Safety-based Reinforcement Learning Longitudinal Decision for Autonomous Driving in Crosswalk Scenarios

Fangzhou Xiong, Dongchun Ren, Mingyu Fan, Shuguang Ding, Zhiyong Liu

Abstract: Autonomous vehicles (AVs) need to make driving decisions to interact with other traffic participants. By adapting to different scenarios with specific parameters, traditional strategies attempt to leverage rule-based methods to solve the decision problems. In this paper, we present a novel reinforcement learning method for resolving interaction uncertainty in the decision-making problem. We construct prior knowledge by introducing traffic regulations and constraints and then converting them into rules that govern the learning of driving policies. To promote safe driving, a safety-aware module equipped with a mathematical collision correlation analysis is developed to anticipate and handle dangerous traffic scenarios. A realistic scenario involving an AV approaching a crosswalk is used to validate the proposed method. The experimental results indicate that the proposed method improves driving safety and efficiency significantly when compared to alternative approaches and can be generalized to more difficult scenarios.