Europe would be close to solving problems related to congestion, traffic safety and environmental challenges if people, vehicles, infrastructure and business were connected into one cooperative ecosystem combining integrated traffic and transport management with new elements of ubiquitous data collection and system self-management. The main objective is increasing the safety, sustainability, flexibility and efficiency of road transport systems by taking advantage of cooperative communication and by processing open data related to travel through a cooperative open web based platform and mobile application, developed with the purpose of delivering information and services to drivers, businesses and Vulnerable Road Users in real time. The operative objectives will be to: - Analyse stakeholder’s needs incorporating them in the specification of the transport services delivered by the platform; - Identify transport open data sources of information and harmonizing this data to be used as real-time information; - Define and implement and fully distributed global architecture to enable cooperative sensing in ITS; - Leverage the information gathered from vehicular communications, GNSS and open data by means of artificial intelligence techniques; - Develop hybrid networks, supporting 802.11p and mobile communications, which will allow assuring a stable communication channel between vehicles and VRU (usually bringing smartphone). - Empower drivers to deliver data to the platform by leveraging the information generated by their mobile phones exponentially increasing the information available on traffic status. - Implement ITS services arranged in the following main areas: driver assistance, vulnerable users and multimodal dynamic commuter, enhanced real time traffic information API and TIMON collaborative ecosystem; - Design two validation environments, a test bed site and another located in an (inter)urban area;

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.

Cooperative intelligent transport system (C-ITS) applications rely on knowledge of the geographical positions of vehicles. Unfortunately, satellite-based positioning systems (e.g., GPS and Galileo) are unable to provide sufficiently accurate position information for many important applications and in certain challenging but common environments (e.g., urban canyons and tunnels). This project addresses this problem by combining traditional satellite systems with an innovative use of on-board sensing and infrastructure-based wireless communication technologies (e.g., Wi-Fi, ITS-G5, UWB tracking, Zigbee, Bluetooth, LTE...) to produce advanced, highly-accurate positioning technologies for C-ITS. The results will be integrated into the facilities layer of ETSI C-ITS architecture and will thereby become available for all C-ITS applications, including those targeting the challenging use cases Traffic Safety of Vulnerable Users and Autonomous Driving/platooning. The project will therefore go beyond ego- and infra-structure-based positioning by incorporating them as building blocks to develop an enhanced European-wide positioning service platform based on enhanced Local Dynamic Maps and built on open European standards. Proof-of-concept systems developed in the project will combine infrastructure devices, reference vehicles, communication between road users and offline processing, and will be evaluated under real conditions at TASS' test site in Helmond, with the objective of assessing its capabilities to provide high precision positioning to C-ITS applications. When possible, codes and prototypes will be fully open-source and made available to the larger research community as well as to the automotive industry at the end of the project. All achievements will be published in top-tier events further guaranteeing an open-access to all technical publications produced. The project also aims at a strong commitment to bringing the developed solutions to standardization bodies

The mission of CoEXist is to systematically increase the capacity of road authorities of getting ready for the transition towards a shared road network with increasing levels of automated vehicles (AVs), both in terms of vehicle penetration rates and levels of automation using the same road network as conventional vehicles (CVs). CoEXist will enable mobility stakeholders to get “AV-ready” – which CoEXist defines as conducting transport and infrastructure planning for automated vehicles in the same comprehensive manner as for existing modes such as conventional vehicles, public transport, pedestrians and cyclists, while ensuring continued support for conventional vehicles on the same network. AV-ready transport and infrastructure planning in cities is a key precondition for fulfilling the promises of AVs to reduce road space demand and improve traffic efficiency and safety – without it, AVs could simply increase the urban mobility problems. CoEXist will address three key steps in transport and infrastructure development: • AV-ready transport modelling: Validated extension of existing microscopic and macroscopic transport models to include different types of AVs (passenger car/ light-freight vehicle, automation levels). • AV-ready road infrastructure: Tool to assess the impact of AVs on safety, traffic efficiency and space demand and development of design guidance for hybrid (AV-/CV-shared) infrastructure. • AV-ready road authorities: Elaboration of eight use cases in four road authorities (Gothenburg, Helmond, Milton Keynes and Stuttgart), used to evaluate AV impacts on safety, traffic efficiency and road space requirements (with CoEXist tools) and making detailed hybrid infrastructure design recommendations. Due consideration of CEDR’s Transnational Research Programme is shown through the participation of TRL, the coordinator of the CEDR-funded Dragon project.

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.