  Fast and reliable trajectory planning is a key requirement of autonomous
vehicles. In this paper we introduce a novel technique for planning the route
of an autonomous vehicle on a straight rural road using the Spin model checker.
We show how we can combine Spins ability to identify paths violating temporal
properties with sensor information from a 3D Unity simulation of an autonomous
vehicle, to plan and perform consecutive overtaking manoeuvres on a traffic
heavy road. This involves discretising the sensory information and combining
multiple sequential Spin models with a Linear Time Temporal Logic specification
to generate an error path. This path provides the autonomous vehicle with an
action plan. The entire process takes place in close to realtime using no
precomputed data and the action plan is specifically tailored for individual
scenarios. Our experiments demonstrate that the simulated autonomous vehicle
implementing our approach can drive on average at least 40km and overtake 214
vehicles before experiencing a collision, which is usually caused by
inaccuracies in the sensory system. While the proposed system has some
drawbacks, we believe that our novel approach demonstrates a potentially
powerful future tool for efficient trajectory planning for autonomous vehicles.
