# Augmenting CCAM Infrastructure for Creating Smart Roads and Enabling Autonomous Driving
## 5 Proposed CCAM Solution for Autonomous Driving  
The motivation to contribute to the proposed CCAM infrastructure is driven by the needed solutions to realize the use-cases of autonomous driving in a complex and urban environment. It should be highlighted that challenges of urban and open environments are a lot different then those of controlled, rural, and highway environments. Hence, the classical methods of creating local perception, finalizing the trajectory, local planning, etc., may not cope with the use-cases of the higher automation levels. Enriched perception and support for decision making is required, which will be provided by the CCAM infrastructure. This section focuses on detailing the proposed CCAM infrastructure and relevant enabling technologies therein. It should be highlighted that the proposed infrastructure is based on the experience collected by the authors in practical implementation of the concepts (as PoCs) in Europe and UAE. The readers are referred to [17] and Table 17 in [3] for engagement and contribute in such activities. In what follows next, we provide the details of the needed CCAM infrastructure. The core idea is to extend the classical roadside unit to a more sophisticated and intelligent edge infrastructure that does not only perform the task of relaying the messages between and among vehicles and infrastructure. To understand the need and gains of the CCAM infrastructure, let us discuss the big picture, as shown in Figure 2. The figure shows three layers, where the middle layer provide the details of the CCAM infrastructure. Meaning thereby, the evolved roadside unit with the CCAM infrastructure is the intermediate level and it hosts compute hardware and communication interfaces. We propose that roadside infrastructure should go beyond serving as the roadside unit of an existing vehicular network to develop it in a manner that allows for the development of the following features:* The ability to create dynamic segments for roads. * The ability to adapt and understand dynamic and complex environments. * The ability to process raw data and build intelligent machine learning pipelines. * The ability to perform layer-to-layer communication using upstream and downstream channels. An evolved roadside unit with CCAM infrastructure, with its features and applications, is shown in Figure 2. The following categories may describe a result of using on-road deployed sensors to capture data, applying ML/AI methods, and making relevant decisions:  
* Categorization--AI/ML methods can be used to categorize vehicles based on the sensory data captured by the on-road sensors. * Activity Analysis--The data can be processed and analyzed using AI/ML methods to create heatmaps of various activities across the road segments. * Tracking--AI/ML methods can be used to track the objects, once the objects are properly detected, for creating an enrich perception of the complex road segment. * Traffic Analysis--AI/ML methods can process raw data to provide useful insights on traffic signs, traffic signals, speed, mobility patterns, and traffic density. In what follows next, we provide the details of the design, involved technologies, and interfaces. The proposed CCAM infrastructure may broadly be categorized into the following major technological and solution categories:  
* Sensors and devices--This category contains all the devices and sensors that are used for collecting the right set of information/data from the road segment, i.e., devices that help capture the dynamics of the road segment. More details about this for the proposed CCAM infrastructure may be found in Section 5.1. * Computation--This category corresponds to the right set of accelerators, e.g., GPUs, CPUs, TPUs, FPGA, etc. More details about this for the proposed CCAM infrastructure may be found in Section 5.2. * Communication--This category defines the set of communication technologies that help achieve the objectives of the right communication bit-pipes for executing different  
Figure 2: Details of the CCAM and evolved roadside unit.