CAV research team and vehicle available for new project

In partnership with the Queensland Department of Transport and Main Roads (TMR), Queensland University of Technology’s (QUT) Australian Centre of Robotic Vision are using an ITS-teched-up Renault ZOE as part of the ‘Connected and Highly Automated Driving (CHAD) Pilot’ iMOVE project.

This research is now coming to an end, and so both the QUT research team, and the Renault ZOE are available for additional research projects and opportunities.

The ZOE vehicle, and the research team attached to it, are available to anyone ‘… looking for opportunities to conduct research in automated vehicles through artificial intelligence.’

The vehicle and team have recently finished a three-month, 1,200 kilometre road trip on rural and urban roads in Queensland, all of which will feed into a report summarising local asset readiness to support automated vehicles, with implications for future road infrastructure upgrade strategies.

Now there’s room for the research platform and its team to conduct more work.

What’s available?

The people

A team of artificial intelligence (AI) experts with experience in:

  • Robotisation
  • Machine and deep learning
  • Computer vision, including automatic scene understanding, scene segmentation, and hazard detection
  • Mapping, localisation, and navigation
  • Engineering complex software and hardware

The vehicle

A research platform built on an electric Renault ZOE, to support research in the field of automated vehicles through artificial intelligence. Features of the vehicle include:

  • LiDAR, a high resolution 32-layer solid state device
  • 360° vision camera
  • 1x pair of stereo cameras
  • 1x wide field mono camera
  • Dual band GPS and inertial measurement unit with N-RTK
  • 3x GoPro cameras
  • 2x on-board industrial computers with its own power supply inside the vehicle

Possible research opportunities

  • Studies investigating positioning, mapping and navigation capabilities using modern autonomous vehicle sensors
  • Investigations and evaluations of modern artificial intelligence techniques for detecting pedestrians, cyclists, other vehicles, and vulnerable road users
  • Development of automatic systems for mapping and performing inventory assessment of signs, road markings, and other assets
  • Evaluations of autonomous vehicle sensing and artificial intelligence technologies under extreme Australian conditions including tropical thunderstorms


If this research opportunity is of interest to you, contact Professor Michael Milford via the Australian Centre for Robotic Vision website.

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