论文标题
ROAD-R：具有逻辑要求的自动驾驶数据集
ROAD-R: The Autonomous Driving Dataset with Logical Requirements
论文作者
论文摘要
事实证明，神经网络在计算机视觉任务上非常强大。但是，它们经常表现出意想不到的行为，违反了表达背景知识的已知要求。这要求模型（i）能够从要求中学习，并且（ii）保证符合要求本身。不幸的是，由于缺乏配备正式指定要求的数据集，这种模型的开发受到了阻碍。在本文中，我们介绍了具有逻辑要求（ROAD-R）的道路事件意识数据集，这是第一个用于自动驾驶的公开可用数据集，其要求表示为逻辑约束。考虑到ROAD-R，我们表明当前的最新模型通常违反其逻辑限制，并且可以利用它们创建（i）具有更好性能的模型，并且（ii）保证自己符合要求本身。
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.
