Preprint Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 2 August 2018 / Approved: 2 August 2018 / Online: 2 August 2018 (13:06:39 CEST)

A peer-reviewed article of this Preprint also exists.

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, 2905.

Journal reference: Sensors 2018, 18, 2905
DOI: 10.3390/s18092905

Abstract

Aiming at 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 obtain an effective control action sequence directly. Firstly, an abstract model of the real environment is extracted. On this basis, Deep Deterministic Policy Gradient (DDPG) and 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 equivalent virtual abstract scene by transfer model, furthermore, the control action and trajectory sequences are calculated according to trained deep reinforcement learning model. Thirdly, the optimal trajectory sequence is selected according to 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 generalization performance. Compared with the traditional trajectory planning, the proposed method output continuous rotation angle control sequence, meanwhile, the lateral control error is also reduced.

Subject Areas

intelligent driving vehicle; trajectory planning; end-to-end; deep reinforcement learning; model transfer

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