## 3 The Proposed FED-XAI Framework for QoE Predictions in V2X Environments
### Exemplary 6G Use Cases in V2X Environments  
The use of Information and Communication Technology, and especially AI techniques, in the automotive sector is gaining increasing attention [16]. Given the large amount of data generated by multiple, distributed sources, AI is one of the key technologies to enable innovative use cases, such as autonomous driving [17; 18], improved safety [19] and platooning [20].  
In the Hexa-X project, a general AI-assisted V2X use case is described ([1], Section 4.2.7.3). In future enhanced automotive uses cases or services, collecting a high volume of contextual and sensor data from traffic participants and road infrastructure will be common practice. With these data, a Digital Twin (DT) of the traffic environment can be created in the cloud or the edge of future mobile networks. This DT can be distributed across multiple edge nodes corresponding to a coverage area or to higher hierarchical edge-cloud nodes in different locations of the network and employed to optimize vehicle traffic by generating inputs to traffic management as well as driving or manoeuvring instructions to traffic participants. Moreover, DTs can be used to support ToD, by providing real-time information and predictions of road traffic information, as well as predictions for the QoS of the radio access network (e.g., radio signal quality). Real-time management of DTs is very challenging and requires network capabilities not available today. To guarantee safety, system operation requires extremely low latency, high reliability and ultra-high location accuracy along with efficient and explainable AI algorithms. Multiple edge nodes can be part of MNO or road infrastructure, and, at a given time, geographically proximate nodes might contain similar AI models which are either part of the same learning federation or updated by applying knowledge sharing. Moreover, when it comes to safety-related V2X services for automated and connected vehicles, most of the use cases analysed by 5 GAA [2], e.g., See Through, Vulnerable Road User protection, Intersection Movement Assist, or In-Vehicle Entertainment, are evaluated, attributing great significance to security and privacy. In this perspective, the FED-XAI approach provides an intrinsic benefit, compared to its centralized learning counterparts.  
In [1] authors proposed to further improve the ToD use case by applying the AI-as-a-Service approach: a driver planning to perform a journey would like to be informed of any V2X service degradations along the planned route by means of in-advance QoS predictions based on a plurality of data, gathered, e.g., from the Uu and PC5 interfaces but also from vehicle sensors (RADAR, LiDAR, etc.). Such notifications will allow for the decision upon activation of other V2X-related functionalities: for instance, automated driving features should be avoided if the predicted QoS in a certain part of the route would not allow such features to be used; hence, the driver should take control of the car until new, favourable QoS predictions suggest switching to automated driving mode. Moreover, QoS predictions could also be used to schedule the execution of non-V2X functionalities, e.g., software over-the-air downloads. In addition, in this scenario, the various data owners are encouraged to participate in the FED-XAI procedure, because the collaborative model will blend the knowledge extracted from all data rather than only from local data.  
In the following section, we describe an illustrative automotive scenario, which can be the basis of evaluating the performance of the FED-XAI concept.