期刊
APPLIED ERGONOMICS
卷 50, 期 -, 页码 207-217出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.apergo.2015.03.017
关键词
Lane change prediction; Naturalistic driving experiment; Neural network model
资金
- National Natural Science Foundation of China [51178053]
- Natural Science Foundation of Chongqing [cstc2013jcyjA30015]
- Fundamental Research Funds for the Central Universities [2014G1502015]
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. (C) 2015 Elsevier Ltd and The Ergonomics Society. All rights reserved.
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