An Efficient and Scalable Simulation Model for Autonomous Vehicles with Economical Hardware

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dc.contributor.authorSajjad, Muhammad
dc.contributor.authorIrfan, Muhammad
dc.contributor.authorMuhammad, Khan
dc.contributor.authorSer, Javier Del
dc.contributor.authorSanchez-Medina, Javier
dc.contributor.authorAndreev, Sergey
dc.contributor.authorDing, Weiping
dc.contributor.authorLee, Jong Weon
dc.contributor.institutionIA
dc.date.accessioned2024-09-10T14:05:06Z
dc.date.available2024-09-10T14:05:06Z
dc.date.issued2021-03
dc.descriptionPublisher Copyright: © 2000-2011 IEEE.
dc.description.abstractAutonomous vehicles rely on sophisticated hardware and software technologies for acquiring holistic awareness of their immediate surroundings. Deep learning methods have effectively equipped modern self-driving cars with high levels of such awareness. However, their application requires high-end computational hardware, which makes utilization infeasible for the legacy vehicles that constitute most of today's automotive industry. Hence, it becomes inherently challenging to achieve high performance while at the same time maintaining adequate computational complexity. In this paper, a monocular vision and scalar sensor-based model car is designed and implemented to accomplish autonomous driving on a specified track by employing a lightweight deep learning model. It can identify various traffic signs based on a vision sensor as well as avoid obstacles by using an ultrasonic sensor. The developed car utilizes a single Raspberry Pi as its computational unit. In addition, our work investigates the behavior of economical hardware used to deploy deep learning models. In particular, we herein propose a novel, computationally efficient, and cost-effective approach. The designed system can serve as a platform to facilitate the development of economical technologies for autonomous vehicles that can be used as part of intelligent transportation or advanced driver assistance systems. The experimental results indicate that this model can achieve real-time response on a resource-constrained device without significant overheads, thus making it a suitable candidate for autonomous driving in current intelligent transportation systems.en
dc.description.sponsorshipManuscript received August 15, 2018; revised June 29, 2019, September 25, 2019, and December 16, 2019; accepted January 24, 2020. Date of publication May 15, 2020; date of current version March 1, 2021. This research was supported by the Department of Education of the Basque Government (Consolidated Research Group MATHMODE, IT1294-19), the MSIT (Ministry of Science, ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2019-2016-0-00312) supervised by the IITP (Institute for Information & Communications Technology Promotion), the Natural Science Foundation of Jiangsu Province under Grant BK20191445, the Six Talent Peaks Project of Jiangsu Province under Grant XYDXXJS-048, and in part by the RADIANT Project, Academy of Finland. The Associate Editor for this article was E. I. Vlahogianni. (Corresponding author: Khan Muhammad.) Muhammad Sajjad is with the Digital Image Processing Laboratory, Islamia College University, Peshawar 25000, Pakistan (e-mail: muhammad.sajjad@icp.edu.pk).
dc.description.statusPeer reviewed
dc.format.extent15
dc.identifier.citationSajjad , M , Irfan , M , Muhammad , K , Ser , J D , Sanchez-Medina , J , Andreev , S , Ding , W & Lee , J W 2021 , ' An Efficient and Scalable Simulation Model for Autonomous Vehicles with Economical Hardware ' , IEEE Transactions on Intelligent Transportation Systems , vol. 22 , no. 3 , 9094331 , pp. 1718-1732 . https://doi.org/10.1109/TITS.2020.2980855
dc.identifier.doi10.1109/TITS.2020.2980855
dc.identifier.issn1524-9050
dc.identifier.urihttps://hdl.handle.net/11556/5162
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85102441205&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofIEEE Transactions on Intelligent Transportation Systems
dc.relation.projectIDDepartment of Education of the Basque Government, IT1294-19
dc.relation.projectIDSuomen Akatemia
dc.relation.projectIDMinistry of Science, ICT and Future Planning, MSIP, IITP-2019-2016-0-00312
dc.relation.projectIDNatural Science Foundation of Jiangsu Province, BK20191445
dc.relation.projectIDSix Talent Peaks Project in Jiangsu Province, XYDXXJS-048
dc.relation.projectIDInstitute for Information and Communications Technology Promotion, IITP
dc.relation.projectIDMinistry of Science and ICT, South Korea, MSIT
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsAutonomous driving
dc.subject.keywordsRaspberry Pi
dc.subject.keywordsintelligent transportation systems
dc.subject.keywordsscalar-visual sensor
dc.subject.keywordsAutomotive Engineering
dc.subject.keywordsMechanical Engineering
dc.subject.keywordsComputer Science Applications
dc.subject.keywordsSDG 11 - Sustainable Cities and Communities
dc.titleAn Efficient and Scalable Simulation Model for Autonomous Vehicles with Economical Hardwareen
dc.typejournal article
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