无需高精地图的鸟瞰视角轨迹预测，提升自动驾驶灵活性与鲁棒性。
BEVTraj: Map-Free End-to-End Trajectory Prediction in Bird's-Eye View with Deformable Attention and Sparse Goal Proposals
- 用可变形注意力从密集鸟瞰特征中自适应提取关键上下文
- 通过稀疏目标候选生成模块实现端到端多模态预测
- 不依赖地图，适合动态或未测绘场景，部署更灵活
在自动驾驶中，轨迹预测对安全高效导航至关重要。现有方法多依赖高精地图提供结构化环境先验，但高精地图维护成本高、地理覆盖有限，且在动态或未测绘场景下不可靠。直接利用鸟瞰视图（BEV）中的原始传感器数据更具灵活性，但BEV特征密集且无结构，导致以智能体为中心的空间推理困难且计算效率低。为此，我们提出鸟瞰视图轨迹预测（BEVTraj），一种无需地图的框架，采用可变形注意力自适应地从密集BEV特征的稀疏位置聚合任务相关上下文。我们进一步引入稀疏目标候选生成（SGCP）模块，预测少量合理目标，实现完全端到端的多模态预测，无需启发式后处理。大量实验表明，BEVTraj性能媲美最先进的基于高精地图的方法，同时在不依赖预建地图的前提下展现出更强的鲁棒性和灵活性。源代码已开源。
原文摘要 · Abstract (English)
In autonomous driving, trajectory prediction is essential for safe and efficient navigation. While recent methods often rely on high-definition (HD) maps to provide structured environmental priors, such maps are costly to maintain, geographically limited, and unreliable in dynamic or unmapped scenarios. Directly leveraging raw sensor data in Bird's-Eye View (BEV) space offers greater flexibility, but BEV features are dense and unstructured, making agent-centric spatial reasoning challenging and computationally inefficient. To address this, we propose Bird's-Eye View Trajectory Prediction (BEVTraj), a map-free framework that employs deformable attention to adaptively aggregate task-relevant context from sparse locations in dense BEV features. We further introduce a Sparse Goal Candidate Proposal (SGCP) module that predicts a small set of realistic goals, enabling fully end-to-end multimodal forecasting without heuristic post-processing. Extensive experiments show that BEVTraj achieves performance comparable to state-of-the-art HD map-based methods while providing greater robustness and flexibility without relying on pre-built maps. The source code is available at https://github.com/Kongminsang/bevtraj.
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