不依赖高精地图也能精准预测车辆轨迹，训练时用地图知识，推理时完全不用。
Map-Free Trajectory Prediction with Map Distillation and Hierarchical Encoding
- 训练时通过知识蒸馏学习地图先验，推理时不依赖地图
- 用分层编码器提取时空特征，生成多路轨迹查询
- 迭代解码器逐步生成最终轨迹，性能领先现有方法
可靠预测周围交通参与者运动轨迹对保障自动驾驶安全至关重要。现有许多轨迹预测方法高度依赖高精地图作为强先验信息，但受限于高昂的构建成本、车辆定位误差或道路施工变化，地图的可用性与准确性难以保证。本文提出MFTP（Map-Free Trajectory Prediction），具备多项优势：首先，在推理阶段完全无需高精地图，同时在训练阶段通过知识蒸馏保留地图先验信息；其次，设计了一种新型分层编码器，有效提取时空特征并聚合为多路轨迹查询；此外，引入迭代解码器，逐次解码轨迹查询以生成最终预测。大量实验表明，该方法在无地图设置下于Argoverse数据集上达到当前最优性能。
原文摘要 · Abstract (English)
Reliable motion forecasting of surrounding agents is essential for ensuring the safe operation of autonomous vehicles. Many existing trajectory prediction methods rely heavily on high-definition (HD) maps as strong driving priors. However, the availability and accuracy of these priors are not guaranteed due to substantial costs to build, localization errors of vehicles, or ongoing road constructions. In this paper, we introduce MFTP, a Map-Free Trajectory Prediction method that offers several advantages. First, it eliminates the need for HD maps during inference while still benefiting from map priors during training via knowledge distillation. Second, we present a novel hierarchical encoder that effectively extracts spatial-temporal agent features and aggregates them into multiple trajectory queries. Additionally, we introduce an iterative decoder that sequentially decodes trajectory queries to generate the final predictions. Extensive experiments show that our approach achieves state-of-the-art performance on the Argoverse dataset under the map-free setting.
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