Abstract

Automated driving systems (ADS) have the potential for improving safety but also pose the risk of extending the transportation system beyond its edge conditions, beyond the operating conditions (operational design domain (ODD)) under which a given ADS or feature thereof is specifically designed to function. The ODD itself is a function of the known bounds and the unknown bounds of operation. The known bounds are those defined by vehicle designers; the unknown bounds arise based on a person operating the system outside the assumptions on which the vehicle was built. The process of identifying and mitigating risk of possible failures at the edge conditions is a cornerstone of systems safety engineering (SSE); however, SSE practitioners may not always account for the assumptions on which their risk mitigation resolutions are based. This is a particularly critical issue with the algorithms developed for highly automated vehicles (HAVs). The injury prevention community, engineers and designers must recognise that automation has introduced a fundamental shift in transportation safety and requires a new paradigm for transportation epidemiology and safety science that incorporates what edge conditions exist and how they may incite failure. Towards providing a foundational organising framework for the injury prevention community to engage with HAV development, we propose a blending of two classic safety models: the Swiss Cheese Model, which is focused on safety layers and redundancy, and the Haddon Matrix, which identifies actors and their responsibilities before, during and after an event.