This work provides an in-depth-view of driver motivational aspects when driver assistance Systems (DAS) are considered. Thereby, the role of driver actual experience with DAS use was also identified and highlighted. A central outcome of this thesis is the STADIUM model describing the interplay of motivational factors that determine the engagement in secondary activities while taking actual DAS use experience into account. The role of motives in showing attentive behaviour depending on DAS (the navigation system) could also be underlined. The relevance, enrichment and need of combining qualitative and quantitative approaches when the effects of safety countermeasures on driver behaviour are investigated could also be shown. The results are discussed in terms of hierarchical driver behaviour models, the theory of planned behaviour and its extended versions and the strengths of the introduced studies and limitations. Implications for traffic safety are provided and future research issues are recommended.:Table of Content ACKNOWLEDGEMENTS III LIST OF INCLUDED PUBLICATIONS III SUMMARY VII ZUSAMMENFASSUNG XIII TABLE OF CONTENT XXI LIST OF FIGURES XXVII LIST OF TABLES XXXI 1. INTRODUCTION 1 1.1. Outline 2 1.2. Theoretical Background 3 1.2.1. Understanding driver behaviour: models and approaches that aim at describing driver behaviour. 3 1.2.2. Motivation and driving. 13 1.2.3. The role of motivation in behavioural adaptation due to driver assistance system use. 15 1.2.3.1. Driver assistance systems 15 1.2.3.2. Actual DAS use experience 24 1.2.4. Relevant motivational influence factors based on the Theory of Planned Behaviour. 25 1.2.4.1. Perceived risk 29 1.2.4.2. Perceived behavioural control 30 1.2.4.3. Norms 32 1.2.4.4. Attitudes towards reckless driving 33 1.2.4.5. Attitudes towards DAS 34 1.2.4.6. The intention to carry out concurrent activities to the driving tasks 35 1.2.5. Applying qualitative or quantitative methods when effects of DAS use on driver behaviour are investigated? 37 1.3. Objectives 41 2. STUDY I - ON THE INTERPLAY OF ACTUAL DAS USE EXPERIENCE AND MOTIVATIONAL FACTORS DETERMINING DRIVERS’ ENGAGEMENT IN SECONDARY ACTIVITIES – A THEORETICAL MODEL 45 2.1. Introduction 46 2.2. Methods 49 2.2.1. Focus group discussions. 49 2.2.2. Participants. 49 2.2.3. Procedure. 51 2.2.4. Data analysis. 54 2.3. Findings and Model Development 55 2.3.1. Perceived risk while driving. 55 2.3.2. Perceived behavioural control. 57 2.3.3. Safety-related beliefs concerning DAS: attitudes towards-, and norms concerning-, DAS. 59 2.4. Discussion 65 2.4.1. The STADIUM model. 65 2.4.2. Strengths and limitations. 66 3. STUDY IIA – THE STADIUM MODEL: SECONDARY ACTIVITY ENGAGEMENT DEPENDING ON THE INFLUENCE OF DAS USE EXPERIENCE ON MOTIVATIONAL FACTORS 69 3.1. Introduction 70 3.2. Methods 72 3.2.1. Participants. 72 3.2.2. Questionnaire. 73 3.2.2.1. DAS use experience 74 3.2.2.2. Motivational factors 75 3.2.2.3. Target behaviour: Intentions to carry out secondary activities while driving 76 3.2.3. Data analysis. 76 3.3. Results 78 3.3.1. DAS use experience: Chi-Squares and correlations. 78 3.3.2. Item analysis. 78 3.3.3. Correlations and Partial correlations. 79 3.3.4. The path analysis. 80 3.4. Discussion 82 3.4.1. Strengths and limitations. 87 4. STUDY IIB – THE ROLE OF DRIVER ASSISTANCE EXPERIENCE, SYSTEM FUNCTIONALITY, GENDER, AGE AND SENSATION SEEKING IN ATTITUDES TOWARDS THE SAFETY OF DRIVER ASSISTANCE SYSTEMS 91 4.1. Introduction 92 4.1.1. System functionality. 93 4.1.2. Driver characteristics. 94 4.1.2.1. Actual DAS use experience 94 4.1.2.2. Gender 96 4.1.2.3. Sensation seeking & age 97 4.1.3. Objectives & hypotheses. 98 4.2. Methods 98 4.2.1. Participants. 98 4.2.2. Questionnaire. 99 4.2.2.1. DAS use experience 99 4.2.2.2. Attitudes towards DAS 99 4.2.3. Data analysis. 