Autonomous Vehicles

QUT leads research and policy development in autonomous vehicles across the transport, defence, mining and agricultural sectors.

We particularly specialize in technological capabilities around positioning systems for GPS- and communication-denied environments, visual intelligence systems including advanced terrain traversability analysis and full autonomy stack development.


HD maps for automated driving

13/07/2020 - 14/12/2020

A review on map creation, monitoring and maintenance to facilitate automated driving including government's potential role.

AgBot II

Funded by Queensland Department of Agriculture and Fisheries

Meet AgBot II, a new generation tool for robotic site-specific crop and weed management.

Mini Autonomous Vehicles

03/01/2017 - Ongoing

QUT has led development of a fleet of miniature autonomous vehicles as part of the Australian Centre for Robotic Vision.

Automation-enabling positioning for underground mining

Advance Queensland Innovation Partnership, Caterpillar, Mining3

The project offers potential solutions to the challenge of accurately estimating the position of vehicles in underground mining environments.

[COMPLETED] How automated vehicles will interact with road infrastructure

QUT researchers who took an Artificial Intelligence (AI) system on a south-east Queensland road trip have identified the key role high-definition annotated maps will likely play in autonomous driving on Australian roads.

Rheinmetall Defence Australia: Advanced Terrain Detection (ATD)

We're solving complex developmental problems related to autonomous driving, to help deliver game-changing autonomous vehicle technologies in Australia.

AUSMURI: Neuro-Autonomy: Neuroscience-Inspired Perception, Navigation, and Spatial Awareness for Autonomous Robots

09/01/2019 - 09/01/2024

State-of-the-art Autonomous Vehicles (AVs) are trained for specific, well-structured environments and, in general, would fail to operate in unstructured or novel settings. This project aims at developing next-generation AVs, capable of learning and on-the-fly adaptation to environmental novelty.