Prognostic Techniques in Automated Driving System (ADS) Vehicle Safety

The recent advancements in fields such as sensors, AI, and IoT are majorly impacting the automotive industry. Automated Driving Systems (ADS) are developing rapidly, meaning that SAE level 3 and above vehicles are quickly becoming a reality. As a result, maintenance of such systems becomes essential to ensure their safe and efficient navigation. Prognostic techniques in particular are crucial to predict the future health state of the system and its components. Many methods have been developed and are being used for prognostic of conventional vehicles as well as other industries. However, ADS is a new technology, and several of these approaches are still being developed and adapted to it. In this paper, we first present a review of the most used prognostic techniques across different safety-critical domains such as aerospace, power, and manufacturing. Then, we summarize the main challenges that must be faced to successfully develop novel approaches for ADS prognostic and provide a set of recommendations to support future research in the field. Finally, we present a future project consisting in a scenario-based prognostic framework for ADS vehicles.