早上看到waymo最新的基座模型分享，柱哥抓紧解读了下，核心信息： 基本上可以断定waymo在follow国内的快慢双系统端到端方案，和理想的E2E+VLM以及小 鹏VLA2.0有相似之处。 在Waymo，我们正通过将"可验证安全的人工智能"置于核心优先级来攻克这一挑战——安全是我们从底层设计模型与人工智能生态系统的核心准则。由此，我们打 造出了一套极其先进的人工智能系统，已实现大规模安全落地于物理世界。截至目前，我们的完全自动驾驶里程已远超1亿英里，在运营区域持续提升道路安全性 ——与人类驾驶员相比，严重事故发生率降低了十倍以上。 点击下方 卡片 ，关注" 自动驾驶之心 "公众号 戳我-> 领取 自动驾驶近30个 方向 学习 路线 >>自动驾驶前沿信息获取 → 自动驾驶之心知识星球 现在，我们邀请你走进这一技术核心。本文将详细解析Waymo的人工智能战略，以及该战略如何为我们注入发展动力，让安全的自动驾驶服务以史无前例的速度惠 及更多用户。我们将拆解这套以Waymo基础模型（Waymo Foundation Model）为核心的整体人工智能方案，该模型支撑起统一的可验证安全人工智能生态系统，进 而实现加速、 ...

点击下方 卡片 ，关注" 自动驾驶之心 "公众号 戳我-> 领取 自动驾驶近30个 方向 学习 路线 >>自动驾驶前沿信息获取 → 自动驾驶之心知识星球 早上看到waymo最新的基座模型分享，柱哥抓紧解读了下，核心信息： 基本上可以断定waymo在follow国内的快慢双系统端到端方案，和理想的E2E+VLM以及小鹏 VLA2.0有相似之处。 在Waymo，我们正通过将"可验证安全的人工智能"置于核心优先级来攻克这一挑战——安全是我们从底层设计模型与人工智能生态系统的核心准则。由此，我们打造 出了一套极其先进的人工智能系统，已实现大规模安全落地于物理世界。截至目前，我们的完全自动驾驶里程已远超1亿英里，在运营区域持续提升道路安全性—— 与人类驾驶员相比，严重事故发生率降低了十倍以上。 现在，我们邀请你走进这一技术核心。本文将详细解析Waymo的人工智能战略，以及该战略如何为我们注入发展动力，让安全的自动驾驶服务以史无前例的速度惠及 更多用户。我们将拆解这套以Waymo基础模型（Waymo Foundation Model）为核心的整体人工智能方案，该模型支撑起统一的可验证安全人工智能生态系统，进而实 现加速、 ...

Core Insights - Waymo has developed a large-scale AI model called the Waymo Foundation Model, which supports vehicle perception, behavior prediction, scene simulation, and driving decision-making [5][11] - The model integrates data from multiple sensors to understand the environment, similar to how large language models operate [5][11] - The focus on data quality and selection is crucial for ensuring that the model addresses the right problems effectively [25][30] Group 1: World Model Development - Waymo's world model encodes all sensor data and incorporates world knowledge, enabling it to decode driving-related tasks [11] - The model allows for real-time perception and decision-making on the vehicle while simulating real driving environments in the cloud for testing [7][11] - The long-tail problem in autonomous driving, which includes complex scenarios like adverse weather and construction, remains a significant challenge [11][12] Group 2: Addressing Long-Tail Problems - Weather conditions such as rain and snow present unique challenges for autonomous driving, requiring high precision in judgment [12][14] - Low visibility scenarios necessitate the use of multi-modal sensors to detect objects effectively [15] - Occlusion reasoning is critical for understanding hidden objects and ensuring driving safety [18][21] Group 3: Complex Scene Understanding - Understanding complex scenes like construction zones and dynamic environments requires advanced reasoning capabilities [24] - Real-time responses to dynamic signals, such as traffic officer gestures, are essential for safe navigation [24] - The use of large language models is being explored to enhance scene understanding and decision-making [24] Group 4: Importance of Data, Algorithms, and Computing Power - The three critical components for successful autonomous driving are data, algorithms, and computing power, with a strong emphasis on data quality [25][30] - Efficient data mining from vast video datasets is vital for understanding driving events [30] - Quick decision-making is essential for safety and smooth operation, with a focus on reducing response times across the algorithmic chain [30][31] Group 5: Operational Infrastructure - Waymo's operational facilities, including depots and modification workshops, are crucial for the efficient deployment of Level 4 autonomous vehicles [33] - Vehicles can autonomously navigate to charging stations and begin operations after sensor installation [33] - The engineering challenges of scaling autonomous driving technology require collaboration with traditional automotive engineers [34] Group 6: Sensor and Algorithm Response - The responsiveness of sensors, such as camera frame rates, is critical for effective autonomous driving [36] - Algorithms must process data at high frequencies to ensure timely execution of driving commands [36] - The evolution of vehicle control systems is moving towards higher frequency responses, particularly in electric and electronically controlled systems [36]