Browse Publications Technical Papers 2020-01-0701

A Novel Prediction Algorithm for Heavy Vehicles System Rollover Risk Based on Failure Probability Analysis and SVM Empirical Model 2020-01-0701

The study of heavy vehicles rollover prediction, especially in algorithm-based heavy vehicles active safety control for improving road handling, is a challenging task for the heavy vehicle industry. Due to the high fatality rate caused by vehicle rollover, how to precisely and effectively predict the rollover of heavy vehicles became a hot topic in both academia and industry. Because of the strong non-linear characteristics of Human-Vehicle-Road interaction and the uncertainty of modeling, the traditional deterministic method cannot predict the rollover hazard of heavy vehicles accurately. To deal with the above issues, this paper applies a probability method of uncertainty to the design of a dynamic rollover prediction algorithm for heavy vehicles and proposes a novel algorithm for predicting the rollover hazard based on the combined empirical model of reliability index and failure probability. Moreover, the paper establishes a classification model of heavy vehicles based on the support vector machine (SVM) and uses the Monte Carlo method to calculate the failure probability of rollover limit state of heavy vehicles. The fishhook, double lane change, and slalom maneuver tests of heavy vehicles are used to predict and validate the proposed algorithm in real-time. The simulation results show that the rollover prediction method based on failure probability is accurate and real-time, and can effectively improve the rollover prediction accuracy. Meanwhile, the proposed approach reduces the external interference of strong non-linear characteristics of Human-Vehicle-Road interaction and the uncertainty of the modeling to the system, thus significantly improving the prediction accuracy of active safety performance of heavy vehicles.


Subscribers can view annotate, and download all of SAE's content. Learn More »


Members save up to 17% off list price.
Login to see discount.