Driving examiners’ views on data-driven assessment of test candidates:An interview study

Vehicles are increasingly equipped with sensors that capture the state of the driver, the vehicle, and the environment. These developments are relevant to formal driver testing, but little is known about the extent to which driving examiners would support the use of sensor data in their job. This semi-structured interview study examined the opinions of 37 driving examiners about datadriven assessment of test candidates. The results showed that the examiners were supportive of using data to explain their pass/fail verdict to the candidate. According to the examiners, data in an easily accessible form such as graphs of eye movements, headway, speed, or braking behaviour, and colour-coded scores, supplemented with camera images, would allow them to eliminate doubt or help them convince disagreeing test-takers. The examiners were sceptical about higher levels of decision support, noting that forming an overall picture of the candidate’s abilities requires integrating multiple context-dependent sources of information. The interviews yielded other possible applications of data collection and sharing, such as selecting optimal routes, improving standardization, and training and pre-selecting candidates before they are allowed to take the driving test. Finally, the interviews focused on an increasingly viable form of data collection: simulator-based driver testing. This yielded a divided picture, with about half of the examiners being positive and half negative about using simulators in driver testing. In conclusion, this study has provided important insights regarding the use of data as an explanation aid for examiners. Future research should consider the views of test candidates and experimentally evaluate different forms of data-driven support in the driving test

Model-based estimation of the state of vehicle automation as derived from the driver’s spontaneous visual strategies

When manually steering a car, the driver’s visual perception of the driving scene and his or her motor actions to control the vehicle are closely linked. Since motor behaviour is no longer required in an automated vehicle, the sampling of the visual scene is affected. Autonomous driving typically results in less gaze being directed towards the road centre and a broader exploration of the driving scene, compared to manual driving. To examine the corollary of this situation, this study estimated the state of automation (manual or automated) on the basis of gaze behaviour. To do so, models based on partial least square regressions were computed by considering the gaze behaviour in multiple ways, using static indicators (percentage of time spent gazing at 13 areas of interests), dynamic indicators (transition matrices between areas) or both together. Analysis of the quality of predictions for the different models showed that the best result was obtained by considering both static and dynamic indicators. However, gaze dynamics played the most important role in distinguishing between manual and automated driving. This study may be relevant to the issue of driver monitoring in autonomous vehicles

