## 5 Transport
### Challenges and Future Directions  
From this use case, we can observe that we are expecting a ubiquitous connection between the vehicle and the user. Additionally, we are expecting that the user position is communicated to the vehicle in real-time. Manufacturers adopting and implementing this framework would find these requirements challenging, especially since the vehicles might be in remote car parks.  
This brings us to the key drivers for 6G, shown in Figure 1. This use case falls under two of the three key drivers, that is, (i) connectivity for all things, and (ii) time-sensitive applications. It can be argued that the information here is not very time-sensitive; however, for better performance, the frequency of transmitting the location should be increased. Although in this use case the vehicle is stationary, in future work, this vehicle can be moving, such as in the case of car sharing services. The aim then would be to have the vehicle  
Figure 16: Inference results for the two scenarios, where the user walk back/past the vehicle, with respect to time in seconds. In box pictures, the car is marked as a red circle and the trajectory of the walked track is displayed in yellow. The direction of travel is indicated in the arrows. The red cross is the starting point and selected timestamps of the GPS trajectory are marked with stars. The graphs show the corresponding destination prediction as a function of time, that is, the probability that the user is returning to the vehicle. (**a**) First scenario where the driver returns to the car. (**b**) Second scenario where the driver walks past the car without returning to it.  
ready before it reaches the passenger. Having both the passenger and the vehicle moving adds an extra level of complexity. Therefore, both, the connectivity and the frequency of transmitting the locations must be reliable.  
The position in this case study is determined, using the GNSS. However, to achieve full automation, we must use a more accurate positioning technology, hence the currently achieved few-meter accuracy is not sufficient to stir the vehicles. The 6G and its enablers, such as wider bandwidths, higher frequencies and massive antenna arrays, can achieve accurate positioning [94]. Sophisticated machine-learning algorithms can use the data to further improve the positioning accuracy and reliability. For instance, in [95], it was shown that we can achieve cm-level accuracy from a single-snapshot, using millimeter-wave frequencies and maximum likelihood estimation to achieve very high accuracy, despite using a single antenna receiver.  
In general, vehicles have several computers on board, and their processing power (capacity) is already consumed with the current demand. To facilitate the demands of autonomous cars, it is necessary to determine the location of these calculations, as can be done in vehicles, the network edge, or the cloud. If we take the approach of offloading these calculations, it is necessary to have reliable communication with a very short round trip duration. Additionally, if in the future, vehicles communicate with each other to optimise the stirring, the complexity of this requirement will significantly increase.