Recently, anchor-based trajectory prediction methods have shown promising performance, which directly selects a final set of anchors as future intents in the spatio-temporal coupled space. However, such methods typically neglect a deeper semantic interpretation of path intents and suffer from inferior performance under the imperfect High-Definition (HD) map. To address this challenge, we propose a novel Planning-inspired Hierarchical (PiH) trajectory prediction framework that selects path and speed intents through a hierarchical lateral and longitudinal decomposition. Especially, a hybrid lateral predictor is presented to select a set of fixed-distance lateral paths from map-based road-following and cluster-based free-move path candidates. {Then, the subsequent longitudinal predictor selects plausible goals sampled from a set of lateral paths as speed intents.} Finally, a trajectory decoder is given to generate future trajectories conditioned on a categorical distribution over lateral-longitudinal intents. Experiments demonstrate that PiH achieves competitive and more balanced results against state-of-the-art methods on the Argoverse motion forecasting benchmark and has the strongest robustness under the imperfect HD map.

翻译：近期，基于锚点的轨迹预测方法展现出显著性能，该方法直接在时空耦合空间中选取最终锚点集作为未来意图。然而，这类方法通常忽略了对路径意图的深层语义解析，在非理想高精地图条件下表现欠佳。为应对这一挑战，我们提出一种新型面向规划的层次化轨迹预测框架，通过横向-纵向层次化分解选取路径与速度意图。具体而言，我们设计了一种混合横向预测器，从基于地图的道路跟随候选路径与基于聚类的自由移动候选路径中选取固定间距的横向路径。随后，纵向预测器从横向路径集合中采样可行目标点作为速度意图。最终，轨迹解码器基于横向-纵向意图的类别分布生成未来轨迹。实验表明，在Argoverse运动预测基准上，PiH方法相比当前最优方法取得了更具竞争力且更均衡的结果，并在非理想高精地图条件下展现出最强的鲁棒性。