Self-Supervised Steering Angle Prediction for Vehicle Control Using Visual Odometry
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.
Cite
Text
Khan et al. "Self-Supervised Steering Angle Prediction for Vehicle Control Using Visual Odometry." Artificial Intelligence and Statistics, 2021.Markdown
[Khan et al. "Self-Supervised Steering Angle Prediction for Vehicle Control Using Visual Odometry." Artificial Intelligence and Statistics, 2021.](https://mlanthology.org/aistats/2021/khan2021aistats-selfsupervised/)BibTeX
@inproceedings{khan2021aistats-selfsupervised,
title = {{Self-Supervised Steering Angle Prediction for Vehicle Control Using Visual Odometry}},
author = {Khan, Qadeer and Wenzel, Patrick and Cremers, Daniel},
booktitle = {Artificial Intelligence and Statistics},
year = {2021},
pages = {3781-3789},
volume = {130},
url = {https://mlanthology.org/aistats/2021/khan2021aistats-selfsupervised/}
}