Intelligent Agents in Mobile Vehicular Ad-Hoc Networks: Leveraging Trust Modeling Based on Direct Experience with Incentives for Honesty

In this paper we introduce a multi-faceted trust model of use for the application of ad-hoc vehicular networks (VANETs) - scenarios where agents representing drivers exchange information with other drivers regarding road and traffic conditions. We argue that there is a need to model trust in various dimensions and that combining these elements effectively can assist agents in making transportation decisions. We then introduce two new elements to our proposed model: i) distinguishing direct and indirect reports that are shared ii) employing a penalty for misleading reports, to promote honesty. We demonstrate how these two elements together serve to increase the value of the trust model, through a series of experiments of simulated traffic. In brief, we present a framework to facilitate the effective sharing of information in VANET environments between agents representing the vehicles.