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.

翻译：准确预测周围车辆的未来轨迹对于自动驾驶汽车的安全运行至关重要。本研究提出了一种具备结构感知能力的车道图Transformer（LGT）模型。其核心贡献在于将地图拓扑结构编码至注意力机制中。为处理不同方向车道信息的差异，模型引入了四个相对位置编码（RPE）矩阵以捕捉地图拓扑结构的局部细节。此外，采用两个最短路径距离（SPD）矩阵来获取两条可通行车道间的距离信息。数值结果表明，所提出的LGT模型在Argoverse 2数据集上实现了显著更高的预测性能。具体而言，minFDE$_6$指标较Argoverse 2基线模型（最近邻）降低了60.73%，b-minFDE$_6$指标较基线LaneGCN模型降低了2.65%。进一步的消融实验表明，对地图拓扑结构的考量使b-minFDE$_6$指标下降了4.24%，验证了该模型的有效性。