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Enhancing V2x Communication in Intelligent Vehicles Using Deep Learning Models

Year: 2024

Type: article

Abstract: The surge in Vehicle-to-Everything (V2X) communication marks a pivotal evolution in intelligent transportation systems, enhancing safety, efficiency, and sustainability in urban mobility.This thesis introduces a groundbreaking framework that harnesses deep learning models to elevate V2X communication in intelligent vehicles.This framework focuses on adapting and optimizing communication protocols via an innovative algorithm.It specifically targets the prevalent challenges of latency, reliability, and scalability by dynamically predicting and managing communication patterns using advanced deep learning techniques.A key innovation of this study is the development of an adapted algorithm that real-time optimizes data transmission paths and schedules, considering the dynamic nature of urban traffic environments.

Source: International Journal of Academic Research in Business and Social Sciences

DOI: 10.6007/ijarbss/v14-i10/23255

Authors: Jiang Yi, Shamsul Arrieya Bin Ariffin

Institutions: Jiangxi University of Water Resources and Electric Power, City University Malaysia, Nanchang Institute of Technology

Language: English

Topic: Vehicular Ad Hoc Networks (VANETs)

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: diamond

OpenAlex ID: W4403489771