Don't double it: Efficient Agent Prediction in Occlusions

2026-01-29

AI Summary

This work addresses the challenge of trajectory prediction for traffic participants in occluded scenarios, where redundant detections often complicate motion planning and increase computational overhead. To mitigate this, we propose MatchInformer, an approach that extends SceneInformer by introducing Hungarian matching for the first time to establish one-to-one correspondences between predicted trajectories and ground-truth annotations. Our method decouples agent heading from motion modeling to enhance prediction accuracy and is built upon a Transformer architecture. We further employ the Matthews Correlation Coefficient (MCC) to evaluate occupancy prediction performance in sparse scenes. Experiments on the Waymo Open Motion Dataset demonstrate that MatchInformer significantly improves reasoning capability in occluded regions, effectively reduces redundant predictions, and achieves higher trajectory accuracy.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Computer Vision: Motion & Tracking
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• occluded agents
• redundant predictions
• autonomous driving
• occupancy prediction
• trajectory forecasting

Innovation

• Hungarian Matching
• trajectory decoupling
• occlusion prediction
• Matthews Correlation Coefficient
• transformer-based agent prediction