Synthetic Training Data Generation for Autonomous Vehicle Edge Cases
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Solution Overview
Problem
Conventional training data for machine learning models, such as LIDAR data and images, often lack information on rare or abnormal events, leading to models being unprepared to handle unobserved occurrences, particularly in scenarios like vehicle behaviors on roads.
Innovation Solution
A system that generates enhanced training information by combining observed behaviors of moving objects with unobserved or rare behaviors, compartmentalized by context such as terrain, driving mode, and environmental conditions, and updates this information based on actual outcomes to improve the model's preparedness for various scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If training data is limited to real observations of objects, then the training data size is manageable and collection is straightforward, but the training data does not include information on rare or abnormal events
Solution Approach 1:
The system performs preliminary actions by generating synthetic training data for rare and abnormal events before actual deployment. Simulation environments pre-generate diverse scenarios including edge cases that would be difficult or impossible to capture in real-world observations, ensuring the model is prepared for rare events without requiring extensive real-world data collection.
Solution Approach 2:
The system creates copies of real-world scenarios through simulation, generating virtual training data that replicates real observations while also incorporating rare and abnormal events. These synthetic copies augment the limited real observation data, providing comprehensive training examples without requiring proportional increases in real-world data collection.
2Reliability
If training data includes only observed behaviors, then the data collection process is simple, but the model is not prepared to deal with unobserved events
Solution Approach 1:
The system introduces simulation environments as an intermediary between real-world observations and model training. This intermediary layer generates virtual training data that bridges the gap, allowing the model to learn from both real observations and synthesized rare events without requiring direct access to all possible real-world scenarios.
Solution Approach 2:
The system varies parameters in simulation environments to generate diverse training scenarios. By changing environmental conditions, object properties, and event parameters in controlled ways, the system creates comprehensive training data covering rare and abnormal events while maintaining systematic control over the data generation process.
3Loss of time
If training data is collected from limited real-world observations, then the collection time is short, but the training data lacks comprehensive scenario coverage
Solution Approach 1:
The system performs preliminary data generation through simulation before actual model training begins. This allows comprehensive scenario coverage to be prepared in advance, including rare and edge cases that would require extensive real-world observation time to capture naturally.
Solution Approach 2:
The system creates virtual copies of real-world scenarios through simulation, augmenting limited real observation data with synthesized examples. This copying approach provides comprehensive scenario coverage across diverse conditions without requiring proportional increases in real-world data collection time.
Data Source
AI summary
Systems, methods, and non-transitory computer readable media configured to generate enhanced training information. Training information may be obtained. The training information may characterize behaviors of moving objects. The training information may be determined based on observations of the behaviors of the moving objects. Behavior information may be obtained. The behavior information may characterize a behavior of a given object. Enhanced training information may be generated by inserting the behavior information into the training information.