Cooperative Intelligent Transport Systems (C-ITS) enable vehicles to ‘talk’ to other connected vehicles, roadside infrastructure and traffic management centre systems to share relevant safety-related messages for drivers. The driver remains in control of the vehicle, and there is no element of vehicle automation. The Department of Transport and Main Roads (TMR) led the largest Australian on-road pilot of C-ITS, of which CARRS-Q was a partner. This ran for 12 months from September 2020 in Ipswich, known as the Ipswich Connected Vehicle Pilot (ICVP). Around 350 public participants’ vehicles were retrofitted with this equipment, and received safety information and warnings from six use cases during their drives. The ICVP was delivered by TMR, supported by Motor Accident Insurance Commission, CARRS-Q, iMOVE Australia, Telstra, Ipswich City Council and Department of Infrastructure, Transport, Regional Development and Communications.
One of the objectives of the ICVP was to validate the safety impacts and user perceptions of C-ITS and pilot use cases (types of safety warnings). CARRS-Q contributed to this research through establishing and conducting a standardised research methodology for user perceptions (including questionnaires, interviews and focus groups), and contributing to the analysis of objective safety evaluation data. CARRS-Q also managed the participants throughout the pilot. The data and research findings will be used by transport agencies – local, state and federal – to support the investment of infrastructure, both digital and physical, that supports emerging C-ITS. In the future, these data and results could also be used by others to perform, compare or supplement the pilot findings (subject to relevant approvals).
CARRS-Q Research & Outcomes
To understand the safety impacts and user perceptions of C-ITS, CARRS-Q:
- Researched the usability and ergonomic design of the human machine interface (HMI) for drivers to receive safety-related messages.
- Conducted a series of questionnaires administered at four different points during the pilot to understand the participants’ experiences of the C-ITS over time, and particularly after experimental changes, for example, a change from seeing warnings, to no longer seeing warnings (that is, Treatment condition to baseline condition).
- Interviewed over 50 participants at four different points during the pilot to further explore trends that arose in the questionnaires.
- Conducted around 10 focus groups with approximately 50 participants at the end of the pilot to identify whether there were changes after the equipment was removed from their vehicles, and the participants had time to reflect.
- Supplemented the ICVP with simulator studies with a different sample of drivers to those in the ICVP, to test use cases that were not available in the ICVP.
Study Findings
Subjective Evaluation findings
Media
Queensland wraps up connected vehicle road safety pilot
This Connected Driving System Helps Motorists See Motorcyclists
Drivers wanted for connected vehicle study
Testing new tech in cars of the future led by QUT research
CAVI 500-vehicle C-ITS Field Operational Test
Vehicle trial to boost road safety
Funding / Grants
- iMOVE CRC, Queensland Government (2017 - 2021)
Team
Publications
- Elhenawy, Mohammed, Bond, Andy, Rakotonirainy, Andry, Barth, M, Sanchez-Medina, J (2018) C-ITS safety evaluation methodology based on cooperative awareness messages Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp.2471-2477.
- Komol, Md Mostafizur Rahman, Elhenawy, Mohammed, Masoud, Mahmoud, Glaser, Sebastien, Rakotonirainy, Andry, Wood, Merle, Alderson, David (2021) Deep Transfer Learning Based Intersection Trajectory Movement Classification for Big Connected Vehicle Data IEEE Access, 9, pp.141830-141842.
- Elrose, Francine, Lewis, Ioni, Hassan, Heidi, Murray, Clare (2022) Insights into the effectiveness of messaging promoting intentions to use connected vehicle technology Transportation Research Part F: Traffic Psychology and Behaviour, 88, pp.155-167.
- Komol, Md Mostafizur Rahman, Elhenawy, Mohammed, Masoud, Mahmoud, Rakotonirainy, Andry, Glaser, Sebastien, Wood, Merle, Alderson, David (2023) Deep RNN Based Prediction of Driver’s Intended Movements at Intersection Using Cooperative Awareness Messages IEEE Transactions on Intelligent Transportation Systems.
- Rodwell, David, Ho, Bonnie, Pascale, Michael T., Elrose, Francine, Neary, Alexandra, Lewis, Ioni (2023) In their own words: A qualitative study of users’ acceptance of connected vehicle technology after nine months of experience with the technology Transportation Research Part F: Traffic Psychology and Behaviour, 97, pp.73-93.
