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Learning unsupervised world models for autonomous driving has the potentialto improve the reasoning capabilities of today's systems dramatically. However,most work neglects the physical attributes of the world and focuses on sensordata alone. We propose MUVO, a MUltimodal World Model with Geometric VOxelRepresentations to address this challenge. We utilize raw camera and lidar datato learn a sensor-agnostic geometric representation of the world, which candirectly be used by downstream tasks, such as planning. We demonstratemultimodal future predictions and show that our geometric representationimproves the prediction quality of both camera images and lidar point clouds.

MUVO: A Multimodal Generative World Model for Autonomous Driving with Geometric Representations