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.

翻译：在自动驾驶中，轨迹预测对于安全高效的导航至关重要。尽管现有方法通常依赖高精地图来提供结构化的环境先验，但此类地图维护成本高昂、地理覆盖有限，且在动态或未测绘场景中不可靠。直接在鸟瞰图空间中利用原始传感器数据提供了更大的灵活性，但鸟瞰图特征密集且非结构化，使得以智能体为中心的空间推理具有挑战性且计算效率低下。为此，我们提出鸟瞰图轨迹预测框架，该无地图框架采用可变形注意力机制，从密集鸟瞰图特征的稀疏位置自适应聚合任务相关上下文信息。我们进一步引入稀疏目标候选提议模块，该模块预测一小组真实可行的目标点，从而实现完全端到端的多模态轨迹预测，无需启发式后处理。大量实验表明，BEVTraj在无需依赖预建地图的情况下，实现了与最先进的基于高精地图方法相当的性能，同时提供了更强的鲁棒性和灵活性。源代码发布于 https://github.com/Kongminsang/bevtraj。