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Sustainable Smart Cities through Multi-Agent Reinforcement Learning-Based Cooperative Autonomous Vehicles

Author

Listed:
  • Ali Louati

    (Department of Information Systems, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia)

  • Hassen Louati

    (SMART Lab, Higher Institute of Management, University of Tunis, Tunis 2000, Tunisia)

  • Elham Kariri

    (Department of Information Systems, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia)

  • Wafa Neifar

    (Arabic Natural Language Processing Research Group (ANLP-RG), MIRACL Laboratory, University of Sfax, Sfax 3018, Tunisia)

  • Mohamed K. Hassan

    (Faculty of Telecommunications Engineering, Future University, Khartoum 10553, Sudan)

  • Mutaz H. H. Khairi

    (Faculty of Engineering, Future University, Khartoum 10553, Sudan)

  • Mohammed A. Farahat

    (Computer Science Department, Higher Future Institute for Specialized Technological Studies, Cairo 11765, Egypt)

  • Heba M. El-Hoseny

    (Computer Science Department, Higher Future Institute for Specialized Technological Studies, Cairo 11765, Egypt)

Abstract

As urban centers evolve into smart cities, sustainable mobility emerges as a cornerstone for ensuring environmental integrity and enhancing quality of life. Autonomous vehicles (AVs) play a pivotal role in this transformation, with the potential to significantly improve efficiency and safety, and reduce environmental impacts. This study introduces a novel Multi-Agent Actor–Critic (MA2C) algorithm tailored for multi-AV lane-changing in mixed-traffic scenarios, a critical component of intelligent transportation systems in smart cities. By incorporating a local reward system that values efficiency, safety, and passenger comfort, and a parameter-sharing scheme that encourages inter-agent collaboration, our MA2C algorithm presents a comprehensive approach to urban traffic management. The MA2C algorithm leverages reinforcement learning to optimize lane-changing decisions, ensuring optimal traffic flow and enhancing both environmental sustainability and urban living standards. The actor–critic architecture is refined to minimize variances in urban traffic conditions, enhancing predictability and safety. The study extends to simulating realistic human-driven vehicle (HDV) behavior using the Intelligent Driver Model (IDM) and the model of Minimizing Overall Braking Induced by Lane changes (MOBIL), contributing to more accurate and effective traffic management strategies. Empirical results indicate that the MA2C algorithm outperforms existing state-of-the-art models in managing lane changes, passenger comfort, and inter-vehicle cooperation, essential for the dynamic environment of smart cities. The success of the MA2C algorithm in facilitating seamless interaction between AVs and HDVs holds promise for more fluid urban traffic conditions, reduced congestion, and lower emissions. This research contributes to the growing body of knowledge on autonomous driving within the framework of sustainable smart cities, focusing on the integration of AVs into the urban fabric. It underscores the potential of machine learning and artificial intelligence in developing transportation systems that are not only efficient and safe but also sustainable, supporting the broader goals of creating resilient, adaptive, and environmentally friendly urban spaces.

Suggested Citation

  • Ali Louati & Hassen Louati & Elham Kariri & Wafa Neifar & Mohamed K. Hassan & Mutaz H. H. Khairi & Mohammed A. Farahat & Heba M. El-Hoseny, 2024. "Sustainable Smart Cities through Multi-Agent Reinforcement Learning-Based Cooperative Autonomous Vehicles," Sustainability, MDPI, vol. 16(5), pages 1-18, February.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:5:p:1779-:d:1343192
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    References listed on IDEAS

    as
    1. Ali Louati & Elham Kariri, 2023. "Enhancing Intersection Performance for Tram and Connected Vehicles through a Collaborative Optimization," Sustainability, MDPI, vol. 15(12), pages 1-17, June.
    2. Shuo Feng & Haowei Sun & Xintao Yan & Haojie Zhu & Zhengxia Zou & Shengyin Shen & Henry X. Liu, 2023. "Dense reinforcement learning for safety validation of autonomous vehicles," Nature, Nature, vol. 615(7953), pages 620-627, March.
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