100 4.3. Results 101 4.3.1. Gender differences. 106 4.3.2. Sensation seeking. 108 4.3.3. Age. 108 4.3.4. Actual DAS use experience. 110 4.4. Discussion 111 5. STUDY III – LOOK WHERE YOU HAVE TO GO! A FIELD STUDY COMPARING GLANCE BEHAVIOUR AT URBAN INTERSECTIONS USING A NAVIGATION SYSTEM OR A PRINTED ROUTE INSTRUCTION 117 5.1. Introduction 118 5.1.1. Behavioural effects of navigation system use. 119 5.1.2. Errors in visual attention allocation: The looked but failed to see phenomenon. 121 5.1.3. The navigation task and hierarchical models of driver behaviour. 122 5.1.4. Objectives. 124 5.1.5. Hypotheses. 124 5.2. Methods 126 5.2.1. Participants. 127 5.2.2. Standardised test drives. 127 5.2.3. Materials. 128 5.2.4. Data analysis procedure. 129 5.3. Results 132 5.3.1. Results from the descriptive, qualitative observation analysis. 132 5.3.2. Quantitative results. 135 5.3.2.1. Drivers’ reactions to pedestrians and/or cyclists who intend to cross 135 5.3.2.2. Driving Speed 136 5.3.2.3. Number of Glances 139 5.3.2.3.1. Areas of interest 139 5.3.2.4. Time looking to the areas of interest 142 5.3.2.4.1. Areas of interest: distribution of glances 142 5.3.2.4.2. Proportionate time looking to the areas of interest related to intersection passing duration 142 5.3.3. Summary of results from the qualitative and the quantitative analyses. 145 5.4. Conclusion 146 5.4.1. Driving speed. 147 5.4.2. Glance behaviour 147 5.4.3. Drivers’ reactions to pedestrians and cyclists. 150 5.4.4. Overall safety effect of type of route guidance 151 5.5. Discussion 152 5.5.1. Field drives: internal and external validity. 152 5.5.2. Experienced navigation system users. 154 5.5.3. Combination of qualitative and quantitative research. 154 5.5.4. Areas of interest. 156 5.5.5. Look but failed to see. 156 5.5.6. Presence of pedestrians and/ or cyclists. 157 5.5.7. Transition towards higher levels of automation. 157 6. FINAL DISCUSSION AND OVERALL CONCLUSION 161 6.1. Looking back, looking ahead 162 6.2. Hierarchical driver behaviour models: Be motivated to pay attention 163 6.3. The STADIUM model 166 6.3.1. Comparison with the Theory of Planned Behaviour. 167 6.3.2. The STADIUM model and its relevance for understanding driver behaviour. 168 6.4. Applying qualitative or quantitative methods when effects of DAS use on driver behaviour are investigated? 169 6.5. Limitations of this research 171 6.6. Implications 175 6.6.1. Individual & DAS. 175 6.6.2. Society & DAS. 177 6.7. Outline: Recommendations for future research 179 7. REFERENCES 183 8. ANNEX: OVERVIEW OF STUDIES THAT INVESTIGATED DRIVER BEHAVIOUR 209 EIDESSTATTLICHE ERKLÄRUNG 219 CURRICULUM VITAE 220 Personal Details 220 Scientific Education 221 Professional Experience in Science 222 Awards & Fellowship 223 Publications 224 Journal Paper 224 Book Chapter 225 Conferences 226 / Diese Arbeit liefert einen gründlichen Einblick, welche Rolle motivationale Aspekte spielen, wenn Fahrerassistenzsysteme (FAS) genutzt werden. Dabei wurde auch die Funktion der tatsächlichen Erfahrung mit FAS identifiziert und hervorgehoben. Ein zentrales Ergebnis dieser Arbeit ist das STADIUM Modell, welches das Zusammenspiel motivationaler Faktoren in Abhängigkeit von der tatsächlichen Erfahrung mit FAS erklärt, die wiederum bestimmen, inwieweit und ob andere Aktivitäten während des Fahrens ausgeführt werden. Außerdem konnte unterstrichen werden, welche Rolle Motive spielen, aufmerksames Verhalten in Abhängigkeit von der Nutzung von FAS (dem Navigationssystem) zu zeigen. Zusätzlich konnte dargestellt werden, wie relevant, bereichernd und nützlich es ist, qualitative und quantitative Methoden zu kombinieren, wenn die Effekte von FAS auf das FahrerInnenverhalten untersucht werden. Die Ergebnisse