[{"data":1,"prerenderedAt":343},["ShallowReactive",2],{"tags-amnon-shashua":3,"tag-posts-amnon-shashua":125},[4,11,19,26,32,40,47,54,62,69,76,83,89,97,105,112,119],{"id":5,"tag":6,"url":7,"list_order":5,"tag_title":8,"tag_description":9,"tag_image":10,"is_visible":5},1,"Amnon Shashua","amnon-shashua","Mobileye CEO and founder Prof. Amnon Shashua","Read the latest news and updates about Professor Amnon Shashua, the Chief Executive Officer and founder of Mobileye.","https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002Fea37d7e7751aae1f5f2e8d70c10c4cd1_1711444846898.jpg",{"id":12,"tag":13,"url":14,"list_order":15,"tag_title":16,"tag_description":17,"tag_image":18,"is_visible":5},4,"ADAS","adas",2,"Advanced Driver-Asssistance Systems from Mobileye","Read about assisted-driving technologies from Mobileye, the pioneering market leader in computer vision for advanced driver-assistance systems.","https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002F36fcc73cd14c1cb4528f155e534bcd24_1710330031175.jpg",{"id":20,"tag":21,"url":22,"list_order":20,"tag_title":23,"tag_description":24,"tag_image":25,"is_visible":5},3,"Autonomous Driving","autonomous-driving","Autonomous Driving Technologies from Mobileye","Discover the full range of innovative advancements, groundbreaking technologies, and comprehensive solutions developed by Mobileye for autonomous vehi","https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002Fd68043f24c36c755b43d169bddaef49f_1710330004977.jpg",{"id":15,"tag":27,"url":28,"list_order":12,"tag_title":29,"tag_description":30,"tag_image":31,"is_visible":5},"AV Safety","av-safety","RSS™ and Autonomous Vehicle Safety at Mobileye","Delve into the latest updates and information about autonomous-vehicle safety and the Responsibility-Sensitive Safety™ model (RSS™) from Mobileye.","https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002F1a93aad27f5322b98336318873549b35_1710329987351.jpg",{"id":33,"tag":34,"url":35,"list_order":36,"tag_title":37,"tag_description":38,"tag_image":39,"is_visible":5},6,"Driverless MaaS","driverless-maas",5,"Driverless Mobility-as-a-Service from Mobileye","Dive into the technologies Mobileye develops to enable autonomous Mobility-as-a-Service solutions delivering self-driving mobility on demand.","https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002Fa04eac1da266c8dd0caf775a243c6ff2_1716967989684.jpg",{"id":41,"tag":42,"url":43,"list_order":33,"tag_title":44,"tag_description":45,"tag_image":46,"is_visible":5},7,"Mapping & REM","mapping-rem","REM™ mapping technology from Mobileye","Discover the latest about Road Experience Management™ (REM™), Mobileye's innovative crowdsourced cloud mapping solution for assisted and autonomous dr","https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002Fd8847e0a8281f0cf9cb0f8bc2c051b1e_1716968001971.jpg",{"id":48,"tag":49,"url":50,"list_order":41,"tag_title":51,"tag_description":52,"tag_image":53,"is_visible":5},12,"Mobileye Inside","mobileye-inside","The latest about vehicles with Mobileye technology inside","Take a look under the hood at the latest vehicles incorporating the groundbreaking assisted and autonomous driving technologies from Mobileye.","https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002Fe8b350759a9f7e7ca5c521f0866b9a48_1716968050502.jpg",{"id":55,"tag":56,"url":57,"list_order":58,"tag_title":59,"tag_description":60,"tag_image":61,"is_visible":5},9,"Opinion","opinion",8,"Opinion