Being able to rapidly respond to the changing scenes and traffic situations by generating feasible local paths is of pivotal importance for car autonomy. We propose to train a deep neural network (DNN) to plan feasible and nearly-optimal paths for kinematically constrained vehicles in small constant time. Our DNN model is trained using a novel weakly supervised approach and a gradient-based policy search. On real and simulated scenes and a large set of local planning problems, we demonstrate that our approach outperforms the existing planners with respect to the number of successfully completed tasks. While the path generation time is about 40 ms, the generated paths are smooth and comparable to those obtained from conventional path planners.
@article{arxiv.2012.03707,
title = {Learning from Experience for Rapid Generation of Local Car Maneuvers},
author = {Piotr Kicki and Tomasz Gawron and Krzysztof Ćwian and Mete Ozay and Piotr Skrzypczyński},
journal= {arXiv preprint arXiv:2012.03707},
year = {2023}
}