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

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