Balanced Datasets for the Automotive Industry
Increase Scenario
Coverage
Synthetic data can create rare or dangerous driving scenarios not commonly found in real-world datasets, ensuring that autonomous driving systems are well-prepared for unusual situations.
Customization and
Control
Developers can tailor synthetic datasets to specific needs or gaps in existing data, such as varying lighting conditions or obstructed views, enhancing the robustness of the training process.
Safety and Ethical Considerations
Generating synthetic data avoids the ethical and safety issues associated with capturing real-world data for hazardous scenarios, such as accidents or extreme weather conditions.
Repeatability and Consistency
Synthetic data generation allows for the reproduction of exact scenarios multiple times, which is crucial for debugging and improving the algorithms of autonomous systems. ​
Take Full Control
Physical simulations for data generation offer the advantage of precisely replicating complex real-world phenomena under controlled conditions, allowing for the exploration of scenarios that might be too risky, expensive, or impractical to perform in reality. This capability not only aids in understanding intricate systems and their behaviors but also significantly enhances the development and testing of technologies by providing high-fidelity data that can predict real-world outcomes, thereby reducing the costs for extensive real data capturing and labeling.
Complex Outdoor Environments
Procedural models and physics-based simulations are effectively used to simulate dynamically changing outdoor environments, such as mining operations, allowing for the realistic modeling of terrain alterations, equipment interactions, and environmental impacts over time.
