The invention discloses an urban road vehicle trajectory prediction method based on an LSTM network and an attention mechanism, and belongs to the field of vehicle trajectory prediction. The method comprises the following steps: predicting a track sequence of a vehicle and surrounding vehicles by using LSTM network coding, predicting a driving intention of a target vehicle by using a method based on an attention mechanism or a residual network, and embedding the intention into a coding vector of the target vehicle; using a multi-scale lane map to carry out convolutional coding on lane center and boundary information, and capturing environment information needing to be focused on through a dot product attention mechanism; and after the target vehicle state and the environment state of the fused intention are spliced, a predicted track is obtained through LSTM network decoding. According to the invention, the position relation between the target vehicle and the road can be dynamically updated, and more excellent trajectory prediction performance is shown.
本发明公开了一种基于LSTM网络和注意力机制的城市道路车辆轨迹预测方法,属于车辆轨迹预测领域。本发明利用LSTM网络编码预测车辆和周围车辆的轨迹序列,并使用基于注意力机制或残差网络的方法预测目标车辆行驶意图,将意图嵌入目标车辆的编码向量;利用多尺度车道图卷积编码车道中心和边界信息,通过点积注意力机制捕获需要重点关注的环境信息;将融合意图的目标车辆状态和环境状态拼接后,经LSTM网络解码得到预测的轨迹。本发明能够动态更新目标车辆和道路的位置关系,表现出更优异的轨迹预测性能。
Urban road vehicle trajectory prediction method based on LSTM network and attention mechanism
一种基于LSTM网络和注意力机制的城市道路车辆轨迹预测方法
2025-05-23
Patent
Electronic Resource
Chinese
IPC: | G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS / G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung |
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