title: Multi-parameter prediction of drivers' lane-changing behaviour with neural network model.
abstract: Accurate prediction of driving behaviour is essential for an active safety system to ensure driver safety. A model for predicting lane-changing behaviour is developed from the results of naturalistic on-road experiment for use in a lane-changing assistance system. Lane changing intent time window is determined via visual characteristics extraction of rearview mirrors. A prediction index system for left lane changes was constructed by considering drivers' visual search behaviours, vehicle operation behaviours, vehicle motion states, and driving conditions. A back-propagation neural network model was developed to predict lane-changing behaviour. The lane-change-intent time window is approximately 5 s long, depending on the subjects. The proposed model can accurately predict drivers' lane changing behaviour for at least 1.5 s in advance. The accuracy and time series characteristics of the model are superior to the use of turn signals in predicting lane-changing behaviour.
authors.affiliation: Chongqing Key Lab of Traffic System & Safety in Mountain Cities, Chongqing Jiaotong University, Chongqing 400074, China. Electronic address: pengjinshuan@163.com.
authors.forename: Jinshuan
authors.initials: J
authors.lastname: Peng
authors.affiliation: Key Laboratory of Automotive Transportation Safety Technology, Ministry of Transport, Chang'an University, Xi'an 710064, China.
authors.forename: Yingshi
authors.initials: Y
authors.lastname: Guo
authors.affiliation: Key Laboratory of Automotive Transportation Safety Technology, Ministry of Transport, Chang'an University, Xi'an 710064, China.
authors.forename: Rui
authors.initials: R
authors.lastname: Fu
authors.affiliation: Key Laboratory of Automotive Transportation Safety Technology, Ministry of Transport, Chang'an University, Xi'an 710064, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Yuan
authors.affiliation: Key Laboratory of Automotive Transportation Safety Technology, Ministry of Transport, Chang'an University, Xi'an 710064, China.
authors.forename: Chang
authors.initials: C
authors.lastname: Wang
mesh_terms: D000328:Adult; D001334:Automobile Driving / Q000523:psychology* / Q000706:statistics & numerical data; D005133:Eye Movements; D005260:Female; D019416:Head Movements; D006801:Humans; D008297:Male; D008875:Middle Aged; D009038:Motion; D016571:Neural Networks, Computer; D013997:Time Factors
keywords: Lane change prediction; Naturalistic driving experiment; Neural network model