Neural Network Ensembles for Out-of-Distribution Scenario Annotation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current deep learning models for autonomous vehicles face challenges in accurately characterizing complex and rare scenarios due to reliance on stringent training data and inability to extrapolate beyond their training regimes, leading to unreliable predictions in unconventional situations.
Innovation Solution
The proposed method involves a computer-implemented system using ensembles of machine learning algorithms trained with diverse data sets to identify and annotate out-of-distribution scenarios, which are then integrated into the training regime to enhance the reliability and robustness of neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural networks are trained with stringent training data schemes, then they can perform complex tasks accurately within their training regime, but they cannot extrapolate reliably beyond their training data
Solution Approach 1:
The system performs preliminary identification of out-of-distribution scenarios by analyzing discrepancies in ensemble predictions before these scenarios cause failures. By detecting unusual patterns early and flagging them for further analysis, the system prepares for potential extrapolation challenges proactively rather than reactively.
Solution Approach 2:
The patent introduces an intermediary annotation system that bridges the gap between training data and real-world scenarios. Human annotators or auxiliary systems label out-of-distribution scenarios, creating a intermediate layer of understood examples that help the neural network gradually learn to handle unseen situations without direct exposure to them during initial training.
2Adaptability or versatility
If diverse data sets are used to train neural network ensembles, then the system can identify out-of-distribution scenarios, but the complexity of the system increases
Solution Approach 1:
The system segments the complex task of handling diverse scenarios into distinct components: multiple neural networks each specialized in different aspects, an ensemble coordination layer that aggregates their predictions, and a discrepancy detection module that identifies out-of-distribution cases. This segmentation allows each component to remain relatively simple while the system as a whole achieves high adaptability.
Solution Approach 2:
The neural network ensemble is designed with universal components that can handle multiple types of scenarios through a common architecture. Each network in the ensemble uses the same fundamental structure but is trained on different aspects of the data, allowing the system to generalize across diverse scenarios without requiring entirely separate systems for each scenario type.
3Reliability
If ensembles of neural networks are used to characterize complex scenarios, then reliability improves, but the computational cost and training time increase
Solution Approach 1:
The system applies partial action by using the ensemble of neural networks only for identifying out-of-distribution scenarios rather than for all processing tasks. For common in-distribution scenarios, a single network or simplified model suffices, while the full ensemble is activated only when discrepancy detection indicates potential out-of-distribution cases require more thorough analysis.
Solution Approach 2:
The patent implements preliminary filtering where a lightweight discrepancy detection mechanism screens scenarios before they undergo full ensemble analysis. This preliminary action identifies which scenarios require the computationally intensive ensemble evaluation, reducing overall training and inference time by avoiding unnecessary full ensemble processing for routine cases.
Data Source
AI summary
A method for automated data generation by neural network ensembles is disclosed. The method includes obtaining a cluster of trained ensembles of machine learning (ML) algorithms. The cluster includes two or more ML algorithm ensembles, wherein each ML ensemble includes a plurality of ML algorithms that are trained based on a first set of training data. The method further includes obtaining sensor data representative of a scenario, in a surrounding environment of a vehicle, wherein the sensor data includes at least two sensor data sets. The method further includes providing each obtained sensor data set as input to a corresponding ML algorithm ensemble. The method further includes selecting the ensemble-prediction output of one ML algorithm ensemble associated with an absent determined discrepancy for generating an annotation for one or more data samples of the sensor data set of at least one ML algorithm ensemble associated with a determined discrepancy.