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Abstract

The primary elements of Intelligent Transportation Systems (ITSs) have become Vehicular Ad-hoc NETworks (VANETs), allowing communication between the infrastructure environment and vehicles. The large amount of data gathered by connected vehicles has simplified how Deep Learning (DL) techniques are applied in VANETs. DL is a subfield of artificial intelligence that provides improved learning algorithms able to analyzing and process complex and heterogeneous data. This study explains the power of DL in VANETs, considering applications like decision-making, vehicle localization, anomaly detection, traffic prediction and intelligent routing, various types of DL, including Recurrent Neural Networks (RNNs), and Convolutional Neural Networks (CNNs) are mentioned for their efficiency in VANET applications. The DL algorithms in VANETs have garnered attention from academia and industry, leading to the development of architectures and algorithms tailored for VANETs. The challenges and advantages of DL in VANETs are expected as future research directions in this field. Moreover, this study explains the operations of Swarm Intelligence (SI) techniques, such as Ant Colony Optimization (ACO), Stochastic Diffusion Search (SDS), Particle Swarm Optimization (PSO), and Artificial Swarm Intelligence (ASI) in VANETs. The techniques of SI offer solutions for improving problems and can be utilized to diagnose and manage routing protocols and traffic congestion malicious nodes in VANETs. This study offers a detailed diagnose of how SI and DL help improve the efficiency and performance of VANETs. This improvement facilitates the development more safer and active transportation systems with intelligent capabilities.

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