Real time risk mapping of agricultural catchments is time consuming and for the purposes of flood risk mapping (particularly after an event) is made difficult due to flooding or soil conditions. The challenge is to map the risk factors associated with flooding or agricultural diffuse pollution in a non-invasive - less labour intensive way using an airborne system with built-in intelligence . The added benefit is that the land surface is not damaged at a critical time, and that mitigation strategies can be rapidly deployed.

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 UK Foundation Industries (Glass, Metals, Cement, Ceramics, Bulk Chemicals and Paper), are worth £52B to the UK economy, produce 28 million tonnes of materials per year and account for 10% of the UK total CO2 emissions. These industries face major challenges in meeting the UK Government's legal commitment for 2050 to reduce net greenhouse gas emissions by 100% relative to 1990, as they are characterised by highly intensive use of both resources and energy. While all sectors are implementing steps to increase recycling and reuse of materials, they are at varying stages of creating road maps to zero carbon. These roadmaps depend on the switching of the national grid to low carbon energy supply based on green electricity and sustainable sources of hydrogen and biofuels along with carbon capture and storage solutions. Achievement of net zero carbon will also require innovations in product and process design and the adoption of circular economy and industrial symbiosis approaches via new business models, enabled as necessary by changes in national and global policies. Additionally, the Governments £4.7B National Productivity Investment Fund recognises the need for raising UK productivity across all industrial sectors to match best international standards. High levels of productivity coupled with low carbon strategies will contribute to creating a transformation of the foundation industry landscape, encouraging strategic retention of the industries in the UK, resilience against global supply chain shocks such as Covid-19 and providing quality jobs and a clean environment. The strategic importance of these industries to UK productivity and environmental targets has been acknowledged by the provision of £66M from the Industrial Strategy Challenge Fund to support a Transforming Foundation Industries cluster. Recognising that the individual sectors will face many common problems and opportunities, the TFI cluster will serve to encourage and facilitate a cross sectoral approach to the major challenges faced. As part of this funding an Academic Network Plus will be formed, to ensure the establishment of a vibrant community of academics and industry that can organise and collaborate to build disciplinary and interdisciplinary solutions to the major challenges. The Network Plus will serve as a basis to ensure that the ongoing £66M TFI programme is rolled out, underpinned by a portfolio of the best available UK interdisciplinary science, and informed by cross sectoral industry participation. Our network, initially drawn from eight UK universities, and over 30 industrial organisations will support the UK foundation industries by engaging with academia, industry, policy makers and non-governmental organisations to identify and address challenges and opportunities to co-develop and adopt transformative technologies, business models and working practices. Our expertise covers all six foundation industries, with relevant knowledge of materials, engineering, bulk chemicals, manufacturing, physical sciences, informatics, economics, circular economy and the arts & humanities. Through our programme of mini-projects, workshops, knowledge transfer, outreach and dissemination, the Network will test concepts and guide the development of innovative outcomes to help transform UK foundation industries. The Network will be inclusive across disciplines, embracing best practice in Knowledge Exchange from the Arts and Humanities, and inclusive of the whole UK academic and industrial communities, enabling access for all to the activity programme and project fund opportunities.