用动态切换模型统一预测轨迹与遮挡推理，提升自动驾驶安全性。
Navigation under uncertainty: Trajectory prediction and occlusion reasoning with switching dynamical systems
- 基于切换动力系统建模，统一处理可见与遮挡物体的轨迹预测。
- 在Waymo数据集上验证了对遮挡场景的合理不确定性建模能力。
- 适合关注长尾安全场景的自动驾驶与机器人导航研究者。
预测周围物体的未来轨迹，尤其是在遮挡情况下，是自动驾驶和安全机器人导航中的关键任务。以往方法通常忽略遮挡物体的不确定性，仅使用高容量模型（如Transformer）在大规模数据集上训练，以预测可观测物体的轨迹。尽管这些方法在常规场景中表现良好，但在长尾、高危场景中泛化能力有限。本文提出一个统一框架，将轨迹预测与遮挡推理纳入同一类结构化概率生成模型——切换动力系统中。我们基于Waymo开放数据集开展初步实验，展示了该方法在遮挡场景下建模不确定性的潜力。
原文摘要 · Abstract (English)
Predicting future trajectories of nearby objects, especially under occlusion, is a crucial task in autonomous driving and safe robot navigation. Prior works typically neglect to maintain uncertainty about occluded objects and only predict trajectories of observed objects using high-capacity models such as Transformers trained on large datasets. While these approaches are effective in standard scenarios, they can struggle to generalize to the long-tail, safety-critical scenarios. In this work, we explore a conceptual framework unifying trajectory prediction and occlusion reasoning under the same class of structured probabilistic generative model, namely, switching dynamical systems. We then present some initial experiments illustrating its capabilities using the Waymo open dataset.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。