特斯拉、英伟达集体盯上的物理AI，中国玩家已亮出“王炸”

车东西01-30

特斯拉的“世界模型”对手来了？这家中国公司用AI造了个无限试炼场。作者 |郭月编辑 |志豪“物理AI的‘ChatGPT时刻’已经到来。”在2026年CES展上，英伟达CEO黄仁勋断言，那些能理解并规划物理世界的AI模型将重塑千行百业，而“自动驾驶将是其首个大规模主流落地场景”。然而，在自动驾驶时代全面到来之前，那些1%的长尾场景成为了核心障碍。特斯拉CEO埃隆·马斯克感同身受，就像他说的，“让...

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|志豪“物理AI的‘ChatGPT时刻’已经到来。”在2026年CES展上，英伟达CEO黄仁勋断言，那些能理解并规划物理世界的AI模型将重塑千行百业，而“自动驾驶将是其首个大规模主流落地场景”。然而，在自动驾驶时代全面到来之前，那些1%的长尾场景成为了核心障碍。特斯拉CEO埃隆·马斯克感同身受，就像他说的，“让自动驾驶达到99%容易，解决剩下的‘长尾问题’却非常困难。”但科技巨头们已经达到了共识，必须构建一个无限逼近现实、甚至能主动创造未知的高保真的“数字宇宙”。这个虚拟世界不仅要能精准复现已知的各种极端路况，更要能主动合成未知的、甚至超出人类想象的复杂交互场景，让自动驾驶系统得以持续进行“饱和式”的训练与迭代。为此，特斯拉研发了世界模型，英伟达通过高精仿真构建虚拟试验场Cosmos，中国自动驾驶明星企业文远知行则发布了通用仿真模型WeRide GENESIS，它们都在教AI理解物理世界。不难看出，仿真模型正在成为推动自动驾驶跨越长尾鸿沟、驶向规模化落地的关键。01.仿真模型破局自动驾驶“最后一公里”自动驾驶汽车需要经历多少测试才算足够安全？业界的一份测算指出：至少110亿英里（约177亿公里）的测试里程，才能获得高置信度的安全验证。而传统的实地路测因成本高昂、周期漫长、法规限制、极端危险场景难以复现以及安全风险高等问题，已成为自动驾驶商业化的主要障碍。在这一背景下，自动驾驶仿真凭借其安全、可控、可无限重复的核心优势，成为推动自动驾驶跨越商业化临界点的关键“试金石”。国际调研机构Fortune Business Insights预示了仿真的广阔前景：到2032年，全球仿真测试市场规模预计将达341.4亿美元（约合人民币2374亿元），维持高速增长。全球科技巨头已在此领域展开激烈角逐。Waymo推出了自动驾驶模拟软件Simulation City，用以高效生成极端场景、训练自动驾驶系统。特斯拉官宣了“世界模拟器”，旨在用AI直接模拟物理世界，扩充算法应对边缘场景的能力。然而，构建真正有效的“数字试金石”远非易事，当前技术仍面临几大核心挑战：1、保真度鸿沟：虚拟环境往往在关键细节上与真实世界存在差距。例如精确模拟暴雨对激光雷达的干扰、夜间复杂的光影反射等场景，仍是技术难点。2、交互真实性不足：许多仿真系统中的交通参与者（车辆、行人）行为模型过于呆板，难以复现人类驾驶员的复杂决策，导致交互场景失真。3、闭环迭代难打通：打造能够自动发现问题、精准诊断根因、持续优化算法并即时验证效果的自我进化体系，对许多企业来说仍是挑战。这些局限性共同导致了仿真在应对极端场景时的乏力，成为自动驾驶突破商业化落地的“最后一公里”阻碍。仿真模型必须进行一场从“场景复现”到“智能进化”的范式升级。换句话说，它不应是回放已知困难场景的“录像机”，而需进化成为能够主动发现系统未知弱点、生成高价值对抗性场景的“陪练”。02.WeRide GENESIS刷“副本”自己训练自己面对“百亿公里”的验证鸿沟与长尾场景的现实挑战，行业讨论重心已从“是否需要仿真”转向“需要多强的仿真”。有观点认为，必须通过高保真、高效率的仿真技术，构建一个能无限逼近现实、甚至能主动创造未知的“数字宇宙”。文远知行发布的自研通用仿真模型WeRide GENESIS，正是朝这一方向进行的关键探索。▲文远知行发布WeRide GENESIS文远知行的WeRide GENESIS仿真模型具备哪些能力？WeRide GENESIS基于生成式AI技术，可在几分钟内生成高度真实的仿真城市环境，还原现实道路中罕见的极端长尾场景，高保真复刻任意现实路况。该仿真平台还允许自由编写与组合任意场景，例如移除或增加特定的交通参与者，或模拟车辆变道博弈。在传感器层面，WeRide GENESIS可合成任意不同位置和视角的传感器数据，并适配从L2++到L4不同自动驾驶等级的任意传感器套件，确保了仿真与真实车辆配置的一致性。最终，这些能力可以扩展至模拟任意大范围的数字街区，使自动驾驶系统在虚拟环境中完成充分训练与测试，从而大幅提升算法应对复杂场景的能力与迭代效率。