Towards System-Level Testing with Coverage Guarantees for Autonomous Vehicles
Towards System-Level Testing with Coverage Guarantees for Autonomous Vehicles
复制标题
实现自动驾驶汽车覆盖范围保证的系统级测试
DOI：
10.1109/models.2019.00-12
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Dániel Varró
中科院分区：
文献类型：
--
作者：
I. Majzik;Oszkár Semeráth;C. Hajdu;Kristóf Marussy;Z. Szatmári;Zoltán Micskei;András Vörös;Aren A. Babikian;Dániel Varró
由于安全关键型自动驾驶汽车需要与极其复杂且不断变化的环境进行交互，因此其保证是一项重大挑战。虽然系统工程实践需要在多个级别上保证，但现有的研究主要集中在组件级保证上，而忽略了复杂的系统级流量场景。在本文中，我们的目标是解决自动驾驶汽车的情况依赖的行为的系统级测试，通过结合各种基于模型的技术在不同的抽象层次。(1)在具有挑战性的测试场景中（在模拟器或现场测试中获得），使用图形查询和复杂事件处理技术连续监测安全属性。为了精确地量化现有测试套件的覆盖范围与安全标准的规定，（2）我们提供了定性抽象的因果关系，时间，或地理空间数据记录在个人运行到情况图，这允许系统地测量系统级的情况覆盖范围（抽象级别）wrt。领域专家掌握的安全概念。此外，（3）我们可以系统地推导出新的挑战性（抽象）的情况下，可解释地导致运行时的行为，还没有被测试到目前为止，通过适应一致的图形生成技术，从而增加情况的覆盖率。最后，（4）这些抽象的测试用例被具体化，以便它们可以在真实的或模拟的上下文中被研究。
Since safety-critical autonomous vehicles need to interact with an immensely complex and continuously changing environment, their assurance is a major challenge. While systems engineering practice necessitates assurance on multiple levels, existing research focuses dominantly on component-level assurance while neglecting complex system-level traffic scenarios. In this paper, we aim to address the system-level testing of the situation-dependent behavior of autonomous vehicles by combining various model-based techniques on different levels of abstraction. (1) Safety properties are continuously monitored in challenging test scenarios (obtained in simulators or field tests) using graph query and complex event processing techniques. To precisely quantify the coverage of an existing test suite with respect regulations of safety standards, (2) we provide qualitative abstractions of causal, temporal, or geospatial data recorded in individual runs into situation graphs, which allows to systematically measure system-level situation coverage (on an abstract level) wrt. safety concepts captured by domain experts. Moreover, (3) we can systematically derive new challenging (abstract) situations which justifiably lead to runtime behavior which has not been tested so far by adapting consistent graph generation techniques, thus increasing situation coverage. Finally, (4) such abstract test cases are concretized so that they can be investigated in a real or simulated context.