Scene Informer: 部分観測環境におけるアンカーベースの遮蔽推論と軌道予測
Scene Informer: Anchor-based Occlusion Inference and Trajectory Prediction in Partially Observable Environments
部分観測環境で観測エージェントの軌道予測と遮蔽領域の推論を統合するTransformerベースの手法を提案し、Waymoデータセットで性能向上を示した。
著者: Bernard Lange, Jiachen Li, Mykel J. Kochenderfer
分類: cs.RO, cs.AI, cs.CV
原文アブストラクト
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.
