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dc.contributor.authorde Saxe, Christopher Charles
dc.date.accessioned2017-11-08T10:46:26Z
dc.date.available2017-11-08T10:46:26Z
dc.date.issued2017-11-01
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/268169
dc.description.abstractArticulated Heavy Goods Vehicles (HGVs) are more efficient than conventional rigid lorries, but exhibit reduced low-speed manoeuvrability and high-speed stability. Technologies such as autonomous reversing and path-following trailer steering can mitigate this, but practical limitations of the available sensing technologies restrict their commercialisation potential. This dissertation describes the development of practical vision-based articulation angle and trailer off-tracking sensing for HGVs. Chapter 1 provides a background and literature review, covering important vehicle technologies, existing commercial and experimental sensors for articulation angle and off-tracking measurement, and relevant vision-based technologies. This is followed by an introduction to pertinent computer vision theory and terminology in Chapter 2. Chapter 3 describes the development and simulation-based assessment of an articulation angle sensing concept. It utilises a rear-facing camera mounted behind the truck or tractor, and one of two proposed image processing methods: template-matching and Parallel Tracking and Mapping (PTAM). The PTAM-based method was shown to be the more accurate and versatile method in full-scale vehicle tests. RMS measurement errors of 0.4-1.6$^\circ$ were observed in tests on a tractor semi-trailer (Chapter 4), and 0.8-2.4$^\circ$ in tests on a Nordic combination with two articulation points (Chapter 5). The system requires no truck-trailer communication links or artificial markers, and is compatible with multiple trailer shapes, but was found to have increasing errors at higher articulation angles. Chapter 6 describes the development and simulation-based assessment of a trailer off-tracking sensing concept, which utilises a trailer-mounted stereo camera pair and visual odometry. The concept was evaluated in full-scale tests on a tractor semi-trailer combination in which camera location and stereo baseline were varied, presented in Chapter 7. RMS measurement errors of 0.11-0.13 m were obtained in some tests, but a sensitivity to camera alignment was discovered in others which negatively affected results. A very stiff stereo camera mount with a sub-0.5 m baseline is suggested for future experiments. A summary of the main conclusions, a review of the objectives, and recommendations for future work are given in Chapter 8. Recommendations include further refinement of both sensors, an investigation into lighting sensitivity, and alternative applications of the sensors.
dc.description.sponsorshipThis work was supported by a "CSIR South Africa Cambridge Scholarship", funded jointly by the Cambridge Commonwealth, European & International Trust and the Council for Scientific & Industrial Research (CSIR South Africa).
dc.language.isoen
dc.rightsNo Creative Commons licence (All rights reserved)
dc.rightsAll Rights Reserveden
dc.rights.urihttps://www.rioxx.net/licenses/all-rights-reserved/en
dc.subjectArticulated vehicles
dc.subjectArticulation angle
dc.subjectComputer vision
dc.subjectOff-tracking
dc.subjectPose estimation
dc.subjectStereo vision
dc.subjectLong Combination Vehicles
dc.subjectHeavy Goods Vehicles
dc.subjectTrailer steering
dc.subjectAutonomous reversing
dc.titleVision-based trailer pose estimation for articulated vehicles
dc.typeThesis
dc.type.qualificationlevelDoctoral
dc.type.qualificationnameDoctor of Philosophy (PhD)
dc.publisher.institutionUniversity of Cambridge
dc.publisher.departmentEngineering
dc.date.updated2017-11-08T09:59:01Z
dc.identifier.doi10.17863/CAM.14370
dc.contributor.orcidde Saxe, Christopher Charles [0000-0002-9458-809X]
dc.publisher.collegeJesus
dc.type.qualificationtitlePhD in Engineering
cam.supervisorCebon, David
cam.supervisor.orcidCebon, David [0000-0003-2828-6445]


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