论文标题
基于场景的自动驾驶系统测试的传递标准
Pass-Fail Criteria for Scenario-Based Testing of Automated Driving Systems
论文作者
论文摘要
MusicC项目创建了一个概念验证方案数据库，该数据库用作验证自动驾驶系统（ADS）类型批准过程的一部分。该过程必须包括评估测试结果的高度自动化方法，因为所需的规模的手动审查是不切实际的。 本文制定了一个框架，以评估正常操作中广告的行为安全性（即，在没有组件故障或恶意动作的动态驾驶任务的执行情况下执行）。确定了五个针对ADS性能的顶级评估标准。实施这些需要两种类型的结果评分规则：规定（必须始终遵循的可衡量规则）和基于风险的（不可能发生的不良结果，必须经常发生）。评分规则以编程语言定义，并将作为方案描述的一部分存储。 基于风险的规则不能从单个测试案例中提供通过/失败的决定。取而代之的是，定义了一个框架以对每个功能方案（具有共同特征的测试用例集）做出决定。这考虑了许多单个测试中的统计性能。确定了该框架对假设检验和方案选择的含义。
The MUSICC project has created a proof-of-concept scenario database to be used as part of a type approval process for the verification of automated driving systems (ADS). This process must include a highly automated means of evaluating test results, as manual review at the scale required is impractical. This paper sets out a framework for assessing an ADS's behavioural safety in normal operation (i.e. performance of the dynamic driving task without component failures or malicious actions). Five top-level evaluation criteria for ADS performance are identified. Implementing these requires two types of outcome scoring rule: prescriptive (measurable rules which must always be followed) and risk-based (undesirable outcomes which must not occur too often). Scoring rules are defined in a programming language and will be stored as part of the scenario description. Risk-based rules cannot give a pass/fail decision from a single test case. Instead, a framework is defined to reach a decision for each functional scenario (set of test cases with common features). This considers statistical performance across many individual tests. Implications of this framework for hypothesis testing and scenario selection are identified.
