论文标题
卡尔曼过滤器符合主观逻辑：使用主观逻辑的自我评估的卡尔曼过滤器
Kalman Filter Meets Subjective Logic: A Self-Assessing Kalman Filter Using Subjective Logic
论文作者
论文摘要
自我评估是自动驾驶中安全性和鲁棒性的关键。为了设计更安全，更强大的自动驾驶功能，目标是自我评估整个自动化驾驶系统中每个模块的性能。自动驾驶系统中的一个关键组件是跟踪周围物体，其中卡尔曼过滤器是最基本的跟踪算法。对于Kalman过滤器，存在一些基于经典概率理论的自我评估的经典在线一致性措施。但是，这些经典方法缺乏测量自我评估中明确的统计不确定性的能力，这是一个重要的质量指标，特别是如果只有少数样本可用于自我评估。在这项工作中，我们提出了一种使用主观逻辑的新颖的在线自我评估方法，这是概率逻辑的现代扩展，可显式地对统计不确定性进行建模。因此，通过将经典的卡尔曼过滤嵌入主观逻辑中，我们的方法还具有明确的措施，以实现自我评估中的统计不确定性。
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.
