Waymo最近的基座模型分享：快慢双系统端到端 & 世界模型仿真

Core Viewpoint - Waymo is advancing its autonomous driving technology by prioritizing "verifiable safe AI" as a core principle, significantly reducing accident rates compared to human drivers, with over 100 million miles of fully autonomous driving achieved [2][4]. Group 1: Waymo's AI Strategy - Waymo's AI ecosystem integrates a driver, simulator, and evaluator, all powered by the Waymo Foundation Model, ensuring safety is a foundational element rather than an afterthought [4][11]. - The Waymo Foundation Model serves as a multifunctional "world model," providing a robust framework for the AI ecosystem, enhancing interaction between components and supporting end-to-end signal backpropagation [7][9]. Group 2: Components of the AI Ecosystem - The driver model generates safe and compliant action sequences, with a distillation process transferring knowledge to more efficient student models for real-time deployment [13]. - The simulator creates high-fidelity virtual environments for training and testing the driver model, covering diverse and challenging scenarios [15][16]. - The evaluator system analyzes driving behavior, providing feedback for continuous improvement and ensuring the driver model's performance is rigorously tested [17]. Group 3: Learning and Optimization Mechanisms - Waymo's internal learning loop, powered by the simulator and evaluator, utilizes reinforcement learning to enhance the driver model's capabilities in a controlled environment [18]. - The external learning loop leverages real-world driving data to identify suboptimal behaviors, generating training data for the driver model, which is then validated through the simulator [20]. - This continuous learning cycle is supported by a vast amount of fully autonomous driving data, which is critical for ongoing optimization and cannot be replicated through simulation alone [20][21].