发表机构
Northeastern University; University of Connecticut（东北大学; 康涅狄格大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对联邦轨迹预测中场景不确定性与复杂度异质性挑战，提出不确定性感知及联合复杂度-不确定性感知的主动客户端选择方法，在Argoverse上加速收敛并提升预测性能。
AI 中文摘要
训练诸如Transformer之类的序列模型已成为自动驾驶车辆轨迹预测的标准做法，然而，由于真实世界的轨迹分散在不同地区和车辆中，组装高质量的中心化数据集仍然具有挑战性。联邦学习（FL）提供了一种自然的替代方案，但面临两个独特的挑战：由轨迹或地图模糊性引起的高场景不确定性，以及由多样化的地图拓扑、交通密度、智能体组成和驾驶行为导致的跨场景复杂度异质性。我们提出了一系列主动客户端选择方法，这些方法逐步纳入对场景不确定性和复杂度的感知，以优先选择信息量丰富的客户端。我们的不确定性感知选择器在不确定性感知的全局目标下使用每个客户端的负对数似然以及估计的偶然不确定性。我们进一步开发了一种联合考虑场景复杂度和不确定性的选择器，其动机源于复杂场景的知识可以迁移到更简单场景的直觉。在Argoverse上的实验表明，联邦轨迹预测优于本地训练的模型。不确定性感知选择加速了收敛，并改善了minADE、minFDE和MR指标。在强场景复杂度异质性下，我们联合的复杂度与不确定性感知选择器实现了最佳的泛化性能，并进一步加速了收敛，证明了优先选择复杂且信息量丰富的场景的益处。
英文摘要
Training sequence models such as transformers is now standard for autonomous vehicle trajectory prediction, yet assembling high-quality centralized datasets remains challenging because real-world trajectories are fragmented across regions and vehicles. Federated Learning (FL) offers a natural alternative, but faces two distinctive challenges: high scene uncertainty arising from trajectory or map ambiguity, and cross-scene complexity heterogeneity caused by diverse map topology, traffic density, agent composition, and driving behaviors. We propose a family of active client selection methods that progressively incorporate awareness of scene uncertainty and complexity to prioritize informative clients. Our uncertainty-aware selectors use per-client negative log-likelihood under an uncertainty-aware global objective and estimated aleatoric uncertainty. We further develop a selector that jointly considers scene complexity and uncertainty, motivated by the intuition that knowledge from complex scenes can transfer to easier ones. Experiments on Argoverse show that federated trajectory prediction outperforms locally trained models. Uncertainty-aware selection accelerates convergence and improves minADE, minFDE, and MR. Under strong scene-complexity heterogeneity, our joint complexity- and uncertainty-aware selector achieves the best generalization and further accelerates convergence, demonstrating the benefit of prioritizing complex and informative scenes.