werden diskutiert indem auf hierarchische Fahrerverhaltensmodelle, auf die Theorie des geplanten Verhaltens und ihre erweiterten Versionen und auf die Stärken und Schwächen der Studien Bezug genommen wird. Es werden Implikationen dargestellt und zukünftige Forschungsfragen und Problemstellungen empfohlen.:Table of Content ACKNOWLEDGEMENTS III LIST OF INCLUDED PUBLICATIONS III SUMMARY VII ZUSAMMENFASSUNG XIII TABLE OF CONTENT XXI LIST OF FIGURES XXVII LIST OF TABLES XXXI 1. INTRODUCTION 1 1.1. Outline 2 1.2. Theoretical Background 3 1.2.1. Understanding driver behaviour: models and approaches that aim at describing driver behaviour. 3 1.2.2. Motivation and driving. 13 1.2.3. The role of motivation in behavioural adaptation due to driver assistance system use. 15 1.2.3.1. Driver assistance systems 15 1.2.3.2. Actual DAS use experience 24 1.2.4. Relevant motivational influence factors based on the Theory of Planned Behaviour. 25 1.2.4.1. Perceived risk 29 1.2.4.2. Perceived behavioural control 30 1.2.4.3. Norms 32 1.2.4.4. Attitudes towards reckless driving 33 1.2.4.5. Attitudes towards DAS 34 1.2.4.6. The intention to carry out concurrent activities to the driving tasks 35 1.2.5. Applying qualitative or quantitative methods when effects of DAS use on driver behaviour are investigated? 37 1.3. Objectives 41 2. STUDY I - ON THE INTERPLAY OF ACTUAL DAS USE EXPERIENCE AND MOTIVATIONAL FACTORS DETERMINING DRIVERS’ ENGAGEMENT IN SECONDARY ACTIVITIES – A THEORETICAL MODEL 45 2.1. Introduction 46 2.2. Methods 49 2.2.1. Focus group discussions. 49 2.2.2. Participants. 49 2.2.3. Procedure. 51 2.2.4. Data analysis. 54 2.3. Findings and Model Development 55 2.3.1. Perceived risk while driving. 55 2.3.2. Perceived behavioural control. 57 2.3.3. Safety-related beliefs concerning DAS: attitudes towards-, and norms concerning-, DAS. 59 2.4. Discussion 65 2.4.1. The STADIUM model. 65 2.4.2. Strengths and limitations. 66 3. STUDY IIA – THE STADIUM MODEL: SECONDARY ACTIVITY ENGAGEMENT DEPENDING ON THE INFLUENCE OF DAS USE EXPERIENCE ON MOTIVATIONAL FACTORS 69 3.1. Introduction 70 3.2. Methods 72 3.2.1. Participants. 72 3.2.2. Questionnaire. 73 3.2.2.1. DAS use experience 74 3.2.2.2. Motivational factors 75 3.2.2.3. Target behaviour: Intentions to carry out secondary activities while driving 76 3.2.3. Data analysis. 76 3.3. Results 78 3.3.1. DAS use experience: Chi-Squares and correlations. 78 3.3.2. Item analysis. 78 3.3.3. Correlations and Partial correlations. 79 3.3.4. The path analysis. 80 3.4. Discussion 82 3.4.1. Strengths and limitations. 87 4. STUDY IIB – THE ROLE OF DRIVER ASSISTANCE EXPERIENCE, SYSTEM FUNCTIONALITY, GENDER, AGE AND SENSATION SEEKING IN ATTITUDES TOWARDS THE SAFETY OF DRIVER ASSISTANCE SYSTEMS 91 4.1. Introduction 92 4.1.1. System functionality. 93 4.1.2. Driver characteristics. 94 4.1.2.1. Actual DAS use experience 94 4.1.2.2. Gender 96 4.1.2.3. Sensation seeking & age 97 4.1.3. Objectives & hypotheses. 98 4.2. Methods 98 4.2.1. Participants. 98 4.2.2. Questionnaire. 99 4.2.2.1. DAS use experience 99 4.2.2.2. Attitudes towards DAS 99 4.2.3. Data analysis. 100 4.3. Results 101 4.3.1. Gender differences. 106 4.3.2. Sensation seeking. 108 4.3.3. Age. 108 4.3.4. Actual DAS use experience. 110 4.4. Discussion 111 5. STUDY III – LOOK WHERE YOU HAVE TO GO! A FIELD STUDY COMPARING GLANCE BEHAVIOUR AT URBAN INTERSECTIONS USING A NAVIGATION SYSTEM OR A PRINTED ROUTE INSTRUCTION 117 5.1. Introduction 118 5.1.1. Behavioural effects of navigation system use. 119 5.1.2. Errors in visual attention allocation: The looked but failed to see phenomenon. 121 5.1.3. The navigation task and hierarchical models of driver behaviour. 122 5.1.4. Objectives. 124 5.1.5. Hypotheses. 124 5.2. Methods 126 5.2.1. Participants. 