A new decentralized Bayesian approach for cooperative vehicle localization based on fusion of GPS and inter-vehicle distance measurements

Embedded intelligence in vehicular applications is becoming of great interest since the last two decades. The significant growth of sensing, communication and computing capabilities over the recent years has opened new fields of applications, such as ADAS and active safety systems, and has brought the ability of exchanging information between vehicles. In this paper, a new method for improving vehicle positioning is proposed. This method is a decentralized method based on sharing GPS data and inter-vehicular distance measurements within a cluster of vehicles. A Bayesian approach is used to fuse the GPS data and inter-vehicular distances. In order to investigate the performance of this new approach on vehicle localization, a Kalman filter has been employed to incorporate the dynamics of the vehicle. The effect of this method on the reduction of the localization uncertainty, over-convergence issues and identification of the vehicles are also discussed in this paper.