# Augmenting CCAM Infrastructure for Creating Smart Roads and Enabling Autonomous Driving
## 4 Literature Review
Therefore, the vehicles do not share their information with each other to introduce latency and communication overhead. When an AV request for the holistic understanding of the environment, e.g., See-Through use case of Extended Sensors, the MEC provides the exact information of the other vehicle to achieve the cooperative perception with low latency communication. However, it is not clear if the MEC is able to prioritize between vehicles' information when responding to the query of the requested vehicle. In other words, since the database connected to MEC has a time-series structure, it could contain records of vehicles with the same time stamp. Furthermore, the existing solution approach only covers streaming-based CAM applications. In [14], the authors present live video streams (data) delivery while ensuring low latency and study how it can be applied for AD use cases such as remote driving and platooning. To ensure smooth video playback, their solution minimizes video stream outages during handovers between two different network operators (cross-border). The use of live video feeds can assist drivers in making timely decisions. However, timely video delivery is only possible with a low glass-to-glass latency (<500 ms), which depends on the available bandwidth, hardware, and technologies. Remote driving makes use of a computer in the vehicle that is connected to the vehicle's OBU (5G connectivity) and an HD webcam. Whereas, platooning makes use of cloud-based media server for providing live video streams to follower vehicles. The RTSP protocol is used to deliver streams to receivers (streams are announced via REST API calls) in real time. There are three main streaming scenarios (i.e., cloud-based, fog-based, and V2V-based) supported by their architectural entities (i.e., sender, server, and receiver). The experimental setup is two-fold: (1) fog-based includes entities connected via local wired network; (2) cloud-based includes entities connected via 4G LTE network and OpenStack cloud platform. The experimental machines are:  
* Jetson TX2, GPU with 256 NVIDIA CUDA cores, Quad-Core, 8 GB LPDDR4 RAM, and Ubuntu 18.04. * A virtual machine (in OpenStack cloud server), 4 cores Intel Processor (Haswell, no TSX, IBRS), 4 GB RAM, and Ubuntu Server 18.04. * Intel(R) Xeon(R) CPU [EMAIL_ADDRESS] GHz, 8 GB RAM, and Ubuntu Desktop 18.04. The authors leveraged the NVIDIA GPU hardware encoder (H.264/H.265) in Jetson TX2 for achieving low end-to-end latency. A recovery module (written in C language that used GStreamer framework) is implemented to cope with failure cases such as network disruption. As video streaming in the aforementioned AD use cases requires low latency (<500 ms) in order to achieve a safe experience, the authors conducted 11 different executions which resulted in latency of 80-100 ms in the fog-based scenario and 105-125 ms in the cloud-based scenario. In order to guarantee smooth playback during handover, 30 handover operations resulted in recovery times less than 300 ms, satisfying requirements of no disruption during network outage. The CarMap [15] creates a lean representation of the 3D feature-map using crowdsourcing and updating the map in near real-time on a cellular network. The idea behind crowdsourcing is to leverage the vehicles' sensors, so that each vehicle is able to upload map updates to a cloud service, making the updated map available to other vehicles. Among the features of CarMap are the following: lean representation of feature maps, use of approximate position information (e.g., from GPS), localization based on dynamic object filtering via resource-aware algorithm, map diff (differences) algorithm, and stitching algorithm. These features address challenges such as map size, environmental dynamics, effective feature matching, and fast map-updates. The CarMap's architecture collects data, generates segments via map segment generator, filters objects, performs stitching for map reconstruction in the cloud, and then shares with other vehicles for localization.