Vehicle Behavior Label Training With Weak Classifier Fusion
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Solution Overview
Problem
Existing machine learning models for vehicle behavior classification face challenges in accurately determining good, bad, or unknown behavior using weak classifier labels, which have low recall and precision, especially in complex driving environments.
Innovation Solution
A machine learned model is trained using a combination of weak classifier labels to classify vehicle behavior as good, bad, or unknown, allowing it to generalize and predict behavior in scenarios not represented in the training data by combining weak label data with other data types, such as sensor and simulation data, and applying functions like sigmoid or hyperbolic tangent to determine safe regions for navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If weak classifier labels are used to train machine learning models, then the model can process large amounts of data quickly, but the accuracy and reliability of behavior classification deteriorates due to low recall and precision
Solution Approach 1:
The patent combines multiple weak classifier labels from different sources (sensor data, simulation data, and other weak labels) into a unified training dataset. This merging allows the model to leverage the quantity advantage of weak labels while compensating for individual label deficiencies through ensemble diversity, thereby maintaining high productivity while improving reliability.
Solution Approach 2:
The training data is constructed as a composite of multiple data types (sensor data, simulation data, weak labels) similar to composite materials. Each data source contributes different strengths, and their combination creates a more robust training dataset that overcomes the limitations of any single source, improving classification reliability without sacrificing processing efficiency.
2Reliability
If hand-tuned strong labeled data is used for training, then the classification accuracy is high, but the time and resources required for data preparation increases significantly
Solution Approach 1:
The patent uses simulation data to create synthetic training examples that copy real-world driving scenarios without requiring actual human annotation. This allows the generation of large volumes of training data with known ground truth labels, achieving high reliability without the time-consuming process of hand-tuning real sensor data.
Solution Approach 2:
The patent performs preliminary labeling through simulation and automated processes before actual model training. By pre-generating labeled training data through simulation environments and automated classifier labels, the system avoids the need for time-consuming manual annotation during the actual training pipeline, significantly reducing data preparation time while maintaining accuracy.
3Adaptability or versatility
If multiple data types are combined for training, then the model's ability to generalize to unseen scenarios improves, but the device complexity increases
Solution Approach 1:
The patent segments the training data into distinct components (sensor data, simulation data, weak labels) that can be processed independently through specialized pipelines. Each data type undergoes separate preprocessing and validation steps, which are then integrated into the final training dataset. This segmentation manages complexity by handling each data source separately rather than processing all data types uniformly.
Data Source
AI summary
Techniques for determining classifications associated with vehicle behavior by a machine learned model are discussed herein. A computing device can combine a variety of classifier labels as input to train a machine learned model to determine classifications that represent a vehicle behavior as either good behavior or bad behavior. The machine learned model may receive weak classifier labels that classify an aspect of a vehicle behavior as “good,”“bad,” or “unknown” (relative to the classification) to generate training data for training the machine learning model. Data associated with a vehicle or simulated vehicle may be input to the trained machine learned model to classify good vehicle behavior or bad vehicle behavior even when the model has not been exposed to the precise scenarios represented in the training data.