## 6 Architectures Comparison
### [PERSON] et al. [27] Architecture  
The system proposed by [PERSON] et al. [27] defines a system that can detect high-speed head-on collisions and accidents in the golden hour for survival--the emergency services can be contacted, and the survival probability of the affected people increases. As depicted in Figure 5, the proposed architecture in the paper uses various in-vehicle (I-V) networks including BT, Bluetooth low energy (BLE), WiFi, etc. These networks are connected using an in-vehicle infotainment telematics unit. The system is divided into three layers: sensing layer, networking layer, application layer (with deep learning model). The sensing layer is comprised of the sensors that are used to detect the accident, like a micro-electro-mechanical system (MEMS) sensor, Global Positioning System (GPS) module, and on-board diagnostics (OBD)-II bridge. The network layer consists of communication protocols like cellular, 3G/4G, BT, BLE, WiFi, universal asynchronous receiver/transmitter (UART), and ethernet. The application layer is the main layer responsible for the analysis of the accident and contacting emergency services. This layer also involves the deep learning model for assessing the situation, based on the cloud.  
1. Sensing layer: This layer is defined as the layer responsible for the sensing of data values to detect the head-on collision in a vehicle. The sensing layer includes the six-axis gyroscope and MEMS sensor with an embedded accelerometer, OBD-II bridge, and GPS. A collision detection system is a continuous process. When a collision occurs, the window of the collision is 0.1 to 0.2 s, as per several reports. When a collision occurs, the data fetched from the above-mentioned sensors is sent to the network layer to further communicate it to the cloud for deep learning analysis.
2. Networking layer: Since the development and use of vehicular networks and infrastructure has increased in the past years to automate vehicles and services, there is a dedicated use of the Telemetrics platform in vehicles, which is the main basis for the communication here. The telematics unit uses in-vehicle infotainment (IVI) to update the mobile application. This system uses 3G/4G mobile networks for sending the data to the cloud from the network layer. Messages from the IVI system are directly transmitted to the user via this interface: 1. Driver information screen: The information is digitized and transmitted to the driver directly through the driving information screen of the IVI system. 2. Collision detection and alarm screen: The collision detection and alarm screen updates the driver of a car during the driving process. A camera is placed in the front of the vehicle and connected to the proposed IVI system to record real-time streaming images. The collision threshold is defined when the braking is at 4 g and the speed of the car is greater than 80 km/h, as per the Insurance Institute for Highway Safety (IIHS) report for a fatal accident. In such a case, the IVI system will record an image of the abnormal event and upload the relevant information to the cloud-based platform for further vehicle crash-event analysis.
3. Application layer: The IVI system utilizes 3G/4G mobile networks to send relevant data to a cloud server database. The recorded data in the cloud database is then utilized to create a data model that can recognize high-speed head-on collisions or accidents involving a single vehicle. In this research, the cloud-based information platform was built using web development tools like hypertext preprocessor (PHP) and Structured Query Language (SQL). This platform facilitates real-time notifications for incidents of high-speed head-on collisions or single-vehicle accidents.