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The research on driver fatigue detection is of great significance to improve driving safety. This paper proposes a real-time comprehensive driver fatigue detection algorithm based on facial landmarks to improve the detection accuracy, which detects the driver’s fatigue status by using facial video sequences without equipping their bodies with other intelligent devices. A tasks-constrained deep convolutional network is constructed to detect the face region based on 68 key points, which can solve the optimization problem caused by the different convergence speeds of each task. According to the real-time facial video images, the eye feature of the eye aspect ratio (EAR), mouth aspect ratio (MAR) and percentage of eye closure time (PERCLOS) are calculated based on facial landmarks. A comprehensive driver fatigue assessment model is established to assess the fatigue status of drivers through eye/mouth feature selection. After a series of comparative experiments, the results show that this proposed algorithm achieves good performance in both accuracy and speed for driver fatigue detection.

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An experimental investigation and performance analysis of inertia properties measurement for heavy truck cab is presented. This method is specifically intended for measuring the inertia properties of irregular rigid bodies, and it has the potential to be applied to the measurement of the inertia properties of vehicle bodies, such as the cab, engine, and gearbox. This article, based on CATARC moment of inertia measurement system test rig, develops an accurate measuring method to identify inertia parameters of heavy truck cab. First, corresponding tests are carried out, and the lever principle and moments of inertia parallel theorem are employed to calculate and analyse the test results, which leads to the accurate value of inertia parameters. Second, the performance analysis of the proposed method is verified through the measurement error analysis. As a result, the proposed method shows high accuracy, which provides an experimental basis for accurate inertia parameter measurement of heavy truck cab.

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.