Intelligent Land-Vehicle Model Transfer Trajectory Planning Method Based on Deep Reinforcement Learning
AbstractTo 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
Share & Cite This Article
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.
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.Chicago/Turabian Style
Yu, Lingli; Shao, Xuanya; Wei, Yadong; Zhou, Kaijun. 2018. "Intelligent Land-Vehicle Model Transfer Trajectory Planning Method Based on Deep Reinforcement Learning." Sensors 18, no. 9: 2905.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.