ROAD-R: The Autonomous Driving Dataset with Logical Requirements
Published in Machine Learning, 2023
Recommended citation: Eleonora Giunchiglia, Mihaela C. Stoian, Salman Khan, Fabio Cuzzolin, Thomas Lukasiewicz. ROAD-R: The Autonomous Driving Dataset with Logical Requirements. Machine Learning, 112, 3261–3291 (2023). https://doi.org/10.1007/s10994-023-06322-z
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.
Notes: The paper was presented at:
- IJCLR 2022, where it received the Best Student Paper Prize.
- IJCAI 2022 Workshop on Artificial Intelligence for Autonomous Driving, where it received the Best Paper Award.
Paper available here.