  Validating the safety of Autonomous Vehicles (AVs) operating in open-ended,
dynamic environments is challenging as vehicles will eventually encounter
safety-critical situations for which there is not representative training data.
By increasing the coverage of different road and traffic conditions and by
including corner cases in simulation-based scenario testing, the safety of AVs
can be improved. However, the creation of corner case scenarios including
multiple agents is non-trivial. Our approach allows engineers to generate
novel, realistic corner cases based on historic traffic data and to explain why
situations were safety-critical. In this paper, we introduce Probabilistic Lane
Graphs (PLGs) to describe a finite set of lane positions and directions in
which vehicles might travel. The structure of PLGs is learnt directly from
spatio-temporal traffic data. The graph model represents the actions of the
drivers in response to a given state in the form of a probabilistic policy. We
use reinforcement learning techniques to modify this policy and to generate
realistic and explainable corner case scenarios which can be used for assessing
the safety of AVs.
