ResearchResearch paperRobotics & Embodied AI · Computer Vision · Large Language Models1 source · Oct 10, 2026

InfraWorld: Motion-Aware World Modeling for Roadside 4D Occupancy Forecasting

Roadside 4D occupancy forecasting predicts future semantic occupancy in a fixed infrastructure coordinate system.

Roadside scenes are globally structured yet locally evolving, requiring comprehensive scene-level context while precisely modeling sparse localized motion.
To address this limitation, we propose InfraWorld, a world model formulating roadside occupancy forecasting as selective state evolution.
Specifically, we propose Dynamic Query Initiation (DQI) to initialize queries in regions supported by motion in the observed history.
Extensive experiments on the InfraOcc and Occ3D--nus datasets demonstrate that InfraWorld achieves state-of-the-art performance for roadside 4D occupancy forecasting while retaining its effectiveness under ego-centric settings.

[1]InfraWorld: Motion-Aware World Modeling for Roadside 4D Occupancy Forecasting
arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 10, 04:00 AM
Roadside 4D occupancy forecasting predicts future semantic occupancy in a fixed infrastructure coordinate system.
Roadside scenes are globally structured yet locally evolving, requiring comprehensive scene-level context while precisely modeling sparse localized motion.

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