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Evaluation the Effects of Delay Predictor in Remote Driving: A Driving Simulator Based 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
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).ORCID iD: 0000-0002-3672-5316
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Remote driving has emerged as an effective solution to bridge the gap between Level-4 and Level-5 autonomous vehicles by allowing human operators to take control when full autonomy is challenged by real-world complexities. However, delay remains a critical issue in remote driving systems. To address this, various predictors—such as the model-free predictor(MFP) and the square-root cubature Kalman filter-based predictor(SRCKP)—have been proposed. In this study, a simulator based remote driving experiment is conducted to evaluate the influence of these predictors on driver performance, experience, and workload. In addition, differences in driving behavior and performance across diverse driver profiles are explored, including comparisons between gamers and non-gamers, experienced and non-experienced drivers, as well as racing gamers and non-racing gamers. The findings indicate that the SRCKP nearly replicates the performance of a no-delay condition, whereas the MFP introduces oscillations due to overshoot issues, resulting in a less comfortable driving experience. Furthermore, experienced drivers consistently outperform non-experienced drivers. These results underscore the importance of advanced predictive algorithms and experienced drivers in enhancing the robustness and performance of remote driving systems.

Keywords [en]
Remote driving, delay compensation, model free predictor, square-root-cubature-kalman-filter, autonomous vehicles
National Category
Vehicle and Aerospace Engineering
Research subject
Vehicle and Maritime Engineering
Identifiers
URN: urn:nbn:se:kth:diva-365293OAI: oai:DiVA.org:kth-365293DiVA, id: diva2:1973398
Projects
REDO2
Funder
Vinnova, 2022-01647
Note

QC 20250702

Available from: 2025-06-19 Created: 2025-06-19 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, MalteMårtensson, Jonas

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