Graph-based Motion Planning for Automated Vehicles using Multi-model Branching and Admissible Heuristics
This work addresses real-time planning for automated driving, but it appears incremental as it builds on existing graph-based methods.
The paper tackled the problem of efficient motion planning for automated vehicles in urban scenarios by proposing a graph-based method with multi-model branching and admissible heuristics, resulting in improved trajectory quality and reduced runtime.
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.