LAUSR

Abstract Tire-road peak adhesion coefficient is not only a key parameter to achieve accurate vehicle motion control, but also an important input for decision-making and planning of intelligent vehicles. The estimation method should be timely and reliable to meet requirements of decision, planning and control, which means the tire and road maximum adhesion ability should be identified before reaching it to ensure vehicle safety. In this paper, a disturbance observer of tire force and tire-road peak adhesion coefficient is designed based on the modified Burckhardt tire model. In order to improve the convergence speed of road estimation algorithm, a tire-road peak adhesion coefficient estimation method based on vehicle-mounted camera is designed. The color and texture features of road surface are extracted by color moment method and gray level co-occurrence matrix method, and the road surface is classified based on support vector machine. The fusion strategy of dynamic estimator and visual estimator is designed based on gain scheduling method. Simulation and experiment results show that the proposed method can make full use of multi-source sensor information and improve the estimation accuracy. The convergence speed of the fusion estimator is faster than the dynamic estimator.