Computer implemented method for estimating a global uncertainty of output data of a computer implemented main neural network of a perceptual system of an at least partially automated vehicle, which is controlled, based on the global uncertainty, the method comprising the following steps: ∘ Determining a first measure, quantifying to which extent the current input data of the main neural network is following the same distribution as the data, which was used for training the main neural network, wherein the input data originates from at least one sensor; ∘ Generating a second measure quantifying the main neural network's certainty in its own prediction based on the input data, especially by using at least one Bayesian method or an ensemble based approach; o Ascertaining a third measure, based on an estimation of class-discriminative features in the input data and a comparison of these features with a class activation probability distribution, especially wherein the class activation probability distribution was created based on estimated class-discriminative features during the training of the main neural network, wherein the class-discriminative features are estimated by computing scaled and/or unscaled features using activation heat maps, wherein scaled activation heat maps of detected objects are compared to class activation heat maps, which were computed for all detected objects during training and were then combined class-wise and/or pixel-wise in order to obtain the class activation probability distributions; ∘ Systematic combination of said three measures to determine the global uncertainty based on the first, second and third measure. ∘ Determining a first measure, quantifying to which extent the current input data of the main neural network is following the same distribution as the data, which was used for training the main neural network, wherein the input data originates from at least one sensor; ∘ Generating a second measure quantifying the main neural network's certainty in its own prediction based on the input data, especially by using at least one Bayesian method or an ensemble based approach; o Ascertaining a third measure, based on an estimation of class-discriminative features in the input data and a comparison of these features with a class activation probability distribution, especially wherein the class activation probability distribution was created based on estimated class-discriminative features during the training of the main neural network, wherein the class-discriminative features are estimated by computing scaled and/or unscaled features using activation heat maps, wherein scaled activation heat maps of detected objects are compared to class activation heat maps, which were computed for all detected objects during training and were then combined class-wise and/or pixel-wise in order to obtain the class activation probability distributions; wherein the class-discriminative features are estimated by computing scaled and/or unscaled features using activation heat maps, wherein scaled activation heat maps of detected objects are compared to class activation heat maps, which were computed for all detected objects during training and were then combined class-wise and/or pixel-wise in order to obtain the class activation probability distributions; ∘ Systematic combination of said three measures to determine the global uncertainty based on the first, second and third measure. <EOS>