The Innovative Manufacturing and Construction Research Centre (IMCRC) will undertake a wide variety of work in the Manufacturing, Construction and product design areas. The work will be contained within 5 programmes:1. Transforming Organisations / Providing individuals, organisations, sectors and regions with the dynamic and innovative capability to thrive in a complex and uncertain future2. High Value Assets / Delivering tools, techniques and designs to maximise the through-life value of high capital cost, long life physical assets3. Healthy & Secure Future / Meeting the growing need for products & environments that promote health, safety and security4. Next Generation Technologies / The future materials, processes, production and information systems to deliver products to the customer5. Customised Products / The design and optimisation techniques to deliver customer specific products.Academics within the Loughborough IMCRC have an internationally leading track record in these areas and a history of strong collaborations to gear IMCRC capabilities with the complementary strengths of external groups.Innovative activities are increasingly distributed across the value chain. The impressive scope of the IMCRC helps us mirror this industrial reality, and enhances knowledge transfer. This advantage of the size and diversity of activities within the IMCRC compared with other smaller UK centres gives the Loughborough IMCRC a leading role in this technology and value chain integration area. Loughborough IMCRC as by far the biggest IMRC (in terms of number of academics, researchers and in funding) can take a more holistic approach and has the skills to generate, identify and integrate expertise from elsewhere as required. Therefore, a large proportion of the Centre funding (approximately 50%) will be allocated to Integration projects or Grand Challenges that cover a spectrum of expertise.The Centre covers a wide range of activities from Concept to Creation.The activities of the Centre will take place in collaboration with the world's best researchers in the UK and abroad. The academics within the Centre will be organised into 3 Research Units so that they can be co-ordinated effectively and can cooperate on Programmes.

The CDT in Advanced Automotive Propulsion Systems will produce the graduates who will bring together the many technical disciplines and skills needed to allow propulsion systems to transition to a more sustainable future. By creating an environment for our graduates to research new propulsion systems and the wider context within which they sit, we will form the individuals who will lead the scientific, technological, and behavioural changes required to effect the transformation of personal mobility. The CDT will become an internationally leading centre for interdisciplinary doctoral training in this critical field for UK industrial strategy. We will train a cohort of 84 high quality research leaders, adding value to academia and the UK automotive industry. There are three key aspects to the success of the CDT - First, a diverse range of graduates will be recruited from across the range of first degrees. Graduates in engineering (mechanical, electrical, chemical), sciences (physics, chemistry, mathematics, biology), management and social sciences will be recruited and introduced to the automotive propulsion sector. The resulting skills mix will allow transformational research to be conducted. Second, the training given to this cohort, re-enforced by a strong group working ethos, will prepare the graduates to make an effective contribution to the industry. This will require training in the current and future methods (technical and commercial) used by the industry. We also need the graduates to have highly developed interpersonal skills and to be experienced in effective group working. Understanding how people and companies work is just as important as an understanding the technology. On the technology side, a broad system level understanding of the technology landscape and the relationship between the big picture and the graduate's own expertise is essential. We have designed a programme that enriches the student's knowledge and experience in these key areas. Third, underpinning all of these attributes will be the graduate's research skills, acquired through the undertaking of an intensive research project within the new £60 million Institute for Advanced Automotive Propulsion Systems (IAAPS), designed from the outset to provide a rich collaborative environment and add value to the UK economy. IAAPS will be equipped with world leading experimental facilities designed for future powertrain systems and provides dedicated space for industry and academia to collaborate to deliver research valued at over £100 million during the lifetime of the CDT. The cohort will contribute to and benefit from this knowledge development, providing opportunities to conduct research at a whole system level. This will address one of the most pressing challenges of our age - the struggle to provide truly sustainable, affordable, connected, zero emissions transport needed by both industrialised and emerging economies. To enable these benefits we request funding for 40 studentships and the infrastructure to provide a world class training environment. The university will enhance this through the funding of an additional 20 studentships and access to research facilities, together valued at £5 million. Cash and in-kind contributions from industrial partners valued at a total of £4.5 million will enhance the student experience, providing 9 fully funded PhD places and 30 half funded places. The research undertaken by the students will be co-created and supervised by our industrial partners. The people and research outputs that from the CDT will be adopted directly by these industrial partners to generate lasting real world impact.

