ROAD-R: The Autonomous Driving Dataset with Logical Requirements

@article{Giunchiglia2022ROADRTA,
  title={ROAD-R: The Autonomous Driving Dataset with Logical Requirements},
  author={Eleonora Giunchiglia and Mihaela Stoia and Salman Khan and Fabio Cuzzolin and Thomas Lukasiewicz},
  journal={ArXiv},
  year={2022},
  volume={abs/2210.01597}
}
Neural networks have proven to be very powerful at computer vision tasks. However, they often exhibit unexpected behaviours, violating known requirements expressing background knowledge. This calls for models (i) able to learn from the requirements, and (ii) guaranteed to be compliant with the requirements themselves. Unfortunately, the development of such models is hampered by the lack of datasets equipped with formally specified requirements. In this paper, we introduce the ROad event… 
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