Autonomous Vehicle Data Engine for Rare Object Auto-Annotation
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Solution Overview
Problem
Current AI models for autonomous vehicles struggle to detect rare or unseen objects due to limited data availability, requiring costly and inefficient human annotation and data curation processes.
Innovation Solution
A self-improving data engine (SIDE) is developed, utilizing multi-modality dense captioning (MMDC) models and vision-language-models (VLMs) to automatically detect unrecognized classes, generate annotations, and continuously train the model with feedback, eliminating the need for human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human annotation and data curation processes are used to train AI models, then the quality of training data is improved, but the cost and efficiency deteriorate
Solution Approach 1:
The system uses AI models to automatically perform data curation and annotation tasks that were previously requiring human intervention. The self-improving data engine continuously trains and refines itself using automated processes, eliminating the need for manual data processing while maintaining high data quality.
Solution Approach 2:
The patent implements a feedback mechanism where the AI model's performance is continuously evaluated and used to improve future data curation and annotation processes. The system learns from its own outputs and adjusts its data processing strategies accordingly, creating a self-improving cycle that enhances both quality and efficiency over time.
2Measurement precision
If more training data is collected to detect rare or unseen objects, then the detection accuracy is improved, but the time and resources required increase
Solution Approach 1:
The system performs preliminary data processing and annotation automatically before formal training begins. By pre-processing data with automated AI models and creating initial annotations in advance, the system reduces the time needed for actual training while ensuring sufficient data quality for detecting rare objects.
Solution Approach 2:
The patent uses synthetic data generation and copying techniques to create additional training examples for rare objects. Instead of collecting vast amounts of real-world data, the system generates synthetic copies and variations of existing data to augment the training set, significantly reducing data collection time while maintaining detection accuracy.
3Productivity
If automated data processing is implemented, then the efficiency is improved, but the quality of annotations may deteriorate
Solution Approach 1:
The system implements multiple feedback loops where automated annotation outputs are continuously evaluated and refined. The AI model assesses its own annotation quality and adjusts its processing strategies accordingly, ensuring that automated efficiency does not compromise annotation accuracy. Human feedback mechanisms are also integrated to correct and improve automated outputs.
Solution Approach 2:
The patent introduces intermediate verification steps and intermediary models that act as mediators between automated processing and final annotations. These intermediary components validate and refine automated outputs, ensuring that efficiency gains from automation do not result in quality deterioration.
Data Source
AI summary
Systems and methods for a self-improving data engine for autonomous vehicles is presented. To train the self-improving data engine for autonomous vehicles (SIDE), multi-modality dense captioning (MMDC) models can detect unrecognized classes from diversified descriptions for input images. A vision-language-model (VLM) can generate textual features from the diversified descriptions and image features from corresponding images to the diversified descriptions. Curated features, including curated textual features and curated image features, can be obtained by comparing similarity scores between the textual features and top-ranked image features based on their likelihood scores. Generate annotations, including bounding boxes and labels, can be generated for the curated features by comparing the similarity scores of labels generated by a zero-shot classifier and the curated textual features. The SIDE can be trained using the curated features, annotations, and feedback.