<?xml version="1.0" encoding="UTF-8"?><ns2:project xmlns:ns1="http://gtr.rcuk.ac.uk/gtr/api" xmlns:ns2="http://gtr.rcuk.ac.uk/gtr/api/project" xmlns:ns3="http://gtr.rcuk.ac.uk/gtr/api/fund" xmlns:ns4="http://gtr.rcuk.ac.uk/gtr/api/person" xmlns:ns5="http://gtr.rcuk.ac.uk/gtr/api/project/outcome" xmlns:ns6="http://gtr.rcuk.ac.uk/gtr/api/organisation" ns1:created="2026-08-26T13:36:10Z" ns1:href="http://gtr.ukri.org/gtr/api/projects/B6C8A41E-5750-4D83-A35C-4DFF3C98F5CB" ns1:id="B6C8A41E-5750-4D83-A35C-4DFF3C98F5CB"><ns1:links><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/persons/5E654132-2C7F-485D-9E65-EECD52DC4902" ns1:rel="PM_PER"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/2A5F62FB-C182-4342-9D94-98442C6D0783" ns1:rel="LEAD_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/2A5F62FB-C182-4342-9D94-98442C6D0783" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/D9B78237-355E-4324-9B26-CD81883D87F6" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/C55FBF09-963E-4674-90B4-1D26B9E9F278" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/5F757471-288A-4A3C-8E66-E5C76C12C99C" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/A05FBCF3-D9BA-4D59-93B5-DD91B26A8D36" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:end="2019-12-31T00:00:00Z" ns1:href="http://gtr.ukri.org/gtr/api/funds/A5B5A8B0-0FBA-48AA-8F40-2F011491B400" ns1:rel="FUND" ns1:start="2017-06-30T23:00:00Z"/></ns1:links><ns2:identifiers><ns2:identifier ns2:type="RCUK">103285</ns2:identifier></ns2:identifiers><ns2:title>Smart ADAS Verification and Validation Methodology (SAVVY)</ns2:title><ns2:status>Closed</ns2:status><ns2:grantCategory>Collaborative R&amp;D</ns2:grantCategory><ns2:leadFunder>Innovate UK</ns2:leadFunder><ns2:abstractText>There is an emerging and strong demand for new techniques to enable the robust design and verification &amp;amp; validation (V&amp;amp;V) of ADAS features in a safe, repeatable, controlled and scientifically rigorous environment. This is driven by a number of challenges: reduced engagement of, and reliance on, the driver in the driving task; the very high number and complexity of use cases &amp;amp; test scenarios; reduced access to prototype vehicles; and limited test time, human resources and cost constraints. This project will therefore deliver a novel, efficient and accelerated simulation and simulator based V&amp;amp;V process for ADAS technologies. This project will create the building blocks for the V&amp;amp;V of future technologies based on Field Programmable Gate Array (FPGA) using deep learning and Convolutional Neural Network (CNN) algorithms. These methodologies will be evaluated throughout a product development lifecycle of a real-time ADAS control system. This project will facilitate collaboration between AVL (consortium lead), Vertizan, Myrtle Software, Warwick University and Horiba MIRA, and will bring together the learning and innovations from 3 current Innovate UK funded feasibility studies.</ns2:abstractText></ns2:project>