Two Major Applications in Vehicular Ad Hoc Networks

Abstract

Vehicular ad hoc networks (VANETs) have emerged in the past decades as a significant type of networks, which consists of vehicles with sensors to communicate. For its ad-hoc nature, VANETs have great potential in a large number of applications, within which rear-end collision warning and traffic automatic incidents detection are two major applications. Because of the large number of injury and consequent economic loss, rear-end traffic collision has become an important issue and attracted a large number of attentions. In the past decades, there have been lots of efforts paid on this field. Existing work usually employed mathematical approaches or machine-learning approaches. In this study, we develop a collaborative rear-end collision warning algorithm (CORECWA), which is able to estimate and assess traffic risk in a collaborative and real-time way, and further notify drivers the warning message timely. Experiments results have shown that our algorithm outperforms the predominant method, HONDA algorithm. On the other hand, traffic incidents detection has been a critical problem in the past decades, due to the considerable economical cost and inestimable disgruntlement from numerous drivers. We present a support vector machines (SVM)-based approach for automatic incident detection (AID), in which the traffic data are collected by VANETs techniques. We process collected data and utilize traffic variables in the SVM model to confirm whether an incident occurs. Several experiments have been conducted to evaluate our approach’s performance, and the results show that our approach could outperform the other two approaches in most cases.