Safety Assurance for Automated Driving Systems: Open Problems and Learnings from a Review of Other Domains

Abstract

Automated Driving Systems (ADSs) for road vehicles will be capable of performing the entire Dynamic Driving Task (DDT) without the active involvement of a human driver. Further, many ADSs will use Machine Learning (ML) to progressively adapt their driving functionality during in-service operation. This presents challenges for traditional regulatory frameworks, which do not readily support automated driving without a human driver or support safety-critical systems using ML to modify driving functionality post-market entry. However, these challenges are not entirely unique to ADSs. A review was undertaken into approaches taken in other domains to assure safety-critical systems that enable auto..