Research on a Novel Vehicle Rollover Risk Warning Algorithm Based on Support Vector Machine Model

Abstract

A novel vehicle rollover warning algorithm based on support vector machine (SVM) empirical model is proposed to improve the real-time of un-tripped rollover warning algorithm and accuracy of dynamic rollover warning. Considering the nonlinear characteristic of driver-vehicle-road interaction and the uncertainty of modeling, the traditional deterministic methods cannot meet the requirements of accurate vehicle rollover warning modeling. The probability method considering issues of uncertainty is applied to design vehicle dynamic rollover warning algorithm. The SVM empirical model considers the uncertainties of the driver-vehicle-road system and the real variability of the parameters, provides an explicit function of vehicle rollover safety limit and its gradient, and utilizes the hypersurface visualization boundary to define the rollover safety area and the unsafe area. Targeting on sport utility vehicle under the condition of high-speed emergency obstacle avoidance, simulations are carried out to verify the proposed vehicle rollover warning algorithm based on SVM empirical model and of the simulation results show that the proposed algorithm has accurate warning and good real-time performance. It can effectively improve the warning accuracy of vehicle dynamic rollover, reduce the interference of nonlinear and uncertainty, and significantly improve the active safety performance with vehicle rollover prevention.