def METHOD_NAME(self, mean, covariance):
    """Run Kalman filter prediction step.
    Parameters
    ----------
    mean : ndarray
        The 8 dimensional mean vector of the object state at the previous
        time step.
    covariance : ndarray
        The 8x8 dimensional covariance matrix of the object state at the
        previous time step.
    Returns
    -------
    (ndarray, ndarray)
        Returns the mean vector and covariance matrix of the predicted
        state. Unobserved velocities are initialized to 0 mean.
    """
    std_pos = [
        self._std_weight_position * mean[3],
        self._std_weight_position * mean[3],
        1e-2,
        self._std_weight_position * mean[3],
    ]
    std_vel = [
        self._std_weight_velocity * mean[3],
        self._std_weight_velocity * mean[3],
        1e-5,
        self._std_weight_velocity * mean[3],
    ]
    motion_cov = np.diag(np.square(np.r_[std_pos, std_vel]))
    super().METHOD_NAME(Q=motion_cov)
    return self.x.T, self.P