# 5G Cross-Border Operation for Connected and Automated Mobility: Challenges and Solutions
## 1 Introduction
In a V2X architecture featuring mobile edge computing (MEC), this problem should be solved, and the solution cannot be to have just one MEC provider. The second unique property is the existence of the multi-OEM (original equipment manufacturer), multi-MNO (mobile network operator), and multi-vendor interoperability challenge [5]. For example, some CCAM services may require real-time response and ultra-high reliability when vehicles, having been manufactured by different OEMs, drive through various national borders and need to roam between different MNOs operated by different companies and under different regulations and also using telco equipment provided by different vendors. Even in these scenarios, service continuity must be guaranteed, and thequality of services must not be degraded when crossing borders [6]. If degraded, it is important to anticipate this degradation so that countermeasures can be taken; e.g., reduce the level of driving automation with enough anticipation or simply stop a driverless vehicle until connectivity quality is resumed and allows for safe operation. The third unique property is the role of the road authority as another source and sink of information [7]. This comes with often closed, sometimes even proprietary, information technology (IT) systems needing integration in a MEC-enabled distributed computing V2X network architecture. A particular challenge arising from this is that crossing national, in some cases also regional, borders results in a new road authority with own IT infrastructure becoming responsible. The fourth unique property is the availability of information about the mobility of the vehicle which can be obtained from its navigation system. Many past and present research activities, e.g., [8; 9] focus on exploiting position and route information to improve quality of services (QoS) or, at least, make delivering a guaranteed QoS less challenging. In practice, most of those approaches fail due to the lack of precise routing information. While this is technically feasible [10] in a vehicular context, security, particularly privacy, and architecture challenges need to be solved. For all these reasons, and in order to be able to meet the objectives set out by the strategy defined in the 5G action plan, there is a need to trial and validate 5G technologies at large scale in cross-border settings with the mission to reduce uncertainties before CCAM services running on top of 5G communication infrastructures can be offered to the market. In this context, the European Commission is partially funding innovation actions, which are working to deploy 5G-enabled CCAM cross-border large scale trials. They are 5 GCroCo (5G Cross Border Control) [11; 12], 5G-CARMEN (5G for Connected and Automated Road Mobility in the European unioN) [13], and 5G-MOBIX (5G for cooperative & connected automated MOBILITY on X-border corridors) [14], and belong to the family of projects under the umbrella of the 5G-PPP (5G public private partnership) [15]. These three projects gather effort from key European partners from both the telco and automotive industries to contribute to the definition of a successful path towards CCAM services in cross-border scenarios and to reduce the uncertainties of a real 5G cross-border deployment. These projects not only aim at deploying the trials, but also at identifying business opportunities and defining new business models for disruptive CCAM services, which can be possible thanks to 5G technology, as well as ensuring the appropriate impact upon relevant standardization bodies both from the telco and automotive sectors. In this paper, the focus is on the 5 GCroCo project to describe its motivation, key objectives, and initial achievements and findings. First, the aim is to describe the cross-border challenges that have been identified by 5 GCroCo in Section 2, and then describe the use cases that have been selected to conduct the trials in Section 3. The requirements for each of the use cases are described and quantified also in Section 3.