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 paper 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 towards 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.
@article{arxiv.1812.01127,
title = {Learning to Predict Ego-Vehicle Poses for Sampling-Based Nonholonomic Motion Planning},
author = {Holger Banzhaf and Paul Sanzenbacher and Ulrich Baumann and J. Marius Zöllner},
journal= {arXiv preprint arXiv:1812.01127},
year = {2019}
}
Comments
Extended version of DOI 10.1109/LRA.2019.2893975, IEEE Robotics and Automation Letters, 2019