论文标题
将机器学习和基于规则的算法结合起来的关键安全决策和控制框架
A Safety-Critical Decision Making and Control Framework Combining Machine Learning and Rule-based Algorithms
论文作者
论文摘要
尽管基于人工智能的方法缺乏透明度，但基于规则的方法占据了安全 - 关键系统中的主导地位。然而，后者无法与最初的鲁棒性竞争多种要求，例如同时解决安全性，舒适性和效率。因此，要从两种方法中受益，它们必须加入单个系统。本文提出了一个决策和控制框架，该框架从基于规则和机器学习的技术的优势中获利，同时弥补其缺点。所提出的方法体现了两个并行运行的控制器，称为安全和学习。基于规则的切换逻辑选择了两个控制器传输的操作之一。每次学习的安全控制器都会优先考虑，当时学习者不符合安全限制，并且直接参加了安全学习的控制器培训。选择自动驾驶中的决策和控制是系统案例研究，在该系统案例研究中，自动驾驶汽车学习了多任务策略以安全地越过未保护的交叉路口。设定多个要求（即安全，效率和舒适性）进行车辆操作。为提出的框架验证执行了数值模拟，在该框架验证中，其满足对不断变化环境的需求和鲁棒性的能力已成功证明。
While artificial-intelligence-based methods suffer from lack of transparency, rule-based methods dominate in safety-critical systems. Yet, the latter cannot compete with the first ones in robustness to multiple requirements, for instance, simultaneously addressing safety, comfort, and efficiency. Hence, to benefit from both methods they must be joined in a single system. This paper proposes a decision making and control framework, which profits from advantages of both the rule- and machine-learning-based techniques while compensating for their disadvantages. The proposed method embodies two controllers operating in parallel, called Safety and Learned. A rule-based switching logic selects one of the actions transmitted from both controllers. The Safety controller is prioritized every time, when the Learned one does not meet the safety constraint, and also directly participates in the safe Learned controller training. Decision making and control in autonomous driving is chosen as the system case study, where an autonomous vehicle learns a multi-task policy to safely cross an unprotected intersection. Multiple requirements (i.e., safety, efficiency, and comfort) are set for vehicle operation. A numerical simulation is performed for the proposed framework validation, where its ability to satisfy the requirements and robustness to changing environment is successfully demonstrated.
