Comfortable driving control for connected automated vehicles based on deep reinforcement learning and knowledge transfer

Author:

Wu Chuna12,Chen Jing3ORCID,Yao Jinqiang4,Chen Tianyi4,Cao Jing5ORCID,Zhao Cong3ORCID

Affiliation:

1. Key Laboratory of MOT of Operation Safety Technology on Transport Vehicles Research Institute of Highway of Ministry of Transport Beijing China

2. Automotive Transportation Research Center Research Institute of Highway of Ministry of Transport Beijing China

3. Key Laboratory of Road and Traffic Engineering of the Ministry of Education Tongji University Shanghai China

4. ITS Branch Zhejiang Communications Investment Group Co., Ltd. Hangzhou China

5. Center for Automotive Intelligence and Future Mobility Society of Automotive Engineers of China Beijing China

Abstract

AbstractWith the development of connected automated vehicles (CAVs), preview and large‐scale road profile information detected by different vehicles become available for speed planning and active suspension control of CAVs to enhance ride comfort. Existing methods are not well adapted to rough pavements of different districts, where the distributions of road roughness are significantly different because of the traffic volume, maintenance, weather, etc. This study proposes a comfortable driving framework by coordinating speed planning and suspension control with knowledge transfer. Based on existing speed planning approaches, a deep reinforcement learning (DRL) algorithm is designed to learn comfortable suspension control strategies with preview road and speed information. Fine‐tuning and lateral connection are adopted to transfer the learned knowledge for adaptability in different districts. DRL‐based suspension control models are trained and transferred using real‐world rough pavement data in districts of Shanghai, China. The experimental results show that the proposed control method increases vertical comfort by 41.10% on rough pavements, compared to model predictive control. The proposed framework is proven to be applicable to stochastic rough pavements for CAVs.

Funder

National Key Research and Development Program of China

Publisher

Institution of Engineering and Technology (IET)

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