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Evaluation of motion sickness prediction models for autonomous driving
KTH, School of Engineering Sciences (SCI), Centres, VinnExcellence Center for ECO2 Vehicle design. KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Vehicle Engineering and Solid Mechanics, Vehicle Dynamics. Volvo Car Corporation, 405 31, Gothenburg, Sweden. (Vehicle Dynamics)
KTH, School of Engineering Sciences (SCI), Centres, VinnExcellence Center for ECO2 Vehicle design. KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Vehicle Engineering and Solid Mechanics, Vehicle Dynamics.ORCID iD: 0000-0002-1426-1936
KTH, School of Engineering Sciences (SCI), Centres, VinnExcellence Center for ECO2 Vehicle design. KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Vehicle Engineering and Solid Mechanics, Vehicle Dynamics.ORCID iD: 0000-0001-8928-0368
2022 (English)In: Advances in Dynamics of Vehicles on Roads and Tracks II, Springer Nature , 2022, p. 875-887Conference paper, Published paper (Refereed)
Abstract [en]

The introduction of autonomous vehicles is expected to change the transportation system radically. One of the essential factors that affect the acceptance and choice of autonomous driving is passenger comfort. All people in the autonomous vehicle will be passengers and be able to perform non-driving tasks like reading etc. which increases the likelihood of motion sickness. This makes accurate estimation of motion sickness a necessity in the design stages of autonomous vehicles. The aim of this work is to review and apply two motion sickness prediction models (ISO-2631 and the 6D-SVC model) and evaluate their ability to capture individual motion sickness feelings using measured data and subjective assessment ratings from field tests. The comparison with the experimental results shows that the applied estimation models can be tuned to capture the individual motion sickness feelings. The results also show that habituation of motion sickness is an important property that needs to be taken into consideration and modelled.

Place, publisher, year, edition, pages
Springer Nature , 2022. p. 875-887
Series
Lecture Notes in Mechanical Engineering, ISSN 2195-4356, E-ISSN 2195-4364
National Category
Vehicle Engineering
Research subject
Vehicle and Maritime Engineering
Identifiers
URN: urn:nbn:se:kth:diva-309459DOI: 10.1007/978-3-031-07305-2_81Scopus ID: 2-s2.0-85136920730OAI: oai:DiVA.org:kth-309459DiVA, id: diva2:1642094
Conference
27th Symposium of the International Association of Vehicle System Dynamics, IAVSD 2021, Virtual, 17-19 August 2021
Funder
Vinnova
Note

Part of ISBN 978-303107304-5

QC 20220408

Available from: 2022-03-03 Created: 2022-03-03 Last updated: 2024-05-16Bibliographically approved
In thesis
1. Motion sickness in autonomous driving: Prediction models and mitigation using trajectory planning
Open this publication in new window or tab >>Motion sickness in autonomous driving: Prediction models and mitigation using trajectory planning
2024 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The development of autonomous vehicles is progressing rapidly through extensive efforts by the automotive industry and researchers. One of the key factors for the adoption of autonomous driving technology is motion comfort and the ability to engage in non-driving tasks such as reading, socialising, and relaxing without experiencing motion sickness while travelling. Therefore, for the full success of autonomous vehicles, it is necessary to learn how to design and control the vehicles to mitigate motion sickness for the passengers. 

This thesis aims to investigate methods for prediction of motion sickness in autonomous vehicles and how to mitigate it using vehicle dynamics based solutions, with an emphasis on trajectory planning. As a first step, a review and evaluation of existing motion sickness prediction methods were performed. The review highlighted the importance of accurate motion sickness assessment in the early phases of autonomous vehicle design. Two chosen methods (ISO 2631-based and sensory conflict theory-based) were evaluated to estimate individual motion sickness feelings using measured data and subjective assessment ratings from field tests. It can be concluded that the methods can be adjusted to predict individual motion sickness feelings, as shown by the comparison with the experimental data.

