Urban Intersection Collision Warning System Utilizing Traffic Surveillance Cameras

Ensuring safety at intersections is a significant challenge, highlighting the urgent need for robust collision prevention systems. This study proposed an innovative Collision Warning (CW) system designed to proactively detect and warn of potential collisions between different traffic participants at urban intersections. The system seamlessly integrates real-time video surveillance, YOLOv3-based object detection, and sophisticated data processing technology to accurately identify collision risks and issue alerts in a timely manner. The system was validated using MATLAB and Unreal Engine for a variety of scenarios including vehicles, pedestrians, and cyclists, considering reaction times, communication delays, and braking distances, and ultimately proved to be capable of accurately identifying potential collision risks, even in complex scenarios with multiple interacting objects. The system output displays target locations and collision predictions, allowing operators to quickly perform evasive maneuvers to prevent potential collisions. The test results show that the system can correctly output warning information in all the test scenarios in the virtual environment, the system positioning error is less than 1m, and the reaction time reserved for the driver is more than 1.42s. This solution offers significant advantages over other CW systems in terms of flexibility, cost-effectiveness, and privacy protection.