Generating risky and realistic scenarios for autonomous vehicle tests involving powered two-wheelers: A novel reinforcement learning framework

Abstract

Emerging technologies have the potential to revolutionize transportation, with Autonomous Vehicles (AVs) enhancing traffic safety, improving efficiency, and reducing emissions by optimizing driving patterns and minimizing idling time. However, despite their great potential, the actual utility and functionality of AVs have yet to be fully realized. Testing remains a critical method for advancing AVs adoption, and given that Powered Two-Wheelers (PTWs) is a major contributor to crashes, this paper proposes a novel scenario generation method for PTWs interactions with AVs. First, we extracted 314 car-to-PTWs crashes from the China In-depth Mobility Safety Study-Traffic Accident (CIMSS-TA) database as the initial state of the test scenarios. Subsequently, Reinforcement Learning (RL) was employed to control PTWs, using a reward function guided by a potential energy function that mirrors human driving characteristics to enhance the risk and realism of the generated scenarios. Finally, the effectiveness and scientific validity of the generated scenarios are verified by comparing and analyzing the risk, realism, and crash severity through multiple indicators. The results demonstrate that our proposed method increases riskiness while maintaining a high level of realism. It is hoped that this process will be applied in the future to not only test AV functions but also encourage AVs to be more mindful of crash severity.