特斯拉的Grok和FSD融合，座舱和辅助驾驶要整合在一起了？

芝能汽车08:32

芝能汽车出品曾经，电动汽车、辅助驾驶NOA还是两个不同的风口，现在 “AI Everywhere”，如何体现出人工智能是每个汽车行业公司需要考虑的内容。算力、模型以及各式各样的硬件载体进入物理世界，成为AI应用的重要因素。当特斯拉砍掉Model S和Model X，用上FSD V14.2 + Grok：大模型上车进入实战阶段；然后还映射到机器人的载体——特斯拉又在技术方向上给大家新出了一张卷子。...

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integrated?","themeId":null,"isJumpTheme":false,"ttsUrl":null,"symbols_score_info":{"TSLT":0.6,"TSLI":0.6,"TSDD":0.6,"TSLG":0.6,"TSLA":1.97,"TESL":0.6,"TSLW":0.6,"TSLO":0.6,"TSII":0.6,"TSLP":0.6,"07766":0.6,"CRSH":0.6,"TSL":0.6,"TSLS":0.6,"TSLZ":0.6,"TSLQ":0.6,"TSLY":0.6,"TSYY":0.6,"TSLR":0.6},"content_text":"芝能汽车出品曾经，电动汽车、辅助驾驶NOA还是两个不同的风口，现在 “AI Everywhere”，如何体现出人工智能是每个汽车行业公司需要考虑的内容。算力、模型以及各式各样的硬件载体进入物理世界，成为AI应用的重要因素。当特斯拉砍掉Model S和Model X，用上FSD V14.2 + Grok： 大模型上车进入实战阶段；然后还映射到机器人的载体——特斯拉又在技术方向上给大家新出了一张卷子。Part1FSD 和Grok的融合特斯拉最近推送的FSD V14，在 FSD V13 端到端模型的基础上，V14的参数规模相比V13提升了一个数量级（10倍）。同时，Grok被引入智能座舱，与车机导航和交互系统深度融合，在端云结合的架构下，Grok已经具备了相当完善的大模型能力。这种组合非常接近于专家级真人，有了强逻辑推理能力的“助理”。FSD与Grok的组合，让目前和用户有关的两个重要的AI板块，用更大的模型规模通往更强智能，这是条可行路径吗？FSD V14 + Grok 的组合完成了一次系统层级的演化，模型规模大幅扩张，导航、路径规划被彻底并入神经网络，车辆更像是在“理解环境并做选择”。车位到车位、策略差异极大的驾驶风格、对警车与特殊车辆的多模态识别，具备相当成熟的“类人驾驶一致性”。真正拉开体验差距的，是 Grok 的加入，从更聪明的语音助手，开始实现“意图理解”第一次比较自然地接入了驾驶系统——模糊指令、多目标描述、场景约束，都能被转译为可执行的行程规划，这种从“操作车”到“对车说话”的转变，是以往车机系统很难做到的。Grok 目前还没真正介入车道选择、临停等智驾动作层，座舱 AI 与自动驾驶融合的方向是个确定的路线。事实上，现在从组织架构看，特斯拉不光是车辆部门，人形机器人 Optimus 项目也不是一个“孤立部门”，嵌入特斯拉既有的 AI、自动驾驶与制造体系之中，呈现出高度扁平化、以技术主线为核心的矩阵式结构。在顶层组织上，Optimus 已从早期偏机械与自动化工程驱动，转向由 AI 体系主导。2025 年中后，项目整体并入以 Ashok Elluswamy 为核心的特斯拉 AI 组织之下，与 FSD 在算法、算力平台和数据闭环层面高度协同，这是和车辆相似的具身智能终端，智能上限由统一的世界模型和学习框架决定。在中层结构上，Optimus 团队大致分为三条主线：机器人硬件与执行系统、机器人 AI 与控制算法、以及制造与系统集成。◎硬件侧以执行器、关节和手部为核心单元，形成若干高度专业化的小团队，直接对性能、成本和量产可行性负责；◎AI 与软件团队则与自动驾驶共享底层模型与工程方法，专注于感知、运动学习和任务规划；◎制造与集成团队则连接工厂场景，负责把原型能力转化为可复制、可部署的系统。