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Multimodal Machine Learning for Intelligent Mobility MULTIMODAL MACHINE LEARNING FOR INTELLIGENT MOBILITY Multimodal Machine Learning for Intelligent Mobility by Aubrey James Roche Doctoral Thesis Submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy Institute of Digital Technologies, Loughborough University London © Aubrey James Roche May 2020 i MULTIMODAL MACHINE LEARNING FOR INTELLIGENT MOBILITY Dedicate to my partner Sádhbh & my son Ruadhán – Nunc scio quid sit amor, semper gratiam habebo. Ever tried. Ever failed. No matter. Try Again. Fail again. Fail better. (Samuel Beckett) ii MULTIMODAL MACHINE LEARNING FOR INTELLIGENT MOBILITY Certificate of Originality Thesis Access Conditions and Deposit Agreement Students should consult the guidance notes on the electronic thesis deposit and the access conditions in the University’s Code of Practice on Research Degree Programmes Author: Jamie Roche .............................................................................................................................. Title: Multimodal Machine Learning for Intelligent Mobility ............................................................ I Jamie Roche, 2 Herbert Tec, Herbert Road, Bray, Co Wicklow, Ireland, “the Depositor”, would like to deposit Multimodal Machine Learning for Intelligent Mobility, hereafter referred to as the “Work”, once it has successfully been examined in Loughborough University Research Repository Status of access OPEN / RESTRICTED / CONFIDENTIAL Moratorium Period: 3 years 3 months ................... years, ending 13 ............ / Dec ....... 2019 .............. Status of access approved by (CAPITALS) ........................................................................................... Supervisor (Signature) ............................................................................................................................. School of: Loughborough University London ...................................................................................... Author's Declaration I confirm the following: CERTIFICATE OF ORIGINALITY This is to certify that I am responsible for the work submitted in this thesis, that the original work is my own except as specified in acknowledgements or in footnotes, and that neither the thesis nor the original work therein has been submitted to this or any other institution for a degree NON-EXCLUSIVE RIGHTS The licence rights granted to Loughborough University Research Repository through this agreement are entirely non-exclusive and royalty free. I am free to publish the Work in its present version or future versions elsewhere. I agree that Loughborough University Research Repository administrators or any third party with whom Loughborough University Research Repository has an agreement to do so may, without changing content, convert the Work to any medium or format for the purpose of future preservation and accessibility. DEPOSIT IN LOUGHBOROUGH UNIVERSITY RESEARCH REPOSITORY I understand that open access work deposited in Loughborough University Research Repository will be accessible to a wide variety of people and institutions - including automated agents - via the World Wide Web. An electronic copy of my thesis may also be included in the British Library Electronic Theses On-line System (EThOS). iii MULTIMODAL MACHINE LEARNING FOR INTELLIGENT MOBILITY I understand that once the Work is deposited, a citation to the Work will always remain visible. Removal of the Work can be made after discussion with Loughborough University Research Repository, who shall make best efforts to ensure removal of the Work from any third party with whom Loughborough University Research Repository has an agreement. Restricted or Confidential access material will not be available on the World Wide Web until the moratorium period has expired. - That I am the author of the Work and have the authority to make this agreement and to hereby give Loughborough University Research Repository administrators the right to make available the Work in the way described above. - That I have exercised reasonable care to ensure that the Work is original, and does not to the best of my knowledge break any UK law or infringe any third party’s copyright or other Intellectual Property Right. I have read the University’s guidance on third party copyright material in theses. - The administrators of Loughborough University Research Repository do not hold any obligation to take legal action on behalf of the Depositor, or other rights holders, in the event of breach of Intellectual Property Rights, or any other right, in the material deposited. The statement below shall apply to ALL copies: This copy has been supplied on the understanding that it is copyright material and that no quotation from the thesis may be published without proper acknowledgement. Restricted/confidential work: All access and any copying shall be strictly subject to written permission from the University Dean of School and any external sponsor, if any. Author's signature ............................................................... Date 13/12/2019 ........................................ user’s declaration: for signature during any Moratorium period (Not Open work): I undertake to uphold the above conditions: Date Name (CAPITALS) Signature Address iv MULTIMODAL MACHINE LEARNING FOR INTELLIGENT MOBILITY Abstract Scientific problems are solved by finding the optimal solution for a specific task. Some problems can be solved analytically while other problems are solved using data driven methods. The use of digital technologies to improve the transportation of people and goods, which is referred to as intelligent mobility, is one of the principal beneficiaries of data driven solutions. Autonomous vehicles are at the heart of the developments that propel Intelligent Mobility. Due to the high dimensionality and complexities involved in real-world environments, it needs to become commonplace for intelligent mobility to use data-driven solutions. As it is near impossible to program decision making logic for every eventuality manually. While recent developments of data-driven solutions such as deep learning facilitate machines to learn effectively from large datasets, the application of techniques within safety-critical systems such as driverless cars remain scarce. Autonomous vehicles need to be able to make context-driven decisions autonomously in different environments in which they operate. The recent literature on driverless vehicle research is heavily focused only on road or highway environments but have discounted pedestrianized areas and indoor environments. These unstructured environments tend to have more clutter and change rapidly over time. Therefore, for intelligent mobility to make a significant impact on human life, it is vital to extend the application beyond the structured environments. To further advance intelligent mobility, researchers need to take cues from multiple sensor streams, and multiple machine learning algorithms so that decisions can be robust and reliable. Only then will machines indeed be able to operate in unstructured and dynamic environments safely. Towards addressing these limitations, this thesis investigates data driven solutions towards crucial building blocks in intelligent mobility. Specifically, the thesis investigates multimodal sensor data fusion, machine learning, multimodal deep representation learning and its application of intelligent mobility. This work demonstrates that mobile robots can use multimodal machine learning to derive driver policy and therefore make autonomous decisions. To facilitate autonomous decisions necessary to derive safe driving algorithms, we present an algorithm for free space detection and human activity recognition. Driving these decision-making algorithms are specific datasets collected throughout this study. They include the Loughborough London Autonomous Vehicle dataset, and the Loughborough London Human Activity Recognition dataset. The datasets were collected using an autonomous v MULTIMODAL MACHINE LEARNING FOR INTELLIGENT MOBILITY platform design and developed in house as part of this research activity. The proposed framework for Free-Space Detection is based on an active learning paradigm that leverages the relative uncertainty of multimodal sensor data streams (ultrasound and camera). It utilizes an online learning methodology to continuously update the learnt model whenever the vehicle experiences new environments. The proposed Free Space Detection algorithm enables an autonomous vehicle to self-learn, evolve and adapt to new environments never encountered before. The results illustrate that online learning mechanism is superior to one-off training of deep neural networks that require large datasets to generalize to unfamiliar surroundings. The thesis takes the view that human should be at the centre of any technological development related to artificial intelligence. It is imperative within the spectrum of intelligent mobility where an autonomous vehicle should be aware of what humans are doing in its vicinity. Towards improving the robustness of human activity recognition, this thesis proposes a novel algorithm that classifies point-cloud data originated from Light Detection and Ranging sensors. The proposed algorithm leverages multimodality by using the camera data to identify humans and segment the region of interest in point cloud data. The corresponding 3- dimensional data was converted to a Fisher Vector Representation before being classified by a deep Convolutional Neural
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