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

TL;DR

This paper systematically reviews how DevOps practices are applied to autonomous driving systems, highlighting challenges and open issues in ensuring safety and reliability in AI-enabled functions.

Contribution

It provides a structured overview of existing literature on DevOps in autonomous driving, identifying challenges and open research topics for safe AD development.

Findings

Several open topics remain for enabling safe DevOps in autonomous driving.

Current literature highlights challenges in integrating safety with rapid development cycles.

The review synthesizes solutions and identifies gaps in safety assurance for AI in AD.

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.