@article{ref1,
title="Multi-parameter prediction of drivers' lane-changing behaviour with neural network model",
journal="Applied ergonomics",
year="2015",
author="Peng, Jinshuan and Guo, Yingshi and Fu, Rui and Yuan, Wei and Wang, Chang",
volume="50",
number="",
pages="207-217",
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.<p /> <p>Language: en</p>",
language="en",
issn="0003-6870",
doi="10.1016/j.apergo.2015.03.017",
url="http://dx.doi.org/10.1016/j.apergo.2015.03.017"
}