Representing diverse and plausible future trajectories of actors is crucial for motion forecasting in autonomous driving. However, efficiently capturing the true trajectory distribution with a compact set is challenging. In this work, we propose a novel approach for generating scene-specific trajectory sets that better represent the diversity and admissibility of future actor behavior. Our method constructs multiple trajectory sets tailored to different scene contexts, such as intersections and non-intersections, by leveraging map information and actor dynamics. We introduce a deterministic goal sampling algorithm that identifies relevant map regions and generates trajectories conditioned on the scene layout. Furthermore, we empirically investigate various sampling strategies and set sizes to optimize the trade-off between coverage and diversity. Experiments on the Argoverse 2 dataset demonstrate that our scene-specific sets achieve higher plausibility while maintaining diversity compared to traditional single-set approaches. The proposed Recursive In-Distribution Subsampling (RIDS) method effectively condenses the representation space and outperforms metric-driven sampling in terms of trajectory admissibility. Our work highlights the benefits of scene-aware trajectory set generation for capturing the complex and heterogeneous nature of actor behavior in real-world driving scenarios.

翻译：在自动驾驶的运动预测中，准确表征交通参与者多样且合理的未来轨迹至关重要。然而，如何用紧凑的轨迹集高效捕捉真实的轨迹分布仍具挑战性。本研究提出一种生成场景特定轨迹集的新方法，以更好地表征未来交通参与者行为的多样性和可采纳性。我们的方法利用地图信息和参与者动态，构建了针对不同场景上下文（如交叉口与非交叉口）的多个轨迹集。我们引入了一种确定性目标采样算法，该算法识别相关地图区域并基于场景布局生成条件轨迹。此外，我们通过实证研究了多种采样策略和集合规模，以优化覆盖度与多样性之间的权衡。在Argoverse 2数据集上的实验表明，与传统的单一集合方法相比，我们的场景特定轨迹集在保持多样性的同时实现了更高的合理性。所提出的递归分布内子采样方法有效压缩了表征空间，在轨迹可采纳性方面优于基于度量的采样方法。本研究凸显了场景感知的轨迹集生成在捕捉现实驾驶场景中交通参与者行为复杂性与异质性方面的优势。