Datasets › MUAD

MUAD (Multiple Uncertainties for Autonomous Driving)

Introduced by Gianni Franchi et al. in MUAD: Multiple Uncertainties for Autonomous Driving, a benchmark for multiple uncertainty types and tasks7 Oct 2022 archive 2025-07-28

The MUAD dataset (Multiple Uncertainties for Autonomous Driving), consisting of 10,413 realistic synthetic images with diverse adverse weather conditions (night, fog, rain, snow), out-of-distribution objects, and annotations for semantic segmentation, depth estimation, object, and instance detection. Predictive uncertainty estimation is essential for the safe deployment of Deep Neural Networks in real-world autonomous systems and MUAD allows to a better assess the impact of different sources of uncertainty on model performance.

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Benchmarks archive 2025-07-28

No leaderboard in the archive resolves to this dataset.

Papers archive 2025-07-28

No paper in the archive has a leaderboard row on this dataset; the archive counts 4 papers for it but never published that list.

Dataset loaders archive 2025-07-28

torch-uncertainty.github.io/ENSTA-U2IS-AI/torch-uncertaintypytorch

1 loader as listed in the archive; links are outbound and not re-checked here.

Tasks archive 2025-07-28

Object Detection Semantic Segmentation Anomaly Detection Monocular Depth Estimation Autonomous Driving Out of Distribution (OOD) Detection Depth Aleatoric Uncertainty Estimation Decision Making Under Uncertainty Uncertainty Visualization Uncertainty Quantification

License archive 2025-07-28

No licence recorded in the archive. Absence here is not a statement about the dataset's terms.

Modalities archive 2025-07-28

Images Environment RGB-D

Languages archive 2025-07-28

No language tagged.

Variants archive 2025-07-28

MUAD

1 variant name, as the archive lists them.

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