A Probabilistic Prediction Model for the Safety Assessment of HDVs Under Complex Driving Environments

• Published in: IEEE transactions on intelligent transportation systems Vol. 18; no. 4; pp. 858 - 868
• Main Authors: He, Yi, Yan, Xinping, Chu, Duanfeng, Lu, Xiao-Yun, Wu, Chaozhong
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accident prediction; Accidents; Aerodynamics; Approximation; first-order reliability method (FORM); Heavy vehicles; Heavy-duty vehicle (HDV) rollover and sideslip accident; Mathematical models; Parameter sensitivity; Prediction models; Probabilistic logic; Probabilistic methods; probabilistic prediction model; Reliability analysis; road environment; Roads; Safety; Safety margins; second-order reliability method (SORM); Statistical analysis; Vehicle dynamics; Vehicles; Wind speed
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2016.2592699

Accidents such as those caused by rollovers and sideslips in complex driving environments involving heavy-duty vehicles (HDVs) often have serious consequences. Such accidents can be due to many factors. In this paper, a probabilistic method for predicting and preventing these accidents is presented. First, a specific vehicle dynamics model based on various random parameters that consider the wind velocity and road curvature is developed. Second, a safety margin function is defined to divide the safe and dangerous domains in the parameter space. Then, the first-order reliability method and second-order reliability method approximations are developed to evaluate the probability of such an accident by using the vehicle dynamics model. Finally, the probability model is applied to explore the interrelations and sensitivities of those parameters with regard to their effects on the accident probability in different scenarios. The study suggests that the presented probabilistic methodology can effectively estimate rollovers and sideslips of HDVs in complex environments, which represent a challenge for the prediction of accidents based on sensors alone.