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Motion sickness in autonomous driving

Prediction models and mitigation using trajectory planning

Time: Wed 2024-06-12 10.00

Location: Munin, Teknikringen 8, Stockholm

Language: English

Subject area: Vehicle and Maritime Engineering

Doctoral student: Ilhan Yunus , Teknisk mekanik

Opponent: Dr Cyriel Diels, Royal College of Art

Supervisor: Lars Drugge, VinnExcellence Center for ECO2 Vehicle design, Väg- och spårfordon samt konceptuell fordonsdesign; Jenny Jerrelind, VinnExcellence Center for ECO2 Vehicle design, Farkostteknik och Solidmekanik

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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.