Don't double it: Efficient Agent Prediction in Occlusions

TL;DR

This paper introduces MatchInformer, a transformer-based method that reduces redundant predictions of occluded agents in autonomous driving, improving accuracy and efficiency in trajectory forecasting.

Contribution

We propose MatchInformer, integrating Hungarian Matching into a transformer architecture to enforce one-to-one predictions and decouple heading from motion for better occlusion reasoning.

Findings

Improved accuracy in occluded agent prediction on Waymo dataset

Reduced redundant occupancy predictions compared to prior methods

Enhanced trajectory forecast interpretability and robustness

Abstract

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.