Public Funding for Cga Simulation Limited

Quantum Annealing for Transport Optimisation (QATO) is a research project exploring the practical use of quantum for smart mobility solutions for smart cities. It uses a logistics planning tool developed by CGA Simulation for Transport for Greater Manchester, and the Hartree Centre will provide advanced computing facilities for developing software that can run on a D-Wave quantum annealer. The project aims to develop a formulation for quantum annealing, evaluate hybrid classical-quantum solutions, and analyse the hardware and software requirements for solving these industrial problems with quantum computers. The project also aims to demonstrate the commercial benefits of using quantum computing for predictive analytics. By accurately predicting outcomes, decision-makers can save time on deliveries, resulting in significant value. The project aims to use quantum annealing to solve complex optimization problems related to last mile delivery logistics and Mobility as a Service, which classical optimization methods find computationally expensive and time-consuming. This is addressing a need identified by Transport for Greater Manchester in the transport industrial sector that consultation with the Hartree Centre suggests would be a suitable use-case for Quantum Computing. Hartree can apply Quantum Computing approaches to an existing set of software solutions developed by CGA Simulation to increase the accuracy of predication provided by classical optimisation methods such as Agent Based Modelling. During past Research and Development cycles on the project, consultation with Stakeholders such as Liverpool City Region Combined Authority, the Transport Research Laboratory and industry indicates a commercial need for better tools to simulate the impact of new approaches to logistics and travel on future transport patterns. Demonstrating the advantage of quantum in predictive accuracy has a clear industrial application. If decision makers installing infrastructure can accurately predict outcomes they can save time on deliveries,. Small savings on individual journeys add up to vast profits. For problems observed in industry we want to develop a formulation for quantum annealing and evaluate hybrid classical quantum solutions and analyse the hardware and software requirements for solving these industrial problems with quantum computers. Hartree intends to test the Atos Quantum Learning Machine (QLM), with simulated quantum annealing, to develop a software approach that can run on a D-Wave quantum annealer. The problem formulation will be valid for both gate-based quantum computers and quantum annealing. The team intends to evaluate quantum annealing to support the development of different technologies complementary to existing efforts in gate-based quantum computing.

Quantum-Enhanced Sustainable Transportation (QuEST) is a feasibility study that aims to explore how quantum technologies can enhance transport infrastructure utilisation, particularly in the areas of logistics and traffic management. The project seeks to answer the question, "Can quantum help agent-based modelling more accurately represent people and, if it can, what can you do with it?" CGA Simulation, a deep technology company specialising in synthetic and movement data, AI, and digital twins for smart cities, is partnering with the Hartree Centre to explore how Quantum Annealing and Quantum Gates can optimise transport models. The use of Quantum Gates provides potential advantages to improving agent-based models, with a general-purpose computing model enabling answers beyond optimisation. The unique properties of quantum computers, such as superposition and entanglement, allow them to explore states in a complex system in parallel, rather than sequentially, as is the case with classical computing. QuEST has set several goals for Net Zero: * Shifting the Modes People Use: Encourage behavioural change towards low or no-emission transport options. * Making Services Accessible via Sustainable Transport Modes: Align land use planning with transport planning to create compact, walkable communities. * Implementing Infrastructure Change: For example, building charging infrastructure for electric vehicles, dedicated lanes for cycling and buses, and incorporating renewable energy into transport infrastructure. By leveraging the power of quantum computing, QuEST aims to improve network efficiency and support the UK's journey towards achieving net-zero emissions. The project intends to build a quantum-powered solution that can optimise transport routes, reduce traffic congestion, improve reliability, and ultimately, reduce carbon emissions. This initiative is in alignment with the UK government's Net Zero Strategy and National Quantum Technologies Programme. It aims not only to reduce greenhouse gas emissions but also contribute to the UK's leading position in the quantum technology space. Phase 1 of the project will involve working with quantum computing specialists and government bodies to define the practical activities to be delivered in Phase 2, which will probably involve testing principles with current qubit levels and in simulation. The success of this project could set a new standard for transport modelling, helping to pave the way for a cleaner, greener, and more efficient future for transportation in the UK.

