# Augmenting CCAM Infrastructure for Creating Smart Roads and Enabling Autonomous Driving
## 1 Introduction
To address the issue of cross-sector collaboration for connected, cooperative, and automated mobility, [PERSON] et al. [4] proposed an integrated perspective. They analyzed the taxonomies from three different perspectives; namely: the user, the vehicle, and the road infrastructure. The proposed taxonomies are comprised of the user communication of automated driving, the SAE cooperation classes, Infrastructure Support for automated driving, and levels of service for automated driving. They concluded that the proposed taxonomies should be used and applied as a shared knowledge that necessitates tight cooperation between the players aiming to plan, prototype, test, and implements CCAM services in the upcoming decade. In another study, [PERSON] et al. [5] presented a flexible and modular testbed, with a focus on evaluating the CCAM applications. They demonstrated a use case leveraging a 4G system, where an AV offloaded processing tasks to an edge server, which analyzed the images, provided routes, and sent guidance commands back to the vehicle, demonstrating the potential of edge computing and wireless technologies. [PERSON] et al. [6] emphasized that the challenges including interoperability among interested stakeholders, seamless connectivity, and the uninterrupted supply of real-time services across borders need to be carefully examined to successfully realize the cross-border CCAM services. To cope with these challenges, they presented a summary of the challenges, demands, technical difficulties, and gaps from the standardization, regulatory, commercial, and legal perspectives that need to be resolved for the quick and effective adoption of 5G-enabled CCAM services, particularly in cross-border settings. [PERSON] et al. [7] also researched the CCAM with an emphasis on cross-border environments, and a novel architecture for CCAM service continuity was developed in the cross-border corridor that integrated a federation of multi-access edge computing concepts to maintain the service continuity. Moreover, they developed an idea for a simulative/emulative platform for C-V2X applications to make it easier to construct complex CCAM use cases, particularly in cross-border or cross-domain environments. Going over the literature, we found that CCAM infrastructure has been actively used for various mobility applications. However, the literature still lacks in providing the right set of sensory infrastructure, communication infrastructure, and machine learning-based algorithms for traffic and road analysis. Therefore, there is a dire need for advanced CCAM infrastructure. In what follows next, we provide the contributions of this research. In this paper, we leverage the CCAM infrastructure for the development of smart infrastructure, which helps in enabling higher levels of autonomous driving capabilities. The aims and objectives of the paper are as follows:  
* To describe the Level 5 autonomous driving relevant concepts,
* To identify the common challenges in the roadside infrastructure for AD,
* To lay down the technical requirements of the CCAM Infrastructure,
* To identify the right set of sensory infrastructure and communication interfaces,
* To parameterize road and network infrastructures for advancements and evaluations,
* To process data and execute intelligent machine learning algorithms,
* To validate CCAM infrastructure's mobility applications for traffic flow and mobility predictions. With the view of highlighting the design goals of level 5 AD, challenges, and directing the readers to the potential solution candidates, we carefully structured the paper. In Section 2, we provide the background (tutorial) type information to equip the readers with the needed information, enabling them to comprehend the contents of this article. Following which, we dedicate a full section, Section 3, on the challenges that captures the crucial challenges hampering the realization of level 5 AD.