Advanced driver assistance systems (ADAS) with increasing automation maturity and availability in urban contexts are entering the market.Meanwhile, the situational context has been identified to play a crucial role in system comprehension and usage, yet its subcomponents and their relation to system comprehension remain an open research question.To gain insights in the role of the situation complexity regarding subjective system comprehension and different methodological aspects, this study applies a mixed quantitative and qualitative approach, focusing on signaled intersections as an exemplary scenario.An on-road study with forty-six participants was conducted, involving six traffic light scenarios (all experienced twice).Results indicate that while comprehension was generally high, the situational context, including environmental and traffic-related factors, affected subjective system understanding.The proposed approach sheds light on the role of mixed methods in ADAS research, which may provide insights for system developers and suggestions for user training content.

Human factors in the context of advanced driver assistance systems (ADAS) are commonly investigated in driving simulators. However, the validity of this approach has not been sufficiently verified. This article compares human factors concerning a level 2 ADAS between real-world driving and two different simulators (simBIG vs. simSMALL) in a three-part user study (N = 93). The results show that usability and the mental model were valid in both simulators. Cognitive load, acceptance, and user experience were also valid, but only in simBIG, with some anecdotal support for the validity of simSMALL. Surprisingly, trust was higher in the simulators compared to real-world driving. Participants consistently took over control from the level 2 system at the same locations throughout simulated and real-world drives, and system usage increased over time in all three test environments. These findings indicate relative validity of the simulators. Further research is necessary to verify the transferability of the presented findings to other ADAS and simulators.

New Advanced Driver Assistance Systems (ADAS) are now available to support urban driving. To adequately use ADAS, especially in complex situations, drivers must comprehend them. An on-road study was conducted to investigate the mental model development while interacting with a state-of-the-art ADAS in both a rural (less complex) and an urban context (more complex). Forty-six participants experienced two rounds of each context. After each round, drivers rated their mental model, acceptance, and trust. Results indicate that for the rural context participants learned the system functionality in the first round without further improvement. In the urban context the mental model was generally less accurate, but improved in the second round. Trust increased from the first to the second rural round while acceptance did not show a significant change within the context. The results provide a first glimpse into the importance of evaluating different contexts and interaction scenarios for ADAS.

Virtual Reality (VR) is commonly utilized to examine driver interactions with vulnerable road users (VRUs) in an effective and secure manner. Recent studies, however, have highlighted issues in VR simulations, particularly concerning the authenticity of state-of-the-art pedestrian agent behaviors. These inaccuracies can compromise the perceived realism of the situation, potentially leading to unrepresentative driver reactions. This paper aims to show-case enhancements in pedestrian agent models and evaluate their subsequent advantages. To this end, real pedestrian movements, captured via motion-capture technology, were compared with outputs from a contemporary pedestrian agent model within a VR driving simulator experiment. The findings underpin the advantages of using motion-captured pedestrians to enhance social presence. Additionally, participant feedback emphasized that certain elements, such as head movements, explicit gestures, and subtle cues like hesitation before entering the road, were crucial in distinguishing realistic from unrealistic agents. These insights contribute significantly to refining the focus for systematic advancements in (pedestrian) agent models in VR environments. Such improvements are pivotal in augmenting the users’ sense of presence and the behavioral accuracy of the simulations.

Driving automation is being pushed towards widespread adoption, with significant progress being made continuously. Once the automated vehicle takes over the driving task, the question arises as to how people want to be driven by automation. In order to gain insights into this, a driving simulator study was conducted, in which N = 49 participants experienced an automated urban drive where pedestrians crossed or attempted to cross the road in front of the automated vehicle at various points. The driving style of the automated vehicle was manipulated (aggressive/defensive), while participants rated their desire for control, trust in automation, and acceptance. The results show that there is no general preference for one driving style over the other. Rather, the preferred behavior of the automation depended on the respective traffic scenario, with drivers preferring defensive driving in some crossing situations and aggressive driving in other situations. The present study indicates that, generally, defensive driving behavior is not necessarily the solution preferred by the user. Instead, a more nuanced approach based on the traffic scenario is recommended.

