PEON 方法：用概率本体建模运行设计域，让 AI 系统测试有统计依据
Probabilistic Modelling of Operational Design Domains, A New Approach for Testing AI Systems
搞自动驾驶或列车感知测试的可以看看：PEON 把运行设计域建成概率模型，能自动采出代表性测试用例，还给出何时能停测的判据。
arXiv 论文提出概率扩展本体 PEON，用于描述机器学习系统的运行设计域（ODD），并在本体划分上叠加概率分布。论文指出传统测试在车辆障碍物检测这类系统上失效：漏检无法靠修 bug 解决，测试结果只能做统计解读，因此测试集必须对 ODD 既完备又有代表性。PEON 不需要维护难以管理的条件概率表，只需指定边际分布和函数化描述的依赖，再由基于耦合与最优传输的算法补全成贝叶斯网络。从 PEON 可导出代表性测试用例的采样方法、给定质量目标和显著性水平下的终止测试判据，以及复评既有测试结果、评估训练数据平衡度的方法。作者以自动列车运行为例演示了复杂运行设计域的实际建模。
Probabilistic Modelling of Operational Design Domains, A New Approach for Testing AI Systems
The conventional testing process quickly fails when applied to ML-based systems such as obstacle detection in vehicles: if an obstacle is not detected in a test, classical bug fixing is impossible and an AI system will always retain shortcomings. Test results can therefore only be interpreted statistically, which in turn requires test sets that are not only complete with respect to the operational design domain (ODD) of the system, but also representative of it. To this end, we introduce probabilistically extended ontologies (PEONs): ontologies describing the ODD, augmented with a probability distribution over the partitioning they induce. Instead of unmaintainable conditional probability tables, only marginal distributions and functionally described dependencies need to be specified; algorithms based on couplings and optimal transport complete this specification to a Bayesian network. From a PEON we derive the sampling of representative test cases, rigorous end-of-test criteria for given quality targets and significance levels, and methods for re-evaluating existing test results and for assessing the balance of training data. We demonstrate the practical modelling of a complex ODD using the example of automatic train operation.