The operating environment of a highly automated vehicle is subject to change, e.g., weather, illumination, or the scenario containing different objects and other participants in which the highly automated vehicle has to navigate its passengers safely. These situations must be considered when developing and validating highly automated driving functions. This already poses a problem for training and evaluating deep learning models because without the costly labeling of thousands of recordings, not knowing whether the data contains relevant, interesting data for further model training, it is a guess under which conditions and situations the model performs poorly. For this purpose, we present corner case criteria based on the predictive uncertainty. With our corner case criteria, we are able to detect uncertainty-based corner cases of an object instance segmentation model without relying on ground truth (GT) data. We evaluated each corner case criterion using the COCO and the NuImages dataset to analyze the potential of our approach. We also provide a corner case decision function that allows us to distinguish each object into True Positive (TP), localization and/or classification corner case, or False Positive (FP). We also present our first results of an iterative training cycle that outperforms the baseline and where the data added to the training dataset is selected based on the corner case decision function.

翻译：高度自动化车辆的运行环境会发生变化，例如天气、光照、或场景中包含不同物体及其他参与者，车辆必须在此类环境中安全运送乘客。在开发和验证高度自动化驾驶功能时，必须考虑这些情况。这已经对深度学习模型的训练与评估构成挑战：若未耗费高昂成本对数以千计的记录进行标注，便无法知晓数据是否包含对进一步模型训练具有相关性或趣味性的样本，从而只能猜测模型在哪些条件或情境下性能不佳。为此，我们提出了基于预测不确定性的角落案例检测准则。利用这些准则，我们无需依赖真实标注（GT）数据即可检测物体实例分割模型中基于不确定性的角落案例。我们使用COCO和NuImages数据集对每个角落案例准则进行了评估，以分析本方法的潜力。我们还提供了一种角落案例决策函数，该函数能够将每个物体区分为真阳性（TP）、定位和/或分类角落案例，或假阳性（FP）。此外，我们展示了迭代训练周期的初步结果，该周期优于基线模型，且训练数据集的扩充基于角落案例决策函数进行筛选。