Data-driving Reinforcement Learning on the Path Planning for Autonomous Vehicles
The path planning for autonomous vehicles is a hot topic in academic world.The goal of this problem is to design a vehi-cle with learning ability to approach the target without any collision.In this paper,we focus on data-driving reinforcement learning(RL)design for the path planning for autonomous vehicles problems.We proposed the method of sensor detection,and a self-learning strategy for a vehicle seeking the target with obstacle avoidance.Specifically,we designed a continuous reinforcement signal to improve the system ” s preferential decision between the target seeking and the obstacle avoidance.To verify the learning ability of our strategy,we developed an in-door environment with different experiments.The simulation results show that the RL presents an effective learning ability for the path planning for autonomous vehicles problems.
autonomous vehicle path planning reinforcement learning adaptive dynamic programming
Fang Xiao Xie Ping Gao Hongbo He Qun Li Naiyi
Chery Scientific Research Institute Wuhu;Shanghai Jiao Tong University Yanshan University Chery Scientific Research Institute Wuhu;Nanjing University of Science and Technology Chery Scientific Research Institute Wuhu
国内会议
上海
英文
2022-2025
2016-10-26（万方平台首次上网日期，不代表论文的发表时间）