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Achieving level-5 driving automation in autonomous vehicles necessitates arobust semantic visual perception system capable of parsing data from differentsensors across diverse conditions. However, existing semantic perceptiondatasets often lack important non-camera modalities typically used inautonomous vehicles, or they do not exploit such modalities to aid and improvesemantic annotations in challenging conditions. To address this, we introduceMUSES, the MUlti-SEnsor Semantic perception dataset for driving in adverseconditions under increased uncertainty. MUSES includes synchronized multimodalrecordings with 2D panoptic annotations for 2500 images captured under diverseweather and illumination. The dataset integrates a frame camera, a lidar, aradar, an event camera, and an IMU/GNSS sensor. Our new two-stage panopticannotation protocol captures both class-level and instance-level uncertainty inthe ground truth and enables the novel task of uncertainty-aware panopticsegmentation we introduce, along with standard semantic and panopticsegmentation. MUSES proves both effective for training and challenging forevaluating models under diverse visual conditions, and it opens new avenues forresearch in multimodal and uncertainty-aware dense semantic perception. Ourdataset and benchmark will be made publicly available.

MUSES: The Multi-Sensor Semantic Perception Dataset for Driving under Uncertainty