• What methodologies best ensure dataset completeness and class balance across long-tail driving scenarios in supervised and self-supervised pipelines?
• How can spatiotemporal perception models fuse asynchronous, multimodal sensor data to achieve low-latency, high-accuracy environmental understanding?
• What are the current best practices for compressing and optimizing deep neural networks for real-time inference on automotive-grade edge hardware?
• How can simulation environments be used to close the domain gap between synthetic and real-world data for perception and planning models?
• Which frameworks or methods enable interpretability and functional safety validation of black-box deep learning components in AV stacks?

Presenting the results of our World Cafés: The moderators summarize the key takeaways of their World Café Round Tables on the main stage and share the main findings with all attendees.