Trustworthy AI for Safe Autonomy of Smart Railways: Directions and Lessons Learnt from Other Sectors

Abstract:

In this paper, we present some of the results achieved during the ongoing RAILS (Roadmaps for Artificial Intelligence Integration in the Rail Sector) research project, funded by the European Union Shift2Rail Joint Undertaking, and specifically in Work Package 2, addressing AI for autonomous and cooperative driving in future smart railways. We will provide a brief state-of-the-art and opportunities for future research, focusing on the trustworthiness of intelligent train control, also based on the analysis of related transportation sectors. As for other safety-critical sectors, the use of AI for autonomous driving in railways represents a challenge. This happens when an appropriate “safety envelope” cannot be guaranteed, such as when the Automatic Train Protection (ATP) is missing or not working properly (e.g., in limited supervision operating modes), or in cases when it is specifically used to improve safety (e.g., when employed for on-track obstacle detection). In many situations, however, AI can be advantageously adopted to optimize several parameters when supporting Automatic Train Operation (ATO), while the ATP supervises safety. Furthermore, in limited or no supervision by the ATP, AI can be effectively used to support train drivers in respecting signals and keeping safe headways, i.e., as an advanced driving assistance system, in analogy with the latest developments in the automotive sector.

Fundings and Disclaimer:

This research has received funding from the Shift2Rail Joint Undertaking (JU) under grant agreement No 881782 RAILS (Roadmaps for Artificial Intelligence (A.I.) integration in the raiL Sector). The JU receives support from the European Union’s Horizon 2020 research and innovation programme and the Shift2Rail JU members other than the Union.

The information and views set out in this document are those of the author(s) and do not necessarily reflect the official opinion of Shift2Rail Joint Undertaking. The JU does not guarantee the accuracy of the data included in this document. Neither the JU nor any person acting on the JU’s behalf may be held responsible for the use which may be made of the information contained therein.

Publication Notes:

This Conference Paper is available at: https://www.rssb.co.uk/spark/sparkitem/pb029335