Learning to Predict Ego-Vehicle Poses for Sampling-Based Nonholonomic Motion Planning

no code implementations • 3 Dec 2018 • Holger Banzhaf, Paul Sanzenbacher, Ulrich Baumann, J. Marius Zöllner

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.

Motion Planning