Synthetic Training Data Generation for Autonomous Vehicle Control

The patent creates synthetic copies of real-world driving environments through simulation. Instead of collecting actual labeled data from physical tests, the system generates virtual representations of road scenes, vehicles, and environmental conditions that replicate real-world scenarios. This copying approach eliminates the time-consuming field data collection while maintaining training data quality for control software validation.

The patent performs preliminary synthesis of training data before actual testing begins. By pre-generating diverse driving scenarios, environmental conditions, and edge cases in virtual environments, the system prepares comprehensive training datasets in advance. This preliminary action eliminates the need for time-consuming data collection during the testing phase while ensuring reliability through pre-validated synthetic data.

The patent uses synthetic copying to reproduce rare and low-probability events that would be extremely difficult to capture in real-world data collection. The simulation system can deliberately generate edge cases such as unusual weather conditions, rare traffic scenarios, and exceptional environmental situations without the prohibitive costs and time requirements of waiting for these events to occur naturally during field testing.

The patent systematically varies environmental parameters, object positions, and scenario conditions in the simulation to generate diverse training data. By changing parameters such as weather conditions, lighting, road types, and vehicle configurations, the system achieves comprehensive scenario coverage including low-probability events while maintaining ease of data production through controlled virtual experimentation rather than expensive field deployment.

The patent eliminates manual labeling by copying real-world scenarios into simulation environments where ground truth is automatically known. Since the synthetic data is generated from predefined models and parameters, the correct labels and annotations are inherently embedded in the generation process itself. This approach maintains measurement precision through accurate virtual representations while dramatically increasing productivity by removing the manual labeling bottleneck entirely.

The simulation system performs self-labeling by automatically generating annotated training data with inherent ground truth information. The synthetic environment inherently knows the correct positions, classifications, and relationships of all objects since it generates them according to defined rules and parameters. This self-service capability eliminates the need for external manual labeling while maintaining high accuracy, thereby大幅提升 data production rate.