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Remote Driving of Road Vehicles: A Survey of Driving Feedback, Latency, Support Control, and Real Applications
Department of Engineering Mechanics, KTH Royal Institute of Technology, Stockholm, Sweden.ORCID iD: 0000-0001-6695-848X
Department of Engineering Mechanics, KTH Royal Institute of Technology, Stockholm, Sweden.ORCID iD: 0000-0002-2265-9004
Swedish National Road and Transport Research Institute, Traffic and road users, Vehicle Systems and Driving Simulation..ORCID iD: 0000-0003-4951-5315
Department of Engineering Mechanics, KTH Royal Institute of Technology, Stockholm, Sweden.ORCID iD: 0000-0002-2480-5554
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2024 (English)In: IEEE Transactions on Intelligent Vehicles, ISSN 2379-8858, E-ISSN 2379-8904Article in journal (Refereed) Epub ahead of print
Abstract [en]

This literature survey explores the domain of remote driving of road vehicles within autonomous vehicles, focusing on challenges and state-of-the-art solutions related to driving feedback, latency, support control, as well as remote driving platform and real applications. The advancement towards Level-5 autonomy faces challenges, including sensor reliability and diverse scenario feasibility. Currently, remote driving is identified as vital for commercialization, however, it comes with challenges like low situational awareness, latency, and a lack of comprehensive feedback mechanisms. Solutions proposed include enhancing visual feedback, developing haptic feedback, employing prediction techniques, and use control methods to support driver. This paper reviews the existing literature on remote driving in these fields, revealing research gaps and areas for future studies. Additionally, this paper reviews the industry applications of remote driving and shows the state-of-art use cases.

Place, publisher, year, edition, pages
IEEE, 2024.
Keywords [en]
Remote driving, teleoperation, autonomous vehicles, situational awareness, latency, driving feedback, support control
National Category
Transport Systems and Logistics
Identifiers
URN: urn:nbn:se:vti:diva-20356DOI: 10.1109/tiv.2024.3362597Scopus ID: 2-s2.0-85184824344OAI: oai:DiVA.org:vti-20356DiVA, id: diva2:1840570
Available from: 2024-02-26 Created: 2024-02-26 Last updated: 2024-03-01Bibliographically approved

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Aramrattana, Maytheewat

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Zhao, LinNybacka, MikaelAramrattana, MaytheewatRothhämel, MalteHabibovic, AzraDrugge, LarsJiang, Frank
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IEEE Transactions on Intelligent Vehicles
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