# Augmenting CCAM Infrastructure for Creating Smart Roads and Enabling Autonomous Driving
## 1 Introduction  
The advancement in sensor technologies, mobile network technologies, and artificial intelligence has pushed the boundaries of different verticals, e.g., eHealth, and autonomous driving. Autonomous driving has the potential to cope with major challenges in the transportation industry such as road safety and traffic efficiency. According to the Society of Automotive Engineers (SAE), ref. [1] there are five levels of autonomous driving (AD), ranging from Level 1 (L-1) to Level 5 (L-5). The L-1 indicates no automation, whereas L-5 indicates full automation; however, a fully automated vehicle will not have a driver's cockpit. The advancement of autonomous vehicles (AVs) has evolved through the early stages of automation, and soon L-4 AVs will be observed operating on roads. The L-3 automation-equipped vehicles have already been manufactured by automakers such as the Audi A8 Sedan [2]. The five levels of AD are discussed in detail in Section 2.1. The race to L-5 AVs is still going on, and the stakeholders, including automakers and technology (sensor and communication) experts, are constantly refining their approaches to reach the necessary level of automation for their vehicles. The construction of testbeds anddemonstrations all around the world is a new step toward the reality of AD. These testing grounds play a crucial role in assessing the capabilities of AVs in various environmental dynamics. The main goal of the testbed is the creation of a realistic environment that accurately represents the world in which AVs will function. The complex dynamic traffic environment on roads necessitates the cooperation between AVs, classical vehicles, road infrastructure, and other road users. Classical purely sensor-based collaborative driving is vulnerable to line-of-sight limitations, processing lag, and sensor mistakes [3]. The concept of AD is based on a vehicle's perception--also known as situational awareness--or its ability to comprehend its surroundings and respond to their changing dynamics. The vehicles are capable to create a perception of their surroundings using the knowledge and sensory data from on-vehicle sensors. Some degree of automation is made possible by the perception of vehicles and their ability to communicate with other vehicles, although it still relies on the visibility of the individual vehicle. How well the AV can adapt to various circumstances and dynamics is unknown. Autonomous driving promises to improve traffic safety. However, the promise of greater safety suffers because the quality and scope of a vehicle's perception is constrained by its sensors. To cope with the above challenges, a paradigm shift known as cooperative, connected, and automated mobility (CCAM) is required to enhance the environmental understanding of vehicles in complex urban settings. The CCAM has the ability to advance current automated driving to L-4 and L-5 vehicle automation. The deployment of CCAM infrastructure necessitates the upgrading of essential assets such as vehicles, network infrastructure, roadside infrastructure, and cloud infrastructure to achieve greater AD performance in complicated urban environments. The CCAM will shift the mobility paradigm towards mobility as a service (MaS), resulting in more reliable, comfortable, and safe transportation systems. To realize this imaginative scenario, vehicular communications, automated driving, and cooperative transportation systems are increasingly observed as the building blocks of CCAM applications. To achieve this, standardizing and regulatory bodies, automotive Original Equipment Manufacturers (OEMs), road operators, telecommunication operators, and other interested stakeholders are collaborating to build massive infrastructures and conduct field tests of cooperative intelligent transport systems, which will pave the way for connected, automated, and cooperative vehicles. In connection with this, we provide an overview of the recent state-of-the-art research conducted on CCAM and highlight the need for this research.