Anti-Skid System for Ice-Snow Curve Road Surface Based on Visual Recognition and Vehicle Dynamics

Preventing skidding is essential for studying the safety of driving in curves. However, the adhesion of the vehicle during the driving process on the wet and slippery road will be significantly reduced, resulting in lateral slippage due to the low adhesion coefficient of the road surface, the speed exceeding the turning critical, and the turning radius being too small when passing through the corner. Therefore, to reduce the incidence of traffic accidents of passenger cars driving in curves on rainy and snowy days and achieve the purpose of planning safe driving speed, this paper proposes a curve active safety system based on a deep learning algorithm and vehicle dynamics model. First,we a convolutional neural network (CNN) model is constructed to extract and judge the characteristics of snow and ice adhesion on roads. By training the residual network, the road surface can be identified and classified under 7 different weather conditions, and the adhesion coefficient of the road surface at this time can be obtained. In addition, the magic formula is used to establish a tire curve driving dynamics model and combined with the curve radius and other parameters to solve the safety speed threshold in the curve driving process. Finally, MATLAB and CarSim software are used to build a simulation platform for verification, and real vehicle experiments verify that the system has strong reliability and robustness. The research shows that the prediction accuracy of the training set and verification set of the system reaches 93.7% and 85.93% respectively. Compared with the traditional back propagation (BP) neural network method, the recognition accuracy of the road adhesion coefficient is improved by 4.53%. Therefore, the recognition algorithm combined with road surface parameter information in this paper has higher prediction accuracy and robustness, which can significantly improve the safety of vehicle driving on curves on rainy and snowy days.