# Augmenting CCAM Infrastructure for Creating Smart Roads and Enabling Autonomous Driving
## 4 Literature Review  
Perception is one of the core layers of autonomous driving. The perception is generated through the edge dynamic map (EDM) by the roadside unit (RSU or Edge) available in the external infrastructure. The most generic challenges of EDM include:  
* Challenging representation of various events (data) happening over the road,
* Right communication bit-pipes that meet the dynamically updating requirements,
* Processing the data to have conclusive outcomes, e.g., recommendation, warning, etc. Obviously, it is difficult to update information (maps) frequently given the constrained transmission and computing resources at the vehicle. Therefore, researchers are focusing on generating information (maps) over the RSU. It should also be highlighted that these challenges kept evolving as we progress to achieve the goals of higher automation levels. Research community and relevant stakeholders have been active in evolving the EDM by enhancing the data representation, processing, transforming the data into events and driving relevant information, etc. The Table 1 shows a quick overview to the reader about achieving perception through information (map) generated over RSU. In what follows next, we discuss the most relevant approaches that falls nearer our domain. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Study and Year** & **Challenge** & **Condition/Focus** & **Solution Approach** & **Architecture** & **Use Case/Scenario** & **Shortcomings** \\ \hline \multirow{4}{*}{EdgeMap Wur 2022} & Frequent updates of HD Maps & A new HD map (EdgeMap) & A crowdsourcing HD Map & \multirow{4}{*}{Netdoor} & \multirow{4}{*}{Netdoor} \\  & under limited network networks & Cybersourcing data in & (EdgeMap) for minimum & & & \\  & resources, \(\alpha\), transmission & MIC, Design and network access with & & & & \\  & and computation resources, & Implementation of DATE & balance frequency requirement & & & \\  & Maintaining up-date map & Algorithm, Minimum & A DARE Algorithm based on & & & \\  & through personalized collection & resource utilization and & multi-agent deep & & & \\  & vehicles, & visualization offloading decision & reinforcement learning for & Real-Experimentation: Yes & & \\  & and resource- & & presented via extensive & vehicular offloading and & & \\  & researching problem & network simulations. & & & & \\  & & & & & \\ \hline \multirow{4}{*}{CAMMO Vur 2021} & Real flexible and modular & Enabling cross-technology & Integration of different V2X & \multirow{4}{*}{Netdoor} \\  & platforms for hybrid & support CONAMICS, & subport CONAMICS, & & & \\  & communication, Different communication techniques & Maximizing and design & C-V2X PC, TESS, 4 GC, 5 GC & & & \\  & between different vehicles & Helping GANs & vehicle/infrastructure sensors, & Real-Experimentation: Yes & & \\  & from different OEMs, & towards management & act act, & \(\text{IMB}\), and external & & \\  & and integration & service providers) & & & & \\ \hline \multirow{4}{*}{5C-MEC Vur 2021} & \multirow{4}{*}{V2V} & [PERSON] orchestration of & \multirow{4}{*}{Netdoor} & \multirow{4}{*}{Netdoor} \\  & & the surrounded & & & & \\  & environment. Cooperative & MIC provides the exact & & & \\  & communication overhead and & perspective between Avs & & & & \\  & latency issues & Enable streaming-based & communication latency. & &  
The EdgeMap [11] explores crowdsourcing data from connected vehicles to HD map as a way to minimize the usage of network resources while maintaining latency. This is accomplished by designing the DATE (distributed adaptive offloading and resource reservation) algorithm and formulating offloading decisions and resource usage. In order to minimize resource usage and maintain latency, they proposed a vehicular offloading agent (e.g., subseconds) based on multi-agent DRL technique and a resource reservation controller (e.g., minutes) based on Gaussian process regression (GPR).