Discovery Early Career Researcher Award - Grant ID: DE150100083
Funder
Australian Research Council
Funding Amount
$360,000.00
Summary
Driving as a model for investigating and improving visual search abilities. Visual search is a fundamental skill that is required in several aspects of everyday life. Driving represents an example of high-stakes search: we must constantly scan the environment in order to identify both potential hazards and informational cues, such as traffic lights and signs. While most drivers are experienced (they have been driving for years) they are not experts (they have no special training or skills); this ....Driving as a model for investigating and improving visual search abilities. Visual search is a fundamental skill that is required in several aspects of everyday life. Driving represents an example of high-stakes search: we must constantly scan the environment in order to identify both potential hazards and informational cues, such as traffic lights and signs. While most drivers are experienced (they have been driving for years) they are not experts (they have no special training or skills); this lack of expertise potentially affects search accuracy and, in turn, road safety. This project aims to use and extend existing models of visual search performance in order to explore factors that influence drivers' visual search abilities, and to identify strategies for reducing these perceptual failures and, in turn, road crashes.Read moreRead less
Intention-aware cooperative driving behaviour model for Automated Vehicles. This project aims to investigate humans' cooperation with automated systems by conceptualising joint intention awareness. This project expects to generate knowledge about a new cooperative driving behaviour model for automated vehicles, utilising a transdisciplinary approach that mixes human-centric methods with deep learning techniques. Intended outcomes are new joint intention awareness theory, new interface for automa ....Intention-aware cooperative driving behaviour model for Automated Vehicles. This project aims to investigate humans' cooperation with automated systems by conceptualising joint intention awareness. This project expects to generate knowledge about a new cooperative driving behaviour model for automated vehicles, utilising a transdisciplinary approach that mixes human-centric methods with deep learning techniques. Intended outcomes are new joint intention awareness theory, new interface for automated vehicles, new methodology for cooperative behaviour research, and enhanced research capacity. The expected significant benefits are for automated systems to become more predictable, acceptable, readable and safer to use by everyday people.Read moreRead less
A new training approach to address the novice driver problem. This project aims to develop a new approach to driver training. For the second consecutive year, road deaths in Australia have increased by 150 from 2014 to 2016. The increase in deaths was greatest for young drivers between the ages of 17-25 years, who remain over-represented in road deaths. The majority of these deaths occur in the first few months after licensing. This project expects to generate new knowledge, where the focus is o ....A new training approach to address the novice driver problem. This project aims to develop a new approach to driver training. For the second consecutive year, road deaths in Australia have increased by 150 from 2014 to 2016. The increase in deaths was greatest for young drivers between the ages of 17-25 years, who remain over-represented in road deaths. The majority of these deaths occur in the first few months after licensing. This project expects to generate new knowledge, where the focus is on developing young driver’s cognitive skills about speed choice through the provisions of a training program that focuses on feedback. The results will have the potential to be used by road authorities and driver training organisations to improve road safety.Read moreRead less
Industrial Transformation Training Centres - Grant ID: IC230100001
Funder
Australian Research Council
Funding Amount
$5,000,000.00
Summary
ARC Training Centre for Automated Vehicles in Rural and Remote Regions. The Centre will build skills and capability to test and deploy safe, socially acceptable, automated vehicles (AV) for rural, regional and remote Australian public roads, where manufacturing, agriculture, mining and defence industries face significant challenges of driver shortages, rising costs, long distances, rough roads, and environmental impacts. The centre will unite technology providers, regulators, government and end ....ARC Training Centre for Automated Vehicles in Rural and Remote Regions. The Centre will build skills and capability to test and deploy safe, socially acceptable, automated vehicles (AV) for rural, regional and remote Australian public roads, where manufacturing, agriculture, mining and defence industries face significant challenges of driver shortages, rising costs, long distances, rough roads, and environmental impacts. The centre will unite technology providers, regulators, government and end users with world-leading interdisciplinary researchers to create new human-AV systems, datasets, frameworks, case studies, platforms, and a vastly upskilled workforce. This will reduce transport costs, increase capacity, boost supply chain efficiency and resilience, improve road safety, and elevate Australian capability.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE200101079
Funder
Australian Research Council
Funding Amount
$426,241.00
Summary
