Waymo刚刚的基座模型分享：快慢双系统端到端 & 世界模型仿真

Core Insights - Waymo is advancing its autonomous driving technology by prioritizing "verifiable safe AI" as a core principle, significantly reducing the accident rate compared to human drivers by over ten times [2][5][19] - The company has achieved over 100 million miles of fully autonomous driving, continuously improving road safety in its operational areas [2][5] Group 1: Waymo's AI Strategy - Waymo's AI ecosystem integrates a driver, a simulator, and an evaluator, all powered by the Waymo Foundation Model, ensuring safety is a foundational element rather than an afterthought [5][12] - The Waymo Foundation Model serves as a multifunctional "world model," providing a robust interface for interaction among various components and supporting end-to-end signal backpropagation during training [8][10] Group 2: Components of the AI Ecosystem - The driver model generates safe and compliant action sequences, with its capabilities distilled into more efficient student models for real-time deployment in vehicles [14] - The simulator creates high-fidelity virtual environments for testing the driver model under diverse and challenging scenarios, while the evaluator analyzes driving behavior to provide feedback for continuous improvement [14][15] Group 3: Learning and Optimization Mechanisms - Waymo employs a dual learning loop: an internal loop driven by the simulator and evaluator for reinforcement learning, and an external loop utilizing real-world driving data to enhance the driver model [17][19] - The company has amassed a vast amount of fully autonomous driving data, which is crucial for training and optimizing its systems, surpassing the reliance on human driving data [19]