Cars capture and generate huge volumes of data in real-time about the driving dynamics, the environment, and the driver and passengers’ activities. With greater proliferation of connected and automated mobility applications, the value of data from vehicles is getting strategic not just for the automotive industry but in a wider scope, and not limited to the onboard systems and services. 5GMETA open platform aims to leverage car-captured data to stimulate, facilitate and feed with them innovative products and services. The result of 5GMETA will empower the automotive ecosystem from industry players to new entrants, such as SMEs and high-tech start-ups granting access to interoperable car-captured data according to data licenses. The access to data coming from relevant geographical regions will catalyse the generation of new opportunities and business models coming from valuable services, where data liability and billing will rely on accountability dashboard of dataflow subscription and volume consumption. 5GMETA expands 5G network functions to enable data monetization with a secure and private pipeline that manages data computing, dataflows according to service subscriptions and geographic queries. 5GMETA will realise representative demonstrators in terms of data heterogeneity, value creation and business models to ensure that third parties and new players’ interests and requirements are considered beyond traditional automotive industries. 5GMETA will focus on technology transfer activities performing different dissemination, tutorials and hackathons to incubators and clusters to capture attention of SMEs and high-tech start-ups with a platform leading to new opportunities in an incoming profitable market.

Cloud-LSVA will create Big Data Technologies to address the open problem of a lack of software tools, and hardware platforms, to annotate petabyte scale video datasets. The problem is of particular importance to the automotive industry. CMOS Image Sensors for Vehicles are the primary area of innovation for camera manufactures at present. They are the sensor that offers the most functionality for the price in a cost sensitive industry. By 2020 the typical mid-range car will have 10 cameras, be connected, and generate 10TB per day, without considering other sensors. Customer demand is for Advanced Driver Assistance Systems (ADAS) which are a step on the path to Autonomous Vehicles. The European automotive industry is the world leader and dominant in the market for ADAS. The technologies depend upon the analysis of video and other vehicle sensor data. Annotations of road traffic objects, events and scenes are critical for training and testing computer vision techniques that are the heart of modern ADAS and Navigation systems. Thus, building ADAS algorithms using machine learning techniques require annotated data sets. Human annotation is an expensive and error-prone task that has only been tackled on small scale to date. Currently no commercial tool exists that addresses the need for semi-automated annotation or that leverages the elasticity of Cloud computing in order to reduce the cost of the task. Providing this capability will establish a sustainable basis to drive forward automotive Big Data Technologies. Furthermore, the computer is set to become the central hub of a connected car and this provides the opportunity to investigate how these Big Data Technologies can be scaled to perform lightweight analysis on board, with results sent back to a Cloud Crowdsourcing platform, further reducing the complexity of the challenge faced by the Industry. Car manufacturers can then in turn cyclically update the ADAS and Mapping software on the vehicle benefiting the consumer.

Road accidents continue to be a major public safety concern. Human error is the main cause of accidents. Intelligent driver systems that can monitor the driver’s state and behaviour show promise for our collective safety. VI-DAS will progress the design of next-gen 720° connected ADAS (scene analysis, driver status). Advances in sensors, data fusion, machine learning and user feedback provide the capability to better understand driver, vehicle and scene context, facilitating a significant step along the road towards truly semi-autonomous vehicles. On this path there is a need to design vehicle automation that can gracefully hand-over and back to the driver. VI-DAS advances in computer vision and machine learning will introduce non-invasive, vision-based sensing capabilities to vehicles and enable contextual driver behaviour modelling. The technologies will be based on inexpensive and ubiquitous sensors, primarily cameras. Predictions on outcomes in a scene will be created to determine the best reaction to feed to a personalised HMI component that proposes optimal behaviour for safety, efficiency and comfort. VI-DAS will employ a cloud platform to improve ADAS sensor and algorithm design and to store and analyse data at a large scale, thus enabling the exploitation of vehicle connectivity and cooperative systems. VI-DAS will address human error analysis by the study of real accidents in order to understand patterns and consequences as an input to the technologies. VI-DAS will also address legal, liability and emerging ethical aspects because with such technology comes new risks, and justifiable public concern. The insurance industry will be key in the adoption of next generation ADAS and Autonomous Vehicles and a stakeholder in reaching L3. VI-DAS is positioned ideally at the point in the automotive value chain where Europe is both dominant and in which value can be added. The project will contribute to reducing accidents, economic growth and continued innovation.