Augmented extended Kalman filter with cooperative Bayesian filtering and multi‐models fusion for precise vehicle localisations

Abstract

Self-localisation is vital for autonomous vehicles. In this study, the authors present an augmented extended Kalman filter (AEKF) framework for intelligent vehicle localisation applications. Compared to the previous approach, the proposed AEKF is enhanced through a model fusion, which incorporates a constant velocity model, constant acceleration model, constant turn rate and velocity, and constant turn rate and acceleration model by using the Takagi–Sugeno fuzzy inference technique, where the typical prediction procedure in the extended Kalman filter is modified by a fusion of those various motion models for the state estimation. Furthermore, they proposed a flexible cooperative Bayesian filter to incorporate the data from nearby-vehicles’ position and lateral distance from the host vehicle to the lane lines, to improve the raw global positioning system (GPS) performance under multi-sensor observation environments. They conduct simulation experiments under vividly, near-realistic scenarios with random traffic-flows to show the superiorities of the proposed framework when compared with the consumer-grade GPS implementation. The results show that the obtained positioning enhancement can significantly reduce the positioning error from the original larger than 5 m to the sub-meter level under various scenarios.