• 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?

High-quality labeled data is the foundation of AV perception systems, but manual annotation is time-consuming, expensive, and prone to inconsistencies. Automated labeling, powered by AI-driven techniques, offers a scalable and efficient solution to enhance the accuracy and reliability of AV training datasets. This presentation will explore the latest advancements in automated labeling, showcasing how machine learning, sensor fusion, and active learning techniques streamline the data annotation process. Attendees will gain insights into how automated labeling improves dataset quality, reduces development costs, and accelerates AV deployment.

In this session, you will learn more about:

• How machine learning models can automate annotation tasks, reducing reliance on manual efforts while maintaining high accuracy
• The role of multi-modal data (LiDAR, radar, and camera) in improving annotation precision and minimizing edge cases
• Strategies for integrating automated labeling into AV pipelines to handle large-scale datasets efficiently while lowering operational costs