针对基于车载监控摄像机的车辆行为监测可能会侵犯驾驶人和其他道路参与者隐私的问题，研究建立了一种以车辆运动和驾驶行为操作数据为输入的公交车车辆行为预测模型。开展了城市公交车的自然驾驶数据采集实验，通过CAN协议采集车辆运动和驾驶人行为操作数据。随后提取了车辆进站、出站、经过交叉口、转向和换道典型活动片段。使用相空间重构算法将时序数据集映射至高维空间生成RGB图像。基于ConvNeXt卷积网络建立了电动公交车车辆行为预测模型E-VBPM (E-bus Vehicle Behavior Prediction Model)。结果表明：E-VBPM在预测5种车辆活动任务中具有84.62%的准确率，与以时序数据作为输入的传统机器学习算法相比，准确率提升了6.8%。研究可为电动公交车载系统判别当前运营模式并更加智能的辅助驾驶人安全行驶提供支持。

To address the issue that vehicle behavior prediction based on in-vehicle monitoring cameras may infringe on the privacy of drivers and other road participants, this study develops a bus activity prediction model with vehicle motion and behavioral operation data as input. First, a naturalistic urban bus driving experiment was conducted, collecting vehicle motion and driver operation data via the CAN bus. Subsequently, segments corresponding to station entry, station exit, intersections, turning, and lane changing were selected. The phase space reconstruction algorithm was used to map the time-series data into a high-dimensional space, thereby generating an RGB image dataset. Finally, an E-bus Vehicle Behavior Prediction Model (E-VBPM) was established based on the ConvNeXt network. The results indicate that the developed E-VBPM achieved an accuracy of 84.62% in predicting driving activities, representing an improvement of approximately 6.8% over machine learning models that utilize time-series data inputs. These findings support the development of more intelligent on-board systems for electric buses, enabling better identification of vehicle operating modes and enhanced driver assistance.