**An exciting project focussed on developing and maturing the simulation, modelling and physical testing supply chain for UK-centric CAM perception sensor and systems developers.** Sim4CAMSens will build a UK supply chain that will advance the quality of modelling, simulation, test and characterisation capability in the UK to accelerate and de-risk the design, development, validation and usage of perception systems sensors and algorithms for automated driving functions. The project will create clear links between the tools, methodologies, standards and safety cases. With state-of-the-art modelling and simulation environments, Sim4CAMSens will deliver much needed synthetic training data of suitable quality for the training of AI systems used in autonomous vehicles. We are bringing together an expert, world-class consortium of partners to support the development of an emerging UK-based perception sensors and systems industry by accelerating the development of perception sensors for assisted and automated driving functions There is a nascent perception sensor design and development industry in the UK, with the potential to challenge global innovation with the right investment and coordination. In parallel, the UK has developed outstanding modelling, simulation and testing capabilities for automated vehicle systems through previous Innovate UK supported projects as well as continued industry investment. We see a major opportunity for the UK by bringing these two worlds together to create a globally competitive sensor design, development, modelling, simulation and testing supply chain. The supply chain will focus on the specific use case of the development and testing of three sensor technologies within the virtual and physical test framework: 1\. RADAR (Oxford RF, Claytex), 2\. Camera technology (rFpro), 3\. LiDAR development (CSAC, Claytex).

Autonomous Vehicle (AV) pilot studies have demonstrated that AVs can handle "normal" driving; 99% of our day-to-day driving experience. However it is proving harder than expected to train AVs to deal with the multitudinous unusual events that can happen on the road, known as edge-cases. When an AV fails to understand an edge-case, driving behaviour becomes unreliable and unsafe, with examples include over-braking causing rear-end collisions, lane-keeping failures, and in the worst cases fatal high-speed collisions. It is essential for AVs to be able to handle edge-cases safely and reliably for public and consumer acceptance of AVs on the road, for the technology developers and automotive manufacturers to meet upcoming regulatory standards and so that the automotive industry can realise a return on the considerable investments made to date in AV technology. Training AV perception systems for edge-cases is challenging; the volume of real-life training data is limited. Simulation and synthetic data are widely recognised as being needed but currently available synthetic training data is not sufficiently sensor-real for simulation-trained AI to be successful in improving perception and decision-making on edge cases. DeepSafe brings together leaders in the simulation supply chain to resolve synthetic data issues inhibiting successful training for AV perception using simulation.

This project will combine risk analytic methods with high-fidelity physics-realistic simulations to rigorously assess the first- and third-party risk associated with Unmanned Aircraft Systems (UAS) and Urban Air Mobility (UAM). The project-level outputs from phase 2 will be an online risk analytics tool and a realistic physics-based simulation environment for testing autonomous systems. These two technologies are enablers for the expansion of the UAS and UAM sectors.

"**Need.** Simulation will be a vital component in the Connected Autonomous Vehicle (CAV) safety-case, enabling the validation and certification of autonomous Vehicle Control Systems (VCS). This is essential in cities where the density and diversity of other agents, including road-users, pedestrians, infrastructure and signage, as well as intermodal-connectivity, present a limitless set of potential scenarios that must be safely negotiated, either individually or collectively. No existing simulation system enables a full safety-case approach to certification within an overarching risk architecture based on clearly-defined key performance indicators and areas. Limitations on incumbent physical simulators and models fail to capture: * High complexity of edge-case scenarios in these contexts, which are difficult to predict and occur with low frequency compared to the majority of everyday driving scenarios * Unpredictable responses of other road users and pedestrians to CAVs * Rapidly changing technology and regulatory landscapes * Relevant socioeconomic and human factors Fundamentally, too many individual types and levels of risk exist for just one kind of continuous real-world simulation and corresponding simulation platform. **Disruptive solution.** In response, aiPod have brought together an ambitious cross-industry consortium, including Imperial College London, Claytex, DG Cities and Transport for London, to develop and integrate, as part of a simulator system, a novel platform-agnostic scenario generator for simulated testing of SAE Level-4/5 urban autonomy at the scale of the individual sensor, CAV and entire transport network. Applicable to all potential CAV variants and any simulation architecture feeding a safety-case approach, the solution targets a step change in capability to: 1. Identify and structure complex realistic edge-case scenarios 2. Adaptively formulate the most relevant test routines to virtually-validate CAV decision-making performance 3. Qualify and quantify overall risk, aggregated across the relevant scenarios and parameter space A multi-level framework targets unprecedented capability to identify complex edge-case test scenarios from multi-mode real and simulated data, human-driven fault-detection and public responses to CAV. Overcoming limitations of existing simulators and models, the project targets a functional architecture that is inherently evolvable and extendable, whilst allowing constraints to test and certify specific deployments. Actionable feedback delivered by the solution and wider project can be leveraged to evolve the overall UK autonomous vehicle simulation capability and regulatory position."

StreetWise aims to develop and demonstrate the technology, safety validation methods, insurance and service models for delivering an autonomous personal mobility solution targeted at replacing the urban commuter car. The project will show that the technology is now sufficiently mature to be safe in urban environments, sufficiently intelligent to co-exist with human drivers, road users and pedestrians and will demonstrate how we can use this technology to build compelling service offers to recover commuting time, reduce commuting costs, cut accident rates, reduce congestion and cut emissions. The StreetWise project will be delivered by a consortium led by FiveAI - a company specialising in perception and artificial intelligence in-vehicle technologies - working in collaboration with component technology providers (McLaren Applied Technologies, University of Oxford), transport sector related innovators (Arriva, TRL Limited & Transport for London) and the UK’s largest automotive personal insurance provider (Direct Line Group).

In today's competitive market, Automotive Manufacturers and Suppliers must achieve faster time to market as well as improved quality and reliability. Additionally they must satisfy customer and regulatory demand for greater powertrain efficiency and refinement. Product development and design must be optimised and verified with limited number of available physical prototypes. This means much of the electronic control systems testing and verification must be carried out automatically through mathematical modelling and simulation. These models must cover multiple physical domains such as Mechanical, Electrical, Hydraulic and Thermal and satisfy sufficient accuracy to replace the real prototype. To validate the functional requirements of the real electronic control systems with embedded software one has to simulate these models in ‘real-time’, i.e. the responses of the model must have the same profile and take the same amount of time as the real system. MOdel-based Real-time Systems Engineering (MORSE) project tries to address some of the challenges in this approach, particularly the trade-off between accuracy and real-time capability of the generated models.