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.

翻译：遮挡的交通参与者对自动驾驶车辆构成重大挑战，因为隐藏的行人或车辆可能突然出现，但该问题仍未得到充分研究。现有的基于学习的方法虽然能够推断隐藏参与者的存在，但常产生冗余的占用预测，即单个参与者被重复识别多次。该问题使下游规划复杂化并增加计算负荷。为此，我们提出MatchInformer——一种基于Transformer的创新方法，该方法建立在最先进的SceneInformer架构之上。我们的方法通过将匈牙利匹配（目标检测领域最先进的目标匹配算法）整合到训练过程中，改进了先前工作，从而强制实现预测与真实标注之间的一一对应关系，以此减少冗余。我们进一步通过解耦参与者的航向与其运动来优化轨迹预测，该策略提升了预测路径的准确性与可解释性。为更好地处理类别不平衡问题，我们建议使用马修斯相关系数（MCC）来评估占用预测。通过综合考虑混淆矩阵中的所有条目，MCC即使在稀疏或不平衡场景下也能提供稳健的度量。在Waymo开放运动数据集上的实验表明，相较于现有方法，我们的方法提升了对遮挡区域的推理能力，并产生了更准确的轨迹预测。