Information and Communications Technology (ICT) is the use of computers and communication systems to collect, send, store, process and use data. ICT is the basis for intelligent transport systems (ITS), comprising a broad range of diverse technologies in the transport domain, using sensor, communication, information processing and control technology. Communication between vehicles and infrastructure defines the area of Cooperative ITS (C -ITS). General goal is to enhance comfort, safety, efficiency and effectiveness of transport and mobility. C -ITS as a prelude for automated road transport is the topic of this book, as outlined in this chapter.

This paper presents the status of C -ITS deployments in different parts of the world. The focus is on current deployment initiatives. Previous and current research efforts are only discussed, as far as these are essential for understanding the current deployment activities. Where needed, the policy framework as well as the roles of different stakeholders and actors are analysed. The drivers for C -ITS deployments from a policy perspective are taken as the starting point At the end of the chapter, common issues are discussed based on the analysis of the singular deployment initiatives.

The purpose of this chapter is to introduce various initiatives related to developing an overall system architecture for Cooperative Intelligent Transportation Systems (C -ITS). C -ITS is also often programmatically referred to in North America as 'Connected Vehicles (CVs)'. Regardless of terminology, C -ITS is focused upon the following forms of connectivity: Vehicle -to -Infrastructure (V21) communications - Tying vehicles to roadside systems, in particular those components owned and operated by local or state transport departments and authorities or private road operators. V2I data can support local transport systems management and operations activities for improving mobility, safety, and environment. V2I can support real-time traffic control as well as tolling, pricing, and payment services, and the data can be highly beneficial to transportation performance analytics, providing transport operators with an assessment of how their current strategies are performing over various time -based, daily, or seasonal condition

The objective of this paper is to present a set of service-dominant BM blueprints designed in collaboration with several stakeholders in eight European cities. The BM blueprints have emerged from stakeholder workshops in these cities. As such, they are catered to the needs and context of the respective cities and aimed at guiding the implementation of C-ITS services for large-scale and sustainable business. However, the blueprints are designed in such a way that they can be adopted in other cities to address similar challenges they have.

This chapter will cover some of the issues mentioned by European Road Transport Research Advisory Council (ERTRAC) and has its starting point in the knowledge and experience from four different EU-funded projects dealing with automation and human factors. It might be expected that there is no difference in how human factors should be considered in relation to automation depending on the country it is used in. However, there are reasons to believe that there are differences in understanding and in acceptance of new functions depending on experience or not from modern vehicles with, for example integrated driver support systems. This chapter has its starting point mainly from work done in Europe, which may be considered to be generic and valid for other countries in the world with the same type of car fleets. The focus will be on challenges covering: the need to have an adaptive Human-Machine Interface (HMI) to achieve trust and acceptance in relation to automated functionalities and system, the importance of considering different driver states and finally the evaluations of automated systems.

Increased automation and connectivity are major trends that are shaping the future of road transport and mobility. They hold the promise of addressing many of the major challenges that transport system of today is facing, such as user safety, energy efficiency, air quality, traffic congestion and enhancing driver comfort and convenience. Automated transport will also have an impact on the role, operations and costs of road operators and authorities. This chapter addresses the existing EU legal framework for road transport from the perspective of future automated driving and the required changes. It also presents a brief description of some topics related to national legal frameworks of EU Member States.

The V2X technology is a quite complex field in the communication sector. It is required for the automotive industry to further evolve the travel experience and safety, as well as to reduce the environmental impact of vehicles. The chapter reviewed various V2X technologies. Two of the key challenges are interoperability and compatibility. The competing access technologies, i.e. WLAN-based and cellular -based communication, are very different in their whole philosophy. Therefore, it is assumed that both technologies will coexist and spread on their own. There will be no consensus that will favour one over the other. This also means that the communication devices have to support both main access technologies resulting in multi -stack equipment. Of course, this has fmancial consequences as well. To complete this thought, it should be noted that WLAN-based V2X communication has geographical restrictions as well. However, this stands for a vehicle itself and its features as well, thus this issue is less significant.

