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

Abstract : 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.
Complete list of metadatas

https://hal.archives-ouvertes.fr/hal-00996254
Contributor : Frédéric Davesne <>
Submitted on : Monday, May 26, 2014 - 11:58:10 AM
Last modification on : Monday, February 10, 2020 - 11:42:10 AM

Identifiers

Collections

Citation

Mohsen Rohani, Denis Gingras, Vincent Vigneron, Dominique Gruyer. A new decentralized Bayesian approach for cooperative vehicle localization based on fusion of GPS and inter-vehicle distance measurements. 2nd IEEE International Conference on Connected Vehicles and Expo (ICCVE 2013), Dec 2013, Las Vegas, NV, United States. pp.473--479, ⟨10.1109/ICCVE.2013.6799839⟩. ⟨hal-00996254⟩

Share

Metrics

Record views

277