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

Muhammad Khan, Khawaja Alamdar, Aiman Junaid, Muhammad Farhan

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.

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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 - Volume 4: VISAPP, ISBN 978-989-758-555-5, pages 470-475. DOI: 10.5220/0010839900003124


in Bibtex Style

@conference{visapp22,
author={Muhammad Khan and Khawaja Alamdar and Aiman Junaid and Muhammad 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 - 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 - 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