The online self-calibration is a required capability from an autonomous vehicle that should operate lifelong in a safe manner. This paper introduces relative weighting by a neural network into the batch Gauss–Newton calibration method of the wheel odometry model. A wheel odometry model with accurately estimated parameters could improve the motion estimation task of an autonomous vehicle, but the online parameter identification from only onboard measurements is a challenge due to the noises and the nonlinear behavior of the dynamic system. A possible solution to deal with the effect of noises is to calibrate the model with more segments at once forming a batch, but this can only reduce the distortion effect, not eliminate it. Our proposed algorithm improves this batch formulation by integrating relative weights for the segments to mitigate the distorting effect of noisy measurements. The method applies an AI-based tool to extract a proper weighting strategy from the previously recorded data that utilizes only online signals during operation. With the usage of the proposed architecture, the calibration accuracy significantly increased with the reduction of the distortion effect of faulty measurements, while the same amount of data is used as the raw batch estimation. The performance of the method is demonstrated with real measurements in a city driving with a passenger vehicle, where the calibration signals come from the equipped automotive-grade type of Global Navigation Satellite System and Inertial Measurement Unit.