这套能力是如何打造的？WeRide GENESIS将其核心能力拆解并内化为四个相互协同的AI模块，即AI场景、AI主体、AI指标、AI诊断。▲WeRide GENESIS的四大AI模块AI场景模块负责构建各类关键情境，通过生成式AI技术，它可以组合衍生出近乎无限的复杂情境，如临车加塞、行人“鬼探头”、火灾地震、极端天气以及其他稀有事件等，确保自动驾驶系统具备应对各种复杂边界场景的能力。这相当于将测试从“在路上等待Bug出现”转变为“在仿真中主动进行饱和式压力测试”，系统性地触探算法的边界。AI主体模块其实就是让系统告别“呆板NPC”，拥抱复杂人性。传统仿真中的交通参与者（车辆、行人等）行为往往简单、平均，且可预测，这与现实世界中充满不确定性和主观意图的复杂交互相去甚远。GENESIS的AI主体模块则致力于为每一位交通参与者构建智能行为模型，使其能够模拟从日常驾驶到高风险行为的全谱系反应。例如，它可以模拟在路口犹豫不决最终又突然加速抢行的驾驶员，或者在车缝中穿梭的外卖骑手。这种对客体不确定性交互的模拟，对自动驾驶系统提升在实际复杂交通流中的应变能力极为重要。▲自车驾驶表现对比如对比视频所示，在左侧“原始算法+无AI主体”的组合下，自车表现犹豫，直至对向车辆完全通过后才开始通行，无法满足效率要求；在中间“新算法+无AI主体”的模拟中，自车仅按预设轨迹行驶，缺乏对周边车辆行为的预测，最后发生碰撞，无法满足安全要求；在最右侧“新算法+AI主体”的加持下，自车能够实时判断周边车辆的行驶意图，在确保安全的前提下流畅通过，实现了效率和安全双重保障。当系统出现问题时，如何客观评估其影响？AI指标模块建立了一套覆盖安全、合规、舒适、效率的多维度量化评估体系。例如，一次急刹车带来的乘客不适感，可以被转化为舒适度评分；一次变道的流畅与否，可以通过轨迹平滑度、加速度变化等多个指标客观衡量。▲舒适度曲线画面中的舒适度曲线（Comfort Score）是“AI指标”模块的核心指标之一，动态量化了行驶过程中的乘客舒适度，为算法评估和迭代提供了实时判断依据。画面均由WeRide GENESIS生成。这使得算法迭代的效果变得一目了然，算法优化有了精准的数据导航，而非依赖工程师的主观经验。当算法在某个场景下表现不佳时，“AI诊断”模块会自动介入，像一位资深专家一样进行问题溯源，更能进一步分析根本原因，并提供可执行的修复建议。随后，修复后的算法可被立即重新投入该场景进行验证，形成“测试-诊断-修复-验证”的快速闭环。值得注意的是，这四大AI模块并非孤立运作，而是构成了一个完整的自动驾驶研发闭环迭代体系。AI场景源源不断制造高难度考题；AI主体在其中扮演狡黠的“考官”；AI指标进行毫秒级、全方位的“阅卷”；AI诊断则对错题进行深度复盘并给出“解题思路”。原本需要耗时数年、耗费巨资的真实道路测试与算法调优过程，可以在虚拟世界中以天为单位的高效迭代中完成。▲WeRide GENESIS为自动驾驶技术迭代提供“加速飞轮”03.加速全球商业化部署进度条对文远知行而言，WeRide GENESIS已超越单一研发工具，成为实现规模商业化的战略基石，它将从四个维度构建关键支撑：首先，破解泛化难题，为跨区域落地铺平道路。面对全球不同城市路网、交通习惯、法规等差异，传统一地一测的模式效率低下，WeRide GENESIS大幅提升了自动驾驶系统的泛化能力。通过虚拟仿真，WeRide GENESIS突破了真实路测在场景覆盖、成本与效率上的局限，为多城市、多场景的规模化商业落地提供了可靠支撑。其次，闭环迭代体系，提升研发效率与安全性。通过四大AI模块的协同，WeRide GENESIS实现了“生成场景-量化评估-诊断优化”的完整闭环。该系统能持续生成高价值场景、找到性能瓶颈并提供优化方向，将数百万公里测试压缩至数天的虚拟仿真，提升算法迭代效率与行车安全性。此外，降低测试成本，加速技术落地。WeRide GENESIS在虚拟环境中进行自动驾驶测试，节省了车队运营、人力等巨额边际成本，为解决Robotaxi规模化盈利难题提供了技术前提。最终，构建可扩展的“数字宇宙”，支撑全球化部署。文远知行CTO李岩将WeRide GENESIS视为可随时生成、扩展的“数字宇宙”。它能为任何目标城市构建“数字副本”，让自动驾驶系统可以进行超大规模的运营推演和算法调优，为全球商业部署打下基础。在这一能力底座的驱动下，文远知行取得了一系列行业瞩目的运营成果。其L4级Robotaxi服务已在北京、广州、阿布扎比等全球超10座城市落地。截至2026年1月12日，文远知行全球Robotaxi车队规模达到1023辆，正式迈入“千辆时代”。