论文标题
SausicyOps
SafetyOps
论文作者
论文摘要
安全保证是大规模部署各种自动驾驶系统（例如自动驾驶车辆）的最高因素。但是，安全工程实践和流程的执行受到现代关键安全系统的复杂性日益复杂的挑战。对于涉及人工智能（AI）和数据驱动技术以及物理世界和数字计算平台的复杂相互作用的自治系统，这种属性变得更加至关重要。在该立场论文中，我们重点介绍了将当前安全过程应用于现代自治系统的一些挑战。然后，我们介绍了SafetyOps的概念 - 一组实践，它结合了DevOps，Testops，DataOps和MLOPS，以提供有效，连续和可追溯的系统安全寿命。我们认为，SafetyOps可以在依靠AI和数据的各个行业中的安全工程适应和适应安全工程中发挥重要作用。
Safety assurance is a paramount factor in the large-scale deployment of various autonomous systems (e.g., self-driving vehicles). However, the execution of safety engineering practices and processes have been challenged by an increasing complexity of modern safety-critical systems. This attribute has become more critical for autonomous systems that involve artificial intelligence (AI) and data-driven techniques along with the complex interactions of the physical world and digital computing platforms. In this position paper, we highlight some challenges of applying current safety processes to modern autonomous systems. Then, we introduce the concept of SafetyOps - a set of practices, which combines DevOps, TestOps, DataOps, and MLOps to provide an efficient, continuous and traceable system safety lifecycle. We believe that SafetyOps can play a significant role in scalable integration and adaptation of safety engineering into various industries relying on AI and data.
