Intelligent Transport Systems

Our research investigates road safety benefits and human factors aspects of future Intelligent Transport Systems technologies. This includes the design, use and assessment of information systems and in-vehicular technologies, as well as their simulation, real-world impact and cost-benefit analysis.

We use the Safe System approach to understand the human factors challenges arising from the introduction of new ITS, such as Connected Automated Vehicles (CAV), e-bikes and mobility services, by focusing on the pattern of cooperation between road users. We capitalise on research progress in the Internet of Things (IoT), automated/connected cars, Deep learning (AI), person’s data acquisition (Quantified Self) and ubiquitous computing to reduce behavioral uncertainties in transport.

Team members have expertise in computer science, civil engineering, psychology, sociology, HMI/HCI, design, mathematics and statistics.

Our research benefits from access to high quality infrastructure, including an Advanced Driving Simulator and a Level 4 (SAE) Cooperative Automated Vehicle, and data associated with naturalistic driving studies and the Cooperative Intelligent Transport Systems (C-ITS) Pilot, plus a range of analysis software, vehicle sensors and physiological devices. The team collaborates with prestigious road safety centres, including IFSTTAR (LIVIC, LPC), UMTRI, Cornell University, Ludwig-Maximilian University of Munich (LMU), Technical University of Munich (TUM), and Tongji University.

Contact:


Professor Andry Rakotonirainy: r.andry@qut.edu.au or +61 7 3138 4683.