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<div class="section abstract"><span class="label">1</span><div class="htmlview paragraph">Autonomous Driving Systems (ADS) are developing rapidly. As vehicle technology advances to SAE level 3 and above (L4, L5), there is a need to maximize and verify safety and operational benefits. As a result, maintenance of these ADS systems is essential which includes scheduled, condition-based, risk-based, and predictive maintenance. A lot of techniques and methods have been developed and are being used in the maintenance of conventional vehicles as well as other industries, but ADS is new technology and several of these maintenance types are still being developed as well as adapted for ADS. In this work, we are presenting a systematic literature review of the “State of the Art” knowledge for the maintenance of a fleet of ADS which includes fault diagnostics, prognostics, predictive maintenance, and preventive maintenance. We are providing statistical inference of different methodologies, comparison between methodologies, and providing our inference of different techniques that are used in other industries for maintenance that can be utilized for ADS. This paper presents a summary, main result, challenges, and opportunities of these approaches and supports new work for the maintenance of ADS.</div></div>