On the tyre–road friction coefficient fusion identification framework utilising computer vision-based and vehicle dynamics-based road surface information

The tyre–road friction coefficient (TFC) is a critical structural parameter in the vehicle–road closed-loop system for intelligent safety and automated driving functions. However, it is vulnerable to the variations of the environment conditions and road surface states, thereby difficult to be determined in advance. This article proposes the TFC fusion identification framework, which primarily comprises the computer vision-based road surface image classification module, the vehicle dynamics-based TFC observation module, and the reliability factor-based road surface information fusion module. First, full-frame images are transformed into block images of eight types of road surface and a special category of interfering objects. These block images are then fed into the road surface block image classification network under the GhostNetV2-based contrastive learning framework. The outputs of the proposed classification network are integrated using the evidence reasoning-based road surface block information fusion strategy to obtain the classification results for the left and right sides, which are subsequently converted into computer vision-based TFCs through mapping and spatio-temporal synchronisation. Meanwhile, the primary–secondary adaptive unscented Kalman filter (UKF) is constructed based on tyre force observation and the modified Dugoff model to acquire vehicle dynamics-based TFCs. The reliability factor-based TFC fusion strategy is then proposed to integrate the two types of TFCs. A series of offline and field tests demonstrate that the proposed classification network exhibits high precision in the block image classification task compared to those under GhostNetV2, MobileNetV3, and ShuffleNetV2 with augmentation techniques, and the proposed identification framework presents rapid responsiveness and strong robustness when compared with TFC observation algorithms based solely on the UKF and the primary–secondary adaptive UKF.