Published November 21, 2022 | Version v1
Dataset Open

MUAD: Multiple Uncertainties for Autonomous Driving

Creators

  • 1. École Nationale Supérieure de Techniques Avancées
  • 2. Valeo (France)
  • 3. ROR icon Laboratoire des systèmes et applications des technologies de l'information et de l'énergie

Description

We introduce MUAD, a synthetic dataset for autonomous driving with multiple uncertainty types and tasks. It contains 10413 in total: 3420 images in the train set, 492 in the validation set and 6501 in the test set. The test set is divided as follows: 551 in the normal set, 102 in the normal set with no shadows, 1668 in the OOD set, 605 in the low adversity set and 602 images in the high adversity set, 1552 in the low adversity with OOD set and 1421 images in the high adversity with OOD set. All of these sets cover day and night conditions, with 2/3 being day images and 1/3 night images. Test datasets address diverse weather conditions (rain, snow, and fog with two different intensity levels) and multiple OOD objects.

Files

train.zip

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Additional details

Related works

Is published in
Conference paper: arXiv:2203.01437 (arXiv)

Software

Repository URL
https://github.com/ENSTA-U2IS-AI/torch-uncertainty
Programming language
Python
Development Status
Active