Citation

Alozi AR, Hussein M. Transp. Res. C Emerg. Technol. 2024; 161: e104572.

Copyright

(Copyright © 2024, Elsevier Publishing)

DOI

10.1016/j.trc.2024.104572

PMID

unavailable

Abstract

The inevitable impact of autonomous vehicles (AV) on traffic safety is becoming a reality with the progressive deployment of these vehicles in different parts of the world. Still, many questions linger in the minds of road users that will share the road and interact with these AVs on a daily basis. To answer some of these questions, this study utilized recently collected real-world AV data from the United States, with the focus mainly targeting active road users. Specifically, the 1,492 h of recorded trips were processed to extract AV-pedestrian and AV-cyclist interactions of different movement types. The interactions were then investigated to gain a better understanding of the active road users' behavior, while excluding any interactions that involved intervention from the AVs' human test drivers. Through deep maximum entropy inverse reinforcement learning (DME-IRL), the reward functions describing the utility of active road users were retrieved and assessed for five interaction scenarios, including parallel, opposing, crossing, and turning (left and right) interactions. In addition, the policies developed as part of the solution were used to simulate the behavior of active road users and validate the resulting conflicts in terms of safety and evasive actions. Overall, the utilized approach demonstrated high accuracy in mimicking the interaction behavior of active road users when encountering an AV, with 81-84% accuracy in predicting evasive actions in parallel and opposing interactions and 12-17% mean absolute error for safety indicators in crossing and turning interactions. In addition, the resulting reward functions provided reliable insight onto the preferences and considerations of the active road users in these situations. Overall, cyclists tend to be less cautious around AVs compared to pedestrians, while the AVs tend to slow down and leave sufficient distance from all active road users in most cases. Still, the robotic behavior of the AVs, which can sometimes be inconsistent, leads to risky evasive actions by the active road users, which can affect other road users in busy intersections.

Language: en

Keywords

Active road users; Autonomous vehicles; Interaction behavior; Inverse reinforcement learning; Traffic safety