title: Driving risk assessment using near-crash database through data mining of tree-based model.
abstract: This paper considers a comprehensive naturalistic driving experiment to collect driving data under potential threats on actual Chinese roads. Using acquired real-world naturalistic driving data, a near-crash database is built, which contains vehicle status, potential crash objects, driving environment and road types, weather condition, and driver information and actions. The aims of this study are summarized into two aspects: (1) to cluster different driving-risk levels involved in near-crashes, and (2) to unveil the factors that greatly influence the driving-risk level. A novel method to quantify the driving-risk level of a near-crash scenario is proposed by clustering the braking process characteristics, namely maximum deceleration, average deceleration, and percentage reduction in vehicle kinetic energy. A classification and regression tree (CART) is employed to unveil the relationship among driving risk, driver/vehicle characteristics, and road environment. The results indicate that the velocity when braking, triggering factors, potential object type, and potential crash type exerted the greatest influence on the driving-risk levels in near-crashes.
authors: State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 10084, China.
authors: Jianqiang
authors: J
authors: Wang
authors: State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 10084, China.
authors: Yang
authors: Y
authors: Zheng
authors: State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 10084, China.
authors: Xiaofei
authors: X
authors: Li
authors: State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 10084, China.
authors: Chenfei
authors: C
authors: Yu
authors: Honda R&D Co. Ltd., Automobile R&D Center, Tochigi 321-3393, Japan.
authors: Kenji
authors: K
authors: Kodaka
authors: State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 10084, China. Electronic address: likq@tsinghua.edu.cn.
authors: Keqiang
authors: K
authors: Li
mesh_terms: D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D001334:Automobile Driving / Q000706:statistics & numerical data*; D002681:China; D057225:Data Mining; D016208:Databases, Factual; D006801:Humans; D008962:Models, Theoretical; D018570:Risk Assessment / Q000379:methods*
keywords: Classification and regression tree (CART); Driving risk; K-mean cluster; Naturalistic driving study; Near-crash