With the emerging automated tasks in vehicle domain, the development of in-vehicle communications is increasingly important and subjected to new applications. Although both wired and wireless communications have been largely used for supporting diverse applications, most of in-vehicle applications with mission-critical nature, such as brake and engine controls, still prefer dedicated wired networks for reliable and secure transmission. One of the key challenges for data wiring is to facilitate the interconnectivity of increasing devices, e.g., sensors and electronic control units (ECU), effectively creating an in-vehicle network with low response latency, improved reliability and less complexity. The space requirement, weight, and installation costs for these wires can become significant, especially in future vehicles, which are highly sophisticated electronic systems. Given that vehicle components, sensors and ECUs are already connected to power wires, we apply vehicle power lines, which have recently been utilized for in-vehicle communications at the physical layer, to in-vehicle networks in this proposal. Taking mass air flow sensor as an example, it has one power wire and two signal wires, it will be efficient to use power line communications to replace the current signal wires, so 66% of wiring can be reduced. The advancement of vehicular power line communications (VPLC) can provide a very low complexity and free platform for in-vehicle networks, which is ideal for the increasing demand of applications in particular with future vehicles. However, the emerging VPLC is constrained by lack of protocol support, which pose significant challenges to deploy it in practise and ensure mission-critical communications. The following example illustrates the motivation of this proposal. An example for the motivation: A future vehicle is equipped with advanced driver assistance systems (ADAS) which can be connected with multiple sensors and ECUs to provide safety monitoring and control. An important demand of this scenario is that the systems, viewed as sources, should have stable connections with all ECUs, or network destinations. And it is also important that such in-vehicle networks must guarantee ultra-low latency for emerging control services since any seconds of delay may cause fatal accident. Therefore, an effective protocol design is crucial for VPLC to support future applications with mission-critical and high-bandwidth demands. The aim of the project is to improve the reliability of the network and guarantee stringent mission-critical requirements of in-vehicle applications in vehicular power line communications. We will partner with automotive specialists and construct the project to develop innovative and intelligent in-vehicle communication protocols. The solution this proposal is seeking is two fold. One is to pursue new design of intelligent access and congestion control solutions by fully exploring the practical and theoretical analysis, dynamic nature of channels/traffic patterns and self-learning techniques, which provides the theoretic aspect of the proposal. Then, the second step is from the practical aspect, where the proposed power line method shall be able to coexist and cooperate with existing state-of-the-art solutions, and its performance will be validated by practical in-vehicle traffic data. Obviously the two are inseparable not just because the ultimate goal of reliable communication for in-vehicle networks is only possible with the accomplishment of the both two parts, but also because the interaction between the two parts is the key for effective system design.

The Office for Artificial Intelligence (AI) estimates that AI could add £232 billion to the UK economy by 2030, increasing productivity in some industries by 30%. However, to be truly transformational, the integration of AI throughout the global economy requires understanding and trust in the AI systems deployed. The super-human ability for decision-making in new AI systems requires huge volumes of data with thousands of variables, dependencies and uncertainties. Unregulated application of uncertified data-driven AI, limited by data bias and a lack of transparency, brings huge risks and necessitates a community-wide change. AI systems of the future must also be able to learn on-the-job to avoid becoming a high-interest credit card of huge technical debt. There is thus a timely and unmet need for a new theory and framework to enable the creation and analysis of data-driven AI systems that are adaptive, resilient, robust, explainable, and certifiable, with provable and practically relevant performance guarantees. This ambitious fellowship, ARaISE, will deliver a radically new framework for the creation of beneficial data-driven AI systems advancing far beyond classical theories by including certifiable robustness and learning in the problem setting. These new theories will enable a formal understanding of the fundamental limits of large-scale data-driven AI, independent of the application area and learning algorithms. This will enable AI practitioners, through understanding such limitations, to influence policy and prevent incidents before they occur. By connecting different and disparate areas of AI and Machine Learning, working with a world-class team of experts, and by engaging with stakeholders across strategic UK industries and sectors (Healthcare, Manufacturing, Space and Earth Observation, Smart Materials, and Security), ARaISE will create high-value, trustworthy, transformative and responsible AI, capable of reliably 'learning on-the-job' from humans to guarantee capability and trust. Novel human-centric AI, designed to function for the benefit of society, will complement and connect to existing work in the