Cloned Vehicle Behavior Analysis Framework

Abstract

Cloned vehicles brought tremendous harm to transportation management and public safety, which necessitates an efficient detection mechanism to discern the behaviors of cloned vehicles. The ubiquitous inspection spots deployed in the city have been collecting moving information of passing vehicles. Thus the positional sequences of inspection spots that vehicles passed by could form into their travelling traces. This provides us unprecedented opportunity to detect cloned vehicles. In this paper, we first propose a framework to discern the behaviors of cloned vehicles, called CVAF. It consists of three parts, including cloned vehicle detection, trajectory differentiation using matching degree-based clustering, and behavior pattern extraction. The experimental results on the real-world data show that our CVAF framework can identify cloned vehicle and discern their behavior patterns effectively. Our proposal can assist traffic control and public security department to solve the crime of cloned vehicle.