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Constructive Modelling of Parallelized Environmental Models for Structured Testing of Automated Driving Systems

机译:自动驾驶系统结构化测试的并行环境模型的构造建模

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In the automotive industry, current activities focus heavily on the development of automated driving systems (ADS). ADS process environmental data from different sensors, which are fused to generate a model of the surrounding world. Actors in the generated model are objects, which are e.g., classified as vehicles or pedestrians. The actors run in parallel, as in the real world actions from traffic participants can be taken independently and asynchronously from each other. For verification and validation of these systems a method is required, that allows for a realistic and hence parallel modeling of the system under test's environment. Additionally, the method should allow for structured testing in compliance with international norms such as the ISO 26262 and the first international standard for software testing ISO/IEC/IEEE 29119, published in 2013. In this paper we present an approach for creating environmental models for structured testing of automated driving systems with a constructive method. One step is the enumeration of all possible sequences, but we first decompose the task into manageable units by input/output dependency analysis. The expected behavior is formalized in temporal logic. In doing so, the effort for the creation of the model is feasible in industry. On the other hand, the test model guarantees the representation of all possible scenarios of use, making it a stable basis to derive significant test cases. We applied the method on an embedded system functionality in the automotive industry at AUDI. The system was architectured using the AUTOSAR 3.2 standard and implemented with Matlab Simulink. An existing, previously created test suite was available. This existing test suite served as a benchmark to assess the quality of the new test suite, derived from the environmental models. We compared the reachability of the test cases inside the implementation with code coverage measures and examined the variance of use imposed by the test suites. We present the promising results in this paper.
机译:在汽车工业中,当前的活动主要集中在自动驾驶系统(ADS)的开发上。 ADS处理来自不同传感器的环境数据,这些数据融合在一起以生成周围世界的模型。所生成的模型中的参与者是对象,其例如被分类为车辆或行人。角色并行运行,因为在现实世界中,流量参与者的动作可以独立且彼此异步地执行。为了验证和验证这些系统,需要一种方法,该方法允许在测试环境下对系统进行现实的并行建模。此外,该方法应允许遵循国际规范(例如ISO 26262和于2013年发布的软件测试的第一个国际标准ISO / IEC / IEEE 29119)进行结构化测试。在本文中,我们提出了一种用于创建环境模型的方法用建设性的方法对自动驾驶系统进行结构化测试。第一步是对所有可能的序列进行枚举,但是我们首先通过输入/输出依赖性分析将任务分解为可管理的单元。预期行为在时间逻辑中形式化。这样,创建模型的努力在工业上是可行的。另一方面,测试模型可以保证表示所有可能的使用情况,从而为得出重要的测试用例奠定了稳定的基础。在AUDI,我们将该方法应用于汽车行业的嵌入式系统功能。该系统使用AUTOSAR 3.2标准进行架构,并通过Matlab Simulink实施。现有的,先前创建的测试套件可用。该现有测试套件可作为评估从环境模型派生的新测试套件质量的基准。我们将实现中测试用例的可及性与代码覆盖率进行了比较,并检查了测试套件所带来的使用差异。我们在本文中提出了令人鼓舞的结果。

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