| Issue |
ITM Web Conf.
Volume 82, 2026
International Conference on NextGen Engineering Technologies and Applications for Sustainable Development (ICNEXTS’25)
|
|
|---|---|---|
| Article Number | 03021 | |
| Number of page(s) | 8 | |
| Section | Information and Technology | |
| DOI | https://doi.org/10.1051/itmconf/20268203021 | |
| Published online | 04 February 2026 | |
Collision Alert System: Computer Vision for Vehicle Safety
1 Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education Krishnankoil, Virudhunagar, Tamil Nadu, India, This email address is being protected from spambots. You need JavaScript enabled to view it.
2 Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil, Virudhunagar, Tamil Nadu, India, This email address is being protected from spambots. You need JavaScript enabled to view it.
3 Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education Krishnankoil, Virudhunagar, Tamil Nadu, India This email address is being protected from spambots. You need JavaScript enabled to view it.
4 Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil, Virudhunagar, Tamil Nadu, India, This email address is being protected from spambots. You need JavaScript enabled to view it.
5 Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil, Virudhunagar, Tamil Nadu, India This email address is being protected from spambots. You need JavaScript enabled to view it.
6 Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil, Virudhunagar, Tamil Nadu, India This email address is being protected from spambots. You need JavaScript enabled to view it.
This paper introduces an innovative, vision-based Collision Alert System (CAS) aimed at significantly enhancing vehicle safety in complex driving environments. Our approach advances beyond existing YOLO-based Advanced Driver- Assistance Systems (ADAS) through its unique integration of YOLOv8’s real-time object detection with specialized Dynamic Region of Interest (ROI) processing and Angular Methods for precise trajectory analysis. This framework, combined with robust object tracking, enables proactive collision prediction. We detail the system’s architecture and operational flow, emphasizing its real-time performance. Rigorous evaluation confirms a True Positive Rate exceeding 95% and a remarkably low False Positive Rate under 2%, while maintaining over 30 Frames Per Second (FPS) for timely driver alerts. The paper also discusses core algorithmic principles and effective strategies for addressing real- world challenges like variable illumination and camera instability. A video demonstrating the system’s live operation is available online. Future work focuses on multi-camera data fusion and integration with active vehicle control.
© The Authors, published by EDP Sciences, 2026
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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