TY - JOUR PY - 2015// TI - Multi-parameter prediction of drivers' lane-changing behaviour with neural network model JO - Applied ergonomics A1 - Peng, Jinshuan A1 - Guo, Yingshi A1 - Fu, Rui A1 - Yuan, Wei A1 - Wang, Chang SP - 207 EP - 217 VL - 50 IS - N2 - 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

LA - en SN - 0003-6870 UR - http://dx.doi.org/10.1016/j.apergo.2015.03.017 ID - ref1 ER -