Trustworthy positioning for intelligent transport systems. This project aims to develop a holistic approach for reliable positioning for Intelligent Transport Systems (ITS). This project will address the challenges of integrity monitoring in ITS when using satellite-based technology, its integration with other sensors, and when supported by the proposed Australia National Positioning Infrastructure. It will consider Australian geography, large area, and sparse population, and emphasise rural tra ....Trustworthy positioning for intelligent transport systems. This project aims to develop a holistic approach for reliable positioning for Intelligent Transport Systems (ITS). This project will address the challenges of integrity monitoring in ITS when using satellite-based technology, its integration with other sensors, and when supported by the proposed Australia National Positioning Infrastructure. It will consider Australian geography, large area, and sparse population, and emphasise rural transport. Expected primary outputs include algorithms, a detailed analysis of required systems and recommendations that will help prepare Australia for the importation of self-driving vehicles.Read moreRead less
Containment and Reduction of Rework in Transport Mega Projects. Mega transport projects (>$1 billion) are poorly managed during their construction with significant cost and schedule overruns and benefit shortfalls regularly being experienced. Having to perform rework has been identified as a major factor that contributes to these unintended consequences. As there has been limited research that has empirically examined rework causation, an inability to develop effective rework containment and red ....Containment and Reduction of Rework in Transport Mega Projects. Mega transport projects (>$1 billion) are poorly managed during their construction with significant cost and schedule overruns and benefit shortfalls regularly being experienced. Having to perform rework has been identified as a major factor that contributes to these unintended consequences. As there has been limited research that has empirically examined rework causation, an inability to develop effective rework containment and reduction strategies prevails. This research aims to develop a theoretical model that can be used to develop robust containment and reduction strategies to mitigate the adverse economic, productivity and safety consequences that materialize from performing rework during the construction of mega transport projects.Read moreRead less
Defense against adversarial attacks on deep learning in computer vision. Computer vision applications rely heavily on deep learning, which is highly vulnerable to being fooled by adding subtle perturbations to object/image textures that are imperceptible to humans. This project aims to develop defense mechanisms to detect and remove adversarial patterns from the input images. The project expects to advance knowledge in understanding the vulnerabilities of deep learning, and to design deep learni ....Defense against adversarial attacks on deep learning in computer vision. Computer vision applications rely heavily on deep learning, which is highly vulnerable to being fooled by adding subtle perturbations to object/image textures that are imperceptible to humans. This project aims to develop defense mechanisms to detect and remove adversarial patterns from the input images. The project expects to advance knowledge in understanding the vulnerabilities of deep learning, and to design deep learning architectures that are inherently robust. The outcomes of this project will increase the security and reliability of computer vision by detecting, reporting and nullifying such attacks and will benefit the general public and industry on many fronts.Read moreRead less
Robust and Explainable 3D Computer Vision. Computer vision is increasingly relying on deep learning which is fragile, opaque and fails catastrophically without warning. This project aims to address these problems by developing new theory in graph representation of 3D geometric and image data, hierarchical graph simplification and novel modules designed specifically for deep learning over geometric graphs. Using these modules, it aims to design graph convolutional network architectures for self-s ....Robust and Explainable 3D Computer Vision. Computer vision is increasingly relying on deep learning which is fragile, opaque and fails catastrophically without warning. This project aims to address these problems by developing new theory in graph representation of 3D geometric and image data, hierarchical graph simplification and novel modules designed specifically for deep learning over geometric graphs. Using these modules, it aims to design graph convolutional network architectures for self-supervised learning that are robust to failures and provide explainable decisions for object detection and scene segmentation. The outcomes are expected to advance theory in robust deep learning and benefit 3D mapping, surveying, infrastructure monitoring, transport and robotics industries.Read moreRead less