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Effectively determining automated driving system (ADS)-equipped vehicle (AV) safety without relying on testing an infeasibly large number of driving scenarios is a challenge with wide recognition in industry and academia. The following paper builds on previous work by the Institute of Automated Mobility (IAM) and Science Foundation Arizona (SFAz), and proposes a test selection and scoring methodology (TSSM) as part of a safety case-based framework being developed by the SFAz to ensure the safety of AVs while addressing the scenario testing challenge. The TSSM is an AV verification and validation (V&V) process that relies, in part, on iterative, partially random generation of AV driving scenarios. These scenarios are generated using an operational design domain (ODD) and behavioral competency portfolio, which expresses the vehicle ODD and behavioral competencies in terms of quantifiable amounts or intensities of discrete components.

The driving safety performance of automated driving system (ADS)-equipped vehicles (AVs) must be quantified using metrics in order to be able to assess the driving safety performance and compare it to that of human-driven vehicles. In this research, driving safety performance metrics and methods for the measurement and analysis of said metrics are defined and/or developed. A comprehensive literature review of metrics that have been proposed for measuring the driving safety performance of both human-driven vehicles and AVs was conducted. A list of proposed metrics, including novel contributions to the literature, that collectively, quantitatively describe the driving safety performance of an AV was then compiled, including proximal surrogate indicators, driving behaviors, and rules-of-the-road violations.