Edge Computing for AI-Optimized Traffic Management in Autonomous Cars

This study proposes an edge computing framework for Artificial Intelligence (AI) optimized traffic management in autonomous vehicles aimed at enhancing real-time responsiveness, reducing network latency and improving traffic flow efficiency. Using Vehicle-to-Everything (V2X) connectivity, the system architecture incorporates a three-layered method with vehicular, edge and cloud levels. Computationally expensive activities are offloaded from autonomous cars to neighbouring edge nodes. While Reinforcement Learning (RL) agents placed at the network's periphery make real-time adjustments to traffic signals, Federated Learning (FL) allows for collaborative method refinement decentralising sensitive data. Furthermore, for precise short-term traffic flow prediction, hybrid deep learning models are used which combine Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) algorithms. Through experimental validation utilising SUMO and CARLA simulations it has been shown that the edge-based system outperforms typical cloud-based models in terms of inference latency reduction (by more than 50%), traffic throughput increases by 22% and predict accuracy improvement by 3.1%. The effectiveness and scalability of the suggested method were confirmed by the fact that resource utilisation was uniform across edge devices. In addition to demonstrating the promise of edge-AI systems for future autonomous transportation infrastructures, the findings show that these systems are better at intelligent traffic management.

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