▲文远知行Robotaxi已进入全球超10座城市作为全球唯一在8个国家获得自动驾驶牌照的公司，文远知行已经在全球11个国家超40个城市开展自动驾驶研发、测试及运营，运营天数超2300天，持续驱动其商业版图高效、快速地向全球新市场复制与落地。04.结语：全球自动驾驶商业化提速随着WeRide GENESIS的不断完善与应用，文远知行自动驾驶技术正持续良性循环：更完备的仿真平台催生更强大的自动驾驶算法，更强大的算法加快了商业部署，而规模化运营产生的海量数据，又推动仿真平台迭代优化……在一个可以无限生成、无限测试的“数字宇宙”中，未来自动驾驶的成熟速度将远超我们想象。文远知行通过WeRide GENESIS，已经在全球自动驾驶竞争中占据了有利位置，而全球自动驾驶商业化也正加速驶来。","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":[{"id":528831526478112,"gmtCreate":1770140492199,"gmtModify":1770140494702,"author":{"id":"4204462656078822","authorId":"4204462656078822","name":"持久战","avatar":"https://static.tigerbbs.com/d3b3e1f07131c06c7ce545ca8b5d510e","vip":1,"crmLevel":7,"crmLevelSwitch":0,"authorIdStr":"4204462656078822","idStr":"4204462656078822"},"htmlText":"股价也起飞啊！","listText":"股价也起飞啊！","text":"股价也起飞啊！","images":[],"top":1,"highlighted":1,"essential":1,"paper":1,"likeSize":1,"commentSize":0,"repostSize":0,"link":"https://laohu8.com/post/528831526478112","repostId":2607066734,"repostType":2,"repost":{"id":"2607066734","kind":"news","pubTimestamp":1769735290,"share":"https://www.laohu8.com/m/news/2607066734?lang=zh_CN&edition=full","pubTime":"2026-01-30 09:08","market":"us","language":"zh","title":"特斯拉、英伟达集体盯上的物理AI，中国玩家已亮出“王炸”","url":"https://stock-news.laohu8.com/highlight/detail?id=2607066734","media":"车东西","summary":"特斯拉的“世界模型”对手来了？在2026年CES展上，英伟达CEO黄仁勋断言，那些能理解并规划物理世界的AI模型将重塑千行百业，而“自动驾驶将是其首个大规模主流落地场景”。AI场景源源不断制造高难度考题；AI主体在其中扮演狡黠的“考官”；AI指标进行毫秒级、全方位的“阅卷”；AI诊断则对错题进行深度复盘并给出“解题思路”。","content":"<html><body><article><img src=\"https://fid-75186.picgzc.qpic.cn/20260130092322821v152l65sucl88fi\"/><img src=\"https://fid-75186.picgzc.qpic.cn/20260130092323432v152rz7hu7ymaqp\"/><a href=\"https://laohu8.com/S/TSLA\">特斯拉</a>的“世界模型”对手来了？这家中国公司用AI造了个无限试炼场。作者 |郭月编辑 |志豪“物理AI的‘ChatGPT时刻’已经到来。”在2026年CES展上，<a href=\"https://laohu8.com/S/NVDA\">英伟达</a>CEO黄仁勋断言，那些能理解并规划物理世界的AI模型将重塑千行百业，而“自动驾驶将是其首个大规模主流落地场景”。然而，在自动驾驶时代全面到来之前，那些1%的长尾场景成为了核心障碍。特斯拉CEO埃隆·马斯克感同身受，就像他说的，“让自动驾驶达到99%容易，解决剩下的‘长尾问题’却非常困难。”但科技巨头们已经达到了共识，必须构建一个无限逼近现实、甚至能主动创造未知的高保真的“数字宇宙”。