Artificial Intelligence and Machine Learning Implementation in Intelligent Vehicular Coordination

Abstract

Artificial Intelligence (AI) and Machine Learning (ML) are transforming vehicular networks by enabling autonomous, collaborative, and context-aware decision-making. Modern transportation systems demand ultra-low latency, high reliability, and rapid adaptation, which traditional rule-based or centralized approaches fail to provide. This work explores the role of distributed intelligence—spanning centralized cloud AI, edge AI, federated learning (FL), and multi-agent AI—in ensuring real-time vehicular coordination. Reinforcement Learning (RL) and Multi-Agent RL (MARL) support dynamic tasks such as lane changing, adaptive routing, collision avoidance, and resource allocation in highly variable traffic conditions. Cooperative Perception (CP) enhances situational awareness by enabling vehicles and roadside sensors to share processed features or decisions, significantly improving detection accuracy under occlusions and adverse conditions. Additionally, AI-driven resource allocation optimizes spectrum, power, and computing distribution across 6G-enabled IoV architectures, ensuring efficient QoS management under dense mobility. Performance analyses—via convergence plots, reward evolution, perception trade-off curves, and newly evaluated latency, communication overhead, and PDR graphs—highlight the superiority of AI-driven mechanisms over static or heuristic baselines. Overall, this study demonstrates that AI/ML techniques form the backbone of next-generation intelligent transportation systems, enabling scalable, secure, and cooperative vehicular ecosystems.