AI research arena, enabling co-development with project partners and focus on strategic industry challenges to ensure real-world relevance is built into research programme and its outputs, facilitating capacity and capability growth. ARaISE will generate gold standard tools for tasks that are currently heavily reliant upon human input and will support long-term global transformation. Impact and knowledge exchange activities, embedded throughout this programme of work, will support uptake of developed novel AI systems and, through leadership and ambassadorial activities, will support a step-change in how AI systems are built and maintained to ensure resilient, robust, adaptive and trustworthy operation. The inclusive research programme has been designed to support the career development of the project team and wider stakeholder group maximising the potential for flexible career paths whilst maintaining flexibility to creatively support the team to develop exciting new technology with real world relevance and guide future AI research. The issues of AI and ethics underpin the programme with responsible research and innovation embedded throughout its activities. Raising public and AI practitioners' awareness, and ultimately influencing policy by active engagement with the UK and AI ethics expertise and policymakers, will ensure that the outcomes are socially beneficial, ethical, trusted and deployable in real world situations. Planned engagement with the ATI, CDTs, partners, and their networks, the development of new partnerships, methodologies and applications, will encourage links between these organisations, build UK expertise, skills and capacity in AI and contribute to realising government investment in UK Societal Challenges and ensure that the UK remains at the forefront of the AI revolution.

The Office for Artificial Intelligence (AI) estimates that AI could add £232 billion to the UK economy by 2030, increasing productivity in some industries by 30%. However, to be truly transformational, the integration of AI throughout the global economy requires understanding and trust in the AI systems deployed. The super-human ability for decision-making in new AI systems requires huge volumes of data with thousands of variables, dependencies and uncertainties. Unregulated application of uncertified data-driven AI, limited by data bias and a lack of transparency, brings huge risks and necessitates a community-wide change. AI systems of the future must also be able to learn on-the-job to avoid becoming a high-interest credit card of huge technical debt. There is thus a timely and unmet need for a new theory and framework to enable the creation and analysis of data-driven AI systems that are adaptive, resilient, robust, explainable, and certifiable, with provable and practically relevant performance guarantees. This ambitious fellowship, ARaISE, will deliver a radically new framework for the creation of beneficial data-driven AI systems advancing far beyond classical theories by including certifiable robustness and learning in the problem setting. These new theories will enable a formal understanding of the fundamental limits of large-scale data-driven AI, independent of the application area and learning algorithms. This will enable AI practitioners, through understanding such limitations, to influence policy and prevent incidents before they occur. By connecting different and disparate areas of AI and Machine Learning, working with a world-class team of experts, and by engaging with stakeholders across strategic UK industries and sectors (Healthcare, Manufacturing, Space and Earth Observation, Smart Materials, and Security), ARaISE will create high-value, trustworthy, transformative and responsible AI, capable of reliably 'learning on-the-job' from humans to guarantee capability and trust. Novel human-centric AI, designed to function for the benefit of society, will complement and connect to existing work in the AI research arena, enabling co-development with project partners and focus on strategic industry challenges to ensure real-world relevance is built into research programme and its outputs, facilitating capacity and capability growth. ARaISE will generate gold standard tools for tasks that are currently heavily reliant upon human input and will support long-term global transformation. Impact and knowledge exchange activities, embedded throughout this programme of work, will support uptake of developed novel AI systems and, through leadership and ambassadorial activities, will support a step-change in how AI systems are built and maintained to ensure resilient, robust, adaptive and trustworthy operation. The inclusive research programme has been designed to support the career development of the project team and wider stakeholder group maximising the potential for flexible career paths whilst maintaining flexibility to creatively support the team to develop exciting new technology with real world relevance and guide future AI research. The issues of AI and ethics underpin the programme with responsible research and innovation embedded throughout its activities. Raising public and AI practitioners' awareness, and ultimately influencing policy by active engagement with the UK and AI ethics expertise and policymakers, will ensure that the outcomes are socially beneficial, ethical, trusted and deployable in real world situations. Planned engagement with the ATI, CDTs, partners, and their networks, the development of new partnerships, methodologies and applications, will encourage links between these organisations, build UK expertise, skills and capacity in AI and contribute to realising government investment in UK Societal Challenges and ensure that the UK remains at the forefront of the AI revolution.