The chapter provides an overview of the use cases, the communication technologies, the stakeholders, the past and current R&D activities, and the future plans for CAD in Europe. The evolution towards CAD is spanning over more than two decades, a clear indication that Europe has a rich R&D history in the domain and that it will continue driving the future definition of CAD in the wider sense of 'cooperative, connected, and automated mobility.

The paper introduces the development of standards for C -ITS, often programmatically referred to in North America as 'connected vehicles'. In addition, the chapter also discusses C -ITS implementation. Regarding standards, this section references a number of Standards Development Organizations (SD0s). These include the Institute for Electrical and Electronics Engineers (IEEE), Society of Automotive Engineers (SAE), International Standards Organization (ISO), Comite Europeen de Normalisation (CEN), and European Telecommunications Standards Institute (ETSI). The focus areas presented here relate to wireless (also frequently referred to as 'over -the -air') mobile communications including Vehicle -to -Infrastructure (V2I), Vehicle -to -Vehicle (V2V) and Vehicle -to -Everything (V2X), interfaces between central systems and other centres, road infrastructure, and vehicles or other mobile devices, which may entail some combination of wireline and wireless communications.

The European Commission has recognized therefore C -ITS as a disruptive factor that could increase the efficiency of traffic and ameliorate its adverse effects on the environment and the economy. C -ITS have been previously tested in various European cities with a focus on signalized intersections. These services provide advice to drivers with respect to optimal behaviour under specific situations, with the purpose of increasing safety and traffic efficiency. With regard to traffic efficiency, the Energy Efficient Intersection Service (EEIS) aims at reducing energy consumption and vehicle emissions in the vicinity of signalized intersections. Hardware can be installed on vehicles (either passenger cars or trucks), and when they approach the traffic lights, fuel-efficient acceleration/ deceleration advice is conveyed to them. Hence, stops and delays are avoided reducing vehicle emissions that are associated with the start -stop episodes of the vehicle.

For vehicles and road users, accurate and real-time weather, road condition and traffic situation information are key elements when improving road safety. This chapter discussed novel use cases and system architecture, utilizing the enhanced capacity and properties of next -generation 5G mobile technology in the automotive and transport domains. The use cases aimed to improve road safety through new road weather and safety services, real-time V2V video and lidar data streaming, as well as the enhancement of automated driving in diverse conditions. The overall solution encompassed sophisticated services and procedures, designed into the vehicle entity as well as in the distributed cloud, including MEC. The use cases were implemented into real pilot constructions and this chapter provided an overview of the experiences and results. The real service pilots gave valuable insights into the benefits that 5G and supporting technologies like MEC will bring to vehicular services. However, for some of the non -critical or less bandwidth consuming services, current 4G/LTE networks and ITS -G5 technology (if supported) could provide sufficient performance to already facilitate the development and deployment of network -assisted vehicular services now in the pre -5G era

This chapter shows how the integration and testing was done, starting from simple simulation runs, doing Hardware -in -the -Loop (HiL) tests, preparing on test tracks with augmented reality and fmally testing on the public road. First, the target system is briefly described, followed by the descriptions of the individual infrastructure and vehicle developments. Finally, the different integration steps are described leading to the fully integrated system at the end.

This paper provides an overview of several favourable approaches and solutions for infrastructure support of automated vehicles. Two inherent generic goals for this are to increase traffic efficiency and to enhance traffic safety. For efficiency, there are several measures that can be taken with a traffic light controller. An already established service for cooperative vehicles is Green Light Optimal Speed Advice (GLOSA). This service gives a speed advice to approaching vehicles that prevents them from stopping at the intersection. With automated vehicles, the potential of this service can be further increased due to the higher precision with which instructions can be provided and executed. However, it is also widely known that dynamic behaviour of traffic light control algorithms can deteriorate the impact of the GLOSA service and in some cases even make the service harmful.

