Abstract:

Effectively integrating lane information is significant for accurately predicting the future trajectory of vehicles. For the low efficiency problems existing in the fusion of lane information of the prediction model， a vehicle trajectory prediction method of Goal Supervised Attention （GSA） is proposed. Based on fusing the geometric and position information of the lane segment through the graph network， a lane goal prediction module is constructed in this paper starting from the attention model to directly supervise the model to fuse the lane goal features associated with vehicle motion into the vehicle’s motion characteristics while encoding changes in the surrounding lane topology over time. Through two Transformer networks with improved residual structure， low-level motion features are extracted and the correlation information of the lane goal at the time scale is fused sequentially to gradually update the vehicle motion features. An interaction fusion module based on a graph network is constructed to aggregate and propagate vehicle motion features globally. Experiments on the Argoverse and Changan vehicle trajectory prediction datasets show that the proposed GSA method can effectively improve the accuracy and quality of vehicle trajectory prediction in complex traffic scenarios.

Key words: trajectory prediction, map feature encoding, goal guided