A comparison of pedestrian behavior in interactions with autonomous and human-driven vehicles: An extreme value theory approach

Abstract

Autonomous Vehicle (AV) technologies are expected to result in significant safety and mobility benefits to the road system. However, one of the most important issues that autonomous vehicle technology faces is ensuring safe interactions with active road users such as pedestrians who can have unpredictable behavior. Moreover, road user behavior varies considerably across different traffic environments, which might represent a challenge to implementing AVs as they lack the intuition common in human-driven vehicles (HDV). This study proposes an approach to evaluate crash risk in vehicle–pedestrian interactions. An Extreme Value Theory (EVT) Peak Over Threshold (POT) model is used to compare the crash risk of AV-pedestrian and HDV-pedestrian interactions in four different cities, namely Boston, Las Vegas, Pittsburgh, and Singapore. A Bayesian hierarchical structure is used to incorporate the effect of several behavioral covariates, which enables estimating the crash risk of each interaction. Results show that the risk varies considerably depending on the type of interaction and the environment. For example, the impact of behavioral covariates (i.e., minimum distance between road users and maximum pedestrian speed) on the risk of AV-pedestrian interactions is greater when compared to the risk of HDV-pedestrian interactions in Boston, Las Vegas, and Singapore. This might indicate that, in busy and congested environments, road users may not be entirely comfortable with the presence of AVs. In addition, Singapore presented a higher percentage of riskier AV-pedestrian interactions when compared to the other cities. Finally, this study offers significant insights into the challenges of introducing AVs in diverse environments as behavior plays a crucial role in traffic and can influence conflict occurrence.