pieces from Mobileye Leadership","Read opinion pieces and editorials from the thought leaders at the helm of Mobileye, the company driving the autonomous vehicle evolution.","https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002F1de50bef1e21d144583a09eba0cc23ff_1716968025874.jpg",{"id":63,"tag":64,"url":65,"list_order":55,"tag_title":66,"tag_description":67,"tag_image":68,"is_visible":5},13,"Industry","industry","Updates about the automotive tech industry from Mobileye","Read the newest updates and insights about the industry from Mobileye, a leading developer and supplier of automotive technologies.","https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002F616c61e1687b24b92c10c22ffddc0d3f_1716968063749.jpg",{"id":36,"tag":70,"url":71,"list_order":72,"tag_title":73,"tag_description":74,"tag_image":75,"is_visible":5},"Awards","awards",10,"Awards & Honors at Mobileye","Mobileye is honored to be recognized as a leader in assisted and autonomous driving technologies. Read about the awards and citations we've received.","https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002Fcbe44f82e8943ecb87748a0b21769a79_1716967974564.jpg",{"id":77,"tag":78,"url":79,"list_order":77,"tag_title":80,"tag_description":81,"tag_image":82,"is_visible":5},11,"Events","events","Events & Conferences at Mobileye","Find out about conferences, trade shows, and other events where Mobileye has showcased the innovations creating the autonomous driving future today. ","https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002F091d61264ea4fdabfab5061ac817b098_1716968037984.jpg",{"id":58,"tag":84,"url":85,"list_order":48,"tag_title":86,"tag_description":87,"tag_image":88,"is_visible":5},"Video","video","Videos about Mobileye","Watch videos about Mobileye and our groundbreaking technologies and solutions that are driving the evolution from assisted to autonomous driving.","https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002Facab36f96b5bf20ca9f7898eea8595c9_1716968015561.jpg",{"id":90,"tag":91,"url":92,"list_order":63,"tag_title":93,"tag_description":94,"tag_image":95,"is_visible":96},15,"Press Kit","press-kit","Press kits and media information from Mobileye","Find the latest press kits, media information, and other assets for journalists from Mobileye, the company driving the autonomous vehicle evolution.","https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fcorporate\u002Fsocial\u002Fabout.jpg",0,{"id":98,"tag":99,"url":100,"list_order":101,"tag_title":102,"tag_description":103,"tag_image":104,"is_visible":96},16,"News","news",14,"News updates from Mobileye","Read the latest news from Mobileye, the innovative and pioneering developer and supplier of assisted and autonomous driving technologies.","https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002Fc14e966bdd3a7bc6585a879ca05b6714_1716968104360.jpg",{"id":106,"tag":107,"url":108,"list_order":90,"tag_title":109,"tag_description":110,"tag_image":111,"is_visible":5},17,"Financial","financial","Financial updates from Mobileye [Nasdaq: MBLY]","Receive the updates on the financial performance and quarterly results of Mobileye, listed on Nasdaq stock exchange under the ticker symbol