论文标题
一个统治所有这些的本体论：自动驾驶的角落案例场景
One Ontology to Rule Them All: Corner Case Scenarios for Autonomous Driving
论文作者
论文摘要
目前，大规模部署自动驾驶汽车的核心障碍在于罕见事件的长尾。这些非常具有挑战性，因为它们不经常发生在深度神经网络的培训数据中。为了解决这个问题，我们建议生成其他合成训练数据，涵盖各种角落情况。由于本体可以在实现计算处理的同时代表人类专家知识，因此我们使用它们来描述场景。我们提出的主体本体论能够对文献中所有常见的角案例类别的场景进行建模。从这个主体的本体论中，可以得出任意的场景描述本体论。以自动化的方式，可以将它们转换为OpenScenario格式，然后在模拟中执行。这样，也可以生成具有挑战性的测试和评估方案。
The core obstacle towards a large-scale deployment of autonomous vehicles currently lies in the long tail of rare events. These are extremely challenging since they do not occur often in the utilized training data for deep neural networks. To tackle this problem, we propose the generation of additional synthetic training data, covering a wide variety of corner case scenarios. As ontologies can represent human expert knowledge while enabling computational processing, we use them to describe scenarios. Our proposed master ontology is capable to model scenarios from all common corner case categories found in the literature. From this one master ontology, arbitrary scenario-describing ontologies can be derived. In an automated fashion, these can be converted into the OpenSCENARIO format and subsequently executed in simulation. This way, also challenging test and evaluation scenarios can be generated.
