## 4 Overview of Research Projects on AI/ML for Communications and Networking
### Europe--H2020 Research Framework
#### 4.1.4 5g-Carmen  
5G for connected and automated road mobility in the European Union (5G-CARMEN) (approximately EUR 19 million EU contribution between November 2018 and October 2021) focuses on four automotive use-case families, namely, cooperative manoeuvring, situation awareness, video streaming, and green driving. Big data analytics and AI/ML tools become essential in addressing the KPIs associated with the selected use cases [121].  
Firstly, the cooperative manoeuvring and situation awareness use cases comprise several subcases such as cooperative lane merging, back-situation awareness of an emergency vehicle arrival, and motorbike awareness. To improve safety and enhance the ride quality at complex merges, vehicles share their current position, speed, and trajectory with a roadside unit (RSU). A MEC server at the RSU processes the received data, using AI/ML, and recommends optimal actions for the drivers, e.g., speed profiles creating sufficient gaps between successive vehicles. As a result, accurate position information, in the order of a meter or less, also becomes essential [122]. In this regard, cooperative perception algorithms at the vehicles that optimally fuse the location data received over the global navigation satellite system and inertial monitoring unit (GNSS/IMU) with that obtained from on-board sensors, such as lidar and cameras, can be employed to meet the required constraints on localisation accuracy before the vehicles share their locations with the RSU. Furthermore, in the considered use cases, the exchange of communication messages between the vehicles and the RSU is likely to involve short packets to satisfy the low-latency communication constraints, and thus, ML-based decoding of channel codes also becomes relevant; see Table 1.  
Secondly, the video streaming use case concerns the continuous reception of high-definition on-demand video while onboard and in challenging situations, where the video quality is likely to deteriorate, due to handovers in cross-border areas. In this scenario, given the trajectory of the subject vehicle, AI/ML models running at the edge/cloud can assist in predicting the quality of the received video stream, while driving through the cross-border area. To do that, the 5G-Carmen platform should account for the historical data about the propagation channel conditions in the area, the current demand at the RAN, and the traffic congestion at the core network. Besides predictive analytics, the AI/ML algorithms shall also prescribe in advance appropriate courses of action to avoid streaming disruptions, e.g., prompt the downloading of more video chunks when the network conditions are strong to compensate while travelling across the problematic areas [122].  
Thirdly, the objective of green driving is to improve the environmental quality in sensitive regions by regulating the density of road traffic and the emissions of hybrid vehicles with an electric powertrain. This use case involves extensive data collection from smart sensors on the vehicles and RSUs for monitoring the air quality, which is then sent to the edge/cloud for analysis and inference. Using AI/ML, the collected data are associated with historical measurements, the current weather conditions, and the types of vehicles on the move to showcase the air quality status and its predicted future quality to the responsible stakeholders. Apart from showcasing, the 5G-CARMEN system ultimately supports prescriptive analytics suggesting environment-friendly actions, e.g., speed profiles for individual vehicles depending on their type (motorcycles, cars, or lorries), alternative routes for non-electric vehicles, and definition of electric zones to relieve air pollution in critical areas [122].