TL;DR
SafetyOps proposes a set of integrated practices combining DevOps, TestOps, DataOps, and MLOps to enhance safety assurance in complex autonomous AI systems through continuous and traceable safety processes.
Contribution
The paper introduces SafetyOps, a novel framework that integrates multiple operational practices to improve safety engineering for autonomous AI systems.
Findings
SafetyOps enables continuous safety monitoring and updates.
It facilitates scalable safety integration across industries.
Enhances traceability and efficiency in safety processes.
Abstract
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…
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Taxonomy
TopicsSafety Systems Engineering in Autonomy · Risk and Safety Analysis · Software Reliability and Analysis Research