Omnia Smart Technologies Limited Public Funding

The "FleetfootAI for Driver Advancement" project, led by Hodos in collaboration with Specialist Vehicle Rental and Omnia Smart Technologies, represents a major step forward in driver training and fleet management within the UK logistics sector. Utilising the latest advancements in artificial intelligence and machine learning, the FAIDA project aims to significantly enhance driver skills and fleet performance, promoting safer and more efficient operations across the industry. Building on the insights gained from the successful AIPTO project, FAIDA seeks to address the critical gaps in traditional fleet management systems, which often focus primarily on vehicle tracking and overlook the essential aspect of driver behaviour. By enhancing our existing Fleetfoot platform with advanced AI and sophisticated CCTV telematics, FAIDA will provide a dynamic, data-driven training experience that actively engages drivers both before and after training sessions. The core of FAIDA is its innovative, data-led training package that seamlessly integrates AI analytics, video footage and practical, hands-on instruction. Before and after each training session, instructors will utilise the platform to review driving patterns and behaviours with students, supported by detailed video feedback. Each significant driving event captured during sessions is video-tagged and geographically mapped, allowing for comprehensive analysis. This feature enables instructors and drivers to collaboratively review and discuss driving incidents, facilitating a deeper understanding and improvement of eco-driving techniques. The real-time data collected through vehicle-mounted telematics, coupled with AI-triggered CCTV that monitors both the driver and the road, provides a resource for creating customised training modules. This approach not only enhances drivers' understanding of their own performance but also ensures that the skills learned are consistently applied, leading to tangible improvements in fleet safety and operational efficiency. FAIDA's collaboration includes Specialist Vehicle Rental, which provides access to a fleet of vehicles for several driver training organisations, and Omnia, which supplies the cutting-edge hardware and technological expertise necessary for the project. This partnership ensures that the FAIDA platform remains at the forefront of technological innovation, is deeply relevant to industry needs, and has a direct route to market through established channels. As an innovative solution to modern fleet management and driver training challenges, FAIDA stands to deliver significant advancements in safety, efficiency, and environmental responsibility within the logistics sector. This project is not just about enhancing the way we train drivers; it's about setting new standards for the integration of technology in transportation, paving the way for a safer and more sustainable future.

FleetMind is an ambitious project poised to redefine waste management for local councils in the UK. It tackles the pressing need for more efficient and responsive waste collection services, a challenge magnified by urban growth and changing waste patterns. Traditional collection methods, often rigid and outdated, fail to keep pace with the dynamic demands of modern communities, leading to inefficiencies that strain budgets, the environment, and public satisfaction. At the heart of FleetMind is a sophisticated Artificial Intelligence (AI) and Machine Learning (ML) framework designed to overhaul the control room operations of waste collection fleets. Rather than relying on static, inflexible systems, FleetMind will leverage real-time data to adapt and optimise waste collection routes and schedules continuously. This data includes a variety of inputs such as collection frequency, traffic conditions, vehicle capacities, and predictive waste generation models. The system's innovation centres on its ability to recalibrate in real-time, ensuring waste collection is both efficient and adaptable. The project is set to deliver a multitude of benefits: 1. **Cost Reduction:** By enhancing operational efficiency, FleetMind will generate significant savings for local councils. This financial relief could enable reinvestment into community services or potentially reduce waste collection costs for residents. 2. **Resource Optimisation:** With live data analysis, FleetMind will ensure better asset deployment, leading to improved service delivery, such as reduced missed collections and wider coverage. 3. **Environmental Benefits:** Optimised routes and schedules will curtail unnecessary vehicle movement, thereby decreasing emissions and contributing to cleaner air, in line with environmental sustainability goals. Initiating with Flintshire County Council and Cumberland Council, FleetMind aims to craft a new benchmark in waste management that marries cutting-edge technology with practical application. This collaboration, which includes expertise from Omnia, TRL, and Hodos, is a step towards a more efficient, eco-friendly, and cost-effective future for UK waste services.

