基于图像识别与动力学融合的路面附着系数估计方法

Accurate estimation of tire-road friction is a prerequisite for vehicle active safety control. Firstly，a single-wheel dynamics model is established，and precise estimation of the longitudinal tire force is realized using the Kalman filter. Then a particle filter（PF）-based tire-road friction estimator is developed based on the Magic Formula tire model. Secondly，a forward road adhesion coefficient prediction method based on image recognition is proposed. The DeeplabV3+，semantic segmentation network and the MobilNetV2 lightweight convolutional neural network are used for road segmentation and classification，based on which the tire-road friction is obtained through table look-up. Finally，the spatiotemporal synchronization method and fusion mechanism of dynamics and image recognition are established to realize effective correlation and reliable fusion of the two estimation methods. The Carsim-Simulink co-simulation shows that the proposed estimation method based on image recognition and dynamics fusion can efficiently improve the tire-road friction estimation accuracy.