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Intelligent Land-Vehicle Model Transfer Trajectory Planning Method Based on Deep Reinforcement Learning

by 1,2,3, 1,*, 1 and 4
1
School of Information Science and Engineering, Central South University, Changsha 410083, China
2
State Key Laboratory of Robotics and System, Harbin Institute of Technology, Haerbin 150001, China
3
State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400044, China
4
School of Computer and Information Engineering, Hunan University of Commerce 410205, Changsha, China
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(9), 2905; https://doi.org/10.3390/s18092905
Received: 1 August 2018 / Revised: 28 August 2018 / Accepted: 29 August 2018 / Published: 1 September 2018
(This article belongs to the Section Intelligent Sensors)
To address the problem of model error and tracking dependence in the process of intelligent vehicle motion planning, an intelligent vehicle model transfer trajectory planning method based on deep reinforcement learning is proposed, which is able to obtain an effective control action sequence directly. Firstly, an abstract model of the real environment is extracted. On this basis, a deep deterministic policy gradient (DDPG) and a vehicle dynamic model are adopted to jointly train a reinforcement learning model, and to decide the optimal intelligent driving maneuver. Secondly, the actual scene is transferred to an equivalent virtual abstract scene using a transfer model. Furthermore, the control action and trajectory sequences are calculated according to the trained deep reinforcement learning model. Thirdly, the optimal trajectory sequence is selected according to an evaluation function in the real environment. Finally, the results demonstrate that the proposed method can deal with the problem of intelligent vehicle trajectory planning for continuous input and continuous output. The model transfer method improves the model’s generalization performance. Compared with traditional trajectory planning, the proposed method outputs continuous rotation-angle control sequences. Moreover, the lateral control errors are also reduced. View Full-Text
Keywords: intelligent driving vehicle; trajectory planning; end-to-end; deep reinforcement learning; model transfer intelligent driving vehicle; trajectory planning; end-to-end; deep reinforcement learning; model transfer
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MDPI and ACS Style

Yu, L.; Shao, X.; Wei, Y.; Zhou, K. Intelligent Land-Vehicle Model Transfer Trajectory Planning Method Based on Deep Reinforcement Learning. Sensors 2018, 18, 2905. https://doi.org/10.3390/s18092905

AMA Style

Yu L, Shao X, Wei Y, Zhou K. Intelligent Land-Vehicle Model Transfer Trajectory Planning Method Based on Deep Reinforcement Learning. Sensors. 2018; 18(9):2905. https://doi.org/10.3390/s18092905

Chicago/Turabian Style

Yu, Lingli, Xuanya Shao, Yadong Wei, and Kaijun Zhou. 2018. "Intelligent Land-Vehicle Model Transfer Trajectory Planning Method Based on Deep Reinforcement Learning" Sensors 18, no. 9: 2905. https://doi.org/10.3390/s18092905

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