Deep Learning for the Estimation of the Longitudinal Slip Ratio

Abstract

In a road vehicle, the interaction forces between tire and road are strongly influenced by the longitudinal slip ratio. This kinematic quantity, therefore, represents one of the most important in the study of vehicle dynamics. The real-time knowledge of this quantity can allow the estimation of the interaction forces and the development of control systems to improve safety and handling. In particular, Anti-lock Braking Systems (ABS) and Traction Control Systems (TCS). Direct measurements of this quantity would require the insertion of sensors inside the tire, with consequent manufacturing complexity and increased costs. This paper proposes an estimate of the longitudinal slip ratio based on other easily measurable or estimable quantities. This estimator makes use of Neural Networks and is based on preliminary physical considerations.