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. INDEX TERMS rollover warning algorithm, support vector machine, empirical model TIANJUN ZHU was born in Xingtai City, Hebei province, China in 1977. He received the B.S. degree in vehicle engineering from Hebei Agricultural University, Baoding, in 2000. and M.S. degree in mechanical engineering from
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.
An image shadow in an autonomous vehicle often causes failures in image segmentation and object tracking and in recognition algorithms. In this paper, a shadow detection method based on a support vector machine (SVM) is proposed. Firstly, an RGB image was converted to LAB color space, and a shadow detection model based on an SVM was obtained by training the image with a shadow. Then, the image was divided into a shadow region, a shadow boundary, and a light region. Moreover, the light intensity in the shadow region was adjusted by eliminating the pixel difference between the shadow region and the light region. Meanwhile, the image gradient was established within the shadow boundary, and the boundary shadow was replaced by smooth interpolation to achieve a smooth transition from the light region to the shadow region. Finally, a clear image without a shadow was recovered using wavelet gradient data. Experimental results show that this method can detect the shadow region in an image and reproduce the image without the shadow effectively.
<div class="section abstract"><div class="htmlview paragraph">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.</div></div>
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