차량용 헤드업 디스플레이 설계에 관한 인간공학 연구

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 산업공학과, 2020. 8. 박우진.Head-up display (HUD) systems were introduced into the automobile industry as a means for improving driving safety. They superimpose safety-critical information on top of the drivers forward field of view and thereby help drivers keep their eyes forward while driving. Since the first introduction about three decades ago, automotive HUDs have been available in various commercial vehicles. Despite the long history and potential benefits of automotive HUDs, however, the design of useful automotive HUDs remains a challenging problem. In an effort to contribute to the design of useful automotive HUDs, this doctoral dissertation research conducted four studies. In Study 1, the functional requirements of automotive HUDs were investigated by reviewing the major automakers' automotive HUD products, academic research studies that proposed various automotive HUD functions, and previous research studies that surveyed drivers HUD information needs. The review results indicated that: 1) the existing commercial HUDs perform largely the same functions as the conventional in-vehicle displays, 2) past research studies proposed various HUD functions for improving driver situation awareness and driving safety, 3) autonomous driving and other new technologies are giving rise to new HUD information, and 4) little research is currently available on HUD users perceived information needs. Based on the review results, this study provides insights into the functional requirements of automotive HUDs and also suggests some future research directions for automotive HUD design. In Study 2, the interface design of automotive HUDs for communicating safety-related information was examined by reviewing the existing commercial HUDs and display concepts proposed by academic research studies. Each display was analyzed in terms of its functions, behaviors and structure. Also, related human factors display design principles, and, empirical findings on the effects of interface design decisions were reviewed when information was available. The results indicated that: 1) information characteristics suitable for the contact-analog and unregistered display formats, respectively, are still largely unknown, 2) new types of displays could be developed by combining or mixing existing displays or display elements at both the information and interface element levels, and 3) the human factors display principles need to be used properly according to the situation and only to the extent that the resulting display respects the limitations of the human information processing, and achieving balance among the principles is important to an effective design. On the basis of the review results, this review suggests design possibilities and future research directions on the interface design of safety-related automotive HUD systems. In Study 3, automotive HUD-based take-over request (TOR) displays were developed and evaluated in terms of drivers take-over performance and visual scanning behavior in a highly automated driving situation. Four different types of TOR displays were comparatively evaluated through a driving simulator study - they were: Baseline (an auditory beeping alert), Mini-map, Arrow, and Mini-map-and-Arrow. Baseline simply alerts an imminent take-over, and was always included when the other three displays were provided. Mini-map provides situational information. Arrow