Safety Analysis of Autonomous Railway Systems: An Introduction to the SACRED Methodology

TL;DR

SACRED is introduced, a safety methodology for producing an initial safety case and determining important safety metrics for autonomous systems, and is motivated by the proposed GoA-4 light-rail system in Berlin.

Abstract

As the railway industry increasingly seeks to introduce autonomy and machine learning (ML), several questions arise. How can safety be assured for such systems and technologies? What is the applicability of current safety standards within this new technological landscape? What are the key metrics to classify a system as safe? Currently, safety analysis for the railway reflects the failure modes of existing technology; in contrast, the primary concern of analysis of automation is typically average performance. Such purely statistical approaches to measuring ML performance are limited, as they may overlook classes of situations that may occur rarely but in which the function performs consistently poorly. To combat these difficulties we introduce SACRED, a safety methodology for producing an initial safety case and determining important safety metrics for autonomous systems. The development of SACRED is motivated by the proposed GoA-4 light-rail system in Berlin.

Figures (4)

• Figure 1: The Grades of Automation as represented in railway, as well as a display of Fully Autonomous Operation (FAO).
• Figure 2: Manifestations of Uncertainty as defined by Lovell, B. E. (1995). lovell_taxonomy_1995
• Figure 3: Safety Assurance of autonomous systems in Complex Environments.
• Figure 4: The SACRED methodology.