Development and classification of autonomous vehicle's ambiguous driving scenario.

Human drivers are gradually being replaced by highly automated driving systems, and this trend is expected to persist. The response of autonomous vehicles to Ambiguous Driving Scenarios (ADS) is crucial for legal and safety reasons. Our research focuses on establishing a robust framework for developing ADS in autonomous vehicles and classifying them based on AV user perceptions. To achieve this, we conducted extensive literature reviews, in-depth interviews with industry experts, a comprehensive questionnaire survey, and factor analysis. We created 28 diverse ambiguous driving scenarios and examined 548 AV users' perspectives on moral, ethical, legal, utility, and safety aspects. Based on the results, we grouped ADS, with all of them having the highest user perception of safety. We classified these scenarios where autonomous vehicles yield to others as moral, bottleneck scenarios as ethical, cross-over scenarios as legal, and scenarios where vehicles come to a halt as utility-related. Additionally, this study is expected to make a valuable contribution to the field of self-driving cars by presenting new perspectives on policy and algorithm development, aiming to improve the safety and convenience of autonomous driving.

Trust in automated vehicles: constructs, psychological processes, and assessment.

There is a growing body of research on trust in driving automation systems. In this paper, we seek to clarify the way trust is conceptualized, calibrated and measured taking into account issues related to specific levels of driving automation. We find that: (1) experience plays a vital role in trust calibration; (2) experience should be measured not just in terms of distance traveled, but in terms of the range of situations encountered; (3) system malfunctions and recovery from such malfunctions is a fundamental part of this experience. We summarize our findings in a framework describing the dynamics of trust calibration. We observe that methods used to quantify trust often lack objectivity, reliability, and validity, and propose a set of recommendations for researchers seeking to select suitable trust measures for their studies. In conclusion, we argue that the safe deployment of current and future automated vehicles depends on drivers developing appropriate levels of trust. Given the potentially severe consequences of miscalibrated trust, it is essential that drivers incorporate the possibility of new and unexpected driving situations in their mental models of system capabilities. It is vitally important that we develop methods that contribute to this goal.

Evaluation of Imminent Take-Over Requests With Real Automation on a Test Track.

OBJECTIVE: Investigating take-over, driving, non-driving related task (NDRT) performance, and trust of conditionally automated vehicles (AVs) in critical transitions on a test track. BACKGROUND: Most experimental results addressing driver take-over were obtained in simulators. The presented experiment aimed at validating relevant findings while uncovering potential effects of motion cues and real risk. METHOD: Twenty-two participants responded to four critical transitions on a test track. Non-driving related task modality (reading on a handheld device vs. auditory) and take-over timing (cognitive load) were varied on two levels. We evaluated take-over and NDRT performance as well as gaze behavior. Further, trust and workload were assessed with scales and interviews. RESULTS: Reaction times were significantly faster than in simulator studies. Further, reaction times were only barely affected by varying visual, physical, or cognitive load. Post-take-over control was significantly degraded with the handheld device. Experiencing the system reduced participants' distrust, and distrusting participants monitored the system longer and more frequently. NDRTs on a handheld device resulted in more safety-critical situations. CONCLUSION: The results confirm that take-over performance is mainly influenced by visual-cognitive load, while physical load did not significantly affect responses. Future take-over request (TOR) studies may investigate situation awareness and post-take-over control rather than reaction times only. Trust and distrust can be considered as different dimensions in AV research. APPLICATION: Conditionally AVs should offer dedicated interfaces for NDRTs to provide an alternative to using nomadic devices. These interfaces should be designed in a way to maintain drivers' situation awareness. PRÉCIS: This paper presents a test track experiment addressing conditionally automated driving systems. Twenty-two participants responded to critical TORs, where we varied NDRT modality and take-over timing. In addition, we assessed trust and workload with standardized scales and interviews.

The influence of experienced severe road traffic accidents on take-over reactions and non-driving-related tasks in an automated driving simulator study.

Road traffic accidents (RTAs) are an ever-existing threat to all road users. Automated vehicles (AVs; SAE Level 3-5) are developed in many countries. They are promoted with numerous benefits such as increased safety yielding less RTAs, less congestion, less greenhouse gas emissions, and the possibility of enabling non-driving related tasks (NDRTs). However, there has been no study which has investigated different NDRT conditions, while comparing participants who experienced a severe RTA in the past with those who experienced no RTA. Therefore, we conducted a driving simulator study (N = 53) and compared two NDRT conditions (i.e., auditory-speech (ASD) vs. heads-up display (HUD)) and an accident (26 participants) with a non-accident group (27; between-subjects design). Although our results did not reveal any interaction effect, and no group difference between the accident and the non-accident group on NDRT, take-over request (TOR), and driving performance, we uncovered for both groups better performances for the HUD condition, whereas a lower cognitive workload was reported for the ASD condition. Nevertheless, there was no difference for technology trust between the two conditions. Albeit we observed higher self-ratings of PTSD symptoms for the accident than for the non-accident group, there were no group differences on depression and psychological resilience self-ratings. We conclude that severe RTA experiences do not undermine NDRT, TOR, and driving performance in a SAE Level 3 driving simulator study, although PTSD symptoms after an RTA may affect the psychological wellbeing.