# Estimation of Tire-Road Friction for Road Vehicles: a Time Delay Neural Network Approach

## TL;DR

This paper presents a novel time delay neural network approach for estimating tire-road friction coefficients independently for each wheel, improving robustness and efficiency over traditional model-based methods.

## Contribution

The paper introduces a TDNN-based method for real-time tire-road friction estimation that avoids standard tire models and estimates friction per wheel.

## Key findings

- The TDNN method accurately estimates friction on various road surfaces.
- It outperforms classical nonlinear regression approaches.
- The approach is robust across different driving maneuvers.

## Abstract

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.