The invention provides a vehicle trajectory prediction method based on a spatial motion relation and a long-short time memory network, and the method comprises the following steps: collecting and preprocessing real-time data of a vehicle, extracting absolute features and relative features of the vehicle as input features, and constructing a spatial interaction diagram based on the relative features of the vehicle; constructing an LSTM (Long Short Term Memory) prediction model, and training the LSTM prediction model by taking the space interaction diagram and the absolute characteristics of the vehicle as input; and outputting a vehicle track prediction result at a future moment through the trained LSTM prediction model. According to the method, the advantages of the space motion relation and the LSTM network are combined, accurate prediction of the vehicle track is achieved, and field test results show that the method has high prediction accuracy and can provide powerful technical support for development of an intelligent traffic system.
本发明提供一种基于空间运动关系和长短时间记忆网络的车辆轨迹预测方法,包括以下步骤:收集并预处理车辆的实时数据,提取车辆的绝对特征和相对特征作为输入特征基于根据车辆的相对特征,构建空间交互图;构建LSTM预测模型,以空间交互图和车辆的绝对特征作为输入,训练LSTM预测模型;通过训练后的LSTM预测模型输出未来时刻的车辆轨迹预测结果。本发明通过结合空间运动关系和LSTM网络的优势,实现了对车辆轨迹的准确预测,实地测试结果表明,该方法具有较高的预测准确性,可以为智能交通系统的发展提供有力的技术支持。
Vehicle track prediction method based on spatial motion relation and long-short time memory network
一种基于空间运动关系和长短时间记忆网络的车辆轨迹预测方法
2024-12-06
Patent
Electronic Resource
Chinese
IPC: | G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS |
Vehicle track prediction method based on spatial motion relation and long-short time memory network
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