Potential of Synthetic Data for Automotive Development

The article delves into the critical role of synthetic data in advancing AI-based perception systems for autonomous vehicles. It highlights the limitations of real-world data collection, such as high costs, data protection issues, and the inability to capture rare or dangerous scenarios. Synthetic data offers a solution by providing an unlimited, controllable, and cost-effective source of high-quality training data. The article explores various methods of generating synthetic data, including generative neural networks and game engines like Unreal and Unity. It also discusses the importance of data quality and the challenges of achieving photorealism. ITK Engineering's innovative data generation pipeline, which combines traditional and generative approaches, is presented as a promising solution. This pipeline automates scene creation, ensures variability, and integrates sensor simulation for realistic data generation. The article concludes by emphasizing the potential of synthetic data to revolutionize automotive development and the importance of ongoing research and development in this field.