IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v20y2023i1p893-d1024287.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/20/1/893/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/20/1/893/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Chunjie & Xu, Chengcheng & Chen, Yusen & Li, Zhibin, 2024. "Development and experiment of an intelligent connected cooperative vehicle infrastructure system based on multiple V2I modes and BWM-IGR method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 635(C).
    2. Jing Cao & Haichao Ling & Tao Li & Shiyu Wang & Shengchuan Jiang & Cong Zhao, 2024. "A Graph-Based Scheme Generation Method for Variable Traffic Organization in Parking Lots," Sustainability, MDPI, vol. 16(11), pages 1-18, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:20:y:2023:i:1:p:893-:d:1024287. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.