def METHOD_NAME():
    predictor = KalmanPredictor(ConstantVelocity(noise_diff_coeff=0))
    timestamp = datetime.datetime.now()
    state = GaussianState([[0.], [1.]], np.diag([1., 1.]), timestamp)
    track = Track([state])
    prediction_time = timestamp + datetime.timedelta(seconds=1)
    prediction1 = predictor.predict(track, prediction_time)
    assert np.array_equal(prediction1.state_vector, np.array([[1.], [1.]]))
    prediction2 = predictor.predict(track, prediction_time)
    assert prediction2 is prediction1
    track.append(GaussianState([[1.], [1.]], np.diag([1., 1.]), prediction_time))
    prediction3 = predictor.predict(track, prediction_time)
    assert prediction3 is not prediction1