Safe learning-based optimal motion planning for automated driving
This work addresses a domain-specific issue for automated driving systems, offering an incremental improvement in motion planning efficiency.
The paper tackled the problem of inconsistent search efficiency in optimal motion planning for automated driving by introducing a machine learning-based heuristic that accounts for dynamic obstacles, achieving improved performance consistency for real-time implementation.
This paper presents preliminary work on learning the search heuristic for the optimal motion planning for automated driving in urban traffic. Previous work considered search-based optimal motion planning framework (SBOMP) that utilized numerical or model-based heuristics that did not consider dynamic obstacles. Optimal solution was still guaranteed since dynamic obstacles can only increase the cost. However, significant variations in the search efficiency are observed depending whether dynamic obstacles are present or not. This paper introduces machine learning (ML) based heuristic that takes into account dynamic obstacles, thus adding to the performance consistency for achieving real-time implementation.