ADSA – Association-Driven Safety Analysis to Expose Unknown Safety Issues

Abstract

Autonomous systems are susceptible to unknown safety issues due to overlooked dependencies among components of the system and the entities that are part of its operating environment. The current safety analysis techniques aids in identifying known safety issues but not overlooked/unknown safety issues. To identify unknown safety issues due to problematic interactions between components, in our previous work, we proposed safety assessment for concurrent components (SACC). Despite being more effective than FMEA and goal modeling, SACC suffers from some limitations such as not considering environmental entities and their properties, and a manual process for identifying associated components for the collective analysis. For a complex system with a large number of components, such an analysis can result in overlooking safety issues. To address these limitations, in this paper, we propose an association-driven safety analysis (ADSA) approach, which is extended and built on SACC. The approach uses a property-relation (PR) table and modified association rule mining algorithm to identify components and environmental entities that need to be considered together to detect overlooked or unknown safety issues. We evaluated our approach using four robotic systems and compared with SACC and systems theoretic process analysis (STPA). Our results show that our proposed approach, in particular using behavioral dependencies, is effective at exposing unknown safety issues.