Dynamic Polygon-Based Reverse Driving Detection Technique for Enhanced Road Safety

Reverse driving and lane collapse pose serious risks to road safety, especially on complex infrastructures such as multi-lane highways, intersections, and roundabouts. Existing detection systems often depend on rigid lane configurations and struggle to adapt to varied road geometries and environmental conditions. Prior works are typically limited to straight, multi-lane roads and rely on automated boundary extraction, making them unsuitable for irregular traffic layouts. To address this gap, the objectives of this research paper is to propose a vision-based detection system that combines the YOLOv8 object detector with a dynamic polygon-based zone management strategy. The system aims to detect reverse driving and lane collapse incidents in real time using CCTV footage, without requiring additional sensors. Its key novelty lies in manually configurable zones and the integration of ByteTrack for robust vehicle tracking across complex scenes. The system was tested under diverse real-world parameters, including different road types (single-lane, multi-lane, roundabouts), lighting conditions (day and night), and traffic behaviors (normal flow, reverse, and collapse) and visual evaluations highlight consistent and logically coherent results across scenarios, highlighting its practical effectiveness for real-time intelligent traffic monitoring.

@article{Kit2025,
  title     = {Dynamic Polygon-Based Reverse Driving Detection Technique for Enhanced Road Safety},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {6},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Tara Kit and Youngsun Han and Anand Nayyar and Tae-Kyung Kim},
  doi       = {10.14569/IJACSA.2025.0160686},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160686}
}