a starting point to inspire lines of inquiry. The interviews were also a way to verify the information amassed through academic literature and journalistic sources, as well as the reasoning behind the methodology of the project. An example would be the hazard analysis. The basic plan for how to proceed was at first developed by analysing what was known about how AV systems function, and thereafter envisioning those hazards which would likely be most significant for such a system. This list of hazards was then discussed independently with a consultant who had worked with developing risk assesments for AV systems previously1, as well as with a project leader from a company which develops AV systems.2 Both of these sources verified that the reasoning behind the hazard analysis was correct, advised to drop certain variables whose importance had been overestimated, as well as offering other hazards which had been missed in and should be investigated.
14 1B. ENQVIST, COMBITECH, PERSONAL COMMUNICATION, 01-12-2020
2C. WALLBÄCKS, EINRIDE, PERSONAL COMMUNICATION, 30-10-2020
4 Current state of autonomous vehicles
The first step to making informed decisions in any venture is to amass the required knowledge on the subject. What follows is an exploration of the technology of autonomous vehicles, including an overview of how the technology works, highlighting some of the challenges which must still be faced, as well as an examination of the main actors on the market.
4.1 CLASSIFICATION OF AUTONOMOUS VEHICLES The degree to which a vehicle is autonomous varies from vehicle to vehicle, and is more than just a function of the sum of all of the autonomizing features included in the machine. The factor which truly lies at the centre of the level of autonomy of any vehicle is the degree to which is can operate unsupervised, as it is through this such a system would create value. SAE International have devised J3016 as a generally accepted industry standard which attempts to categorize the level of autonomy present in an autonomous vehicle on a scale from 0 to 5. Each grade of the scale represents a different level of autonomous functionality and, conversely, driver responsibility (Paden et al., 2016).
SAE J3016
Level 0 Vehicle requires driver input for all functions. The system has only the capability of warning the driver of any dangers its sensors picks up (Paden et al., 2016).
Level 1 Certain basic functions necessary for vehicle operation have been automated. Examples include adaptive cruise control, anti lock breaking and lane centring. The system can control either acceleration/braking or steering at the same time: not both (Paden et al., 2016).
Level 2 The functions of level 1 have been integrated so as to allow the vehicle to control steering and acceleration/braking at the same time (Paden et al., 2016).
Level 3 The vehicle is able to drive autonomously, but the driver’s full attention is needed as the system can at any point in time require manual input (Paden et al., 2016).
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Level 4 The system is able to drive completely autonomously as long as certain conditions are met, as well as being able to abort the journey safely once a situation arises which is beyond its limits. In the context of trucking, a driver may still be necessary, but can have his/her attention entirely elsewhere: sleeping or working another job remotely, for instance (Paden et al., 2016).
Level 5 The vehicle is entirely autonomous under all conditions; a driver is no longer necessary (Paden et al., 2016). .
It is from the perspective of removal of driver engagement where the scale distinguishes the difference in usefulness of the autonomous systems at its different levels. This is also the main source of the usefulness of the scale itself is. For example, the largest breakthrough for autonomous trucking would be reaching a level 4 autonomous vehicle for public roads, as this would open up a new degree of efficiency and productivity (Daimler, 2020).
4.2 DECISION MAKING HIERARCHY OF SELF-DRIVING CARS Autonomous vehicles are in essence self governing decision making machines. In order to function they need real time information upon which to base their actions. This is acquired through sensors like LIDAR, cameras and odometry, as well as through operator input such as the final destination. Like any other decision making entity they then need a framework of rules upon which to structure and evaluate the input, as well as to determine the actual decisions made. While the details vary from system to system, in general this takes the form of a hierarchy consisting of four components. These are listed below, in the order from highest to lowest level, where the preceding level informs what the decisions the next level will make (Paden et al., 2016).
Route planning The system attempts to plan the most efficient route through combining information from roadmaps, GPS localisation and traffic reports. The road network is first reconstructed as a directed graph. Each road segment is assigned a cost, which is required to travel along it based on factors such as time-use, distance and elevation. An algorithm is then used to calculate the minimum-cost route to arrive at the final destination (Paden et al., 2016).
Behavioural decision-making Once the route has been established, it is the responsibility of the system to travel that route whilst maintaining correct and safe behaviour for the situation at hand. Decisions include engaging in such behaviours as performing stops at stop signs, waiting to observe pedestrian actions and continuing the route one the way ahead is all clear. To determine what behaviour is appropriate, decisions are based upon the position and behaviour of other road users, laws and rules of the road, the layout
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and functions of the infrastructure as well as other more general factors, such as visibility and road conditions. These are usually analysed through modelling each available decision as a state in a finite state machine, and selecting the decision which brings the optimal outcome (Paden et al., 2016).
Motion planning The motion planning system is tasked with creating the specific path trajectory which the vehicle will follow. Examples include which line to take in a crossing or how to switch lanes in a highway. This trajectory is determined through creating a virtual model of the world surrounding the vehicle. By using various techniques, the optimal path within the model is identified, whereby inputs may be generated for the vehicle controls in order to recreate this path in the real world. When a path is chosen priority is given to working within the physical limits of the vehicle, avoiding collisions with all obstacles and creating as comfortable a ride as possible for any passengers (Paden et al., 2016).
Vehicle control The actual operation of the vehicle is performed through feedback controllers. Due to small errors and inaccuracies in the motion planning system and vehicle model, small adjustments and error corrections must be performed continuously throughout operation in order to secure the path and trajectory stabilisation (Paden et al., 2016).
4.3 ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING While the hierarchy described above presents a procedure for the way decisions have to be made on different timeframes and as a function of different kinds of information, at some level there needs to be a system which determines what decisions are actually going to be taken. Throughout the hierarchy, but especially in the behavioural decision making and the vehicle control levels, this is done through some manner of artificial intelligence (AI) system. In some cases the system has no problem making a decision: the correct decision can be easily determined ahead of time and programmed into the system, or the system receives such good information that a decision is self evident. An example could be what speed to hold on a stretch of empty road with ideal weather and road conditions. In this case the AI could use its GPS localisation system, combined with a connection to some database of the speed limits of all roads in the area, determine what the officially mandated speed limit is. If all other things are equal, the vehicle would then proceed at that speed (Cummings, in press).
In the world domain, however, things are rarely this simple: there is usually some degree of uncertainty which the system has got to factor into the decision making process. The system has to first perceive environmental factors through the information it gathers with its sensors. It then has to act appropriately based on these factors. An example would be if the sensors detected a long thin object on the roadside. The system would need to establish whether the object is a tree, a road sign or a person, and adjust its speed accordingly. The source of uncertainty in this case arises in perceiving and recognizing what object the system is sensing
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(Cummings, in press).
Although all the systems on the market are likely slightly different, for the most part it can be said that the ability to perceive the world around it is gained through machine learning. Oftentimes a more advanced variant of machine learning called deep learning is used (Cummings, in press). While there are differences, both methods at the most base level use similar methodology to acquire their decision making capabilities. The system is given algorithms with which to reach a desired state. It is then presented with data which to parse and upon which it applies said algorithm. The system is able to change these algorithms based on the results of past runs in order to bring itself closer to the desired state; in essence it teaches itself to become better by trying different decisions in different situations (Grossfeld, 2020).
In the case of AV, the data used to train the machine learning systems to perceive the world around them would principally be images of known objects, such as road signs and traffic lights. When confronted with such an object, the system would likely use edge detection software in order to create an image which is easier to process (Cummings, in press). It would then use an object detection algorithm, such as the viola-jones object detection framework, to identify the features of an object in order to be able to recognize similar objects in the future (Lee, 2020).
Although in theory, with enough computing power and training, such a system would be entirely adequate for the task of controlling an AV. At this point in time though, with the technology currently available, there are serious problems with this methodology. Since the control of an AV is such a safety-critical system, an extremely high standard must be placed on its control system. However, with current existing technologies, the previously discussed perception system when applied to the world domain is brittle to the point of inadequacy. In this context, the term brittleness applies to the inability of an algorithm to “generalize or adapt to conditions outside a narrow set of assumptions”, as Dr. Cummings puts it (in press). As an example, the system may learn to recognize a stop sign. It collects a set of assumptions of characteristics, such as colours and shape, with which it can successfully identify the signs. However, were the sign to be covered with snow, suddenly the systems assumptions do not hold true, and so it may no longer recognize the stop sign. However, even if the system learned that this was also a stop sign, the next one could have a layer of snow and frost in a different configuration to the preceding sign, leading again to the system not being able to identify the object. It would have failed to create a generalized algorithm to identify the object, as the model by which it attempts to do so has been generated through parsing assumptions made by analysing specific stop signs, which end up not holding true due to slight changes in the conditions.
These issues are symptomatic of the “bottom-up” approach by which machine learning systems develop their algorithms. While such a system is working in chartered territory, it works absolutely fine. However, as soon as it faces uncertainty, its algorithms cannot cope. Uncertainty, instead, can be more effectively tackled with “top-down” reasoning, with causal inference to be able to
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fill in the gaps. A human, by comparison, would be much more likely to inductively identify the sign (Cummings, in press).
4.4 SENSORS USED IN AUTONOMOUS VEHICLES Although there is variation from system to system, the information an AV requires to operate can generally be divided in two categories: information from external sources, such as GPS positioning, traffic reports and roadmaps, and information from sensors. This information is that upon which the car must model the world around it in order to implement its behavioral decision making and base its motion planning. The information is acquired through a selection of visual sensors, of which the most commonly used are described here, along with a comparison of their most frequently encountered uses in Table 2 (Hecht, 2018).
Lidar Lidar, which is a portmanteau for “light detection and ranging”, is a relatively new and expensive technology used to detect the distance between the vehicle and surrounding obstacles, as well as in some cases the speed at which these are moving relative to the sensor. It can be used to produce high resolution images. In the most basic form, the sensor works by emitting a laser pulse and recording the time taken for the pulse to be reflected back towards the sensor. The maximum range of the lidars currently on the market is around 200 to 300 metres, although this number varies depending on the reflectivity of the target which the sensor is attempting to detect (Hecht, 2018).
Cameras Cameras produce a high resolution image of the world surrounding the vehicle. They cannot in and of themselves provide information about the distance or relative speed of surrounding obstacles, although this can in some cases be accomplished through data analysis, or by using two cameras to produce a binocular image. (Kehtarnavaz et al., 1991) Colour can provide important information, although the data analysis requires a great deal of processing time/power (Hecht, 2018). The sensor has no inherent source of light, and so is reliant on the external conditions, such as time of day and weather. In good conditions, the range can be as far as 1 kilometer (TuSimple, 2020).
Radar A highly mature technology, which results in relatively cheap sensors, amongst other benefits. The sensor emits a pulse of electromagnetic radiation at a certain frequency, usually in the radio or microwave bands, and measures the time taken for the pulse to return to the sensor. Different frequencies are used at different ranges. In all cases, the sensors can detect both distance and speed. However, the resolution is low, and oftentimes results in a 2D image of the surrounding objects, as seen from a top-down perspective (Hecht, 2018).
Ultrasound The sensors emit a high frequency sound wave pulse and record the time taken to
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return to the sensor. These re only useful at extremely close range, making them ideal for use in slow speed, close quarters maneuvering (Hecht, 2018).
Table 2 Comparison of the uses of the different sensors Long range Short/medium Lidar Cameras radar range radar Ultrasound
Adaptive cruise control x
Collision avoidance x x
Emergency braking x x
Pederstian detection x x x
Environment mapping x x x x
Traffic sign recognition x
Lane departure warning x
Cross traffic alert x
Digital side mirror x
Blind spot detection x x
Surround View x x
Rear collision warning x x
Park assistance x x x x (Hecht, 2018)
4.5 TECHNOLOGY READINESS LEVELS When a new technology is being developed, such as that of autonomous vehicles, it is important to gauge the maturity of the technology in order to find appropriate applications in which to implement it in an effective and safe manner. In the 1970s, NASA proposed the Technology Readiness Levels framework (TRL) in order to accurately assess the capabilities of the new technologies being developed in the space program (Cummings, in press).