这个虚拟世界不仅要能精准复现已知的各种极端路况，更要能主动合成未知的、甚至超出人类想象的复杂交互场景，让自动驾驶系统得以持续进行“饱和式”的训练与迭代。为此，特斯拉研发了世界模型，英伟达通过高精仿真构建虚拟试验场Cosmos，中国自动驾驶明星企业文远知行则发布了通用仿真模型WeRide GENESIS，它们都在教AI理解物理世界。不难看出，仿真模型正在成为推动自动驾驶跨越长尾鸿沟、驶向规模化落地的关键。01.仿真模型破局自动驾驶“最后一公里”自动驾驶汽车需要经历多少测试才算足够安全？业界的一份测算指出：至少110亿英里（约177亿公里）的测试里程，才能获得高置信度的安全验证。而传统的实地路测因成本高昂、周期漫长、法规限制、极端危险场景难以复现以及安全风险高等问题，已成为自动驾驶商业化的主要障碍。在这一背景下，自动驾驶仿真凭借其安全、可控、可无限重复的核心优势，成为推动自动驾驶跨越商业化临界点的关键“试金石”。国际调研机构Fortune Business Insights预示了仿真的广阔前景：到2032年，全球仿真测试市场规模预计将达341.4亿美元（约合人民币2374亿元），维持高速增长。全球科技巨头已在此领域展开激烈角逐。Waymo推出了自动驾驶模拟软件Simulation City，用以高效生成极端场景、训练自动驾驶系统。特斯拉官宣了“世界模拟器”，旨在用AI直接模拟物理世界，扩充算法应对边缘场景的能力。然而，构建真正有效的“数字试金石”远非易事，当前技术仍面临几大核心挑战：1、保真度鸿沟：虚拟环境往往在关键细节上与真实世界存在差距。例如精确模拟暴雨对激光雷达的干扰、夜间复杂的光影反射等场景，仍是技术难点。2、交互真实性不足：许多仿真系统中的交通参与者（车辆、行人）行为模型过于呆板，难以复现人类驾驶员的复杂决策，导致交互场景失真。3、闭环迭代难打通：打造能够自动发现问题、精准诊断根因、持续优化算法并即时验证效果的自我进化体系，对许多企业来说仍是挑战。这些局限性共同导致了仿真在应对极端场景时的乏力，成为自动驾驶突破商业化落地的“最后一公里”阻碍。仿真模型必须进行一场从“场景复现”到“智能进化”的范式升级。换句话说，它不应是回放已知困难场景的“录像机”，而需进化成为能够主动发现系统未知弱点、生成高价值对抗性场景的“陪练”。02.WeRide GENESIS刷“副本”自己训练自己面对“百亿公里”的验证鸿沟与长尾场景的现实挑战，行业讨论重心已从“是否需要仿真”转向“需要多强的仿真”。有观点认为，必须通过高保真、高效率的仿真技术，构建一个能无限逼近现实、甚至能主动创造未知的“数字宇宙”。文远知行发布的自研通用仿真模型WeRide GENESIS，正是朝这一方向进行的关键探索。<img src=\"https://fid-75186.picgzc.qpic.cn/20260130092323813v1527uiq1ncauuq\"/>▲文远知行发布WeRide GENESIS文远知行的WeRide GENESIS仿真模型具备哪些能力？WeRide GENESIS基于生成式AI技术，可在几分钟内生成高度真实的仿真城市环境，还原现实道路中罕见的极端长尾场景，高保真复刻任意现实路况。该仿真平台还允许自由编写与组合任意场景，例如移除或增加特定的交通参与者，或模拟车辆变道博弈。在传感器层面，WeRide GENESIS可合成任意不同位置和视角的传感器数据，并适配从L2++到L4不同自动驾驶等级的任意传感器套件，确保了仿真与真实车辆配置的一致性。最终，这些能力可以扩展至模拟任意大范围的数字街区，使自动驾驶系统在虚拟环境中完成充分训练与测试，从而大幅提升算法应对复杂场景的能力与迭代效率。这套能力是如何打造的？WeRide GENESIS将其核心能力拆解并内化为四个相互协同的AI模块，即AI场景、AI主体、AI指标、AI诊断。<img src=\"https://fid-75186.picgzc.qpic.cn/20260130092326246v152cwl6jsbnjxz\"/>▲WeRide GENESIS的四大AI模块AI场景模块负责构建各类关键情境，通过生成式AI技术，它可以组合衍生出近乎无限的复杂情境，如临车加塞、行人“鬼探头”、火灾地震、极端天气以及其他稀有事件等，确保自动驾驶系统具备应对各种复杂边界场景的能力。这相当于将测试从“在路上等待Bug出现”转变为“在仿真中主动进行饱和式压力测试”，系统性地触探算法的边界。AI主体模块其实就是让系统告别“呆板NPC”，拥抱复杂人性。传统仿真中的交通参与者（车辆、行人等）行为往往简单、平均，且可预测，这与现实世界中充满不确定性和主观意图的复杂交互相去甚远。GENESIS的AI主体模块则致力于为每一位交通参与者构建智能行为模型，使其能够模拟从日常驾驶到高风险行为的全谱系反应。