The volume, velocity, and variety of data are foundational to advancing autonomous driving systems, yet manual data collection for training machine learning models remains costly, time-intensive, and limited in covering edge cases. This presentation explores how synthetic data generation and AI-driven data augmentation techniques address these challenges, enabling scalable, high-quality training datasets for perception systems. Attendees will discover how synthetic data enriches real-world datasets, enhances model robustness, and accelerates development cycles while maintaining accuracy. Through case studies and technical insights, the session will highlight best practices for balancing synthetic and real-world data, ensuring safety-critical scenarios are thoroughly validated.

In this session, you will learn more about:

• How synthetic data accelerates AV development without compromising accuracy, enabling rapid iteration and validation of perception models.
• Strategies to simulate edge cases (e.g., rare weather conditions) using advanced augmentation techniques.
• How using synthetic data helps to overcome region-specific challenges.

• How can AI and ML models be trained to handle unpredictable real-world conditions, including rare and adversarial scenarios?
• What are the limitations of simulation-based training, and how can real-world data be effectively incorporated to improve model robustness?
• With massive sensor data streams, how can V2X communication and edge computing optimize real-time decision-making in autonomous vehicles?
• How can AI models be designed to continuously learn, adapt, and generalize across different geographies, weather conditions, and traffic patterns?
• What are the key AI safety and validation strategies required to meet regulatory standards and ensure reliable deployment at scale?