Comparing the Crash Risk of Vehicle-Pedestrian Interactions using Autonomous Vehicle Data

Abstract

There is an increasing interest in autonomous vehicles (AVs) research as they are expected to provide considerable safety and mobility benefits. These vehicles should be able to interact with road users safely, which requires understanding the behavior of actual interactions between human-driven vehicles (HDV) and vulnerable road users (e.g., pedestrians). However, such behavior may vary considerably depending on the driving environment as culture plays an important role in traffic safety. This study uses an Extreme Value Theory Peak Over Threshold framework to estimate the risk of vehicle-pedestrian interactions in four different cities in the US and Asia (i.e., Boston, Las Vegas, Pittsburgh, and Singapore). A Bayesian hierarchical structure is considered to incorporate the effect of different covariates, which enables estimating the risk for each interaction. A large-scale AV dataset is used. As AVs are equipped with several sensors, they can capture information about the environment in real-time, including other road users’ positions and speeds. Results show that the risk varies significantly across different cities. For example, Pittsburgh has a greater risk than Singapore for regular vehicle-pedestrian interactions, which indicates that some cities require additional efforts for the implementation of AVs as the risk of interactions with pedestrians varies. Therefore, modeling frameworks that account for site-specific behavioral parameters should be proposed for the safe coexistence between advanced technologies and vulnerable road users.