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 Awareness Dataset with logical Requirements (ROAD-R), the first publicly available dataset for autonomous driving with requirements expressed as logical constraints. Given ROAD-R, we show that current state-of-the-art models often violate its logical constraints, and that it is possible to exploit them to create models that (i) have a better performance, and (ii) are guaranteed to be compliant with the requirements themselves.
@article{arxiv.2210.01597,
title = {ROAD-R: The Autonomous Driving Dataset with Logical Requirements},
author = {Eleonora Giunchiglia and Mihaela Cătălina Stoian and Salman Khan and Fabio Cuzzolin and Thomas Lukasiewicz},
journal= {arXiv preprint arXiv:2210.01597},
year = {2023}
}