  The development of driving functions for autonomous vehicles in urban
environments is still a challenging task. In comparison with driving on
motorways, a wide variety of moving road users, such as pedestrians or
cyclists, but also the strongly varying and sometimes very narrow road layout
pose special challenges. The ability to make fast decisions about exact
maneuvers and to execute them by applying sophisticated control commands is one
of the key requirements for autonomous vehicles in such situations. In this
context we present an algorithmic concept of three correlated methods. Its
basis is a novel technique for the automated generation of a free-space
polygon, providing a generic representation of the currently drivable area. We
then develop a time-dependent hybrid-state A* algorithm as a model-based
planner for the efficient and precise computation of possible driving maneuvers
in arbitrary dynamic environments. While on the one hand its results can be
used as a basis for making short-term decisions, we also show their
applicability as an initial guess for a subsequent trajectory optimization in
order to compute applicable control signals. Finally, we provide numerical
results for a variety of simulated situations demonstrating the efficiency and
robustness of the proposed methods.