presents the action direction information for the take-over. Mini-map-and-Arrow provides the action direction together with the relevant situational information. This study also investigated the relationship between drivers initial trust in the TOR displays and take-over and visual scanning behavior. The results indicated that providing a combination of machine-made decision and situational information, such as Mini-map-and-Arrow, yielded the best results overall in the take-over scenario. Also, drivers initial trust in the TOR displays was found to have significant associations with the take-over and visual behavior of drivers. The higher trust group primarily relied on the proposed TOR displays, while the lower trust group tended to more check the situational information through the traditional displays, such as side-view or rear-view mirrors. In Study 4, the effect of interactive HUD imagery location on driving and secondary task performance, driver distraction, preference, and workload associated with use of scrolling list while driving were investigated. A total of nine HUD imagery locations of full-windshield were examined through a driving simulator study. The results indicated the HUD imagery location affected all the dependent measures, that is, driving and task performance, drivers visual distraction, preference and workload. Considering both objective and subjective evaluations, interactive HUDs should be placed near the driver's line of sight, especially near the left-bottom on the windshield.자동차 헤드업 디스플레이는 차내 디스플레이 중 하나로 운전자에게 필요한 정보를 전방에 표시함으로써, 운전자가 운전을 하는 동안 전방으로 시선을 유지할 수 있게 도와준다. 이를 통해 운전자의 주의 분산을 줄이고, 안전을 향상시키는데 도움이 될 수 있다. 자동차 헤드업 디스플레이 시스템은 약 30년 전 운전자의 안전을 향상시키기 위한 수단으로 자동차 산업에 처음 도입된 이래로 현재까지 다양한 상용차에서 사용되고 있다. 안전과 편의 측면에서 자동차 헤드업 디스플레이의 사용은 점점 더 증가할 것으로 예상된다. 그러나 이러한 자동차 헤드업 디스플레이의 잠재적 이점과 발전 가능성에도 불구하고, 유용한 자동차 헤드업 디스플레이를 설계하는 것은 여전히 어려운 문제이다. 이에 본 연구는 이러한 문제를 해결하고, 궁극적으로 유용한 자동차 헤드업 디스플레이 설계에 기여하고자 총 4가지 연구를 수행하였다. 첫 번째 연구는 자동차 헤드업 디스플레이의 기능 요구 사항과 관련된 것으로서, 헤드업 디스플레이 시스템을 통해 어떤 정보를 제공할 것인가에 대한 답을 구하고자 하였다. 이에 주요 자동차 제조업체들의 헤드업 디스플레이 제품들과, 자동차 헤드업 디스플레이의 다양한 기능들을 제안한 학술 연구, 그리고 운전자의 정보 요구 사항들을 체계적 문헌 고찰 방법론을 통해 포괄적으로 조사하였다. 자동차 헤드업 디스플레이의 기능적 요구 사항에 대하여 개발자, 연구자, 사용자 측면을 모두 고려한 통합된 지식을 전달하고, 이를 통해 자동차 헤드업 디스플레이의 기능 요구 사항에 대한 향후 연구 방향을 제시하였다. 두 번째 연구는 안전 관련 정보를 제공하는 자동차 헤드업 디스플레이의 인터페이스 설계와 관련된 것으로, 헤드업 디스플레이 시스템을 통해 안전 관련 정보를 어떻게 제공할 것인가에 대한 답을 구하고자 하였다. 실제 자동차들의 헤드업 디스플레이 시스템에서는 어떤 디스플레이 컨셉들이 사용되었는지, 그리고 학계에서 제안된 디스플레이 컨셉들에는 어떤 것들이 있는지 체계적 문헌 고찰 방법론을 통해 검토하였다. 검토된 결과는 각 디스플레이의 기능과 구조, 그리고 작동 방식에 따라 정리되었고, 관련된 인간공학적 디스플레이 설계 원칙과 실험적 연구 결과들을 함께 검토하였다. 검토된 결과를 바탕으로 안전 관련 정보를 제공하는 자동차 헤드업 디스플레이의 인터페이스 설계에 대한 향후 연구 방향을 제시하였다. 세 번째 연구는 자동차 헤드업 디스플레이 기반의 제어권 전환 관련 인터페이스 설계와 평가에 관한 것이다. 제어권 전환이란, 자율주행 상태에서 운전자가 직접 운전을 하는 수동 운전 상태로 전환이 되는 것을 의미한다. 따라서 갑작스런 제어권 전환 요청이 발생하는 경우, 운전자가 안전하게 대처하기 위해서는 빠른 상황 파악과 의사 결정이 필요하게 되고, 이를 효과적으로 도와주기 위한 인터페이스 설계에 대해 연구할 필요성이 있다. 이에 본 연구에서는 자동차 헤드업 디스플레이 기반의 총 4개의 제어권 전환 관련 디스플레이(기준 디스플레이, 미니맵 디스플레이, 화살표 디스플레이, 미니맵과 화살표 디스플레이)를 제안하였고, 제안된 디스플레이 대안들은 주행 시뮬레이터 실험을 통해 제어권 전환 수행 능력과 안구의 움직임 패턴, 그리고 사용자의 주관적 평가 측면에서 평가되었다. 또한 제안된 디스플레이 대안들에 대해 운전자들의 초기 신뢰도 값을 측정하여 각 디스플레이에 따른 운전자들의 평균 신뢰도 점수에 따라 제어권 전환 수행 능력과 안구의 움직임 패턴, 그리고 주관적 평가가 어떻게 달라지는지 분석하였다. 실험 결과, 제어권 전환 상황에서 자동화된 시스템이 제안하는 정보와 그와 관련된 주변 상황 정보를 함께 제시해 주는 디스플레이가 가장 좋은 결과를 보여주었다. 또한 각 디스플레이에 대한 운전자의 초기 신뢰도 점수는 디스플레이의 실제 사용 행태와 밀접한 관련이 있음을 알 수 있었다. 