Study Of Statistical Models For Route Prediction Algorithms In VANET

Abstract

Vehicle-to-vehicle communication is a concept greatly studied during the past years. Vehicles equipped with devices capable of short-range wireless connectivity can form a particular mobile ad-hoc network, called a Vehicular Ad-hoc NETwork (or VANET). The users of a VANET, drivers or passengers, can be provided with useful information and with a wide range of interesting services. Route prediction is the missing piece in several proposed ideas for intelligent vehicles. In this paper, we are studying the algorithms that predict a vehicle's entire route as it is driven. Such predictions are useful for giving the driver warnings about upcoming traffic hazards or information about upcoming points of interest, including advertising. This paper describes the route Prediction algorithms using Markov Model, Hidden Markov Model (HMM), Variable order Markov model (VMM).

Keywords: VANET, MANET, ITs, GPS, HMM, VMM, PST.