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Learning-based control strategies for personalised and socially aware automated vehicles

Project summary

Program
PhD
Location
St Lucia
Research area
Engineering

Project description

Automated vehicles need to be both safe and trusted by the public. A key challenge is enabling automated vehicles to adapt their behaviour to individual passenger preferences while interacting well with other road users in mixed traffic.
 
This project focuses on developing learning-based control and artificial intelligence methods that combine control theory with data-driven techniques to address this challenge. The approach draws on perspectives from robotics and transport engineering to ensure the resulting strategies are practical and scalable for real-world deployment.
 
Through modelling, simulation and driving simulator experiments, the work aims to advance safe, trustworthy control strategies for automated vehicles operating in mixed traffic.

Research environment

You will work within The University of Queensland (UQ), a member of Australia's Group of Eight and consistently ranked among the world's top 50 universities. 
 
You will be based in the Transport Engineering research group in the School of Civil Engineering, which is one of Australia's leading centres for transport and intelligent transportation systems research.
 
The research environment offers access to a range of specialist facilities and expertise, including:
  • Multi-Modal Connected & Automated Transport Lab (M2CAT): established by Professor Zuduo Zheng, the lab includes a Tesla Model Y with Full Self-Driving (Supervised), a desktop driving simulator, an e-scooter VR simulator, and a dedicated simulation platform for modelling mixed traffic of human-driven, connected and automated vehicles.
  • Transport Engineering research group: led by Professor Mark Hickman (Chair of Transport Engineering) with other group members including Professor Zuduo Zheng, Associate Professor Jiwon Kim, and Associate Professor Mehmet Yildirimoglu, and strong links to industry and government partners including iMOVE CRC and the Queensland Department of Transport and Main Roads.
  • High-Performance Computing (HPC): access to UQ's Bunya supercomputer, including GPU-accelerated nodes suited to training and evaluating deep learning and reinforcement learning models.

Scholarship

This is an Fellowship support scheme scholarship project that aligns with a recently awarded Australian Government grant.

The scholarship includes:

  • living stipend of $39,220 per annum tax free (2026 rate), indexed annually
  • your tuition fees covered

Learn more about the Fellowship support scheme scholarship.

Supervisor

Preferred educational background

Your application will be assessed on a competitive basis.

We take into account your:

  • previous academic record
  • publication record
  • honours and awards
  • employment history.

A working knowledge of programming (Python, MATLAB or C++) and/or simulation tools for vehicle dynamics or traffic systems would be of benefit to someone working on this project.

You will demonstrate academic achievement in the fields of control theory, robotics, artificial intelligence/machine learning, transport engineering, or a related engineering discipline (electrical, mechanical, civil, or software engineering) and the potential for scholastic success.

A background or knowledge of machine learning-based control is highly desirable.

How to apply

This project requires candidates to commence no later than Research Quarter 4, 2027. You can start in an earlier research quarter.

You must submit an expression of interest (EOI) by the closing date for the research quarter (RQ) you want to start in:

  • RQ 1 (January): 30 September
  • RQ 2 (April): 31 December
  • RQ 3 (July): 31 March
  • RQ 4 (October): 30 June.
  • RQ 1 (January): 30 June
  • RQ 2 (April): 30 September
  • RQ 3 (July): 31 December
  • RQ 4 (October): 31 March.

Before you apply

  1. Check your eligibility for the Doctor of Philosophy (PhD).
  2. Prepare your documentation.
  3. If you have any questions about whether the project is suitable for your research interests, contact Dr Weiming Zhao (weiming.zhao@uq.edu.au).

When you apply

To apply, submit an expression of interest (EOI) for the program. You don't need to apply separately for the project or scholarship. How to submit an EOI

In your EOI, complete the 'Scholarship/Sponsorship' section with the following details:

  1. Are you applying for an advertised project: 'Yes'
  2. Project: 'Fellowship project scholarship'
  3. Scholarship Code Listed in the Advertisement: AUTOMATED-ZHAO
  4. Link to Scholarship Advertisement: https://study.uq.edu.au/study-options/phd-mphil-professional-doctorate/projects/learning-based-control-strategies-personalised-and-socially-aware-automated-vehicles

Submit an EOI

This project is not available to international students

Do you have questions?

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