The DevSafeOps Dilemma: A Systematic Literature Review on Rapidity in Safe Autonomous Driving Development and Operation

2025-06-26

AI Summary

Autonomous driving system development faces a fundamental tension between safety compliance and continuous delivery. This study conducts a systematic literature review (SLR) following the PRISMA guidelines to address this challenge. It introduces “DevSafeOps” — the first conceptual framework explicitly delineating the interface between continuous integration and delivery (CI/CD) and compliance-driven verification in safety-critical AI systems. The SLR identifies 12 core challenges and 7 corresponding mitigation strategies, revealing three critical gaps: (1) absence of safety-aware CI/CD pipelines, (2) insufficient verifiability of the simulation-to-real-world closed-loop validation, and (3) lack of automated mechanisms for generating auditable safety evidence. These findings establish a theoretical foundation and practical roadmap for developing DevOps practices in autonomous driving that simultaneously ensure agility and assurance.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Computer Vision: Vision for Robotics & Autonomous Driving
• Natural Language Processing: Safety and Robustness

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Developing autonomous driving (AD) systems is challenging due to the complexity of the systems and the need to assure their safe and reliable operation. The widely adopted approach of DevOps seems promising to support the continuous technological progress in AI and the demand for fast reaction to incidents, which necessitate continuous development, deployment, and monitoring. We present a systematic literature review meant to identify, analyse, and synthesise a broad range of existing literature related to usage of DevOps in autonomous driving development. Our results provide a structured overview of challenges and solutions, arising from applying DevOps to safety-related AI-enabled functions. Our results indicate that there are still several open topics to be addressed to enable safe DevOps for the development of safe AD.

Problem

• Challenges in ensuring safe autonomous driving development
• DevOps application in AI-driven autonomous systems
• Identifying gaps in safe DevOps for autonomous vehicles

Innovation

• Systematic literature review on DevOps in AD
• Continuous development and monitoring solutions
• Safety challenges in AI-enabled DevOps