A Vision-Based Vehicle Collision Warning Algorithm Integrating YOLOv8 and DeepSORT

With the continuous development of autonomous driving technology, vehicle collision warning systems are playing an increasingly important role in this field, promoting the progress and improvement of the whole autonomous driving field. But existing methods have low detection rates and unstable multi-target tracking performance. In particular, the estimation of relative motion parameters is still inaccurate due to the loss of direction information when relative speed is described as a scalar quantity. These limits produce easy collisions in the judgment of the car in a dangerous traffic environment. To solve these problems, a vehicle collision warning algorithm based on YOLOv8 and DeepSORT is proposed in this paper. YOLOv8 is introduced to detect the vehicle precisely, and DeepSORT is used to enhance multi-target vehicle tracking. The geometric principles of monocular vision are applied to extract key motion parameters such as distance and direction-signed relative speed. A classification logic is designed to distinguish between positive and negative relative velocities, enabling more accurate judgment of collision risk levels. In order to further enhance the reliability of the system, a four-stage cascaded false-alarm suppression mechanism is proposed. By adding velocity direction validation, distance validity checks, adaptive confidence thresholds, and a temporal consistency verification mechanism, the false alarm rate is reduced, and the proposed approach can realize direction- aware velocity estimation without requiring additional sensors and can be easily integrated into the existing YOLOv8 perception system.