# Survey on Ranging Sensors and Cooperative Techniques for Relative Positioning of Vehicles
## 2 Requirement Analysis  
In this work, the ego vehicle is defined as the vehicle that requires information on the position of surrounding vehicles with respect to it. The surrounding vehicles, the target vehicles, are located around the ego vehicle, possibly in another lane, another direction or street. It can also happen that the relative positioning system might detect vehicles driving on another level, as for instance on an overhead bridge. The relative position of a target vehicle as seen from the ego vehicle is generally supplied in the ego vehicle coordinate frame. In automotive applications, usually a coordinate system is defined at the foremost center point of the vehicle with the \(x\)-axis pointing in the driving direction, the \(y\)-axis perpendicular to it in the left direction and the \(z\)-axis pointing up. The relative position is, in general, the three-dimensional vector pointing from the origin of the ego coordinate system to a predefined point in the target vehicle. Since vehicles generally drive on the same plane, a two-dimensional relative position is provided by many systems and is sufficient for most safety critical applications. This is depicted in Figure 2a. Many driver assistant applications require, along with the relative position, information about the relative velocity towards surrounding vehicles. For instance, FCA applications profit from having a measurement of the relative speed in order to predict more accurately the Time to Collision (TTC) with another car. In order to compare non-cooperative and cooperative approaches, the way in which relative position information in each of the technologies is obtained has to be explained. Radar, laser scanners and vision-based systems first perform the measurement step, in which raw measurement data are extracted out of a raw signal by applying signal processing algorithms. The raw measurement data are a cloud of reflecting points in radar and laser scanners or one or several pixel matrices in vision-based systems. In the detection step, the raw measurement data are segmented and clustered into objects. An object classification step aims at sorting objects into different classes, as for instance vehicles, bicycles and pedestrians. Usually a geometric model is used to describe objects in a parametrized way. Vehicles are thus abstracted as rectangular cuboids, which are described by its position, its dimension and its orientation in the ego vehicle coordinate frame (see Figure 2b). After the detection, the position of the vehicle needs to be estimated over time. This is known as tracking and can be performed in many different ways. Usually, movement models that predict the future path of the vehicle are used. Common movement models range from simple kinematic models as the rectilinear-constant-velocity model or the constant-accelerated-constant-turn-rate models, to dynamic models that take forces into account, as the well-known two-wheel bicycle model or the four-wheel Ackermann model. New measurements are included by performing a data association step  
Figure 2: The figure (**a**) depicts the ego and target vehicle driving in an Earth-fixed navigation frame \([x^{n},y^{n}]\). A ranging sensor in the ego vehicle is able to detect the target vehicle, whose position is given in an ego vehicle coordinate frame \([x^{i},y^{i}]\). The figure (**b**) shows how a geometric model of the target vehicle can be constructed from the ranging sensor measurement data. Using a tracking algorithm, the future location of the target vehicle can be predicted. in which the target that most probably produced the measurement is found. In the last step, a semantic classification provides a broader understanding of the surrounding situation and enables higher level reasoning, as for instance to control an automated vehicle. Further information regarding the different processing steps in object detection and tracking for vehicular environments can be found in [4; 5; 6].