Cooperative vehicle localisation method based on the fusion of GPS, inter‐vehicle distance, and bearing angle measurements

Abstract

A new cooperative localisation method based on the Bayesian framework is proposed to obtain accurate and reliable vehicle localisation in intelligent transportation system applications. The new position estimation is achieved by the fusion of the filtered global positioning system (GPS) data, the inter-vehicle distance, and bearing angle. The simulation results indicate that the accuracy of vehicle localisation is effectively improved with the consideration of bearing angle, when compared with the fusion of GPS and inter-vehicle distance. A simulated scenario with multi-target dynamic environment is designed to discuss an appropriate number of nearby vehicles for cooperative localisation. The simulation results show that four nearby vehicles around the host vehicle for localisation is the most appropriate while balancing the accuracy and computing burden. Moreover, the proposed localisation method has also been proved to provide a well-robustness performance as well as localisation accuracy.