例如，它可以模拟在路口犹豫不决最终又突然加速抢行的驾驶员，或者在车缝中穿梭的外卖骑手。这种对客体不确定性交互的模拟，对自动驾驶系统提升在实际复杂交通流中的应变能力极为重要。▲自车驾驶表现对比如对比视频所示，在左侧“原始算法+无AI主体”的组合下，自车表现犹豫，直至对向车辆完全通过后才开始通行，无法满足效率要求；在中间“新算法+无AI主体”的模拟中，自车仅按预设轨迹行驶，缺乏对周边车辆行为的预测，最后发生碰撞，无法满足安全要求；在最右侧“新算法+AI主体”的加持下，自车能够实时判断周边车辆的行驶意图，在确保安全的前提下流畅通过，实现了效率和安全双重保障。当系统出现问题时，如何客观评估其影响？AI指标模块建立了一套覆盖安全、合规、舒适、效率的多维度量化评估体系。例如，一次急刹车带来的乘客不适感，可以被转化为舒适度评分；一次变道的流畅与否，可以通过轨迹平滑度、加速度变化等多个指标客观衡量。<img src=\"https://fid-75186.picgzc.qpic.cn/20260130092406656v152lp4q9q1s2a8\"/>▲舒适度曲线画面中的舒适度曲线（Comfort Score）是“AI指标”模块的核心指标之一，动态量化了行驶过程中的乘客舒适度，为算法评估和迭代提供了实时判断依据。画面均由WeRide GENESIS生成。这使得算法迭代的效果变得一目了然，算法优化有了精准的数据导航，而非依赖工程师的主观经验。当算法在某个场景下表现不佳时，“AI诊断”模块会自动介入，像一位资深专家一样进行问题溯源，更能进一步分析根本原因，并提供可执行的修复建议。随后，修复后的算法可被立即重新投入该场景进行验证，形成“测试-诊断-修复-验证”的快速闭环。值得注意的是，这四大AI模块并非孤立运作，而是构成了一个完整的自动驾驶研发闭环迭代体系。AI场景源源不断制造高难度考题；AI主体在其中扮演狡黠的“考官”；AI指标进行毫秒级、全方位的“阅卷”；AI诊断则对错题进行深度复盘并给出“解题思路”。原本需要耗时数年、耗费巨资的真实道路测试与算法调优过程，可以在虚拟世界中以天为单位的高效迭代中完成。<img src=\"https://fid-75186.picgzc.qpic.cn/20260130092408776v152iy3b38v13fe\"/>▲WeRide GENESIS为自动驾驶技术迭代提供“加速飞轮”03.加速全球商业化部署进度条对文远知行而言，WeRide GENESIS已超越单一研发工具，成为实现规模商业化的战略基石，它将从四个维度构建关键支撑：首先，破解泛化难题，为跨区域落地铺平道路。面对全球不同城市路网、交通习惯、法规等差异，传统一地一测的模式效率低下，WeRide GENESIS大幅提升了自动驾驶系统的泛化能力。通过虚拟仿真，WeRide GENESIS突破了真实路测在场景覆盖、成本与效率上的局限，为多城市、多场景的规模化商业落地提供了可靠支撑。其次，闭环迭代体系，提升研发效率与安全性。通过四大AI模块的协同，WeRide GENESIS实现了“生成场景-量化评估-诊断优化”的完整闭环。该系统能持续生成高价值场景、找到性能瓶颈并提供优化方向，将数百万公里测试压缩至数天的虚拟仿真，提升算法迭代效率与行车安全性。此外，降低测试成本，加速技术落地。WeRide GENESIS在虚拟环境中进行自动驾驶测试，节省了车队运营、人力等巨额边际成本，为解决Robotaxi规模化盈利难题提供了技术前提。最终，构建可扩展的“数字宇宙”，支撑全球化部署。文远知行CTO李岩将WeRide GENESIS视为可随时生成、扩展的“数字宇宙”。它能为任何目标城市构建“数字副本”，让自动驾驶系统可以进行超大规模的运营推演和算法调优，为全球商业部署打下基础。在这一能力底座的驱动下，文远知行取得了一系列行业瞩目的运营成果。其L4级Robotaxi服务已在北京、广州、阿布扎比等全球超10座城市落地。截至2026年1月12日，文远知行全球Robotaxi车队规模达到1023辆，正式迈入“千辆时代”。<img src=\"https://mmbiz.qpic.cn/mmbiz_png/vHUB2sDB7M6s3WQZtHibo4CGzPgWeJamTDP7xqs2eLkfUyycjcO1VKvQ17QFrqwNf0S5icYNCeKWHeN8w9EyricDg/640?from=appmsg\"/>▲文远知行Robotaxi已进入全球超10座城市作为全球唯一在8个国家获得自动驾驶牌照的公司，文远知行已经在全球11个国家超40个城市开展自动驾驶研发、测试及运营，运营天数超2300天，持续驱动其商业版图高效、快速地向全球新市场复制与落地。