Neural Network Global Uncertainty Estimation Method
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Solution Overview
Problem
Deep neural networks (DNNs) used in safety-critical tasks, such as automated driving, face challenges in generalizing error from test sets to new and unseen samples, especially in complex open contexts, leading to uncertainty in system safety due to the dependence on specific distribution assumptions that cannot be met in real-world applications.
Innovation Solution
A method to estimate global uncertainty in output data by combining three measures: confidence in input data distribution similarity, introspective uncertainty through Bayesian methods or ensemble approaches, and output uncertainty based on class-discriminative features, using techniques like variational autoencoders and Grad-Cam for visualization, to systematically determine the reliability of neural network predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If deep neural networks are used for safety-critical tasks in automated driving, then the system can perform complex perception tasks, but the system cannot reliably generalize error from test sets to new and unseen samples due to distribution assumptions
Solution Approach 1:
The patent segments the uncertainty estimation into three distinct measures: (1) distributional uncertainty measuring how well input data fits the training distribution, (2) model uncertainty quantifying the neural network's confidence in its predictions, and (3) output uncertainty assessing the reliability of specific prediction outputs. This segmentation allows each aspect to be evaluated and combined systematically to provide comprehensive safety assessment.
Solution Approach 2:
The patent introduces uncertainty measures as intermediary components between the neural network and the decision-making system. These measures act as mediators that translate the neural network's internal state and predictions into quantifiable reliability indicators, enabling the safety system to make informed decisions about when to trust the neural network's outputs.
2Reliability
If sophisticated safety architectures including advanced safety measures are implemented, then system safety is improved, but the device complexity increases
Solution Approach 1:
The patent creates a universal uncertainty estimation framework that can be applied to various neural network architectures and safety-critical applications. The three-measure approach serves multiple functions: it assesses input data quality, evaluates model confidence, and validates output reliability, making it a multi-functional safety mechanism that can be integrated into different automated driving systems without requiring application-specific customization.
Solution Approach 2:
The patent implements feedback mechanisms where uncertainty measures are continuously computed and fed back to the safety system. The distributional uncertainty feedback indicates when input data deviates from training conditions, model uncertainty feedback adjusts prediction confidence weighting, and output uncertainty feedback validates result reliability. This feedback loop enables dynamic safety assessment without requiring complex predetermined safety rules.
Data Source
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AI summary
The invention describes a method for estimating a global uncertainty of output data of a computer implemented main neural network comprising the following steps: - Determining (102) 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; - Generating (103) 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, such as Bayesian neural networks, or an ensemble based approach. - Ascertaining (104) 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; - Determining (105) the global uncertainty based on at least two measures out of the first, second and third measure.