Christian Chabanon

New Results on the Relation Between Tyre-Road Longitudinal Stiffness and Maximum Available Grip for Motor Car

Tyre–road estimation methods have been the objective of many research programmes throughout the world. Most of these methods aim at estimating the friction components such as tyre longitudinal slip rate κ and friction coefficient μ in the contact patch area. In order to estimate the maximum available friction coefficient μmax, these methods generally use a probabilistic relationship between the grip obtained for low tyre excitations (such as constant speed driving) and the grip obtained for high tyre excitations (such as emergency braking manoeuvre). Confirmation or invalidation of this relationship from experimental results is the purpose of this paper. Experiments have been carried out on a reference track including several test boards corresponding to a wide textural spectrum. The main advantage of these experiments lies in the use of a vehicle allowing us to accurately build point-by-point relationship between κ and μ. This relationship has been determined for different tyres and pavement textures. Finally, the curves obtained are analysed to check the validity of the relationship between the current friction coefficient used by the car during normal driving conditions and μmax.

A Novel Approach to Real Time Tire-Road Grip and Slip Monitoring

Abstract It is a very challenging task to measure longitudinal slip of a tire on road surface in normal driving conditions, because of the very small value of the slip (a few 1/1000). This paper presents an industrially deployable method for Tire-Road Friction (TRF) monitoring applied to passenger vehicle. This method estimates the longitudinal wheel slip κ and the normalized friction coefficient µ, for low grip requirement driving conditions, in real time. It uses wheels velocity and forces applied on wheels bearings. Due to the imperfections in the wheel speed sensors used, signal to noise ratio from speed measurement is very low. A correction, taking into account the measured deterministic component of the speed measurement noise (called sensor signature), allows for a correction of wheels speed signals and, accordingly, a better estimation of slip. The obtained results demonstrate the ability of our method to distinguish between wet and dry roads during a longitudinal stabilized drive. This method finds applications in Adaptive Cruise Control systems, Driving Assistance systems and Intelligent Highways.