IEEE Transactions on Intelligent Vehicles · 2022 · 15 citations · 25 references
EngineeringAutomotive EngineeringAutonomous SystemsAutonomous VehiclesTransportationVehicle DynamicsIntelligent Transportation SystemsLocalization UncertaintyMagnetic SensorsRobot LocalizationAccuracyExtended Kalman FilterExtended Kalman Filter LocalizationGlobal Positioning SystemLocalization RobustnessRobust StabilitySideslip Angle MeasurementSmall-gain Theorems
This paper describes a localization method based on the global magnetic positioning system (GMPS) using a magneto-impedance sensor, which was developed for automated driving systems to obtain precise vehicle positions under all conditions, including snow or rain. The aim of this study is to verify the stability, robustness, and high accuracy of the proposed method, and propose appropriate intervals for magnetic markers. This localization method accurately calculates the sideslip angle because a noisy acceleration sensor is not required except for the identification process. The estimated positions and orientation are filtered through an extended Kalman filter as the vehicle passes the magnetic markers. The robust stability of the proposed method is verified using the small-gain theorem, which shows that the intervals for magnetic markers should be less than 38.6 m for a city bus. Experimental results using a city bus and GMPS show that there is a possibility that the intervals of magnetic markers are extended to 30 m at small curvature, and 10 m at large curvature from the current 2 m intervals. These results indicate that the maximum intervals to avoid lane deviation of vehicles depend on the reference path curvature.
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