[{"data":1,"prerenderedAt":215},["ShallowReactive",2],{"blog-post-driving-ai":3,"blog-featured-driving-ai":24,"header-links":59,"corporate-footer":146,"footer-year":214},[4],{"id":5,"type":6,"lang":7,"primary_tag":8,"url":9,"title":10,"short_title":11,"subtitle":11,"description":12,"meta_description":12,"image":13,"img_alt":14,"author_name":11,"download_doc":11,"download_title":11,"download_btn":11,"webinar_video":11,"thumbnail":11,"author_id":11,"content":15,"publish_date":16,"featured":17,"is_gated":18,"is_infographic":11,"is_hidden":18,"is_active":19,"tags":20,"updated_at":23},280,"blog","en","Amnon Shashua","driving-ai","Mobileye Driving AI Day 活动的5大要点",null,"首席执行官 Amnon Shashua 教授和首席技术官 Shai Shalev-Shwartz 教授介绍自动驾驶出行领域的关键人工智能技术的进展。","https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002F9d5275f3635f6d26bd00f9edf597d298_1732784251661.jpg","Shai Shalev-Shwartz 教授和 Mobileye 总裁兼首席执行官 Amnon Shashua 教授出席 Driving AI Day","\u003Cp>Mobileye是致力于开发完全自动驾驶系统的领军企业之一。我们的目标是搭建完全自动驾驶“可脱眼”系统，但这是一项十分复杂的工作，需要极高的安全标准，以及大量的长期投资。为了达成这一里程碑，我们凭借在高级驾驶辅助技术市场的领先地位创造当前收入，确保业务的可持续发展，同时始终聚焦于完全自动驾驶的最终目标。\u003C\u002Fp>\n\u003Cp>但要如何实现这一目标？上个月，我们的首席执行官Amnon Shashua教授和首席技术官Shai Shalev-Schwartz教授在Mobileye Driving AI Day活动中发表了演讲，讨论并分享了Mobileye为实现这一里程碑所采用的创新人工智能方法。\u003C\u002Fp>\n\u003Cp>观看演讲的五大要点，阐释了Mobileye以智能方式利用人工智能，从而逐步攻克自动驾驶技术的挑战。\u003C\u002Fp>\n\u003Cp>\u003Ciframe title=\"YouTube video player\" src=\"https:\u002F\u002Fwww.youtube.com\u002Fembed\u002F92e5zD_-xDw?si=AFtw2AQWwgfm0Eoz\" width=\"560\" height=\"315\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\">\u003C\u002Fiframe>\u003C\u002Fp>\n\u003Ch3>\u003Cstrong>\u003Cspan style=\"font-size: 14pt;\">单一方法无法解决自主性问题\u003C\u002Fspan>\u003C\u002Fstrong>\u003C\u002Fh3>\n\u003Cp>在开场演讲中，Mobileye首席执行官Amnon Shashua教授概述了解决自主性问题的三种技术路线。以激光雷达为核心的复合人工智能系统（CAIS）方法，以纯视觉和端到端人工智能方法，以及Mobileye以摄像头为核心的复合人工智能系统（CAIS）方法。\u003C\u002Fp>\n\u003Cp>Shashua教授强调了评估每种方法时，需要基于四大关键支柱来衡量其是否成功实现自主性：成本、模块化、地域可扩展性和平均故障间隔时间（MTBF）。虽然每种方法都有其独特的优势和局限性，但没有一种方法能满足全部四大支柱并完全解决自主性问题。\u003C\u002Fp>\n\u003Cp>每种方法都有利有弊。某种方法可以提供高精度但生产效率却极度低下，而另一种方法本身则可能存在局限性或并不可靠。例如，以激光雷达为核心方法可提供高精度，从而实现极高（出色）的平均故障间隔时间，但其成本却限制了区域可扩展性，因此其难以适应更广泛的市场。\u003C\u002Fp>\n\u003Cp>\u003Cspan data-contrast=\"auto\">\u003Cimg src=\"https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002F53a8091b10a0f6baa10a8cac1aec0a67_1732785971870.png\" alt=\"\" \u002F>\u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>相反，依靠纯视觉或以摄像头为核心方法更为经济实惠，但通常会在实现高MTBF方面面临更多挑战，而且纯端到端方法会带来自动驾驶汽车领域的对齐问题（难以确保人工智能系统\u002F机器学习模型的目标与人类目标保持一致）。不过，经过平衡的方法可以弥合这些差距，既提供安全性，又能实现自动驾驶市场的可扩展性。\u003C\u002Fp>\n\u003Ch3>\u003Cstrong>\u003Cspan style=\"font-size: 14pt;\">纯端到端方法存在局限性\u003C\u002Fspan>\u003C\u002Fstrong>\u003C\u002Fh3>\n\u003Cp>端到端方法的前提是，向系统输入的数据越多，系统在模仿人类驾驶行为方面的能力就越优秀，最终达到甚至超越人类的驾驶水平。这种方法无需“粘合代码”或手动编码，而是完全依赖数据，尤其是无监督数据。\u003C\u002Fp>\n\u003Cp>基于Transformer的神经网络从数以百万计的汽车发送的驾驶数据中不断学习，消除了人工标注或解释数据的手动过程。。然而，这种方法面临三大挑战：缺乏抽象概念、存在捷径学习问题以及长尾问题。这三个挑战都凸显了当前系统在有效处理复杂现实驾驶场景方面的局限性。\u003C\u002Fp>\n\u003Cp>在演讲中，Shashua教授以“计算器问题”为例解释了缺乏抽象概念的局限性。其中，“计算器问题”是指ChatGPT因其基于语言的架构局限性，难以可靠地处理复杂多步骤计算的问题。\u003C\u002Fp>\n\u003Cp>要解决这个问题，答案其实极其简单：必须集成计算器工具（即集成Python环境来提高计算准确性）。然而，Shashua教授认为，仅仅依靠无监督数据的端到端方法来解决自动驾驶汽车这样的安全关键系统的所有复杂问题，既不可靠又充满风险。\u003C\u002Fp>\n\u003Cp>这种方法还有可能在学习阶段嵌入不良甚至危险的驾驶行为，且自动驾驶汽车的“对齐问题”也会更加显而易见，因为模型可能会优先考虑“常见但错误”的行为，而不是“罕见但正确”的行为。\u003C\u002Fp>\n\u003Cp>\u003Cspan data-contrast=\"auto\">\u003Cimg src=\"https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002Fc090cff3e255d37cf9c145fd17435972_1732784761464.jpg\" alt=\"\" width=\"1272\" height=\"825\" \u002F>\u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>例如，人类驾驶员通常会在接近停车标线时选择缓慢减速后继续前进，或做出鲁莽驾驶行为，此时，尽管这些行为并不正确，系统仍可能会将其视为常见行为进行学习。