In order to deploy automated vehicles to the public, it has to be proven that
the vehicle can safely and robustly handle traffic in many different scenarios.
One important component of automated vehicles is the perception system that
captures and processes the environment around the vehicle. Perception systems
require large datasets for training their deep neural network. Knowing which
parts of the data in these datasets describe a corner case is an advantage
during training or testing of the network. These corner cases describe
situations that are rare and potentially challenging for the network. We
propose a pipeline that converts collective expert knowledge descriptions into
the extended KI Absicherung ontology. The ontology is used to describe scenes
and scenarios that can be mapped to perception datasets. The corner cases can
then be extracted from the datasets. In addition, the pipeline enables the
evaluation of the detection networks against the extracted corner cases to
measure their performance.

为了证明自动驾驶汽车可以安全、稳健地处理多种不同情况的交通，提出了一个通过 KI Absicherung 本体论将集体专家知识描述转化为场景和情境来提取自动驾驶数据集中的极端案例，并评估检测网络性能的流程。