Vol. 3 No. 2 (2023): Journal of AI-Assisted Scientific Discovery
Articles

Autonomous Vehicle Swarm Robotics: Real-Time Coordination Using AI for Urban Traffic and Fleet Management

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  • Jaswinder Singh
Jaswinder Singh
Director, Data Wiser Technologies Inc., Brampton, Canada
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Published 17-08-2023

Keywords

  • swarm robotics,
  • autonomous vehicles,
  • vehicle-to-vehicle communication,
  • urban traffic management
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
J. Singh, “Autonomous Vehicle Swarm Robotics: Real-Time Coordination Using AI for Urban Traffic and Fleet Management”, Journal of AI-Assisted Scientific Discovery, vol. 3, no. 2, pp. 1–44, Aug. 2023, Accessed: Nov. 21, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/183

Abstract

Autonomous vehicle swarm robotics represents a transformative advancement in the field of intelligent transportation systems, with significant implications for urban traffic management and fleet optimization. This research paper delves into the application of artificial intelligence (AI) and machine learning techniques in real-time coordination of autonomous vehicle swarms, addressing the complexities of vehicle-to-vehicle (V2V) communication, traffic congestion reduction, and overall fleet efficiency. Swarm robotics, inspired by natural systems like insect colonies and bird flocks, provides a decentralized and scalable framework for coordinating large groups of autonomous vehicles in dynamic urban environments. The principles of swarm intelligence, particularly distributed problem-solving, collective behavior, and adaptive response to changing conditions, are pivotal to the success of this approach in managing traffic and logistics challenges.

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