Image-based environment perception is an important component especially for driver assistance systems or autonomous driving. In this scope, modern neuronal networks are used to identify multiple objects as well as the according position and size information within a single frame. The performance of such an object detection model is important for the overall performance of the whole system. However, a detection model might also predict these objects under a certain degree of uncertainty. [...] In this work, we examine the semantic uncertainty (which object type?) as well as the spatial uncertainty (where is the object and how large is it?). We evaluate if the predicted uncertainties of an object detection model match with the observed error that is achieved on real-world data. In the first part of this work, we introduce the definition for confidence calibration of the semantic uncertainty in the context of object detection, instance segmentation, and semantic segmentation. We integrate additional position information in our examinations to evaluate the effect of the object's position on the semantic calibration properties. Besides measuring calibration, it is also possible to perform a post-hoc recalibration of semantic uncertainty that might have turned out to be miscalibrated. [...] The second part of this work deals with the spatial uncertainty obtained by a probabilistic detection model. [...] We review and extend common calibration methods so that it is possible to obtain parametric uncertainty distributions for the position information in a more flexible way. In the last part, we demonstrate a possible use-case for our derived calibration methods in the context of object tracking. [...] We integrate our previously proposed calibration techniques and demonstrate the usefulness of semantic and spatial uncertainty calibration in a subsequent process. [...]

翻译：基于图像的環境感知是驾驶员辅助系统或自动驾驶的重要组成部分。在此领域中，现代神经网络被用于识别单个帧中的多个目标及其对应的位置与尺寸信息。此类目标检测模型的性能对整个系统的整体表现至关重要。然而，检测模型也可能在某种程度的不确定性下预测这些目标。[...] 在本工作中，我们研究了语义不确定性（目标类型？）以及空间不确定性（目标位置及尺寸？）。我们评估了目标检测模型预测的不确定性是否与其在实际数据上观测到的误差相匹配。在本工作的第一部分，我们引入了目标检测、实例分割及语义分割背景下语义不确定性的置信度校准定义。我们在研究中整合了额外位置信息，以评估目标位置对语义校准特性的影响。除了测量校准效果，还可以对可能失准的语义不确定性进行事后重校准。[...] 本工作的第二部分探讨了由概率检测模型获得的空域不确定性。[...] 我们回顾并扩展了常见的校准方法，从而能够以更灵活的方式获得位置信息的参数化不确定性分布。在最后一部分，我们在目标跟踪的背景下展示了所推导校准方法的一个潜在用例。[...] 我们整合了先前提出的校准技术，并展示了语义与空间不确定性校准在后续流程中的实用性。[...]