MBLY.","https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002F3bd26e0f4e9f88d3184254ab9d601fe1_1716968119194.jpg",{"id":113,"tag":114,"url":115,"list_order":98,"tag_title":116,"tag_description":117,"tag_image":118,"is_visible":5},18,"From our CEO","from-our-ceo","From the Desk of Mobileye CEO & Founder Prof. Amnon Shashua","Messages, editorials, and videos communicated directly from the mind and desk of Mobileye CEO and founder Professor Amnon Shashua.","https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002Fd0b71dedc5a444b7ecf4959a28b5e4c5_1716968130886.jpg",{"id":120,"tag":121,"url":122,"list_order":106,"tag_title":123,"tag_description":124,"tag_image":61,"is_visible":5},19,"Physical AI","physical-ai","Mobileye Blog | Physical AI","Read the latest news and updates about Mobileye's Physical AI technologies",[126,139,149,160,170,180,190,200,210,220,231,242,252,262,272,282,292,303,313,323,333],{"id":127,"type":100,"url":128,"title":129,"description":130,"primary_tag":20,"author_name":131,"is_hidden":96,"lang":132,"meta_description":130,"image":133,"img_alt":134,"content":135,"download_doc":136,"download_title":136,"download_btn":136,"webinar_video":136,"thumbnail":136,"is_gated":96,"featured":5,"publish_date":137,"tags":138},329,"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","","en","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>",null,"2026-06-16T07:00:00.000Z",[99,6,34,64,21],{"id":140,"type":100,"url":141,"title":142,"description":143,"primary_tag":5,"author_name":131,"is_hidden":96,"lang":132,"meta_description":143,"image":144,"img_alt":145,"content":146,"download_doc":136,"download_title":136,"download_btn":136,"webinar_video":136,"thumbnail":136,"is_gated":96,"featured":96,"publish_date":147,"tags":148},318,"prof-amnon-shashua-elected-to-us-national-academy-of-engineering","Amnon Shashua 教授当选美国国家工程院院士","Mobileye总裁兼首席执行官凭借计算机视觉及其自动驾驶应用成果获此殊荣","https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002Ffb56d3e56621d10b6f0acc4fde141090_1770890393267.png","Mobileye首席执行官兼总裁Amnon Shashua教授","\u003Cp>Mobileye欣然宣布，公司总裁兼首席执行官Amnon Shashua教授当选2026届美国国家工程院（NAE）外籍院士。NAE院士称号是工程领域授予从业者的最高专业荣誉之一，旨在表彰其在工程研究、实践或教育领域的卓越贡献。  \u003C\u002Fp>\n\u003Cp>Shashua教授凭借其在计算机视觉及相关技术在自动驾驶领域的应用成果，经由同行提名并当选。 他是本届28位外籍院士之一。目前，美国国家工程院全球外籍院士共356名。  \u003C\u002Fp>\n\u003Cp>Shashua教授于1999年联合创立Mobileye，坚信人工智能将从根本上提升驾驶安全性。至今，全球已有约2.3亿辆汽车搭载Mobileye技术，将计算机视觉与机器学习基础研究落地为全球部署最广泛的物理人工智能系统之一。Shashua教授的研究覆盖业界领先的感知系统、形式安全框架及可扩展AI架构，有力推动了高级驾驶辅助系统的（ADAS）普及，并促成自动驾驶从科研项目走向商业化落地。  \u003C\u002Fp>\n\u003Cp>Shashua教授奠定的计算机视觉与机器学习技术基础，如今已延伸至汽车领域之外。今年1月，Mobileye宣布收购Mentee Robotics，开启了Mobileye 3.0时代的战略新篇章：将人工智能突破性成果应用于物理世界，依托在感知、仿真与安全模型方面的核心优势，前瞻性布局人形机器人与自动驾驶领域。\u003C\u002Fp>\n\u003Cp>NAE荣誉是对Shashua教授科研贡献的又一项肯定。Shashua教授的过往荣誉包括：2020年度丹·大卫奖（Dan David Prize）人工智能领类奖项，2022年度汽车名人堂出行创新者奖（Automotive Hall of Fame Mobility Innovator Award），以及2019年度电子成像科学家奖（Electronic Imaging Scientist of the Year）。  \u003C\u002Fp>\n\u003Cp>\u003Cspan data-contrast=\"auto\">如需阅读美国国家工程院完整公告及新晋院士名单，请\u003Ca href=\"https:\u002F\u002Fwww.nae.edu\u002F19579\u002F31222\u002F20095\u002F343222\u002F345149\u002FNAENewClass2026#:~:text=The%20National%20Academy%20of%20Engineering,of%20international%20members%20to%20356\" target=\"_blank\" rel=\"noopener\">点击此处。