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

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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.

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Acknowledgements

This study is sponsored by open fund of China Design Group Co., Ltd. & Research and Development Center On ITS Technology and Equipment, Ministry of Transport (2020-04); Hubei Provincial Natural Science Foundation of China (2018CFC863, 2019CFC837); China Postdoctoral Science Foundation (2019M661913, 2018M642181); National Science Foundation of China (61906076); Natural Science Foundation of Jiangsu Province (BK20190853); JITRI Suzhou Automotive Research Institute Project (CEC20190404); KIT-JITRI-TSARI Collaboration Foundation; the Scientific Research Project of Huanggang Normal University.

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Authors and Affiliations

  1. Suzhou Automobile Research Institute, Tsinghua University, Suzhou, China

    Chuan Sun, Sifa Zheng & Yulin Ma

  2. School of Vehicle and Mobility, Tsinghua University, Beijing, China

    Chuan Sun & Sifa Zheng

  3. School of Electromechanical and Automobile Engineering, Huanggang Normal University, Huanggang, China

    Chuan Sun

  4. Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan, China

    Duanfeng Chu & Junru Yang

  5. China Design Group Co., Ltd., Nanjing, China

    Yuncheng Zhou

  6. Research and Development Center on ITS Technology and Equipment, Ministry of Transport, Nanjing, China

    Yuncheng Zhou

  7. Automotive Engineering Research Institute, Jiangsu University, Zhenjiang, China

    Yicheng Li

  8. International Department, The Affiliated High School of SCNU, Guangzhou, China

    Tingxuan Xu

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  1. Chuan Sun
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  2. Sifa Zheng
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  3. Yulin Ma
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  4. Duanfeng Chu
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  6. Yuncheng Zhou
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  7. Yicheng Li
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  8. Tingxuan Xu
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Correspondence to Yulin Ma.

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Sun, C., Zheng, S., Ma, Y. et al. An active safety control method of collision avoidance for intelligent connected vehicle based on driving risk perception. J Intell Manuf 32, 1249–1269 (2021). https://doi.org/10.1007/s10845-020-01605-x

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  • DOI: https://doi.org/10.1007/s10845-020-01605-x

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