127 5.2.2. Standardised test drives. 127 5.2.3. Materials. 128 5.2.4. Data analysis procedure. 129 5.3. Results 132 5.3.1. Results from the descriptive, qualitative observation analysis. 132 5.3.2. Quantitative results. 135 5.3.2.1. Drivers’ reactions to pedestrians and/or cyclists who intend to cross 135 5.3.2.2. Driving Speed 136 5.3.2.3. Number of Glances 139 5.3.2.3.1. Areas of interest 139 5.3.2.4. Time looking to the areas of interest 142 5.3.2.4.1. Areas of interest: distribution of glances 142 5.3.2.4.2. Proportionate time looking to the areas of interest related to intersection passing duration 142 5.3.3. Summary of results from the qualitative and the quantitative analyses. 145 5.4. Conclusion 146 5.4.1. Driving speed. 147 5.4.2. Glance behaviour 147 5.4.3. Drivers’ reactions to pedestrians and cyclists. 150 5.4.4. Overall safety effect of type of route guidance 151 5.5. Discussion 152 5.5.1. Field drives: internal and external validity. 152 5.5.2. Experienced navigation system users. 154 5.5.3. Combination of qualitative and quantitative research. 154 5.5.4. Areas of interest. 156 5.5.5. Look but failed to see. 156 5.5.6. Presence of pedestrians and/ or cyclists. 157 5.5.7. Transition towards higher levels of automation. 157 6. FINAL DISCUSSION AND OVERALL CONCLUSION 161 6.1. Looking back, looking ahead 162 6.2. Hierarchical driver behaviour models: Be motivated to pay attention 163 6.3. The STADIUM model 166 6.3.1. Comparison with the Theory of Planned Behaviour. 167 6.3.2. The STADIUM model and its relevance for understanding driver behaviour. 168 6.4. Applying qualitative or quantitative methods when effects of DAS use on driver behaviour are investigated? 169 6.5. Limitations of this research 171 6.6. Implications 175 6.6.1. Individual & DAS. 175 6.6.2. Society & DAS. 177 6.7. Outline: Recommendations for future research 179 7. REFERENCES 183 8. ANNEX: OVERVIEW OF STUDIES THAT INVESTIGATED DRIVER BEHAVIOUR 209 EIDESSTATTLICHE ERKLÄRUNG 219 CURRICULUM VITAE 220 Personal Details 220 Scientific Education 221 Professional Experience in Science 222 Awards & Fellowship 223 Publications 224 Journal Paper 224 Book Chapter 225 Conferences 226

This thesis is about designing and implementing an affordable reversing camera sys-tem with object detection and OBD-2 integration for commercial vehicles. The aim is to improve the safety and efficiency of these vehicles by giving drivers a clear view of their surroundings behind the vehicle and alerting them to the presence of nearby obstacles. Ultrasonic sensors are used for object detection and give the driver control over the environment behind the vehicle and warn of present obstacles. The system is also integrated with the vehicle's on-board diagnostics system (OBD-2), which provides important information on speed and engine performance, among other things. This project contributes to making safety systems more accessible to com-mercial vehicles and reduces the risk of accidents and collisions. / Detta examensarbete handlar om att utforma och implementera ett prisvärt backkamerasystem objektdetektering och integration med On-Board Diagnostics 2 för kommersiella fordon. Syftet är att förbättra säkerheten och effektiviteten för dessa fordon genom att ge förarna en tydlig vy av deras omgivningar bakom fordonet och varna dem för närvaron av hinder i närheten. Ultraljudssensorer används för objekt-detektering och ger föraren en kontroll över omgivningen bakom fordonet samt var-nar för närvarande hinder. Systemet är också integrerat med fordonets omborddia-gnostiksystem (OBD-2), som ger viktig information om bland annat hastighet och motorprestanda. Detta projekt bidrar till att göra säkerhetssystem mer tillgängliga för kommersiella fordon och minskar risken för olyckor och kollisioner.

