英伟达还是放不下自动驾驶

远川科技评论01-12

2026年第一个星期，车企牛马们收到了黄仁勋的开年大礼包。刚刚结束的CES展，英伟达早早宣布没有新显卡发布，同时掏出了面向数据中心的Rubin平台和面向自动驾驶的VLA大模型Alpamayo。2026 CES英伟达展台VLA即Vision-Language-Action（视觉-语言-行动），具体在自动驾驶中，模型会将传感器数据先变成语言和符号，再通过推理形成决策交由车辆执行。好处是理解能力强，有可...

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autonomous driving","themeId":null,"isJumpTheme":false,"ttsUrl":null,"symbols_score_info":{"NVDY":0.6,"NVDA":1.96,"NVIW.SI":0.6,"3NVD.UK":0.6,"NVD3.UK":0.6,"NVD2.UK":0.6,"NVDS":0.6,"NVDS.UK":0.6,"SNVD.UK":0.6,"NVDX":0.6,"NVDU":0.6,"NVD":0.6,"NVDD":0.6,"2NVD.UK":0.6},"content_text":"2026年第一个星期，车企牛马们收到了黄仁勋的开年大礼包。刚刚结束的CES展，英伟达早早宣布没有新显卡发布，同时掏出了面向数据中心的Rubin平台和面向自动驾驶的VLA大模型Alpamayo。2026 CES英伟达展台VLA即Vision-Language-Action（视觉-语言-行动），具体在自动驾驶中，模型会将传感器数据先变成语言和符号，再通过推理形成决策交由车辆执行。好处是理解能力强，有可解释的推理痕迹，避免了黑箱情况。去年，VLA接棒BEV和端到端，是自动驾驶领域当之无愧的年度热词。理想、小鹏、Momenta、地平线等企业都有类似技术方案。英伟达的Alpamayo则是首个开源的VLA大模型。作为英伟达汽车业务负责人，吴新宙在采访中表示，Alpamayo与特斯拉FSD“非常接近”，在一对一测试中，内部接管率处于同一水平[1]。看热闹不嫌事儿大的网友第一时间@马斯克，表示马总这下要睡不着觉了。6G网速的马斯克也在第一时间回应，说自己睡眠质量还行，并“真心希望英伟达获得成功”。关于英伟达与特斯拉之争，子弹还得再飞一会儿，但无论马斯克睡不睡得着，都不会改变一个清晰的事实：这一次，英伟达要对自动驾驶发起总攻了。大模型预制菜作为当前自动驾驶最热门的技术路线之一，VLA模型在英伟达发布Alpamayo之前就已经吸引了诸多势力押注。小鹏据称早在2024年就开始开发VLA模型，在Alpamayo发布后没几天，小鹏就跳过了第一代，发布了自研的第二代VLA模型。理想、长城、奇瑞等车企也选择了VLA路线。特斯拉虽然没有完全公开自己的技术路线，但从FSD V14的种种信息看，特斯拉也采用了类似VLA的技术架构。早期的自动驾驶算法高度依赖人工补丁，工程团队要尽可能穷尽所有的路况，并写入对应规则，效率非常低。特斯拉的端到端方案大幅简化了架构，通过高强度的训练，实现传感器输入数据-算法输出决策的效果，不再依赖人为穷举。但端到端也有局限性。一是输入和输出的中间过程是个黑箱，没办法追溯；二是由于这种“黑箱化”，优化算法只能不断投喂数据训练，效果不可控。VLA的思路是把语言模型融入中间过程，让算法的决策可追溯、可解释。相比端到端纯粹“模仿”正确的驾驶行为，VLA让算法有了自主的推理决策能力，效果更加可控。英伟达Alpamayo的轰动性不在于VLA本身，而在于开源。VLA虽好，但好东西都不便宜。车企开发VLA模型有三大大山，一是高质量数据获取、二是推理能力的训练、三是算力与成本，总之就是成本高。英伟达Alpamayo相当于给车企提供了一套完整的底层模型，车企可以基于自有数据和需求微调，形成自己的技术方案。既降低了开发难度，又保证了算法差异性。相当于英伟达提供预制菜，车企按照自己的需求调整火候和调味，翻炒加热即可“出锅”上车。按照英伟达自己的说法[4]：Alpamayo并不是直接部署于车端的模型，而是作为大规模教师模型，供开发者调优、蒸馏，成为其完整辅助驾驶技术栈的核心基础。除了Alpamayo开源模型，英伟达还送佛送到西，附赠仿真框架AlpaSim用以闭环测试，以及包含超过1727小时驾驶数据的Physical AI开放数据集，预制菜、锅碗、燃气灶一应俱全，不失为另一个角度的“智驾平权”。Alpamayo（模型）+AlpaSim（测试）+ Physical AI（数据）在CES展当天的媒体采访环节，黄仁勋谈到特斯拉FSD时依然是一通乱吹，“世界级标杆”、“表现完美”、“我自己都在用”赞美三连[2]。