  Automated driving in urban scenarios requires efficient planning algorithms
able to handle complex situations in real-time. A popular approach is to use
graph-based planning methods in order to obtain a rough trajectory which is
subsequently optimized. A key aspect is the generation of trajectories
implementing comfortable and safe behavior already during graph-search while
keeping computation times low. To capture this aspect, on the one hand, a
branching strategy is presented in this work that leads to better performance
in terms of quality of resulting trajectories and runtime. On the other hand,
admissible heuristics are shown which guide the graph-search efficiently, where
the solution remains optimal.
