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Journal Article

Citation

Peng J, Guo Y, Fu R, Yuan W, Wang C. Appl. Ergon. 2015; 50: 207-217.

Affiliation

Key Laboratory of Automotive Transportation Safety Technology, Ministry of Transport, Chang'an University, Xi'an 710064, China.

Copyright

(Copyright © 2015, Elsevier Publishing)

DOI

10.1016/j.apergo.2015.03.017

PMID

25959336

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.


Language: en

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