因此，区分正确和不正确的操作（尤其是在“罕见但正确”的场景中）仍然是一项复杂的挑战。\u003C\u002Fp>\n\u003Cp>这些问题可以通过特斯拉完全自动驾驶（FSD）系统收集的数据得到实际例证。我们可以看到，完全自动驾驶系统的数据表明，长尾问题在此显现，即使输入大量数据，模型也很难充分解决这些罕见事件，从而影响系统的整体安全性和可靠性，并阻碍系统在每一次产品升级中提高平均故障间隔时间（MTBF）的能力。\u003C\u002Fp>\n\u003Cp>这一问题凸显了仅依靠大型数据集的局限性，因为罕见但关键的驾驶场景往往在数据中被低估或缺失。\u003C\u002Fp>\n\u003Ch3>\u003Cstrong>\u003Cspan style=\"font-size: 14pt;\">PGF(Primary-Guardian-Fallback) 融合对于打造安全系统至关重要\u003C\u002Fspan>\u003C\u002Fstrong>\u003C\u002Fh3>\n\u003Cp>在整个驾驶过程中，我们需要进行无数次决策。从非常简单的二元选择——例如左转还是右转、刹车还是不刹车，到更复杂和微妙的判断——比如，如果我要刹车，是应该轻踩还是急刹？\u003C\u002Fp>\n\u003Cp>经典的处理方式是：多个系统提供答案，并采取少数服从多数的原则 – 如果三个系统中有两个决定向左并线，那么就向左并线。但是，如果这三个系统提供了三种不同的建议，例如分别是左转、右转或保持直行呢？在这种情况下，多数原则并不能解决问题。\u003C\u002Fp>\n\u003Cp>这时候就需要PGF（Primary-Guardian-Fallback）融合方法。PGF系统是一个分层决策模型。其工作原理如下：\u003C\u002Fp>\n\u003Cul>\n\u003Cli data-leveltext=\"\" data-font=\"Symbol\" data-listid=\"13\" data-list-defn-props=\"{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}\" aria-setsize=\"-1\" data-aria-posinset=\"1\" data-aria-level=\"1\">\n\u003Cp>\u003Cstrong>\u003Cspan data-contrast=\"auto\">Primary 主系统: \u003C\u002Fspan>\u003C\u002Fstrong>该系统是标准自动驾驶系统（SDS），用于生成车辆的轨迹 - 规划的路线或行动方案。在大多数情况下，它是主要决策者，输出初步建议的行驶路线或行动方案。\u003C\u002Fp>\n\u003C\u002Fli>\n\u003Cli data-leveltext=\"\" data-font=\"Symbol\" data-listid=\"13\" data-list-defn-props=\"{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}\" aria-setsize=\"-1\" data-aria-posinset=\"1\" data-aria-level=\"1\">\n\u003Cp>\u003Cstrong>\u003Cspan data-contrast=\"auto\">Fallback 备用系统: \u003C\u002Fspan>\u003C\u002Fstrong>与Primary主系统一样，Fallback备用系统是另一种能自动生成路径轨迹或替代路线的自动驾驶系统。它是“Primary”主系统遇到问题或Guardian监护系统检测到潜在问题时的备用系统。\u003C\u002Fp>\n\u003C\u002Fli>\n\u003Cli data-leveltext=\"\" data-font=\"Symbol\" data-listid=\"13\" data-list-defn-props=\"{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}\" aria-setsize=\"-1\" data-aria-posinset=\"1\" data-aria-level=\"1\">\n\u003Cp>\u003Cstrong>\u003Cspan data-contrast=\"auto\">Guardian 监护系统: \u003C\u002Fspan>\u003C\u002Fstrong>\u003Cspan data-contrast=\"auto\">Guardian监护系统是一个监控层。它不会直接生成路径，而是对Primary主系统的轨迹进行评估，以确保其符合一定的安全性和可行性标准。简而言之，Guardian监护系统会评估Primary主系统建议的行动方案是否安全可行。如果Guardian监护系统发现问题，则可以触发Primary主系统切换到Fallback备用系统，以确保车辆的安全领航行驶。\u003C\u002Fspan>\u003C\u002Fp>\n\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>在决定是否刹车的二元情景中，以使用三重传感器系统为例：摄像头（“Primary”主系统）、雷达（“Guardian”监护系统）和激光雷达（“Fallback”备用系统），按多数原则进行决策：如果摄像头和雷达决策一致，就直接执行；如果不一致，就遵从激光雷达的建议，而激光雷达的建议必然会与另外两个系统其中之一的建议保持一致，确保服从多数 - 因此，PGF相当于秉持了2\u002F3多数原则。\u003C\u002Fp>\n\u003Cp>“三个子系统中的多数”这一概念只在二元决策中具有明确的定义。然而，我们在驾驶过程中需要做出的许多决策都不是二元决策。\u003C\u002Fp>\n\u003Cp>最值得注意的是，车道的几何形状并不是二元决策，而这种几何形状对责任敏感安全模型（RSS）的决策有着深远的影响。因此，我们提出了一种对多数原则的概括，称之为PGF（Primary-Guardian-Fallback）融合系统。这种融合系统遵循“Primary”主系统或“Fallback”备用系统的建议，具体取决于“Guardian”监护系统的输出。\u003C\u002Fp>\n\u003Ch3>\u003Cspan data-ccp-props=\"{}\">\u003Cimg src=\"https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002Fb241684e79737202ff86b19c995c3fb1_1732784889378.jpg\" alt=\"\" width=\"1272\" height=\"825\" \u002F>\u003C\u002Fspan>\u003C\u002Fh3>\n\u003Ch3>\u003Cstrong>\u003Cspan style=\"font-size: 14pt;\">Transformer 效率可提高100倍\u003C\u002Fspan>\u003C\u002Fstrong>\u003C\u002Fh3>\n\u003Cp>在目标物检测领域，通信至关重要。将图像准确输入标记化流程 - 将其转换为可解读和可处理的数据 -需要尽可能精确的执行。而要进一步提升效率，不仅需要智能的标记化过程，还需要构建一个系统，使标记能够以高效且有序的方式进行大规模相互通信，从而实现更高级别的协同处理。\u003C\u002Fp>\n\u003Cp>需要注意的是，由于单个芯片的计算要求很高，通信过程必须高效。为使芯片流畅运行，则必须优化数据流，以防止芯片性能滞后，从而导致通信速度变慢。\u003C\u002Fp>\n\u003Cp>在演讲中，Mobileye首席技术官Shai Shalev-Schwartz教授解释了Mobileye利用稀疏类型注意力（STAT）方法解决这一问题，并将Transformer效率提高100倍。