自动驾驶车辆在无信号交叉口右转驾驶决策技术研究.

The application of deep reinforcement learning technology for autonomous driving decision has become a research topic at domestic and overseas. The existing research on vehicle traffic flow lacks randomness and authenticity,and the effective exploration of autonomous vehicles in the environment has limitations. Therefore, this paper used the 1D3 algorithm to study the right turn driving decision of autonomous vehicles in signal-free intersections. Firstly, it developed the training and testing scenario of signal-free intersection in Carla simulation,and added the traffic flow management function to improve the randomness of system training and testing. Secondly,in order to improve the exploratory of autonomous vehicles, it added OU noise for the target action to improve the Actor network in TD3 algorithm. Finally, it used the success rate and average travel time to evaluate the autonomous driving behavior decisions. The results show that in different traffic flow scenarios, the traffic success rate of the improved 1D3 algorithm is 6. 2% higher than the DDPG algorithm controlled vehicles, and 23% higher than the rule-based AEB model. The improved TD3 algorithm not only can explore more possibilities, but also has a more prominent performance of prevailing decision. [ABSTRACT FROM AUTHOR]