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An active safety control method of collision avoidance for intelligent connected vehicle based on driving risk perception

Author

Listed:
  • Chuan Sun

    (Tsinghua University
    Tsinghua University
    Huanggang Normal University)

  • Sifa Zheng

    (Tsinghua University
    Tsinghua University)

  • Yulin Ma

    (Tsinghua University)

  • Duanfeng Chu

    (Wuhan University of Technology)

  • Junru Yang

    (Wuhan University of Technology)

  • Yuncheng Zhou

    (China Design Group Co., Ltd.
    Ministry of Transport)

  • Yicheng Li

    (Jiangsu University)

  • Tingxuan Xu

    (The Affiliated High School of SCNU)

Abstract

As the complex driving scenarios bring about an opportunity for application of deep learning in safe driving, artificial intelligence based on deep learning has become a heatedly discussed topic in the field of advanced driving assistance system. This paper focuses on analysing vehicle active safety control of collision avoidance for intelligent connected vehicles (ICVs) in a real driving risk scenario, and driving risk perception is based on the ICV technology. In this way, trajectories of surrounding vehicles can be predicted and tracked in a real-time manner. In this paper, vehicle dynamics based state-space equations conforming to model predictive controllers are set up to primarily explore and identify a safety domain of active collision avoidance. Furthermore, the model predictive controller is also designed and calibrated, thereby implementing the active collision avoidance strategy for vehicles based on the model predictive control method. At last, functional testing is conducted for the proposed active collision avoidance control strategy in a designed complex traffic scenario. The research findings here can effectively improve automatic driving, intelligent transportation efficiency and road traffic safety.

Suggested Citation

  • Chuan Sun & Sifa Zheng & Yulin Ma & Duanfeng Chu & Junru Yang & Yuncheng Zhou & Yicheng Li & Tingxuan Xu, 2021. "An active safety control method of collision avoidance for intelligent connected vehicle based on driving risk perception," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1249-1269, June.
    • Handle: RePEc:spr:joinma:v:32:y:2021:i:5:d:10.1007_s10845-020-01605-x
    DOI: 10.1007/s10845-020-01605-x
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    References listed on IDEAS

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    1. Erick J. Rodríguez-Seda & Dušan M. Stipanović & Mark W. Spong, 2016. "Guaranteed Collision Avoidance for Autonomous Systems with Acceleration Constraints and Sensing Uncertainties," Journal of Optimization Theory and Applications, Springer, vol. 168(3), pages 1014-1038, March.
    2. Sullivan-Wiley, Kira A. & Short Gianotti, Anne G., 2017. "Risk Perception in a Multi-Hazard Environment," World Development, Elsevier, vol. 97(C), pages 138-152.
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    5. Duck Bong Kim, 2019. "An approach for composing predictive models from disparate knowledge sources in smart manufacturing environments," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1999-2012, April.
    6. Wang, Jian & Gong, Siyuan & Peeta, Srinivas & Lu, Lili, 2019. "A real-time deployable model predictive control-based cooperative platooning approach for connected and autonomous vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 128(C), pages 271-301.
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