To ensure the transmission quality of real-time communications on the road, the research of routing protocol is crucial to improve effectiveness of data transmission in Vehicular Ad Hoc Networks (VANETs). The existing work Q-Learning based routing algorithm, QLAODV, is studied and its problems, including slow convergence speed and low accuracy, are found. Hence, we propose a new routing algorithm FLHQRP by considering the characteristics of real-time communication in VANETs in the paper. The virtual grid is introduced to divide the vehicle network into clusters. The node’s centrality and mobility, and bandwidth efficiency are processed by the Fuzzy Logic system to select the most suitable cluster head (CH) with the stable communication links in the cluster. A new heuristic function is also proposed in FLHQRP algorithm. It takes cluster as the environment state of heuristic Q-learning, by considering the delay to guide the forwarding process of the CH. This can speed up the learning convergence, and reduce the impact of node density on the convergence speed and accuracy of Q-learning. The problem of QLAODV is solved in the proposed algorithm since the experimental results show that FLHQRP has many advantages on delivery rate, end-to-end delay, and average hops in different network scenarios.

中文翻译：

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基于模糊逻辑和强化学习的VANET V2V路由

为了确保道路上实时通信的传输质量，路由协议的研究对于提高车辆自组织网络（VANET）中数据传输的有效性至关重要。研究了现有的基于Q学习的路由算法QLAODV，发现了收敛速度慢，精度低的问题。因此，本文考虑了VANET中实时通信的特点，提出了一种新的路由算法FLHQRP。引入虚拟网格将车辆网络划分为集群。该节点的中心性和移动性以及带宽效率由模糊逻辑系统处理，以选择具有群集中稳定通信链路的最合适的群集头（CH）。FLHQRP算法中还提出了一种新的启发式函数。通过考虑延迟来指导CH的转发过程，将集群作为启发式Q学习的环境状态。这样可以加快学习收敛速度，减少节点密度对收敛速度和Q学习精度的影响。实验结果表明，FLHQRP在不同网络场景下，在传输速率，端到端延迟和平均跳数方面具有很多优势，从而解决了QLAODV问题。