The rapid development of cooperative vehicle-infrastructure system (CVIS) improves the communication reliability between vehicles and road environment. These communications enable the accurate vehicle rollover prediction in Human-Vehicle-Road interaction. However, considering the strong nonlinear characteristics of Human-Vehicle-Road interaction and the uncertainty of modeling, the traditional deterministic method cannot meet the requirement of accurate prediction of rollover hazard for heavy vehicles. In order to improve the accuracy of vehicles rollover prediction, this paper proposes a developed rollover prediction algorithm based on the multiple observed variables by combining the failure probability in reliability and the empirical model. This approach applies the probability method of uncertainty to the design of dynamic rollover prediction algorithm for heavy vehicles and establishes a classification model of heavy vehicles based on support vector machine (SVM) with multiple observed variables. The failure probability of rollover limit state of heavy vehicles is calculated by Monte Carlo Sampling (MCS), Radial-Based Importance Sampling (RBIS), and Truncated Importance Sampling (TIS), respectively. Then the Fishhook, Double Lane Change tests, and J-turn tests, simulated in TruckSim, are carried out to validate the proposed algorithm. The simulation results show that the rollover prediction algorithm based on failure probability can effectively improve the rollover prediction accuracy for heavy vehicles. Moreover, based on the communication in CVIS, the failure probability can be obtained before entering the specific road. Meanwhile, this approach can reduce the external interference of strong non-linear characteristics of Human-Vehicle-Road interaction and the uncertainty of the modeling to the system, thus improving the prediction accuracy of active safety performance of heavy vehicles significantly. INDEX TERMS Failure probability, heavy vehicles, load transfer ratio, rollover risk prediction, SVM classification model. BIN LI received the Ph.D. degree from Shanghai Jiao Tong University, China, in 2010. He was Research Fellow on electric vehicle project with the University of Waterloo and on mobile robotic control project with McGill University. He is currently a Researcher working on stability and safety control of commercial vehicle and passenger car with Concordia University. His research interests include vehicle system modeling, dynamics and control, vehicle control system design and optimization, electrified vehicle, integrated vehicle motion control, and autonomous vehicle control. WEI MA was born in Xingtai, Hebei, China, in 1991. He received the B.S. degree in vehicle service engineering from the Hebei University of Engineering, Handan, China, in 2018, where he is currently pursuing the M.S. degree in equipment intelligence and safety engineering. His research interest includes vehicle dynamic control.
