Enhanced Training Data Generation for Autonomous Systems
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Solution Overview
Problem
Conventional training data, such as LIDAR data and images, often lack information on rare or abnormal events, leading to machine learning models being unprepared to handle unobserved behaviors of objects like vehicles, which can result in inadequate performance in real-world scenarios.
Innovation Solution
The system generates enhanced training information by inserting behavior information into existing training data, characterizing both observed and unobserved behaviors of moving objects, including rare and dangerous actions, using three-dimensional point cloud models and frequency of occurrence to ensure comprehensive object behavior representation.
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 that includes rare and abnormal behaviors before actual deployment. Behavior information is obtained in advance and inserted into training data to prepare the machine learning model for uncommon events that would be difficult to capture through real-world observation alone.
2Reliability
If training data includes only observed behaviors, then the data collection process is simple, but the machine learning model is not prepared to deal with unobserved events
Solution Approach 1:
The system introduces an intermediary component that generates and inserts behavior information into training data. This intermediary process bridges the gap between observed real-world data and unobserved rare events, enabling the machine learning model to learn from both without requiring direct observation of every possible scenario.
3Loss of information
If observations are limited to a finite amount of time, then the observation process is practical and manageable, but rare events with very low likelihood are not captured
Solution Approach 1:
The system creates copies of training data with inserted behavior information representing rare events. Instead of extending observation time to capture rare events naturally, the system generates synthetic copies of training scenarios that include uncommon behaviors, allowing the model to learn from these rare events without requiring extended real-world observation periods.
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.