World models for autonomous driving have the potential to dramatically improve the reasoning capabilities of today's systems. However, most works focus on camera data, with only a few that leverage lidar data or combine both to better represent autonomous vehicle sensor setups. In addition, raw sensor predictions are less actionable than 3D occupancy predictions, but there are no works examining the effects of combining both multimodal sensor data and 3D occupancy prediction. In this work, we perform a set of experiments with a MUltimodal World Model with Geometric VOxel representations (MUVO) to evaluate different sensor fusion strategies to better understand the effects on sensor data prediction. We also analyze potential weaknesses of current sensor fusion approaches and examine the benefits of additionally predicting 3D occupancy.
@article{arxiv.2311.11762,
title = {MUVO: A Multimodal Generative World Model for Autonomous Driving with Geometric Representations},
author = {Daniel Bogdoll and Yitian Yang and Tim Joseph and Melih Yazgan and J. Marius Zöllner},
journal= {arXiv preprint arXiv:2311.11762},
year = {2025}
}
Comments
Daniel Bogdoll and Yitian Yang contributed equally. Accepted for publication at IV 2025