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Driving Experience and Behavior Change in Remote Driving: An Explorative Experimental Study
KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Vehicle engineering and technical acoustics.ORCID iD: 0000-0001-6695-848x
KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Vehicle engineering and technical acoustics.ORCID iD: 0000-0002-2265-9004
KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Vehicle engineering and technical acoustics.ORCID iD: 0000-0002-2480-5554
Scania CV AB, Södertälje, Sweden.ORCID iD: 0000-0002-0885-9560
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2024 (English)In: IEEE Transactions on Intelligent Vehicles, ISSN 2379-8858, E-ISSN 2379-8904, Vol. 9, no 2, p. 3754-3767Article in journal (Refereed) Published
Abstract [en]

Remote driving plays an essential role in coordinating automated vehicles in some challenging situations. Due to the changed driving environment, the experiences and behaviors of remote drivers would undergo some changes compared to conventional drivers. To study this, a continuous real-life and remote driving experiment is conducted under different driving conditions. In addition, the effect of steering force feedback (SFF) on the driving experience is also investigated. In order to achieve this, three types of SFF modes are compared. According to the results, no SFF significantly worsens the driving experience in both remote and real-life driving. Additionally, less force and returnability on steering wheel are needed in remote driving, and the steering force amplitude appears to influence the steering velocity of remote drivers. Furthermore, there is an increase in lane following deviation during remote driving. Remote drivers are also prone to driving at lower speeds and have a higher steering reversal rate. They also give larger steering angle inputs when crossing the cones in a slalom manoeuvre and cause the car to experience larger lateral acceleration. These findings provide indications on how to design SFF and how driving behavior and experience change in remote driving.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2024. Vol. 9, no 2, p. 3754-3767
Keywords [en]
driving behavior, driving experience, driving performance, Remote driving, steering force feedback
National Category
Vehicle and Aerospace Engineering
Identifiers
URN: urn:nbn:se:kth:diva-348450DOI: 10.1109/TIV.2023.3344890ISI: 001215322100017Scopus ID: 2-s2.0-85181805259OAI: oai:DiVA.org:kth-348450DiVA, id: diva2:1877235
Note

QC 20240702

Available from: 2024-06-25 Created: 2024-06-25 Last updated: 2025-08-20Bibliographically approved
In thesis
1. Remote Driving of Road Vehicles: Feedback Effects, Latency Compensation, and Driver Behavior
Open this publication in new window or tab >>Remote Driving of Road Vehicles: Feedback Effects, Latency Compensation, and Driver Behavior
2025 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Remote driving has appeared as an effective solution to address challenges in achieving full autonomy for vehicles, bridging the gap between Level 4 and Level 5autonomy. Beyond autonomous vehicles (AVs), remote driving can be widely applied in various industries, such as mining, timber cutting, and warehouse logistics, where it can enhance safety, efficiency, and operational reliability. Despite its advantages, remote driving faces significant challenges, including latency, and reduced situational awareness, which impact remote drivers’ performance and experience. This thesis delves into these challenges and investigates solutions to enhance teleoperated driving systems, focusing on user experience, driving feedback and delay compensation.

The research is structured around six research questions, examining the influence of driving feedback on driving behavior and user experience, and strategies to mitigate latency in remote driving and its influence on remote drivers, as well as the learning rate of remote drivers. An integrated approach including quantitative and qualitative analysis is employed, combining experimental studies on areal-life remote driving platform and hardware-in-the-loop (HIL) simulations using IPG CarMaker. Comprehensive experiments evaluate the impact of steering force, motion-cueing, and sound and vibration feedback on driving behavior and experience. Additionally, innovative delay compensation strategies, including an enhanced model-free predictor and a square-root cubature Kalman filter-based predictor, are developed and validated to address signal transmission challenges. Finally, the learning rate of remote drivers under the delayed environment are also explored on a driving simulator.

The research results demonstrate that integrating multimodal driving feedback, such as steering force, motion-cueing, sound, and vibration, can substantially enhance remote drivers’ situational awareness and perceived confidence. However, delays in these feedback channels, particularly motion cues, are found to degrade driving precision and control stability. These challenges highlight the need for more robust delay compensation strategies. In response, a square-root cubature Kalman filter-based predictor is developed, significantly outperforming conventional approaches by maintaining accurate state prediction under latency. It is also found that remote drivers can be used for a certain driving task after 4–5 training rounds in delayed scenarios, suggesting a low adaptation threshold. These findings not only validate the technical feasibility of the proposed methods but also offer practical advantages in deploying scalable, operator-friendly remote driving systems in dynamic, real-world environments.

While the experiments provide meaningful results, certain limitations exist, including the use of a single 4G SIM card for communication and controlled testing environments. Future studies could explore dual-carrier 5G setups and advanced feedback systems to further enhance remote driving platforms.

