IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i7p6156-d1114936.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/7/6156/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/7/6156/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sidney Afonso Sobrinho-Junior & Azriel Cancian Nepomuceno de Almeida & Amanda Aparecida Paniago Ceabras & Carolina Leonel da Silva Carvalho & Tayla Borges Lino & Gustavo Christofoletti, 2022. "Risks of Accidents Caused by the Use of Smartphone by Pedestrians Are Task- and Environment-Dependent," IJERPH, MDPI, vol. 19(16), pages 1-9, August.
    2. 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.
    3. Oriol Vinyals & Igor Babuschkin & Wojciech M. Czarnecki & Michaël Mathieu & Andrew Dudzik & Junyoung Chung & David H. Choi & Richard Powell & Timo Ewalds & Petko Georgiev & Junhyuk Oh & Dan Horgan & M, 2019. "Grandmaster level in StarCraft II using multi-agent reinforcement learning," Nature, Nature, vol. 575(7782), pages 350-354, November.
    Full references (including those not matched with items on IDEAS)

    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. Wang, Xianjia & Yang, Zhipeng & Liu, Yanli & Chen, Guici, 2023. "A reinforcement learning-based strategy updating model for the cooperative evolution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).
    2. Shuo Sun & Rundong Wang & Bo An, 2021. "Reinforcement Learning for Quantitative Trading," Papers 2109.13851, arXiv.org.
    3. Li, Wenqing & Ni, Shaoquan, 2022. "Train timetabling with the general learning environment and multi-agent deep reinforcement learning," Transportation Research Part B: Methodological, Elsevier, vol. 157(C), pages 230-251.
    4. Christoph Graf & Viktor Zobernig & Johannes Schmidt & Claude Klöckl, 2024. "Computational Performance of Deep Reinforcement Learning to Find Nash Equilibria," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 529-576, February.
    5. Wang, Xin & Liu, Shuo & Yu, Yifan & Yue, Shengzhi & Liu, Ying & Zhang, Fumin & Lin, Yuanshan, 2023. "Modeling collective motion for fish schooling via multi-agent reinforcement learning," Ecological Modelling, Elsevier, vol. 477(C).
    6. Weifan Long & Taixian Hou & Xiaoyi Wei & Shichao Yan & Peng Zhai & Lihua Zhang, 2023. "A Survey on Population-Based Deep Reinforcement Learning," Mathematics, MDPI, vol. 11(10), pages 1-17, May.
    7. Xuan-Kun Li & Jian-Xu Ma & Xiang-Yu Li & Jun-Jie Hu & Chuan-Yang Ding & Feng-Kai Han & Xiao-Min Guo & Xi Tan & Xian-Min Jin, 2024. "High-efficiency reinforcement learning with hybrid architecture photonic integrated circuit," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    8. János Kramár & Tom Eccles & Ian Gemp & Andrea Tacchetti & Kevin R. McKee & Mateusz Malinowski & Thore Graepel & Yoram Bachrach, 2022. "Negotiation and honesty in artificial intelligence methods for the board game of Diplomacy," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    9. Mohit Sewak & Sanjay K. Sahay & Hemant Rathore, 2023. "Deep Reinforcement Learning in the Advanced Cybersecurity Threat Detection and Protection," Information Systems Frontiers, Springer, vol. 25(2), pages 589-611, April.
    10. Tulika Saha & Sriparna Saha & Pushpak Bhattacharyya, 2020. "Towards sentiment aided dialogue policy learning for multi-intent conversations using hierarchical reinforcement learning," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-28, July.
    11. Mahmoud Mahfouz & Angelos Filos & Cyrine Chtourou & Joshua Lockhart & Samuel Assefa & Manuela Veloso & Danilo Mandic & Tucker Balch, 2019. "On the Importance of Opponent Modeling in Auction Markets," Papers 1911.12816, arXiv.org.
    12. Woo Jae Byun & Bumkyu Choi & Seongmin Kim & Joohyun Jo, 2023. "Practical Application of Deep Reinforcement Learning to Optimal Trade Execution," FinTech, MDPI, vol. 2(3), pages 1-16, June.
    13. Lu, Yu & Xiang, Yue & Huang, Yuan & Yu, Bin & Weng, Liguo & Liu, Junyong, 2023. "Deep reinforcement learning based optimal scheduling of active distribution system considering distributed generation, energy storage and flexible load," Energy, Elsevier, vol. 271(C).
    14. Yuhong Wang & Lei Chen & Hong Zhou & Xu Zhou & Zongsheng Zheng & Qi Zeng & Li Jiang & Liang Lu, 2021. "Flexible Transmission Network Expansion Planning Based on DQN Algorithm," Energies, MDPI, vol. 14(7), pages 1-21, April.
    15. Michelle M. LaMar, 2018. "Markov Decision Process Measurement Model," Psychometrika, Springer;The Psychometric Society, vol. 83(1), pages 67-88, March.
    16. Yang, Ting & Zhao, Liyuan & Li, Wei & Zomaya, Albert Y., 2021. "Dynamic energy dispatch strategy for integrated energy system based on improved deep reinforcement learning," Energy, Elsevier, vol. 235(C).
    17. Wang, Yi & Qiu, Dawei & Sun, Mingyang & Strbac, Goran & Gao, Zhiwei, 2023. "Secure energy management of multi-energy microgrid: A physical-informed safe reinforcement learning approach," Applied Energy, Elsevier, vol. 335(C).
    18. Julia Sajewicz & Alicja Dziuba-Słonina, 2023. "Texting on a Smartphone While Walking Affects Gait Parameters," IJERPH, MDPI, vol. 20(5), pages 1-8, March.
    19. Neha Soni & Enakshi Khular Sharma & Narotam Singh & Amita Kapoor, 2019. "Impact of Artificial Intelligence on Businesses: from Research, Innovation, Market Deployment to Future Shifts in Business Models," Papers 1905.02092, arXiv.org.
    20. Ande Chang & Yuting Ji & Chunguang Wang & Yiming Bie, 2024. "CVDMARL: A Communication-Enhanced Value Decomposition Multi-Agent Reinforcement Learning Traffic Signal Control Method," Sustainability, MDPI, vol. 16(5), pages 1-17, March.

    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:jsusta:v:15:y:2023:i:7:p:6156-:d:1114936. 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.