Estimation of Tire-Road Friction for Road Vehicles: a Time Delay Neural Network Approach
This addresses the need for accurate friction estimation in vehicle control systems, but it is incremental as it builds on existing neural network approaches for a specific automotive domain.
The paper tackles the problem of estimating tire-road friction coefficient for vehicle safety systems by using a time delay neural network (TDNN) to detect it under lateral force excitations, achieving robust results without standard tire models and enabling independent wheel estimation, with simulations showing effectiveness compared to a classical model-based method.
The performance of vehicle active safety systems is dependent on the friction force arising from the contact of tires and the road surface. Therefore, an adequate knowledge of the tire-road friction coefficient is of great importance to achieve a good performance of different vehicle control systems. This paper deals with the tire-road friction coefficient estimation problem through the knowledge of lateral tire force. A time delay neural network (TDNN) is adopted for the proposed estimation design. The TDNN aims at detecting road friction coefficient under lateral force excitations avoiding the use of standard mathematical tire models, which may provide a more efficient method with robust results. Moreover, the approach is able to estimate the road friction at each wheel independently, instead of using lumped axle models simplifications. Simulations based on a realistic vehicle model are carried out on different road surfaces and driving maneuvers to verify the effectiveness of the proposed estimation method. The results are compared with a classical approach, a model-based method modeled as a nonlinear regression.