title: Modeling Driver Behavior near Intersections in Hidden Markov Model.
abstract: Intersections are one of the major locations where safety is a big concern to drivers. Inappropriate driver behaviors in response to frequent changes when approaching intersections often lead to intersection-related crashes or collisions. Thus to better understand driver behaviors at intersections, especially in the dilemma zone, a Hidden Markov Model (HMM) is utilized in this study. With the discrete data processing, the observed dynamic data of vehicles are used for the inference of the Hidden Markov Model. The Baum-Welch (B-W) estimation algorithm is applied to calculate the vehicle state transition probability matrix and the observation probability matrix. When combined with the Forward algorithm, the most likely state of the driver can be obtained. Thus the model can be used to measure the stability and risk of driver behavior. It is found that drivers' behaviors in the dilemma zone are of lower stability and higher risk compared with those in other regions around intersections. In addition to the B-W estimation algorithm, the Viterbi Algorithm is utilized to predict the potential dangers of vehicles. The results can be applied to driving assistance systems to warn drivers to avoid possible accidents.
authors.affiliation: MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China. juanli@bjtu.edu.cn.
authors.forename: Juan
authors.initials: J
authors.lastname: Li
authors.affiliation: MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China. 15120815@bjtu.edu.cn.
authors.forename: Qinglian
authors.initials: Q
authors.lastname: He
authors.affiliation: MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China. 13251029@bjtu.edu.cn.
authors.forename: Hang
authors.initials: H
authors.lastname: Zhou
authors.affiliation: MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China. 13251158@bjtu.edu.cn.
authors.forename: Yunlin
authors.initials: Y
authors.lastname: Guan
authors.affiliation: MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China. 13251155@bjtu.edu.cn.
authors.forename: Wei
authors.initials: W
authors.lastname: Dai
mesh_terms: D000063:Accidents, Traffic / Q000517:prevention & control; D000465:Algorithms; D001334:Automobile Driving / Q000523:psychology*; D001519:Behavior; D006801:Humans; D008390:Markov Chains; D011336:Probability; D018570:Risk Assessment; D012307:Risk Factors; D012449:Safety
keywords: Baum-Welch estimation algorithm; Hidden Markov Model; driver assistance system; driver behavior; intersections