Quantum-Enhanced Sustainable Transportation (QuEST) is a feasibility study that aims to explore how quantum technologies can enhance transport infrastructure utilisation, particularly in the areas of logistics and traffic management. The project seeks to answer the question, "Can quantum help agent-based modelling more accurately represent people and, if it can, what can you do with it?" CGA Simulation, a deep technology company specialising in synthetic and movement data, AI, and digital twins for smart cities, is partnering with the Hartree Centre to explore how Quantum Annealing and Quantum Gates can optimise transport models. The use of Quantum Gates provides potential advantages to improving agent-based models, with a general-purpose computing model enabling answers beyond optimisation. The unique properties of quantum computers, such as superposition and entanglement, allow them to explore states in a complex system in parallel, rather than sequentially, as is the case with classical computing. QuEST has set several goals for Net Zero: * Shifting the Modes People Use: Encourage behavioural change towards low or no-emission transport options. * Making Services Accessible via Sustainable Transport Modes: Align land use planning with transport planning to create compact, walkable communities. * Implementing Infrastructure Change: For example, building charging infrastructure for electric vehicles, dedicated lanes for cycling and buses, and incorporating renewable energy into transport infrastructure. By leveraging the power of quantum computing, QuEST aims to improve network efficiency and support the UK's journey towards achieving net-zero emissions. The project intends to build a quantum-powered solution that can optimise transport routes, reduce traffic congestion, improve reliability, and ultimately, reduce carbon emissions. This initiative is in alignment with the UK government's Net Zero Strategy and National Quantum Technologies Programme. It aims not only to reduce greenhouse gas emissions but also contribute to the UK's leading position in the quantum technology space. Phase 1 of the project will involve working with quantum computing specialists and government bodies to define the practical activities to be delivered in Phase 2, which will probably involve testing principles with current qubit levels and in simulation. The success of this project could set a new standard for transport modelling, helping to pave the way for a cleaner, greener, and more efficient future for transportation in the UK.

Created by CGA Simulation and PINTEL, ViCAL integrates simulated data for training the deep learning algorithms behind the AI that analyses video and other sensor data from traffic control systems. Korean based company, PINTEL, has an existing product called Precise Video Analytics Experience (PreVAX), already in use in Korea. PreVAX is an intelligent traffic system that collects real-time data by applying an artificial intelligence-based algorithm to video from traffic control systems. CGA Simulation has developed ALEAD, a simulation platform, previously optimised for training Connected and Autonomous Vehicles (CAVs), planning installation of 5G infrastructure and transport infrastructure planning. ALEAD uses agent based modelling (ABM), which generates 'patterns-of-life' created via the interaction of digital agents (simulated vehicles), which travel from home, to work, to leisure, to retail, in manners informed by real life. This is better for predicting reactions to unprecedented dilemmas, like the introduction of new modes of travel or a pandemic. ViCAL will add the simulation techniques developed by CGA to the AI video platform developed by PINTEL. This newly enhanced product will be a substantial revenue earner for both companies as this innovation adds unique features that distinguish them in the Smart City, V2X, traffic management and CAV markets CGA provides synthetic data that can be combined with PINTEL's technology to predict edge cases. Using CGA's complex simulated environment, PINTEL will generate synthetic camera feeds. The simulation will visualise and remove an accessibility barrier to scenes that in real life would be dangerous or difficult to generate. CGA can create a broad range of simulated environments. During ViCAL we will label simulated data for easier use and transference across platforms, localities, and technologies. Our created cases can then be applied to multiple scenarios and variables. One example would be a car pulling out unexpectedly, but with a range of delays, from very close, to collision, to further away. Ease of access and clear data visualisation means ViCAL can be used by anyone, not just specialists, and shared easily with wider teams and stakeholders. CGA's hazard editor allows users to easily create and share specific driving situations for either pass/fail test of AI or equally to create a range of edge cases, with a customisable capability.