One way to improve road safety for cyclists is the development of hazard notification systems. Instead of in field experiments, such systems could be tested in safe and more controlled simulated environments; however, their validity needs verification. We evaluated the validity of mixed reality (MR) simulation for bicycle support systems notifying of dooring hazards. In a mixed-design study (N=43) with environment type(MR/test track) as within and hazard notifications (with/without) as between factor, comparing subjective and objective measures across environments.

Perceived security is crucial for the widespread adoption of shared automated vehicles (SAVs) and shuttle buses. However, there is currently no validated instrument to measure perceived security in this context, and little research has been done to determine the factors that contribute to perceived security. We propose the Perceived Security Scale for Shared Automated Vehicles (PSSAV), a questionnaire that assesses various aspects of perceived security in SAVs. The scale was evaluated using an exploratory, data-driven approach in a pilot study with 60 German participants, and a main study with 114 German and 101 Colombian participants experiencing a positive or negative ride in an automated shuttle bus (between-subjects design) presented as videos in an online study. The results suggest that trust, privacy, and control are key factors that influence security in the context of SAVs. The PSSAV questionnaire is reliable and sensitive to manipulation, indicating its construct validity.

Head-mounted displays (HMDs) are considered a promising, highly immersive display technology, which has been widely discussed in the context of driving simulation. The literature is heterogeneous to date with regard to the effects of HMDs on simulator sickness, the sense of presence, and perception. In the present study, a comparison between a modern HMD with a screen-based (LED wall) simulator is conducted in a repeated-measures driving simulator study including N = 31 subjects. The results indicate that the HMD is neither better nor worse, but performs equally well as the screen-based simulator in terms of simulator sickness, presence, and active distance perception. Evidence for passive distance and speed perception was only anecdotal, though also mostly points at a null-effect. The only (anecdotal) evidence of worse performance in the HMD simulator was in an active braking task. Accordingly, the present study did not identify disadvantages of using current HMDs in driving simulation.

Driving simulation is becoming an increasingly important component of research and development in the automotive industry. When performing simulator studies in urban scenarios, the challenge is to create a realistic driving context including natural interactions between the subject and artificial traffic participants, which are simulated by agent models. These traffic agents should behave as similar as possible to real humans. This raises the question of how to define realistic or human-like behaviour of traffic agents and how to measure this. Furthermore, it is necessary to investigate the influence of the surrounding traffic on the driver’s behaviour and perception of reality in the simulator. Accordingly, we present a method for quantifying the degree of realism of virtual traffic agents’ behaviour and their impact on subjects’ experience in a simulator experiment. By means of questionnaires, participants rated their perception of reality and the behaviour of present agent models. The experiment shows that surrounding traffic has a positive effect on subjects’ perception and behaviour, indicating that more realistic traffic agents have the potential to improve the validity of simulator studies. Moreover, our results provide new insights regarding required characteristics for the development of human-like traffic agents and give an overview of current strengths and weaknesses.

Measuring user experience is highly important for human-centered development and thus for designing automated driving systems. Multi-item measures such as the System Usability Scale (SUS) [7] or the Usability Metric for User Experience (UMUX) [14] are commonly used for collecting user feedback on technical systems or products. The goal of the present study was to investigate the potentials of a single-item approach as an economic alternative for measuring user experience compared to multi-item scales. Therefore, a single-item measure was developed to assess both event-related and cumulative user experience in automated driving. User experience was manipulated in a between-subject design implemented in a real-world driving task and feedback was collected using the newly developed Single Item User Experience (SIUX) scale, the UMUX, and the SUS. Results indicate that the SIUX scale is more sensitive than the UMUX to differences in event-related user experience, but not in cumulative user experience. Both the SIUX and the UMUX were more sensitive than the SUS when measuring differences in cumulative user experience. Future studies should be aimed at investigating the applicability of the SIUX scale to domains other than automated driving and at collecting more extensive data on validity and reliability of all three instruments.