Investigation of communication systems powered by AI for autonomous transportation

Abstract

Autonomous transportation is transforming mobility by allowing cars to function independently using new technology. Artificial intelligence (AI)-driven communication systems—Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), and Vehicle-to-Pedestrian (V2P)—are key to this revolution because they enable smooth data interchange and intelligent decision-making, hence improving safety and operating efficiency. This review investigates how AI approaches, notably deep learning (DL) and machine learning (ML), improve autonomous vehicle communication, traffic management, and security. The emphasis is on assessing approaches used in real-time traffic scenarios and mixed mobility contexts to promote safer and more efficient transportation networks. The study compiles research on deep learning models such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and Reinforcement Learning (RL), as well as machine learning techniques such as Random Forests (RF) and decision trees (DT). These techniques are used in traffic signal control, intrusion detection, driving intention identification, and dynamic routing, using both simulation-based and real-world traffic datasets. The examined models provide significant gains in traffic flow, danger prediction, and cyberattack avoidance. V2V technologies enable cooperative driving, V2I improves infrastructure-based decision-making, and V2P provides pedestrian safety. AI integration enables autonomous platforms to respond in real time, route intelligently, and increase situational awareness in both urban and industrial situations. Advanced AI-powered communication and management systems dramatically improve autonomous cars' safety, operational efficiency, and reliability. Although these technologies show promise in simulated and semicontrolled environments, more research is needed to address real-world challenges, ensure model adaptability, and build resilient cybersecurity frameworks for scalable and secure deployment in complex, dynamic settings.