신뢰도 점수에 따라 신뢰도가 높은 그룹과 낮은 그룹으로 분류되었고, 신뢰도가 높은 그룹은 제안된 디스플레이들이 보여주는 정보를 주로 믿고 따르는 경향이 있었던 반면, 신뢰도가 낮은 그룹은 룸 미러나 사이드 미러를 통해 주변 상황 정보를 더 확인 하는 경향을 보였다. 네 번째 연구는 전면 유리창에서의 인터랙티브 헤드업 디스플레이의 최적 위치를 결정하는 것으로서 주행 시뮬레이터 실험을 통해 디스플레이의 위치에 따라 운전자의 주행 수행 능력, 인터랙티브 디스플레이 조작 관련 과업 수행 능력, 시각적 주의 분산, 선호도, 그리고 작업 부하가 평가되었다. 헤드업 디스플레이의 위치는 전면 유리창에서 일정한 간격으로 총 9개의 위치가 고려되었다. 본 연구에서 활용된 인터랙티브 디스플레이는 음악 선택을 위한 스크롤 방식의 단일 디스플레이였고, 운전대에 장착된 버튼을 통해 디스플레이를 조작하였다. 실험 결과, 인터랙티브 헤드업 디스플레이의 위치가 모든 평가 척도, 즉 주행 수행 능력, 디스플레이 조작 과업 수행 능력, 시각적 주의 분산, 선호도, 그리고 작업 부하에 영향을 미침을 알 수 있었다. 모든 평가 지표를 고려했을 때, 인터랙티브 헤드업 디스플레이의 위치는 운전자가 똑바로 전방을 바라볼 때의 시야 구간, 즉 전면 유리창에서의 왼쪽 아래 부근이 가장 최적인 것으로 나타났다.Abstract i Contents v List of Tables ix List of Figures x Chapter 1 Introduction 1 1.1 Research Background 1 1.2 Research Objectives and Questions 8 1.3 Structure of the Thesis 11 Chapter 2 Functional Requirements of Automotive Head-Up Displays: A Systematic Review of Literature from 1994 to Present 13 2.1 Introduction 13 2.2 Method 15 2.3 Results 17 2.3.1 Information Types Displayed by Existing Commercial Automotive HUD Systems 17 2.3.2 Information Types Previously Suggested for Automotive HUDs by Research Studies 28 2.3.3 Information Types Required by Drivers (users) for Automotive HUDs and Their Relative Importance 35 2.4 Discussion 39 2.4.1 Information Types Displayed by Existing Commercial Automotive HUD Systems 39 2.4.2 Information Types Previously Suggested for Automotive HUDs by Research Studies 44 2.4.3 Information Types Required by Drivers (users) for Automotive HUDs and Their Relative Importance 48 Chapter 3 A Literature Review on Interface Design of Automotive Head-Up Displays for Communicating Safety-Related Information 50 3.1 Introduction 50 3.2 Method 52 3.3 Results 55 3.3.1 Commercial Automotive HUDs Presenting Safety-Related Information 55 3.3.2 Safety-Related HUDs Proposed by Academic Research 58 3.4 Discussion 74 Chapter 4 Development and Evaluation of Automotive Head-Up Displays for Take-Over Requests (TORs) in Highly Automated Vehicles 78 4.1 Introduction 78 4.2 Method 82 4.2.1 Participants 82 4.2.2 Apparatus 82 4.2.3 Automotive HUD-based TOR Displays 83 4.2.4 Driving Scenario 86 4.2.5 Experimental Design and Procedure 87 4.2.6 Experiment Variables 88 4.2.7 Statistical Analyses 91 4.3 Results 93 4.3.1 Comparison of the Proposed TOR Displays 93 4.3.2 Characteristics of Drivers Initial Trust in the four TOR Displays 102 4.3.3 Relationship between Drivers Initial Trust and Take-over and Visual Behavior 104 4.4 Discussion 113 4.4.1 Comparison of the Proposed TOR Displays 113 4.4.2 Characteristics of Drivers Initial Trust in the four TOR Displays 116 4.4.3 Relationship between Drivers Initial Trust and Take-over and Visual Behavior 117 4.5 Conclusion 119 Chapter 5 Human Factors Evaluation of Display Locations of an Interactive Scrolling List in a Full-windshield Automotive Head-Up Display System 121 5.1 Introduction 121 5.2 Method 122 5.2.1 Participants 122 5.2.2 Apparatus 123 5.2.3 Experimental Tasks and Driving Scenario 123 5.2.4 Experiment Variables 124 5.2.5 Experimental Design and Procedure 126 5.2.6 Statistical Analyses 126 5.3 Results 127 5.4 Discussion 133 5.5 Conclusion 135 Chapter 6 Conclusion 137 6.1 Summary and Implications 137 6.2 Future Research Directions 139 Bibliography 143 Apeendix A. Display Layouts of Some Commercial HUD Systems Appendix B. Safety-related Displays Provided by the Existing Commercial HUD Systems Appendix C. Safety-related HUD displays Proposed by Academic Research 국문초록 187Docto