The framework, consisting of 9 levels (see fig. 5), may be applied to autonomous vehicle technology to assess the suitability of running such systems on public roads. Dr. Cummings argues that the AV systems currently on the market are at level 6 (in press). This would imply that the technologies are adequate to operate in a “relevant environment”. In the case of AV, this specifically refers to a controlled environment where the levels of uncertainty for both the perception and sensor systems are at a minimum. An example would be the trials performed with autonomous trucking in Southern California, where hub to hub transfers are performed along stretches of relatively empty highway, with negligible pedestrian and cyclist traffic, as well as predictable weather conditions conducive to the 7
systems abilities (Kirsher, 2019). Some of the technologies most in need of scrutiny in the regard are their readiness is the software used for perception of the world, as well LIDAR sensors, which constitute relatively new technologies in relation to others technologies in use, such as radar RADAR (Cummings, in press).
Fig. 5 The Technology Readiness Levels (NASA, 2019)
4.6 TESTING Autonomous vehicles are a new technology aimed to operate in the public space with serious safety implications and potential economic damage for both users and third parties. Therefore, one of the most important problems to solve before their widespread implementation is to create a functional, secure and proven methodology to test autonomous systems (Cummings & Britton, 2019). While the most basic safety functionalities of the systems have already been developed to the point where level three autonomous systems are able to operate satisfactorily safely on the roads, the standard needed to be attained in order for SAE level 4 technologies to be allowed in public spaces is equivalent or better levels of safety (EBLS) than those currently achieved by human drivers (Cummings, 2018). 8
One might assume that a simplistic approach to achieving EBLS might be to allow the autonomous systems out on the roadways in order to gather enough data to statistically prove this standard is met. However, in order to produce enough data for this conclusion to be statistically significant, an unreasonable amount of testing must be performed. Due in part to the rarity of traffic accidents per total miles travelled by traditional cars, a fleet of 100 cars would have to be driven for over 12 years non-stop in order to statistically prove that their fatality rate is lower. Naturally, alternative approaches will have to be considered (Kalra & Paddock, 2016).
4.7 JUNCTION CLASSIFICATION One of the great challenges in operating an AV system, or any vehicle for that matter, is necessarily the handling of junctions; this is one of the main places where the vehicle must cross paths with another vehicle on a regular basis. The Swedish Transport Administration has classified junctions between two public roads into 6 types: A through F. These are ranked depending on the amount of risk-managing features they incorporate. These have been divided into two distinct groups depending on the conditions provided for vehicles traveling on the joining roads: types A-C are the “minor junctions” and types D-F are the “major junctions”. The main difference between the groups is that the minor junctions do not employ any major features to facilitate travel from the joining roads, whereas the major junctions all employ features which put vehicles on the joining roads on equal footing with those on the main road. The minor junctions may or may not include a shoulder to facilitate right hand turns off of the main road (Swedish Transport Administration & Frid, 2015).
Type A A simple junction with no features to separate traffic.
Type B There is a separation of the traffic on the joining road.
Type C Traffic is separated both on the joining road and the main road. Space is reserved for vehicles on the main road to turn left without disturbing traffic. 9
Type D The roundabout, where priority is always given to those vehicles already in the roundabout.
Type E Traffic is controlled through stoplights.
Type F Interchanges, typically seen on larger roads like highways.
Fig. 6 The types of junction seen on Swedish roads (Swedish Transport Administration & Frid, 2015)
4.8 AUTONOMOUS TRUCKING SYSTEMS On the market today, there are a number of companies currently at work developing autonomous trucking systems. What follows is a description of some of the biggest, as well as those who are of greatest interest for the client.
Scania/TuSimple Scania, a Swedish company, is an international leading producer of transport solutions, such as busses and trucks for heavy transport applications (Facts and Figures, 2020). In 2019, the company presented two autonomous concepts: the Scania AXL and the Scania NXT. The Scania AXL is an autonomous truck developed for mining applications. The truck is designed to operate with no driver on board, and therefore has no cab (A New Cabless Concept – Revealing Scania
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AXL, 2019). The Scania NXT concept consists of a modular urban autonomous transport vehicle. The vehicle is built up of different modules, starting with a base consisting of a fully electric drivetrain. On top of this can be fitted passenger modules for public transport or shuttle service applications, or a cargo-hauling module for trucking applications. One of the ideas is for the vehicle to function as a bus during the day to then haul freight at night. The design also puts a great deal of focus on new ways of communication with passengers, infrastructure and other road users. It has been released as a concept of how urban transport may function post 2030 (A New Cabless Concept – Revealing Scania AXL, 2019).
When questioned on the matter of autonomous trucking in regards to the case at the centre of this inquiry, representatives from Scania cautioned of having too lofty ambitions. There is no “plug in and go” autonomous transportation solution available on the market. The technology is still a new one, and as such there are many conditions which must still be met for any autonomous system to be operated safely and efficiently. Even if a sufficient number of these are met, any such system would require investments of both time and capital, likely alongside significant infrastructure changes, in order to operate the system. What was suggested was that they might look at the case and advise gradual changes to simplify such a systems future implementation.1
In September of 2020 it was announced that Traton, the parent company of Scania, had entered into a partnership with the autonomous trucking developer TuSimple specifically to augment Scanias autonomous vehicles with their technology (TRATON, 2020). Founded in 2015, TuSimple has been hailed as “the world’s largest and most advanced self-driving truck company”. The company has been at work developing a logistics network in the American southwest, with SAE level 3 autonomous trucks running freight hub-to-hub. This means that the customer brings the cargo to a terminal owned by TuSimple. Here, TuSimple load the cargo onto one of their autonomous trucks for transport along their predetermined route. At the end of this route is another terminal, where the cargo is offloaded on a conventional, driver-controlled truck for the last mile delivery. These routes are set routes and have previously been mapped in detail by the machine learning AI of the autonomous system. While their network only covers the American South-West at the moment, they project covering the entire nation of the United States as early as 2023. (TuSimple, 2021) In the summer of 2020, TuSimple’s fleet consisted of 40 category 8 trucks (the heaviest category of trucks on US roads) (Heilweil, 2020).
Einride A Swedish startup, Einride aims to be at the forefront of the next generation of environmentally sustainable road freight transportation. As such, they have developed a series of electric, battery powered freight vehicles. One of their flagship products is the Einride Pod, a battery driven autonomous transportation vehicle. The vehicle does not include a cab for a conventional driver, and is therefore supervised through remote control. As such, excellent connectivity is required in order to assure proper operation of the system (5G or greater is
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preferred). The vehicle has an operational uptime of 20 hours a day and a maximum payload of 16 tons (Einride, 2020). Einride view autonomous vehicles through a scale of four different Autonomous Electric Transport (AET) levels:
AET 1 Fenced The AV is restricted in its operation to closed areas, such as ports and industrial facilities. This allows for extremely detailed mapping of the routes, accurate scheduling of operations and control over a series of uncertainties, as well as facilitating the guarantee of good connectivity. The vehicle is limited to an operating speed of 30km/h and has a range of 130-180km (Einride, 2020).
AET 2 Nearby Essentially the same as AET 1, but is able to traverse short stretches of public road between different closed areas (Einride, 2020).
AET 3 Rural The vehicles are designed to operate on back roads and smaller main roads. The vehicle is limited to an operating speed of 45km/h and has a range of 200-300km (Einride, 2020).
AET 4 Highway The vehicles are designed to be able to operate on all roads, including highways. The vehicle is limited to an operating speed of 80km/h and has a range of 200- 300km (Einride, 2020).
AET 1 and 2 are already in production and can be installed early 2021, depending on their legality in the relevant jurisdiction. AET 3 and 4 are projected to be shipped in 2023. (Einride, 2020)
As was laid clear during interviews with representatives from the company, perspective from which Einride motivates their development of AV technology arises directly from their core mission to develop environmentally sustainable road freight transportation. Their position stems from a belief that this issue is of urgent importance for our continued existence on this planet. Their solution to this issue is to disrupt the fossil fuel industry by developing battery driven trucks and turning these into the new industry standard. However, these have a significantly higher initial investment than more traditional combustion engine vehicles, due in no small part to the cost of batteries. As previously discussed, though, AV technology has the potential to significantly lower the running costs of logistics operations, chiefly through the reduction of labour costs. As such, Einride sees two main incentives to implementing AV technology in their vehicles. One of these is that the lowered operation cost will be part of their tactical arguments for their solution being a sounder long term investment than traditional trucking. The other incentive is far more strategic in nature. The argument goes that if AV technology is first implemented in traditional trucks, this will leave customers even less incentive to switch over to sustainable alternatives. As such, it is essential for Einride to stay at the forefront of this technology in order to safeguard the viability of its core products.2
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Einride was the first fully driverless vehicle to be tested on public roadways. The tests took place in Jönköping, Sweden, in partnership with the logistics company DB Schenker. The test consisted of taking over a part of their logistical flow for the period of the pilot, in order to test the capabilities and adjust the algorithms of their vehicles. The portion of the test which took part on public roadways consisted of a road between two closed facilities. The stretch of road in question was approximately 300m long and the maximum speed of the vehicles on the public roads was limited to 5 km/h. Furthermore, Einride has entered a partnership with Coca Cola to perform a similar pilot. However, the stretch of public road used for these tests will be more than 2 km, and the maximum speed limit of the vehicles will be around 30 km/h. This pilot has already begun in 2020 with tests on closed areas and will continue, with the public road testing starting in 2021. 2
Volvo Group The Volvo Group is one of the world’s largest suppliers of transport and infrastructure solutions, headquartered in Gothenburg, Sweden. The range of products they produce is vast, but featured prominently among them are their lines of busses and trucks (“About us | Volvo Group,” 2020). The group presented their first prototype of an autonomous truck in 2016, and has since produced a number of concepts for autonomous heavy vehicles.
One of the most widely publicised Volvo Group concepts is Volvo Vera. The vehicle is a cabless, battery driven autonomous hauler for semi-trailers. The intended application of the vehicle are large cargo loads to be transported over short distances. Examples could include intra facility transports for large industrial facilities, as well as short trips between logistic hubs. The autonomous system is projected to result in high delivery precision. Several vehicles are to be monitored and directed from a central control hub; the system therefore requires high levels of connectivity (“Ground-breaking innovations | Volvo Group,” 2018).
Vera has been deployed in a pilot study at DFDS logistics centre in Gothenburg, Sweden. DFDS is Danish shipping and logistics provider, and as such the Vera concept was used to move shipping containers between their logistics centre and a container terminal at the port. The vehicles were limited to a top speed of 40km/h, and did not use public roadways. The testing is concluded, and took place through 2019/2020 (“Vera’s first assignment: Volvo Trucks presents an autonomous transport between a logistics centre and port,” 2019).
The Volvo Group has also developed autonomous transport systems for use in mining applications. One of the trials took place at the Boliden mining area in Skellefteå, Sweden. According to a representative from Boliden, this trial took place over the period of a year, with 6 testing periods of a couple of weeks at a time. Between these periods, adjustments and any repairs were made to the system. The trials took place in the tunnels underground. The vehicles were never allowed to operate completely unmanned, and the tests took place in areas completely cleared of pedestrian and other traffic. The vehicles had a limit of 30 km/h, which was the maximum speed limit of the mine tunnels in general. It was impossible to
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use GPS navigation underground, since the signals from the satellites were not able to penetrate the ground. As such, mapping the routes thoroughly beforehand was of paramount importance. This was accomplished chiefly through the use of LIDAR. After the tests were completed at the Boliden mines, Volvo continued testing at the Brønnøy Kalk AS chalk mine in Norway, where the system was later implemented as Volvo’s first commercial autonomous transportation solution.3
Waymo Originally a Google project, Waymo broke off as a separate company in late 2016, though it remains a subsidiary of the same parent company: Alphabet. Its trucking division, Waymo Via, was launched in march 2020. It is currently testing its fleet of class 8 trucks in a number of states in the USA: California, Arizona, New Mexico and Texas (“Via,” 2020). The company also sells their LIDAR system commercially (“Lidar - Laser Bear Honeycomb,” 2020). Waymo were among those developers of AV technology who denied requests to be interviewed, resulting in the relevant information about them being rather limited. However, as one of the developers most frequently discussed in media they have been deemed of a sufficient importance to include in this report.