可惜马斯克并不领情，回应完自己睡眠质量的担忧后再次开炮，一边大夸特夸特斯拉的技术水平，一边嘲讽英伟达：“英伟达为汽车行业提供了有用的工具，但对汽车行业本身却鲜有作为。”不管马斯克是不是在嘴硬，他这句话确实也没说错。英伟达的心事英伟达的汽车业务，远看像个宝，近看像根草。从市占率来看，英伟达DRIVE系列是L2+及以上中高阶智驾多年霸主，Drive Thor量产拖延一度让头部车企新车发布节奏被集体打乱，地位超然可见一斑。但从汽车业务实际贡献的收入看，虽然增长不算低，但相比在数据中心市场的统治力，恐怕也没达到黄仁勋的预期。拿下高速增长的汽车市场，也是英伟达从小鹏挖走吴新宙的重要原因。作为一家“卖铲子”的芯片公司，英伟达在软件上的所有投入，都是为了更好的卖芯片。从游戏到数据中心，再到人形机器人，英伟达的思路都是给客户提供标准化的工具箱（如cuda）和开发环境，并将这些软件服务与自家硬件绑定，最终目的还是卖芯片。不过在汽车业务上，英伟达遇到了严峻的问题：卖铲子的人不会用铲子。英伟达只卖芯片，具体的自动驾驶算法需要车企基于芯片自行研发。因此，英伟达汽车业务的大客户，一直是小鹏、蔚来这些软件、算法能力比较强的国内新势力。但问题是，这些客户多少都有自研芯片脱离英伟达的心思，蔚来的芯片已经上车，小鹏的图灵芯片也量产在即。而大众、奔驰、宝马这些销量更高的老牌车企，基本都没什么自动驾驶算法的研发能力，就很难给英伟达提供订单。而英伟达自己身为顶级卖铲人，对自动驾驶算法的研发也是知之甚少。于是黄仁勋痛定思痛，既然老牌车企都不会用铲子，那就写一份详细的使用说明书，让车企在这个基础上发挥主观能动性。吴新宙跳槽来英伟达，做的就是这个工作。Alpamayo就是英伟达痛定思痛的结果之一，不会写代码，那就给一个代码模板，虽不能照抄，但改一改也能用。同时从硬件到开发工具，全部进化到“保姆式服务”。最新的DRIVE Orin/Thor硬件平台，不仅提供芯片，还提供完整的车载计算机硬件设计蓝图；物理级精确仿真平台NVIDIA DRIVE Sim，可以为企业生成合成数据、进行极端的安全测试。再加上最新一代数据中心芯片Vera Rubin、世界模型Cosmos，英伟达真正建成了一个“云端训练（Rubin+Cosmos）+车端推理（Thor+Alpamayo）”的智能驾驶工具链闭环，饭喂到嘴边，闭眼吃就是。英伟达智能驾驶形成了一个工具链闭环Alpamayo的另一面，是英伟达在“标准化”和“定制化”之间的一次重新校准。对英伟达来说，最理想的商业模式是销售标准化的芯片，这样毛利率最高，估值空间最大。一旦涉足定制化的算法开发，就成了干一个项目结一次钱的施工队。所以过去这些年，英伟达汽车业务的经营思路就是不断提升软件工具箱的丰富度，降低车企使用自家芯片的门槛，但坚决不亲自下场做车企的自动驾驶项目。Alpamayo就是这一策略的最新产物，英伟达宁愿研发一套开源的算法交给汽车行业，也不愿意自己当乙方。基于英伟达平台建立的全球L4和robotaxi生态换句话说，为了卖自家燃气灶，英伟达开发了配套的抽油烟机、锅碗瓢盆、各式调味料，又研发了一系列预制菜，离自己亲自下厨，只有一步之遥了。","kind":"news","is_publish_news":true,"is_publish_highlight":false,"is_publish_live":false,"is_publish_wemedia":null,"editions":null,"column":"","sentiment":"0","news_tag":"","news_rank":0,"isVideo":false,"video":null,"symbols":[],"gpt_button":0,"need_auth":false,"need_login_tip":false,"code":"91000000","status":"200"},"commentList":[],"isCommentEnd":true,"newsSizeData":{"likeSize":0,"commentSize":0,"repostSize":0,"favoriteSize":0,"likeStatus":false,"favoriteStatus":false},"APP":{"userAgent":"Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; 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