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Open AccessArticle

Hazardous Traffic Event Detection Using Markov Blanket and Sequential Minimal Optimization (MB-SMO)

by Lixin Yan 1,2,3, Yishi Zhang 4, Yi He 1,2,*, Song Gao 1,2, Dunyao Zhu 1,2, Bin Ran 3 and Qing Wu 1,2
1
Intelligent Transport Systems Research Center, Wuhan University of Technology, Wuhan 430063, China
2
Engineering Research Center for Transportation Safety, Ministry of Education, Wuhan 430063, China
3
Department of Civil and Environmental Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
4
Management School, Jinan University, Guangzhou 510632, China
*
Author to whom correspondence should be addressed.
Academic Editor: Wilmar Hernandez
Sensors 2016, 16(7), 1084; https://doi.org/10.3390/s16071084
Received: 9 April 2016 / Revised: 29 June 2016 / Accepted: 7 July 2016 / Published: 13 July 2016
(This article belongs to the Section Physical Sensors)
The ability to identify hazardous traffic events is already considered as one of the most effective solutions for reducing the occurrence of crashes. Only certain particular hazardous traffic events have been studied in previous studies, which were mainly based on dedicated video stream data and GPS data. The objective of this study is twofold: (1) the Markov blanket (MB) algorithm is employed to extract the main factors associated with hazardous traffic events; (2) a model is developed to identify hazardous traffic event using driving characteristics, vehicle trajectory, and vehicle position data. Twenty-two licensed drivers were recruited to carry out a natural driving experiment in Wuhan, China, and multi-sensor information data were collected for different types of traffic events. The results indicated that a vehicle’s speed, the standard deviation of speed, the standard deviation of skin conductance, the standard deviation of brake pressure, turn signal, the acceleration of steering, the standard deviation of acceleration, and the acceleration in Z (G) have significant influences on hazardous traffic events. The sequential minimal optimization (SMO) algorithm was adopted to build the identification model, and the accuracy of prediction was higher than 86%. Moreover, compared with other detection algorithms, the MB-SMO algorithm was ranked best in terms of the prediction accuracy. The conclusions can provide reference evidence for the development of dangerous situation warning products and the design of intelligent vehicles. View Full-Text
Keywords: hazardous traffic event; Markov blanket; sequential minimal optimization; naturalistic driving; traffic safety hazardous traffic event; Markov blanket; sequential minimal optimization; naturalistic driving; traffic safety
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Yan, L.; Zhang, Y.; He, Y.; Gao, S.; Zhu, D.; Ran, B.; Wu, Q. Hazardous Traffic Event Detection Using Markov Blanket and Sequential Minimal Optimization (MB-SMO). Sensors 2016, 16, 1084.

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