14 1M. VAN HORIK, SCANIA, PERSONAL COMMUNICATION, 18-11-2020
2C. WALLBÄCKS, EINRIDE, PERSONAL COMMUNICATION, 30-10-2020 3P. WESTERLUND, BOLIDEN, PERSONAL COMMUNICATION, 28-10-2020
5 Regulations: current state and looking to the future
One important hurdle on the path to implementing an autonomous transportation system at the production facility is compliance with current Swedish regulatory standards. However, as AV technology is relatively new and progress is happening rapidly, there are likely great changes afoot. As such, there is value in also looking ahead to what the situation might look like as the industry matures. What follows is first a summation of the current regulatory reality for AV operation in Sweden, after which is offered a glimpse of one possible path we might take in the future.
5.1 PERMISSION TO PERFORM AV TESTS In order for a vehicle to be allowed on the public roadways in Sweden, it has to receive an official approval from the Swedish Transportation Agency. These approvals most commonly take the form of a “type approval” (typgodkännande), where the vehicle or component has been proven to meet the current engineering standards for safety and environmental sustainability. Currently, no AV systems above level 2 on the SAE classification are eligible for type approval in Sweden. However, one may apply for permission to test AV systems on public roadways through Transportstyrelsen once certain criteria are met (Swedish Transport Agency, 2017).
Transportstyrelsen are able to grant permission to perform AV trials on public roadways through a government ordinance (Riksdagen, 2021). A trial is in this case defined as operating an AV for the purposes of testing and evaluating the autonomous functions which cannot be certified through a type approval. The permission may be contingent on certain conditions being met. A time limit on the permission shall be put in place, although acquiring an extension is possible if needed (Swedish Transport Agency, 2017).
There are a couple of basic conditions which all applications must meet in order for the permission to be granted. First and foremost, the traffic safety of the trial has to be proven. It must also be shown that the trial will not cause any significant disruption or inconvenience to any third party. The permission is also contingent on the existence of a physical person to be held responsible for the trial adhering to the conditions set up through the permission documentation. In the case of a corporation, this is by default the CEO. During AV operation, there must also exist a human “driver” who has direct responsibility for the vehicle from the time it is activated until the time the system has been shut down. However, this driver need 15
not actually be physically inside the vehicle. Finally, a report detailing the results of the trial must be submitted to the Swedish Transport Agency. (Swedish Transport Agency, 2017)
5.2 APPLICATION FOR PERMISSION The application for permission is initiated through submitting to Transportstyrelsen a document outlining the trial. As well as the contact details of those responsible for the trial, the document must account for the following 9 aspects of the planned trial.
Aims and objectives These are mainly used in order for Transportstyrelsen to gain an understanding of the background of the trial. They do not have any particular bearing on whether the permission will be granted or not. 1 However, these aims and objectives should be reflected in the final report (Swedish Transport Agency, 2017).
Method A brief description of how the trial is to take place, as well as how it will be evaluated. Furthermore, the applicant must motivate why the trial must take place on the public roadways (Swedish Transport Agency, 2017).
Automated functions An accounting for precisely which automated (and, by extension, autonomous) functions will be tested and evaluated during the trial (Swedish Transport Agency, 2017).
Accountability A description of how responsibility, tasks and authority will be distributed through those responsible for the project (Swedish Transport Agency, 2017).
Competence and qualifications An accounting for the competence and qualifications of those who hold critical roles in the project (Swedish Transport Agency, 2017). These must be relevant and sufficient for them to responsibly perform the tasks for which they are held responsible; eg, a driver/supervisor of a level 3 autonomous heavy truck must hold a license to operate heavy trucks.1
Management of documents The documents which are important to the project must be handled and accounted for in a secure and appropriate manner (Swedish Transport Agency, 2017).
Accidents and incidents An accounting for the procedures to be put in place to investigate any accidents or incidents which may occur during the trial, as well as the measures and precautions taken in order to prevent these from happening (Swedish Transport Agency, 2017).
Communication A description of how the transmission of important information will be carried out throughout the project. This does not only concern those working directly on 16
different parts of the project within to organisation itself, but also the communication with outside entities including the Swedish Transport Agency, Swedish Transport Administration, other road users and any other third party upon who the project may have an effect (Swedish Transport Agency, 2017).
Risk management A description of any risks identified with the project, as well as measures to be taken in order to minimise these risks. There must also be a plan put in place to continually monitor and manage risks throughout the entirety of the project (Swedish Transport Agency, 2020).
5.3 STATE REGULATION: LOOKING TO THE FUTURE While the regulation for AV operation in Sweden currently leaves room only for testing, in the future, regulation will have to be drawn up to allow for the development and implementation of these systems for use on public roads. How this could take form may be gathered from looking at existing regulation for established industries. One industry which may be relied upon to set a good example is that of commercial passenger airlines. Although there are not yet any truly autonomous technologies out on this market, the technology includes a great deal of automation, which can be seen as a logical step on the path towards autonomy. In addition to this, the industry is known to use a highly effective methodology in order to minimise safety risks within their technology. Their accident rate reached an all time low in 2017, with one fatal accident per 16 million flights and numbers not showing a significant increase in the years since then (Cummings & Britton, 2019; Ranter, 2020).
In the USA, the regulatory body for new technologies entering the commercial airline market is the Federal Aviation Administration (FAA). In assuring the safety standard of these technologies are met, the FAA ensure that their regulatory certification is a part of the design process from the ground up. The certification process consists of five phases:
Conceptual design A series of “familiarization meetings” with the aerospace company take place in order that the FAA may take part in the goals and objectives of the new technology, as well as helping to identify difficult and critical areas to focus on during the certification process. A “Project Specific Certification Plan” is also commenced as a part of this phase (Cummings & Britton, 2019).
Requirements definition Focus is placed on how exactly the aerospace company is going to adhere to the specific regulations set up for the industry by the FAA. In cases where the existing regulatory framework is insufficient or inapplicable to the new technology, “special conditions” may need to be implemented. Attention can be brought to
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these issues at this early stage in order to help streamline this time consuming process (Cummings & Britton, 2019).
Compliance planning The “Project Specific Certification Plan” is completed, where airworthiness standards are completed and testing is planned in order to prove adherence to said standards. During this phase, an employee of the company is designated the “Designee”, and as such is given some certification responsibilities by the FAA. The role is also meant to streamline the communication between the FAA and the company, and as such to help reduce the energy, time and money needed for the regulatory process (Cummings & Britton, 2019).
Implementation The aforementioned partners work together to assure that the certification requirements are satisfied. This is accomplished to a large degree through testing where, for a test to be considered valid by the FAA, its methodology and scope will have to be planned and developed jointly by the company and the FAA, and accepted by the latter (Cummings & Britton, 2019).
Post certification The goals achieved in previous stages are secured and sustained through maintenance and awareness of safety and regulatory standards, as well as continual adherence to approved manufacturing processes. The Designees perform subsequent inspections and submit these for approval to the FAA (Cummings & Britton, 2019).
While this process is certainly effective in reducing the risk of accidents tied to the technology, the system is inherently cumbersome: the type certification proces may take as much as 8 years from the conceptual design to implementation, and the post certification process is associated with further costs and time expenditure. (Cummings & Britton, 2019) Here, the give and take between a costly and lengthy certification process and the ease of bringing new technology to the market bears some thought. While it is essential that new technologies not be unnecessarily burdened by regulation in order to allow for efficient progress, the backlash of potential future safety failures may cause more harm to technological advancement and could prove to be a greater hinder to wide market acceptance in the long run (Cummings & Britton, 2019).
While the overall procedure of regulation for AV might well be similar to that of existing certification processes in established industries, the use of autonomy ensures that the content of such regulations will need to be different. Autonomous systems leverage the use of probabilistic reasoning in order for the system to operate and make decisions; an area of technology which has previously never been regulated. As a result of this, any such endeavours will necessarily prove to be a venture in uncharted territory (Cummings & Britton, 2019).
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5.4 LICENSING AS A MODEL FOR CERTIFICATION In regards to certification processes in existence today, perhaps one of the closest parallels of AV certification could be drawn to licensing professional human drivers and pilots. The point of such licensing is to ensure that these operators are able use the technologies at a sufficient level of efficiency and safety. While there are aspects of the licensing procedures which are not applicable to AV technology, there are also many many features where direct comparisons may be drawn (Cummings, 2018).
The first testing phase of the human licensing procedures is usually a physical examination, the most usual being a vision screening test. Vision, whether it be through LIDAR, Radar or just visual light cameras, is of course equally important for AV. While the equipment itself must of course function perfectly, one issue which requires special attention in the case of AV is that of perception. The tests would have to be constructed to prove that the equipment can not only sense obstacles and signals in varied weather and lighting conditions, but also to prove that the system AI correctly identifies these as such (Cummings, 2018).
The next stage in human licensing is usually a knowledge test, with regards to laws, rules of the road and official recommendations for safe operation of the vehicles. In the case of AV, achieving competence in this area would seem to be comparatively straightforward. This knowledge would be a part of the AI programming from the start, and would be used to inform the machine learning algorithms and probabilistic reasoning engines as to what their ideal state should be. As rules change, this knowledge would have to be updated within the software. The systems compliance with these rules could then be fairly easily tested through simulations or test tracks (Cummings, 2018).
The final stage of the human licensing process is usually what is known as a “checkride”; the license-seeker is tasked with operating the vehicle while an observer monitors and evaluates the performance. In the case of AV, this may be done either through test tracks or through simulation; presumably the most effective method will be to use both methods to test different aspects of the system’s capability. An aspect of testing which is of particular interest to the certification of AV is where the focus of the testing should lie. The tests performed for the usual drivers licence is designed to test the driver’s abilities to operate the vehicle over the minimum levels of safety required under normal conditions. In the case of AV, the way the AI operates results in a great aptitude at performing low level driving skills consistently in normal conditions. A more effective way of testing AV would be to expose them to extreme situations with decision making under great uncertainty, similar to the testing pilots undergo to show their proficiency at handling emergency situations (Cummings, 2018).
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1A. ERIKSSON, SWEDISH TRANSPORT AGENCY, PERSONAL COMMUNICATION, 10-14-2020
6 Hazard analysis of the proposed route
Between the production facility and the Skellefteå harbour runs the 372, a county road specifically designed for heavy transport. What follows is a breakdown of some of the most significant hazards for safe AV operation, as well as an assessment of their prevalence along the proposed route.
6.1 THE PROPOSED ROUTE: SCOPE AND LIMITATIONS The journey between the site of the production facility and Skellefteå harbour along existing roads is currently approximately 11 km long. At this point only an approximation of the length of the route can be produced as the exact layout of the route has yet to be decided. Due to these uncertainties, this hazard assessment will focus on the portion of the route along the 372, which is to remain relatively unchanged in the near future (see fig.7). However, the uncertain portions of the route and the current plans for these are discussed below.
