The anatomy of a fatigue-related motor vehicle crash or near-crash. The anatomy of a fatigue-related motor vehicle crash or near-crash. This project aims to investigate the time course of multiple physiological and behavioural signals that lead to fall-asleep (on-road) driving events, to inform the next generation of driver state monitoring technologies. Falling asleep at the wheel remains a major cause of road crashes worldwide. Although technologies to monitor driver sleepiness are integral to ....The anatomy of a fatigue-related motor vehicle crash or near-crash. The anatomy of a fatigue-related motor vehicle crash or near-crash. This project aims to investigate the time course of multiple physiological and behavioural signals that lead to fall-asleep (on-road) driving events, to inform the next generation of driver state monitoring technologies. Falling asleep at the wheel remains a major cause of road crashes worldwide. Although technologies to monitor driver sleepiness are integral to the rapidly evolving autonomous vehicle industry, such technologies are limited because they measure the end-state of falling asleep, rather than the physiological and behavioural precursors, thus providing little opportunity for intervention. This project is expected to lead to new driver monitoring systems that reduce fall-asleep crashes.Read moreRead less
Difficulties of monitoring for rare events. This project aims to identify cognitive and neural processes involved in sustaining attention to moving displays under monitoring conditions.People are poor at monitoring for rare events: they tend to miss infrequent targets. This is a problem in automated systems for transport, rail and air traffic control. If a computer error occurs, the operator needs to intervene quickly. This project will develop a tool for studying monitoring and determine patter ....Difficulties of monitoring for rare events. This project aims to identify cognitive and neural processes involved in sustaining attention to moving displays under monitoring conditions.People are poor at monitoring for rare events: they tend to miss infrequent targets. This is a problem in automated systems for transport, rail and air traffic control. If a computer error occurs, the operator needs to intervene quickly. This project will develop a tool for studying monitoring and determine patterns of brain activity that predict a lapse of attention. The results should contribute to theories of vigilance and improve performance in real-world monitoring situations.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
Improving performance in high risk environments using guided distraction and iconic cues. This project tests a novel strategy to assist operators in high-risk automated environments, in order to maintain their performance in low workload situations. Using guided distraction, this project will be able to show improvements in attention to critical tasks and in overall system performance, thereby reducing the potential for error.