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Modeling Interactions of Autonomous/Manual Vehicles and Pedestrians with a Multi-Agent Deep Deterministic Policy Gradient

Author

Listed:
  • Weichao Hu

    (School of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China
    Research Institute for Road Safety of the Ministry of Public Security, Beijing 100062, China)

  • Hongzhang Mu

    (Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100085, China
    School of Cyber Security, University of Chinese Academy of Sciences, Beijing 100085, China)

  • Yanyan Chen

    (School of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China)

  • Yixin Liu

    (Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China)

  • Xiaosong Li

    (Research Institute for Road Safety of the Ministry of Public Security, Beijing 100062, China)

Abstract

This article focuses on the development of a stable pedestrian crash avoidance mitigation system for autonomous vehicles (AVs). Previous works have only used simple AV–pedestrian models, which do not reflect the actual interaction and risk status of intelligent intersections with manual vehicles. The paper presents a model that simulates the interaction between automatic driving vehicles and pedestrians on unsignalized crosswalks using the multi-agent deep deterministic policy gradient (MADDPG) algorithm. The MADDPG algorithm optimizes the PCAM strategy through the continuous interaction of multiple independent agents and effectively captures the inherent uncertainty in continuous learning and human behavior. Experimental results show that the MADDPG model can fully mitigate collisions in different scenarios and outperforms the DDPG and DRL algorithms.

Suggested Citation

  • Weichao Hu & Hongzhang Mu & Yanyan Chen & Yixin Liu & Xiaosong Li, 2023. "Modeling Interactions of Autonomous/Manual Vehicles and Pedestrians with a Multi-Agent Deep Deterministic Policy Gradient," Sustainability, MDPI, vol. 15(7), pages 1-14, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:7:p:6156-:d:1114936
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    References listed on IDEAS

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    3. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
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