Recurrent Neural Network Model for On-Board Estimation of the Side-Slip Angle in a Four-Wheel Drive and Steering Vehicle

Abstract

A valuable quantity for analyzing the lateral dynamics of road vehicles is the side-slip angle, that is, the angle between the vehicle’s longitudinal axis and its speed direction. A reliable real-time side-slip angle value enables several features, such as stability controls, identification of understeer and oversteer conditions, estimation of lateral forces during cornering, or tire grip and wear estimation. Since the direct measurement of this variable can only be done with complex and expensive devices, it is worth trying to estimate it through virtual sensors based on mathematical models. This article illustrates a methodology for real-time on-board estimation of the side-slip angle through a machine learning model (SSE—side-slip estimator). It exploits a recurrent neural network trained and tested via on-road experimental data acquisition. In particular, the machine learning model only uses input signals from a standard road car sensor configuration. The model adaptability to different road conditions and tire wear levels has been verified through a sensitivity analysis and model testing on real-world data proves the robustness and accuracy of the proposed solution achieving a root mean square error (RMSE) of 0.18 deg and a maximum absolute error of 1.52 deg on the test dataset. The proposed model can be considered as a reliable and cheap potential solution for the real-time on-board side-slip angle estimation in serial cars.