Data-Driven Detection of Hit-and-Run Truck Incidents Using Computer Vision and Telemetry

Hit-and-run incidents involving heavy-duty trucks are a persistent and dangerous phenomenon on highways, especially in developing countries. Despite the rise in road safety technologies, the ability to track, identify, and respond to trucks that flee accident scenes remains insufficient. This paper proposes a real-time, AI-powered detection system that uses computer vision and vehicular telemetry to identify trucks involved in collisions and monitor their post-accident behavior. The framework integrates visual input (license plate recognition, object tracking) with vehicular data (GPS trajectory, acceleration, CAN bus signals) to classify “escape behavior” patterns such as rapid acceleration, route deviation, and abnormal braking following collision-like events. A hybrid model combining YOLOv8, custom LPR modules, and route deviation analysis detects hit-and-run suspects with over 92% accuracy in simulated environments. Telemetry anomalies such as high jerk values (derivative of acceleration) and abrupt heading changes serve as core behavioral indicators. All processing occurs on edge hardware (e.g., NVIDIA Jetson Nano) ensuring real-time performance and privacy preservation. This system creates a new post-accident response paradigm—where trucks are not only monitored for fatigue (as in previous research) but also held accountable for unsafe or evasive actions. Together, these intelligent modules form a comprehensive smart-truck safety suite for both accident prevention and accountability. The implementation is available on the presented GitHub repository,