谷歌浏览器插件
订阅小程序
在清言上使用

Learning Monitorable Operational Design Domains for Assured Autonomy.

Automated Technology for Verification and Analysis (ATVA)(2022)

引用 1|浏览12
暂无评分
摘要
AI-based autonomous systems are increasingly relying on machine learning (ML) components to perform a variety of complex tasks in perception, prediction, and control. The use of ML components is projected to grow and with it the concern of using these components in systems that operate in safety-critical settings. To guarantee a safe operation of autonomous systems, it is important to run an ML component in its operational design domain (ODD), i.e., the conditions under which using the component does not endanger the safety of the system. Building safe and reliable autonomous systems which may use machine-learning-based components, calls therefore for automated techniques that allow to systematically capture the ODD of systems. In this paper, we present a framework for learning runtime monitors that capture the ODDs of black-box systems. A runtime monitor of an ODD predicts based on a sequence of monitorable observations whether the system is about to exit the ODD. We particularly investigate the learning of optimal monitors based on counterexample-guided refinement and conformance testing. We evaluate the applicability of our approach on a case study from the domain of autonomous driving.
更多
查看译文
关键词
monitorable operational design domains,learning
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要