"Every day 1.2 billion vehicle-driver combinations worldwide use 7 billion tyres (Wards, 2016) to maintain safe contact with roads. To capture essential safety data from all vehicles, this project seeks to develop Vuelytics(tm) - a disruptive IoT cloud-connected on-Vehicle device (iGuard) and Tyre Attribute Management & Brokerage (TAMB) Platform. Vuelytics(tm) iGuard will monitor tyre integrity, the way they wear, grip, perform on different surfaces and vehicles, in different environments, and merge that data with driver behaviour, presenting a gold mine of data to sector OEMs (tyre, wheel, automotive), aftermarket firms (insurance, fast fits, service centres, telematics), and enforcement agencies. Supported by 5 patents, 3 already at PCT National stage, we'll engage sector players in the utilisation of captured, codified, mined and meaningfully analysed data to yield opportunities for new digital business models."

The purpose of this project is to spend time in overseas markets to validate the needs of global customers for our novel MyTyreManager™ (MTM) product; to build international networks; and to determine the nature of future collaborations and follow-on work. We'll visit sector players in UK, Europe, US, Canada and USA. MTM is a multi-patented sensing and imaging technology enabling machines instead of people to make complex decisions about tyre and wheel condition(s) using a SmartPhone. MTM was built thanks to a combination of previously successful IUK innovation grants and company investment in 2013, 2014 and 2015. Since then we’ve broadened our technology to include a range of other Software, Hardware and Connected Vehicle options. Today MTM produces big data based on tyres, their users, and associated vehicle characteristics. It enables 2-way exchange between user and stakeholders, generating data, stored in a cloud-based MTM ‘attribute engine’, from which inferences can be taken on a host of engineering, quality, asset management and risk / safety related matters - targeting a US$138bn segment.

Smartphones have extended Global Positioning System (GPS) capabilities, Micro Electro Mechanical System (MEMS) inertial sensors, powerful computing and very high res cameras (>13MegaPixel). We aim to demonstrate feasibility of using on-board sensors and imaging capability of smartphones to scan dents, chips and scratches in car bodywork, and use that data to produce photogrammetric 3D full colour models that can be used for remote quoting purposes by Repair Centres. Demand: Every 4 seconds, a bodywork dent is repaired in UK. Problem: Time and convenience. Preparing a bodywork quote takes on average 4.4 hrs (Audatex, 2014). To obtain a fixed quote, customers need to visit a garage. Alternatively, garage owners visit customers, usually after normal working hours. Some employ mobile teams, increasing their cost base. No automated remote quoting technology exists. Addressable market: 3.8million dents jobs completed/year in UK. 135m completed in the EU and USA. Result: a $50+billion industry (ABPC, 2014). Key challenges: feature recognition, spatial accuracy, influence of environment (reflection, shaddows, etc).

In 2013 we successfully built a machine system for tyre-wear analysis, allowing, machines to make complex decisions about tyre road-worthiness. To complement it, this project investigates a further machine system for determining tyre pressure. This system will be developed for mobile platforms, and used to inform drivers about safety, economic, and environmental benefits of correct tyre pressure. Data will stream to cooperating devices wireless for enhanced user experience/historical analysis. Key challenges are, feature vector algorithm, machine learning, influence of environment, and software performance. A demo system will show operation in a range of scenarios. To reinforce market potential, 1bn car tyres sold in 2011 (1.5bn by 2030, OECD). Incorrect tyre pressure negatively affects proper tyre wear and road user safety. 4.4m UK cars have at least 1 illegally worn tyre(TyreSafe). 1bn smartphone users. 50% in the West.

This project aims to allow machines instead of people to make complex decisions about tyrewear, building on a successfully completed TSB ICT Technical Feasibility project (File Ref 131363). That feasibility study stated that current 3d scanners are expensive (>£1000) and not fit for use by law enforcers, or consumers (car owner/drivers). We showed that manual methods are open to misinterpretation and are often ignored. We demonstrated that it was possible to embed intelligent machine learning and novel algorithms into a Smart Phone for tyre-wear image analysis, allowing for the first time, a Smart Phone-based 'machine' to make complex decisions about the road-worthiness of tyres on vehicles. This project seeks to progress that technology to Proof of Concept stage, targeting the development of a consumer 'App' and platform architecture to enable car owners to manage their tyre wear, and condition, on an on-going basis using their Smart Phone. This will enable users to alter their driving style to optimise tyre longevity, and maximise safety. Other interested parties (like insurers) could analyse that data. Challenges to address include, optimisation of feature vector algorithms across a range of Smart-Phones, characterise tyre differences and impact on machine vision, learning and accuracy, realtime cloud integration, influence of weather, and optimisation of acquisition speed. Significance: 2011-2020 is the United Nations Decade of Action for Road Safety(UN). Worldwide road accidents kill 1.3million. Increasing 30%/yr. 45% linked to tyre wear. According to TyreSafe in UK alone 4.4m cars have at least 1 illegal tyre. The problem is enormous. The serviceable market is equally large therefore. One measure could be tyres sold vs smart phone users worldwide, suggesting a serviceable market in excess of 500m users. This is an opportunity for wide adoption of patented machine learning at the consumer ‘App’ level and a chance to positively impact road safety.

3D scanning innovations turn physical objects into precise, usable 3D digital models. This enables users to invent bespoke customer-driven freeform products, capitalising on design freedoms offered by Additive Manufacturing (AM). These plug-and-play devices are set to be mainstream in manufacturing in the next few years, and will accelerate the concept-to-commercialisation journey by enabling NPD steps to be combined or skipped. However, an unanswered question is whether 3D scanning, combined with AM tools like 3D printing, makes this technology commercially viable and accessible as a highstreet/retail service for consumers. We will undertake a study to explore a possible hybrid PPU/MTO-BS1 (pay-per-use/make-to-order batch-size of 1) business model that takes these HVM skills into retail and the public eye, carrying out business model research and running a public demonstrator in a retail space.

3d scanners are popular for capturing our surroundings. However, current systems are expensive, difficult to use, and therefore not fit for use in road safety scenarios such as tyre wear. Similarly, manual methods are open to misinterpretation. This project investigates the use of intelligent machine learning for tyre-wear analysis, aiming to allow machines instead of people to make complex decisions about the road-worthiness of tyres on vehicles. Key challenges ahead are, feature recognition, machine learning , influence of environmental conditions, and performance of software on 'off the shelf' hardware. A demonstrator system will be built showing operation in a range of tyre-wear scenarios.

This project will investigate an Additive Manufacturing (AM) PPU (Pay Per Use) business model to underpin possible global developments in new surface marking techniques that build advanced ultra-reflective markings layer by layer, cured using laser or microwave technology. Surface Markings applied to asphalt and concrete give facilities and highways owners the ability to add safety, education, and advertising in prime locations, leading to global opportunities in schools, retail, car parks, stadia; as well as road surface markings. Current systems typically apply thermoplastics bonded to the surface with a naked gas flame. This gives variable results, is time consuming, and presents health and safety issues, as well as being banned from use in all 144 underground applications Worldwide. If viable, this business model will migrate HVM technology into a traditional unskilled industry, creating new products and jobs, and raising awareness of HVM in a new audience.