Representing diverse and plausible future trajectories is critical for motion forecasting in autonomous driving. However, efficiently capturing these trajectories in a compact set remains challenging. This study introduces a novel approach for generating scene-specific trajectory sets tailored to different contexts, such as intersections and straight roads, by leveraging map information and actor dynamics. A deterministic goal sampling algorithm identifies relevant map regions, while our Recursive In-Distribution Subsampling (RIDS) method enhances trajectory plausibility by condensing redundant representations. Experiments on the Argoverse 2 dataset demonstrate that our method achieves up to a 10% improvement in Driving Area Compliance (DAC) compared to baseline methods while maintaining competitive displacement errors. Our work highlights the benefits of mining such scene-aware trajectory sets and how they could capture the complex and heterogeneous nature of actor behavior in real-world driving scenarios.

翻译：表示多样且合理的未来轨迹对于自动驾驶中的运动预测至关重要。然而，在紧凑集合中高效捕获这些轨迹仍然具有挑战性。本研究提出了一种新颖方法，通过利用地图信息和参与者动态，为不同场景（如交叉路口和直道）生成定制的场景特定轨迹集。一种确定性的目标采样算法识别相关地图区域，而我们的递归分布内子采样方法通过压缩冗余表示来增强轨迹合理性。在Argoverse 2数据集上的实验表明，与基线方法相比，我们的方法在驾驶区域合规性指标上实现了高达10%的提升，同时保持了具有竞争力的位移误差。我们的工作凸显了挖掘此类场景感知轨迹集的优势，以及它们如何能够捕捉现实世界驾驶场景中参与者行为复杂且异构的本质。