Bayesian fusion of GNSS, ITS-G5 and IR–UWB data for robust cooperative vehicular localization

Publication date: Available online 8 April 2019Source: Comptes Rendus PhysiqueAuthor(s): Gia Minh Hoang, Benoît Denis, Jérôme Härri, Dirk SlockAbstractIn the automotive domain, Cooperative Localization (CLoc) is a new promising paradigm that aims at outperforming conventional Global Navigation Satellite Systems (GNSS) in terms of positioning accuracy, robustness, and service continuity, by relying on Vehicle-to-Vehicle (V2V) communications and hybrid data fusion. However, the growing number and the variety of the sensors aboard vehicles raise unprecedented challenges, especially in the context of distributed fusion approaches. This paper thus compares parametric and nonparametric Bayesian data fusion engines (e.g., based on cooperative variants of the Extended Kalman Filter (EKF) and Particle Filter (PF), respectively), while validating a CLoc scheme suitable to Vehicular Ad Hoc Networks (VANETs). More particularly, absolute position information from both onboard GNSS receiver and ITS-G5 V2V messages, as well as relative distance measurements based on the Impulse Radio–Ultra-Wideband (IR–UWB) technology, are combined into a single location solution that is hopefully more robust and more accurate than that of standalone GNSS. First, we investigate V2V ranging accuracy on a highway under real mobility conditions. In the same environment, we then provide offline validations of CLoc positioning, confirming significant performance gains through cooperation over conventiona...
Source: Comptes Rendus Physique - Category: Physics Source Type: research