Over the past decades, congestion and emission problems has increased remarkablywhich escalates the demands on vehicles. The advancements withinthe eld of information and communication systems gives the opportunity todeal with the aforementioned problems. The concept of platooning shows tobe an attractive way of reducing both congestion and emissions by having ashort inter-vehicle spacing. The ndings in studies show that fuel reductionpotentials of 5-20 % are viable as a result of the lowered air drag by drivingin platoon. This thesis investigates the state of the art within the areaof intelligent transport systems (ITS) along with advanced driver assistancesystems (ADAS). Furthermore, the prosecuted work results in a proposedcontrol design for a longitudinal control in a platoon of vehicles. The platoonconsists of two homogeneous radio controlled vehicles (RCV) which aremodelled by taking advantage of system identication methods. The identi-ed plant models are implemented into a Simulink model where the controlsystem is developed. Moreover, the developed control system is implementedinto a real-time demonstrator for experimental evaluation. The results showsthat the modelled dynamics corresponds reasonably well with the real dynamicsof the system. The developed control system proves to work well andagree with the expectations of its performance obtained from simulations.The performance of the proposed controller has been evaluated by means ofsimulations and real experiments. The resulting control system consists ofPID controllers for both speed and spacing control. / Under de senaste decennierna har mangden trakstockningar och problemmed utslapp okat - darmed aven kraven pa vara fordon. Samtidigt skaparframstegen inom informations- och kommunikationssystem mojligheter foratt hantera ovannamnda problem. Kolonnkorning, eller platooning har visatsig vara en eektiv metod for att minska saval trakstockningar som utslappsom en foljd av kortare avstand mellan fordon. Resultat fran studier visarhur en branslereduktion runt 5-20 % ar mojlig till foljd av det sankta luftmotstandet vid kolonnkorning. Avhandlingen undersoker teknikens standpunktinom intelligenta transportsystem (ITS) tillsammans med avancerade drivhjalpsystem(ADAS). Vidare resulterar arbetet i ett forslag till regleringsdesignfor en longitudinell kontroll i en kolonn av fordon. Kolonnen bestar av tvahomogena radiostyrda fordon (RCV) som modelleras genom att utnyttjametoder for systemidentiering. De identierade systemmodellerna implementerasi en Simulink-modell dar styrsystemet utvecklas. Dessutom implementerasdet utvecklade styrsystemet i en realtids-demonstration for experimentellutvardering. Resultaten visar att den modellerade dynamikenstammer bra overens med systemets verkliga dynamik. Det utvecklade styrsystemetvisar sig fungera bra och overensstammer med forvantningarna pa dessprestanda som erhallits genom simuleringar. Den foreslagna regulatorns prestandahar utvarderats med hjalp av simuleringar och verkliga experiment.Det resulterande styrsystemet bestar av PID regulatorer for bade hastighetsochavstandskontroll.

