# 5G Beyond 3 GPP Release 15 for Connected Automated Mobility in Cross-Border Contexts
## 3 Challenging Cross-Border Use Cases
### Use Cases' Requirements  
Each of the identified use cases can be implemented with different levels of automation while they all require high performance parameters in terms of connectivity. In this paper we focus on highly automated driving, that is, SAE L4 and L5. Here, the requirements are even more demanding as there is no handover to manual driving. The spatial- and time-accuracy required by L4 and L5 autonomous vehicles with respect to object localisation are key factors when translating the nominal performance of communication technologies to operational parameters of use cases. The latency in the positioning messages received from another vehicle or entity adds uncertainty to the transmitted localisation value, with a higher impact on the longitudinal direction. This effect is depicted in Figure 1 for the longitudinal error. The localisation requirements for autonomous vehicles were studied in [33], and they concluded that a maximum lateral error of 0.57 m and a maximum longitudinal error of 1.40 m are acceptable for passenger vehicles travelling on highways--the most common road type at cross-border corridors. Considering that state-of-the art ego-vehicle localisation methods have in general sub-metre accuracy in the order of several decimetres [34], there is little room for positioning error induced by network latency. To study the potential effect of the latency on the localisation error, we have measured the position error induced by different latency values using a public driving dataset. More specifically, we have used three highway scenarios from the CommonRoad dataset [35]. The scenarios were partly recorded from real traffic and partly hand-crafted to create challenging situations. The selected scenarios include lane merges and curves to make them more challenging. More information about the selected CommonRoad dataset scenarios can be found in Table 2.  
Figure 1: Left to right in the figure: (1) Actual position of the vehicle when the message is sent (\(t=t_{0}\)). (2) Position estimated by the onboard positioning system. This is the position communicated. Note that there is a \(\varepsilon_{measure}\) longitudinal error. (3) Actual position of the vehicle at \(t=t_{0}+latency\). This is the instant in time that the message is received by another vehicle. There is a \(\varepsilon_{comm}\) error caused by the network latency. The receiver needs to deal with a \(\varepsilon_{total}\) offset. The dataset provides position, velocity, acceleration and heading values of each vehicle recorded at 10 Hz, which is the standard frequency for sending this kind of vehicle data [36]. In this study and based on SAE J2945/1 [37], the receiver estimates the current position of the transmitter vehicle based on the latest received message assuming that the transmitter vehicle is moving at a constant acceleration and heading. For each vehicle position data in the dataset, we have estimated the position after a certain time lapse that would correspond to the message latency. The position error added by the latency is calculated as the Euclidean distance between the vehicle position estimation and the actual position at the time that the message reaches the receiver. This ground truth is obtained by using a quadratic interpolation between the closest points that are part of the dataset. As specified by the European Committee for Standardization (CEN) and European Committee for Electrotechnical Standardization (CENELEC) [38], positioning accuracy is represented with a set of three statistical values given by the 50 th, 75 th and 95 th percentiles of the cumulative distribution function (CDF) of the position error. The empirical CDF obtained in our study with the datasets of Table 2 is depicted in Figure 2 for latencies in the range of 1 to 100 ms. A latency higher than 100 ms would make impossible the real-time processing of the received data, this being the maximum tolerable latency for vehicle-to-vehicle (V2V) communication [39].