We present DeepDECS, a new method for the synthesis of correct-by-construction software 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 software 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 evaluate the method in simulation by using it to synthesise controllers for mobile-robot collision limitation, and for maintaining driver attentiveness in shared-control autonomous driving.

中文翻译：

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具有深度学习感知组件的自主系统的控制器综合


我们提出了 DeepDECS，这是一种为自主系统合成构建正确软件控制器的新方法，该系统使用深度神经网络 (DNN) 分类器来进行决策过程的感知步骤。尽管近年来深度学习取得了重大进展，但为这些系统提供安全保证仍然非常具有挑战性。我们的控制器综合方法通过将 DNN 验证与经过验证的马尔可夫模型的综合相结合来解决这一挑战。综合模型对应于离散事件软件控制器，保证满足自主系统的安全性、可靠性和性能要求，并且对于一组优化目标而言是帕累托最优。我们在仿真中评估该方法，使用它来合成用于移动机器人碰撞限制的控制器，以及在共享控制自动驾驶中保持驾驶员注意力的控制器。