# On the Needs and Requirements Arising from Connected and Automated Driving
## 3 5 GCAR Use Cases  
A set of diverse functionalities, which rely on telecommunication systems providing vehicles with connectivity to other nearby vehicles, road users, and to the network infrastructure, enables automated driving. Each specific use case will require some of these functionalities and, most likely, not all of them with the same level of importance. The 5 GCAR project has classified these functionalities into five use case classes in order to analyze the required functionalities for each use case: (1) \"cooperative maneuvers\", (2) \"cooperative perception\", (3) \"cooperative safety\", (4) \"intelligent autonomous navigation\", and (5) \"remote driving\" [10]. These use case classes are briefly discussed in Table 1 and further described, as follows. **Cooperative maneuvers** is a class including use cases for coordinating the driving maneuvers and optimizing the decision making for a group of vehicles. It is based on the principle of sharing local vehicle information and driving intentions and negotiating the planned trajectories. The information is exchanged between nearby vehicles, or with the roadside infrastructure via wireless communication, and it is used to coordinate the driving trajectories of the considered group and optimize the driving decisions in a centralized or a decentralized manner. This involves potential negotiations, including the exchange of planned or preferred trajectories among vehicles, and possible joint optimization of trajectories. Examples of use cases in this use-case class include convoy driving, cooperative intersection management, and cooperative lane changing. **Cooperative perception** is about extending the vehicle perception range beyond the line-of-sight and local field-of-view and sensing angles. It is built on the basis of merging local data collected from different on-board sensors, such as radars, laser, and stereo-vision sensors with remote information being obtained from nearby vehicles and/or from the infrastructure. The resulting perception scenes can be communicated to the automated vehicle or to the driver in the form of see-through, bird's eye view, sensor and state map sharing, or three-dimensional (3D) video composition. **Cooperative safety** includes use cases wherein information is exchanged with the purpose of detecting other road users, e.g., pedestrians and cyclists, and preventing the occurrence of accidents. Various methods are applied to detect vulnerable road users, including on-board sensors, cameras, radars, and communication systems. In contrast to cooperative perception, where communications are between nearby vehicles or with the infrastructure, cooperative safety relies on communications to and from the handheld communication devices that are carried by pedestrians and cyclists. Some examples of use cases in this category are traffic light violation warning or vulnerable road user warning. **Intelligent autonomous navigation** includes use cases aimed at building and distributing real-time intelligent high definition (HD) maps, which enable optimal route selection for vehicles of higher levels of automated driving. This includes the aggregation of the information collected from vehicle sensors (e.g., in case of cooperative perception) and the delivery of map updates with very precise context information (e.g., road structures, reference objects for localization, etc.). The map data assembly and the distribution of the localized HD map to the vehicles is performed based on the vehicles' locations and, thus, rely on the accurate real-time localization, which is one prerequisite