基于相空间重构的电动公交车辆行为预测OA

To identify the driving activities of urban buses while avoiding privacy infringement on drivers and other road users caused by the use of on-board surveillance cameras,this study establishes a bus vehicle behavior predic-tion model that takes vehicle motion and driving operation data as inputs.First,experiments were carried out to co-llect the natural driving data of urban buses,and vehicle movement and driver behavior operation data were co-llected through the CAN protocol.Then,segments corresponding to station entry,station exit,intersections,turning and lane changing were selected.Based on Takens'delay embedding method,phase space reconstruction was per-formed to map time-series data into a high-dimensional space to generate two-dimension recurrence plots.After-wards,multi-channel stacking was applied to construct RGB images.To address the issue of class imbalance,Focal Loss function was adopted to enhance the model's feature extraction capability for minority classes.On this basis,an E-bus vehicle behavior prediction model marked as E-VBPM was developed using the ConvNeXt network.The results indicate that E-VBPM achieves an accuracy of 84.62%in predicting 5 kinds of driving activities.As com-pared with the machine learning algorithm that uses time-series data as the input,the proposed model achieves an absolute increase in accuracy,precision,and recall by 6.79%,10.98%and 8.86%,respectively.The results of this research provide support for electric bus on-board systems to identify the current operating modes and assist the driver in a safer and more intelligent way.