Abstract:

Effectively predicting the future trajectories for surrounding multiple targets is critical to the success of autonomous vehicle decision-making and motion planning. Most existing studies consider pairwise interactions between individual vehicle behaviors， while ignoring the influence of different reaction patterns among heterogeneous traffic participants and other scene factors on prediction， which reduces the rationality of the predicted trajectories and affects the safety of motion control. In view of this， this paper proposes a heterogeneous multi-target trajectory prediction method HGATP based on hierarchical graph attention. Firstly， a category-target-lane three-level graph is innovatively constructed， and different types of targets are independently coded with categories using GRU and GCN respectively to capture the features of different categories. Secondly， to enhance the edge representation of the heterogeneous target interaction graph， the attention mechanism of hierarchical graph is constructed to separately capture the interaction between categories and categories and the interaction between targets and lanes so as to achieve efficient interaction and sharing of maps among heterogeneous multiple targets. Finally， a prediction network is constructed to predict the trajectories of multiple targets based on the target trajectory information and the lane information of the region. To evaluate the performance of the model， experiments are conducted on the INTERACTION and nuScenes datasets respectively. The experiments show that the proposed model reduces the average displacement error and final displacement error of single-target trajectory output on the nuScenes dataset by more than 20%， with the ADE loss effect of multi-target trajectory output on the INTERACTION dataset reduced by 2 m error compared with the baseline method， which improves the reasonableness of vehicle trajectory prediction under complex road structures.

Key words: intelligent vehicles, trajectory prediction, heterogeneous multi-objective, hierarchical graph interaction