In the rapidly evolving domain of automotives, the integration of advanced sensing technologies and data processing methodologies is crucial for enhancing vehicle safetyand performance. This project focuses on the "Performance Analysis of Mobile Augmented Reality Using Mobile Edge Computing in Automobiles," with a particular emphasis on sensor integration and data processing techniques. 

A comprehensive sensor suite is deployed within a vehicle, including cameras for parking assistance, distance calculation, and lane detection, infrared cameras for nightvision, Ambient Light Sensors (ALS) for adjusting interior and headlight brightness, microphone arrays and noise level sensors for voice command recognition and emergency siren detection, Global Positioning System (GPS) for precise location tracking, Vehicle-to-Everything (V2X) communication sensors for inter-vehicle communication,and rain sensors for automatic wiper control.

The data collected from these sensors is processed using two distinct approaches: cloud-based processing and Mobile Edge Computing (MEC). The vehicle navigates through a custom-designed map, equipped with nodes, transceivers, processing units, and a Base-Station (BS), enabling a thorough comparison of the performance metrics, latency, and throughput of both the techniques.

By comparing the performance of cloud-based processing with MEC, this research aims to demonstrate the potential advantages of edge computing in reducing latency and enhancing the responsiveness of automotive systems. The findings will provide insights into the enhancement of real-time data processing in automobiles, highlighting the efficacy of MEC in supporting complex, safety-critical applications like Mobile Augmented Reality (MAR) and Advanced Driver Assistance Systems (ADAS). This work contributes to the ongoing development of robust and low-latency automotives, with implications for improving safety and efficiency in future intelligent transportation systems.