This thesis consists of two projects focusing on how 5G can be used to make vehicles safer. The first project focuses on conceptualizing near-future use cases of how Advanced Driver Assistance Systems (ADAS) can be enhanced through 5G technology. Four concepts were developed in collaboration with various industry partners. These concepts were successfully demonstrated in a proof-of-concept at the 5G Automotive Association (5GAA) “The 5G Path of Vehicle-to-Everything Communication: From Local to Global” conference in Turin, Italy. This proof-of-concept was the world’s first demonstration of such a system. The second project focuses on a futuristic use case, namely remote operation of semi-autonomous vehicles (sAVs). As part of this work, it was explored if augmented reality (AR) can be used to warn remote operators of dangerous events. It was explored if such augmentations can be used to compensate during critical events. These events are defined as occurrences in which the network conditions are suboptimal, and information provided to the operator is limited. To evaluate this, a simulator environment was developed that uses eye- tracking technology to study the impact of such scenarios through user studies. The simulator establishes an extendable platform for future work. Through experiments, it was found that AR can be beneficial in spotting danger. However, it can also be used to directly affect the scanning patterns at which the operator views the scene and directly affect their visual scanning behavior. / Denna avhandling består av två projekt med fokus på hur 5G kan användas för att göra fordon säkrare. Det första projektet fokuserar på att konceptualisera användningsfall i närmaste framtid av hur Advanced Driver Assistance Systems (ADAS) kan förbättras genom 5G-teknik. Fyra koncept utvecklades i samarbete med olika branschpartner. Dessa koncept demonstrerade i ett proof-of- concept på 5G Automotive Association (5GAA) “5G Path of Vehicle to to Everything Communication: From Local to Global” -konferensen i Turin, Italien. Detta bevis-of-concept var världens första demonstration av ett sådant system. Det andra projektet fokuserar på ett långt futuristiskt användningsfall, nämligen fjärrstyrning av semi-autonoma fordon (sAVs). Som en del av detta arbete undersöktes det om augmented reality (AR) kan användas för att varna fjärroperatörer om farliga händelser. Det undersöktes om sådana förstärkningar kan användas för att kompensera under kritiska händelser. Dessa händelser definieras som händelser där nätverksförhållandena är suboptimala och information som tillhandahålls till operatören är begränsad. För att utvärdera detta utvecklades en simulatormiljö som använder ögonspårningsteknologi för att studera effekterna av sådana scenarier genom en användarstudie. Simulatorn bildar en utdragbar plattform för framtida arbete. Genom experiment fann man att AR kan vara fördelaktigt när det gäller att upptäcka fara. Men det kan också användas för att direkt påverka skanningsmönstret där operatören tittar på scenen och direkt påverka deras visuella skanningsbeteende.

Models based on micro-simulation of traffic flows have proven to be useful tools in the study of various traffic systems. Today, there is a wealth of traffic microsimulation models developed for freeway and urban street networks. The road mileage is however in many countries dominated by rural highways. Hence, there is a need for rural road traffic simulation models capable of assessing the performance of such road environments. This thesis introduces a versatile traffic micro-simulation model for the rural roads of today and of the future. The developed model system considers all common types of rural roads including effects of intersections and roundabouts on the main road traffic. The model is calibrated and validated through a simulation study comparing a two-lane highway to rural road designs with separated oncoming traffic lanes. A good general agreement between the simulation results and the field data is established. The interest in road safety and the environmental impact of traffic is growing. Recent research has indicated that traffic simulation can be of use in these areas as well as in traditional capacity and level-of-service studies. In the road safety area more attention is turning towards active safety improving countermeasures designed to improve road safety by reducing the number of driver errors and the accident risks. One important example is Advanced Driver Assistance Systems (ADAS). The potential to use traffic simulation to evaluate the road safety effects of ADAS is investigated in the last part of this thesis. A car-following model for simulation of traffic including ADAS-equipped vehicles is proposed and the developed simulation framework is used to study important properties of a traffic simulation model to be used for safety evaluation of ADAS. Driver behavior for ADAS-equipped vehicles has usually not been considered in simulation studies including ADAS-equipped vehicles. The work in this thesis does however indicate that modeling of the behavior of drivers in ADAS-equipped vehicles is essential for reliable conclusions on the road safety effects of ADAS. / <p>Report code: LiU-Tek-Lic-2005:60.</p>

Advanced Vehicle Systems promise improved safety and comfort for drivers. Steady advancements in technology are resulting in increasing levels of vehicle automation capabilities, furthering safety benefits. In fact, some of these vehicle automation systems are already deployed and available, but with promised benefits, such systems can potentially change driving behaviors. There is evidence that drivers have increased secondary task engagements while driving with automated vehicle systems, but there is a need for a clearer scientific understanding of any potential correlations between the use of automated vehicle systems and potentially negative driver behaviors. Therefore, this thesis aims to understand the state of knowledge on automated vehicle systems and their possible impact on drivers’ distraction behaviors. I have conducted two systematic literature reviews to examine this question. This thesis reports these reviews and examines the effects of secondary task engagement on driving behaviors such as take-over times, visual attention, trust, and workload, and discusses the implications on driver safety.

No description available.