Data-Driven Evaluation of In-Vehicle Information Systems

Today’s In-Vehicle Information Systems (IVISs) are featurerich systems that provide the driver with numerous options for entertainment, information, comfort, and communication. Drivers can stream their favorite songs, read reviews of nearby restaurants, or change the ambient lighting to their liking. To do so, they interact with large center stack touchscreens that have become the main interface between the driver and IVISs. To interact with these systems, drivers must take their eyes off the road which can impair their driving performance. This makes IVIS evaluation critical not only to meet customer needs but also to ensure road safety. The growing number of features, the distraction caused by large touchscreens, and the impact of driving automation on driver behavior pose significant challenges for the design and evaluation of IVISs. Traditionally, IVISs are evaluated qualitatively or through small-scale user studies using driving simulators. However, these methods are not scalable to the growing number of features and the variety of driving scenarios that influence driver interaction behavior. We argue that data-driven methods can be a viable solution to these challenges and can assist automotive User Experience (UX) experts in evaluating IVISs. Therefore, we need to understand how data-driven methods can facilitate the design and evaluation of IVISs, how large amounts of usage data need to be visualized, and how drivers allocate their visual attention when interacting with center stack touchscreens. In Part I, we present the results of two empirical studies and create a comprehensive understanding of the role that data-driven methods currently play in the automotive UX design process. We found that automotive UX experts face two main conflicts: First, results from qualitative or small-scale empirical studies are often not valued in the decision-making process. Second, UX experts often do not have access to customer data and lack the means and tools to analyze it appropriately. As a result, design decisions are often not user-centered and are based on subjective judgments rather than evidence-based customer insights. Our results show that automotive UX experts need data-driven methods that leverage large amounts of telematics data collected from customer vehicles. They need tools to help them visualize and analyze customer usage data and computational methods to automatically evaluate IVIS designs. In Part II, we present ICEBOAT, an interactive user behavior analysis tool for automotive user interfaces. ICEBOAT processes interaction data, driving data, and glance data, collected over-the-air from customer vehicles and visualizes it on different levels of granularity. Leveraging our multi-level user behavior analysis framework, it enables UX experts to effectively and efficiently evaluate driver interactions with touchscreen-based IVISs concerning performance and safety-related metrics. In Part III, we investigate drivers’ multitasking behavior and visual attention allocation when interacting with center stack touchscreens while driving. We present the first naturalistic driving study to assess drivers’ tactical and operational self-regulation with center stack touchscreens. Our results show significant differences in drivers’ interaction and glance behavior in response to different levels of driving automation, vehicle speed, and road curvature. During automated driving, drivers perform more interactions per touchscreen sequence and increase the time spent looking at the center stack touchscreen. These results emphasize the importance of context-dependent driver distraction assessment of driver interactions with IVISs. Motivated by this we present a machine learning-based approach to predict and explain the visual demand of in-vehicle touchscreen interactions based on customer data. By predicting the visual demand of yet unseen touchscreen interactions, our method lays the foundation for automated data-driven evaluation of early-stage IVIS prototypes. The local and global explanations provide additional insights into how design artifacts and driving context affect drivers’ glance behavior. Overall, this thesis identifies current shortcomings in the evaluation of IVISs and proposes novel solutions based on visual analytics and statistical and computational modeling that generate insights into driver interaction behavior and assist UX experts in making user-centered design decisions

