Stability Analysis in Mixed-Autonomous Traffic with Deep Reinforcement Learning

With the development of deep neural networks and artificial intelligence, Autonomous Driving Systems (ADS) are developing rapidly. According to the commercialization of Autonomous Vehicles (AVs), non-AVs and AVs will drive simultaneously on the road. The stability of autonomous vehicles can significantly affect the entire road condition. In this study, we use a Deep Reinforcement Learning (DRL) approach to making an AV learn a reasonable lane-changing and the acceleration control to keep the desired velocity. For the learning efficiency of the AV, it provides minimal state information and replaces the lane-changing action space with a lower level. Therefore, we modified the action selection method of TD3 and used it. Finally, the driving performance of the TD3-based AV and the LC2013-based vehicle is compared in various environments. The TD3-based AV performed better than the LC 2013.