A Adaptive Collision Warning System Based on the Recognition of Slippery Road Conditions

Aiming at the problems of slow detection speed, large prediction error of warning area and weak environmental adaptability of the current machine vision-based vehicle collision warning technology, this paper proposes a collision warning system based on the recognition of slippery road conditions. Firstly, this paper uses the on-board camera to monitor the environment and road conditions in front of the vehicle in real time, and uses the YOLOv5 algorithm to detect the vehicle in front of it in real time, while accurately identifying the current wet state of the road, such as dry and slippery, through the ResNet50 model in the convolutional neural network. Secondly, a driving safety distance model with adaptive traffic environment characteristics is established by combining different road environments and driving conditions, and an early warning area is generated that changes dynamically with the speed of the vehicle and the slippery state of the road. Finally, possible collisions are predicted and warned in time, based on the relationship between the area of the warning and the position of the vehicle. Experimental results show that the method proposed in this paper improves the overall warning accuracy by 6.72% and reduces the warning false alarm rate for oncoming traffic on both sides by 16.67% compared with the traditional risk warning algorithm. Its application in practical driving can effectively ensure the safety of the driver and has a high application value.