With the advent of Highly Automated or Autonomous Vehicles (AV) on our roads in just a few years’ time, the insurance industry needs to have a thorough understanding of the technologies involved, and their impact on mobility and insurance business models into the future.
As a first step, by building up an autonomous driving prototype based on an everyday car, a deeper insight into the architecture, peculiarities and risks of Autonomous Vehicles will be gained.
Further, research on behavioural economics, the interaction with other traffic participants, and the robustness of implemented technologies and algorithms will result in the development of first risk assessment methods from an insurance point of view.
- University of New South Wales (UNSW Sydney)
Understanding autonomous vehicles (AVs) from a technical and policy perspective is a must-have, not only for solution providers, but also relevant agencies, such as government, insurance companies, and transport authorities.
Based on research at UNSW and the engineering capabilities of IAG, this project will engineer an AV R&D environment and carry out research studies to examine the potential impacts of AVs to Australia, and therefore contribute to the world by taking the results to a global scale.
By having a modular, decoupled, and highly-extensible AV environment, some other insights can be obtained apart from the research described in this proposal, such as:
- the examination of human factors in AVs
- the risk assessment of AVs and some potential industry standards with respect to the safety check of AVs
The generated assets and outputs of this project will form the base of further iMOVE CRC projects for even deeper investigation of the impacts of autonomous vehicles, as well as cybersecurity topics related to AVs, and the impact of adverse environmental conditions on sensor performance, and therefore AV behaviour and safety of operation.
- Buildup of an operational autonomous car to develop infrastructure for future research
- Gain first-hand experience in understanding potential sources of risk in AVs.
- Identify sources of risk from AV sensor systems
- Identify sources of risk from AV algorithmic systems
- Inform development of testing methods
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