The Foot-LITE project will deliver innovative driver/vehicle interface systems and services to encourage sustained changes to driving styles and behaviours which are safer, reduce congestion, enhance sustainability, help reduce traffic pollution emissions, and reduce other social and environmental impacts. Fundamental research will be used to support the strong industry base in the project through prototype systems development and design, impact assessments and the further development of research tools and processes, including the SRIF/TRW funded instrumented vehicle to deliver a credible evidence-based validation of the system through to real-world operational experiences with user feedback and evaluation. The Foot-LITE system is seen as a tool to encourage and challenge drivers to achieve very real benefits that are already available in the current vehicle fleet but whose benefits cannot be readily maximised without an advisory interface to the driver. The approach has the ultimate choice and control still resting with the individual. This is seen to be crucial to the public and commercial acceptability of Foot-LITE. The aim of the Foot-LITE project is to create a revolutionary driver information system designed to educate and encourage safer and greener driving and longer term behavioural changes. The project consists of four Foot-LITE Work Areas. Effective Project Management is crucial to the delivery and testing of technologies and the assessment of their impacts. This Work Area will be the responsibility of the Lead Partner MIRA who encompasses both commercial as well as research expertise. The second Work Area is Market Reviews and Delivery which is focussed on the development of the concept and identification of product opportunities and system enhancements. The third work area, Technical Implementation, will create innovative applications which influence driver behaviour; this will be led by TRW. The fourth Work Area, which is led by TRG (the Academic Lead Partner), uses a variety of approaches, including simulation and large scale fleet trials, to produce an Impact Assessment of the systems and services and to identify those characteristics which will support applications in a future policy and market environment and deliver a tool that has the potential in instigating a step change in driver behaviour to tackle the twin problems of safety and the environment. The project will undertake all the necessary research and development to produce a prototype system which will be evaluated by fleets of drivers in normal driving conditions. The necessary data collection/data base systems for the vehicle fleet will also be developed so that robust evidence of the effectiveness (or otherwise) of the system will be collected, analysed and published to better the overall knowledge in this area. Additional surveys of other user groups to determine long term effects will be undertaken to better determine market opportunities and implementation strategies to deliver future intelligent vehicles and associated infrastructure. The system to be developed in the project comprises an aftermarket, standalone vehicle interface (although installation during vehicle build will not be excluded) giving moment-to-moment feedback during a drive (similar to SatNav), plus a back office support tool for off-line analysis of journeys and retrospective feedback.

AI applications have become pervasive: from mobile phones and home appliances to stock markets, autonomous cars, robots and drones. As AI takes over a wider range of tasks, we gradually approach the times when security laws, or policies, ultimately akin to Isaac Asimov's "3 laws of robotics" will need to be established for all working AI systems. A homonym of Asimov's first name, the project AISEC (``Artificial Intelligence Secure and Explainable by Construction"), aims to build a sustainable, general purpose, and multidomain methodology and development environment for policy-to-property secure and explainable by construction development of complex AI systems. We will create and deploy a novel framework for documenting, implementing and developing policies for complex deep learning systems by using types as a unifying language to embed security and safety contracts directly into programs that implement AI. The project will produce a development tool AISEC with infrastructure (user interface, verifier, compiler) to cater for different domain experts: from lawyers working with security experts to verification experts and system engineers designing complex AI systems. AISEC will be built, tested and used in collaboration with industrial partners in two key AI application areas: autonomous vehicles and natural language interfaces. AISEC will catalyse a step change from pervasive use of deep learning in AI to pervasive use of methods for deep understanding of intended policies and latent properties of complex AI systems, and deep verification of such systems.

Autonomous vehicles (AVs) must be controlled by software, and such software thus has responsibility for safe vehicle behaviour. It is therefore essential that we rigorously test such software. This is difficult to do for AVs, as they have to respond appropriately to a great diversity of external situations as they go about their missions. It is possible to find faults in an AV software specification by testing its behaviour in a variety of external situations, either in reality or in computer simulation. Such testing may reveal that the specification ignores certain situations (e.g. negotiating a motorway contraflow lane) or defines behaviour that is unsafe in a subset of situations (e.g. its policy for adapting to icy surfaces leads to unsafe speed control in crowded urban environments). This project will test the hypothesis that testing based on coverage of possible external situations ("situation coverage") is an effective means of finding AV specification faults. We will test the hypothesis by creating a tool that generates situations for simulated AVs, both randomly and using heuristic search, and assessing whether higher situation coverage correlates with greater success at revealing seeded specification faults. (For the search, the fitness function will be based on the situation coverage achieved) The project will draw on previous work on test coverage measures, on search-based testing, and on automated scenario generation in training simulations. To assess the effectiveness of the approach, we will use a small but practically-motivated case study of an autonomous ground vehicle, informed by the advice of an advisory panel set up for this project.