An Integrated Scheme for Coefficient Estimation of Tire-Road Friction With Mass Parameter Mismatch Under Complex Driving Scenarios

The accurate knowledge of tire-road friction coefficient (TRFC) contributes to the optimization of driver maneuvers for further improving the safety of intelligent vehicles. The performance of the existing estimation methods would decline when a vehicle performs complex driving maneuvers. In addition, the mass parameter mismatch also deteriorates the estimation accuracy of TRFC. To address these problems, in this article, an integrated scheme for TRFC estimation is proposed by combining a strong tracking unscented Kalman filter and an interactive multiple model unscented Kalman filter. Real-time experiments are implemented on a mass-produced vehicle to demonstrate the feasibility and effectiveness of the proposed method. The results show that the proposed approach has better estimation accuracy than the existing ones under various driving scenarios.

Automated summary

This article proposes an integrated scheme for estimating tire-road friction coefficient (TRFC) by combining a strong tracking unscented Kalman filter and an interactive multiple model unscented Kalman filter. Real-time experiments on a mass-produced vehicle demonstrate the feasibility and effectiveness of the proposed method. The results show that the proposed approach has better estimation accuracy than the existing ones under various driving scenarios.