Using visual science to reduce the dangers of night driving. This project aims to develop novel tests of visual function relevant to the modern night driving environment. Night driving is challenging for all drivers and has been linked to poor visibility under low light conditions. This project will characterise the visual challenges of the modern night driving environment, develop visual tests that incorporate the dynamic light levels typical of night-time roads and assess the association of th ....Using visual science to reduce the dangers of night driving. This project aims to develop novel tests of visual function relevant to the modern night driving environment. Night driving is challenging for all drivers and has been linked to poor visibility under low light conditions. This project will characterise the visual challenges of the modern night driving environment, develop visual tests that incorporate the dynamic light levels typical of night-time roads and assess the association of these tests with night driving performance. The outcomes will contribute new knowledge regarding dynamic visual processing and the ageing visual system and will inform vision testing, potential interventions to improve visual function for night driving and reduce the dangers of night driving.Read moreRead less
Unifying Traffic Modelling and Safety Management for Safer and Faster Roads. This project aims to balance road safety and efficiency as conflicting goals of transport systems mixed with connected and automated vehicles (CAVs). This project is expected to generate fundamental knowledge on operational algorithms and analytics for CAVs and develop innovative tools for operating them. Expected outcomes include ground-breaking models capable of the co-estimation of efficiency and safety impacts of CA ....Unifying Traffic Modelling and Safety Management for Safer and Faster Roads. This project aims to balance road safety and efficiency as conflicting goals of transport systems mixed with connected and automated vehicles (CAVs). This project is expected to generate fundamental knowledge on operational algorithms and analytics for CAVs and develop innovative tools for operating them. Expected outcomes include ground-breaking models capable of the co-estimation of efficiency and safety impacts of CAVs, and control strategies to safely and efficiently integrate CAVs into existing transport systems. This should provide significant safety and efficiency benefits that currently cost about 1160 lives and 1.25 billion hours of congestion per year, and make Australia better prepared for the connected and automated vehicle era.Read moreRead less
Coach My Ride: Mentorable Interfaces to support Older Australians' Mobility. This project aims to co-design new interfaces to support older Australians to collaboratively learn the use of automated vehicles. We will seek to understand the needs, expectations, and challenges of urban and rural residents, and the peer support strategies they deploy to learn technology. Mobility is key to the wellbeing of older people, but automated vehicles that are too complex will fail to deliver their promise o ....Coach My Ride: Mentorable Interfaces to support Older Australians' Mobility. This project aims to co-design new interfaces to support older Australians to collaboratively learn the use of automated vehicles. We will seek to understand the needs, expectations, and challenges of urban and rural residents, and the peer support strategies they deploy to learn technology. Mobility is key to the wellbeing of older people, but automated vehicles that are too complex will fail to deliver their promise of independent ageing. Outcomes will be a new theory of collaborative learning and new mentorable interfaces to allow older adults to mentor each other to access and use new mobility solutions. This will contribute to narrow the digital and mobility gap improving the independence, safety and wellbeing of ageing Australians.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
Context-aware verification and validation framework for autonomous driving. This project aims to enhance the reliability and safety of emerging self-driving vehicles, through a framework that supports the validation and verification of autonomous driving systems. This project expects to generate new knowledge in areas of software engineering, intelligent transport, and machine learning, using a multi-disciplinary research combining expertise from various fields. Expected outcomes of this project ....Context-aware verification and validation framework for autonomous driving. This project aims to enhance the reliability and safety of emerging self-driving vehicles, through a framework that supports the validation and verification of autonomous driving systems. This project expects to generate new knowledge in areas of software engineering, intelligent transport, and machine learning, using a multi-disciplinary research combining expertise from various fields. Expected outcomes of this project are a family of new context-aware techniques to verify and validate complex behaviours in autonomous driving. This should provide significant benefits, such as safe autonomous driving systems and the improved journey experience and security for road users.Read moreRead less