Methods and techniques for analyzing human factors facets on drivers

Mención Internacional en el título de doctorWith millions of cars moving daily, driving is the most performed activity worldwide. Unfortunately, according to the World Health Organization (WHO), every year, around 1.35 million people worldwide die from road traffic accidents and, in addition, between 20 and 50 million people are injured, placing road traffic accidents as the second leading cause of death among people between the ages of 5 and 29. According to WHO, human errors, such as speeding, driving under the influence of drugs, fatigue, or distractions at the wheel, are the underlying cause of most road accidents. Global reports on road safety such as "Road safety in the European Union. Trends, statistics, and main challenges" prepared by the European Commission in 2018 presented a statistical analysis that related road accident mortality rates and periods segmented by hours and days of the week. This report revealed that the highest incidence of mortality occurs regularly in the afternoons during working days, coinciding with the period when the volume of traffic increases and when any human error is much more likely to cause a traffic accident. Accordingly, mitigating human errors in driving is a challenge, and there is currently a growing trend in the proposal for technological solutions intended to integrate driver information into advanced driving systems to improve driver performance and ergonomics. The study of human factors in the field of driving is a multidisciplinary field in which several areas of knowledge converge, among which stand out psychology, physiology, instrumentation, signal treatment, machine learning, the integration of information and communication technologies (ICTs), and the design of human-machine communication interfaces. The main objective of this thesis is to exploit knowledge related to the different facets of human factors in the field of driving. Specific objectives include identifying tasks related to driving, the detection of unfavorable cognitive states in the driver, such as stress, and, transversely, the proposal for an architecture for the integration and coordination of driver monitoring systems with other active safety systems. It should be noted that the specific objectives address the critical aspects in each of the issues to be addressed. Identifying driving-related tasks is one of the primary aspects of the conceptual framework of driver modeling. Identifying maneuvers that a driver performs requires training beforehand a model with examples of each maneuver to be identified. To this end, a methodology was established to form a data set in which a relationship is established between the handling of the driving controls (steering wheel, pedals, gear lever, and turn indicators) and a series of adequately identified maneuvers. This methodology consisted of designing different driving scenarios in a realistic driving simulator for each type of maneuver, including stop, overtaking, turns, and specific maneuvers such as U-turn and three-point turn. From the perspective of detecting unfavorable cognitive states in the driver, stress can damage cognitive faculties, causing failures in the decision-making process. Physiological signals such as measurements derived from the heart rhythm or the change of electrical properties of the skin are reliable indicators when assessing whether a person is going through an episode of acute stress. However, the detection of stress patterns is still an open problem. Despite advances in sensor design for the non-invasive collection of physiological signals, certain factors prevent reaching models capable of detecting stress patterns in any subject. This thesis addresses two aspects of stress detection: the collection of physiological values during stress elicitation through laboratory techniques such as the Stroop effect and driving tests; and the detection of stress by designing a process flow based on unsupervised learning techniques, delving into the problems associated with the variability of intra- and inter-individual physiological measures that prevent the achievement of generalist models. Finally, in addition to developing models that address the different aspects of monitoring, the orchestration of monitoring systems and active safety systems is a transversal and essential aspect in improving safety, ergonomics, and driving experience. Both from the perspective of integration into test platforms and integration into final systems, the problem of deploying multiple active safety systems lies in the adoption of monolithic models where the system-specific functionality is run in isolation, without considering aspects such as cooperation and interoperability with other safety systems. This thesis addresses the problem of the development of more complex systems where monitoring systems condition the operability of multiple active safety systems. To this end, a mediation architecture is proposed to coordinate the reception and delivery of data flows generated by the various systems involved, including external sensors (lasers, external cameras), cabin sensors (cameras, smartwatches), detection models, deliberative models, delivery systems and machine-human communication interfaces. Ontology-based data modeling plays a crucial role in structuring all this information and consolidating the semantic representation of the driving scene, thus allowing the development of models based on data fusion.I would like to thank the Ministry of Economy and Competitiveness for granting me the predoctoral fellowship BES-2016-078143 corresponding to the project TRA2015-63708-R, which provided me the opportunity of conducting all my Ph. D activities, including completing an international internship.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: José María Armingol Moreno.- Secretario: Felipe Jiménez Alonso.- Vocal: Luis Mart

The Application of Driver Models in the Safety Assessment of Autonomous Vehicles: A Survey

Driver models play a vital role in developing and verifying autonomous vehicles (AVs). Previously, they are mainly applied in traffic flow simulation to model realistic driver behavior. With the development of AVs, driver models attract much attention again due to their potential contributions to AV certification. The simulation-based testing method is considered an effective measure to accelerate AV testing due to its safe and efficient characteristics. Nonetheless, realistic driver models are prerequisites for valid simulation results. Additionally, an AV is assumed to be at least as safe as a careful and competent driver. Therefore, driver models are inevitable for AV safety assessment. However, no comparison or discussion of driver models is available regarding their utility to AVs in the last five years despite their necessities in the release of AVs. This motivates us to present a comprehensive survey of driver models in the paper and compare their applicability. Requirements for driver models in terms of their application to AV safety assessment are discussed. A summary of driver models for simulation-based testing and AV certification is provided. Evaluation metrics are defined to compare their strength and weakness. Finally, an architecture for a careful and competent driver model is proposed. Challenges and future work are elaborated. This study gives related researchers especially regulators an overview and helps them to define appropriate driver models for AVs