Road Adhesion Coefficient Estimation Based on Vehicle-Road Coordination and Deep Learning

Abstract

Accurate estimation of the road adhesion coefficient can help drivers and vehicles perceive changes in road state effectively, reducing the occurrence of traffic crashes accordingly. Therefore, this paper proposes a road adhesion coefficient estimation method based on vehicle-road coordination and deep learning. Firstly, a vehicle-based data feedback system combined with a vehicle-road network cloud is introduced, and CarSim simulation is used to expand the data set and train the model effectively. Then, the dynamic analysis of the whole vehicle is carried out, and the vehicle operation data related to the adhesion coefficient are obtained as the input of the estimation model. Then a combined model of road adhesion coefficient estimation based on self-attention (SA), convolutional neural network (CNN), and long short-term memory (LSTM) is established, to reduce the instability of the prediction, Q-learning is used to optimize the weight of the model. Finally, the model is verified by the simulation data and the actual vehicle-based data. The results show that the vehicle-based data feedback system combined with the vehicle-road network Ccloud is effective, and compared with other commonly used model, the estimation model proposed in this paper can effectively predict the road adhesion coefficient.

Adhesion Coefficient Identification of Wheeled Mobile Robot under Unstructured Pavement

“…are required to measure the factors associated with tires or roads (for example, the deformation and noise of tires, the road texture, etc. ), and then identify the road adhesion coefficient in the cause-based methods [ 12 , 13 ]. For example, given the critical and difficult problems about the estimation of adhesion coefficients, Bo Leng et al proposed fusing vehicle dynamics with machine vision during the estimation [ 14 ].…”