在近日的2026 CES大会上，英伟达一口气开源了Alpamayo系列视觉-语言-动作推理模型、仿真工具AlpaSim和包含1700多个小时驾驶数据的开放数据集。 有人认为，英伟达此举将一夜削平自动驾驶的门槛，这种言论虽然很炸裂，但离真实情况很远。 不过，这件事确实值得深入探讨一番！ 01 英伟达在CES上投下的Alpamayo系列VLA推理模型、AlpaSim仿真工具、开放数据集这三颗重磅炸弹，不仅标志着英伟达的战略重心正从提供底层算力向 构建覆盖算法、工具链与数据基础设施的全栈开发生态系统转变，还形成了一套组合拳，直指自动驾驶行业最顽固的阿喀琉斯之踵-长尾挑战。 先来看全栈开发生态。 参考英伟达的三个计算机理论（用于模型部署的计算机AGX、用于模型训练的计算机DGX、用于模型评估的计算机RTX），一个完整的物理AI开发平台 包含模型训练、仿真、部署三大环节。 对照来看，数据集中的训练集用于模型训练，评估集用于模型仿真； AlpaSim结合Cosmos生成长尾场景，用于模型训练，结合Omiverse提供虚拟交通世界，用于模型仿真； Alpamayo提供教师模型，用于模型的微调训练和部署。 英伟达此次开源的三 ...

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]