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Modified Driving Safety Field Based on Trajectory Prediction Model for Pedestrian–Vehicle Collision

Author

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  • Renfei Wu

    (Department of Transportation, Nanjing University of Science and Technology, Nanjing 210094, China)

  • Xunjia Zheng

    (State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China)

  • Yongneng Xu

    (Department of Transportation, Nanjing University of Science and Technology, Nanjing 210094, China)

  • Wei Wu

    (Department of Transportation, Nanjing University of Science and Technology, Nanjing 210094, China)

  • Guopeng Li

    (College of Information and Communication, National University of Defense Technology, Xian 710106, China)

  • Qing Xu

    (State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China)

  • Zhuming Nie

    (Department of Education Technology, School of Educational Science Anhui Normal University, Wuhu 241000, China)

Abstract

Pedestrian–vehicle collision is an important component of traffic accidents. Over the past decades, it has become the focus of academic and industrial research and presents an important challenge. This study proposes a modified Driving Safety Field (DSF) model for pedestrian–vehicle risk assessment at an unsignalized road section, in which predicted positions are considered. A Dynamic Bayesian Network (DBN) model is employed for pedestrian intention inference, and a particle filtering model is conducted to simulate pedestrian motion. Driving data collection was conducted and pedestrian–vehicle scenarios were extracted. The effectiveness of the proposed model was evaluated by Monte Carlo simulations running 1000 times. Results show that the proposed risk assessment approach reduces braking times by 18.73%. Besides this, the average value of TTC −1 (the reciprocal of time-to-collision) and the maximum TTC −1 were decreased by 28.83% and 33.91%, respectively.

Suggested Citation

  • Renfei Wu & Xunjia Zheng & Yongneng Xu & Wei Wu & Guopeng Li & Qing Xu & Zhuming Nie, 2019. "Modified Driving Safety Field Based on Trajectory Prediction Model for Pedestrian–Vehicle Collision," Sustainability, MDPI, vol. 11(22), pages 1-15, November.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:22:p:6254-:d:284623
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    References listed on IDEAS

    as
    1. Zhou, Zhuping & Cai, Yifei & Ke, Ruimin & Yang, Jiwei, 2017. "A collision avoidance model for two-pedestrian groups: Considering random avoidance patterns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 475(C), pages 142-154.
    2. Lord, Dominique & Mannering, Fred, 2010. "The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(5), pages 291-305, June.
    3. Catherine Staton & Joao Vissoci & Enying Gong & Nicole Toomey & Rebeccah Wafula & Jihad Abdelgadir & Yi Zhou & Chen Liu & Fengdi Pei & Brittany Zick & Camille D Ratliff & Claire Rotich & Nicole Jadue , 2016. "Road Traffic Injury Prevention Initiatives: A Systematic Review and Metasummary of Effectiveness in Low and Middle Income Countries," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-15, January.
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    Cited by:

    1. Cheng Peng & Chenxiao Ma & Yunhao Dong, 2023. "Unravelling the Formation Mechanism of Sustainable Underground Pedestrian Systems: Two Case Studies in Shanghai," Sustainability, MDPI, vol. 15(15), pages 1-23, August.
    2. Chen, Kai & Zhao, Xiaodong & Huang, Yujie & Fang, Guoyu, 2025. "SocialTrans: Transformer based social intentions interaction for pedestrian trajectory prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 663(C).
    3. Matteo Miani & Matteo Dunnhofer & Christian Micheloni & Andrea Marini & Nicola Baldo, 2021. "Surrogate Safety Measures Prediction at Multiple Timescales in V2P Conflicts Based on Gated Recurrent Unit," Sustainability, MDPI, vol. 13(17), pages 1-18, August.
    4. Ma, Guodong & Sun, Baofeng & Yuan, Quan & Yang, Wenyu, 2025. "The connected vehicle microscopic behavior modeling base on risk field theory: Theoretical developments, methodological overview and future trends," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 668(C).

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