Fig 7. The stretch of the 372 between Torsgatan, leading to the production facility, and Skellefteå Harbour 20
The passage between the production facility and the 372 is one of the portions of the route where major changes are due to occur prior to the factory's operation. Currently, there is uncertainty as to where exactly on the client’s premises the entrance and exit for transportation lorries will be located. It is however certain to be connected to Torsgatan: a minor road connecting the factory to the 372 (see fig. 8). At the moment, this consists of no more than a dirt road with a speed limit of 30 km/h, though it will most likely be upgraded to a limit of 50 to 60 km/h prior to the commencement of operations at the production facility. Torsgatan currently connects to the 372 by a one way junction of type A, directing all traffic away from the harbour. It will be replaced with a type D junction (a roundabout) in conjunction with the additional changes to Torsgatan.1
Fig. 8 Torsgatan from the production facility to the Harbour
The access point to the harbour, as well as the harbour itself, are important sections of the route where there exists a certain amount of uncertainty. Currently, the entrance to the harbour area lies on the southeasternmost stretch of the 372, on the southern side of the harbour bay, and consists of a type A junction followed by a railway crossing. However, in the 4th quarter of 2024, the harbour on the northern side of the harbour bay is projected to be operational. To reach this harbour travelling from the production facility one turns left off of the 372 at the 13th junction, then continues along Näsuddsvägen until one reaches the harbour. (see fig. 9) In the case of either harbour, the precise location of the the client’s container depots, or indeed which harbour is to be used, is undecided as of yet and may well change depending on the needs and volumes of both the client and the other customers of the harbour.
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Fig. 9 The two possible harbour locations
6.2 LANE CHANGES AND OBSTACLE AVOIDANCE While necessary for normal vehicle operation, lane changes pose risks as vehicles may need to use a lane where there is a possibility of traffic coming from the opposite direction. The risks involve both lane changes by other vehicles as well as the AV, on top of which there lies the ever-present risk of other drivers acting in unpredictable ways. Scenarios where lane changes may occur include:
- Other vehicles overtaking the AV - The AV needing to overtake a slow-moving vehicle - The AV needing to pass a planned obstacle, such as road works - The AV needing to pass or avoid temporary spontaneous obstacle, such as a fallen tree
Several variables come into play when assessing the hazards that such manoeuvres pose. One of the principal factors is the speed at which vehicle is expected to travel. This has an effect on a majority of the hazards which one must take into account when operating a vehicle. In the case of the client, the vehicles would be used to transport 40 ft shipping containers with even an empty weight above 3.5 metric tonnes, meaning that they would be considered “heavy lorries”. (“40-foot Container - Dimensions, Measurements and Weight,” 2020) As such, their maximum speed would legally be 90 km/h on highways and expressways and 80km/h on all other roads. This would cause them to be especially prone to overtaking on roads with speed limits higher than these. However, the maximum speed limit along the proposed route in Skellefteå is 80 km/h, so as long as the AV could travel at these speeds the need to overtake them by other vehicles should be minimal. While the majority of the route retains this limit, the limit varies with sections as slow as 50 km/h in the more populated areas. (see fig. 10)
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Fig. 10 Speed limits and yearly average daily traffic (Swedish Traffic Agency & Ramström, 2019)
The 372 has a width varying from 7 to 14 metres (Swedish Traffic Agency & Ramström, 2019). The road has two lanes throughout: one for each direction of travel. However, there is a shoulder with broken lines running during the entire high-speed portion of the road, from the factory until junction 8. (see fig. 11) The road is rated for the heaviest vehicles possible under Swedish standards, and is a preferred route for heavy traffic (Swedish Traffic Agency & Ramström, 2019).
Fig. 11 Road width of the 372 (Swedish Traffic Agency & Ramström, 2019) 23
6.3 JUNCTIONS Junctions, in this case defined as the place where two or more roads meet, pose many risks to the operation of AV as there is a larger probability of contact with other vehicles crossing the path of the AV, and vice versa. The portions of the route most likely to initiate the most frequent interactions with other vehicles are likely the exits of the harbour and the factory, since these junctions require exiting or entering the main road. As with the lane changes, other drivers may act unpredictably.
The 372 is a primary county-road (primär länsväg) and is a priority road throughout the portion of the road which lies between the production facility and the harbour. As such, any vehicle travelling on the road would have the right of way at all regular junctions along it, unless otherwise stated. However, any AV travelling on the 372 must at all times be able to stop completely in case of vehicles whose drivers break the rule regarding right of way.
At present, there are 16 junctions along the relevant section of the 372, discounting the junctions needed to enter/exit the 372 from the factory or harbour. (see fig. 12) In the case of all of these junctions, the right of way always lies with the vehicles on the 372. However, there are some variation in the ways this rule is handled at each of the junctions.
Fig. 12 Position of junctions along the 372
The junctions on the high speed section of road closest to the production facility all use a “stop” sign to signal the right of way of the vehicles on the 372. (see fig. 13) 24
The effect of this is that, according to the law, all cars must come to a complete stop before assessing the traffic situation on the 372, and subsequently joining in a safe manner. This portion of the route has a speed limit of 80km/h, with stretches with a limit of 60km/h immediately surrounding the junctions. All except one of the junctions are of type C, and therefore use a left turn “pocket” to allow cars wishing to exit the 372 to do so safely and without disturbing the remaining traffic flow in their lane. Throughout this section there also exists a shoulder which may be used for vehicles wishing to make a right-turn exit from the 372 to do so in a similarly safe and efficient manner. There are 7 junctions on this stretch of the route.
Fig. 13 Type C junction with a stop sign
The remaining 9 junctions of the route give way to the vehicles on the 372 utilizing a “give way” sign as opposed to “stop” signs. (see fig. 14) The effect of this is that it is up to the drivers to assess whether it is necessary to bring the car to a complete stop before attempting to join the 372. This section of the route starts with a type F interchange, which can be thought of as the end of the high speed portion of the route with a shoulder on both sides of the road. After this, the junctions consist of a mix of the types A, B and C. The speed limitations are lower, but the proximity to residential neighbourhoods increases.
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Fig. 14 Type A junction with a “Give way” sign
A number of the junctions throughout the route have been deemed insufficiently safe for both vehicular and non-protected road users by the Swedish Transport Administration. Road 372 has been proclaimed as one of the 100 most dangerous in Sweden. In the period 2000-2018, 269 accidents have been reported, with 8 fatalities. 102 of the accidents, and 5 of the fatalities, happened on or in the area immediately surrounding a junction (Swedish Traffic Agency & Ramström, 2019). The problems are to a large degree due to the residential neighbourhoods north and south of the 372, which are the reason for the many junctions and ensure that they see relatively heavy traffic. This, in combination with the high speeds of vehicles travelling on the road as well as the traffic travelling from the port area towards central Skellefteå, contributes to many hazardous situations.2
There is a railway line running from the harbour, which crosses the route twice. The closing of a railway crossing due to an approaching train is signalled both through the closing of the gates as well as blinking lights. The railway is only used by cargo trains.
6.4 NON-PROTECTED ROAD USERS Since they are smaller, more irregular in shape and can be more unpredictable than motorised vehicles, pedestrians and cyclists are a great challenge when it comes to operating AV. Children are of special concern, due in part to them being even smaller and less predictable, but also since the PR consequences of any accident may be dire. Although less frequently encountered and with less severe consequences if an accident were to happen, pets and wild animals also pose a risk.
Pedestrian crossings are one of the road features which indicate a more concentrated risk of accidents. This is due to the fact that pedestrians and motorized vehicles have to share this space, and thus regularly have to cross paths. In the case of the 372, there only exists one crossing which is specifically marked such, in this case by way of striped ground markings. Here, a traffic light controls the right of way of the road users, raising the predictability of the pedestrians’ actions, as well as clear markings where the motor vehicle on the 372 is meant to stop in case of pedestrian priority. This pedestrian crossing is located on the 26
portion of the route closest to the harbour, in direct conjunction with residential neighbourhoods and with a speed limit of 50 km/h. The crossing connects the residential neighbourhoods on either side of the 372. There is a gas station with a shop on the northern side of the nearby junction, which may be accessed by pedestrians from the southern side by use of the pedestrian crossing. (See fig. 15 and 16)
Fig. 15 Location of the pedestrian crossings throughout the route
Fig. 16 Pedestrian crossing with traffic light 27
At two separate junctions there are unmarked sections clearly used as pedestrian crossings. The speed limit through these junctions is in both cases 60 km/h, reduced specifically for the junctions from the 80 km/h limit of the sections of road immediately surrounding the junctions. This can be seen through a gap in the curb used to separate the two lanes of opposite direction of the 372. Since these crossings are unmarked, the right of way remains with vehicles travelling on the 372. (Svenska Trafikutbildares Riksförbund, 2019)
In addition to the pedestrian crossings, there are a number of other features along the route which may lead to higher concentrations of pedestrians. In the main, these consist of businesses and services which normally experience a significant number of customers who visit in person, such as a supermarket, a gas station with an attached convenience store, and a health clinic. It is worth noting that the health clinic includes a family clinic, meaning that there may be a higher likelihood of the presence of children around this feature. On the subject of children, it is worth noting that there are several schools in the area, although not with entrances directly on the 372. All of these are separated from the 372 by a block of residential buildings with the exception of Ursviksskolan. Its sporting and outdoor facilities are separated from the 372 by only a minor road and a ditch. (See fig. 17)
Fig. 17 Points of concentration of pedestrians
Bus stations are another possible point of concentration for pedestrians. Currently two of these exist along the route: one on the western side of the route near
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junction 1. This one lies in direct conjunction with a pedestrian tunnel, which allows passengers to cross the 372 without exposing themselves to traffic. Additionally, one bus stop lies on the eastern portion of the route near junction 15 and another lies just 50 metres to the east of the entrance to the harbour. Neither of these lie in the vicinity of any marked pedestrian crossings to allow passengers to cross the 372. (see fig. 18)
Another group of road users which may be encountered on the route are cyclists. There exists a network of bike paths running parallel to the 372, directly next to the main road and stretching into the paralell residential streets. (see fig. 18) Otherwise, one may assume that cyclists may concentrate and attempt to cross the 372 at similar places as pedestrians.
Fig. 18 Bus stops and bike paths along the 372 (Swedish Traffic Agency & Ramström, 2019)
Collisions with wild animals are a further hazard inherent to the 372. Between the years 2010 and 2018, 115 recorded accidents involving wild animals and reindeer occurred along the 372 (all reindeer in Sweden are technically domesticated animals, though during part of the year many are free-roaming). Apart from reindeer, all the other recorded accidents occurred with moose or roe deer.
6.5 WEATHER AND SUN When operating an AV in the world domain, there are a number of external environmental factors which affect the safety of the vehicle. Precipitation of all kinds is known to affect road safety in most vehicles. When falling in the air, it can affect vision, and once on the road it usually has a deleterious effect on vehicle handling. These things are true for AV as well. The sensors chiefly used for world perception during operation are LIDAR and cameras. Both operate through recording data through visible light, and so are understandably fragile at during times of heavy precipitation or reduced visibility. The data from a LIDAR sensor is
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used to measure distances from the vehicle o points on the world immediately surrounding it. It does so by emitting laser pulses and measuring the time taken for this signal to be turned. Were these pulses to be deflected by rain or blocked by snow or fog, the sensor would understandably return false information about the true nature of the obstacles surrounding the vehicle. Cameras also record images of the world surrounding the vehicle, and are equally affected by poor visibility. However, cameras are also used for object recognition for such crucial pieces of information as road signs and road markings. A layer of snow covering the ground and infrastructure could severely obstruct the object recognition algorithm, and as such hamper the systems ability to perceive crucial elements of the world around it. (Stock, 2018; Mardirosian, 2020)
The amount of precipitation in Skellefteå can be seen in the charts below. Fig. 19 shows the average amount of precipitation per month throughout the year. Here, we can see how the summer is the season with the most precipitation, with June, July and August all receiving above 60 mm each. The lowest precipitation is seen in the early winter and spring. These trends are echoed in fig. 20, which shows the probability on any day of the month experiencing more than 10mm of precipitation. The months of June through September lie on about 6%, meaning that on average one would expect approximately 2 days of heavy precipitation each of these months.
