The Road to Safe Automated Driving Systems: A Review of Methods Providing Safety Evidence

Abstract

In recent years, enormous investments in Automated Driving Systems (ADSs) have distinctly advanced ADS technologies. Despite promises made by several high profile auto-makers, it has however become clear that the challenges involved for deploying ADS have been drastically underestimated. Contrary to previous generations of automotive systems, common design, development, verification and validation methods for safety critical systems do not suffice to cope with the increased complexity and operational uncertainties of an ADS. Therefore, the aim of this paper is to provide an understanding of existing methods for providing safety evidence and, most importantly, identifying the associated challenges and gaps pertaining to the use of each method. To this end, we have performed a literature review, articulated around four categories of methods: design techniques, verification and validation methods, run-time risk assessment, and run-time (self-)adaptation. We have identified and present eight challenges, collectively distinguishing ADSs from safety critical systems in general, and discuss the reviewed methods in the light of these eight challenges. For all reviewed methods, the uncertainties of the operational environment and the allocation of responsibility for the driving task on the ADS stand-out as the most difficult challenges to address. Finally, a set of research gaps is identified, and grouped into five major themes: 1) completeness of provided safety evidence, 2) improvements and analysis needs, 3) safe collection of closed loop data and accounting for tactical responsibility on the part of the ADS, 4) integration of AI/ML-based components, and 5) scalability of the approaches with respect to the complexity of the ADS.