Data-driving Reinforcement Learning on the Path Planning for Autonomous Vehicles
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- 英文论文题名：Data-driving Reinforcement Learning on the Path Planning for Autonomous Vehicles
- 论文作者：Fang Xiao ; Xie Ping ; Gao Hongbo ; He Qun ; Li Naiyi
- 英文论文作者：Fang Xiao ; Xie Ping ; Gao Hongbo ; He Qun ; Li Naiyi ; Chery Scientific Research Institute Wuhu ; Shanghai Jiao Tong University ; Yanshan University ; Nanjing University of Science and Technology
- 年：2016
- 作者机构：Chery Scientific Research Institute Wuhu;Shanghai Jiao Tong University;Yanshan University;Nanjing University of Science and Technology;
- 英文论文关键词：autonomous vehicle ; path planning ; reinforcement learning ; adaptive dynamic programming
- 会议召开时间：2016-10-26
- 会议录名称：2016中国汽车工程学会年会论文集
- 英文会议录名称：Proceedings of 2016 SAE-China Congress
- 语种：英文
- 分类号：U463.6
- 学会代码：QCGC
- 会议名称：2016中国汽车工程学会年会暨展览会
- 会议地点：中国上海
- 主办单位：中国汽车工程学会
- 学会名称：中国汽车工程学会
- 页数：4
- 文件大小：214k
- 原文格式：O
- 会议级别：全国
摘要
The path planning for autonomous vehicles is a hot topic in academic world.The goal of this problem is to design a vehicle 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.
The path planning for autonomous vehicles is a hot topic in academic world.The goal of this problem is to design a vehicle 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.
The path planning for autonomous vehicles is a hot topic in academic world.The goal of this problem is to design a vehicle 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.
引文
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[8]X Fang,D Zheng.Goal Seeking of Autonomous Mobile Robot with Obstacle Avoidance using Heuristic Dynamic Programming[J].Transactions of the Chinese Society for Agricultural Machinery,2014,45(7):73-78.
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[10]J Si,Y Wang.On-line Learning Control by Association and Reinforcement[J].IEEE Transactions on Neural Networks,2001,12(2):264-271.
[11]X Fang,D Zheng.Control Strategy Design for Car Autonomous Navigation using Adaptive Dynamic Programming[J].Journal of Yanshan University,2014,38(1):57-65.
[12]H He,Z Ni,J Fu.A Three-network Architecture for On-line learning and Optimization Based on Adaptive Dynamic Programming[J].Neurocomputing,2012,78(1):3-13.
[13]J Si,AG Barto,W B Powell,et al.Handbook of Learning and Approximate Dynamic Programming[M].IEEE Press,2004.
[2]A Zhu,S X Yang.Neurofuzzy-Based Approach to Mobile Robot Navigation in Unknown Environments[J].IEEE Transaction on Systems,Man and Cybernetics,Part C;Application and reviews,2007,37(4):610-621.
[3]X Fang,X Chen,H Gao,et al.Self-driving Car Navigation with Obstacle Avoidance Using Reinforcement Learning[C].SAE-China Congress Proceedings,2015:498-501.
[4]R S Sutton,A G Barto.Reinforcement Learning:An Introduction[M].Cambridge,MA;MIT Press,1998.
[5]LP Kaelbling,ML Littman,A W Moore.Reinforcement Learning:A Survey[J].Journal of Artificial Intelligence Research,19%,4:237-285.
[6]X Fang,D Zheng,H He,et al.Data-driven Heuristic Dynamic Programming with Virtual Reality[J].Neurocomputing,2015,166;244-255.
[7]Z Ni,X Fang,H He,et al.Real-time Tracking on Adaptive Critic Design with Uniformly Ultimately Bounded Condition[C].IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning(ADPRL'13),IEEE Symposium Series on Computational Intelligence(SSCI),2013:39-46.
[8]X Fang,D Zheng.Goal Seeking of Autonomous Mobile Robot with Obstacle Avoidance using Heuristic Dynamic Programming[J].Transactions of the Chinese Society for Agricultural Machinery,2014,45(7):73-78.
[9]X Fang,H He,Z Ni,et al.Learning and Control in Virtual Reality for Machine Intelligence[C].IEEE International Conference on Intelligent Control and Information Processing,2012:63-64.
[10]J Si,Y Wang.On-line Learning Control by Association and Reinforcement[J].IEEE Transactions on Neural Networks,2001,12(2):264-271.
[11]X Fang,D Zheng.Control Strategy Design for Car Autonomous Navigation using Adaptive Dynamic Programming[J].Journal of Yanshan University,2014,38(1):57-65.
[12]H He,Z Ni,J Fu.A Three-network Architecture for On-line learning and Optimization Based on Adaptive Dynamic Programming[J].Neurocomputing,2012,78(1):3-13.
[13]J Si,AG Barto,W B Powell,et al.Handbook of Learning and Approximate Dynamic Programming[M].IEEE Press,2004.