稀疏类型注意力方法通过将标记组织成结构化的组来优化标记之间的通信。想象一下，成千上万人试图在一个巨大的体育场内互相交谈，必然会导致混乱。同样的概念也适用于成千上万的标记，在相同的场景下，它们很难有效地相互交流。而STAT方法正是为解决这一问题而设计的。\u003C\u002Fp>\n\u003Cp>除非将其划分为特定的角色 - 例如“常规标记”和“管理者标记” - 以创建更有组织的通信结构，或者更准确地说，是相关的连接方式。这基本上就是稀疏类型注意力背后的理念，通过改进类型和组织方式，引入结构和参数来提高模型效率。\u003C\u002Fp>\n\u003Cp>那我们是如何做到这一点的呢？我们通过用“管理者”或链接标记，对标记进行划分和分组来建立秩序，让常规标记可以独立地与链接标记（“管理者”）进行通信。例如，通过将300个常规标记与32个链接标记分组，常规标记就可以与链接标记通信，而链接标记之间也可以相互通信。这种结构化方法大大降低了复杂性，将模型效率提高了100倍。\u003C\u002Fp>\n\u003Ch3>\u003Cstrong>\u003Cspan style=\"font-size: 14pt;\">在效率与灵活性之间寻找最佳平衡点\u003C\u002Fspan>\u003C\u002Fstrong>\u003C\u002Fh3>\n\u003Cp>Shai Shalev-Schwartz 教授阐述了芯片运行中效率和灵活性之间的平衡。简而言之，如果我们要设计只具单一内置用途的超高效芯片，那么它的效率会非常高，但同时功能也非常有限。\u003C\u002Fp>\n\u003Cp>另一方面，如果设计一款多任务处理芯片，那么它的灵活性就会很高，但其性能却不会那么高效。这就是效率和灵活性之间的基本权衡 - 尤其是对于车载芯片而言。然而，EyeQ™6 High芯片恰好能够满足自动驾驶的需求，实现了灵活性和效率之间的完美结合。\u003C\u002Fp>\n\u003Cp>为实现这一目标，EyeQ™6 High芯片内部包含了多种组件，每种组件都具有不同程度的灵活性和效率。Shai Shalev-Schwartz教授提到了五种不同架构的组件，这些组件从高度专用、高效到高度灵活，不一而足。从两种高度灵活的中央处理器 - MPC和MIPS，到高效且特定的XNN，再到介于两者之间的两种加速器，EyeQ™6 High芯片可根据操作的不同进行调整，以适用于不同的应用范围。\u003C\u002Fp>\n\u003Cp>在执行苛刻的人工智能深度学习任务时，Mobileye EyeQ6 High的效率令人印象深刻。其34 TOPS（每秒万亿次运算）的算力大大超越了前代产品EyeQ5。然而，单纯比较TOPS数字并不能完全反映其优势。\u003C\u002Fp>\n\u003Cp>衡量芯片在自动驾驶应用中有效性的真正标准，在于其在各种神经网络任务中每秒可处理帧数的能力。例如，EyeQ5每秒仅能处理91帧的像素标注神经网络，而EyeQ6 High每秒可处理超过1000帧，效率提高了十倍以上。这一升级不仅来自于更高的时钟速度，还源于XNN的专用架构，该架构针对特定应用的高效利用进行了优化。\u003C\u002Fp>\n\u003Cp>\u003Cspan data-ccp-props=\"{}\">\u003Cimg src=\"https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002F53a8091b10a0f6baa10a8cac1aec0a68_1732785971875.png\" alt=\"\" \u002F>\u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>例如，英伟达Orin芯片的算力可达到275 TOPS，相比之下，其原始数据可能更胜我们一筹。然而，在运行标准的ResNet-50网络时，两者的帧数处理能力差距仅为2倍。\u003C\u002Fp>\n\u003Cp>这表明，仅凭TOPS并不足以衡量芯片的有效性；环境和效率才是关键。总体而言，EyeQ6High的设计侧重于量身定制的功能和效率，并辅以强大的软件堆栈（在各种加速器之间优化任务分配）。从本质上讲，EyeQ6 High的真正优势在于它的智能设计。而这种为高效处理特定任务而量身定制的设计证明，单纯TOPS数据并不能反映其真正的性能。\u003C\u002Fp>\n\u003Cp>总而言之，Mobileye在人工智能领域的专业技术与为提高效率而优化的专用硬件相结合，为实现区域可扩展的自动驾驶技术开辟了一条清晰的道路。随着我们不断获得突破，我们正一步步实现完全自动驾驶的未来愿景。\u003C\u002Fp>\n\u003Cp> \u003C\u002Fp>","2024-11-27T06:00:00.000Z",0,false,true,[8,21,22],"Autonomous Driving","AV Safety","2026-08-04T08:00:37.472Z",[25,37,48],{"id":26,"type":27,"lang":7,"primary_tag":21,"url":28,"title":29,"short_title":11,"subtitle":11,"description":30,"meta_description":30,"image":31,"img_alt":32,"author_name":11,"download_doc":11,"download_title":11,"download_btn":11,"webinar_video":11,"thumbnail":11,"author_id":11,"content":33,"publish_date":34,"featured":35,"is_gated":18,"is_infographic":11,"is_hidden":18,"is_active":19,"tags":36,"updated_at":23},329,"news","mobileye-to-establish-vertically-integrated-robotaxi-business","Mobileye 将打造垂直整合Robotaxi业务","全新业务布局突破原有自动驾驶系统供应商定位，与现有车企、移动出行合作项目形成互补","https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002F86fdfc495d3212c3c26221fbe4f90e66_1781548502451.webp","Mobileye 拟推出的Robotaxi服务概念示意图。","\u003Cp>\u003Cstrong>中国上海，2026年6月16日\u003C\u002Fstrong> — Mobileye 今日宣布，计划进一步拓展自动驾驶出租车（Robotaxi）业务版图，从提供自动驾驶技术延伸至自主运营自动驾驶网约车服务。该新项目计划于2027年率先落地美国一座城市，标志着Mobileye战略的重要升级。届时，Mobileye将整合行业领先自动驾驶技术、车队运营、乘客出行服务与出行管理能力，打造垂直整合解决方案。这一新业务是在Mobileye现有商业模式基础上的进一步拓展 —— Mobileye将继续作为自动驾驶技术供应商，为全球汽车制造商和出行服务提供商提供解决方案，在持续为客户部署提供支持的同时，增设全新运营板块。\u003C\u002Fp>\n\u003Cp>目前 Mobileye Drive 作为独立自动驾驶系统，正整合进合作伙伴项目。根据新举措的规划，Mobileye将打通Robotaxi价值链，依托Mobileye Drive系统，整合旗下Moovit出行平台及消费端应用、多模式出行规划、自动驾驶运营调度、车队管理技术以及远程协助基础设施。\u003C\u002Fp>\n\u003Cp>这一新举措不会改变Mobileye继续向汽车制造商、出行运营商及其他客户提供Mobileye Drive的承诺。公司将自营Robotaxi业务视作互补市场化路径，其将有助于加速自动驾驶落地部署，沉淀一线运营经验，并进一步验证Mobileye Drive平台的规模化应用能力。公司计划客户合作项目与自有运营车队双线同步推进。\u003C\u002Fp>\n\u003Cp>Mobileye规划首批部署约100台自动驾驶车辆，并于2027年开始在美国一座大都市投入运营。