04.结语：全球自动驾驶商业化提速随着WeRide GENESIS的不断完善与应用，文远知行自动驾驶技术正持续良性循环：更完备的仿真平台催生更强大的自动驾驶算法，更强大的算法加快了商业部署，而规模化运营产生的<a href=\"https://laohu8.com/S/603138\">海量数据</a>，又推动仿真平台迭代优化……在一个可以无限生成、无限测试的“数字宇宙”中，未来自动驾驶的成熟速度将远超我们想象。文远知行通过WeRide GENESIS，已经在全球自动驾驶竞争中占据了有利位置，而全球自动驾驶商业化也正加速驶来。</article></body></html>","source":"tencent","collect":0,"html":"<!DOCTYPE html>\n<html>\n<head>\n<meta http-equiv=\"Content-Type\" content=\"text/html; charset=utf-8\" />\n<meta name=\"viewport\" 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href=http://gu.qq.com/resources/shy/news/detail-v2/index.html#/?id=nesSN2026013009243897a40dfe&s=b>车东西</a>\n\n\n</h4>\n\n</header>\n<article>\n<div>\n特斯拉的“世界模型”对手来了？这家中国公司用AI造了个无限试炼场。作者 |郭月编辑 |志豪“物理AI的‘ChatGPT时刻’已经到来。”在2026年CES展上，英伟达CEO黄仁勋断言，那些能理解并规划物理世界的AI模型将重塑千行百业，而“自动驾驶将是其首个大规模主流落地场景”。然而，在自动驾驶时代全面到来之前，那些1%的长尾场景成为了核心障碍。特斯拉CEO埃隆·马斯克感同身受，就像他说的，“让自动...\n\n<a href=\"http://gu.qq.com/resources/shy/news/detail-v2/index.html#/?id=nesSN2026013009243897a40dfe&s=b\">网页链接</a>\n\n</div>\n\n\n</article>\n</div>\n</body>\n</html>\n","type":0,"thumbnail":"","relate_stocks":{"LU0417517546.SGD":"Allianz US Equity Cl AT Acc SGD","LU1803068979.SGD":"FTIF - Franklin Technology A (acc) SGD-H1","LU1934455863.HKD":"AB SICAV I LOW VOLATILITY TOTAL RETURN EQUITY PORT \"A\" (HKD) ACC","LU1127390331.HKD":"AB SICAV I - ALL MARKET INCOME PORTFOLIO \"A\" (HKD) ACC","LU0061474705.USD":"THREADNEEDLE (LUX) GLOBAL DYNAMIC REAL RETURN \"AU\" (USD) ACC","LU1623119135.USD":"Natixis Mirova Global Sustainable Equity 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GENESIS，它们都在教AI理解物理世界。不难看出，仿真模型正在成为推动自动驾驶跨越长尾鸿沟、驶向规模化落地的关键。01.仿真模型破局自动驾驶“最后一公里”自动驾驶汽车需要经历多少测试才算足够安全？业界的一份测算指出：至少110亿英里（约177亿公里）的测试里程，才能获得高置信度的安全验证。而传统的实地路测因成本高昂、周期漫长、法规限制、极端危险场景难以复现以及安全风险高等问题，已成为自动驾驶商业化的主要障碍。在这一背景下，自动驾驶仿真凭借其安全、可控、可无限重复的核心优势，成为推动自动驾驶跨越商业化临界点的关键“试金石”。国际调研机构Fortune Business Insights预示了仿真的广阔前景：到2032年，全球仿真测试市场规模预计将达341.4亿美元（约合人民币2374亿元），维持高速增长。全球科技巨头已在此领域展开激烈角逐。Waymo推出了自动驾驶模拟软件Simulation City，用以高效生成极端场景、训练自动驾驶系统。