Neural Network Error Signatures for Autonomous Driving Accuracy
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Solution Overview
Problem
Current perception systems for autonomous vehicles face challenges in achieving high accuracy beyond level-2 ADAS, particularly in handling the long tail of edge cases, due to limitations in deep learning algorithms and computational resources.
Innovation Solution
The adaptable AI system addresses these challenges by dynamically adapting to errors through an error resolving unit that generates accurate signatures, reducing the need for extensive retraining or increased computational power.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deeper and heavier neural networks are used to improve accuracy, then perception accuracy improves, but computational power requirements increase by over 10x
Solution Approach 1:
The patent segments the neural network into an ensemble of multiple smaller, specialized sub-networks (expert networks), each trained to handle specific types of detection tasks. This segmentation allows the system to achieve high accuracy through coordinated specialization rather than relying on a single massive network, thereby reducing overall computational power requirements while maintaining or improving detection precision.
2Measurement precision
If retraining with more labeled data is performed to improve accuracy, then some false-positives/false-negatives are solved, but new false-positives/false negatives are exposed due to saturation
Solution Approach 1:
The patent applies local quality by creating specialized expert networks that focus on specific detection domains or object types. Each expert network develops localized expertise in its specific area, allowing for higher precision in targeted detection tasks without the diminishing returns encountered when uniformly retraining a general-purpose network with additional data.
Solution Approach 2:
The system dynamically selects and combines predictions from multiple expert networks based on the specific input characteristics. This dynamic ensemble approach allows the system to adapt to different detection scenarios, maintaining reliability across diverse cases rather than suffering from the saturation effects that plague static, monolithic networks when repeatedly retrained.
3Manufacturing precision
If the neural network is made deeper to reduce error rate by factor of 2-4, then model quality improves, but the increase in computational power required is over 10x
Solution Approach 1:
Instead of increasing the depth of a single neural network, the patent segments the modeling task across multiple parallel, shallower expert networks. This approach achieves equivalent or superior model quality through the collective capability of specialized sub-networks, avoiding the exponential increase in complexity that would result from deepening a monolithic network structure.
Data Source
AI summary
A method that is computer implemented for improving an accuracy of a neural network (NN) used for classification, the method includes obtaining a signature generated by a signature generator, the signature represents at least a part of a sensed information unit (SIU); calculating, by a controller, a distance between the signature and a reference signature that is associated with an error; and determining, by the controller, that the signature is associated with the error when the distance does not exceed a distance threshold. The reference signature is a cluster signature that represents a cluster of signatures, the cluster of signatures includes (i) first signatures that are determined, during a supervised learning process associated with at least partially autonomous driving, to be associated with the error; and (ii) second signatures that are generated during an unsupervised learning process for the autonomous driving scenario, and exhibit a defined similarity with the first signatures.