Mitigating the Zero Biased Steering Angles in Self-driving Simulator Datasets

Muhammad Ammar Khan, Khawaja Ghulam Alamdar, Aiman Junaid, Muhammad Ammar Khan

2022

Abstract

Autonomous or self-driving systems require rigorous training before making it to the roads. Deep learning is at the forefront of the training, testing, and validation of such systems. Self-driving simulators play a vital role in this process not only due to the data-intensiveness of the deep learning algorithm but also due to several parameters involved in the system. The data generated from self-driving car simulators have an inherent problem of large zero-bias due to the discrete nature of computation arising from computer input devices. In this paper, we analyze this problem and propose filtering to make the steering angles in the dataset smoother and to remove random fluctuations that make our model learn better. After such processing, the test run on simulators showed promising results using a significantly small dataset and a relatively shallow network.

Paper Citation

in Harvard Style

Khan M., Alamdar K., Junaid A. and Farhan M. (2022). Mitigating the Zero Biased Steering Angles in Self-driving Simulator Datasets. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP; ISBN 978-989-758-555-5, SciTePress, pages 470-475. DOI: 10.5220/0010839900003124

in Bibtex Style

@conference{visapp22,
author={Muhammad Ammar Khan and Khawaja Ghulam Alamdar and Aiman Junaid and Muhammad Ammar Farhan},
title={Mitigating the Zero Biased Steering Angles in Self-driving Simulator Datasets},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP},
year={2022},
pages={470-475},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010839900003124},
isbn={978-989-758-555-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP
TI - Mitigating the Zero Biased Steering Angles in Self-driving Simulator Datasets
SN - 978-989-758-555-5
AU - Khan M.
AU - Alamdar K.
AU - Junaid A.
AU - Farhan M.
PY - 2022
SP - 470
EP - 475
DO - 10.5220/0010839900003124
PB - SciTePress