The majority of the research efforts related to automated driving is focused on the vehicle side. Only recently, the role of the road infrastructure and how it can support the gradual insertion of Automated Vehicles (AVs) attracted attention. In particular, the upgrades of the infrastructure in terms of connectivity, along with Traffic-Management Centres (TMCs) deployment, are recognized as indispensable parts for data exchange and management of mixed vehicle traffic flows. Apart from the minimum road infrastructure quality standards (e.g. clear lane markings, visibility of signs), the kind of support provided by the infrastructure to the AVs (e.g. availability of highly accurate maps, guidance via wireless messages, detailed weather information and recommendations for optimum route) is important information which is expected to increase the use of automated functions while driving.

This chapter investigates TA scenarios and performs simulations. In order to start the simulations, several preparatory steps were made. At first, scenarios had to be defined. As capabilities of future AVs are difficult to foresee and as the problem has several dimensions, it has been decided to use service definitions (i.e. the kind of possible infrastructure influences) as the main criterion for clustering the dimensions. Five services have been proposed and converted into five scenarios. In order to perform baseline simulations of the five scenarios, vehicle models had to be chosen and even newly developed, so that simulation of AVs and the TOC became possible. The models needed to be parametrized according to estimations of future traffic shares, future number of TOR, TOC, and MRM. This chapter highlights the measured effects in one of the studied scenarios. It also explains how simulations get more sophisticated and realistic by using the iTETRIS-integrated platform for traffic simulation, communication, and TM. Furthermore, the plans for performing real-world feasibility assessments including V2X message set definitions have been introduced, closing this first report.

Automated driving developments are experiencing a boom. They are not only driven by advances in technology but also have a strong link with the Smart Cities agenda, where automated vehicles, the shared economy and electro-mobility will play an essential role. This section provides a review of automated vehicle literature from research projects completed in recent years.

The market penetration surveys have provided important insights into the state-of play concerning vehicle-safety systems in Germany. The surveys have confirmed that some building blocks are widely penetrated or almost ubiquitous in the vehicle fleet (e.g. airbags, ESC), whereas advanced systems (e.g. emergency braking) are at the beginning of penetrating the fleet. The combination of system equipment and socio-demographic/geographic indicators has revealed that the presence of systems on the road (fleet km) is higher than the equipment suggests, because there is already a rational incentive that large equipment is most beneficial for vehicles with high annual mileage. Taken together in a temporal perspective, the surveys point at the growing intelligence of and in the vehicles. One can also recognise the penetration pattern following the innovation cascade where new systems are introduced as options in upper class segments, usually bundled in packages. Over time, innovation seeps through to the volume segments [18]. Vehicle-safety systems are experience goods. Acknowledging that some of the vehicle -safety systems can be interpreted as automation Level 1 s or 2 systems, users can collect valuable experience on how to drive assisted by such systems.

Within the scope of the C-Roads Platform, a key element is the evaluation and assessment of impacts related to the deployment of Cooperative Intelligent Transport Systems (C -ITS) services, as a first step in the direction of cooperative and automated vehicles. Therefore, while developing its activities, each C-Roads national pilot has to deal with these issues, with the support of the guidelines provided by the "evaluation and assessment plan," drafted by the Working Group WG3 of the C-Roads platform.

Due to restrictions in time and budget, the study focused on five automation use cases only. The selection was made from a tentative list of automation use cases specified by ERTRAC with a potential of being commercially available in Finland in 2030 while also allowing interesting ODD variations. The following use cases were selected in the Finnish study [2]: (1) highway autopilot including highway convoy; (2) highly automated (freight) vehicles on dedicated roads; (3) automated PRT (public rapid transit)/shuttles in mixed traffic; (4) commercial driverless vehicles as taxi services; and (5) driverless maintenance and road works vehicles.

