Synthetic Data for AVs
Our customer is a leading US-based tech company that makes AI-based chips for automobiles and also designs chips for electric vehicles, as well as manufactures home and grid-scale battery energy storage and related equipment.
Problem Statement
The company faced challenges in training their AV perception algorithms on real-world data alone. Real-world data can be limited, biased, and expensive to collect, especially for rare or dangerous scenarios. They needed a solution that could supplement their real-world data with high-quality synthetic data to improve the robustness and accuracy of their perception systems.
Our Solution
- Our solution used photorealistic 3D simulation environments to generate synthetic driving scenarios, including rare and edge cases that would be difficult or dangerous to capture in the real world.
- We employed novel view synthesis techniques to generate synthetic data from different sensor viewpoints, mimicking the diverse sensor configurations found across different vehicle types.
- Automatically generated ground truth annotations, such as 3D bounding boxes and object classifications, to enable efficient training of AV perception models.
- Validated the synthetic data for realism and diversity, and seamlessly integrated it into the customer’s AV perception training pipeline.
Business Benefits
- The synthetic data helped improve the accuracy and robustness of the customer’s AV perception models, particularly in handling rare and edge cases.
- The automated data generation and annotation capabilities significantly reduced the time and cost required to collect and prepare training data.
- The improved perception performance enabled by synthetic data enhanced the overall safety of the customer’s AV systems, reducing the risk of accidents.
- The ability to generate diverse and customized synthetic data allowed the customer to efficiently explore and validate their AV perception algorithms across a wide range of scenarios.
