Study level

  • PhD

Faculty/School

Topic status

We're looking for students to study this topic.

Research centre

Supervisors

Professor Sebastien Glaser
Position
Professor, Intelligent Transport Systems
Division / Faculty
Faculty of Health
Dr Xiaomeng Li
Position
Research Fellow
Division / Faculty
Faculty of Health
Professor Andry Rakotonirainy
Position
Principal Research Fellow
Division / Faculty
Faculty of Health
Associate Professor Ronald Schroeter
Position
Principal Research Fellow
Division / Faculty
Faculty of Health

External supervisors

  • Prof Patricia Delhomme, Gustave Eiffel University

Overview

CARRS-Q has developed a strong expertise in AV and ADAS, and operate an Automated Vehicle for its research on test track and open roads.

We have collected more than 12,000km of sensor data in various Australian conditions, and we are progressing quickly to a broader understanding of safe operation of AV technologies on our roads. We are looking for PhD candidates to progress further on these topics.

PhD positions are available for highly motivated domestic and/or international students to work on a project that aims to improve the interpretability of Automated Vehicle decisions to human users.

Automated Vehicles are developing rapidly and promise to improve road safety. These vehicles are equipped with advanced Artificial Intelligence (AI) techniques to perceive, learn, decide and act. However, the ability of the AI algorithms to explain their decisions to humans is critically poor.

This project seeks an innovative approach to develop a human-centric eXplainable Automated Vehicle Framework to improve user comprehension of the rationale underlying vehicle decisions.

Research activities

This project will involve a multidisciplinary research programme that generates knowledge in the fields of Cognitive Psychology/Applied Social Psychology or Experimental Applied Psychology, Computer Science and Human-Computer-Interaction. You will conduct lab and field experimental research under the supervision of the project chief investigators. Research activity will include:

  • field tests with real-world automated driving*Advanced driving simulator studies
  • in-vehicle Human-Machine-Interface design
  • development of explainable deep learning models
  • qualitative (e.g. interviews) and quantitative (e.g. vehicle dynamics, human behaviours) data collection and analysis.


This project is funded by the Australian Research Council as part of the Discovery Program. It will be primarily based in the Centre for Accident Research and Road Safety-Queensland (CARRS-Q) at the Kelvin Grove campus of Queensland University of Technology in collaboration with Gustave Eiffel University (France). CARRS-Q provides a highly collaborative environment with state-of-the-art research equipment and the opportunity to work with a vibrant community of students and research fellows.

Outcomes

This project will provide a consistent framework for the future design of human-centric explanation of AI. It will help ensure that future transport policies and road safety initiative spending throughout Australia is grounded in real-world data, and is future proof, bringing significant economic and social benefits for the Australian community.

Skills and experience

You must have:

  • good communication skills, motivation and the ability to work as part of a team
  • the ability to meet the eligibility criteria for admission into the QUT-PhD program
  • strong interests in decision-making process, emotions, and experimental research
  • a good level of statistical skills to analyse the data of the studies he/she will program
  • a background in social cognitive psychology or experimental applied psychology, or a background in machine learning, artificial intelligence or human-computer interaction.


Optional:

  • interface design experience
  • 3D modelling experience in Unity
  • programming skills and machine learning experiences.

Scholarships

You may be eligible to apply for a research scholarship.

Explore our research scholarships

Keywords

Contact

Contact the supervisor for more information.