Improving novice drivers' speed and hazard management. The aim of the study is to extend the evidence-based approach we have developed for speed management (cognitive integration speed management training) to hazard management, thereby developing cognitive integration hazard management training for young drivers. Hence, this study is specifically designed to curb the alarming trend in young driver fatalities on Australian roads. The results of the research will provide clear direction to road au ....Improving novice drivers' speed and hazard management. The aim of the study is to extend the evidence-based approach we have developed for speed management (cognitive integration speed management training) to hazard management, thereby developing cognitive integration hazard management training for young drivers. Hence, this study is specifically designed to curb the alarming trend in young driver fatalities on Australian roads. The results of the research will provide clear direction to road authorities and driver training providers as to effective training strategies to improve young driver training, and ultimately improve road safety with this vulnerable population.Read moreRead less
Visual field impairment and injury: A population-based study. This project aims to link a large-scale ophthalmic database of visual field tests to population-based injury data which includes police-reported crash data, hospitalisation, death and trauma data in people aged over 60 years. The identification of individuals with high risk visual fields will enable the development of targeted interventions at the local, national and international level to prevent injuries due to visual field loss. Si ....Visual field impairment and injury: A population-based study. This project aims to link a large-scale ophthalmic database of visual field tests to population-based injury data which includes police-reported crash data, hospitalisation, death and trauma data in people aged over 60 years. The identification of individuals with high risk visual fields will enable the development of targeted interventions at the local, national and international level to prevent injuries due to visual field loss. Significant benefits include a reduction in the number of injuries and consequent reductions in personal harm and health care demands.Read moreRead less
Situational Assessment as a Marker of Cognitive Skill Decay. The aim of this study is to test how differences in exposure to complex tasks change the capacity for situational assessment. Amongst drivers, pilots and electricity controllers, the capacity to assess and respond effectively to changes in the operational environment are critical in sustaining performance and ensuring the safety and security of the public. Establishing the nature of this relationship will enable, for the first time, ob ....Situational Assessment as a Marker of Cognitive Skill Decay. The aim of this study is to test how differences in exposure to complex tasks change the capacity for situational assessment. Amongst drivers, pilots and electricity controllers, the capacity to assess and respond effectively to changes in the operational environment are critical in sustaining performance and ensuring the safety and security of the public. Establishing the nature of this relationship will enable, for the first time, objective measures of cognitive skill decay. In evaluating cognitive skill decay more accurately, we will provide a cost-effective, easily administered tool, enabling practitioners to identify and address areas of development and providing data to anticipate when cognitive skill decay is most likely to occur.Read moreRead less
Understanding and improving sustained attention under vigilance conditions. This project aims to address a major global challenge caused by technological advances: human operators have to monitor computer-control (e.g., in autonomous vehicles, rail and airtraffic control) but sustaining attention is very difficult under these conditions. Developing innovative behavioural and neural methods, this internationally collaborative project bridges basic and applied science to understand lapses of atten ....Understanding and improving sustained attention under vigilance conditions. This project aims to address a major global challenge caused by technological advances: human operators have to monitor computer-control (e.g., in autonomous vehicles, rail and airtraffic control) but sustaining attention is very difficult under these conditions. Developing innovative behavioural and neural methods, this internationally collaborative project bridges basic and applied science to understand lapses of attention under monitoring conditions. It creates a novel intervention, based on brain activity patterns, to improve performance. Outcomes will increase our neural understanding of attention and lay a foundation for a novel system to detect lapses of attention in high-risk environments, preventing errors before they occur.Read moreRead less
Sustainable mobility: city-wide exposure modelling to advance bicycling. This project aims to develop a world-leading platform for city-wide modelling of cycling exposure. This project will provide unparalleled insights into cycling exposure by combining multiple cycling data sources through the use of advanced spatial statistical and machine learning techniques. The expected outcomes of this project are a novel inventory of cycling infrastructure, a cycling route choice modelling system and rob ....Sustainable mobility: city-wide exposure modelling to advance bicycling. This project aims to develop a world-leading platform for city-wide modelling of cycling exposure. This project will provide unparalleled insights into cycling exposure by combining multiple cycling data sources through the use of advanced spatial statistical and machine learning techniques. The expected outcomes of this project are a novel inventory of cycling infrastructure, a cycling route choice modelling system and robust predictions of cycling volumes on individual streets. This project will deliver a step change in cycling that will lead to increased cycling participation, enhanced safety, and improved infrastructure planning, thereby resulting in substantial gains in population and environmental health.Read moreRead less