The invention discloses an automatic driving track prediction method based on global and local spatio-temporal feature extraction, and aims at a complex driving environment, and after historical track data of a vehicle and surrounding vehicles and information such as lane lines are obtained, tracks of the surrounding vehicles within a few seconds in the future are predicted more quickly and more accurately. Specifically, firstly, each vehicle is divided into a local area, the interaction relation between the vehicles in the local area and between the vehicles and lane lines is learned through a self-attention mechanism, the historical track information of each vehicle in the local area is learned through a time attention mechanism, and secondly, due to the fact that certain interaction exists between the local areas, the historical track information of each vehicle in the local area is learned. Therefore, a global attention module is designed, interaction information between local areas is learned through a space attention mechanism, and finally, a decoder is designed to output a plurality of predicted trajectories and uncertainty of the trajectories.
本发明公开一种基于全局和局部时空特征提取的自动驾驶轨迹预测方法,针对复杂的行车环境,在获取了自车和周车的历史轨迹数据,以及车道线等信息之后,更快速并更精准地预测出周车未来几秒之内的轨迹。具体来讲,首先对每辆车划分一个局部的区域,通过自注意力机制学习局部区域内车辆之间以及车辆和车道线之间的交互关系,通过时间注意力机制学习局部区域内各车自身的历史轨迹信息,其次由于局部区域之间也有一定的交互,因此设计了一个全局注意力模块,通过空间注意力机制去学习局部区域之间的交互信息,最后设计了一个解码器去输出所预测出的多条轨迹以及他们的不确定性。
Automatic driving track prediction method based on global and local spatial-temporal feature extraction
一种基于全局和局部时空特征提取的自动驾驶轨迹预测方法
15.03.2024
Patent
Elektronische Ressource
Chinesisch
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