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
Towards equity in crash protection. Women are at increased relative risk for death and serious injury in motor vehicle crashes compared to men and the reasons for this are not clear. This Fellowship aims to build a new model that describes the mechanistic pathways for this inequity to identify where and how intervention could reduce this relative risk. This will establish what population groups have good and poor access to the best vehicle safety technologies, the differences, and what might cau ....Towards equity in crash protection. Women are at increased relative risk for death and serious injury in motor vehicle crashes compared to men and the reasons for this are not clear. This Fellowship aims to build a new model that describes the mechanistic pathways for this inequity to identify where and how intervention could reduce this relative risk. This will establish what population groups have good and poor access to the best vehicle safety technologies, the differences, and what might cause these differences in the benefits of vehicle safety technology between women and men. The outcomes will be of use to academics, policy makers and industry designing to new ways to protect women in crashes and close this gender gap.Read moreRead less
Crashworthiness topology optimisation for light-weight battery compartments. This project uses computational modelling and optimisation methods to the design of battery compartments for electric vehicles. As the use of electric vehicles becomes more extensive, awareness of the consequences of catastrophic failure of high energy battery in a crash has increased. This project will develop novel design methodologies, using multi-disciplinary techniques for battery compartment structure. The methodo ....Crashworthiness topology optimisation for light-weight battery compartments. This project uses computational modelling and optimisation methods to the design of battery compartments for electric vehicles. As the use of electric vehicles becomes more extensive, awareness of the consequences of catastrophic failure of high energy battery in a crash has increased. This project will develop novel design methodologies, using multi-disciplinary techniques for battery compartment structure. The methodology will expand conventional crashworthiness design to the coupled mechanical-electrochemical-thermal problems. The proposed crashworthiness optimisation of battery compartment structure will enhance safety and reliability of electric vehicles, potentially benefiting consumers and manufacturers.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
User-centric solutions to optimise safety of children in cars. Globally, incorrect use of child restraints and inappropriate use of adult seat belts are widespread and longstanding problems. In Australia alone >2 million children are at increased risk of injury because of the lack of understanding about how to solve these problems. We have pioneered development of methods to allow novel user-centred approaches coupled with behavioural theory to effectively tackle these problems. In this project ....User-centric solutions to optimise safety of children in cars. Globally, incorrect use of child restraints and inappropriate use of adult seat belts are widespread and longstanding problems. In Australia alone >2 million children are at increased risk of injury because of the lack of understanding about how to solve these problems. We have pioneered development of methods to allow novel user-centred approaches coupled with behavioural theory to effectively tackle these problems. In this project we will build on and extend these innovative methods to to deliver new understanding about how (i) to communicate with parents to ensure behaviours and decisions about restraint optimise safety, and (ii) user-driven design can solve problems inhibiting optimal protection of in cars.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
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
Condition Monitoring of Aircraft Propulsion for Automated Diagnostics. The integrity of the steering system is crucial for the safe operation of autonomous vehicles. This project aims at developing a new condition monitoring system able to diagnose steering faults earlier, provide a root-cause-analysis of malfunctions, and estimate associated failure risks in the future. The outcomes of this project will be a better understanding of steering faults and their effect on autonomous driving, timely ....Condition Monitoring of Aircraft Propulsion for Automated Diagnostics. The integrity of the steering system is crucial for the safe operation of autonomous vehicles. This project aims at developing a new condition monitoring system able to diagnose steering faults earlier, provide a root-cause-analysis of malfunctions, and estimate associated failure risks in the future. The outcomes of this project will be a better understanding of steering faults and their effect on autonomous driving, timely diagnostics and prognostics and innovative proactive control measures that mitigate their impact on autonomous driving quality and safety. The expected benefits for the automotive industry and end-users include increased safety and reliability of steering systems, and higher confidence in autonomous driving.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
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