140 120 100 80 60 40 20
Average precipitation (mm) 0 1 2 3 4 5 6 7 8 9 10 11 12 Month of the year
Fig 19. The average monthly precipitation in Skellefteå. The bars show standard deviation (SMHI, 1989–2020b)
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8.00% 7.00% 6.00% 5.00% 4.00% 3.00% 2.00% 1.00% Probability of >10mm rain 0.00% 1 2 3 4 5 6 7 8 9 10 11 12 Month of the year
Fig. 20 The probability of experiencing daily precipitation of more than 10 mm on any given day each month in Skellefteå. (SMHI, 1989–2020b)
Winter conditions have a great impact on all road users. The Swedish Transport Agency define winter conditions as the existence of snow ice slush or frost on any part of the roadway (“Vinterdäck - Transportstyrelsen,” 2020). This means that at any time when the daily minimum temperature is below zero, there is a risk of winter conditions (see fig. 21). Another clear indicator of winter conditions is snow cover. As can be seen in fig. 22, Skellefteå experiences snow from the month of October to the month of May, and from December to mid April snow is almost a certainty.
40 Average Lowest Temperature (°C) 30 Average Highest Temperature (°C) 20
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0 1 2 3 4 5 6 7 8 9 1 1 1
Temperature (°C) Months of the year -10 0 1 2
-20
-30
Fig. 21 Average daily maximum and minimum temperatures throughout the year in Skellefteå. The bars show the standard deviation. (SMHI, 1989–2020a)
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0.8 0.7 0.6 0.5 0.4 0.3
Snow depth (m) 0.2 0.1 0 1 2 3 4 5 6 7 8 9 1 1 1 0 1 2 Month of the year Fig. 22 Average snow depth through the year in Skellefteå. The bars show standard deviation. (SMHI, 1989–2020d)
The Swedish Meteorological and Hydrological institute describes visibility as a measurement of the airs transparency, and defines it as the largest distance at which a dark and sufficiently large object is recognizable with the sky as a background. Fog is defined as visibility under 1000m (“Sikt,” 2020). As can be seen in fig. 23 and 24, Skellefteå experiences a higher probability of fog at night, as well as during autumn and winter.
5.00% 4.50% 4.00% 3.50% 3.00% 2.50% 2.00% 1.50%
%probability of fog 1.00% 0.50% 0.00% 0:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 Time of day
Fig. 23 The probability of fog throughout the day in Skellefteå. (SMHI, 1989– 2020c) 32
40
35
30
25
20 Hours 15
10
5
0 1 2 3 4 5 6 7 8 9 10 11 12 Months of the Year
Fig. 24 The average number of hours each month experiences fog in Skellefteå. (SMHI, 1989–2020c)
There are reports of the sun having an impact on the effectiveness of autonomous vehicles camera systems. The problems occur when the sun is low in the sky, as the brightness of the image and lens-flare can cause the system to fail; for instance a it may miss a traffic light. (Williams, 2016) As Skellefteå is located relatively far north, on the 64th parallel, the sun remains low in the sky for larger portions of the day than it would closer to the equator (“Skellefteå, Sweden latitude longitude,” 2015).
6.6 COMMUNICATION Most, if not all AV technologies rely on digital communication for safe and consistent operation. The most appropriate communications technology for the case at hand is connection through a 4G or 5G network. If public networks were to be used, Telia currently holds “very good” coverage throughout the route. The meaning of “very good” coverage is coverage using the 4g+, or LTA Advanced mobile tecommunication standard (“Täckningskartor - Telia.se,” 2020). The significance on this standard in this scenario is the ability to connect to several 4G towers simultaneously in order to achieve larger bandwidth than one could by only connecting to one tower (Thomas, 2020). In the case of Telia, this means a normal bandwidth of 20-60 mb/s, and a theoretical max of 300mb/s (“Täckningskartor - Telia.se,” 2020). The only widely used technology which would provide higher badwidth is 5g. Sources in the municipality claim that Telia will be the first carrier to implement such a network in Skellefteå. However, as of yet there have not been any announcements about a timeframe for this expansion.1
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6.7 DANGEROUS GOODS The majority of the client’s cargo will consist of dangerous goods of some form, according to representatives within the company.3 While the 372 is considered a “recommended route” for dangerous goods by the Swedish Transport Administration, the risks associated with such cargo still pose issues when operating AV (Swedish Traffic Agency & Ramström, 2019). In the case of an accident, for example, dangerous goods may increase the severity of the situation and complicate the response.
A dialogue was initiated with the Swedish Civil Contingencies Agency (MSB) in order to discuss the matter.4 From that discussion, it was made clear that there had been no previous trials in Sweden where an AV had transported dangerous goods. Therefore, there are no concrete precedents to refer to as to how the existing regulations might be applied to the case of AV. However, the source was able to draw some conclusions from the way the current regulatory system handles the transportation of dangerous goods with regular trucks. As such, the source supplied a series of issues which may prove particularly difficult to overcome, as well as recommendations as to how operating an AV might be possible.4
The current regulatory framework has no way of handling a truly driverless vehicle; in order to operate such an AV, significant changes to the regulation would have to take place. However, trials where the vehicle operates autonomously but there is a supervisor present inside the vehicle would be possible under the current regulations so long as the supervisor had all the necessary training to transport the goods in question. Issues would likely arise if this supervisor were to control the vehicles remotely. In the case of an accident, it would be problematic for such a supervisor to properly secure the area surrounding the accident in order to protect third parties from the effect of the dangerous goods. The security of the system would also have to be proven, so as to secure the truck from potential hijacking or other theft of the cargo.4
An initial recommendation was to avoid the transportation of explosive goods. The reasoning was that both the safety and security ramifications of such goods meant that the regulations need to be so stringent as to prove entirely prohibitive towards the utilization of any but the most tried and tested technologies. Furthermore, any goods requiring tankers would pose a problem, as tanker trucks have to be type certified. 4 As the conditions for receiving a permission to perform trials from the Swedish Transport Agency is contingent on the certification of the vehicle not being possible under current type certification parameters, this obviously causes a conflict between these regulatory requirements (Riksdagen, 2021).
1 H. A NDERSSON, SKELLEFTEÅ MUNICIPALITY,34 PERSONAL COMMUNICATION, 03-12-2020 2L. RAMSTRÖM, S WEDISH TRANSPORT ADMNINISTRATION, PERSONAL COMMUNICATION, 18-12-2020
3 A. BRUNDIN, THE CLIENT, PERSONAL COMMUNICATION, 30-10-2020 4K. STRÖM, MSB, PERSONAL COMMUNICATION, 24-11-2020
7 Discussion
In the case at hand, it will be some time before an autonomous system will be able to handle the transportation needs from the facility to the harbour. Currently, there are neither possibilities under Swedish regulation, nor a system on the market where the thechnology is sufficiently advanced enough to handle such a route. However, there are possibilities to perform trials on public roadways. This section of the report will analyse the potential of the the client’s route to be used for such a trial. After this, the future of autonomous transportation will be discussed.
7.1 AV TRIALS AT THE PRODUCTION FACILITY While the technology of autonomous road transportation shows potential for a great many advantages over traditional, human-controlled options, there are still significant hurdles to overcome before these benefits may be reaped. While opinions differ on what and how severe these hurdles are, there is a generally held consensus that the most important part of developing these systems must be safety. This stems not only because of the fact that autonomous vehicles are safety-critical systems, where failure can, and have, resulted in death and severe damage to equipment, infrastructure and the environment (Gonzales, 2019). A large part of true cost of safety failures may very well be the knock on effects from poor public relations. As a new technology entering into a space already used by millions of people each day, and stringently regulated by the state, the public perception is likely going to be a determining factor in the speed at which this technology may advance. In fact, the technology of artificial intelligence, which is central to that of autonomous vehicles, has proven itself highly dependent on public perception. Throughout its history, it has been notorious for suffering “AI winters”, periods of low public confidence in the technology, where funding and investment dried up and technological progress thus slowed to a crawl (Schuchmann, 2020). As such, it is imperative that any company pursuing this technology truly understand the limits thereof in order to be able to make decisions which prioritise safety above all else. Otherwise, an ill-judged attempt to implement AV may in fact severely retard the advancement of the technology for the entire industry, thereby incurring a significant opportunity cost in regards to the benefits which humanity may have enjoyed.
A natural follow up question to this might be: what could cause AV systems to be unsafe? Among the benefits of AV systems being discussed by most proponents of the technology is the improved levels of safety possible through eliminating human error. However, it is important to understand that an AV system is capable of causing errors all on its own. What then, one might ask, would cause an AV system to err? In the interviews with academics, users and developers of the technology, all have claimed the same thing: uncertainty. The AI systems making the decisions in these vehicles, steered by the knowledge base gained by machine learning, excels at computing the correct decision when faced with known variables. However, when faced with the unknown, these systems are completely unable to
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use inductive reasoning to fill in the blanks. If they have not been given the chance to simulate or test the input they are presented with thousands of times prior to exposure on the road, they are completely unable to form a plan with which to tackle it. Dr. Cummings of the Duke Pratt School of Engineering discusses this at length in her work, and highlights the large gap between what is possible through artificial intelligence and what is necessary in order to operate at the level of reasoning humans are able to (Cummings, in press).
As a result of this, the most important factor affecting the ability of AV to operate safely is controlling the environment in which it operates. This is one of the main reasons that the technology first found widespread use in mining. Not only does this use-case place the systems in a closed area; this area is exceptionally closely monitored and controlled from the get go. Because of the dangerous nature of the industry, safety is “in the spinal chord”, as one source from the Boliden mine put it.1 Algorithms for safe operation already exist for all entities within the environment, such as precise limits of where humans and machines are able to go and exact timeframes of when they are able to do so. Due to this, operating an AV safely within this domain is a relatively trivial case of integrating its algorithm with the other well-defined and documented safety algorithms already in use.1
With this in mind, the hazard analysis performed on the the client’s case can be considered in order to deduce what an AV system would need to be capable of in order to run this route, as well as which portions of the route would cause the most significant difficulties. The first thing to note is the speed of the vehicles on the 372, as this has an effect on both the time the system has to compute its environment, as well as the severity of any mistakes committed while doing so. The majority of the route has a speed limit of 80 km/h. This is the maximum speed of the type of heavy vehicles the client has decided to acquire to run the route: at least for the type of road in question.
This is essentially the worst-case scenario for an AV system operating in Sweden. Although such a truck could legally operate at a higher speed of 90km/h on a highway or two-lane expressway, these roads have several features which facilitate operating at such speeds. There are usually two lanes to facilitate overtaking. Junctions are usually interchanges and never designed to cross the road, only to join it or leave it. Slow moving traffic is prohibited and significant measures have been put in place to separate pedestrians, cyclists and wild animals from the roadway. In short, these roads have been designed to raise the predictability of the users by limiting the range of speed they travel and the directions traffic my come from, in order to be able to operate vehicles at a higher speed with a greater margin of safety. The 372 incorporates few of these adaptations, and the ones that do exist are sporadically distributed throughout the route.
This brings us to the crux of the route, and a large part of what makes it one of Sweden’s 100 most dangerous roads: residential neighbourhoods surround it on both sides. This in and of itself does not necessarily pose a problem, as there obviously exists roads in residential neighbourhoods which are perfectly safe. However, problems start arising when with the high speeds on the 372 are
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combined with the lack of separation between the traffic on the 372 and the residential traffic. While there are a small number of tunnels along the road, the majority of the roads running adjacent to the 372 do so in the form of junctions. Here, the motorists, cyclists and pedestrians alike enter the same space as the traffic of the 372, and thereby cause severe variations in the range of speeds of all vehicles involved. When discussing the matter with a representative from the Swedish Transport Administation it was made clear that this is one of the reasons for the large accident rate which has plagued the road.2
The weather in Skellefteå is another factor which would need to be taken into account when deploying an AV system on the route. What we may look for where this is concerned is not only the effect the weather phenomena would have on the AV, but also the degree to which one may predict when and how these occur in order to be able to handle them effectively for use in developing AV systems. Precipitation for example, as we may see from fig. 19, experiences a definite increase in the summer months. However, when taking into account the standard deviation of this data, a conclusion may be reached that there is so much variance in amount of precipitation that this trend is not very reliable. For instance, in July, the month with the highest amount of precipitation, the lowest bound of one standard deviation under the mean is well within the upper bounds of April, the month with the lowest precipitation; in fact, the lower bound of July is less than 5 mm off the mean of April.
