Control for motion sickness minimisation in autonomous vehicles.
| dc.contributor.advisor | Longo, Stefano | |
| dc.contributor.advisor | Velenis, Efstathios | |
| dc.contributor.author | Htike, Zaw Lin | |
| dc.date.accessioned | 2024-04-10T09:45:13Z | |
| dc.date.available | 2024-04-10T09:45:13Z | |
| dc.date.issued | 2021-06 | |
| dc.description.abstract | Automated vehicles are expected to push towards the evolution of transportation systems and exploit the use of vehicular technologies. This thesis investigates the fundamentals of motion planning for minimising motion sickness in transportation systems of higher automation levels. The optimum velocity pro le is sought for a predefined road path from a specific starting point to a final one within specific and given boundaries and constraints in order to minimise the motion sickness and the journey time. Motion sick- ness is minimised by taking the optimum trajectory and velocity profile for any given road path generated by the motion planner. The trajectory tracking controllers based on PID control method were able to track the reference trajectory with good performances. The trade-off between motion sickness and journey time was solved using the application of multi-objective optimisation by altering the weighting factors to find a compromise solution. The Pareto front representing the correlation between the two components is obtained and this front also allows user to select their preference driving style. From the three case studies, driving styles have a bigger impact on reducing motion sickness and journey time rather than vehicle speed and the road width. However, the effect of road width is negligible when travelling on longer road for the reduction of motion sickness and journey time. This finding is crucial considering the need for automated vehicles to drive on a fixed road path in respect to road safety and also to allow the employment of connected and automated vehicles in the future. Finally, an approach combining two optimisation algorithms, the optimal control problem and the k - є method, is applied successfully to seek the best trajectory profile that ensures the optimum compromise between motion comfort and driving behaviour, energy efficiency, vehicle stability, occupant's confidence to ride and journey time. | en_UK |
| dc.description.coursename | PhD in Transport Systems | en_UK |
| dc.identifier.uri | https://dspace.lib.cranfield.ac.uk/handle/1826/21174 | |
| dc.language.iso | en_UK | en_UK |
| dc.publisher | Cranfield University | en_UK |
| dc.publisher.department | SATM | en_UK |
| dc.rights | © Cranfield University, 2021. All rights reserved. No part of this publication may be reproduced without the written permission of the copyright holder. | en_UK |
| dc.subject | optimum trajectory | en_UK |
| dc.subject | optimum velocity | en_UK |
| dc.subject | multi-objective optimisation | en_UK |
| dc.subject | weighting factors | en_UK |
| dc.subject | driving styles | en_UK |
| dc.subject | energy efficiency | en_UK |
| dc.title | Control for motion sickness minimisation in autonomous vehicles. | en_UK |
| dc.type | Thesis or dissertation | en_UK |
| dc.type.qualificationlevel | Doctoral | en_UK |
| dc.type.qualificationname | PhD | en_UK |