Self-assessment is a key to safety and robustness in automated driving. In order to design safer and more robust automated driving functions, the goal is to self-assess the performance of each module in a whole automated driving system. One crucial component in automated driving systems is the tracking of surrounding objects, where the Kalman filter is the most fundamental tracking algorithm. For Kalman filters, some classical online consistency measures exist for self-assessment, which are based on classical probability theory. However, these classical approaches lack the ability to measure the explicit statistical uncertainty within the self-assessment, which is an important quality measure, particularly, if only a small number of samples is available for the self-assessment. In this work, we propose a novel online self-assessment method using subjective logic, which is a modern extension of probabilistic logic that explicitly models the statistical uncertainty. Thus, by embedding classical Kalman filtering into subjective logic, our method additionally features an explicit measure for statistical uncertainty in the self-assessment.

中文翻译：

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卡尔曼滤波器遇到主观逻辑：使用主观逻辑的自我评估卡尔曼滤波器

自我评估是自动驾驶安全性和稳健性的关键。为了设计更安全、更稳健的自动驾驶功能，目标是对整个自动驾驶系统中每个模块的性能进行自我评估。自动驾驶系统中的一个关键组件是对周围物体的跟踪，其中卡尔曼滤波器是最基本的跟踪算法。对于卡尔曼滤波器，存在一些用于自我评估的经典在线一致性度量，它们基于经典概率论。然而，这些经典方法缺乏在自我评估中测量显式统计不确定性的能力，这是一个重要的质量衡量标准，尤其是在只有少量样本可用于自我评估的情况下。在这项工作中，我们提出了一种使用主观逻辑的新型在线自我评估方法，这是概率逻辑的现代扩展，它明确地对统计不确定性进行建模。因此，通过将经典卡尔曼滤波嵌入主观逻辑，我们的方法还具有对自我评估中统计不确定性的显式度量。