When taking variance into account, what may be gathered from the data? Concerning the snow cover and winter conditions, one is safe in assuming that these are not going to be factors from mid May until mid October, as this has never occurred since the year 1989. The opposite is true for the period from December to April, which have historically always experienced some snowfall or temperatures below freezing. In the case of fog, although there are clear trends, the proportion of time these conditions exist cause their effect to be negligible in regard to using these to test an AV system: less than one day in 20 will experience fog at 1 am, which is the hour at which the probability of fog peaks.
When taking all these factors into account, what conclusions may be drawn about the route from the production facility to the harbour for use in trials developing AV technology? There are some interesting features of the route, which could be of some use to researchers. The route presents a great variety of speeds and traffic situations, all of which any complete AV system would be expected to deal with. There are certainly dependable winter conditions, which could be very useful in testing such features. The fact that the route is located so far north also means that the sun is lower in the sky for a larger part of the day. This could be useful in developing camera and LIDAR systems to be able to cope with these conditions, which is known to be a factor they have struggled with in the past.
However, this great variety of conditions also pose some problems in regard to the route’s use as a trial case. The first thing which is taught in most primary school science classrooms in regard to experimental trials is that for the experiment to
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produce valid results, all variables must be controlled except for the independent and dependent variables. Though a great simplification, the same principle may be applied to trials of AV. If a system is to be developed to cope with urban conditions, a route should be selected where the speed is kept low in order to minimise the severity of any failings of the system. Likewise, a trial to test the ability of a system to cope with weather conditions would choose a route where there were no other significant hurdles to overcome. If one adheres to this principle, the testing which could take place on the route would necessarily be of a highly advanced system. In order for there to be any value in testing a system there, the system must already be able to cope with all of the “controlled variables”of the experiment. In the client’s case, it is imaginable that the only real testing which should be performed would be a final polish of a system, to verify how it well it functions under combinations of some of the more arduous conditions it would be expected to cope with.
In addition to the usefulness of performing trials on the route, the appropriateness and desirability of such a project must be considered. The first thing one may note here is the importance of the 372 to Skellefteå. The road is the preferred route for heavy traffic from the harbour to the numerous industries in the municipality. As such, any disturbance to such traffic due to the needs of such a trial would necessarily come at a large economic cost. In the statute permitting trials to be performed, one of the conditions which must be met is that the trial must cause no significant disturbance to usual traffic (Riksdagen, 2021). There are doubtlessly routes upon which to perform a trial where the level of disturbance which could be classified as significant would be far more lenient. In addition to this, there is an aspect of the case for the client which further complicates the situation: the transportation of dangerous goods. The great majority of the client’s cargo will be classified as dangerous goods. This is a factor which is quite unrelated to the operation of any AV system, but nonetheless would complicate the regulatory situation for any trial considerably, as indicated by a representative from the Swedish Civil Contingencies Agency.3 In addition to this, the ramifications of any accident occurring during the trial may be much more severe; an accident which, if the cargo was not dangerous, may have resulted only in the AV itself being damaged could turn into an ecological, economic and humanitarian disaster. If such a thing were to happen, it is reasonable to assume the public relations damage done to the autonomous transportation industry would likely be equally great.
Another thing to appreciate when considering implementing such a trial would be the disturbance it would cause the production at the facility. When discussing their trial with the Volvo system, a representative from Boliden claimed that testing new technologies as a part of active production systems always results in a great disturbance to the running of the affected parts of those systems. On top of this, it was noted that the trials were much easier to perform if they could be completely separated from the other production processes, as those would likely interfere with the needs of the trial and vice versa.1 As such, one could argue that these trials should only be considered for the client once their own system is robust enough to be able to handle disturbances to the supply chain without incurring a great cost. As a part of this, the scope of the possible disturbances must of course be studied
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and possible mitigations must be developed. However, the impact of performing trials on such a crucial part of the the client’s supply chain must be fully understood before any such activities may take place.
7.2 THE FUTURE OF AUTONOMOUS TRANSPORTATION History is full of examples showing the wisdom of exercising caution when making predictions. With this in mind, what can be said of the future of autonomous transportation? To start off, it is important to establish the capabilities and limitations of the technology today. As it turns out, it is exceptionally hard for an outsider to say exactly what the existing systems are capable of. Press releases and journalistic articles often describe the routes used and the sensor technology in a fair amount of detail, and usually go to great lengths to proselytize the importance of the technology and the great potential it has; yet these seldom go into much detail about the degree of autonomy the system is capable of, except perhaps that it reaches SAE level 3. It is understandable that these companies would prefer to keep the secrets of their systems close to their chests as this is a highly competitive field where a real breakthrough could lead to a complete paradigm shift in the transportation industry. However, there is of yet very little evidence that any of the projects being worked on today being close to this level of technological readiness. One thing which is certain is that none of those involved in developing the technology today are bold enough to claim that their technology is operating at SAE level 4 autonomy on public roadways. This level, where the machines are able to operate completely unsupervised so long as some specified conditions are met, is a great divide beyond which a huge number of the potential benefits of the technology are unlocked.
What, then, are the barriers which prevent the systems of today from being able to operate at this level? Why has no AV been shown to perform to the same level of safety which humans are able to do on public roadways? According to Dr. Cummings, by far the most significant hurdle is the ability of AI to satisfactorily perceive the world around it. The strategy employed to try to accomplish this is machine- or deep learning. It is true that this technology can certainly perform incredible feats of what could be called “intelligence”, and has shown an incredible progression in recent years. However, the method by which it operates is, at the base level, incredibly simplistic and shallow. It develops the algorithms by which it accomplishes its tasks through analysing countless examples and trials, and thereby selecting the decisions which give it the greatest probability of reaching its goals. This works astonishingly well for operating in spaces where all the inputs are recognizable by the computer. Examples in gaming, such as Alpha Go, which was able to beat the greatest human players of the board game Go, and Open AI success with the competitive esport DOTA 2, where the AI system was able to beat a team comprised of the best players in the world. Both of these systems have a possible set of states too vast to all be computed and tried by the AI; Go has a number of possible games which far outnumbers the particles in the universe. However, they are able to selectively try out more and more effective decision trees in order to develop an algorithm which is essentially unbeatable by humans (DeepMind, 2020; Statt, 2019).
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The domain in which any autonomous transportation AI has to operate is different than those which it has hereto conquered. In the case of both Go and DOTA2, while the states of these systems may be incalculably large, at the very least the possible inputs into the AI system is limited enough that the system may reasonably handle all the relevant permutations. In the case of AV, even the inputs themselves are immeasurably complex, as they are images of the physical world around them. It is for this purpose that perception systems have been developed in order to make sense of the information the sensors are providing. This is usually unproblematic as long as the images are of things it the system has been exposed to before, and therefore can recognize. However, the complexity of the real world is such that the perception system is liable to make mistakes a human would not. The difference that gives humans the upper hand in this regard is reasoning by inference. An example Dr. Cummings provides of a problem an AI system has had trouble dealing with is the Kanisza illusion seen in fig. 25. A human is easily able to identify the star made up of the negative space left by the other shapes; an AI system is likely to only ever perceive those black shapes. A parallel more applicable to an AV system might be a road sign partially obscured by snow or a tree branch (Cummings, in press).
Fig. 25 The Kanisza illusion (Cummings, in press)
So what, then, is the solution? One possible strategy would be to believe that the deep learning methodology is sound in and of itself, and that with sufficient time the AI can be given enough data to parse so as to be adequately skilled at perceiving the world. Dr. Cummings disagrees with this hypothesis. She posits that the computational burden of this method is too large and the method is not effective enough to make significant progress on this application. Instead, the answer would likely lie in the development of a completely new, unknown computational method: one which is able to use contextual and causal reasoning to perceive the world. If this is the case, the technology necessary for completely unsupervised autonomous vehicle systems is currently as near as inconceivable, and as such may lie decades in the future (Cummings, in press).
Another possible answer to the question of AV transportation may lie in adapting the world to a level appropriate to AI system, instead of attempting to raise the AI to be able to perceive the world. As has been noted, autonomous systems already 40
have commercial applications within closed off, highly controlled areas. It is conceivable that infrastructure be built and restrictions be placed on the roads used by AV to such a degree that the uncertainty the system is exposed to may be brought down to manageable levels. In the case of the client, one possibility for this may be to build an entirely new road from the facility to the harbour, and closing it off to all outside traffic. However, there are a number of reasons this may not be realistic. One of these is the economic benefit. The largest cost elimination when operating an AV system is the labour cost. The labour cost for transportation in Sweden is around 300 kr an hour (Statista, 2020). The route as driven currently along the 372 takes approximately 15 minutes, and hooking and unhooking a trailer from a truck takes about 5 minutes each (“CSTT Blog | how to unhook a semi trailer,” 2015). The client’s projected amount of journeys necessary for normal operation amounts to approximately 40,000 a year. This all results in a cost elimination of 5,000,000 SEK a year on wages if such a system were implemented. The cost of building a normal road in Sweden of course varies depending on an enormous amount of factors, but one estimate is 5000kr/m (Serrander, 2019). If this is the case, the minimum of 10 km of road needed in the client’s case would cost 50,000,000 SEK. This would suggest that, with conservative estimates, such an autonomous system would take around 10 years to start returning the investment necessary to just to build the road, not even considering the costs of purchasing the autonomous system or acquiring use of the land necessary to build the road.
In conclusion, the future of AV transportation is uncertain. The most likely scenario is that progress will be incremental. The AV systems currently in operation are confined to closed facilities only. What we will probably see more of is AV making short trips on public roads between such facilities, such as was the case of Einride’s trials performed at the DB Schenker facility. On these short outings, infrastructure and restrictions can be implemented to control the environment to a degree where to safe operation can be assured. As the sensory and perceptive technology improves, and as the network of roads with the necessary adaptations grows, so will the momentum of the technology. The limits of its eventual reach, however, remains to be seen.
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1P. WESTERLUND, BOLIDEN, PERSONAL COMMUNICATION, 28-10-2020 2L. RAMSTRÖM, SWEDISH TRANSPORT ADMINISTRATION, PERSONAL COMMUNICATION, 18-12-2020 3K. STRÖM, PERSONAL COMMUNICATION, 24-11-2020
8 Conclusion and recommendations
Operating autonomous vehicles on the route including the 372 between the production facility and Skellefteå harbour is not feasible in the foreseeable future, due to the need for extensive development of both the regulatory and technological foundation necessary for such a system. However, the current Swedish regulatory framework does allow for supervised trials of such systems to take place as long as certain conditions are fulfilled. The great variety of uncertain and complex elements encountered on the proposed route suggests that any system tried here would need to be exceptionally advanced. The route does provide some useful scenarios for testing, such as reliable winter conditions and a high percentage of daytime to test system functioning with the sun low in the sky. However, there are aspects inherent to the client’s case which impact the feasibility and desirability of using it to perform trials. The route 372 is heavily trafficked and one of the most important routes of the surrounding area, meaning that it would be unnecessarily sensitive to disturbances in a testing scenario. In addition to this, the dangerous goods which make up the majority of the clients’s cargo add unnecessary risks and regulatory complications to any tests performed. One application of autonomous transport which may be feasible would be the use of a closed off road to transport cargo from the factory to the harbour. However, at the volumes projected in the client’s case, this is unlikely to be cost effective.
