Navigating complex and dynamic environments requires autonomous vehicles (AVs) to reason about both visible and occluded regions. This involves predicting the future motion of observed agents, inferring occluded ones, and modeling their interactions based on vectorized scene representations of the partially observable environment. However, prior work on occlusion inference and trajectory prediction have developed in isolation, with the former based on simplified rasterized methods and the latter assuming full environment observability. We introduce the Scene Informer, a unified approach for predicting both observed agent trajectories and inferring occlusions in a partially observable setting. It uses a transformer to aggregate various input modalities and facilitate selective queries on occlusions that might intersect with the AV's planned path. The framework estimates occupancy probabilities and likely trajectories for occlusions, as well as forecast motion for observed agents. We explore common observability assumptions in both domains and their performance impact. Our approach outperforms existing methods in both occupancy prediction and trajectory prediction in partially observable setting on the Waymo Open Motion Dataset.

自主车辆在复杂和动态环境中导航需要考虑可见和遮挡区域，本论文提出了一种统一的方法Scene Informer，在部分可观测的场景中同时预测观察到的代理的轨迹和推断遮挡物。通过聚合各种输入模态并在可能与自主车辆计划路径相交的遮挡物上进行选择性查询，该框架估计了遮挡物的占用概率和可能的轨迹，以及观察代理的未来运动。在Waymo Open Motion数据集的部分可观测设置中，我们的方法在占用预测和轨迹预测方面优于现有方法。

场景信息获取器：基于锚点的遮挡推断与轨迹预测在部分可观测环境中