部署工作将分阶段推进，旨在验证完全无人驾驶条件下的整套运营模式。待首支车队稳定运营后，公司还计划大幅扩大业务规模，目标未来五年内将车队规模扩展至约17,000辆。\u003C\u002Fp>\n\u003Cp>Mobileye创始人兼首席执行官Amnon Shashua教授表示：“Robotaxi领域的变革才刚刚起步，其重塑全球出行方式的潜力仍在持续增长。随着自动驾驶出行赛道热度攀升，行业高度依赖为数不多的技术供应商与商业模式。而我们认为，可以开拓全新的发展路径，依托深厚的自动驾驶技术积淀、强大的产业合作生态、以及覆盖整个出行体系的成熟落地能力，构建差异化布局。 二十余年来，Mobileye持续深耕自动驾驶底层技术研发。如今我们迈出了新的重要一步：自研技术叠加自主运营，开创一套可实现财务层面规模化扩展、跨区域复制的全球化Robotaxi业务。我们计划于2026年底前在美国举办资本市场日活动，届时将分享更多有关商业化、技术及运营方面的详细信息。”\u003C\u002Fp>\n\u003Cp>为完善端到端自动驾驶平台，Mobileye将与自动驾驶整车平台厂商、车队运营商、车辆集成合作伙伴以及关键技术供应商展开合作。依托这套生态体系，Mobileye将通过统一的业务部门，统筹自有自动驾驶网约车服务的建设与运营。”\u003C\u002Fp>\n\u003Cp>Amnon Shashua教授补充道：“这一举措并非取代现有合作伙伴关系，而是在此基础上的延伸。我们将一如既往地通过Mobileye Drive赋能车企与出行服务商；与此同时，运营自有出行服务也有助于我们加快技术的市场采用，积累一手运营经验，充分释放自动驾驶出行的潜力。”\u003C\u002Fp>\n\u003Cp>Mobileye Drive沉淀25年以上的计算机视觉、地图、感知及自动驾驶技术研发经验，搭载最新复合人工智能架构，融合多类AI算法，配套严谨的安全框架，并通过工程化架构实现落地。全球已有超2.3亿台车辆搭载Mobileye技术，在业内积累了业内领先的真实道路经验。\u003C\u002Fp>\n\u003Cp>这一业务布局同时也拓展了Moovit原有的业务战略。Moovit致力于解决复杂城市环境下，用户查询、规划、使用公共交通服务的全流程需求。Moovit出行平台覆盖全球112个国家、3500余座城市，支持45种语言，服务超17亿用户。Moovit在消费者出行服务、多模式线路规划、用户运营和车队运营方面的专业知识，为公司在全球范围内扩展自动驾驶出行服务提供了关键基础。\u003C\u002Fp>\n\u003Cp>___________________________________\u003C\u002Fp>\n\u003Ch6>Mobileye (Nasdaq: MBLY) leads the mobility revolution with our autonomous driving and driver-assistance technologies, harnessing world-renowned expertise in artificial intelligence, computer vision and integrated software and hardware. Since our founding in 1999, Mobileye has enabled the global adoption of advanced driver-assistance systems that save countless lives and reduce crashes, while pioneering groundbreaking technologies such as REM™ crowdsourced road intelligence, Imaging Radar and Compound AI. These technologies drive the ADAS and AV fields towards the future of mobility – enabling self-driving vehicles and mobility solutions at scale, and powering industry-leading ADAS products. Through 2025, more than 230 million vehicles worldwide have been built with Mobileye’s EyeQ technology inside. In 2026, Mobileye acquired Mentee Robotics to pursue the future of physical AI and humanoid robots. Since 2022, Mobileye has been listed independently from Intel (Nasdaq: INTC), which retains majority ownership. For more information, visit \u003Ca href=\"https:\u002F\u002Fwww.mobileye.com\" target=\"_blank\" rel=\"noopener noreferrer\">https:\u002F\u002Fwww.mobileye.com\u003C\u002Fa>.\u003C\u002Fh6>\n\u003Ch6>“Mobileye,” the Mobileye logo and Mobileye product names are registered trademarks of Mobileye Global. All other marks are the property of their respective owners.\u003C\u002Fh6>\n\u003Ch6>Forward-Looking Statements\u003C\u002Fh6>\n\u003Ch6>This press release contains forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995. Statements in this release that are not statements of historical fact are forward-looking statements and should be evaluated as such, including statements regarding Mobileye’s expansion into a full ownership of an autonomous ride-hailing business, and the impact and potential benefits to Mobileye’s business. These statements often include words such as “anticipate,” “expect,” “suggests,” “plan,” “believe,” “intend,” “estimates,” “targets,” “projects,” “should,” “could,” “would,” “may,” “will,” “forecast,” or the negative of these terms, and other similar expressions, although not all forward-looking statements contain these words. We base these forward-looking statements or projections, including Mobileye’s full-year guidance, on our current expectations, plans and assumptions that we have made in light of our experience in the industry, as well as our