4.6 Article

Position Estimation of Vehicle Based on Magnetic Marker: Time-Division Position Correction

期刊

SENSORS
卷 21, 期 24, 页码 -

出版社

MDPI
DOI: 10.3390/s21248274

关键词

automatic driving; error estimation; position detection; guidance control; magnetic marker; magnetic sensing ruler; peak detection; autonomous vehicle; heading angle

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The study proposes a time-division position correction method to achieve stable and continuous vehicle position information. Compared to traditional methods, this approach does not result in discontinuity of the position information and instability in vehicle control.
During the automatic driving of a vehicle, the vehicle's positional information is important for vehicle driving control. If fixed-point land markers such as magnetic markers are used, the vehicle's current position error can be calculated only when a marker is detected while driving, and this error can be used to correct the estimation position. Therefore, correction information is used irregularly and intermittently according to the installation intervals of the magnetic markers and the driving speed. If the detected errors are corrected all at once using the position correction method, discontinuity of the position information can occur. This problem causes instability in the vehicle's route guidance control because the position error fluctuates as the vehicle's speed increases. We devised a time-division position correction method that calculates the error using the absolute position of the magnetic marker, which is estimated when the magnetic marker is detected, along with the absolute position information from the magnetic marker database. Instead of correcting the error at once when the position and heading errors are corrected, the correction is performed by dividing the errors multiple times until the next magnetic marker is detected. This prevents sudden discontinuity of the vehicle position information, and the calculated correction amount is used without loss to obtain stable and continuous position information. We conducted driving tests to compare the performances of the proposed algorithm and conventional methods. We compared the continuity of the position information and the mean error and confirmed the superiority of the proposed method in terms of these aspects.

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