11:00 - 12:30
Parallel sessions 2
11:00 - 12:30
Room: HSZ - N1
Chair/s:
Stefan Brandenburg, Martin Baumann
The facilitated integration of technology into people's lives highlights the importance of examining its impact on experience and behavior. Experimental approaches help to determine the underlying psychological processes of this impact. This symposium summarizes experimental studies examining various contexts of technology use and psychological aspects of Engineering Psychology and Human Factors. Applying various experimental approaches these talks address major concepts of Engineering Psychology and Human Factors, such as situation awareness, cognitive load, technology adaptation in classical domains such as human-AI interaction, human-automation interaction, teleoperation, and highlight the value and the feasibility of rigorous experimental approaches also in complex and applied settings. The first talk by Alexander Reisinger examines how much lead time remote drivers need to effectively regain situation awareness and safely take control of highly automated vehicles during event-based remote driving tasks, highlighting the benefits of providing augmented visual information from the vehicle. The second talk by Andreas Schrank explores how different camera perspectives and visual augmentations influence remote assistants’ performance and situation awareness when supervising highly automated vehicles, showing that the optimal perspective depends on the driving scenario and that augmentation can compensate for poor visibility in adverse weather. The third talk by Matthias Arend introduces and validated a new implicit measure of situation awareness called SAMBA, comparing it with established explicit methods and showing that combining SAMBA with the traditional SAGAT approach can provide a more comprehensive and less intrusive assessment of operator awareness during teleoperation tasks. The fourth talk by Romy Müller examines how people evaluate AI image classifications using concept-based explainable AI, showing that participants preferred explanations with image snippets that precisely matched the original image and rated generalized or imprecise explanations significantly lower—indicating that users value precision over robustness in AI interpretations. The fifth talk by Judith Josupeit highlights the benefits of using virtual reality (VR) for rigorous experimental manipulations in applied contexts. In addition, the talk demonstrates how AI can be used in VR-experiments.
Submission 239
Investigating the Time Required by Remote Drivers to Get into the Loop During Event-Based Takeovers
SymposiumTalk-01
Presented by: Alexander Reisinger
Alexander Reisinger 1, 2, Marc Wilbrink 1, Martin Baumann 2, Michael Oehl 1
1 Deutsches Zentrum für Luft und Raumfahrt, Germany
2 University of Ulm, Germany
Future traffic is expected to be shaped by Highly Automated Vehicles (HAV; SAE Level 4), which promise a wide range of benefits. However, achieving these benefits depends on overcoming traffic situations that are beyond the current technological capabilities of HAVs. Here, one approach could be to issue a take-over request to a remote driver (RD). In these events, the RD proceeds to take over the dynamic driving task for the HAV remotely. This leads to challenges in getting the RD into a state of “being in the loop” (i.e., gaining situation awareness and subsequently taking control without being physically present on site). These challenges can lead to potentially safety-critical RD task performance impairments. To date, this novel challenge has been largely unexplored. To address this gap, we propose to provide the RD with information about the evolvement of the traffic situation, continuously gathered by the HAV. In addition, augmenting the video stream with sensory data gathered by the HAV could further support the RD. In an experimental user study in a simulator setting, we investigated how much Lead Time the RD requires to get into the loop (i.e., to gain situation awareness). N = 47 participants encountered typical event-based remote driving tasks displayed in an urban traffic environment, presented either with or without visual augmentation. These tasks were presented with varying Lead Time and traffic situation complexity. Findings contribute to the understanding of how much Lead Time RDs require to enter the loop, thereby increasing safety of event-based remote driving.