Naive Bayes Classifier Based Driving Habit Prediction Scheme for VANET Stable Clustering
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Naive Bayes Classifier Based Driving Habit Prediction Scheme for VANET Stable Clustering Tong Liu 1
&
Shuo Shi 1 & Xuemai Gu 1
# Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract 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. Keywords Naive Bayes classifier . Driving habit prediction . VANET clustering
1 Introduction Recent years, automatic driving attracts considerable attentions. The most important part of this enormous and complicated project is the reliable communication system which provide intelligent transportation services including driving assistant, road traffic sensing, path planning and emergency warning. Strict latency for transmitting real-time data generated by surround vehicles is required, since the moving pattern of autonomous vehicles on road is significant affected by neighbors. While the delay in cellular networks may deteriorate when a large amount of vehicles access. VANETs are expected to boost the development of automatic driving. It is designed for vehicles to exchange information with surrounding vehicles [1]. VANET is a derived form of mobile ad hoc network (MANET) which is an infrastructureless network and organized with multi-hop communications. Specially,
* Tong Liu [email protected] 1
Harbin Institute of Technology, Harbin 150001, China
VANET communication can be classified two parts: vehicleto-vehicle(V2V) and vehicle-to-infrastructure (V2I). Vehicles are equipped with on board units (OBUs) which make vehicles working as moving communication nodes [2]. Stationary road infrastructure units are assembled along the street to provide Internet access, content cach
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