二重予測は不要：遮蔽環境における効率的なエージェント予測
Don't double it: Efficient Agent Prediction in Occlusions
遮蔽された交通エージェントの予測において、冗長な占有予測を減らすため、ハンガリアンマッチングと進行方向分離を導入したMatchInformerを提案し、Waymoデータセットで精度向上を実証した。
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著者: Anna Rothenhäusler, Markus Mazzola, Andreas Look, Raghu Rajan, Joschka Bödecker
分類: cs.RO
原文アブストラクト
Occluded traffic agents pose a significant challenge for autonomous vehicles, as hidden pedestrians or vehicles can appear unexpectedly, yet this problem remains understudied. Existing learning-based methods, while capable of inferring the presence of hidden agents, often produce redundant occupancy predictions where a single agent is identified multiple times. This issue complicates downstream planning and increases computational load. To address this, we introduce MatchInformer, a novel transformer-based approach that builds on the state-of-the-art SceneInformer architecture. Our method improves upon prior work by integrating Hungarian Matching, a state-of-the-art object matching algorithm from object detection, into the training process to enforce a one-to-one correspondence between predictions and ground truth, thereby reducing redundancy. We further refine trajectory forecasts by decoupling an agent's heading from its motion, a strategy that improves the accuracy and interpretability of predicted paths. To better handle class imbalances, we propose using the Matthews Correlation Coefficient (MCC) to evaluate occupancy predictions. By considering all entries in the confusion matrix, MCC provides a robust measure even in sparse or imbalanced scenarios. Experiments on the Waymo Open Motion Dataset demonstrate that our approach improves reasoning about occluded regions and produces more accurate trajectory forecasts than prior methods.