Optimus 延续了特斯拉一贯的“产品即系统”思路：工程师往往同时隶属于多个项目，既服务于 FSD，也支持机器人开发，避免形成封闭的技术烟囱，降低了重复研发成本，也加快了算法和工程经验在不同载体之间的迁移。整体来看，Optimus 的组织架构并非传统机器人公司的“项目制”，而更像是特斯拉 AI 战略的一个自然外延——以统一技术栈为核心，通过跨部门协作，推动人形机器人从实验室原型走向真实生产环境。Part2小鹏和理想的跟随在学习特斯拉的路线上，小鹏是最积极的。Grok 与 FSD 的融合直接让小鹏调整了组织架构：智驾和座舱合并，成立通用智能中心。这是个面向汽车 + 机器人的全新 AI 组织，将围绕基座模型、Infra 底座、平台化交付、产品和项目质量等圈层结构设立相关二级组织。新部门的 AI 技术中台底座，同时为智能驾驶、智能座舱、机器人等应用层提供支撑。智能座舱与辅助驾驶曾是小鹏 汽车“智能化” 的两大支柱，智能座舱涵盖语音交互、产品设计、技术研发、物联网等板块；智能驾驶的迭代相对独立，之前两边开发节奏不太一致（之前所有的车企都不可能对齐），软件 OTA 更新也节奏不一样。理想汽车持续两小时的全员战略宣贯会，以\"构建硅基生命\"为目标的调整，涉及自动驾驶、机器人等核心业务重组，给出了三大时间节点：2026年完成AI系统级通用Agent构建；2028年实现L4级自动驾驶全量落地；2030年前推出具有完整生命特征的人形机器人。理想研发体系：◎基座模型团队：统筹自研芯片（如M100）与VLA司机大模型的深度融合，聚焦芯片算法协同优化，支撑L4级自动驾驶及通用Agent（智能体）的底层能力。◎软件本体团队：整合智能座舱（MindGPT）与智能驾驶功能，统筹智驾与智舱研发。其核心任务是实现“车机-家庭”场景的无缝衔接，例如车内语音控制智能家居设备。◎硬件本体团队：承接自动驾驶底层技术研发与人形机器人业务，主导具身智能的“感知-决策-执行”全链路研发。复用汽车供应链资源开发机器人关节驱动、环境感知等核心技术，原自动驾驶团队被彻底拆分：算法团队并入基座模型团队，量产与数据团队划归软件本体团队，硬件研发职能则转入硬件本体团队。我们看到这里辅助驾驶和座舱的界限被打破了，AI的模型的重要性变得非常高，也是沿着特斯拉往前走的路在走。Grok 与 FSD 是把智能驾驶从“感知—决策—控制”的工程问题，升级为一个以世界模型为核心的具身智能系统。FSD 负责车辆在真实道路中的感知与执行，而 Grok 代表的大模型能力，则为系统提供更高层次的环境理解、推理与泛化能力，两者共同指向一个依赖数据和算力不断扩展的 Scaling Law 路径。通过海量真实驾驶数据形成持续自我强化的数据闭环，使自动驾驶能力以“涌现”的方式提升，逐步摆脱对高精地图和规则工程的依赖。自动驾驶不再被视为有限场景下的功能堆叠，而是一种可持续进化的智能体。车辆从交付那一刻起并未“定型”，而是在长期 OTA 与模型迭代中不断逼近能力上限，这也使汽车更像一个持续成长的智能终端。这一路线也天然匹配软件订阅式的商业模式，将价值释放从一次性硬件销售（我们看到FSD也开始完全取消了一次性销售），转移到长期的软件与服务收入之中。小结特斯拉的启示在于对数据、组织与产品形态的整体重构：端到端 AI 优先于模块化架构，数据规模优先于场景定制，软件定义优先于硬件配置。","kind":"news","is_publish_news":true,"is_publish_highlight":false,"is_publish_live":false,"is_publish_wemedia":null,"editions":null,"column":"","sentiment":"1","news_tag":"","news_rank":0,"symbols":[],"gpt_button":0,"need_auth":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; ClaudeBot/1.0; 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