  We formalise and study multi-agent timed models MAPTs (Multi-Agent with timed
Periodic Tasks), where each agent is associated to a regular timed schema upon
which all possibles actions of the agent rely. MAPTs allow for an accelerated
semantics and a layered structure of the state space, so that it is possible to
explore the latter dynamically and use heuristics to greatly reduce the
computation time needed to address reachability problems. We apply MAPTs to
explore state spaces of autonomous vehicles and compare it with other
approaches in terms of expressivity, abstraction level and computation time.
