Ordnance Survey's research and development team routinely uses machine learning to extract new information from existing data sources. As machine learning is a relatively new field, there is a need to provide high-quality metrics to help understand the data quality of machine learning outputs.
This project will create a new range of tools and processes to describe and quantify the quality of Ordnance Survey's machine learning outputs. These tools and processes will use different testing methodologies as well as comparative assessments of networks to create benchmarks for accuracy. The project will establish a regulated quality control metric for Ordnance Survey's machine learning models to ensure its processes stand up to the growing accuracy requirements demanded by its widespread customer base.
"OmniCAV will lay the foundations for the development of a comprehensive, robust and secure simulator, aimed at providing a certification tool for Connected Autonomous Vehicles (CAVs) that can be used by regulatory and accreditation bodies, insurers and manufacturers to accelerate the safe development of CAVs.
It brings together a team of eleven internationally renowned organisations, with decades of accumulated knowledge in the area, in order to produce a single-point-of-call simulator to establish when a CAV can safely progress from a testbed to road trial.
To achieve this, OmniCAV will use highly detailed road maps, together with a powerful combination of traffic management, accident and CCTV data, to create a high-fidelity dual (traffic and driving) simulation environment, including AI-trained road users to interact with the AV under test. Scenarios for testing will be developed and randomised in a holistic way to avoid CAVs training to specific conditions, whilst maximising coverage, and the integrity of the testing environment will be taken into consideration through creation of a root-of-trust design to secure the test inputs, simulator configuration and resulting test outputs.
Critically, the simulator will offer market-leading coverage of a representative element of the UK road network, through encompassing rural roads, peri-urban and urban roads, to help enable autonomy for all.
Representatives of the key end-users, including a local authority, an OEM and an insurance provider, will be engaged throughout to understand their needs.
The validity of the synthetic test environment compared to the real-world is of particular importance, and OmniCAV will be tested and refined through an iterative approach involving real-world comparisons and working in conjunction with a CAV test-bed.
This is an ambitious project aiming to step-change the safe trialing of CAVs in a safe, holistic and challenging manner in order to accelerate their training, deployment and adoption."
CityVerve is based around the large-scale deployment of technologies, where everyday objects can be
connected to a network in order to share data. This approach will demonstrate and evidence the benefits to
citizens’ through environmental improvements, economic opportunities, and the more efficient and effective
delivery of services such as transport, healthcare and energy. It will provide the ability to create new services
and operating models through the interoperability between transport, healthcare and energy systems. The
geographical focus of CityVerve will be Greater Manchester; a city region which has been at the forefront of the
city devolution agenda, in particular health and social care provision, leading the way in designing new ways of
delivering services and providing the blueprint for other cities in the UK and beyond. CityVerve will build on
these opportunities to provide a once-in-a-generation opportunity to transform healthcare and other city
services around the needs of people, not only is this is an urgent priority for Greater Manchester, but a
challenge faced by many global cities.
The Atlas Project will study the feasibility of and requirements of the technologies and services required to
deliver autonomous navigation ‘anywhere’ in a safe, reliable and resilient manner. Specifically, the project will
study the navigation, mapping, data, communications and processing requirements; ,identifying the on-vehicle
and infrastructure elements required to support autonomous navigation. The project also considers how data
can be reused for the planning of urban environments more suited to autonomy. The consortium partners
collaborating on this project are: Ordnance Survey (lead), Gobotix Ltd, Oxford Technical Solutions Ltd,
Transport Research Laboratory, Sony Europe Ltd, Royal Borough of Greenwich and Satellite Applications
Catapult.
The advent of new standards and initiatives for data publication in the context of the World Wide Web (in particular the move to linked data formats) has resulted in the availability of rich sources of information about the changing economic, geographic and socio-cultural landscape of the United Kingdom, and many other countries around the world. In order to exploit the latent potential of these linked data assets, we need to provide access to tools and technologies that enable data consumers to easily select, filter, manipulate, visualize, transform and communicate data in ways that are suited to specific decision-making processes.
In this project, we will enable organizations to press maximum value from the UK’s growing portfolio of linked data assets. In particular, we will develop a suite of software components that enables diverse organizations to rapidly assemble ‘goal-oriented’ linked data applications and data processing pipelines in order to enhance their awareness and understanding of the UK’s geographic, economic and socio-cultural landscape.
RAGLD (Rapid Assembly of Geo-Linked Data) is collaboration between the Ordnance Survey, Seme4 Ltd and the University of Southampton.
SENTINEL will continuously monitor GPS signals and warn users of interference, whether from nature or from hostile sources. Today we not only use GPS to navigate in our cars but we also rely on GPS and signals from space as a timing signal to synchronise a wide range of computer based systems, including communication systems.
The SENTINEL project aims to take prototype GPS interference detection probes that have already been developed in the TSB GAARDIAN project forward to real world deployment and applications. This Research will verify and pinpoint the nature and extent of interference and will enable alerts to appropriate authorities where this is as a result of illegal activity or where it could impact on safety.
With the increasing occurrence of GPS interference, both intentional and unintentional, together with the increasing reliance of a wide range of users on such systems, being able to detect and locate interference or a jamming device is an important requirement.
The consortium led by Chronos Technology includes ACPO-ITS, a working group of the Association of Chief Police Officers, the General Lighthouse Authority, Ordnance Survey, the National Physical Laboratory, the University of Bath and Thatcham. The consortium represents the breadth of interest in the public sector and the commercial interests of industry, coupled with applied expertise from the UK’s academic sector.
Ideas in Transit is a five-year project that applies User Innovation to the transport challenges faced by individuals and society. It is a unique collaboration between Government, Commercial and Academic thought leaders and their networks. It will influence intelligent transport decisions at policy, social, personal and commercial levels.
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