A fusion estimation of the peak tire-road friction coefficient based on road images and dynamic information

To accurately acquire the peak tire-road friction coefficient, a fusion estimation framework combining vision and vehicle dynamic information is established. First, information for the road ahead is collected in advance from an image captured by a camera, and the road type with its typical range of tire-road friction coefficients is identified with a lightweight convolutional neural network. Then, an unscented Kalman filter (UKF) method is established to estimate the tire-road friction coefficient value directly according to the dynamic vehicle states. Next, the results from the road-type recognition and dynamic estimation methods are spatiotemporally synchronized. Finally, a confidence-based vision and vehicle dynamic fusion strategy is proposed to obtain an accurate peak tire-road friction coefficient. The virtual and real vehicle test results suggest that the proposed fusion estimation strategy can accurately determine the peak tire- road friction coefficient. The proposed strategy can more precisely acquire the tire-road friction coefficient than can the general vision-based estimation method and is superior to the dynamic -based estimation method in that it eliminates the need for sufficient tire excitation to some extent.