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Scheme : Discovery Projects
Field of Research : Autonomous Vehicles
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  • Active Funded Activity

    Discovery Projects - Grant ID: DP220102598

    Funder
    Australian Research Council
    Funding Amount
    $525,000.00
    Summary
    A human-centric eXplainable Automated Vehicle. The aim is to create a computational model to address the inability of Automated Vehicles (AV), powered by Artificial intelligence, to self explain their behaviours. This project applies novel multidisciplinary methodologies in a real-world self-driving setting to formalise the essence of driving explanations. It explores the when, why and how a driver is seeking an explanation and what type of automated explanation is truly human-interpretable. Exp .... A human-centric eXplainable Automated Vehicle. The aim is to create a computational model to address the inability of Automated Vehicles (AV), powered by Artificial intelligence, to self explain their behaviours. This project applies novel multidisciplinary methodologies in a real-world self-driving setting to formalise the essence of driving explanations. It explores the when, why and how a driver is seeking an explanation and what type of automated explanation is truly human-interpretable. Expected outcomes include the discovery of an acceptable, transparent and ethical explanation system that helps humans to understand the AVs decision making. This field will continue to rise in prominence and produce much-needed work to improve the widespread adoption of AVs.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP200102642

    Funder
    Australian Research Council
    Funding Amount
    $475,000.00
    Summary
    Going wild: Neural processing in freely moving animals. This project aims to use new techniques in wireless neural recording to reveal how small neural networks process visual information to make fast, accurate decisions. The project is designed to generate new knowledge about biological solutions to contextual information processing and how tiny, simple biological neural systems control critical animal behaviours such as predator avoidance. Expected outcomes will be new biological insights with .... Going wild: Neural processing in freely moving animals. This project aims to use new techniques in wireless neural recording to reveal how small neural networks process visual information to make fast, accurate decisions. The project is designed to generate new knowledge about biological solutions to contextual information processing and how tiny, simple biological neural systems control critical animal behaviours such as predator avoidance. Expected outcomes will be new biological insights with which to develop novel bio-inspired decision-making processing systems as required in small, autonomous robots. The anticipated benefits of this project will be advances in fundamental neuroscience and animal behaviour and is expected to provide significant value to a fast-developing industrial sector.
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    Funded Activity

    Discovery Projects - Grant ID: DP180100220

    Funder
    Australian Research Council
    Funding Amount
    $306,832.00
    Summary
    Understanding evolution in natural systems using robotic models. This project aims to build biologically-inspired robotic and computational systems, and then modify these in ways which are either not possible, or have not yet occurred in natural systems. A comparison of these two systems will then allow a quantitative understanding of how well optimised biological structures are and where the limitations to optimisation lie. Expected outcomes include advancing the understanding of evolutionary p .... Understanding evolution in natural systems using robotic models. This project aims to build biologically-inspired robotic and computational systems, and then modify these in ways which are either not possible, or have not yet occurred in natural systems. A comparison of these two systems will then allow a quantitative understanding of how well optimised biological structures are and where the limitations to optimisation lie. Expected outcomes include advancing the understanding of evolutionary processes, and will provide significant benefits, such as aiding the manufacture of efficient autonomous robots.
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