基于可信等价精化的自治系统测试
Testing Autonomous Systems with Believed Equivalence Refinement
摘要：
自动驾驶功能的持续工程通常需要在道路测试中部署车辆，以获得导致问题决策的输入。虽然这一发现导致产生了改进的系统，但它也挑战了使用等价类和相关的相对测试覆盖标准进行测试的基础。在本文中，我们提出了相信等价，建立一个等价类最初是基于专家的信念，并受到一组可用的测试用例具有一致的估值。当一个新遇到的测试用例破坏了一致性时，可能需要细化已建立的分类，以便将最初认为的等价性分成两个。最后，我们将重点介绍使用深度神经网络实现的模块，其中每个类别都在实数域上划分输入。我们提出了分析和懒惰的方法来建议改进。该概念在分析多个自动驾驶模块中得到了证明，表明了我们所提出的方法的潜力。
英文摘要：
Continuous engineering of autonomous driving functions commonly requires deploying vehicles in road testing to obtain inputs that cause problematic decisions. Although the discovery leads to producing an improved system, it also challenges the foundation of testing using equivalence classes and the associated relative test coverage criterion. In this paper, we propose believed equivalence, where the establishment of an equivalence class is initially based on expert belief and is subject to a set of available test cases having a consistent valuation. Upon a newly encountered test case that breaks the consistency, one may need to refine the established categorization in order to split the originally believed equivalence into two. Finally, we focus on modules implemented using deep neural networks where every category partitions an input over the real domain. We present both analytical and lazy methods to suggest the refinement. The concept is demonstrated in analyzing multiple autonomous driving modules, indicating the potential of our proposed approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
