Abstract
In this paper, an innovative collaborative data fusion approach to ego-vehicle localization is presented. This approach called Optimized Kalman Swarm (OKS) is a data fusion and filtering method, fusing data from a low cost GPS, an INS, an Odometer and a Steering wheel angle encoder. The OKS is developed addressing the challenge of managing reactivity and robustness during a real time ego-localization process. For ego-vehicle localization, especially for highly dynamic on-road maneuvers, a filter needs to be robust and reactive at the same time. In these situations, the balance between reactivity and robustness concepts is crucial. The OKS filter represents an intelligent cooperative-reactive localization algorithm inspired by dynamic Particle Swarm Optimization (PSO). It combines advantages coming from two filters: Particle Filter (PF) and Extended Kalman filter (EKF). The OKS is tested using real embedded sensors data collected in the Satory’s test tracks. The OKS is also compared with both the well-known EKF and the Particle Filters (PF). The results show the efficiency of the OKS for a high dynamic driving scenario with damaged and low quality GPS data.
Highlights
Localization of vehicles is a research topic in perpetual evolution
This new type of positioning opens fields for new Intelligent Transport Systems applied to Road applications (ITS-R) and advanced systems for driving assistance (ADAS) such as parking valet, and copilot for autonomous driving
To bypass the model nonlinearity problem, the Extended Kalman Filter (EKF) based localization has been proposed for autonomous vehicles positioning such as in [10,11,12]
Summary
Localization of vehicles is a research topic in perpetual evolution. Nowadays, the location information becomes very important and inevitable in large cities in order to move from point A to a desired point B. The development of new services using an accurate localization should have a slight impact from an economic point of view For this reason, research was directed toward hybrid fusion methods which consisted in using of onboard sensors or new low cost sensors. To bypass the model nonlinearity problem, the Extended Kalman Filter (EKF) based localization has been proposed for autonomous vehicles positioning such. Improved for optimization issues [14,15,16,17], the PSO introduces social behaviors and cognitive concepts to the localization process With these notions, particles are directed toward the high probability positioning region of the state space (sensor information). Inspired by the following localization and tracking research works [1,18,19], this vehicle localization method is called the Optimized Kalman Swarm (OKS). Some future works and improvement ways are proposed
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