论文标题
具有深度学习感知组件的自主系统的离散事件控制器合成
Discrete-Event Controller Synthesis for Autonomous Systems with Deep-Learning Perception Components
论文作者
论文摘要
我们提出了DeepDecs，这是一种用于合成对自动构建系统正确构造的离散事件控制器的新方法，这些系统使用深神经网络（DNN）分类器为其决策过程的感知步骤。尽管近年来在深度学习方面取得了重大进展，但为这些系统提供安全保证仍然非常具有挑战性。我们的控制器合成方法通过将DNN验证与经过验证的Markov模型的合成整合来解决这一挑战。合成的模型对应于保证满足自主系统的安全性，可靠性和性能要求的离散事件控制器，并且相对于一组优化目标，帕累托是最佳的。我们在模拟中使用该方法将控制器综合用于缓解移动机器人碰撞并保持驾驶员的专注于共享控制自动驾驶。
We present DeepDECS, a new method for the synthesis of correct-by-construction discrete-event controllers for autonomous systems that use deep neural network (DNN) classifiers for the perception step of their decision-making processes. Despite major advances in deep learning in recent years, providing safety guarantees for these systems remains very challenging. Our controller synthesis method addresses this challenge by integrating DNN verification with the synthesis of verified Markov models. The synthesised models correspond to discrete-event controllers guaranteed to satisfy the safety, dependability and performance requirements of the autonomous system, and to be Pareto optimal with respect to a set of optimisation objectives. We use the method in simulation to synthesise controllers for mobile-robot collision mitigation and for maintaining driver attentiveness in shared-control autonomous driving.
