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

TL;DR

This paper introduces a graph-based motion planning approach for automated vehicles in urban environments, utilizing multi-model branching strategies and admissible heuristics to improve trajectory quality and computational efficiency.

Contribution

It presents a novel branching strategy and admissible heuristics that enhance planning performance and maintain optimality in urban automated driving scenarios.

Findings

Improved trajectory quality and safety during planning.

Reduced computation times for real-time applications.

Maintained optimal solutions with admissible heuristics.

Abstract

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.