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9 References
40-foot Container - Dimensions, Measurements and Weight. (2020, March 3). Retrieved December 3, 2020, from https://www.icontainers.com/help/40-foot- container/#:%7E:text=A%2040%2Dfoot%20container’s%20empty,29%20tons%2 0(26%2C300kg).
A new cabless concept – revealing Scania AXL. (2019). Retrieved January 5, 2021, from https://www.scania.com/group/en/home/newsroom/news/2019/a-new- cabless-concept-revealing-scania-axl.html
About us | Volvo Group. (2020). Retrieved January 6, 2021, from https://www.volvogroup.com/en-en/about-us.html
Bartz, D. (2017, June 3). Autonomous Cars Will Make Us Safer. Retrieved from https://www.wired.com/2009/11/autonomous-cars/
Bellgran, M., & Säfsten, K. (2005). Produktionsutveckling. Studentlitteratur.
Chen, F., Song, M., Ma, X., & Zhu, X. (2019). Assess the impacts of different autonomous trucks’ lateral control modes on asphalt pavement performance. Transportation Research Part C: Emerging Technologies, 103, 17–29. https://doi.org/10.1016/j.trc.2019.04.001
CSTT Blog | how to unhook a semi trailer. (2015, July 30). Retrieved January 13, 2021, from http://www.csttdrivertraining.com/cstt_blog/unhooking-a-semi- trailer/#:%7E:text=Summary&text=Unhooking%20a%20semi%2Dtrailer%20to,ta ke%20less%20than%205%20minutes.
Cummings, M. L. (in press). Rethinking the maturity of artificial intelligence in safety-critical settings. AI Magazine.
Cummings, M. L. (Ed.). (2018). Adaptation of Human Licensing Examinations to the Certification of Autonomous Systems. In Safe, Autonomous and Intelligent Vehicles (Unmanned System Technologies) (1st ed. 2019 ed., pp. 145–162). Springer. Retrieved from https://hal.pratt.duke.edu/sites/hal.pratt.duke.edu/files/u30/Certification%20of%20 Autonomous%20Systems%20MAR18.compressed.pdf
Cummings, M. L., & Britton, D. (2019). Regulating Safety-Critical Autonomous Systems: Past, Present and Future Perspectives. In Living with Robots: Emerging Issues on the Psychological and Social Implications of Robotics (1st ed., pp. 119– 140). Academic Press. Retrieved from https://www.sciencedirect.com/book/9780128153673/living-with-robots#book- description
Daimler. (2020). Automated Driving at Trucks. Retrieved October 5, 2020, from 43
https://www.daimler.com/innovation/case/autonomous/automated-driving-daimler- trucks.html
DeepMind. (2020). AlphaGo the Story so Far. Retrieved January 12, 2021, from https://deepmind.com/research/case-studies/alphago-the-story-so-far
Einride. (2020). Retrieved January 6, 2021, from https://www.einride.tech/
Eurostat. (2020, March). Labour costs in the EU (52/2020). Author. Retrieved from https://ec.europa.eu/eurostat/documents/2995521/10624905/3-31032020-BP- EN.pdf/055df0e0-980d-27b9-a2a9-83b143d94d5b
Facts and figures. (2020). Retrieved January 5, 2020, from https://www.scania.com/group/en/home/about-scania/scania-in-brief/facts-and- figures.html
Fagnant, D. J., & Kockelman, K. (2015). Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations. Transportation Research Part A: Policy and Practice, 77, 167–181. https://doi.org/10.1016/j.tra.2015.04.003
Gonzales, R. (2019, November 7). Feds Say Self-Driving Uber SUV Did Not Recognize Jaywalking Pedestrian In Fatal Crash. Retrieved January 12, 2021, from https://www.npr.org/2019/11/07/777438412/feds-say-self-driving-uber-suv-did- not-recognize-jaywalking-pedestrian-in-fatal-
Grossfeld, B. (2020, October 12). Deep learning vs machine learning: a simple way to understand the difference. Retrieved December 4, 2020, from https://www.zendesk.com/blog/machine-learning-and-deep- learning/#:%7E:text=To%20recap%20the%20differences%20between,intelligent% 20decisions%20on%20its%20own
Ground-breaking innovations | Volvo Group. (2018). Retrieved January 6, 2021, from https://www.volvogroup.com/en-en/news/2018/sep/news-3048902.html Hecht, J. (2018). Lidar for Self-Driving Cars. Optics and Photonics News, 29(1), 26. https://doi.org/10.1364/opn.29.1.000026
Heilweil, R. (2020, July 1). Self-driving trucks from Waymo and TuSimple hit the road in the US. Retrieved January 6, 2021, from https://www.vox.com/recode/2020/7/1/21308539/self-driving-autonomous-trucks- ups-freight-network
Huff, A. (2020, November 12). Driver shortage credited for buoying freight economy in 2020 and likely into 2021. Retrieved January 5, 2021, from https://www.ccjdigital.com/driver-shortage-tight-capacity-2020- rates/#:%7E:text=Driver%20shortage%20credited%20for%20buoying%20freight %20economy%20in%202020%20and%20likely%20into%202021&text=The%20s upply%20(capacity)%20side%20of,not%20because%20equipment%20is%20scarc e.&text=The%20trucking%20industry%20has%20about,a%20year%20ago%2C%2
44
0he%20said
IRU report forecasts alarming jump in driver shortage in Europe. (2020, March 9). Retrieved January 6, 2021, from https://www.iru.org/resources/newsroom/iru- report-forecasts-alarming-jump-driver-shortage-europe
Kalra, N., & Paddock, S. M. (2016). Driving to safety: How many miles of driving would it take to demonstrate autonomous vehicle reliability? Transportation Research Part A: Policy and Practice, 94, 182–193. https://doi.org/10.1016/j.tra.2016.09.010
Katwala, A. (2018, August 3). The spiralling environmental cost of our lithium battery addiction. Retrieved January 6, 2021, from https://www.wired.co.uk/article/lithium-batteries-environment-impact
Kehtarnavaz, N., Griswold, N. C., & Eem, J. K. (1991). Comparison of mono- and stereo-camera systems for autonomous vehicle tracking. Applications of Artificial Intelligence IX. https://doi.org/10.1117/12.45489
Kirsher, T. (2019, February 4). 5 reasons why autonomous cars aren’t coming anytime soon. AP NEWS. https://apnews.com/article/b67a0d6b6413406fb4121553cdf0b95a
Lee, S. (2020, May 22). Understanding Face Detection with the Viola-Jones Object Detection Framework. Retrieved December 5, 2020, from https://towardsdatascience.com/understanding-face-detection-with-the-viola-jones- object-detection-framework-c55cc2a9da14
Lidar - Laser Bear Honeycomb. (2020). Retrieved January 7, 2021, from https://waymo.com/lidar/
Mardirosian, R. (2020, February 5). Lidar vs. camera: driving in the rain. Retrieved December 6, 2020, from https://ouster.com/blog/lidar-vs-camera-comparison-in- the-rain/
Moore,P. (2018, March 15). Komatsu outlines past and future of its Autonomous Haulage System. Retrieved January 5, 2021, from https://im- mining.com/2018/01/29/komatsu-outlines-past-future-autonomous-haulage- system/
NASA. (2019). Appendix G: Technology Assessment/Insertion. Retrieved December 10, 2020, from https://www.nasa.gov/seh/appendix-g-technology- assessmentinsertion/
NXT concept vehicle represents a vision of the future. (2019). Retrieved January 5, 2020, from https://www.scania.com/uk/en/home/experience-scania/features/nxt- concept-vehicle-represents-a-vision-of-the-future.html
Paden, B., Cap, M., Yong, S. Z., Yershov, D., & Frazzoli, E. (2016). A Survey of
45
Motion Planning and Control Techniques for Self-Driving Urban Vehicles. IEEE Transactions on Intelligent Vehicles, 1(1), 33–55. https://doi.org/10.1109/tiv.2016.2578706
Persyn, D., Díaz-Lanchas, J., & Barbero, J. (2020). Estimating road transport costs between and within European Union regions. Transport Policy, 1–25. https://doi.org/10.1016/j.tranpol.2020.04.006
Ranter, H. (2020, January 5). Aviation Safety Network releases 2019 airliner accident statistics. Retrieved October 20, 2020, from https://news.aviation- safety.net/2020/01/01/aviation-safety-network-releases-2019-airliner-accident- statistics/
Riksdagen. (2021, January 1). Förordning (2017:309) om försöksverksamhet med självkörande fordon. Retrieved October 21, 2020, from https://www.riksdagen.se/sv/dokument-lagar/dokument/svensk- forfattningssamling/forordning-2017309-om-forsoksverksamhet-med_sfs-2017- 309
Sasmal, R. P., Sen, S., & Chakraborty, A. (2016, December). Solar photovoltaic output smoothing: Using battery energy storage system. Presented at the 2016 National Power Systems Conference (NPSC), Bhubaneswar, India. Retrieved from https://ieeexplore.ieee.org/document/7858894
Schuchmann, S. (2020, May 16). History of the first AI Winter - Towards Data Science. Retrieved December 13, 2021, from https://towardsdatascience.com/history-of-the-first-ai-winter-6f8c2186f80b
Serrander, J. (2019, October 3). 15 900 kronor - för en meter väg. Retrieved January 13, 2021, from https://ekuriren.se/artikel/kr2d3pj8
Sikt. (2020, September 24). Retrieved January 10, 2021, from https://www.smhi.se/kunskapsbanken/meteorologi/sikt-1.3925
Singh, S. (2018, March). Critical Reasons for Crashes Investigated in the National Motor Vehicle Crash Causation Survey (DOT HS 812 506). National Highway Traffic Safety Administration. Retrieved from https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812506#:~:text=The% 20critical%20reason%20was%20assigned%20to%20drivers%20in%20an%20esti mated,crashes%20at%20the%20national%20level.&text=The%20driver%2Drelate d%20critical%20reasons,%2C%20and%20non%2Dperformance%20errors. Skellefteå, Sweden latitude longitude. (2015). Retrieved December 10, 2021, from https://latitudelongitude.org/se/skelleftea/
SMHI. (1989–2020a). LUFTTEMPERATUR, MIN OCH MAX (DYGN) Skellefteå Flygplats| SMHI [Dataset]. Retrieved from https://www.smhi.se/data/meteorologi/ladda-ner-meteorologiska- observationer/#param=airTemperatureMinAndMaxOnceEveryDay,stations=all,stat ionid=151380
46
SMHI. (1989–2020b). NEDERBÖRDSMÄNGD (MÅNAD) Kusmark D | SMHI [Dataset]. Retrieved from https://www.smhi.se/data/meteorologi/ladda-ner- meteorologiska- observationer/#param=precipitationMonthlySum,stations=all,stationid=150530
SMHI. (1989–2020c). SIKT (H) Skellefteå Flygplats | SMHI [Dataset]. Retrieved from https://www.smhi.se/data/meteorologi/ladda-ner-meteorologiska- observationer/#param=visibility,stations=all,stationid=151380
SMHI. (1989–2020d). SNÖDJUP (DYGN) Kusmark D | SMHI [Dataset]. Retrieved from https://www.smhi.se/data/meteorologi/ladda-ner-meteorologiska- observationer/#param=snowDepth,stations=all,stationid=150530
Statista. (2020, January 31). Labor costs for transportation and storage in the European Union (EU) in 2015. Retrieved January 12, 2020, from https://www.statista.com/statistics/939986/eu-labor-costs-for-transportation-and- storage/
Statt, N. (2019, April 13). OpenAI’s Dota 2 AI steamrolls world champion e-sports team with back-to-back victories. Retrieved January 12, 2020, from https://www.theverge.com/2019/4/13/18309459/openai-five-dota-2-finals-ai-bot- competition-og-e-sports-the-international-champion
Stock, K. (2018, September 17). Self-Driving Cars Can Handle Neither Rain nor Sleet nor Snow. Retrieved December 8, 2020, from https://www.bloomberg.com/tosv2.html?vid=&uuid=69f34980-573f-11eb-8b87- 05e521000c96&url=L25ld3MvYXJ0aWNsZXMvMjAxOC0wOS0xNy9zZWxmL WRyaXZpbmctY2Fycy1zdGlsbC1jYW4tdC1oYW5kbGUtYmFkLXdlYXRoZXI= #:%7E:text=To%20help%20autonomous%20vehicles%20solve,can%20see%20bel ow%20the%20ground.&text=A%20graphic%20representation%20of%20the,can% 20detect%20various%20driving%20conditions
Svenska Trafikutbildares Riksförbund. (2019). Körkortsteori 2019. Landskrona, Sweden: Svea Trafikutbildning AB.
