# On the Needs and Requirements Arising from Connected and Automated Driving
## 2 The Future Needs of Connected and Automated Driving  
In [9], six driving automation levels have been defined, where higher automated driving levels require the increase of the automation of the vehicle functions while reducing the active involvement of the driver. This development will lead to fully automated driving systems that will, with increasing automation level, gradually allow for the driver to remain completely out of the loop, while vehicles are expected to take over the complete driving task. To achieve this vision, several key capabilities need to be implemented in the vehicles to handle the significant driving scene complexity that might be encountered in different situations. This includes the capability of a vehicle to center itself within the lane and control its trajectory and speed by performing automated steering interventions. However, the vehicle has to perform a continuous sensing of its surrounding to maintain a complete awareness of its environment to be able to make reliable decisions on the best appropriate actions. This is achieved through a combination of information obtained from multiple local sensors, e.g., radar, cameras, etc., which are necessary for generating a complete and reliable vehicle environmental model. This process of sensor data fusion from various sensors to create surrounding awareness is known as perception. The local on-board sensors field-of-view is often limited to line-of-sight (LOS), but it can be complemented by the information received from other vehicles, roadside infrastructure, or from a back-end server through wireless links. Such sensor data shared by other vehicles can be used to extend the vehicle field-of-view beyond the LOS, resulting in \"behind-the-corner\" extension of perception and a 360-degree reliable environment model.  
Beyond the extended environmental perception, fully automated vehicles will require up-to-date and high definition map information, e.g., including road descriptions and context information. Such services could be provided, for example, from a cloud server and would enable the vehicle to navigate intelligently and safely through different types of environments. As part of the automated driving tasks, a vehicle also needs to be able to perform appropriate maneuvers, such as automated lane changing and collision avoidance. To this end, communication with the surroundings and cooperation with nearby vehicles becomes more relevant. Moreover, such cooperation enables the execution of joint maneuvers that increase the road safety and help the vehicles navigate through difficult traffic situations. In addition to cooperation with other vehicles, the detection of vulnerable road users, such as pedestrians, bicycles, and motorcycles, helps in reducing the traffic accidents by providing early notifications regarding potential collisions and avoidance through appropriate maneuvers. In this regard, the accurate localization of the vehicles and the vulnerable road users is a fundamental capability that needs to be developed. Finally, remote driving is another use case that that can assist progression to higher automation levels. Remote driving can be realized by having a remote operator monitoring and controlling the vehicle movements, e.g., via a cloud server, and ready to intervene as the need arises in order to assist the vehicle or the driver through appropriate maneuvers that increase the road safety and efficiency. The information that is shared from different sources, e.g., nearby vehicles, road infrastructure, and map servers, is expected to provide a form of redundancy that is needed for increasing the reliability of the automated driving systems.