Discovery Early Career Researcher Award - Grant ID: DE170101180
Funder
Australian Research Council
Funding Amount
$327,900.00
Summary
Understanding and preventing road deaths using coronial investigations. This project aims to study coronial death investigations of fatal road crashes in Australia using public health and road safety theoretical frameworks. Fatal road crashes are sudden, unexpected and violent. Each fatality has a lasting effect resulting in immeasurable emotional costs and a financial burden in excess of $3.8 billion per year. Intended outcomes will contribute to understanding of fatal road crashes including pr ....Understanding and preventing road deaths using coronial investigations. This project aims to study coronial death investigations of fatal road crashes in Australia using public health and road safety theoretical frameworks. Fatal road crashes are sudden, unexpected and violent. Each fatality has a lasting effect resulting in immeasurable emotional costs and a financial burden in excess of $3.8 billion per year. Intended outcomes will contribute to understanding of fatal road crashes including pre-crash social factors (e.g. alcohol/drug use and dependence, unemployment, age), the use and effect of coronial recommendations on road safety policy and practice, and preventing deaths on Australian roads.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
A real-time traffic signal system for safe and efficient intersections . Road traffic crashes result in 1,200 fatalities and another 36,500 injuries on Australian roads each year. Signalised intersections represent a high-risk node in a transportation network, but the current signal designs only consider efficiency but not safety. This project aims to unleash the power of artificial intelligence (AI) and integrate with the advanced extreme value models for proactive and efficient detection of cr ....A real-time traffic signal system for safe and efficient intersections . Road traffic crashes result in 1,200 fatalities and another 36,500 injuries on Australian roads each year. Signalised intersections represent a high-risk node in a transportation network, but the current signal designs only consider efficiency but not safety. This project aims to unleash the power of artificial intelligence (AI) and integrate with the advanced extreme value models for proactive and efficient detection of crash risk in real-time. Its innovations lie on developing a novel traffic signal control system balancing safety and efficiency of signalised intersections. The proposed real-time traffic signal system will fundamentally transform the intersection operation and lead to reductions of road fatalities, injuries and emissions.Read moreRead less
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
The Safer Scooting Study. E-scooters are a new transport option experiencing rapid uptake, but many people are concerned about their safety. This project aims to provide an understanding of how and why people use e-scooters and how rider behaviour and safety outcomes change with experience. The anticipated goal of this project is to harness the potential benefits of e-scooters as an efficient replacement for short car trips and a way of improving access to public transport, while minimising the ....The Safer Scooting Study. E-scooters are a new transport option experiencing rapid uptake, but many people are concerned about their safety. This project aims to provide an understanding of how and why people use e-scooters and how rider behaviour and safety outcomes change with experience. The anticipated goal of this project is to harness the potential benefits of e-scooters as an efficient replacement for short car trips and a way of improving access to public transport, while minimising the dangers to riders and pedestrians. This knowledge is expected to inform governments at all levels, industry and riders on how to optimise e-scooter design, use and regulation to contribute to improvements in transport, health and environmental outcomes for all Australians.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
Predictive Analytics and Real-time Traffic Control for Urban Corridors. This project aims to develop predictive data analytics and real-time traffic control and safety models for multimodal management of urban corridors, serving two salient objectives: (1) optimising person-throughput of multimodal traffic; while (2) minimising safety risks for all modes. The outcome will be an automated, sensor-based platform to monitor traffic flows from all modes and make proactive and coordinated control dec ....Predictive Analytics and Real-time Traffic Control for Urban Corridors. This project aims to develop predictive data analytics and real-time traffic control and safety models for multimodal management of urban corridors, serving two salient objectives: (1) optimising person-throughput of multimodal traffic; while (2) minimising safety risks for all modes. The outcome will be an automated, sensor-based platform to monitor traffic flows from all modes and make proactive and coordinated control decisions in real-time. The expected benefits are profound; the developed algorithms and platform will significantly reduce traffic congestion, travel delays and safety risks for all modes of transport, especially for vulnerable road users (e.g. pedestrians and cyclists).Read moreRead less
AUSLearn: AUtomated Sample Learning for Object Recognition. This project aims to enable computers to learn how to effectively use training samples for object recognition. Training sample is the only source used by computers to learn recognising objects. This project creates a new research direction that will enable the first full exploration of the power of samples. The aims will be enabled by leveraging the recent advances in reinforcement learning, fast training algorithms, and by developing n ....AUSLearn: AUtomated Sample Learning for Object Recognition. This project aims to enable computers to learn how to effectively use training samples for object recognition. Training sample is the only source used by computers to learn recognising objects. This project creates a new research direction that will enable the first full exploration of the power of samples. The aims will be enabled by leveraging the recent advances in reinforcement learning, fast training algorithms, and by developing novel deep learning algorithms. The new algorithms will benefit a wide range of applications, e.g. to effectively use car crash training samples for accurately identifying potential road crashes in transport and to effectively use rare medical imaging training data for robustly diagnosing diseases in health.Read moreRead less