  One of the major impediments in deployment of Autonomous Driving Systems
(ADS) is their safety and reliability. The primary reason for the complexity of
testing ADS is that it operates in an open world characterized by its
non-deterministic, high-dimensional and non-stationary nature where the actions
of other actors in the environment are uncontrollable from the ADS's
perspective. This leads to a state space explosion problem and one way of
mitigating this problem is by concretizing the scope for the system under test
(SUT) by testing for a set of behavioral competencies which an ADS must
demonstrate. A popular approach to testing ADS is scenario-based testing where
the ADS is presented with driving scenarios from real world (and synthetically
generated) data and expected to meet defined safety criteria while navigating
through the scenario. We present SAFR-AV, an end-to-end ADS testing platform to
enable scenario-based ADS testing. Our work addresses key real-world challenges
of building an efficient large scale data ingestion pipeline and search
capability to identify scenarios of interest from real world data, creating
digital twins of the real-world scenarios to enable Software-in-the-Loop (SIL)
testing in ADS simulators and, identifying key scenario parameter distributions
to enable optimization of scenario coverage. These along with other modules of
SAFR-AV would allow the platform to provide ADS pre-certifications.
