AISTATS 2021poster3 citations
Self-Supervised Steering Angle Prediction for Vehicle Control Using Visual Odometry
Qadeer Khan, Patrick Wenzel, Daniel Cremers
Abstract
Vision-based learning methods for self-driving cars have primarily used supervised approaches that require a large number of labels for training. However, those labels are usually difficult and expensive to obtain. In this paper, we demonstrate how a model can be trained to control a vehicle’s trajectory using camera poses estimated through visual odometry methods in an entirely self-supervised fashion. We propose a scalable framework that leverages trajectory information from several different runs using a camera setup placed at the front of a car. Experimental results on the CARLA simulator demonstrate that our proposed approach performs at par with the model trained with supervision.
BibTeX
@InProceedings{pmlr-v130-khan21a,
title = { Self-Supervised Steering Angle Prediction for Vehicle Control Using Visual Odometry },
author = {Khan, Qadeer and Wenzel, Patrick and Cremers, Daniel},
booktitle = {Proceedings of The 24th International Conference on Artificial Intelligence and Statistics},
pages = {3781--3789},
year = {2021},
editor = {Banerjee, Arindam and Fukumizu, Kenji},
volume = {130},
series = {Proceedings of Machine Learning Research},
month = {13--15 Apr},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v130/khan21a/khan21a.pdf},
url = {https://proceedings.mlr.press/v130/khan21a.html},
abstract = { Vision-based learning methods for self-driving cars have primarily used supervised approaches that require a large number of labels for training. However, those labels are usually difficult and expensive to obtain. In this paper, we demonstrate how a model can be trained to control a vehicle’s trajectory using camera poses estimated through visual odometry methods in an entirely self-supervised fashion. We propose a scalable framework that leverages trajectory information from several different runs using a camera setup placed at the front of a car. Experimental results on the CARLA simulator demonstrate that our proposed approach performs at par with the model trained with supervision. }
}