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Safe, Efficient, and Comfortable Autonomous Driving Based on Cooperative Vehicle Infrastructure System

Author

Listed:
  • Jing Chen

    (Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China)

  • Cong Zhao

    (Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China)

  • Shengchuan Jiang

    (Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China)

  • Xinyuan Zhang

    (Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China)

  • Zhongxin Li

    (Shanghai Utopilot Technology Co., Ltd., Shanghai 201306, China)

  • Yuchuan Du

    (Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China)

Abstract

Traffic crashes, heavy congestion, and discomfort often occur on rough pavements due to human drivers’ imperfect decision-making for vehicle control. Autonomous vehicles (AVs) will flood onto urban roads to replace human drivers and improve driving performance in the near future. With the development of the cooperative vehicle infrastructure system (CVIS), multi-source road and traffic information can be collected by onboard or roadside sensors and integrated into a cloud. The information is updated and used for decision-making in real-time. This study proposes an intelligent speed control approach for AVs in CVISs using deep reinforcement learning (DRL) to improve safety, efficiency, and ride comfort. First, the irregular and fluctuating road profiles of rough pavements are represented by maximum comfortable speeds on segments via vertical comfort evaluation. A DRL-based speed control model is then designed to learn safe, efficient, and comfortable car-following behavior based on road and traffic information. Specifically, the model is trained and tested in a stochastic environment using data sampled from 1341 car-following events collected in California and 110 rough pavements detected in Shanghai. The experimental results show that the DRL-based speed control model can improve computational efficiency, driving efficiency, longitudinal comfort, and vertical comfort in cars by 93.47%, 26.99%, 58.33%, and 6.05%, respectively, compared to a model predictive control-based adaptive cruise control. The results indicate that the proposed intelligent speed control approach for AVs is effective on rough pavements and has excellent potential for practical application.

Suggested Citation

  • Jing Chen & Cong Zhao & Shengchuan Jiang & Xinyuan Zhang & Zhongxin Li & Yuchuan Du, 2023. "Safe, Efficient, and Comfortable Autonomous Driving Based on Cooperative Vehicle Infrastructure System," IJERPH, MDPI, vol. 20(1), pages 1-18, January.
  • Handle: RePEc:gam:jijerp:v:20:y:2023:i:1:p:893-:d:1024287
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    References listed on IDEAS

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    1. Cong Zhao & Delong Ding & Zhouyang Du & Yupeng Shi & Guimin Su & Shanchuan Yu, 2023. "Analysis of Perception Accuracy of Roadside Millimeter-Wave Radar for Traffic Risk Assessment and Early Warning Systems," IJERPH, MDPI, vol. 20(1), pages 1-21, January.
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    Cited by:

    1. Cong Zhao & Delong Ding & Zhouyang Du & Yupeng Shi & Guimin Su & Shanchuan Yu, 2023. "Analysis of Perception Accuracy of Roadside Millimeter-Wave Radar for Traffic Risk Assessment and Early Warning Systems," IJERPH, MDPI, vol. 20(1), pages 1-21, January.

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