State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China.
School of Artificial Intelligence, Henan University, Zhengzhou 450046, China.
Sensors (Basel). 2021 Nov 15;21(22):7588. doi: 10.3390/s21227588.
Connected autonomous vehicles can leverage communication and artificial intelligence technologies to effectively overcome the perceived limitations of individuals and enhance driving safety and stability. However, due to the high dynamics of the vehicular network and frequent interruptions and handovers, it is still challenging to provide stable communication connections between vehicles, which is likely to cause disasters. To address this issue, in this paper, we propose an intelligent clustering mechanism based on driving patterns in heterogeneous Cognitive Internet of Vehicles (CIoVs). In the proposed approach, we analyze the driving mode containing multiple feature parameters to accurately capture the driving characteristics. To ensure the accuracy of pattern recognition, a genetic algorithm-based neural network pattern recognition algorithm is proposed to support the reliable clustering of connected autonomous vehicles. The cognitive engines recognize the driving modes to group vehicles with a similar driving mode into a relatively stable cluster. In addition, we formulate the stability and survival time of clusters and analyze the communication performance of the clustering mechanism. Simulation results show that the proposed mechanism improves the reliable communication throughput and average cluster lifetime by approximately 14.4% and 11.5% respectively compared to the state-of-the-art approaches.
联网自动驾驶车辆可以利用通信和人工智能技术有效地克服个人感知的局限性，提高驾驶安全性和稳定性。然而，由于车辆网络的高动态性以及频繁的中断和切换，仍然难以在车辆之间提供稳定的通信连接，这可能会导致灾难。针对这个问题，在本文中，我们提出了一种基于异构认知互联网车辆（CIoV）驾驶模式的智能聚类机制。在提出的方法中，我们分析了包含多个特征参数的驾驶模式，以准确捕捉驾驶特性。为了确保模式识别的准确性，我们提出了一种基于遗传算法的神经网络模式识别算法，以支持联网自动驾驶车辆的可靠聚类。认知引擎识别驾驶模式，将具有相似驾驶模式的车辆分组到相对稳定的集群中。此外，我们还对集群的稳定性和生存时间进行了公式化，并分析了聚类机制的通信性能。仿真结果表明，与现有方法相比，所提出的机制可将可靠通信吞吐量和平均集群寿命分别提高约 14.4%和 11.5%。