特斯拉官宣了“世界模拟器”，旨在用AI直接模拟物理世界，扩充算法应对边缘场景的能力。然而，构建真正有效的“数字试金石”远非易事，当前技术仍面临几大核心挑战：1、保真度鸿沟：虚拟环境往往在关键细节上与真实世界存在差距。例如精确模拟暴雨对激光雷达的干扰、夜间复杂的光影反射等场景，仍是技术难点。2、交互真实性不足：许多仿真系统中的交通参与者（车辆、行人）行为模型过于呆板，难以复现人类驾驶员的复杂决策，导致交互场景失真。3、闭环迭代难打通：打造能够自动发现问题、精准诊断根因、持续优化算法并即时验证效果的自我进化体系，对许多企业来说仍是挑战。这些局限性共同导致了仿真在应对极端场景时的乏力，成为自动驾驶突破商业化落地的“最后一公里”阻碍。仿真模型必须进行一场从“场景复现”到“智能进化”的范式升级。换句话说，它不应是回放已知困难场景的“录像机”，而需进化成为能够主动发现系统未知弱点、生成高价值对抗性场景的“陪练”。02.WeRide GENESIS刷“副本”自己训练自己面对“百亿公里”的验证鸿沟与长尾场景的现实挑战，行业讨论重心已从“是否需要仿真”转向“需要多强的仿真”。有观点认为，必须通过高保真、高效率的仿真技术，构建一个能无限逼近现实、甚至能主动创造未知的“数字宇宙”。文远知行发布的自研通用仿真模型WeRide GENESIS，正是朝这一方向进行的关键探索。▲文远知行发布WeRide GENESIS文远知行的WeRide GENESIS仿真模型具备哪些能力？WeRide GENESIS基于生成式AI技术，可在几分钟内生成高度真实的仿真城市环境，还原现实道路中罕见的极端长尾场景，高保真复刻任意现实路况。该仿真平台还允许自由编写与组合任意场景，例如移除或增加特定的交通参与者，或模拟车辆变道博弈。在传感器层面，WeRide GENESIS可合成任意不同位置和视角的传感器数据，并适配从L2++到L4不同自动驾驶等级的任意传感器套件，确保了仿真与真实车辆配置的一致性。最终，这些能力可以扩展至模拟任意大范围的数字街区，使自动驾驶系统在虚拟环境中完成充分训练与测试，从而大幅提升算法应对复杂场景的能力与迭代效率。这套能力是如何打造的？WeRide GENESIS将其核心能力拆解并内化为四个相互协同的AI模块，即AI场景、AI主体、AI指标、AI诊断。▲WeRide GENESIS的四大AI模块AI场景模块负责构建各类关键情境，通过生成式AI技术，它可以组合衍生出近乎无限的复杂情境，如临车加塞、行人“鬼探头”、火灾地震、极端天气以及其他稀有事件等，确保自动驾驶系统具备应对各种复杂边界场景的能力。这相当于将测试从“在路上等待Bug出现”转变为“在仿真中主动进行饱和式压力测试”，系统性地触探算法的边界。AI主体模块其实就是让系统告别“呆板NPC”，拥抱复杂人性。传统仿真中的交通参与者（车辆、行人等）行为往往简单、平均，且可预测，这与现实世界中充满不确定性和主观意图的复杂交互相去甚远。GENESIS的AI主体模块则致力于为每一位交通参与者构建智能行为模型，使其能够模拟从日常驾驶到高风险行为的全谱系反应。例如，它可以模拟在路口犹豫不决最终又突然加速抢行的驾驶员，或者在车缝中穿梭的外卖骑手。这种对客体不确定性交互的模拟，对自动驾驶系统提升在实际复杂交通流中的应变能力极为重要。▲自车驾驶表现对比如对比视频所示，在左侧“原始算法+无AI主体”的组合下，自车表现犹豫，直至对向车辆完全通过后才开始通行，无法满足效率要求；在中间“新算法+无AI主体”的模拟中，自车仅按预设轨迹行驶，缺乏对周边车辆行为的预测，最后发生碰撞，无法满足安全要求；在最右侧“新算法+AI主体”的加持下，自车能够实时判断周边车辆的行驶意图，在确保安全的前提下流畅通过，实现了效率和安全双重保障。当系统出现问题时，如何客观评估其影响？AI指标模块建立了一套覆盖安全、合规、舒适、效率的多维度量化评估体系。例如，一次急刹车带来的乘客不适感，可以被转化为舒适度评分；一次变道的流畅与否，可以通过轨迹平滑度、加速度变化等多个指标客观衡量。▲舒适度曲线画面中的舒适度曲线（Comfort Score）是“AI指标”模块的核心指标之一，动态量化了行驶过程中的乘客舒适度，为算法评估和迭代提供了实时判断依据。画面均由WeRide GENESIS生成。这使得算法迭代的效果变得一目了然，算法优化有了精准的数据导航，而非依赖工程师的主观经验。当算法在某个场景下表现不佳时，“AI诊断”模块会自动介入，像一位资深专家一样进行问题溯源，更能进一步分析根本原因，并提供可执行的修复建议。随后，修复后的算法可被立即重新投入该场景进行验证，形成“测试-诊断-修复-验证”的快速闭环。值得注意的是，这四大AI模块并非孤立运作，而是构成了一个完整的自动驾驶研发闭环迭代体系。