Trainable Module Uncertainty Evaluation for Automated Driving Generalization
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Solution Overview
Problem
Trainable modules used in automated driving systems face limitations in generalization, leading to misclassification of traffic signs across different regions, such as European and U.S. traffic signs, which can result in critical errors like incorrect acceleration or deceleration, due to lack of training data variability.
Innovation Solution
The method introduces variations in trainable modules by deactivating neurons, varying parameters, or altering input order and initialization, and assesses uncertainty in output variable values to determine applicability to new situations, using statistical functions to compare against a dynamic distribution of uncertainties, thereby preventing misclassification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If trainable modules are trained with limited learning data from specific regions, then training efficiency is improved, but generalization capability deteriorates leading to misclassification in new regions
Solution Approach 1:
The patent applies parameter changes by systematically varying multiple training parameters including learning data from different regions, weather conditions, road conditions, and lighting conditions. This creates a more comprehensive training dataset that enables the trainable module to generalize better to new regions while maintaining training efficiency through structured parameter variation.
Solution Approach 2:
The patent implements universality by designing the trainable module to handle multiple types of input variations (different regions, weather, road conditions, lighting) through a single unified training framework. This multi-functional training approach enables the module to classify traffic signs across diverse conditions without requiring separate training processes for each scenario.
2Measurement precision
If the trainable module uses high power to generalize from limited training data, then detection capability is improved, but reliability deteriorates due to misclassification in unseen situations
Solution Approach 1:
The patent applies preliminary action by proactively training the module with diverse variations of learning data before deployment to unseen regions. By pre-exposing the module to different weather conditions, road conditions, lighting conditions, and regional variations during training, the system prepares the module to handle unseen situations more reliably without sacrificing detection capability.
3Quantity of substance
If the trainable module is trained only with data from the training region, then training cost is reduced, but adaptability to other regions deteriorates
Solution Approach 1:
The patent systematically varies the regional parameter in training data to include multiple regions during the training phase. This parameter change approach allows the module to learn region-invariant features while maintaining reasonable training data volume, thereby achieving cross-regional adaptability without exponentially increasing training data requirements.
Data Source
AI summary
A method for operating a trainable module. At least one input variable value is supplied to variations of the trainable module, the variations differing so much from each other, that they may not be converted into each other in a congruent manner, using progressive learning. A measure of the uncertainty of the output variable values is ascertained from the difference of the output variable values, into which the variations translate, in each instance, the input variable value. The uncertainty is compared to a distribution of uncertainties, which is ascertained for input variable learning values used during training of the trainable module and/or for further input variable test values, to which relationships learned during the training of the trainable module are applicable. The extent to which the relationships learned during the training of the trainable module are applicable to the input variable value, is evaluated from the result of the comparison.