Understanding human behaviour during automated driving

Overview

https://research.qut.edu.au/ver/https://research.qut.edu.au/ver/

We are seeking a highly motivated and talented Vision Science and Human Factors PhD Researcher to join an interdisciplinary research project, conducted in collaboration with Queensland University of Technology (QUT) and industry partner Seeing Machines—world leader in human-machine interaction and an industry leader in artificial intelligence (AI), that enable machines to see, understand and assist the people who are using them.

A PhD scholarship is available for this project.

Learn more about the scholarship

Our program offers:

• World-class research environment: immerse yourself in a stimulating and supportive research community. Collaborate with leading experts in vision and eye research, road safety and automated driving, and computer vision and AI who are shaping the future of these fields.
• Cutting-edge facilities: access state-of-the-art facilities, ranging from the latest technology in driver monitoring system technologies, prototype Automated Vehicles, and new test track and digital twin infrastructure to facilitate real-world studies. We invest in research infrastructure to provide the tools you need to excel.
• Diverse research opportunities: explore a wide range of research areas, including eye-, gaze-, head- and body-tracking technologies, novel human-machine interfaces. Choose the path that aligns with your passion and expertise.
• Industry collaboration: benefit from strong ties with industry leaders. Collaborate with industry partner Seeing Machines, gain real-world experience, and enhance your career prospects upon graduation.
• Scholarships and funding: we understand the importance of financial support. The PhD program offers competitive scholarships and additional funding and top-up opportunities (e.g. the Australian Government’s National Industry PhD Program) to help you focus on your research.
• Publish and present: showcase your findings on prestigious platforms. Publish in top-tier conferences and journals, and present your work at international conferences to gain recognition in the research community.

Research activities

• conduct literature reviews and stay abreast of the latest developments in human factors, driver engagement, eye tracking, gaze analysis, and head tracking in the context of autonomous driving
• design and conduct empirical studies using eye, gaze, and head tracking technologies to investigate the links between unsafe levels of driver engagement and safety performance/risk
• collaborate with interdisciplinary research teams from QUT and Seeing Machines to integrate findings from other PhD projects into the broader research objectives, particularly in relation to vision, so eye and gaze-tracking, and head- and body-tracking data
• work closely with industry partner Seeing Machines to gain insights into driver monitoring systems and leverage their expertise in eye-, gaze-, head- and body-tracking technologies
• contribute to the development of novel measures of driver engagement in L2-L4 driving environments, incorporating eye-, gaze-, head- and body-tracking technologies
• analyze eye, gaze, and head tracking data collected from DMS and other sensors to understand the relationship between engagement levels, situation awareness, and risk
• publish research findings in high-impact journals, specifically focusing on eye, gaze, and head tracking in the context of human-machine interaction and autonomous driving
• present research outcomes at relevant conferences and workshops, emphasizing the role of eye, gaze, and head tracking in understanding driver engagement and safety
• contribute to the preparation of research reports, project documentation, and funding proposals, highlighting the significance of eye, gaze, and head tracking in the research outcomes
• collaborate with other PhD researchers to support interdisciplinary learning and foster a cohesive research environment, integrating eye, gaze, and head tracking aspects into the broader project goals.

Outcomes

The project aims to advance the understanding of human behaviour during automated driving, with a strong focus on eye, gaze, and head tracking using Seeing Machines’ optimised driver monitoring system technology. The new knowledge is expected to inform novel strategies that contribute to the safe introduction of Automated Vehicles.

Skills and experience

• honours or masters degree and strong research background in vision science, optometry, human factors, psychology or a related discipline
• familiarity with empirical research methods, experimental design, statistical analysis, and eye-, gaze-, head- and body-tracking technologies
• experience in conducting human subjects research, data collection and analysis
• interest in human-machine interaction, driver engagement during automated driving, and the role of eye, gaze, head and body movements in understanding behaviour
• strong analytical thinking, problem-solving abilities, and attention to detail
• excellent communication skills, both written and verbal, for presenting research findings and collaborating with interdisciplinary teams
• ability to work independently and as part of a team, managing multiple tasks and priorities effectively
• it’s advantageous to have a demonstrated publication record (or potential) in peer-reviewed conferences or journals.

Keywords

• human behaviour
• human factors
• eye tracking
• driver monitoring systems
• automated driving

Contact

Contact the supervisor for more information.