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. 14, 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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References

  1. G. A. A. Al-Juboori and K. Al-Jumaily, "A review of swarm robotics: Principles and applications," Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 2, pp. 141-153, 2022.
  2. F. L. de Melo, R. A. S. Ferreira, and J. C. de Araújo, "Communication in swarm robotics: A survey," Robotics and Autonomous Systems, vol. 98, pp. 1-17, 2017.
  3. D. P. de Oliveira, C. F. C. Lima, and T. L. R. Barros, "A Survey on the Use of Machine Learning for Autonomous Vehicles," IEEE Access, vol. 9, pp. 16323-16346, 2021.
  4. R. D'Souza and A. K. Dhamija, "Swarm robotics: A new paradigm for autonomous traffic management," Computers, Environment and Urban Systems, vol. 88, pp. 101068, 2021.
  5. P. K. Kumar and B. H. Gupta, "Review of V2X communication technologies for smart transportation systems," IEEE Communications Surveys & Tutorials, vol. 21, no. 1, pp. 145-171, 2019.
  6. C. C. Ko, W. H. Liu, and W. Y. Wang, "An overview of reinforcement learning techniques in autonomous vehicles," Journal of Intelligent & Robotic Systems, vol. 99, no. 3-4, pp. 561-575, 2020.
  7. Kasaraneni, Ramana Kumar. "AI-Enhanced Virtual Screening for Drug Repurposing: Accelerating the Identification of New Uses for Existing Drugs." Hong Kong Journal of AI and Medicine 1.2 (2021): 129-161.
  8. Ahmad, Tanzeem, et al. "Hybrid Project Management: Combining Agile and Traditional Approaches." Distributed Learning and Broad Applications in Scientific Research 4 (2018): 122-145.
  9. Sahu, Mohit Kumar. "AI-Based Supply Chain Optimization in Manufacturing: Enhancing Demand Forecasting and Inventory Management." Journal of Science & Technology 1.1 (2020): 424-464.
  10. Pattyam, Sandeep Pushyamitra. "Data Engineering for Business Intelligence: Techniques for ETL, Data Integration, and Real-Time Reporting." Hong Kong Journal of AI and Medicine 1.2 (2021): 1-54.
  11. Bonam, Venkata Sri Manoj, et al. "Secure Multi-Party Computation for Privacy-Preserving Data Analytics in Cybersecurity." Cybersecurity and Network Defense Research 1.1 (2021): 20-38.
  12. Thota, Shashi, et al. "Federated Learning: Privacy-Preserving Collaborative Machine Learning." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 168-190.
  13. Jahangir, Zeib, et al. "From Data to Decisions: The AI Revolution in Diabetes Care." International Journal 10.5 (2023): 1162-1179.
  14. Z. M. El-Shafee and A. El-Shafee, "A review of deep learning methods for object detection in autonomous vehicles," Neural Computing and Applications, vol. 33, pp. 10269-10288, 2021.
  15. C. Li, J. Zhang, and Z. Liu, "Traffic management in intelligent transportation systems using swarm intelligence," IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 3, pp. 1445-1454, 2021.
  16. D. A. de Lima and P. S. Gonçalves, "The role of artificial intelligence in the future of autonomous vehicles," IEEE Intelligent Transportation Systems Magazine, vol. 11, no. 2, pp. 27-35, 2019.
  17. L. A. Alcaide, F. J. Garcia, and A. L. F. Dominguez, "V2X communications for connected vehicles: A survey," Journal of Network and Computer Applications, vol. 164, pp. 102683, 2020.
  18. S. M. Sundararajan and S. V. Kumar, "Challenges in implementing AI in autonomous vehicles," IEEE Transactions on Vehicular Technology, vol. 68, no. 11, pp. 10673-10682, 2019.
  19. R. C. M. L. Ribeiro, R. R. de Souza, and A. J. T. de Oliveira, "Ethical implications of autonomous vehicles: A review," IEEE Access, vol. 9, pp. 107523-107535, 2021.
  20. H. G. R. S. Oliveira and M. E. de Oliveira, "Autonomous vehicle fleet management using machine learning techniques," IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 4, pp. 3010-3020, 2022.
  21. C. F. Liu and C. Huang, "Smart city traffic management using AI and IoT technologies," IEEE Internet of Things Journal, vol. 7, no. 9, pp. 7491-7500, 2020.
  22. X. Li, D. Patil, and P. S. V. Kumar, "An overview of swarm intelligence for traffic management," Computational Intelligence and Neuroscience, vol. 2022, pp. 1-12, 2022.
  23. S. Z. L. Wang and R. S. Chen, "Challenges and solutions for autonomous vehicle communication systems," IEEE Communications Magazine, vol. 57, no. 3, pp. 94-101, 2019.
  24. Q. Huang, "The potential impact of V2X communication on traffic efficiency," Transportation Research Part C: Emerging Technologies, vol. 109, pp. 195-211, 2019.
  25. J. Briz and M. J. R. M. D. Vega, "Adaptive traffic signal control using swarm robotics," IEEE Transactions on Automation Science and Engineering, vol. 18, no. 2, pp. 897-905, 2021.
  26. F. Chen and S. H. Zhang, "Survey of reinforcement learning applications in intelligent transportation systems," IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 1, pp. 1-14, 2021.
  27. W. C. Lee, "Public acceptance of autonomous vehicles: An empirical study," IEEE Transactions on Human-Machine Systems, vol. 51, no. 3, pp. 338-347, 2021.

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