AI场景源源不断制造高难度考题；AI主体在其中扮演狡黠的“考官”；AI指标进行毫秒级、全方位的“阅卷”；AI诊断则对错题进行深度复盘并给出“解题思路”。原本需要耗时数年、耗费巨资的真实道路测试与算法调优过程，可以在虚拟世界中以天为单位的高效迭代中完成。▲WeRide GENESIS为自动驾驶技术迭代提供“加速飞轮”03.加速全球商业化部署进度条对文远知行而言，WeRide GENESIS已超越单一研发工具，成为实现规模商业化的战略基石，它将从四个维度构建关键支撑：首先，破解泛化难题，为跨区域落地铺平道路。面对全球不同城市路网、交通习惯、法规等差异，传统一地一测的模式效率低下，WeRide GENESIS大幅提升了自动驾驶系统的泛化能力。通过虚拟仿真，WeRide GENESIS突破了真实路测在场景覆盖、成本与效率上的局限，为多城市、多场景的规模化商业落地提供了可靠支撑。其次，闭环迭代体系，提升研发效率与安全性。通过四大AI模块的协同，WeRide GENESIS实现了“生成场景-量化评估-诊断优化”的完整闭环。该系统能持续生成高价值场景、找到性能瓶颈并提供优化方向，将数百万公里测试压缩至数天的虚拟仿真，提升算法迭代效率与行车安全性。此外，降低测试成本，加速技术落地。WeRide GENESIS在虚拟环境中进行自动驾驶测试，节省了车队运营、人力等巨额边际成本，为解决Robotaxi规模化盈利难题提供了技术前提。最终，构建可扩展的“数字宇宙”，支撑全球化部署。文远知行CTO李岩将WeRide GENESIS视为可随时生成、扩展的“数字宇宙”。它能为任何目标城市构建“数字副本”，让自动驾驶系统可以进行超大规模的运营推演和算法调优，为全球商业部署打下基础。在这一能力底座的驱动下，文远知行取得了一系列行业瞩目的运营成果。其L4级Robotaxi服务已在北京、广州、阿布扎比等全球超10座城市落地。截至2026年1月12日，文远知行全球Robotaxi车队规模达到1023辆，正式迈入“千辆时代”。▲文远知行Robotaxi已进入全球超10座城市作为全球唯一在8个国家获得自动驾驶牌照的公司，文远知行已经在全球11个国家超40个城市开展自动驾驶研发、测试及运营，运营天数超2300天，持续驱动其商业版图高效、快速地向全球新市场复制与落地。04.结语：全球自动驾驶商业化提速随着WeRide GENESIS的不断完善与应用，文远知行自动驾驶技术正持续良性循环：更完备的仿真平台催生更强大的自动驾驶算法，更强大的算法加快了商业部署，而规模化运营产生的海量数据，又推动仿真平台迭代优化……在一个可以无限生成、无限测试的“数字宇宙”中，未来自动驾驶的成熟速度将远超我们想象。文远知行通过WeRide GENESIS，已经在全球自动驾驶竞争中占据了有利位置，而全球自动驾驶商业化也正加速驶来。","news_type":1,"symbols_score_info":{"NVDS.UK":0.6,"TSYY":0.6,"TSLP":0.6,"NVDS":0.6,"TSDD":0.6,"TSLQ":0.6,"TSLS":0.6,"NVDY":0.6,"NVIW.SI":0.6,"TSLY":0.6,"2NVD.UK":0.6,"TSL":0.6,"NVDU":0.6,"TSLW":0.6,"3NVD.UK":0.6,"TESL":0.6,"NVDA":0.9,"TSII":0.6,"00800":1.95,"DIPS":0.6,"07766":0.6,"TSLO":0.6,"NVDX":0.6,"TSLT":0.6,"NVD3.UK":0.6,"NVD2.UK":0.6,"NVII":0.6,"TSLR":0.6,"TSLI":0.6,"SNVD.UK":0.6,"NVDG":0.6,"NVDD":0.6,"WRD":1.95,"NVDQ":0.6,"NVDW":0.6,"CRSH":0.6,"TSLG":0.6,"NVD":0.6,"TSLZ":0.6}},"isVote":1,"likeStatus":false,"favoriteStatus":false,"reportStatus":false,"tweetType":1,"langContent":"CN"}],"isCommentEnd":true,"newsSizeData":{"likeSize":1,"commentSize":1,"repostSize":1,"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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