论文标题
通过监视责任安全规则，基于搜索的测试案例生成
Search-based Test-Case Generation by Monitoring Responsibility Safety Rules
论文作者
论文摘要
自动化车辆（AV）作为网络物理系统（CPS）的安全性取决于它们组成的模块（软件和硬件）的安全性及其严格集成。深度学习是AVS中用于感知，预测和决策的主要技术之一。预测和决策的准确性高度取决于用于培训其潜在深入学习的测试。在这项工作中，我们提出了一种筛选和分类基于模拟的驾驶测试数据的方法，用于培训和测试控制器。我们的方法基于监视和伪造技术，这导致了一个系统的自动化过程，用于生成和选择合格的测试数据。我们使用责任敏感安全（RSS）规则作为我们的预选赛规范，以滤除不满足RSS假设的随机测试。因此，其余的测试涵盖了受控车辆对环境的驾驶场景。我们的框架使用公开可用的S-Taliro和Sim-Atav工具分发。
The safety of Automated Vehicles (AV) as Cyber-Physical Systems (CPS) depends on the safety of their consisting modules (software and hardware) and their rigorous integration. Deep Learning is one of the dominant techniques used for perception, prediction, and decision making in AVs. The accuracy of predictions and decision-making is highly dependant on the tests used for training their underlying deep-learning. In this work, we propose a method for screening and classifying simulation-based driving test data to be used for training and testing controllers. Our method is based on monitoring and falsification techniques, which lead to a systematic automated procedure for generating and selecting qualified test data. We used Responsibility Sensitive Safety (RSS) rules as our qualifier specifications to filter out the random tests that do not satisfy the RSS assumptions. Therefore, the remaining tests cover driving scenarios that the controlled vehicle does not respond safely to its environment. Our framework is distributed with the publicly available S-TALIRO and Sim-ATAV tools.
