无需高精地图，直接从鸟瞰图预测多条合理行车轨迹
CASPFormer: Trajectory Prediction from BEV Images with Deformable Attention
- 用可变形注意力聚焦关键区域，高效生成轨迹
- 在nuScenes上多项指标达当前最优
- 适合追求端到端部署的自动驾驶系统
运动预测是自动驾驶与高级驾驶辅助系统的重要组成部分。现有最先进方法依赖高精地图来获取车辆周围环境信息，但高精地图制作成本高且难以实时更新，限制了实际部署的可扩展性。为此，我们提出上下文感知场景预测变换器（CASPFormer），能够直接从栅格化鸟瞰图（BEV）进行多模态运动预测。该系统可与任意能生成BEV图像的上游感知模块集成，并直接解码向量化轨迹，无需后处理。轨迹通过递归使用可变形注意力生成，兼具计算效率与空间关注能力。此外，我们引入可学习的模式查询，缓解生成多条场景一致轨迹时的模式坍缩问题。在nuScenes数据集上的实验表明，该模型在多个评估指标上达到当前最优性能。
原文摘要 · Abstract (English)
Motion prediction is an important aspect for Autonomous Driving (AD) and Advance Driver Assistance Systems (ADAS). Current state-of-the-art motion prediction methods rely on High Definition (HD) maps for capturing the surrounding context of the ego vehicle. Such systems lack scalability in real-world deployment as HD maps are expensive to produce and update in real-time. To overcome this issue, we propose Context Aware Scene Prediction Transformer (CASPFormer), which can perform multi-modal motion prediction from rasterized Bird-Eye-View (BEV) images. Our system can be integrated with any upstream perception module that is capable of generating BEV images. Moreover, CASPFormer directly decodes vectorized trajectories without any postprocessing. Trajectories are decoded recurrently using deformable attention, as it is computationally efficient and provides the network with the ability to focus its attention on the important spatial locations of the BEV images. In addition, we also address the issue of mode collapse for generating multiple scene-consistent trajectories by incorporating learnable mode queries. We evaluate our model on the nuScenes dataset and show that it reaches state-of-the-art across multiple metrics
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