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In this paper we report the implementation and experimental evaluation of a proposed hybrid communication ecosystem for CCAM applications such as cooperative adaptive cruise control (CACC) and smart intersections. Three wireless technologies have been suggested for communications between vehicles and intelligent traffic lights and are evaluated in this work: ITS-G5 based on IEEE 802.11p and LTE sidelink with PC5 air interface for direct short range links, and regular mobile LTE with LTE Uu air interface for long range or indirect links. The applications used are independent of the communication channel, to enable a comparison on the application level of the different communication technologies. Field experiments were carried out with two CACC-equipped vehicles and three intelligent traffic lights in two field test locations under ideal, i.e., no-traffic, conditions and with real traffic. Experimental results related to CACC show that the best performance in terms of latency is achieved by the ITS-G5 system, while LTE PC5 and LTE Uu links show a penalty of 20 and 50 ms respectively. However, experimental results show that all three communication technologies were still able to guarantee string stable performance of the vehicle platoon. Regarding the smart intersections, an analysis based on field measurements and comparison between long- and short-range solutions is proposed; the analysis includes the impact of each channel on the applications such as speed advisory and green light prediction. The reported experimental evaluation shows the potential of current mobile technologies for CCAM use cases and highlights the way for further CCAM applications based on 5G and beyond mobile networks.

Despite the progress in the development of automated vehicles in the last decade, reaching the level of reliability required at large-scale deployment at an economical price and combined with safety requirements is still a long road ahead. In certain use cases, such as automated shuttles and taxis, where there is no longer even a steering wheel and pedals required, remote driving could be implemented to bridge this gap; a remote operator can take control of the vehicle in situations where it is too difficult for an automated system to determine the next actions. In logistics, it could even be implemented to solve already more pressing issues such as shortage of truck drivers, by providing more flexible working conditions and less standstill time of the truck. An important aspect of remote driving is the connection between the remote station and the vehicle. With the current roll-out of 5G mobile technology in many countries throughout the world, the implementation of remote driving comes closer to large-scale deployment. 5G could be a potential game-changer in the deployment of this technology. In this work, we examine the remote driving application and network-level performance of remote driving on a recently deployed sub-6-GHz commercial 5G stand-alone (SA) mobile network. It evaluates the influence of the 5G architecture, such as mobile edge computing (MEC) integration, local breakout, and latency on the application performance of remote driving. We describe the design, development (based on Hardware-in-the-Loop simulations), and performance evaluation of a remote driving solution, tested on both 5G and 4G mobile SA networks using two different vehicles and two different remote stations. Two test cases have been defined to evaluate the application and network performance and are evaluated based on position accuracy, relative reaction times, and distance perception. Results show the performance of the network to be sufficient for remote driving applications at relatively low speeds (