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About this paper
Ali Nouri, Beatriz Cabrero-Daniel, Fredrik Törner, Christian Berger
This systematic literature review by researchers from Chalmers University of Technology, University of Gothenburg, and Volvo Cars identifies 11 clusters of challenges and corresponding solutions for integrating rapid DevOps practices with stringent safety requirements in autonomous driving (AD) development. The work introduces a conceptual DevSafeOps framework to bridge the perceived dilemma between development speed and safety assurance in AD systems.
Key insights
- Integrating safety activities directly within continuous development and deployment pipelines is crucial to reconcile the demands of rapidity and safety in autonomous driving systems.
- Automation, data-driven methodologies, and AI/ML technologies, including Large Language Models (LLMs), are increasingly vital for addressing safety challenges across analysis, testing, and safety case generation.
- The "DevSafeOps" framework provides a structured approach for embedding safety into every stage of the continuous development and operation of AD systems, moving beyond a simple trade-off.
Summary by alphaXiv.
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