Comparison of Pedestrians’ Gap Acceptance Behavior Towards Automated and Human-Driven Vehicles

Abstract

To protect pedestrian safety, automated vehicles can adopt a conservative strategy by yielding to pedestrians in all interactions and external human-machine interface was suggested to convey vehicle intentions to pedestrians. However, automated vehicles also could convey messages to assist existing pedestrians’ road-crossing decision-making, which is another way to ensure pedestrian safety but has generally been neglected. The current study explored the effect of assistance information on pedestrian gap acceptance behavior by presenting three colors similar to a traffic light to indicate the instant safety to cross road. Forty-eight participants completed the gap acceptance task in a virtual reality environment when interacting with human-driven vehicles or automated vehicles in a mixed or non-mixed traffic environment. The results showed that generally pedestrians had similar gap acceptance trends in rejecting small gaps towards two types of vehicles, but are more likely to accepted a large gap when they interacted with automated vehicles. The assistance information helped pedestrians to make safer road-crossing decisions, but whether the two types of vehicles drove in separately or in mixed condition did not affect pedestrian behavior. The null effect of driving context indicates that pedestrians may rely on their legacy strategy of gap acceptance regardless of vehicle type and the assistance information just only had minor effects.