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 名额有限，仅限前「5名」 最近一个月，柱哥在星球内更新了很多最新的行业动态： 同时，还有很多的答疑解惑： Waymo最新的基座模型分享，快慢双系统+数据飞轮； 2025地平线技术生态大会上，苏箐关于自驾的一些insights； 自动驾驶世界模型论文与代码汇总； 英伟达2025年技术图鉴，自驾、具身、大模型全面开花； 理想披露了的最新技术信息，从数据闭环到训练闭环。 近期柱哥也会邀请嘉宾在星球内部和大家聊一聊最近的一些技术进展，欢迎大家加入自动驾驶之心知识星球。 我们准备了大额新人优惠...... 秋招/社招offer建议； 传统规控实现给端到端大模型兜底的思路； 动态行人的场景高斯重建的方法； 40个问题深度解析自动驾驶领域vla+wm的重磅工作：DriveVLA-W0； BEV融合如何能够提升盲区（很近范围）内3D Box的边界准确程度； 小鹏第二代VLA的延展讨论； 对于很多想入门的同学来说，试错成本有点高。没时间和缺乏完整的体系是最大问题，这也容易导致行业壁垒 越来越高，如果想要卷赢那就更加困难了。 扛 ...

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 最近一个月，柱哥在星球内更新了很多最新的行业动态： 同时，还有很多的答疑解惑： 近期柱哥也会邀请嘉宾在星球内部和大家聊一聊最近的一些技术进展，欢迎大家加入自动驾驶之心知识星球。 我们准备了大额新人优惠...... 名额有限，仅限前「5名」 扛内卷，一个足够有料的社区 Waymo最新的基座模型分享，快慢双系统+数据飞轮； 2025地平线技术生态大会上，苏箐关于自驾的一些insights； 自动驾驶世界模型论文与代码汇总； 英伟达2025年技术图鉴，自驾、具身、大模型全面开花； 理想披露了的最新技术信息，从数据闭环到训练闭环。 秋招/社招offer建议； 传统规控实现给端到端大模型兜底的思路； 动态行人的场景高斯重建的方法； 40个问题深度解析自动驾驶领域vla+wm的重磅工作：DriveVLA-W0； BEV融合如何能够提升盲区（很近范围）内3D Box的边界准确程度； 小鹏第二代VLA的延展讨论； 对于很多想入门的同学来说，试错成本有点高。没时间和缺乏完整的体系是最大问题，这也容易导致行业壁垒 越来越高，如果 ...

Group 1 - The core concept of the article is the definition and function of the "world model," which predicts future scenarios based on past sensory data, similar to how humans anticipate events in daily life [2][3][30] - The world model operates by taking various forms of input, such as images, sounds, and sensor data, and outputs predictions about future states, emphasizing the importance of recognizing patterns and making forecasts [4][30] - The distinction between world models and neural networks is highlighted, where neural networks serve as tools for recognition and imitation, while world models are the core that enables prediction and understanding [5][10][30] Group 2 - The article discusses the limitations of creating a "universal" world model due to the vast differences in rules and requirements across various scenarios, leading to the necessity for specialized models [11][12][30] - Various specialized world models are introduced, including video generation, music generation, game, and industrial production models, each focusing on specific domains to achieve precise predictions [12][14][18][30] - The automatic driving world model is described as the most stringent type, as its predictions directly impact safety, requiring rapid response times and high accuracy [18][22][30] Group 3 - The VLA model is presented as an enhanced version of the automatic driving world model, incorporating language logic to improve the prediction of actions based on user commands and traffic rules [23][26][30] - The article concludes that the future of world models lies in becoming more specialized rather than universal, focusing on improving prediction accuracy and speed in specific scenarios [29][30]

Core Insights - The article discusses advancements in autonomous driving technology, particularly focusing on the development of the Visual-Language-Action (VLA) framework and world models, highlighting the contributions of various experts in the field [1][2][3][4][5]. Group 1: Key Contributors - Jian Kun, a senior director at Li Auto, has built the autonomous driving technology stack from scratch since 2021, achieving milestones such as Highway NoA in 2022 and City NoA in 2023 [1]. - Xu Lingyun, a PhD from the Chinese Academy of Sciences, leads the parking team at Changan Automobile, focusing on autonomous driving perception and end-to-end system research [2]. - Jiang Anqing, a senior algorithm scientist at Bosch, leads research on VLA and closed-loop algorithms [3]. Group 2: Technological Focus - The discussion includes the potential integration of world models and VLA, questioning whether a unified approach is feasible [8]. - The high demand for data and computing power is making it increasingly difficult for academia to participate in intelligent driving, raising questions about future opportunities in the academic sector [8]. Group 3: Event Highlights - A live discussion on the future of autonomous driving technologies, including insights on Tesla's FSD v14 and its implications for domestic technology [4][5]. - The event featured a deep dive into the reliability of VLM in autonomous driving, with expert opinions on data closed-loop engineering [12].