论文标题
在自动驾驶的反馈控制系统中学习关键方案
Learning Critical Scenarios in Feedback Control Systems for Automated Driving
论文作者
论文摘要
测试对于验证和验证控制设计至关重要，尤其是在安全至关重要的应用中。特别是，管理自动驾驶车辆的控制系统必须被证明足以可靠地在市场上接受。最近，许多研究集中在基于方案的方法上。但是，要测试的可能驾驶场景的数量原则上是无限的。在本文中，我们将基于学习的优化框架形式化，以生成角落测试杆，并考虑到操作设计领域。我们研究了用于自动驾驶的反馈控制系统的方法，为此我们建议设计表达场景关键性的目标函数。在案例研究的两个逻辑场景上进行的数值测试表明，该方法可以在有限数量的闭环实验中识别关键方案。
Testing is essential for verifying and validating control designs, especially in safety-critical applications. In particular, the control system governing an automated driving vehicle must be proven reliable enough for its acceptance on the market. Recently, much research has focused on scenario-based methods. However, the number of possible driving scenarios to test is in principle infinite. In this paper, we formalize a learning-based optimization framework to generate corner test-cases, where we take into account the operational design domain. We examine the approach on the case of a feedback control system for automated driving, for which we suggest the design of the objective function expressing the criticality of scenarios. Numerical tests on two logical scenarios of the case study demonstrate that the approach can identify critical scenarios within a limited number of closed-loop experiments.