Safe distractions? Taking the danger out of competing activities. Distracted driving is an increasing safety concern in Australia and worldwide. Smartphones play key roles in today’s professional and social contexts and current road safety policies based on stopping their use while driving have shown little success. Distraction is predicted to be an even greater issue in new semi-automated vehicles. This project proposes an innovative approach that will enable safe engagement in competing tasks ....Safe distractions? Taking the danger out of competing activities. Distracted driving is an increasing safety concern in Australia and worldwide. Smartphones play key roles in today’s professional and social contexts and current road safety policies based on stopping their use while driving have shown little success. Distraction is predicted to be an even greater issue in new semi-automated vehicles. This project proposes an innovative approach that will enable safe engagement in competing tasks while driving non-automated and semi-automated vehicles. The outcomes will underpin the development of new technologies to reduce the potential adverse effects of these distractions and thus reduce deaths and serious injuries, representing significant cost savings to the health system and the community.Read moreRead less
Developing and evaluating a theoretically grounded novice driver education program incorporating simulators. Australian young drivers are 13 per cent of the population but account for nearly a quarter of road deaths. This project aims to develop a research informed, theory-driven education intervention that includes a simulator component to improve their driving skills and attitudes. A process and outcome evaluation aims to assess the effectiveness of the training including the impact on how ind ....Developing and evaluating a theoretically grounded novice driver education program incorporating simulators. Australian young drivers are 13 per cent of the population but account for nearly a quarter of road deaths. This project aims to develop a research informed, theory-driven education intervention that includes a simulator component to improve their driving skills and attitudes. A process and outcome evaluation aims to assess the effectiveness of the training including the impact on how individuals learn to drive, changes in skill and attitudes as well as the influence on crashes and offences. It is intended that a second parallel study will adapt the intervention for Indigenous Australians and examine the effectiveness of this adaptation.Read moreRead less
Attention vs Perception: When is selection optimal, when relational? This project aims to investigate an important, newly discovered dissociation between early visual selection and perceptual decision-making. Contrary to current theories, attentional and perceptual processes are tuned to different stimulus attributes described in the relational vs. optimal account, which implies that current theories of attention do not describe early attention but later, decisional processes. This project will ....Attention vs Perception: When is selection optimal, when relational? This project aims to investigate an important, newly discovered dissociation between early visual selection and perceptual decision-making. Contrary to current theories, attentional and perceptual processes are tuned to different stimulus attributes described in the relational vs. optimal account, which implies that current theories of attention do not describe early attention but later, decisional processes. This project will provide an accurate description of these processes, which promises important theoretical breakthroughs. Work on this project will also significantly advance methods to detect and describe early attentional processes, by identifying error-prone methods of Psychophysics and Neuroscience studies, and proposing remedies.Read moreRead less
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.Read moreRead less
Can the relational account of attention explain search in natural environments and inattentional blindness? This project aims to further extend the relational theory of attention to account for visual search and inattentional blindness in natural environments. In addition, the neuronal correlates for inattentional blindness will be investigated with the use of Functional Magnetic Resonance Imaging (fMRI). The research has fundamental implications for theories of visual attention and awareness, a ....Can the relational account of attention explain search in natural environments and inattentional blindness? This project aims to further extend the relational theory of attention to account for visual search and inattentional blindness in natural environments. In addition, the neuronal correlates for inattentional blindness will be investigated with the use of Functional Magnetic Resonance Imaging (fMRI). The research has fundamental implications for theories of visual attention and awareness, and will advance understandings of how and why we frequently fail to notice potentially important objects and events in the environment.Read moreRead less
Engaging Augmented Reality on 3D Head Up Displays to Reduce Risky Driving. This project aims to reduce risky driving behaviours through novel augmented reality applications for three-dimensional head-up displays, making safe driving more engaging so that drivers will take less risk. Over 1 million people are killed and 50 million are seriously injured on roads each year worldwide. Risky driving behaviours (speeding and distracted driving) are major causes. This project intends to produce novel i ....Engaging Augmented Reality on 3D Head Up Displays to Reduce Risky Driving. This project aims to reduce risky driving behaviours through novel augmented reality applications for three-dimensional head-up displays, making safe driving more engaging so that drivers will take less risk. Over 1 million people are killed and 50 million are seriously injured on roads each year worldwide. Risky driving behaviours (speeding and distracted driving) are major causes. This project intends to produce novel in-car interaction design implementations, provide important visual design guidelines for future display technologies, and provide novel road safety interventions.Read moreRead less