perceptions of historical trends, current conditions, expected future developments and other factors we believe are appropriate under the circumstances and at such time. You should understand that these statements are not guarantees of performance or results. The forward-looking statements and projections are subject to and involve risks, uncertainties and assumptions and you should not place undue reliance on these forward-looking statements or projections. Although we believe that these forward-looking statements and projections are based on reasonable assumptions at the time they are made, you should be aware that many factors could affect our actual financial results or results of operations and could cause actual results to differ materially from those expressed in the forward-looking statements and projections. Detailed information regarding these and other factors that could affect Mobileye’s business and results is included in Mobileye’s SEC filings, including the company’s Annual Report on Form 10-K for the year ended December 27, 2025, particularly in the section entitled “Item 1A. Risk Factors”. Copies of these filings may be obtained by visiting our Investor Relations website at \u003Ca href=\"http:\u002F\u002Fir.mobileye.com\" target=\"_blank\" rel=\"noopener noreferrer\">ir.mobileye.com\u003C\u002Fa> or the SEC’s website at \u003Ca href=\"http:\u002F\u002Fwww.sec.gov\" target=\"_blank\" rel=\"noopener noreferrer\">www.sec.gov\u003C\u002Fa>.\u003C\u002Fh6>\n\u003Cp> \u003C\u002Fp>","2026-06-16T04:00:00.000Z",1,"News, Amnon Shashua, Driverless MaaS, Industry, Autonomous Driving",{"id":38,"type":27,"lang":7,"primary_tag":39,"url":40,"title":41,"short_title":11,"subtitle":11,"description":42,"meta_description":42,"image":43,"img_alt":44,"author_name":11,"download_doc":11,"download_title":11,"download_btn":11,"webinar_video":11,"thumbnail":11,"author_id":11,"content":45,"publish_date":46,"featured":35,"is_gated":18,"is_infographic":11,"is_hidden":18,"is_active":19,"tags":47,"updated_at":23},321,"News","mobileye-secures-major-dms-production-program-with-leading-us-automaker","Mobileye斩获美国头部车企驾驶员监测系统（DMS）大型量产定点项目","Mobileye DMS™平台获全球车企青睐，此次新单将延续Mobileye车内感知业务增长势头","https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002F8bfc2d66bf2b488ba43e4ea74cf956e3_1774268681384.png","Mobileye DMS™能够联动驾驶员视线、注意力状态与实时道路情境，更智能地识别分心驾驶行为","\u003Cp>\u003Cspan data-contrast=\"auto\">\u003Cstrong>中国上海，2026年3月23日\u003C\u002Fstrong> — Mobileye（纳斯达克股票代码：MBLY）今日宣布，一家美国头部车企将在其搭载Mobileye EyeQ6L系统集成芯片的未来车型中集成Mobileye驾驶员监测系统（Mobileye DMS™），计划于2027年启动量产。这一新项目拓展了与该车企客户现有组合辅助驾驶系统（ADAS）项目的合作范围与功能配置，预计量产规模达数百万辆，将覆盖多款车型、多个年款。\u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>Mobileye车内感知平台涵盖驾驶员监测系统（DMS）与乘员监测系统（OMS），可与ADAS感知算法在单颗芯片上并行运行。平台能够融合车辆内部感知与车外道路感知，基于驾驶环境的具体情境来评估驾驶员参与度，不仅可判断驾驶员是否处于警觉状态，还能监测其视线方向，以及注意力是否与实时道路状况匹配。\u003C\u002Fp>\n\u003Cp>之前，Mobileye DMS和OMS已获一家全球车企青睐，将被集成到基于EyeQ6H的SuperVision™及环绕式ADAS™方案中，而此次新项目是此前业务的成功延续。这些项目共同体现出，车企日益希望将驾驶员监测、乘员安全与辅助驾驶功能整合，从而告别独立DMS电子控制单元（ECU）带来的额外成本与系统复杂度。\u003C\u002Fp>\n\u003Cp>Mobileye业务发展与战略执行副总裁Nimrod Nehushtan 表示：“新一代辅助驾驶需要车辆全维度、更丰富的情境信息，包括前方道路、车辆内部以及二者的交互关联。与此同时，车企希望在全系车型中规模化搭载辅助驾驶功能，同时避免额外的硬件成本与复杂的系统集成工作。Mobileye DMS兼顾两大需求，可在单一ADAS芯片及ECU平台上实现情境感知驾驶员监测。这样的功能集成正是Mobileye独有的优势，我们期待助力客户实现规模化量产落地。”\u003C\u002Fp>\n\u003Cp>随着运动脱离驾驶功能向非豪华车型普及，确保驾驶员切实关注道路状况，是该功能安全落地的关键。Mobileye DMS可联动ADAS摄像头采集的真实路况与驾驶员视线，识别仅靠车内独立系统无法捕捉的分心行为，同时判断驾驶员是否已感知路况。该系统能够减少误报，实现更精准的干预控制；针对更高等级的驾驶自动化，可基于驾驶员参与度，更智能地发出接管请求。\u003C\u002Fp>\n\u003Cp>该平台旨在满足欧洲新车安全评鉴协会（Euro NCAP）2026版评分要求，同时前瞻性地着眼于Euro NCAP 2029版规程的潜在升级方向，预计届时评测标准将从单纯的眼球追踪提升至有效驾驶参与度检测。