Interacting with Yielding Vehicles: A Perceptually Plausible Model for Pedestrian Road Crossing Decisions

As autonomous driving technology advances, automated vehicles (AVs) will increasingly share road space with pedestrians, creating significant challenges for AV systems. Effective interaction between AVs and pedestrians is one of the key hurdles. Pedestrian simulation tools offer the potential to expedite the evaluation and refinement of these interactive capabilities. However, existing research lacks efforts to model pedestrian behavior in vehicle-yielding scenarios, resulting in distorted modeling results. This paper proposes a perceptually plausible road-crossing decision model that creates temporaldynamic crossing decisions across a range of vehicle-yielding scenarios. Specifically, a proposed hybrid perception strategy explains how pedestrians may apply psychophysical cues to make crossing decisions. Discrete choice models based on the hybrid perception strategy combined with a crossing initiation model reproduce the details of crossing decisions: the decision and its timing. An empirical dataset collected in a pedestrian simulator is applied to validate the model. Additionally, the latest crossing decision models, i.e., the evidence accumulation model and the artificial neural networks approach, are employed as comparisons. The results show that the proposed model accurately reproduces crossing decision patterns affected by diverse vehicle kinematics in vehicle-yielding scenarios in a perceptually plausible manner. Our results strengthen the notion that there is a perceptual threshold for pedestrians to control their decisionmaking strategy. The proposed theory and approach bring insights into the computational pedestrian road-crossing behavior and have practical implications in traffic simulation and AV development.