Created by CGA Simulation and PINTEL, ViCAL integrates simulated data for training the deep learning algorithms behind the AI that analyses video and other sensor data from traffic control systems. Korean based company, PINTEL, has an existing product called Precise Video Analytics Experience (PreVAX), already in use in Korea. PreVAX is an intelligent traffic system that collects real-time data by applying an artificial intelligence-based algorithm to video from traffic control systems. CGA Simulation has developed ALEAD, a simulation platform, previously optimised for training Connected and Autonomous Vehicles (CAVs), planning installation of 5G infrastructure and transport infrastructure planning. ALEAD uses agent based modelling (ABM), which generates 'patterns-of-life' created via the interaction of digital agents (simulated vehicles), which travel from home, to work, to leisure, to retail, in manners informed by real life. This is better for predicting reactions to unprecedented dilemmas, like the introduction of new modes of travel or a pandemic. ViCAL will add the simulation techniques developed by CGA to the AI video platform developed by PINTEL. This newly enhanced product will be a substantial revenue earner for both companies as this innovation adds unique features that distinguish them in the Smart City, V2X, traffic management and CAV markets CGA provides synthetic data that can be combined with PINTEL's technology to predict edge cases. Using CGA's complex simulated environment, PINTEL will generate synthetic camera feeds. The simulation will visualise and remove an accessibility barrier to scenes that in real life would be dangerous or difficult to generate. CGA can create a broad range of simulated environments. During ViCAL we will label simulated data for easier use and transference across platforms, localities, and technologies. Our created cases can then be applied to multiple scenarios and variables. One example would be a car pulling out unexpectedly, but with a range of delays, from very close, to collision, to further away. Ease of access and clear data visualisation means ViCAL can be used by anyone, not just specialists, and shared easily with wider teams and stakeholders. CGA's hazard editor allows users to easily create and share specific driving situations for either pass/fail test of AI or equally to create a range of edge cases, with a customisable capability.

CGA is a pioneering simulation, modelling, games studio that combines innovative gaming and emerging technologies. The team can visualise future interactions between humans, smart cities, vehicles, and technologies. During lockdown, CGA created an online learner driving game, Virtual Driving School (VDS) to help learner drivers test their driving skills in a safe, virtual space. 'Turn on the ignition, test your driving mettle against a dynamic, constantly changing set of road hazards, junctions, weather conditions and road companions. Navigate the road and manage standard driving test manoeuvres: parallel parking, roundabouts, and junctions. A virtual driving instructor gives you praise or advice where needed.' Many young people learning to drive during the pandemic couldn't access physical driving lessons so CGA Simulation created a virtual driving game to help young people practice driving skills. However, consultation with driving instructors, Department for Transport, parents, revealed a taste for a more realistic driving test game for learner drivers post lockdown (the UK and abroad.) Many young people don't have a car to practice driving, between lessons, and the cost of driving lessons can be prohibitive. In the US, some parents feel their driving test is not thorough enough and a tool to continue learning with would be welcomed. VDS would also teach bus, lorry, and returner drivers online. CGA created other games & simulation solutions for 'real world' problems, prior to VDS. One project, ALEAD, tests the safety of autonomous vehicle technology with simulated road networks/hazards. The vast, complex array of hazards and road scenarios created for ALEAD are now in VDS. The COVSIM project modelled the spread of Covid 19 around a UK town. Techniques perfected in these projects give VDS the innovative edge, against similar driving test sims, whilst the game also benefits from CGA's games development background with its rich graphics. ALEAD, COVSIM, and now VDS use Agent Based Modelling (ABM). ABM mathematically models interactions between things in CGA's simulations - in this case other cars, driving hazards, weather conditions, junctions, test scenarios. ABM differs from other modelling techniques because it gives more agency to the things being modelled. They interact independently, generating randomised 'pattern of life' interactions. ABM is a perfect modelling tool for designing a learner driver game; players are pitted against multiple distractions at once, during a fun game that's realistic in terms of managing complex driving scenarios.

Public description Around 75 million inhalers are prescribed every year in the UK for the treatment of respiratory diseases like asthma, of which 70% are pressurised metered-dose inhalers (pMDIs) made with single use plastic (polypropylene) 'actuators'. Research shows most used inhalers are disposed of at home, ending up in landfill or low-temperature incineration, despite initiatives to encourage recycling. We are seeking to fundamentally re-design these life-saving devices so that the use of plastic is minimised or eliminated. Our vision is to inspire other industry-led initiatives to design out persistent plastic wastes in the healthcare sector through re-imagining how healthcare equipment can be designed for reuse and valued by users. Human-centred design can be used to support a more sustainable NHS, while generating jobs in the UK green economy.

