Trajectory prediction for autonomous driving must continuously reason the motion stochasticity of road agents and comply with scene constraints. Existing methods typically rely on one-stage trajectory prediction models, which condition future trajectories on observed trajectories combined with fused scene information. However, they often struggle with complex scene constraints, such as those encountered at intersections. To this end, we present a novel method, called LAformer. It uses a temporally dense lane-aware estimation module to select only the top highly potential lane segments in an HD map, which effectively and continuously aligns motion dynamics with scene information, reducing the representation requirements for the subsequent attention-based decoder by filtering out irrelevant lane segments. Additionally, unlike one-stage prediction models, LAformer utilizes predictions from the first stage as anchor trajectories and adds a second-stage motion refinement module to further explore temporal consistency across the complete time horizon. Extensive experiments on Argoverse 1 and nuScenes demonstrate that LAformer achieves excellent performance for multimodal trajectory prediction.
翻译:现有方法通常依赖单阶段轨迹预测模型,这些模型使观察到的轨迹上的未来轨迹与引信的现场信息相结合,但往往与复杂的场景限制如交叉点遇到的场景限制相抗衡。为此,我们提出了一个叫LAAUOR的新颖方法。它使用一个时间密集的车道-视界估计模块,只选择HD地图中高度潜力最高的高度航道段,该模型有效并持续地将运动动态与现场信息相匹配,通过过滤无关的航道段来减少对随后以注意为基础的解码器的描述要求。此外,LAAOLEX与一个阶段预测模型不同,利用第一阶段的预测作为锚定轨道,并增加一个第二阶段的调整模块,以进一步探索整个时空范围的时间一致性。 Argoverse 1 和 nuScenes 的广泛实验表明,LAOLEXUT在多式联运轨迹预测方面表现良好。</s>