Vehicular ad hoc network (VANET) is expected one of promising network forms for intelligent transportation system which supports road safety applications, in-vehicle entertainment and arriving automatic driving. Establishing and maintaining stable connections in VANETs are challenging on account of the high mobility of vehicles, dynamic vehicle topology, and time-varying vehicle density. Clustering can provide scalability and reliability for VANETs by grouping vehicles with hierarchical structures. However, keeping cluster stable became a hard nut to crack due to high vehicle speed and unpredictable driving pattern. Recent rapid development of artificial intelligence (AI) provided an innovative solution for this situation. In this paper, a Naive Bayes Classifier based driving habit prediction scheme for stable clustering is proposed, briefly named NBP. According to driving speed and overtaking decisions, vehicles are classified into two alignments with different driving habit. Specifically, Naive Bayes classifier perform driving habit prediction through several relative independent factors, such as relative velocity, vehicle type, number of lanes traveled. The cluster head candidates will be chosen from alignment with mild driving pattern which will benefit for stable clusters. Combined with clustering design, the proposed method has been proven effective for stable clustering in VANET based on the real data of highways in California.

车载自组织网络（VANET）有望成为支持交通安全应用，车载娱乐和自动驾驶的智能交通系统的有前途的网络形式之一。由于车辆的高机动性，动态的车辆拓扑以及随时间变化的车辆密度，在VANET中建立和维护稳定的连接具有挑战性。通过将车辆与分层结构分组，群集可以为VANET提供可伸缩性和可靠性。然而，由于车速高和驾驶模式无法预测，保持集群稳定变得难以破解。人工智能（AI）的快速发展为这种情况提供了创新的解决方案。本文提出了一种基于朴素贝叶斯分类器的稳定聚类驾驶习惯预测方案，简称NBP。根据行驶速度和超车决定，将车辆分为具有不同驾驶习惯的两个路线。具体而言，朴素贝叶斯分类器通过几个相对独立的因素来执行驾驶习惯预测，例如相对速度，车辆类型，行车道数。将从与轻度驾驶模式的对准中选择簇头候选者，这将有利于稳定的簇。结合聚类设计，基于加利福尼亚州高速公路的真实数据，该方法已被证明对VANET中的稳定聚类有效。例如相对速度，车辆类型，行车道数。将从与轻度驾驶模式的对准中选择簇头候选者，这将有利于稳定的簇。结合聚类设计，基于加利福尼亚州高速公路的真实数据，该方法已被证明对VANET中的稳定聚类有效。例如相对速度，车辆类型，行车道数。将从与轻度驾驶模式的对准中选择簇头候选者，这将有利于稳定的簇。结合聚类设计，基于加利福尼亚州高速公路的真实数据，该方法已被证明对VANET中的稳定聚类有效。