MUSES: The Multi-Sensor Semantic Perception Dataset for Driving under
  Uncertainty
Tim Brödermann David Bruggemann Christos Sakaridis Kevin Ta Odysseas Liagouris Jason Corkill Luc Van Gool
Achieving level-5 driving automation in autonomous vehicles necessitates a
robust semantic visual perception system capable of parsing data from different
sensors across diverse conditions. However, existing semantic perception
datasets often lack important non-camera modalities typically used in
autonomous vehicles, or they do not exploit such modalities to aid and improve
semantic annotations in challenging conditions. To address this, we introduce
MUSES, the MUlti-SEnsor Semantic perception dataset for driving in adverse
conditions under increased uncertainty. MUSES includes synchronized multimodal
recordings with 2D panoptic annotations for 2500 images captured under diverse
weather and illumination. The dataset integrates a frame camera, a lidar, a
radar, an event camera, and an IMU/GNSS sensor. Our new two-stage panoptic
annotation protocol captures both class-level and instance-level uncertainty in
the ground truth and enables the novel task of uncertainty-aware panoptic
segmentation we introduce, along with standard semantic and panoptic
segmentation. MUSES proves both effective for training and challenging for
evaluating models under diverse visual conditions, and it opens new avenues for
research in multimodal and uncertainty-aware dense semantic perception. Our
dataset and benchmark will be made publicly available.