Peng Jinshuan, Guo Yingshi, Fu Rui, Yuan Wei, Wang Chang
Chongqing Key Lab of Traffic System & Safety in Mountain Cities, Chongqing Jiaotong University, Chongqing 400074, China.
Key Laboratory of Automotive Transportation Safety Technology, Ministry of Transport, Chang'an University, Xi'an 710064, China.
Appl Ergon. 2015 Sep;50:207-17. doi: 10.1016/j.apergo.2015.03.017. Epub 2015 Apr 11.
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.
准确预测驾驶行为对于主动安全系统确保驾驶员安全至关重要。基于自然主义道路实验的结果开发了一种用于预测变道行为的模型，以用于变道辅助系统。通过后视镜的视觉特征提取来确定变道意图时间窗口。通过考虑驾驶员的视觉搜索行为、车辆操作行为、车辆运动状态和驾驶条件，构建了左变道的预测指标体系。开发了一种反向传播神经网络模型来预测变道行为。变道意图时间窗口大约有5秒长，具体取决于受试者。所提出的模型可以提前至少1.5秒准确预测驾驶员的变道行为。该模型在预测变道行为方面的准确性和时间序列特征优于使用转向灯。
