  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.
