Crash prediction with behavioral and physiological features for advanced vehicle collision avoidance system |
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| Institution: | 1. IBM Research, Beijing, China;2. State Key Laboratory of Automobile Safety and Energy, Department of Industrial Engineering, Tsinghua University, Beijing, China;3. Department of Management Science and Engineering, School of Economics and Management, Beihang University, Beijing, China;1. Centre for Automotive Safety Research, The University of Adelaide, North Terrace, SA 5005, Australia;2. Centre for Road Safety, Transport for NSW, Level 3, 84 Crown St, Wollongong, NSW 2500, Australia;1. Road Safety Engineering & Environment Research Center (REER), Malaysian Institute of Road Safety Research (MIROS), Lot 125-135, Jalan TKS 1,Taman Kajang Sentral, 43000 Kajang, Selangor Darul Ehsan, Malaysia;2. P.O. Box 118, John Ericssons väg 1, 22100, Traffic and Roads Unit (Taffik och väg), Department of Technology and Society (Teknik och Samhälle), Faculty of Engineering (Lunds Tekniska Högskola), Lund University, Lund, Sweden;1. College of Transportation Engineering, Tongji University, 4800 Cao’an Highway, Shanghai, 201804, China;2. Department of Civil & Environmental Engineering, University of Waterloo, 200 University Avenue West, Waterloo, ON, N2L 3G1, Canada;1. Intelligent Transport Systems Research Center, Wuhan University of Technology, 1040 Heping Avenue, Wuhan, Hubei 430063, China;2. Engineering Research Center for Transportation Safety, Ministry of Education, 1040 Heping Avenue, Wuhan, Hubei 430063, China;3. School of Automation, Wuhan University of Technology, 205 Luoshi Road, Wuhan, Hubei 430070, China |
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| Abstract: | Real-time crash prediction is the key component of the Vehicle Collision Avoidance System (VCAS) and other driver assistance systems. The further improvements of predictability requires the systemic estimation of crash risks in the driver-vehicle-environment loop. Therefore, this study designed and validated a prediction method based on the supervised learning model with added behavioral and physiological features. The data samples were extracted from 130 drivers’ simulator driving, and included various features generated from synchronized recording of vehicle dynamics, distance metrics, driving behaviors, fixations and physiological measures. In order to identify the optimal configuration of proposed method, the Discriminant Analysis (DA) with different features and models (i.e. linear or quadratic) was tested to classify the crash samples and non-crash samples. The results demonstrated the significant improvements of accuracy and specificity with added visual and physiological features. The different models also showed significant effects on the characteristics of sensitivity and specificity. These results supported the effectiveness of crash prediction by quantifying drivers’ risky states as inputs. More importantly, such an approach also provides opportunities to integrate the driver state monitoring into other vehicle-mounted systems at the software level. |
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| Keywords: | Crash prediction Driving behavior Fixation Physiological measures Discriminant analysis |
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