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CGA simulation is a 3D modelling and simulation studio with experience of applying mathematical modelling processes to 'real world' issues and finding solutions. We have used 'Agent Based Modelling' (ABM) in the simulated towns and cities we have created to test the safety of autonomous vehicle technology and for meddling disaster planning. For this project, we propose to use ABM to model a series of scenarios and individual behaviours (both of individuals and viral spread), with the aim of predicting more specific outcomes than is currently possible using Bayesian modelling (the mathematical modelling that is currently informing the Government's lockdown interventions.) We believe that at this more mature stage of the pandemic ABM modelling can provide much needed specific data, around which to create more targeted interventions and procedures to help move the lockdown agenda forward and plan more cohesively for future pandemics. This is because ABM does not model the entire population as one homogenous, hive minded entity but assumes individual agency of specific people, groups, or in the case of a recent project in which we used ABM, autonomous vehicles. In this previous project, we modelled individual cars as if the owners were visiting friends, going to work or going to the football. In other words, making their own decisions, with individual impacts. This is the approach we would take to modelling behaviours relating to Covid19 spread and it would enable us to tailor targeted interventions and approaches to specific activities like going to the cinema/ football. We could also investigate the role of super spreaders on viral spread and consider how informed interventions could help keep carers safer from infection, whilst caring for the sick. This approach is innovative because it is a much more detail orientated approach to modelling than the current modelling approaches (which tend in the main to be statistical). In conjunction with existing modelling ABEM would help give policy makers one of the tools they need to get society and the economy back up and running before a vaccine is available. Effects of Extension for Impact Funding The extension will allow us time to improve our viral modeling to incorporate post-code data around virus infection rates and the lockdown strategies in-place to improve the fidelity and realism of our simulation. We will be able to explore specific use-cases around arts and sports and the readmission of audiences from both and R&D and commercial standpoint. It will allow us to move the Technology Readiness Level of ABEM from 5/6 to 7/8 through application to real world problems and working alongside potential customers.

"ALEAD is a digital environment that provides Autonomous Vehicles a virtual space to learn in and so save money and time for the companies developing those systems. CGA will produce a testing environment that encompasses sensors, vehicle modelling, and an example autonomous driving system. A candidate autonomous vehicle can be placed into a simulated driving test and will subjected to the most demanding environmental conditions and extreme events; fog, debris in the road and unpredictable vehicles, pedestrians and cyclists. We believe that by creating a highly realistic simulator of an urban environment based on real road networks and containing a variety of simulated driver behaviours it will be possible to produce results that are good enough to train systems used for driverless cars."

Transport and in particular city congestion are huge issues in the 21st Century, with gridlock and pollution costing global economies billions annually. Using data sets around congestion, pollution and road safety it is possible to calculate the real cost of driving, and its impact on the environment, pedestrians, cyclists and other commuters. The project will assess the technical feasibility of a Holistic Transport Costing (HTC) Model of intelligent "pay-per-mile" micro-transactions where vehicles have to pay more to access busy or dangerous roads but are rewarded for taking more environmentally friendly routes, or carrying more passengers. The system would use machine learning (ML) and artificial intelligence(AI) to continually intelligently negotiate road usage costs between autonomous agents on behalf of the city and cars then later autonomous vehicles. The system would be capable of continual evolution to reflect priorities and behaviours in a single city, ‘evolving’ it to become less congested, more efficient, safer and cleaner. The platform would also be adaptable to multi-modal transport journeys, integrating different transport options as data around times, pricing and access became available.

CGA Simulation's ARplan is an innovative approach to providing decision making tools aimed intially at the defence and security sector but with later applications for civilian and commercial industries. It deploys advanced Augmented Reality approaches and leverages emerging platforms with a specific use case based on a need that has been identified in previous research and development by CGA.

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We wish to create a demonstrator of a system that combines emotion sensing, fitness and health monitoring devices with specific anxiety reduction exercises. We will use existing Brain Computer Interfaces (BCI) and Virtual Reality (VR) headsets to reduce anxiety, anger, low mood and self-harm. Young people increasingly carry and rely on smartphones; they have become diary, communication medium and their one essential possession. There exists an opportunity to provide them with a set of easy to use apps aimed at reducing anxiety. Through this research project we will combine biofeedback devices and EEG measurement with an app that provides Psychoeducation and interventions aimed at reducing anxiety. The app will provide mindfulness, relaxation distraction and self-soothing skills.

"We are on the brink of a new waves of digital content based on Virtual Reality (VR), biofeedback and Augmented Reality (AR) devices. We are interested in how VR and biofeedback systems can be used to enhance pain relief and anxiety reduction. We specifically want to expert advice on our current range of content production and R&D around the use of EEG data to understand immersion in VR."

There is a very real need for robotics that can explore environments; there are a number of hazardous locations within which one would like to deploy robots there are practical applications of field robots in the Nuclear, Military and Agricultural sectors. Even when a human controls a robot it is best practice is to have the robot as aware of its environment as possible. Our objective is to create a virtual environment that allows for the development and testing of autonomous agents. Having good simulators allows for quicker and cheaper iterations in the testing of machine learning and robotic artificial intelligence. This opens up a range of training options including training deep learning systems through continual interation.