Scenario-based testing will help to validate automated driving systems (ADS) and establish safer road traffic. To date, most data-driven test scenario generation methods rely primarily on one data source such as police accident data (PD), naturalistic driving studies, or video-based traffic observations (VOs). However, none of these data sources perfectly satisfies all the layers of the six-layer model for the description of test scenarios. Moreover, not all available data sources cover the same location and period of time. Therefore, we fused information from 1,648 scenarios extracted from a German VO with information from 74 scenarios extracted from German PD into a comprehensive new PD* database. Finally, PD* consisted of 74 accident scenarios extended, for example, by variables containing the dynamic information of the VO scenarios. Thus, PD* contained more than 350 variables, whereas PD contained only 269 and VO only 122 variables. For fusion, we followed the Find-Unify-Synthesize-Evaluation (FUSE) for Representativity (FUSE4Rep) process model using statistical matching. Subsequently, we derived three logical scenarios from PD* to test an autonomous emergency braking system (AEB) in a stochastic traffic simulation incorporating driver-behavior models. The quality of the fusion itself was satisfactory, and we propose improving the VO data collection process and observation time to obtain even better results.

In the field of modern automotive engineering, many researchers are focusing on the development of advanced vehicle control systems such as autonomous vehicle systems and Advanced Driver Assistance Systems (ADAS). Furthermore, Driver Assistance Systems (DAS) such as cruise control, Anti-Lock Braking Systems (ABS), and Electronic Stability Control (ESC) have become widely popular in the automotive industry. Therefore, vehicle control research attracts attention from both academia and industry, and has been an active area of vehicle research for over 30 years, resulting in impressive DAS contributions. Although current vehicle control systems have improved vehicle safety and performance, there is room for improvement for dealing with various situations. The objective of the research is to develop a predictive vehicle control system for improving vehicle safety and performance for autonomous vehicles and ADAS. In order to improve the vehicle control system, the proposed system utilizes information about the upcoming local driving environment such as terrain roughness, elevation grade, bank angle, curvature, and friction. The local driving environment is measured in advance with a terrain measurement system to provide terrain data. Furthermore, in order to obtain the information about road conditions that cannot be measured in advance, this work begins by analyzing the response measurements of a preceding vehicle. The response measurements of a preceding vehicle are acquired through Vehicle-to-Vehicle (V2V) or Vehicle-to-Infrastructure (V2I) communication. The identification method analyzes the response measurements of a preceding vehicle to estimate road data. The estimated road data or the pre-measured road data is used as the upcoming driving environment information for the developed vehicle control system. The metric that objectively quantifies vehicle performance, the Performance Margin, is developed to accomplish the control objectives in an efficient manner. The metric is used as a control reference input and continuously estimated to predict current and future vehicle performance. Next, the predictive control algorithm is developed based on the upcoming driving environment and the performance metric. The developed system predicts future vehicle dynamics states using the upcoming driving environment and the Performance Margin. If the algorithm detects the risks of future vehicle dynamics, the control system intervenes between the driver's input commands based on estimated future vehicle states. The developed control system maintains vehicle handling capabilities based on the results of the prediction by regulating the metric into an acceptable range. By these processes, the developed control system ensures that the vehicle maintains stability consistently, and improves vehicle performance for the near future even if there are undesirable and unexpected driving circumstances. To implement and evaluate the integrated systems of this work, the real-time driving simulator, which uses precise real-world driving environment data, has been developed for advanced high computational vehicle control systems. The developed vehicle control system is implemented in the driving simulator, and the results show that the proposed system is a clear improvement on autonomous vehicle systems and ADAS. / Ph. D. / In the field of modern automotive engineering, many researchers are focusing on the development of advanced vehicle control systems such as autonomous vehicle systems and Advanced Driver Assistance Systems (ADAS). Furthermore, cruise control, Anti-Lock Braking Systems, and Electronic Stability Controls have become widely popular in the automotive industry. Although vehicle control systems have improved vehicle safety and performance, there is still room for improvement for dealing with various situations. The objective of the research is to develop a predictive vehicle control system for improving vehicle safety and performance for autonomous vehicles and ADAS. In order to improve the vehicle control system, the proposed system utilizes information about the upcoming driving conditions such as road roughness, elevation grade, bank angle, and curvature. The driving environment is measured in advance with a terrain measurement system. Furthermore, in order to obtain the information about road conditions that cannot be measured in advance, this work begins by analyzing a preceding vehicle’s response to the road. The combined road data is used as the upcoming driving environment information. The measurement that indicates vehicle performance, the Performance Margin, is developed to accomplish the research objectives. It is used in the developed control system, which predicts future vehicle performance. If the system detects future risks, the control system will intervene to correct the driver’s input commands. By these processes, the developed system ensures that the vehicle maintains stability, and improves vehicle performance regardless of the upcoming and unexpected driving conditions. To implement and evaluate the proposed systems, a driving simulator has been developed. The results show that the proposed system is a clear improvement on autonomous vehicle systems and ADAS.