Automated driving with Level 3 characteristics (Conditional Driving Automation according to SAE [I]) has been intensely promoted by the industry and research scene in the recent years. Level 3 means that driving functions may be taken over by the system, but the human driver must take over control when requested by the system within a certain time frame. To prove the feasibility of such systems, many research activities and pilot projects are undertaken on the international level. Corresponding map services have been piloted in projects for automated driving and demonstrated in real operation.

Here, we propose a model for driver-intention recognition that refrains from driver-based input and instead explores the utilization of information about the traffic situation to extend the predictive capabilities of the model and enable the use in highly automated or autonomous driving. The model is explored in three different scenarios: real-world motorway, simulated rural road, and simulated roundabout scenarios.

In this chapter, we will survey recent advances of two of the most influential and productive projects and initiatives in Japan, i.e., the Cross -ministerial Strategic Innovation Promotion Program (SIP)-adus (automated driving for universal services) [3] and Nagoya University Center of Innovation (NU COI) Mobility Innovation Center [4]. These projects and initiatives mainly focus on the cooperation areas of Cooperative Intelligent Transportation Systems (C -ITS) technologies that automobile OEMs (Original Equipment Manufacturers), suppliers, and service providers can share in their products and applications. Furthermore, we will also briefly introduce how we approach these technologies beyond the cooperation areas.

An automated vehicle is a highly intelligent wheeled (maybe more than four wheels) robot that can sense its surrounding environment on the road, making navigation decisions and regulating its longitudinal and lateral motions without human intervention. Its automated system is often designed to work in an independent and autonomous way without communicating with neighboring vehicles. To achieve human -like intelligence, such a design requires a variety of high -cost sensors for reliable environment perception, a large amount of real -world training data to improve decision -making capabilities, and accurate state measurements for resilient feedback control. Recent progresses on Vehicle -to -Vehicle (V2V) communication allow automated vehicles to cyber-physically interact with each other, yielding the so-called Connected and Automated Vehicles (CAVs). CAVs could use distributed sensing, learning, and control to handle the problems faced by an autonomous system. In the layer of environment perception, distributed sensing enables CAVs to perceive their surrounding environment using not only their own sensors, but also those from neighboring vehicles. This approach can broaden the sensing range of each vehicle and achieve more accurate environmental awareness using much less expensive sensors. Distributed learning allows CAVs to share their decision rules or parameters with each other to achieve "swarm intelligence" in a more efficient way. Lastly, distributed control enables CAVs to coordinate their movements while enhancing their ability to withstand external disturbances and measurement errors. Using these distributed sensing and control techniques can further improve the safety, efficiency, and smoothness of future transportation systems.

Connected and Automated Vehicle (CAV) is one of the most revolutionary technologies of transportation in this century. From the initial concept of automated vehicle, "American Wonder" in the 1920s to the self -driving car companies that burst onto the scene in recent year, the United States emerges as the global leader in CAV research and development. In this chapter, we will review the history and current status of CAV technology in the United States.

Autonomous Vehicles (AVs) are becoming prevalent in our society, from self-driving cars and autonomous shuttle buses on urban roads to delivery robots on the pavements and within buildings. These emerging applications have generated a huge interest in the concept of Mobility-on-Demand (MoD) and specifically, Autonomous MoD (AMoD). In this work, we highlight the initiatives of the Singapore-MIT Alliance for Research and Technology (SMART) in the area of AMoD. We discuss the fundamental building blocks of AMoD systems, the solutions and algorithms that we have developed and successfully deployed for public trials since late 2014, and the challenges that we have encountered during the process.

The context of this book is intelligent road transport, based on Information and Communication Technologies (ICT). The book is written mainly from a technical perspective and especially addresses Cooperative Intelligent Transport Systems (C -ITS), also referred to as connected vehicles. The book takes a technologically neutral position, and also takes policy and business models into account.