Adaptive AI Signature Validation for Rare Object-Detection Errors
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Solution Overview
Problem
Existing perception systems in ADAS and AVs face challenges in achieving accuracy above 0.999 due to the limitations of gradient descent-based deep learning algorithms, which struggle to address rare edge cases and require excessive computational resources.
Innovation Solution
An adaptable AI system that includes an error resolving part (ERP) to dynamically correct errors by generating accurate signatures, adapting parameters, or modifying sensing units, without retraining the neural network, thereby enhancing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deeper and heavier neural networks are used to improve perception accuracy, then accuracy improves, but computational power requirements increase by over x10
Solution Approach 1:
The system segments the perception task into two parts: a lightweight neural network for initial processing and an error resolving part for handling edge cases. This segmentation allows the main network to remain computationally efficient while delegating complex tasks to a specialized module, avoiding the need for an entirely heavier network.
Solution Approach 2:
The error resolving part acts as an intermediary between the neural network output and the final perception result. It receives outputs from the neural network, identifies and corrects errors in edge cases, and produces refined results, thereby improving accuracy without requiring the main network to be heavier.
2Measurement precision
If more labeled data is used for retraining to address false-positives/false-negatives, then some errors are solved, but new false-positives/false negatives are exposed
Solution Approach 1:
The error resolving part enables the system to self-correct by automatically identifying errors in its outputs and generating corrections without requiring external retraining. The system monitors its own performance and uses the error resolving part to fix mistakes, eliminating the need for complex retraining procedures.
Solution Approach 2:
The system implements feedback mechanisms where the error resolving part receives outputs from the neural network, identifies errors, and generates corrected results. This feedback loop allows continuous improvement without requiring complete retraining, as the system learns from its own errors in real-time.
3Measurement precision
If gradient descent-based deep learning algorithms are used, then general accuracy reaches >0.95, but accuracy above 0.999 cannot be achieved due to limitations in addressing rare edge cases
Solution Approach 1:
The error resolving part provides local quality improvement by specifically targeting and correcting errors in edge cases while leaving the general processing unchanged. Instead of attempting to improve all cases uniformly, it focuses computational resources on the specific local problems of rare edge cases, achieving high overall accuracy.
Data Source
AI summary
A method that is automatically validating of signatures for object detection, the method includes automatically evaluating, by a controller, a signature being generated by an adaptable artificial intelligence (AI) system and stored in a memory, by determining whether the signature introduced an error in a previously accurate detection of an object; and triggering a response to address an outcome of the evaluating with respect to a problem found during the automatically evaluating.