Simulated Vehicle Data Augmentation for Autonomous Detection Training
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Solution Overview
Problem
The existing autonomous control systems for vehicles require significant resources and time to obtain and label training data for machine-learned models, particularly for object detection and transformation models, as they rely on human operators to annotate images of vehicles, which is inefficient.
Innovation Solution
The use of computer-simulated models of vehicles allows for the generation and labeling of training data automatically, enabling the training of object detection and transformation models without human intervention, using geometric information and simulated views to create large datasets for improved vehicle detection and recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human operators are used to label or annotate training data images, then the accuracy and quality of training data can be ensured, but the time and resource requirements increase significantly
Solution Approach 1:
The patent uses computer-simulated images as copies of real-world scenarios to train object detection models. These synthetic images contain ground truth annotations by definition, eliminating the need for manual labeling while providing sufficient training data quality. The simulation environment generates images with known object positions, sizes, and attributes, serving as perfect training examples without human intervention.
Solution Approach 2:
The system enables self-service data generation through computer simulations that automatically produce annotated training images. The simulation engine itself provides the ground truth annotations without requiring external human operators, making the data preparation process autonomous and efficient.
2Measurement precision
If human operators manually annotate bounding boxes in images, then accurate object location data can be obtained, but the productivity and output rate decrease
Solution Approach 1:
The patent generates synthetic images that copy real-world visual scenarios while automatically embedding accurate object location data. The simulation environment creates images with precisely known bounding boxes and object attributes, providing both high accuracy and high throughput simultaneously through automated generation processes.
Solution Approach 2:
The system performs preliminary action by pre-computing and embedding ground truth annotations during the image generation process itself. Rather than adding annotations after image capture, the accurate location data is built into the synthetic images from the start, eliminating the need for subsequent manual annotation work.
3Adaptability or versatility
If large amounts of real-world images are collected for training, then the model can learn from diverse scenarios, but the resource requirements and data collection complexity increase
Solution Approach 1:
The patent uses computer simulations to copy diverse real-world scenarios without requiring physical data collection infrastructure. The simulation environment can generate images representing various weather conditions, lighting scenarios, object positions, and environmental contexts, providing model training data with high versatility while avoiding complex data collection systems.
Solution Approach 2:
The simulation-based training system serves multiple functions: it generates training images, provides ground truth annotations, creates diverse scenarios, and scales indefinitely without additional hardware. This universal approach replaces multiple specialized data collection systems with a single multi-functional simulation platform.
Data Source
AI summary
A modeling system trains computer models for an autonomous control system using computer simulated models of objects. The objects may be vehicles, and the computer simulated models may be virtual models of vehicles simulated by computer software. Since the vehicle models are computer simulated, various characteristics of the vehicle can be easily obtained by the modeling system. The various types of data may include geometric information of the vehicle, views of the vehicle from different perspectives, and the like. The modeling system can easily generate and label a large amount of training data using the characteristics of the computer simulated vehicles. The modeling system can use the training data to train computer models for the autonomous control system.