Name
Title | ||
|---|---|---|
Learning To Predict Ego-Vehicle Poses For Sampling-Based Nonholonomic Motion Planning |
Abstract | ||
|---|---|---|
Sampling-based motion planning is an effective tool to compute safe trajectories for automated vehicles in complex environments. However, a fast convergence to the optimal solution can only be ensured with the use of problem-specific sampling distributions. Due to the large variety of driving situations within the context of automated driving, it is very challenging to manually design such distributions. This letter introduces, therefore, a data-driven approach utilizing a deep convolutional neural network (CNN): Given the current driving situation, future ego-vehicle poses can be directly generated from the output of the CNN allowing to guide the motion planner efficiently toward the optimal solution. A benchmark highlights that the CNN predicts future vehicle poses with a higher accuracy compared to uniform sampling and a state-of-the-art A*-based approach. Combining this CNN-guided sampling with the motion planner bidirectional RRT* reduces the computation time by up to an order of magnitude and yields a faster convergence to a lower cost as well as a success rate of 100% in the tested scenarios. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/LRA.2019.2893975 | IEEE ROBOTICS AND AUTOMATION LETTERS |
Keywords | DocType | Volume |
Deep learning in robotics and automation, non-holonomic motion planning, motion and path planning | Journal | 4 |
Issue | ISSN | Citations |
2 | 2377-3766 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Holger Banzhaf | 1 | 1 | 1.42 |
| Paul Sanzenbacher | 2 | 0 | 0.34 |
| Ulrich Baumann | 3 | 0 | 0.34 |
| Johann Marius Zöllner | 4 | 131 | 24.29 |