图注意力模式下融合高精地图的周车轨迹预测

Vehicle trajectory prediction combined with high definition map in graph attention mode

为了准确、合理地预测车辆未来轨迹并且理解周围交通流的变化,提出了一种图注意力模式下融合高精地图的轨迹预测方法。设计了基于长短期记忆(LSTM)网络的编码-解码框架,建立了以车辆历史状态和高精地图信息为输入的模型结构,提出了结合车辆局部特征和全局特征的图查询机制输出车辆预测轨迹。在公开数据集nuScenes上的实验结果表明,该模型的综合预测性能优于Traj++、CoverNet等其他先进方法,且具有良好的抗干扰性。

In order to accurately and reasonably predict the future trajectories of vehicles and understand the changes of surrounding traffic flow,a trajectory prediction method combined with high definition map in graph attention mode was proposed.The encoder-decoder framework based on LSTM network was designed,and the model structure with vehicle historical status and high-precision map information as input was established.A graph query mechanism combining local and global features of vehicles was proposed to output vehicle prediction trajectory.The results of experiments carried out on the nuScenes dataset show that the comprehensive prediction performance of our model is better than other state-of-the-art methods,such as Traj++,CoverNet,etc.,and it has good anti-interference.