As the automotive industry accelerates toward autonomy, the need for high-quality, diverse, and compliant training data has never been more critical. Traditional data collection methods are costly, time-intensive, and constrained by regulatory and privacy concerns. GenAI presents a groundbreaking opportunity to generate high-fidelity synthetic data, enhancing product development across perception, validation, and compliance workflows. This presentation will explore how GenAI-driven synthetic data can improve data quality, boost productivity in AI model training, and ensure alignment with evolving regulatory standards. We will discuss innovative tools and methodologies that enhance realism, mitigate bias, and streamline compliance processes—ultimately transforming the way autonomous vehicle systems are developed and validated.

In this session, you will deep dive into how to:

• Leverage GenAI to generate high-fidelity synthetic data for more accurate and robust AV system training
• Integrate synthetic data into AI training pipelines to accelerate development cycles and reduce reliance on costly real-world data
• Implement strategies to align synthetic data with regulatory and ethical standards, addressing transparency and bias challenges