Overall, this research contributes to the growing field of remote driving by addressing critical challenges and proposing actionable solutions, paving the way for safer, more efficient, and scalable remote driving systems across diverse applications.

Abstract [sv]

Fjärrstyrd körning av fordon  har framträtt som en effektiv lösning för att hantera utmaningarna med att uppnå fullständigt självkörande fordon och överbrygga gapet mellan  nivå 4 och nivå 5  i graden av självkörande. Utöver självkörande fordon (AV) kan fjärrstyrning användas  brett inom olika industrier, såsom gruvdrift, skogsavverkning och lagerlogistik, där det kan förbättra säkerheten, effektiviteten och driftsäkerheten. Trots dess fördelar står fjärrstyrd körning inför betydande utmaningar, såsom latens och minskad situationsmedvetenhet, vilket påverkar fjärrförarnas prestation och upplevelse. Denna avhandling undersöker dessa utmaningar och utforskar lösningar för att förbättra fjärrstyrda körsystem med fokus på användarupplevelse, föraråterkoppling och fördröjningskompensation.

Forskningen struktureras kring sex forskningsfrågor, där man undersöker föraråterkopplingens påverkan på körbeteendet och användarupplevelsen, strategier för att mildra latens vid fjärrstyrd körning samt dess effekt på fjärrförare och fjärrförarnas anpassningsförmåga. Kombinerade metoder  används vilket  inkluderar experimentella studier på en verklig plattform för fjärrstyrning, "hardware-in-the-loop" (HIL) med IPG CarMaker, samt kvantitativa och kvalitativa användarenkäter. Omfattande experiment utvärderar effekten av styrkraft, rörelseåterkoppling samt ljud- och vibrationsåterkoppling på körbeteende och upplevelse. Dessutom utvecklas och valideras innovativa strategier för fördröjningskompensation, däribland en förbättrad modellfri kompensator och en kompensator baserad på en kvadratrot-kubatur-Kalmanfilter, för att hantera utmaningar vid signalöverföring. Slutligen undersöks även fjärrförarnas anpassningsförmåga under fördröjda förhållanden med hjälp av en  körsimulator.

Forskningsresultaten visar att integrering av multimodal föraråterkoppling – såsom styrkraft, rörelseåterkoppling, ljud och vibrationer – avsevärt stärker fjärrförarnas situationsmedvetenhet och upplevda trygghet. Studien utgör ett tidigt empiriskt bidrag som kvantifierar dessa effekter med hjälp av både subjektiva och objektiva mått i en realistisk fjärrkörningsmiljö. Den föreslagna prediktionsmodellen, baserad på kvadratrot-kubatur-Kalmanfilter, uppvisar betydligt högre robusthet och noggrannhet jämfört med konventionella metoder under varierande fördröjningsförhållanden. Studien visar också att fjärrförare kan bli van ett specifikt köruppdrag  efter endast 4–5 träningsomgångar i nya scenarier med fördröjning, vilket tyder på en låg inlärningströskel. Sammantaget bekräftar resultaten metodernas tekniska genomförbarhet och pekar på praktiska fördelar vid implementering av skalbara och användarvänliga fjärrkörningssystem i dynamiska, verkliga miljöer.

Även om experimenten ger meningsfulla resultat finns vissa begränsningar, såsom användningen av ett enda 4G SIM-kort för kommunikation och kontrollerade testmiljöer. Framtida studier kan utforska lösningar med dubbla operatörer och avancerade återkopplingssystem för att ytterligare förbättra plattformar för fjärrstyrning. 

Sammantaget bidrar denna forskning till det växande området för fjärrstyrd körning genom att hantera kritiska utmaningar och föreslå konkreta lösningar, vilket banar väg för säkrare, effektivare och mer skalbara system för fjärrstyrning inom olika tillämpningar.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2025. p. xv, 79
Series
TRITA-SCI-FOU ; 2025:30
Keywords
Remote driving, driving feedback, driving behavior, driving experience, delay compensation, autonomous vehicles, Fjärrkörning, Köråterkoppling, Körbeteende, Körupplevelse, Fördröjningskompensation, Autonoma fordon
National Category
Vehicle and Aerospace Engineering
Research subject
Vehicle and Maritime Engineering
Identifiers
urn:nbn:se:kth:diva-368744 (URN)978-91-8106-335-6 (ISBN)
Public defence
2025-09-15, Sal Kollegiesalen, Brinellvägen 6, Stockholm, 10:00 (English)
Opponent
Supervisors
Funder
Vinnova, 2022-01647
Note

QC-2025-08-22

Available from: 2025-08-22 Created: 2025-08-20 Last updated: 2025-09-15Bibliographically approved

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Zhao, LinNybacka, MikaelRothhämel, MaltePapaioannou, GeorgiosDrugge, Lars

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