This study highlights the challenge of motion sickness (MS) in autonomous vehicles (AVs), providing a comprehensive review of assessing, predicting, and preventing this issue with a special focus on vehicle dynamics and control-based approaches. Unlike previous studies, this review bridges the gap between MS prediction models and vehicle dynamics-based mitigation strategies by presenting an integrated perspective. Effective mitigation requires accurate and reliable prediction. In this context, motion-based prediction approaches, recognised for their practicality, cost-effectiveness, and promising results, are examined in detail with particular focus on ISO-based methods and sensory conflict theory-based models. The importance of identifying MS triggers and validating these models experimentally is also emphasised, alongside recent trends in customised approaches addressing individual variability in MS susceptibility. The study then investigates mitigation strategies centred on vehicle dynamics and control systems, due to their potential for directly controlling motion triggers, calling for tailored and integrated approaches. Furthermore, the critical role of trajectory planning and tracking algorithms in mitigating MS is reviewed, emphasising their potential through optimal control and the incorporation of MS metrics into cost functions. Additionally, integrating trajectory planning with active chassis systems is identified as a promising direction for reducing MS. The study concludes by underscoring the importance of optimised, personalised, integrated and connected vehicle dynamics and control-based methods to effectively mitigate MS in AVs. Finally, a future horizons approach, supported by a vision roadmap, is introduced as a means to address current challenges, define research directions, and ultimately advance the adoption of AVs with minimum MS.

Car passengers experience much more car sickness than car drivers. We assume that this is because drivers can better anticipate the car's motions. Does helping passengers to anticipate the car's motions then mitigate car sickness? Indeed, laboratory studies have shown that anticipatory cues which announce one-dimensional motions of a linear sled mitigate sickness to a small extent. Does this mitigation generalize to real car driving? We tested this in a car ride on a test track along a trajectory involving lane changes, accelerations, and decelerations. We show that vibrotactile cues mitigated car sickness in passengers. Auditory cues were less effective. The mitigating effect of the vibrotactile cue was considerable: a 40% decrease in car sickness symptoms, a larger effect than we found in the laboratory. Automated vehicles can predict their own motion very well. They could thus provide vibrotactile cues to mitigate car sickness in their passengers.

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.

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.

Autonomous driving is expected to significantly affect future mobility. While the technological aspects are often the focus, various subjective challenges such as perceived comfort also need to be addressed before autonomous vehicles are accepted by users. One of the key restrictions on the design is motion sickness, since autonomous vehicles are expected to increase its incidence and severity due to the shift of the user from driver to passenger. To maximise user acceptance a human-centred approach is necessary. In this work, the optimisation of trajectory planning as a trade-off between motion sickness and manoeuvre time is carried out by considering the Motion Sickness Dose Value (MSDV, defined in ISO 2631) within the cost function of an optimal control problem (OCP). All motion directions are considered for the computation of the MSDV, i.e. vertical, longitudinal and lateral, with different frequency weightings in each direction. The filters associated with the frequency weightings increase the dimension, i.e. number of states, of the OCP. Different approximations of the exact (high order) frequency weightings are employed and the analysis is carried out on three-dimensional tracks. The related OCP is solved using a direct approach in combination with a nonlinear programming solver. The results show that lower-order approximations of the weighting filters are sufficient for the computation of the trajectory and speed profiles that mitigate the motion sickness, while optimisation based on minimising acceleration RMS or jerk RMS leads to higher MSDV. The effect of three-dimensionality (slope and banking) is negligible in terms of calculated MSDV, for the selected tracks and vehicle model.

Autonomous vehicle technologies are rapidly growing and are expected to change transportation habits radically. Autonomous cars increase the likelihood of motion sickness by allowing everyone in the vehicle to become passengers and perform non-driving tasks such as reading, working, and socializing. Comfort is one of the critical factors in the acceptance of autonomous vehicles. This makes accurate estimation of motion sickness a necessity in the development stages of autonomous vehicles. The sensory conflict theory is a widely accepted theory that explains the mechanism of motion sickness. Computational models based on the sensory conflict theory are used to predict motion sickness and contain two main parts: a human perception model and a nonlinear fitting function to the subjective feeling of motion sickness. Models of the human perception, including the dynamics of the vestibular system, are used to calculate the difference between sensory inputs and the predicted motions in the brain, i.e. the conflict signal, which is the primary cause of motion sickness. One of the main limitations of motion sickness prediction is how to mathematically model human perception because of the complexity of the psychophysiological systems. The aim of this work is to implement and analyse different human perception modelling techniques, such as observer framework in the control theory and optimal estimator approach using Kalman filters, to evaluate their abilities to integrate with motion sickness prediction. In this study, the different human perception models are implemented and analysed using MATLAB / Simulink and the advantages, as well as disadvantages of the models, are discussed.

Autonomous passenger vehicles are expected to change mobility as we know it. They can potentially offer many advantages over traditional vehicles, but user acceptance is fundamental to benefit from them. In particular, comfort will be one of the most relevant deciding factors. The lack of control over the vehicle's motion will increase the passengers' inability to predict the path, raising their discomfort. The most straightforward way to maximize comfort is to adopt smooth and slow speed and cornering profiles, but the risk is to penalize too much the total travel time. A preliminary study on the optimal trajectory planning for an autonomous vehicle to find the best user-defined trade-off between comfort and travel time is presented. The problem is approached with a Nonlinear Model-Predictive Control strategy. After describing a suitable vehicle model, the complete formulation of the algorithm is presented. A multi-objective analysis is then carried out to obtain the Pareto Front and the Utopian solution for comfort and travel time. Finally, the algorithm is applied to a case study, assessing the overall performance.

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.