"This fellowship will develop pioneering testing methodologies and standards to enable robust and safe use of CAV with a focus on creating both fundamental knowledge and applied research methods and tools using various environments including the digital world via simulated environments, test track testing and real-world testing."
The vision for CAV is coupled with the challenge of testing and safety analysis as it needs complex solutions to include interactions between a large number of variables and the environment. It is suggested that in order to prove that CAVs are safer than human drivers, they will need to be driven for more than 11 billion miles. Fundamental research will be conducted to explore how to make Machine Learning-based systems interpretable, enabling us to explain results. This is an essential requirement for the safety of CAV due to the critical nature of their deployment and the mitigation of risk.
As well as informing policy and legislative frameworks for the UK on testing, evaluation and deployment of autonomous vehicles through research, the Fellowship aims to stimulate learning from the research into all levels of engineering education, including the WMG Academies, the new MSc in Smart, Connected and Autonomous Vehicles launched in Sep 2018, and the Master's level Technical Accreditation Scheme for industry.