Enabling a Novel Evaluation Continuum for Connected & Autonomous Vehicles (CAV)

The global Connected & Autonomous Vehicles (CAV) industry is estimated to be worth over £50billion (by 2035), with the UK CAV industry being projected over £3billion. Additionally, the UK Government's Industrial Strategy aims to bring fully autonomous cars without a human operator on the UK roads by 2021, one of the first countries in the world to achieve this. However, in order to realise this vision and the market potential, safe introduction of CAV is necessary, requiring significant research to overcome diverse barriers (technological, legislative and societal) associated with public deployment of CAV. While prototype CAV technologies have existed for some time now, ensuring the safety level of these technologies has been proving to be a hindrance to the commercialization of CAV technologies. 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 CAV are safer than human drivers, they will need to be driven for more than 11 billion miles. The vision for this fellowship is to support positioning the UK as the world leader in CAV research and innovation for a long lasting societal and economic benefit. 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. At WMG, University of Warwick, UK, we have created a concept of the "evaluation continuum" for CAV, which involves using various environment like digital world, simulated environment, test track testing and real-world for testing. There are two aspects which are common to each of the evaluation continuum environments and also the focus areas of the fellowship research 1) Test Scenarios (input to the environment) 2) Safety Evidence (output of the environment). On the scenarios theme, while the 11-billion-miles requirement has garnered a lot of publicity, the focus needs to be on what happens in those miles (i.e., smart miles which expose failures in CAV) and not on the number of miles themselves. As a part of this fellowship, three approaches will be explored to identify these smart miles. These include 1) using Machine Learning (ML) based methods including Bayesian Optimisation to create test cases for test scenarios, 2) Safety Of The Intended Functionality (SOTIF) (Innovative safety analysis of CAV) based test scenarios and 3) translating real-world data into executable test scenarios for a simulation tool. All these approaches will together contribute to the creation of a UK's National CAV Test Scenario Database, which will help coordinate the research work in various CAV projects part-funded by the UK Government and will prevent "reinventing of the wheel" in each of the projects with respect to test scenario identification. Industry trends in CAV suggest the widespread adoption of machine learning (ML) in the autonomous control systems. ML-systems by their structure are non-deterministic in nature, making the CAV system highly opaque in nature. Therefore, it is difficult to identify the reason of a failure in such ML-based systems and take the corrective measures. Thus, on the safety strand, fundamental research will be conducted as a part of this fellowship to explore how to make ML-based systems interpretable enabling us to explain the results. This is an essential requirement for safety of CAV due to the critical nature of their deployment and the mitigation of risk. In addition, the fellowship will also benefit from the fellow's first-hand experience as the UK's technical representative on the ISO standards committees, providing further insight and a clear route to deliver impact from the proposed research through the development of international standards, while also ensuring that the UK becomes a world leader in this area.