Swedish Traffic Agency, & Ramström, L. (2019, June). Åtgärdsvalsstudie Väg 372 Skellefteå-Skelleftehamn (TRV 2108/8564). Swedish Transport Agency. Retrieved from https://trafikverket.ineko.se/Files/sv- SE/66852/Ineko.Product.RelatedFiles/2019_157_atgardsvalsstudie_vag_372_skell eftea_skelleftehamn.pdf
Swedish Transport Administration, & Frid, L. (2015, June). Vägars och gators utformning - Begrepp och grundvärden (2015:090). Swedish Transport Administration. Retrieved from https://trafikverket.ineko.se/Files/sv- SE/12059/RelatedFiles/2015_090_%20VGU_begrepp_och_grundvarden.pdf
Swedish Transport Agency. (2017, October). Transportstyrelsens föreskrifter och allmänna råd om tillstånd att bedriva försök med självkörande fordon (TSFS
47
2017:92). Author. Retrieved from https://transportstyrelsen.se/TSFS/TSFS%202017_92.pdf
Swedish Transport Agency. (2020, May). Ansökan om tillstånd för självkörande fordon. Author. Retrieved from https://transportstyrelsen.se/globalassets/global/blanketter/vag/fordon/tsv7097- intresseanmalan-tillstand-for-sjalvkorande-fordont.pdf
Täckningskartor - Telia.se. (2020). Retrieved December 14, 2020, from https://www.telia.se/privat/support/tackningskartor
Talebpour, A., & Mahmassani, H. S. (2016). Influence of connected and autonomous vehicles on traffic flow stability and throughput. Transportation Research Part C: Emerging Technologies, 71, 143–163. https://doi.org/10.1016/j.trc.2016.07.007
Thomas, K. (2020, February 28). 4G+ on EE - What you need to know. Retrieved December 14, 2020, from https://www.4g.co.uk/news/what-is-4g-plus-ee/
TRATON – The TRATON GROUP and TuSimple Agreed on Global Partnership for Autonomous Trucks. (2020, November 24). Retrieved January 6, 2021, from https://traton.com/en/newsroom/press_releases/press_release_230920_01.html
TuSimple. (2020, August 20). Autonomous Vehicle Technology | Freight Transportation Services. Retrieved October 10, 2020, from https://www.tusimple.com/technology/
TuSimple. (2021, January 9). Autonomous Trucks | Self-Driving Truck | Driverless Vehicles. Retrieved January 6, 2021, from https://www.tusimple.com
Van Meldert, B., & De Boeck, L. (2016). Introducing autonomous vehicles in logistics: a review from a broad perspective. U Leuven, Faculty of Economics and Business (FEB), Department of Decision Sciences and Information Management. Retrieved from https://lirias.kuleuven.be/bitstream/123456789/543558/1/KBI_1618.pdf
Vera’s first assignment: Volvo Trucks presents an autonomous transport between a logistics centre and port. (2019). Retrieved January 6, 2021, from https://www.volvogroup.com/en-en/news/2019/jun/news-3336083.html
Via. (2020). Retrieved January 6, 2021, from https://waymo.com/waymo-via/
Vinterdäck - Transportstyrelsen. (2020, September 30). Retrieved December 8, 2020, from https://www.transportstyrelsen.se/vinterdack
Wetmore, J. M. (2003). The History and Motivations Behind 60 Years of Automated Highway Systems in America, 4–19. Presented at the Consortium for Science, Policy & Outcomes, Arizona, United States of America: Arizona State University.
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Williams, C. (2016, August 24). Stop lights, sunsets, junctions are tough work for Google’s robo-cars. Retrieved January 10, 2021, from https://www.theregister.com/2016/08/24/google_self_driving_car_problems/
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APPENDIX 1. Interview Swedish Transport Agency page 1/1
Interview template Swedish Transport Agency AV trial Statute § Details on the precise meaning of ”trials? o Can you perform them as a part of a profit-making venture? o What requirements exist to allow them to be called trials? o What exactly is needed to satisfy the requirement to report ones findings? § Can you explain the term “typgodkänna”? § What do the conditions in §4 entail? Could you give some examples? § What speed can the vehicles run at? § Specifically, how could one show that traffic safety can be guaranteed?
The permission form § What are acceptable aims and objectives? What conditions must these meet? § What counts as ”appropriate competence”? How would one prove that this level has been reached? § What responsibility does one have to handle accidents and incidents? To what degree is this instead a police matter?
Earlier trials § What trials have so far been performed on public roads in Sweden? § Are there any reports of these which I could gain access to?
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APPENDIX 2. Interview Dr. Missy Cummings page 1/1
Interview Dr. Missy Cummings
§ What technologies/areas are most in need of work before a level 4 AV can be used on public roads? § (Brief description of the the client’s case) What thoughts/concerns would you have about such a project? § What technologies are the most overhyped? What are some red flags when you hear company press releases about their technology? § What shall one look out for when assessing a potential system to implement? § Under what circumstances can the new technologies be safely tested?’ § There are claims that actually driving the vehicles will not realistically provide sufficient data to decide safety with the required statistical significance. What alternatives are there? § Under what timeframe would you estimate the AV industry would hit a major level 4 breakthrough? What stages of development/testing would you imagine are viable and necessary to get there? § In your truckinginfo.com podcast you mentioned a company by name which was censored. Could you divulge what company that was? What specifically did the CEO overhype? § Have you had a chance to look into TuSimple/einride? Any thoughts on their claims? § What are some red flags when you hear company pr about their technology? § Is there any further reading you’d recommend on the subject?
APPENDIX 3 Interview template Einride page 1/1
Interview template Einride
§ (Brief description of the client’s case) o How does your system work? o What sensors are used for what specific functions? o e.g. § Cameras § Radar § Lidar § What other technology is integral to your system? § How was the trial at DB Schenker? o Whad did you learn? o Is there a report you could share? § How is the trial at Coca Cola in Jordbro proceeding? o What will the experiment look like? § Top speed § Public roads § Loads § How does your system work in winter conditions? o Lane assist with snow on the road? o Sensor weatherproofing? § What should be included in any hazard analysis for AV system? o What are weaknesses for AV? o For your system in particular?
APPENDIX 4 Interview template Scania page 1/1
Interview template Scania
§ (Brief description of the client’s case) o How does your system work? o What sensors are used for what specific functions? o e.g. § Cameras § Radar § Lidar § What other technology is integral to your system? § How does your system work in winter conditions? o Lane assist with snow on the road? o Sensor weatherproofing? § What should be included in any hazard analysis for AV system? o What are weaknesses for AV? o For your system in particular? § Can you tell me a little about the partnership with TuSimple?
APPENDIX 5 Autonomous vehicle hazard assessmen t page 1/3
Autonomous vehicle hazard assessment
For the route between the client and Skellefteå harbour In my work to establish the viability of implementing an autonomous vehicle (AV) based transportation system between the upcoming production facility and Skellefteå harbour, one of the main challenges will necessarily be the use of public roadways. As such, I have compiled a list of hazards which should be investigated, as well as the data collection methods which I will use to gather data for analysis of these hazards.
Background The route is slightly over 11 km, although this is subject to change due to possible infrastructure changes at the harbour and/or factory. The majority of the route consists of the main road 372, with a speed limit of 80km/h. Although separated from the road, the surrounding land mainly consists of residential areas.
Fig 1. The route between the production facility and Skellefteå harbour.
Method The route will be scrutinised from the perspective of a number of hazards which are important in regards to AV technology. The severity of these hazards for the particular route will be established though analysis of data which will be attained by two main methodologies. First, data which is already compiled and available from external sources, such as Trafikverket’s database for weather conditions, will be requested from those sources. Second, a survey of the route will be performed on the ground, where the data needed to assess the hazards will be collected in person.
Hazards and data collection What follows is a list of the different hazards currently identified, as well as the methods used to collect the data for their assessment.
APPENDIX 5 Autonomous vehicle hazard assessmen t page 2/3
Lane changes and obstacle avoidance While necessary for normal vehicle operation, these pose risks as vehicles could need to use a lane where there is a possibility of traffic coming from the opposite direction. The risks involve both lane changes by other vehicles as well as the AV, on top of which there lies the ever-present risk of other drivers acting in unpredictable ways. Scenarios where lane changes may occur include:
Other vehicles overtaking the AV The AV needing to overtake a slow-moving vehicle The AV needing to pass a planned obstacle, such as road works The AV needing to pass or avoid temporary spontaneous obstacle, such as a fallen tree
Areas of data collection The lane configuration throughout the route. Where are lane changes allowed? The runoff areas. A basic assessment of the severity of running off the road throughout the route Legal speed limit throughout the route The road condition. Any road damage which could affect traction or may need to be avoided
Junctions Junctions, in this case defined as the place where two or more roads meet, pose many risks as there is a larger probability of contact with other vehicles crossing the path of the AV, and vice versa. The portions of the route most likely to initiate the most frequent interactions with other vehicles are likely the exits of the harbour and the factory, since these junctions require exiting or entering the main road. As with the lane changes, other drivers may act unpredictably.
Areas of data collection Traffic laws/rules of the road for each junction? Who has the right of way in the different directions? Signalling. Road signs as well as road markings. The distance and placement of any obstacles to the line of sight in the corners of junctions. This may be used to roughly calculate what reaction time may be necessary to avoid unexpected obstacles. Any existing “accident hotspots”.
Non motorised road users Since they are smaller, more irregular in shape and can be more unpredictable than motorised vehicles, pedestrians and cyclists are a great challenge when it comes to operating AV. Children are of special concern, due in part to them being even smaller and less predictable, but also since the PR consequences of any accident may be dire. Although less frequently encountered and with less severe consequences if an accident were to happen, pets and wild animals also pose a risk. Areas of data collection Points of concentration of pedestrians. Public facilities and businesses such as
APPENDIX 5 Autonomous vehicle hazard assessmen t page 3/3
hospitals and supermarkets. Schools and other locations with a high proportion of children will be specially noted Zebra crossings Bike paths. Both running parallel to and crossing the road Wilderness fences
Weather Weather has an effect upon both road traction and the effectiveness of the sensors of AV.
Areas of data collection Statistics will be collected for the frequency, duration and at what time of the year the following weather phenomena are most likely to occur. Winter conditions Snow Rain Fog
Sun The direction of the sun, as well as nighttime darkness, may have a deleterious effect on the quality of the camera images used by AV.
Areas of data collection Suns direction at different times of the day Daylight hours at different times of the year’
Communication Most, if not all AV technologies rely on digital communication for safe and consistent operation.
Areas of data collection 4G coverage maps Any plans to implement 5G coverage
Dangerous goods The majority of the client’s cargo will consist of dangerous goods of some form. This may increase the severity and complicate the response to any accidents.
Areas of data collection A dialogue has been initiated with the Swedish Civil Contingencies Agency (MSB) in order to discuss the matter.
APPENDIX 6. Rules and infrasructure ont the junctions of road 372 page 1/1
Rules and infrastructure of the junctions of road 372