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Big Data Scenarios Simulator for Deep Learning Algorithm Evaluation for Autonomous Vehicle

Ang, J. W. D., Seow, C. K. , Subramanianz, K. and Pranata, S. (2021) Big Data Scenarios Simulator for Deep Learning Algorithm Evaluation for Autonomous Vehicle. In: 2020 IEEE Global Communications Conference (GLOBECOM 2020 ), Taiwan, Taipei, 7-11 Dec 2020, ISBN 9781728182988 (doi: 10.1109/GLOBECOM42002.2020.9322480)

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Abstract

One of the challenges in developing autonomous vehicles (AV) is the collection of suitable real environment data for the training and evaluation of machine learning algorithms for autonomous vehicles. Such environment data collection via various sensors mounted on AV is big data in nature which require massive time and money investment and in some specific scenarios could pose a significant danger to human lives. This necessitates the virtual scenarios simulator to simulate the real environment by generating big data images from a virtual fisheye lens that can mimic the field of view and radial distortion of commercial available camera lens of any manufacturer and model. In this paper, we proposed the novelty of developing a fisheye lens with distortion system to generate big data scenarios images to train and test imaged based sensing functions and to evaluate scenarios according to EuroNCap standards. A total of 10,123 RGB, depth and segmentation images of varying road scenarios were generated by proposed system in approximately 14 hours as compared to existing methods of 20 hours, achieving 42.86% improvement.

Item Type:	Conference Proceedings
Keywords:	Big data, deep learning, autonomous vehicle.
Status:	Published
Refereed:	Yes
Glasgow Author(s) Enlighten ID:	Seow, Dr Chee Kiat
Authors:	Ang, J. W. D., Seow, C. K., Subramanianz, K., and Pranata, S.
Subjects:	Q Science > Q Science (General) T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering
College/School:	College of Science and Engineering > School of Computing Science
ISSN:	2576-6813
ISBN:	9781728182988
Copyright Holders:	Copyright © 2020 IEEE
First Published:	First published in Proceedings of GLOBECOM 2020 - 2020 IEEE Global Communications Conference
Publisher Policy:	Reproduced in accordance with the copyright policy of the publisher

University Staff: Request a correction | Enlighten Editors: Update this record

References

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Deposit and Record Details

ID Code:	234446
Depositing User:	Dr Chee Kiat Seow
Datestamp:	18 Feb 2021 12:02
Last Modified:	19 Apr 2022 12:57
Date of acceptance:	17 August 2020
Date of first online publication:	25 January 2021
Date Deposited:	18 February 2021
Data Availability Statement:	No