To continue the work, a review of vehicle dynamics based motion sickness mitigation methods for autonomous vehicles was performed. Several chassis control strategies in literature like active suspension, rear-wheel steering and torque distribution have demonstrated the potential help to reduce motion sickness. Another effective approach to mitigate motion sickness in autonomous vehicles is to regulate vehicle speed and path using trajectory planning which was chosen to be further investigated. The trajectory planning was constructed as an optimisation problem where there is a trade-off between motion sickness and manoeuvre time. The impact of the trajectory planning algorithm to reduce motion sickness was analysed by simulating two different vehicle models in specific test manoeuvres. The results indicate that driving style has a significant influence on motion sickness and trajectory planning algorithms should be carefully designed to find a good balance between journey time and motion sickness.

The research presented in this thesis contributes to the development of methodologies for predicting and mitigating motion sickness in autonomous vehicles, helping to achieve the goal of ensuring their overall success.

Abstract [sv]

Utvecklingen av autonoma fordon går snabbt framåt tack vare omfattande insatser från fordonsindustrin och forskare. En av de viktigaste faktorerna för införandet av teknik för autonom körning är åkkomfort och möjligheten att ägna sig åt andra saker än körning, som att läsa, umgås och koppla av, utan att drabbas av åksjuka under resan. För att autonoma fordon ska lyckas fullt ut är det därför nödvändigt att förstå hur man utformar och styr fordonen för att minska risken för att passagerarna drabbas av åksjuka. 

Denna licentiatuppsats syftar till att undersöka hur åksjuka kan förutsägas i vägfordon och hur den kan reduceras med hjälp av fordonsdynamikbaserade lösningar, med tonvikt på trajektorieplanering. Som ett första steg genomfördes en granskning och utvärdering av befintliga metoder för åksjukeprediktion. Granskningen belyste vikten av en korrekt bedömning av åksjuka i de tidiga faserna av autonom fordonsdesign. Två valda metoder (ISO 2631-baserad och sensorisk konfliktbaserad) utvärderades för att uppskatta individuell åksjuka med hjälp av uppmätta data och subjektiva bedömningar från fälttester. Slutsatsen är att metoderna kan justeras för att förutsäga individuell åksjuka, vilket framgår av jämförelsen med experimentella data.

För att fortsätta arbetet gjordes en genomgång av fordonsdynamikbaserade metoder för att minska åksjuka i autonoma fordon. Flera chassireglerstrategier i litteraturen, såsom aktiv fjädring, bakhjulsstyrning och drivmomentfördelning, har visat sig kunna bidra till att minska åksjuka. En annan effektiv metod för att minska åksjuka i autonoma fordon är att reglera fordonets hastighet och bana med hjälp av trajektorieplanering, vilket valdes att undersökas ytterligare. Trajektorieplaneringen konstruerades som ett optimeringsproblem där det finns en avvägning mellan åksjuka och manövertid. Effekten av trajektorieplaneringsalgoritmen för att minska åksjuka analyserades genom att simulera två olika fordonsmodeller i specifika testmanövrar. Resultaten indikerar att körstil har en betydande inverkan på åksjuka och att algoritmer för trajektorieplanering bör utformas noggrant för att hitta en bra balans mellan restid och åksjuka.

Forskningen som presenteras i denna uppsats bidrar till utvecklingen av metoder för att förutsäga och mildra åksjuka i autonoma fordon, vilket hjälper till att uppnå målet att säkerställa deras framgång.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2024
Series
TRITA-SCI-FOU ; 2024:34
Keywords
Motion sickness models, motion sickness mitigation methods, vehicle dynamics, trajectory planning, vehicle control, autonomous driving
National Category
Vehicle Engineering
Research subject
Vehicle and Maritime Engineering
Identifiers
urn:nbn:se:kth:diva-346492 (URN)978-91-8040-958-2 (ISBN)
Presentation
2024-06-12, Munin, Teknikringen 8, Stockholm, 10:00 (English)
Opponent
Supervisors
Funder
Vinnova, 2016-05195
Available from: 2024-05-16 Created: 2024-05-16 Last updated: 2024-06-10Bibliographically approved

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Yunus, IlhanJerrelind, JennyDrugge, Lars

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