Alex Kendall, CEO of Wayve, discusses the architectural shift from AV 1.0's hand-engineered robotics to AV 2.0's end-to-end deep learning. He explains how Wayve's generalization-first approach, powered by world models and diverse data, allows them to scale across hundreds of cities and multiple automotive OEMs, creating a path toward a general-purpose embodied AI foundation model.
Waymo Distinguished Engineer Vincent Vanhoucke discusses the core challenges of autonomous driving, explaining how Waymo fuses data from cameras, LiDAR, and radar to build a robust perception system. He delves into the "closed-loop" problem, the critical role of generative AI and simulation in training and validation, and how modern multimodal models are used in a teacher-student framework to distill vast world knowledge into the vehicle's onboard system, aiming for a safety standard that surpasses human performance.
An exploration of Waymo's research into EMMA, an End-to-End Multimodal Model for Autonomous Driving. This summary details how foundation models like Gemini are being adapted to create a single, generalizable system that processes raw sensor data directly into driving decisions, aiming to solve the long-tail problem and improve scalability. It also covers the use of generative AI for advanced sensor simulation and model evaluation.