Omnia is developing a solution to enhance workforce remuneration management in the fleet management industry by utilising AI technology. Our method includes embedding AI directly into fleet management tools, as opposed to utilising an external AI platform to analyse data from existing tools. This approach will result in faster, more accurate, and actionable insights, improved adaptability, reduced complexity and cost, increased trust in AI results, and the ability to tailor the solution to specific industries or companies. To accomplish this, we will undertake several key steps, including data collection, model training, integration, deployment, and maintenance. We plan to offer the solution through different commercial structures, such as a SaaS model, product licence, customizable solution, and service-based model. By integrating AI into our existing all-in-one platform, we aim to become one of the leading operational tools in the industry. This feasibility study will help us evaluate the market, technical, financial, operational, and regulatory feasibility of the proposed solution, and will assist in determining the most appropriate commercial strategy for the solution.

Omnia Smart Technologies Ltd (Omnia) is a UK fleet technology and operational control tower SME with a core project team of Jean-Philippe Taylor (project lead), Roque Duarte (technical lead) and Frans Badenhorst (commercial, sustainability and diversity lead). Fleet operators currently use a range of services to manage their daily operations, most of which are incompatible with each other, leading to slow decision-making and wasted resources. Omnia is solving a significant unmet need with its all-in-one consolidated software solution that will enable fleet operators to improve their efficiency, make savings and reduce their carbon footprint

The project is a Feasibility Study to understand the complexities in implementing a commercially viable electric Vehicle (eV) charging network and associated services within South Africa. South Africa has a unique set of challenges that will effect the sustainability of an Electric vehicle charging network, these challenges include: and its key challenges of, * Electricity Stability. * Crime rate. * The distances travelled by the public in a daily basis requires charging outside of the safety of own home. * Government policies related to procurement which proves difficult for outside suppliers. The project looks to work with partners in South Africa to understand the cost and design of a supply chain that can support an Electric vehicle charging network.

With the current important focus moving away from standard fuel based vehicles to electric, Local Authorities have been given targets to ensure that their operational fleets are fully electric, by 2030\. The ability to transition to an EV fleet requires the analysis of the current operation and capacity but also ensuring that the management of the services the local authority delivers is still able to be done effectively. Our project will be conducting a live demonstration with an electric Refuse Collection Vehicle (eRCV) within an actual local authority's waste collection operation, focusing on ensuring that the council will still be able to manage and complete its daily waste collection rounds using EVs. We will also be looking at how the traditional weekly collection rounds can be changed to cater for the increase in waste due to lock-down and eCommerce deliveries, and how technology can flatted demand to better suit collection using an electric vehicle.

Due to COVID-19, the world has moved to more online shopping which is increasing the amount of residential waste needing to be collected by the local authority. Not only is it increasing the amount of waste by it is affecting the air quality at residential properties due to the increased number of non electric delivery vehicles and the increased frequency of non electric waste vehicles travelling to collect the waste. We are proposing to develop technology that enables the Local authority to know in real time when waste bins are full to enable collection on demand, as well as provide the Government with air quality information at a residential level.

Poor air quality in populated areas is widely recognised as a national public health issue. In an attempt to combat this and reduce emissions in our cities, the UK Gov has allocated £101m for the creation of 6 ‘Clean Air Zones’ - designated areas where restrictions and charges can be placed on the most polluting vehicles. Original proposals contained plans for 16 CAZs however these plans were curtailed by the high cost of current ANPR technology and is a major barrier to any future voluntary adoption. We aim to address this by using telematics, GPS and geofence technology to create a low cost, integrated solution that can be rapidly deployed to improve air quality. The system will be able to track activity within the CAZ and a vehicle's emissions footprint, enabling the development of an emission based pricing model - penalising heavy polluters and incentivising low emission behaviours. Our system requires no heavy road infrastructure, no disruption and will integrate with local authority traffic and parking systems. Our initial findings suggest that we can reduce the capital cost of a CAZ system by >90% and running cost by >50%.

Fleetfoot is our driver engagement and behaviour change tool for fleet telematics companies. We use game mechanics and real world rewards, combined with telematics hardware and data, to motivate and engage drivers of light commercial vehicles. Professional, safe and efficient driving is seen as a way to improve brand image for many companies. However, what does data from a safe driver look like? How can it be visualised, and how does this differ across users? What is needed to convince the insurance community? Can it be historically back-tested and will it result in a reduction of risk and lower insurance premiums for better drivers and safer operators?