Cooperative Multi-sensor Multi-vehicle Localization in Vehicular Adhoc Networks

Intelligent Transportation System (ITS) is an important application domain for information reuse and integration. Efficient integration and deployment of information and communication technologies (ICT) can potentially reduce travel time and emission, improve usage of parking and public spaces, offer personalized travel related services, and more importantly, improve safety for drivers and pedestrians in large municipalities. Personalized travel related services and recommendation systems rely mainly on precise identification of position of the vehicles. In this paper we propose a cooperative multi-sensor multi-vehicle localization algorithm with high accuracy for terrestrial vehicles. Noisy observations in the form of GPS coordinates of nearby vehicles as well as inter-vehicle distance measurements are assumed to be available. These heterogeneous sources of information are fused together and used to estimate the number and motion model parameters of the vehicles in the field. The problem is formulated in the context of Bayesian framework and vehicle locations are estimated via a Sequential Monte-Carlo Probability Hypothesis Density (SMC-PHD) filter. Given that the GPS data and inter-vehicle distance measurements are available except for short periods of time, simulation results indicate that the proposed algorithm provides approximately threefold improvement in location accuracy compared to that achieved with GPS.