\u003C\u002Fp>\n\u003Cp> \u003C\u002Fp>\n\u003Cp>\u003Cstrong>\u003Cspan data-contrast=\"auto\">About Mobileye\u003C\u002Fspan>\u003C\u002Fstrong>\u003Cspan data-ccp-props=\"{"201341983":0,"335559739":0,"335559740":240}\"> \u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan data-contrast=\"auto\">Mobileye (Nasdaq: MBLY) leads the mobility revolution with our autonomous driving and driver-assistance technologies, harnessing world-renowned expertise in artificial intelligence, computer vision and integrated software and hardware. Since our founding in 1999, Mobileye has enabled the global adoption of advanced driver-assistance systems that save countless lives and reduce crashes, while pioneering groundbreaking technologies such as REM™ crowdsourced road intelligence, Imaging Radar and Compound AI. These technologies drive the ADAS and AV fields towards the future of mobility – enabling self-driving vehicles and mobility solutions at scale, and powering industry-leading ADAS products. Through 2025, about 230 million vehicles worldwide have been built with Mobileye’s EyeQ technology inside, and in 2026 Mobileye acquired Mentee Robotics to pursue the future of physical AI and humanoid robots. Since 2022, Mobileye has been listed independently from Intel (Nasdaq: INTC), which retains majority ownership. For more information, visit \u003C\u002Fspan>\u003Ca href=\"https:\u002F\u002Fcts.businesswire.com\u002Fct\u002FCT?id=smartlink&url=https%3A%2F%2Fwww.mobileye.com&esheet=54421863&newsitemid=20260210772101&lan=en-US&anchor=https%3A%2F%2Fwww.mobileye.com&index=1&md5=cfa04b95770b8044fedb93639c4fc56e\">\u003Cspan data-contrast=\"auto\">https:\u002F\u002Fwww.mobileye.com\u003C\u002Fspan>\u003C\u002Fa>\u003Cspan data-ccp-props=\"{"201341983":0,"335559739":0,"335559740":240}\"> \u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan data-contrast=\"auto\">Mobileye's name, product names, product marks and logos are trademarks or registered trademarks of Mobileye Vision Technologies Limited. Other names and brands are the property of their respective owners.\u003C\u002Fspan>\u003Cspan data-ccp-props=\"{"201341983":0,"335559739":0,"335559740":240}\"> \u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan data-contrast=\"auto\">Any reference to \"Mobileye\" in this document means Mobileye Vision Technologies Ltd., Mobileye Global Inc. or any of their subsidiaries.\u003C\u002Fspan>\u003Cspan data-ccp-props=\"{"201341983":0,"335559739":0,"335559740":240}\"> \u003C\u002Fspan>\u003C\u002Fp>","2026-03-23T05:00:00.000Z","ADAS",{"id":49,"type":6,"lang":7,"primary_tag":50,"url":51,"title":52,"short_title":11,"subtitle":11,"description":53,"meta_description":53,"image":54,"img_alt":55,"author_name":11,"download_doc":11,"download_title":11,"download_btn":11,"webinar_video":11,"thumbnail":11,"author_id":11,"content":56,"publish_date":57,"featured":35,"is_gated":18,"is_infographic":11,"is_hidden":18,"is_active":19,"tags":58,"updated_at":23},298,"Industry","where-mobility-meets-the-game-mobileye-partners-with-vfl-wolfsburg","智慧出行邂逅绿茵激情：Mobileye与德甲沃尔夫斯堡足球俱乐部达成战略合作","Mobileye 正式成为德国足球甲级联赛（Bundesliga）劲旅沃尔夫斯堡足球俱乐部的顶级合作伙伴。","https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002F3190dec398225b0d7744d1f29bcae4e6_1751278008967.png","沃尔夫斯堡足球俱乐部是一支独具特色的球队，始终秉持创新精神、前瞻视野与团队协作理念。","\u003Cp>打造卓越出行体验始终是Mobileye的使命核心。如今，我们首次将这份追求卓越的精神延伸至绿茵场，成为德甲劲旅沃尔夫斯堡俱乐部的顶级合作伙伴。\u003C\u002Fp>\n\u003Ch3>智慧出行与绿茵激情的完美碰撞\u003C\u002Fh3>\n\u003Cp>这项\u003Ca href=\"https:\u002F\u002Fwww.vfl-wolfsburg.de\u002Fen\u002Fnewsdetails\u002Fnews-detail\u002Fdetail\u002Fnews\u002Fautomotive-technology-meets-team-spirit-a-vision-for-the-future\">战略合作\u003C\u002Fa>对 Mobileye 意义重大，不仅关乎品牌本身，更体现了我们所坚持的理念。