The emerging development of automated driving demands a mutual understanding and a smooth coordination between human driver and vehicle controller, so as to avoid conflict and mismatch in demands, and instead achieve desirable driving performance, smooth and swift transitions which enhance driving safety during complex operating scenarios. However, such driver-vehicle collaboration during automated driving will impact on the driver's attention and cognition and it is important to consider these effects in order to prevent any negative impact on driving. This project aims to achieve a safe engagement and smooth and swift control-authority shift between the driver and the vehicle controller during adaptive automated driving. To this aim, we will first conduct a comprehensive study of driver attention and cognitive control characteristics when interacting with the vehicle controller. An optimal control authority shifting system which considers driver cognition will then be systematically developed and validated. This cross-disciplinary research challenge will be addressed using a unique combination of researchers from engineering, cognitive neuroscience and human factors. The research will not only contribute to the cutting-edge technology innovations in automated driving, but will also result in a major advance in the science of human attention and cognitive control when interacting with automation.

There are two ongoing revolutions in modern automotive industry. The first is the development of autonomous transportation systems which is leading to greatly improved safety, traffic economy, environment and passenger comfort. The second on is the development of advanced propulsion systems, envisaging reduced fuel consumption and exhaust emissions. The intersection of the autonomous transportation systems and advanced propulsion systems is the future trend, which has been revealed in the strategic partnerships between automakers and IT companies. However, there is a big challenge that the environment information collected by autonomous vehicles is poorly used in propulsion systems, especially when vehicles run in fast changing conditions. Only few examples of using environment data to optimise energy efficiency can be found, although the potential benefits in fuel reduction and mission flexibility are great. This project aims to tackle this challenge by developing a cloud-aided learning framework to merge the two themes as integrity. To establish awareness of the environment, the onboard sensors of autonomous vehicles including cameras, light detecting and ranging (LiDAR), ultrasonic, and radar are used to perceive the environment over short distances. The GPS and intelligent transportation systems are used to perceive the environment at a further distance. The combined information enables the autonomous vehicle to establish a comprehensive model of the external environment. Using advanced machine learning algorithms (e.g., dynamic Bayesian network), the environment information can be used to update the propulsion system model in real time. Combining the real-time updated model with dynamic optimisation methods (e.g., adaptive model predictive control), the optimal actions of the propulsion system can be obtained in s systematic way. Employing high performance computing resources on cloud, the computational intensive modelling and optimisation tasks can be cost-effectively addressed. Aligning with the EPSRC Innovation Fellowship priority area of "Robotics and Artificial Intelligence Systems" through its focus on efficient transport, a cloud-in-the-loop testing platform will be built. This framework focuses on sensing, modelling, control, optimisation and computing of energy efficient autonomous vehicles. In a long term vision, the framework can be generalised from a single vehicle to connected and autonomous for further economic benefits.

There are two ongoing revolutions in modern automotive industry. The first is the development of autonomous transportation systems which is leading to greatly improved safety, traffic economy, environment and passenger comfort. The second on is the development of advanced propulsion systems, envisaging reduced fuel consumption and exhaust emissions. The intersection of the autonomous transportation systems and advanced propulsion systems is the future trend, which has been revealed in the strategic partnerships between automakers and IT companies. However, there is a big challenge that the environment information collected by autonomous vehicles is poorly used in propulsion systems, especially when vehicles run in fast changing conditions. Only few examples of using environment data to optimise energy efficiency can be found, although the potential benefits in fuel reduction and mission flexibility are great. This project aims to tackle this challenge by developing a cloud-aided learning framework to merge the two themes as integrity. To establish awareness of the environment, the onboard sensors of autonomous vehicles including cameras, light detecting and ranging (LiDAR), ultrasonic, and radar are used to perceive the environment over short distances. The GPS and intelligent transportation systems are used to perceive the environment at a further distance. The combined information enables the autonomous vehicle to establish a comprehensive model of the external environment. Using advanced machine learning algorithms (e.g., dynamic Bayesian network), the environment information can be used to update the propulsion system model in real time. Combining the real-time updated model with dynamic optimisation methods (e.g., adaptive model predictive control), the optimal actions of the propulsion system can be obtained in s systematic way. Employing high performance computing resources on cloud, the computational intensive modelling and optimisation tasks can be cost-effectively addressed. Aligning with the EPSRC Innovation Fellowship priority area of "Robotics and Artificial Intelligence Systems" through its focus on efficient transport, a cloud-in-the-loop testing platform will be built. This framework focuses on sensing, modelling, control, optimisation and computing of energy efficient autonomous vehicles. In a long term vision, the framework can be generalised from a single vehicle to connected and autonomous for further economic benefits.

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.

Control systems play a central role in automation and modern industry. By using feedback, control systems are designed to regulate system outputs around a reference setpoint or track a reference trajectory, despite disturbances and variations due to its operational environment; for example, an aircraft follows a specified speed and altitude despite gust, wind and changes typical of an aircraft (e.g. number of passengers). Performance specifications for control systems are typically defined to quantify their behaviours in terms of the defined reference. The analysis and design tools/methods across the entire area of control engineering are mainly developed and built upon these specifications. With the demand for ever increasing levels of automation, we are moving towards goal-oriented operation, where what a system needs to achieve is specified at a high level (e.g. in terms of economic or mission requirements), rather than how it is to be achieved (e.g. through defining a setpoint or trajectory). The goal-oriented operation improves the operation process, offers new opportunities for technological advances and reduces operational costs. A Goal-Oriented Control System (GOCS) is essential to enable this type of operation. Another significant difference in GOCS is the influence of disturbance/uncertainties on design specifications. Traditionally a central role of a control system is to attenuate the influence of external disturbance/uncertainties because they always divert the system away from a reference. However, certain disturbance may be good for the system operation in terms of a high-level goal. For example, changes in raw material or environment may make a chemical process more profitable so they should not be rejected; a favourable change of wind condition should be exploited, rather than rejected in an emergency landing as it helps the aircraft to glide longer, providing a larger safe margin in performing forced landing. In current control system design, constraints (e.g. physical, operational, legal) are often considered explicitly or implicitly through generating appropriate references. However, as the system specifications will be defined at a high level in goal-oriented operation and the control system must work out how to achieve the specifications/goals, constraints must be specified very carefully in order to meet safety and other requirements. Some constraints are difficult to represent in the specifications or to take into account constraints within current control design. This Fellowship will develop analysis and design tools for goal-oriented control systems, build up a unified framework for the next generation of control systems in performance specifications, constraint representation and problem formulating, investigate its performance and other properties in the presence of disturbance, uncertainties and constraints, and benchmark their applications. Temporal logic will be used to represent high level specifications and a wide range of complex constraints. By combining temporal logic with rich representation of dynamic systems in control engineering, it aims to develop a complete new analysis and design framework for GOCS that involves both rich dynamics and complex specifications in performance and constraints, and to provide analysis tools for the propagation of information errors, environmental disturbances and dynamic uncertainties into the high level performance specifications and fulfilment of constraints, and the interplay of these terms with a feedback strategy and controlled dynamics. The Fellowship programme is developed and built upon the Fellow's internationally leading work in disturbance observer-based control, model predictive control and autonomous vehicles in the last 25 years. The successful completion of the Fellowship will open a new field of goal-oriented control systems, transform control engineering and unlock the potential of moving from low levels to high levels of automation