ROAD-R: the autonomous driving dataset with logical requirements

Author:

Giunchiglia Eleonora,Stoian Mihaela Cătălina,Khan Salman,Cuzzolin Fabio,Lukasiewicz Thomas

Abstract

AbstractNeural networks have proven to be very powerful at computer vision tasks. However, they often exhibit unexpected behaviors, acting against background knowledge about the problem at hand. This calls for models (i) able to learn from requirements expressing such background knowledge, and (ii) guaranteed to be compliant with the requirements themselves. Unfortunately, the development of such models is hampered by the lack of real-world 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.

Funder

Engineering and Physical Sciences Research Council

University of Oxford

AXA Research Fund

Huawei Technologies

HORIZON EUROPE Reforming and enhancing the European Research and Innovation system

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. CCN+: A neuro-symbolic framework for deep learning with requirements;International Journal of Approximate Reasoning;2024-01

2. Refining neural network predictions using background knowledge;Machine Learning;2023-03-14

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