作为一家汽车科技公司，这次合作为我们在德国、欧洲乃至全球范围内进一步提升品牌影响力打开了新的机遇。\u003C\u002Fp>\n\u003Cp>沃尔夫斯堡足球队以创新、远见与团队精神著称，正是这些价值观，让这次的合作显得如此契合、自然。这不仅是两个领域的携手同行，更是一次共同塑造未来的旅程——发生在一个长期象征智慧出行与技术进步的国家与城市之中。\u003C\u002Fp>\n\u003Ch3>沃尔夫斯堡俱乐部与德甲联赛\u003C\u002Fh3>\n\u003Cp>沃尔夫斯堡足球俱乐部是德甲联赛的重要成员，而德甲是世界足坛最具影响力的联赛之一。该赛事不仅汇聚了众多国际知名球队，更涌现了无数闪耀足坛的传奇球星与当世巨星，因此，每逢周末，数以千万计的全球球迷都会将目光聚焦于德甲赛场。\u003C\u002Fp>\n\u003Cp>沃尔夫斯堡是德甲联赛的传统劲旅，曾在2009年问鼎德甲冠军，并于2015年捧起德国杯，是21世纪德国足坛最成功的俱乐部之一。俱乐部所在城市沃尔夫斯堡不仅是大众汽车集团总部所在地，更是德国汽车工业的重要象征。\u003C\u002Fp>\n\u003Ch3>品牌影响力跃升\u003C\u002Fh3>\n\u003Cp>随着 Mobileye 品牌正式走上绿茵赛场，我们将迎来前所未有的曝光机会。球衣品牌展示、场边广告横幅以及社交媒体传播，将全面提升 Mobileye 的品牌可见度。\u003C\u002Fp>\n\u003Cp>以下是球迷将在 2025\u002F2026 赛季看到 Mobileye 的一些重点展示场景：\u003C\u002Fp>\n\u003Cp>Mobileye 的logo将出现在沃尔夫斯堡队比赛球衣的左袖上，覆盖所有赛事，包括德甲、德国杯、国际赛事以及友谊赛。除了球衣展示，Mobileye 品牌还将在球场内外拥有强大的曝光版位，全面提升品牌可见度。\u003C\u002Fp>\n\u003Ch3>超越足球的战略意义\u003C\u002Fh3>\n\u003Cp>此次合作以建立深度联结为核心，是Mobileye深化产业布局的重要一步，展现了我们在各个领域追求卓越的坚定决心，旨在将Mobileye的创新故事传递给更多受众，共同见证科技创新与足球碰撞出的精彩火花。让我们期待新赛季的到来。\u003C\u002Fp>\n\u003Cp> \u003C\u002Fp>","2025-06-30T04:00:00.000Z","Events, Industry",{"links_array":60},[61,91,112,133,137],{"id":62,"name":63,"is_link":19,"is_local_link":19,"path":64,"sub_links":65,"is_icon":18},"1","公司信息","\u002Fabout\u002F",[66,68,72,75,79,83,87],{"id":62,"name":67,"is_local_link":19,"path":64},"关于我们",{"id":69,"name":70,"is_local_link":19,"path":71},"2","管理层","\u002Fabout\u002Fmanagement\u002F",{"id":73,"name":8,"is_local_link":19,"path":74},"3","\u002Famnon-shashua\u002F",{"id":76,"name":77,"is_local_link":18,"path":78},"4","投资人","https:\u002F\u002Fir.mobileye.com\u002F",{"id":80,"name":81,"is_local_link":19,"path":82},"5","ESG","\u002Fabout\u002Fesg\u002F",{"id":84,"name":85,"is_local_link":18,"path":86},"6","招贤纳士","https:\u002F\u002Fcareers.mobileye.com\u002F",{"id":88,"name":89,"is_local_link":19,"path":90},"7","联系我们","\u002Fcontact\u002F",{"id":69,"name":92,"is_link":19,"is_local_link":19,"path":93,"sub_links":94,"is_icon":18},"解决方案","\u002Fsolutions\u002F",[95,97,100,103,106,109],{"id":62,"name":96,"is_local_link":19,"path":93},"解决方案概览",{"id":69,"name":98,"is_local_link":19,"path":99},"Mobileye ADAS","\u002Fsolutions\u002Fadas\u002F",{"id":73,"name":101,"is_local_link":19,"path":102},"Mobileye SuperVision™","\u002Fsolutions\u002Fsuper-vision\u002F",{"id":76,"name":104,"is_local_link":19,"path":105},"Mobileye Chauffeur™","\u002Fsolutions\u002Fchauffeur\u002F",{"id":80,"name":107,"is_local_link":19,"path":108},"Mobileye Drive™","\u002Fsolutions\u002Fdrive\u002F",{"id":84,"name":110,"is_local_link":19,"path":111},"EyeQ Kit™","\u002Fsolutions\u002Feyeq-kit\u002F",{"id":73,"name":113,"is_link":19,"is_local_link":19,"path":114,"sub_links":115},"尖端技术","\u002Ftechnology\u002F",[116,118,121,124,127,130],{"id":62,"name":117,"is_local_link":19,"path":114},"技术概览",{"id":69,"name":119,"is_local_link":19,"path":120},"EyeQ™ 系统集成芯片","\u002Ftechnology\u002Feyeq-chip\u002F",{"id":73,"name":122,"is_local_link":19,"path":123},"真正冗余","\u002Ftechnology\u002Ftrue-redundancy\u002F",{"id":76,"name":125,"is_local_link":19,"path":126},"智能路网技术 (REM™)","\u002Ftechnology\u002Frem\u002F",{"id":80,"name":128,"is_local_link":19,"path":129},"责任敏感安全 (RSS)","\u002Ftechnology\u002Fresponsibility-sensitive-safety\u002F",{"id":84,"name":131,"is_local_link":19,"path":132},"安全方法论","\u002Ftechnology\u002Fsafety-methodology\u002F",{"id":76,"name":134,"is_link":19,"is_local_link":19,"path":135,"sub_links":136,"is_icon":18},"CEO专栏","\u002Fceo-corner\u002F",[],{"id":80,"name":138,"is_link":19,"is_local_link":19,"path":139,"sub_links":140},"新闻中心","\u002Fnews\u002F",[141,143],{"id":62,"name":142,"is_local_link":19,"path":139},"新闻",{"id":69,"name":144,"is_local_link":19,"path":145},"博客","\u002Fblog\u002F",{"linksList":147,"socialLinks":199},[148,158,165,172,174,178],{"id":62,"link":149,"name":63,"isLocalLink":19,"subLinks":150},"\u002F",[151,152,153,154,155,156,157],{"id":62,"link":64,"name":67,"isLocalLink":19},{"id":69,"link":71,"name":70,"isLocalLink":19},{"id":73,"link":74,"name":8,"isLocalLink":19},{"id":76,"link":78,"name":77,"isLocalLink":18},{"id":80,"link":82,"name":81,"isLocalLink":19},{"id":84,"link":86,"name":85,"isLocalLink":18},{"id":88,"link":90,"name":89,"isLocalLink":19},{"id":69,"link":93,"name":92,"isLocalLink":19,"subLinks":159},[160,161,162,163,164],{"id":62,"name":98,"isLocalLink":19,"link":99},{"id":69,"name":101,"isLocalLink":19,"link":102},{"id":73,"name":104,"isLocalLink":19,"link":105},{"id":76,"name":107,"isLocalLink":19,"link":108},{"id":80,"name":110,"isLocalLink":19,"link":111},{"id":73,"link":114,"name":113,"isLocalLink":19,"subLinks":166},[167,168,169,170,171],{"id":62,"name":119,"isLocalLink":19,"link":120},{"id":69,"name":122,"isLocalLink":19,"link":123},{"id":73,"name":125,"isLocalLink":19,"link":126},{"id":76,"name":128,"isLocalLink":19,"link":129},{"id":80,"name":131,"isLocalLink":19,"link":132},{"id":76,"link":135,"name":134,"isLocalLink":19,"subLinks":173},[],{"id":80,"link":149,"name":138,"isLocalLink":19,"subLinks":175},[176,177],{"id":62,"link":145,"name":144,"isLocalLink":19},{"id":69,"link":139,"name":142,"isLocalLink":19},{"id":84,"link":149,"name":179,"isLocalLink":19,"subLinks":180},"法务条款",[181,184,187,190,193,196],{"id":62,"link":182,"name":183,"isLocalLink":18},"https:\u002F\u002Fwww.mobileye.com\u002Fterms-of-use\u002F","使用条款",{"id":69,"link":185,"name":186,"isLocalLink":18},"https:\u002F\u002Fwww.mobileye.com\u002Fglobal-candidate-privacy-notice\u002F","候选人隐私声明",{"id":73,"link":188,"name":189,"isLocalLink":18},"https:\u002F\u002Fwww.mobileye.com\u002Fsecurity-and-compliance\u002F","安全与合规",{"id":76,"link":191,"name":192,"isLocalLink":18},"https:\u002F\u002Fwww.mobileye.com\u002Fprivacy-policy\u002F","隐私政策",{"id":80,"link":194,"name":195,"isLocalLink":18},"https:\u002F\u002Fwww.mobileye.com\u002Fcookies-notice\u002F","Cookies通知",{"id":84,"link":197,"name":198,"isLocalLink":18},"https:\u002F\u002Fbrand.mobileye.com\u002Fd\u002FaLZ6VeziuWM7\u002Flegal","Logo及标志使用",[200,204,208,211],{"id":62,"link":201,"icon":202,"isLocalLink":18,"withQr":19,"qrIcon":203},"https:\u002F\u002Fweixin.qq.com\u002Fr\u002FrTju9qbELPtTreKV922-","https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fcorporate\u002Fimg\u002Fhomepage2022\u002Fcn_wechat_icon.svg","https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fcn\u002Fcorporate\u002Ficons\u002Fmobileye_wechat_qr_small.png",{"id":69,"link":205,"icon":206,"isLocalLink":18,"withQr":18,"qrIcon":207},"https:\u002F\u002Fweibo.com\u002Fmobileye","https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fcorporate\u002Fimg\u002Fhomepage2022\u002Fcn_weibo_icon.svg","",{"id":73,"link":209,"icon":210,"isLocalLink":18,"withQr":18,"qrIcon":207},"https:\u002F\u002Fwww.zhihu.com\u002Forg\u002Fmobileye-wu-bi-shi","https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fcorporate\u002Fimg\u002Fhomepage2022\u002Fcn_zhihu_icon_2.svg",{"id":76,"link":212,"icon":213,"isLocalLink":18,"withQr":18,"qrIcon":207},"https:\u002F\u002Fspace.bilibili.com\u002F1730991723","https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fcorporate\u002Fimg\u002Fhomepage2022\u002Fcn_bilibili_icon_2.svg",2026,1785839814674]