AI-Based Autonomous System For Smart Vehicle Transportation For Traffic Optimization, And Accident Prevention
DOI:
https://doi.org/10.29284/6cvgss92Keywords:
Autonomous vehicles; Artificial intelligence; Traffic optimization; Accident prevention; V2X communication.Abstract
Rapid urbanization and increasing vehicle density have intensified traffic congestion and road safety challenges worldwide. Road traffic accidents account for nearly 1.19 million deaths annually, with human error contributing to more than 90% of crashes. This study proposes an AI-based autonomous system for smart vehicle transportation that integrates multi-sensor perception, risk-aware decision-making, and V2X-enabled cooperative intelligence to achieve traffic optimization and accident prevention simultaneously. Using a simulation-based experimental framework, the system is evaluated under varying traffic densities and connectivity conditions. The results indicate that AI-based autonomy reduces average travel time by 20–26%, increases traffic throughput by about 25%, and lowers collision rates by 40–70% compared to conventional traffic systems. Risk-aware control significantly improves safety margins, while V2X cooperation further enhances efficiency and stability by reducing queue lengths and secondary collisions. The findings confirm that safety and efficiency are complementary objectives when intelligent, cooperative AI systems are deployed, offering strong implications for smart city mobility and autonomous transport policy.
References
1. Badue, C., Guidolini, R., Carneiro, R. V., Azevedo, P., Cardoso, V. B., Forechi, A., Jesus, L., Berriel, R., Paixão, T., Mutz, F., Veronese, L., De Souza, A., & Oliveira-Santos, T. (2021). Self-driving cars: A survey. Expert Systems with Applications, 165, 113816.
2. Chen, X., Ma, H., Wan, J., Li, B., & Xia, T. (2015). Multi-view 3D object detection network for autonomous driving. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1907-1915.
3. Elvik, R. (2019). A comprehensive and unified framework for analysing the effects on road safety of measures influencing speed. Accident Analysis & Prevention, 130, 1-12.
4. Feng, S., Yu, H., & Liu, H. X. (2021). Multi-agent reinforcement learning for cooperative adaptive cruise control at road intersections. Transportation Research Part C: Emerging Technologies, 123, 102948.
5. Geiger, A., Lenz, P., Stiller, C., & Urtasun, R. (2013). Vision meets robotics: The KITTI dataset. The International Journal of Robotics Research, 32(11), 1231-1237.
6. Hydén, C., & Linderholm, L. (2015). The Swedish traffic conflict technique. International Transport Forum Discussion Papers, 2015/02.
7. INRIX. (2025). 2024 global traffic scorecard (press release).
8. Kiran, B. R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A. A., Yogamani, S., & Pérez, P. (2021). Deep reinforcement learning for autonomous driving: A survey. IEEE Transactions on Intelligent Transportation Systems, 23(6), 4909-4926.
9. Li, Z., Li, S. E., Li, R., & Cheng, B. (2017). Stability analysis of mixed traffic flow with human-driven and connected vehicles. Transportation Research Part C: Emerging Technologies, 85, 1-18.
10. Ministry of Road Transport and Highways. (2023). Annual report on “Road accidents in India-2022” (press release). Press Information Bureau, Government of India.
11. Papageorgiou, M., Ben-Akiva, M., Bottom, J., Bovy, P., Hoogendoorn, S., Hounsell, N., Kotsialos, A., McDonald, M., & Veldman, S. (2003). ITS and traffic management. Transportation Research Part A: Policy and Practice, 37(9), 781-807.
12. Rudenko, A., Palmieri, L., Herman, M., Kitani, K., Gavrila, D. M., & Arras, K. O. (2020). Human motion trajectory prediction: A survey. The International Journal of Robotics Research, 39(8), 895-935.
13. SaveLIFE Foundation. (2024). India’s road crash statistics: 2022 overview.
14. Shalev-Shwartz, S., Shammah, S., & Shashua, A. (2017). On a formal model of safe and scalable self-driving cars. arXiv preprint arXiv:1708.06374.
15. Talebpour, A., & Mahmassani, H. S. (2016). Influence of connected and autonomous vehicles on traffic flow stability and throughput. Transportation Research Part C: Emerging Technologies, 71, 143-163.
16. TomTom. (2025). TomTom traffic index 2025: India country overview / ranking.
17. Wang, J., Zhang, L., Huang, Q., & Xu, J. (2020). Deep reinforcement learning for traffic signal control: A review. IEEE Transactions on Intelligent Transportation Systems, 22(10), 1-15.
18. Wei, H., Zheng, G., Yao, H., & Li, Z. (2019). IntelliLight: A reinforcement learning approach for intelligent traffic light control. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2496-2505.
19. World Bank. (2020). India needs additional $100-plus billion for safer roads (Press release).
20. World Bank. (2021). How do the poor cope with road crashes in India? (Blog).
21. World Health Organization. (2023). Global status report on road safety 2023.
22. World Health Organization. (n.d.). Road traffic injuries (road safety overview).
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This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
