  Accurate prediction of future trajectories for surrounding vehicles is vital
for the safe operation of autonomous vehicles. This study proposes a Lane Graph
Transformer (LGT) model with structure-aware capabilities. Its key contribution
lies in encoding the map topology structure into the attention mechanism. To
address variations in lane information from different directions, four Relative
Positional Encoding (RPE) matrices are introduced to capture the local details
of the map topology structure. Additionally, two Shortest Path Distance (SPD)
matrices are employed to capture distance information between two accessible
lanes. Numerical results indicate that the proposed LGT model achieves a
significantly higher prediction performance on the Argoverse 2 dataset.
Specifically, the minFDE$_6$ metric was decreased by 60.73% compared to the
Argoverse 2 baseline model (Nearest Neighbor) and the b-minFDE$_6$ metric was
reduced by 2.65% compared to the baseline LaneGCN model. Furthermore, ablation
experiments demonstrated that the consideration of map topology structure led
to a 4.24% drop in the b-minFDE$_6$ metric, validating the effectiveness of
this model.