The development of driver assistance and automated vehicle control is in process and finds its way more and more into urban traffic environments. Here, the complexity of traffic situations is highly challenging and requires system approaches to comprehend such situations. The key element is the process of situation assessment to identify critical situations in advance and derive adequate warning and intervention strategies. This thesis introduces a system approach to establish a situation assessment process with the focus on the prediction of the driver intention. The system design is based on the Situation Awareness model by Endsley. Further, a prediction algorithm is created using Hidden Markov Models. To define the parameters of the models, an existing database is used and previously analyzed to identify reasonable variables that indicate an intended driving direction while approaching the intersection. Here, vehicle dynamics are used instead of driver inputs to enable a further extension of the prediction, i.e.\\ to predict the driving intention of other vehicles detected by sensors. High prediction rates at temporal distances of several seconds before entering the intersection are accomplished. The prediction is integrated in a system for situation assessment including an intersection model. A Matlab tool is created with an interface to the vehicle CAN bus and the intersection modeling which uses digital map data to establish a representation of the intersection. To identify differences and similarities in the process of approaching an intersection dependent on the intersection shape and regulation, a naturalistic driving study is conducted. Here, the distance to the intersection and velocity is observed on driver inputs related to the upcoming intersection (leaving the gas pedal, pushing the brake, using the turn signal). The findings are used to determine separate prediction models dependent on shape and regulation of the upcoming intersection. The system runs in real-time and is tested in a real traffic environment. / Die Entwicklung von Fahrerassistenz und automatisiertem Fahren ist in vollem Gange und entwickelt sich zunehmend in Richtung urbanen Verkehrsraum. Hier stellen besonders komplexe Verkehrssituationen sowohl für den Fahrer als auch für Assistenzsysteme eine Herausforderung dar. Zur Bewältigung dieser Situationen sind neue Systemansätze notwendig, die eine Situationsanalyse und -bewertung beinhalten. Dieser Prozess der Situationseinschätzung ist der Schlüssel zum Erkennen von kritischen Situationen und daraus abgeleiteten Warnungs- und Eingriffsstrategien. Diese Arbeit stellt einen Systemansatz vor, welcher den Prozess der Situationseinschätzung abbildet mit einem Fokus auf die Prädiktion der Fahrerintention. Das Systemdesign basiert dabei auf dem Situation Awareness Model von Endsley. Der Prädiktionsalgorithmus ist mit Hilfe von Hidden Markov Modellen umgesetzt. Zur Bestimmung der Modellparameter wurde eine existierende Datenbasis genutzt und zur Bestimmung von relevanten Variablen für die Prädiktion der Fahrtrichtung während der Kreuzungsannäherung analysiert. Dabei wurden Daten zur Fahrdynamik ausgewählt anstelle von Fahrereingaben um die Prädiktion später auf externe Fahrzeuge mittels Sensorinformationen zu erweitern. Es wurden hohe Prädiktionsraten bei zeitlichen Abständen von mehreren Sekunden bis zum Kreuzungseintritt erzielt. Die Prädiktion wurde in das System zur Situationseinschätzung integriert. Weiterhin beinhaltet das System eine statische Kreuzungsmodellierung. Dabei werden digitale Kartendaten genutzt um eine Repräsentation der Kreuzung und ihrer statischen Attribute zu erzeugen und die der Kreuzungsform entsprechenden Prädiktionsmodelle auszuwählen. Das Gesamtsystem ist als Matlab Tool mit einer Schnittstelle zum CAN Bus implementiert. Weiterhin wurde eine Fahrstudie zum natürlichen Fahrverhalten durchgeführt um mögliche Unterschiede und Gemeinsamkeiten bei der Annäherung an Kreuzungen in Abhängigkeit der Form und Regulierung zu identifizieren. Hierbei wurde die Distanz zur Kreuzung und die Geschwindigkeit bei Fahrereingaben im Bezug zur folgenden Kreuzung gemessen (Gaspedalverlassen, Bremspedalbetätigung, Blinkeraktivierung). Die Ergebnisse der Studie wurden genutzt um die Notwendigkeit verschiedener Prädiktionsmodelle in Abhängigkeit von Form der Kreuzung zu bestimmen. Das System läuft in Echtzeit und wurde im realen Straßenverkehr getestet.