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◆ Transportation Research Part F Traffic Psychology and Behaviour2025-11-16· Aggression

Mitigating aggression toward AVs in mixed traffic: Effects of automated driving styles and human driving styles

Haitao Chen, Yiqi Zhang

原始摘要(英文原文)· Original abstract
• This study investigates driver behavior and subjective evaluation in mixed traffic. • Aggressive drivers behave more aggressively when interacting with defensive AVs. • Moderate drivers made more aggressive decisions when driving around defensive AVs. • Defensive drivers were not significantly influenced by the driving styles of AVs. • Automated and driver’s driving styles influence drivers’ risk perception and trust. As automated vehicles (AVs) technology advances, the deployment of AVs creates a mixed traffic environment in which human-driven vehicles (HVs) and AVs interact and coexist on the roads. This study investigated the effects of AVs’ automated driving styles on HV drivers’ decision-making, driving performance, and subjective evaluation while considering the manual driving styles of HV drivers. Thirty-six drivers participated in a driving simulator experiment, interacting with aggressive and defensive AVs in two separate blocks and experiencing four driving scenarios in each block. The results indicated that drivers with more aggressive driving styles tend to make less aggressive decisions when interacting with aggressive AVs compared to defensive AVs. In contrast, the decision-making of drivers with more defensive driving styles was not significantly influenced by AVs’ driving styles with which they interacted in mixed traffic. Regarding driving performance, all drivers demonstrated less aggressive behaviors when interacting with aggressive AVs than with defensive AVs, as reflected in longer time-to-collision (TTC). Additionally, human drivers reported lower trust and perceived higher risk when interacting with defensive AVs compared to aggressive AVs, with this effect varying based on their driving styles. These insights are crucial in informing the development of AV algorithms and establishing robust traffic regulations to enhance safe interactions between human drivers and AVs in mixed-traffic environments.
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