  The widescale deployment of Autonomous Vehicles (AV) seems to be imminent
despite many safety challenges that are yet to be resolved. It is well known
that there are no universally agreed Verification and Validation (VV)
methodologies to guarantee absolute safety, which is crucial for the acceptance
of this technology. Existing standards focus on deterministic processes where
the validation requires only a set of test cases that cover the requirements.
Modern autonomous vehicles will undoubtedly include machine learning and
probabilistic techniques that require a much more comprehensive testing regime
due to the non-deterministic nature of the operating design domain. A rigourous
statistical validation process is an essential component required to address
this challenge. Most research in this area focuses on evaluating system
performance in large scale real-world data gathering exercises (number of miles
travelled), or randomised test scenarios in simulation.
  This paper presents a new approach to compute the statistical characteristics
of a system's behaviour by biasing automatically generated test cases towards
the worst case scenarios, identifying potential unsafe edge cases.We use
reinforcement learning (RL) to learn the behaviours of simulated actors that
cause unsafe behaviour measured by the well established RSS safety metric. We
demonstrate that by using the method we can more efficiently validate a system
using a smaller number of test cases by focusing the simulation towards the
worst case scenario, generating edge cases that correspond to unsafe
situations.
