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

Graph-based Motion Planning for Automated Vehicles using Multi-model Branching and Admissible Heuristics