Autonomous Vehicle Decision and Control through Reinforcement Learning with Traffic Flow Randomization

• Published in: Machines
• Main Authors: Yuan Lin, Antai Xie, Xiao Liu
• Format: Article
• Language: English
• Published: MDPI AG 2024-04-01
• Subjects: autonomous vehicle; reinforcement learning; decision and control; traffic flow; domain randomization
• Online Access: https://www.mdpi.com/2075-1702/12/4/264
• ISSN: 2075-1702

Most of the current studies on autonomous vehicle decision-making and control based on reinforcement learning are conducted in simulated environments. The training and testing of these studies are carried out under the condition of rule-based microscopic traffic flow, with little consideration regarding migrating them to real or near-real environments. This may lead to performance degradation when the trained model is tested in more realistic traffic scenes. In this study, we propose a method to randomize the driving behavior of surrounding vehicles by randomizing certain parameters of the car-following and lane-changing models of rule-based microscopic traffic flow. We trained policies with deep reinforcement learning algorithms under the domain-randomized rule-based microscopic traffic flow in freeway and merging scenes and then tested them separately in rule-based and high-fidelity microscopic traffic flows. The results indicate that the policies trained under domain-randomized traffic flow have significantly better success rates and episodic rewards compared to those trained under non-randomized traffic flow.