Date of Award
Automated Driving Systems;Cognitive;Eye tracking;Situation Awareness
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With the automotive industry's recent shift towards automation, understanding the interaction between humans and automated system vehicles becomes crucial. The current technology in these systems is level 2 vehicles, where the driver still has to take-over the vehicle manually when needed. However, concerns were raised about the safety of these vehicles due to the monotonous monitoring role of the driver which leads to low cognitive workload. Using the inverted U-model, a low cognitive workload leads to boredom which might cause task disengagement and a severe loss of situation awareness. Thus, this study aimed to investigate attentional costs and recall performance in automated system vehicles to further investigate the relationship between low cognitive workload and situation awareness in automated driving systems. Additionally, the study explores the impact of driving these systems for a long period duration. In total, 26 participants drove in a driving simulator in both manual and automated modes for 10 minutes each while billboards were displayed on the right side of the roadway. Eye movements were recorded using an eye tracker, followed by a surprise memory task to evaluate participants' situation awareness. The findings revealed that drivers in level 2 mode exhibited increased and longer fixations on billboards while maintaining attention to the road as in manual mode. Moreover, drivers in automated mode showed higher recall performance, indicating enhanced attention to visual information and better situation awareness. The temporal analysis did reveal that drivers might face challenges in maintaining their attention with time. These findings contribute to an understanding of how drivers interact with automation, emphasizing the potential positive impact on attention and awareness, with implications for the development and implementation of safety-related in-vehicle systems.
Jalal Eddine, Reem, "Evaluating the effect of automated driving systems on drivers' visual attention" (2024). Electronic Theses and Dissertations. 9142.