Design and experimental validation of a cooperative adaptive cruise control system based on supervised reinforcement learning

Shouyang Wei, Yuan Zou*, Tao Zhang, Xudong Zhang, Wenwei Wang

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

33 引用 (Scopus)

摘要

This paper presents a supervised reinforcement learning (SRL)-based framework for longitudinal vehicle dynamics control of cooperative adaptive cruise control (CACC) system. A supervisor network trained by real driving data is incorporated into the actor-critic reinforcement learning approach. In the SRL training process, the actor and critic network are updated under the guidance of the supervisor and the gain scheduler. As a result, the training success rate is improved, and the driver characteristics can be learned by the actor to achieve a human-like CACC controller. The SRL-based control policy is compared with a linear controller in typical driving situations through simulation, and the control policies trained by drivers with different driving styles are compared using a real driving cycle. Furthermore, the proposed control strategy is demonstrated by a real vehicle-following experiment with different time headways. The simulation and experimental results not only validate the effectiveness and adaptability of the SRL-based CACC system, but also show that it can provide natural following performance like human driving.

源语言英语
文章编号1014
期刊Applied Sciences (Switzerland)
8
7
DOI
出版状态已出版 - 21 6月 2018

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