| Abstract |
Autonomous vehicles (AVs) are anticipated to play a pivotal role in intelligent transportation systems, particularly in the context of future smart cities. Common performance measures like throughput and latency are not sufficient for capturing the timing and freshness of data in applications like autonomous driving and accident prevention. Therefore, this paper addresses the challenge of minimizing the Age of Information (AoI) in AVassisted vehicular networks. First, the problem is mathematically formulated as linear programming to derive optimal solutions. Recognizing the computational complexity, a scalable heuristic method tailored for large networks is proposed. Additionally, for comparative analysis, the problem is modeled as a Markov decision process and solved using Q-learning, an algorithm of Reinforcement Learning (RL). The numerical results highlight the efficacy of the proposed heuristic method in minimizing the average AoI, considering both computational efficiency and its potential to complement RL algorithms in a hybrid approach. © 2025 IEEE. |
