  If autonomous vehicles are to be widely accepted, we need to ensure their
safe operation. For this reason, verification and validation (V&V) approaches
must be developed that are suitable for this domain. Model checking is a formal
technique which allows us to exhaustively explore the paths of an abstract
model of a system. Using a probabilistic model checker such as PRISM, we may
determine properties such as the expected time for a mission, or the
probability that a specific mission failure occurs. However, model checking of
complex systems is difficult due to the loss of information during abstraction.
This is especially so when considering systems such as autonomous vehicles
which are subject to external influences. An alternative solution is the use of
Monte Carlo simulation to explore the results of a continuous-time model of the
system. The main disadvantage of this approach is that the approach is not
exhaustive as not all executions of the system are analysed. We are therefore
interested in developing a framework for formal verification of autonomous
vehicles, using Monte Carlo simulation to inform and validate our symbolic
models during the initial stages of development. In this paper, we present a
continuous-time model of a quadrotor unmanned aircraft undertaking an
autonomous mission. We employ this model in Monte Carlo simulation to obtain
specific mission properties which will inform the symbolic models employed in
formal verification.