\u003C\u002Fa>\u003C\u002Fspan>\u003C\u002Fp>","2026-02-12T08:00:00.000Z",[6],{"id":150,"type":151,"url":152,"title":153,"description":154,"primary_tag":5,"author_name":131,"is_hidden":96,"lang":132,"meta_description":154,"image":155,"img_alt":156,"content":157,"download_doc":136,"download_title":136,"download_btn":136,"webinar_video":136,"thumbnail":136,"is_gated":96,"featured":96,"publish_date":158,"tags":159},315,"blog","takeaways-from-the-mobileye-press-conference-with-ceo-prof-amnon-shashua-at-ces-2026","Amnon Shashua 教授发表CES 2026年度主题演讲：Robotaxi最新动态、人工智能突破与机器人技术革新","展望2026年及未来，Mobileye首席执行官Amnon Shashua教授公布了公司的多项关键进展，包括重大里程碑事件、重磅合作成果、Robotaxi 技术持续推进、创新训练方法和人形机器人领域的最新突破。","https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002F749a8a359e3b53c6c41e870f8770aa0f_1767863157245.jpg","这些进展标志着公司迈入发展新阶段。","\u003Cp>Mobileye 以 Amnon Shashua 教授的年度演讲宣告CES 2026正式启幕。\u003C\u002Fp>\n\u003Cp>Amnon Shashua 教授登台演讲，展望2026年及未来，公布了公司多项关键进展。演讲全面覆盖从高级驾驶辅助系统（ADAS）到自动驾驶汽车（AV）的全产品布局以及核心里程碑事件，包括与大众汽车和MOIA在自动驾驶出租车（Robotaxi）领域的合作进展，以及Mobileye视觉-语言（VLSA）模型与创新训练方法的最新技术突破。最后，宣布当日重磅新闻--通过收购Mentee Robotics，Mobileye正式进军人形机器人领域。\u003C\u002Fp>\n\u003Cp>这些发布共同标志着公司迈入全新的发展阶段--Mobileye 3.0。在这一阶段，Mobileye将其在物理AI领域的技术领导力进一步拓展至两大极具潜力的前沿赛道。\u003C\u002Fp>\n\u003Ch3>\u003Cstrong>公司业绩与解决方案部署\u003C\u002Fstrong>\u003C\u002Fh3>\n\u003Cp>Amnon Shashua 教授首先概述了Mobileye的市场地位、预期营收以及解决方案部署规模等数据，重点强调了关键增长点。\u003C\u002Fp>\n\u003Cp>进入2026年，Mobileye未来八年的预期营收总额达245亿美元，较2023年173亿美元的预期金额增长约42%。2025年，公司斩获过去十年未曾合作过的两家新车企（OEM）的定点项目，同时，EyeQ™6L芯片获得定点数量较2024年增长3.5倍。\u003C\u002Fp>\n\u003Cp>截至2025年第三季度末，全球已有超过2.3亿辆汽车搭载了Mobileye技术。\u003C\u002Fp>\n\u003Ch3>\u003Cstrong>环绕式\u003C\u002Fstrong>\u003Cstrong>ADAS\u003C\u002Fstrong>\u003Cstrong>正在引领面向大众市场量产车型的驾驶辅助技术突破\u003C\u002Fstrong>\u003C\u002Fh3>\n\u003Cp>演讲重点披露了一项重大定点成果：一家\u003Ca href=\"https:\u002F\u002Fwww.mobileyechina.com\u002Fnews\u002Fmobileye-surround-adas-adds-second-top-10-automaker\u002F\">\u003Cu>美国头部车企\u003C\u002Fu>\u003C\u002Fa>已选定Mobileye环绕式ADAS™平台，搭载于其即将推出的面向大众市场量产车型上。此项合作进一步印证了行业趋势--传统基础ADAS正在加速向环绕式ADAS演进。\u003C\u002Fp>\n\u003Cp>环绕式ADAS配置多个摄像头和雷达，通过单个ECU内置的一颗EyeQ™6H系统集成芯片（SoC）来处理数据，实现全车周感知与辅助驾驶功能。该集中式架构旨在降低整车厂系统复杂度与成本的同时，支持更高级别的驾驶辅助功能，包括一体化泊车能力，以及在特定高速公路的运行设计范围内驾驶员”运动脱离|需注视”的驾驶体验。\u003C\u002Fp>\n\u003Cp>\u003Cspan data-ccp-props=\"{"201341983":0,"335559740":257}\">\u003Cimg src=\"https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002F04e658007e1bece0efff765b2e911adb_1767869270652.jpg\" alt=\"\" width=\"1200\" height=\"675\" \u002F>\u003C\u002Fspan>\u003C\u002Fp>\n\u003Ch3>\u003Cspan data-teams=\"true\">\u003Cstrong>至2033年，计划同MOIA联合部署超过10万辆 Robotaxi\u003C\u002Fstrong>\u003Cbr \u002F>\u003C\u002Fspan>\u003C\u002Fh3>\n\u003Cp>\u003Cspan data-contrast=\"auto\">自动驾驶出租车（Robotaxi）在公共道路的安全落地需要端到端生态系统支持，包括持续运营、车队管理以及真实路况适配能力。在主题演讲中，围绕ID. Buzz项目，大众汽车自动驾驶出行公司首席执行官Christian Senger受邀登台，深入探讨了大规模部署的实践路径。\u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>在这项合作中，大众汽车提供具备产业规模的车辆生产能力，Mobileye通过Mobileye Drive™系统提供L4级完全自动驾驶技术，MOIA则负责车队运营与出行服务层搭建，三方共同围绕ID. Buzz平台构建完整的运营生态系统。该项目计划于2026年在美国启动首批部署，随后逐步拓展至欧洲市场。\u003C\u002Fp>\n\u003Cp>此项合作将自动驾驶出租车（Robotaxi）项目扩大到了可观的规模，目前已在多种天气与气候条件（晴天、雨天、雪天）下的多个城市开展测试。据Christian Senger透露，MOIA计划到2033年，部署超10万辆搭载Mobileye系统的高等级驾驶自动化车辆。\u003C\u002Fp>\n\u003Ch3>\u003Cstrong>人工智能如何深度融入自动驾驶 \u003C\u002Fstrong>\u003C\u002Fh3>\n\u003Cp>合作推动业务落地的同时，技术创新也在持续加速，人工智能已成为自动驾驶演进的核心驱动力。更强大的新模型不断涌现，其应用早已不再局限于纯数字世界，而是扩展到在真实物理环境中运行的系统。但即便依托超高性能模型，人工智能领域的核心挑战仍未消除，包括“幻觉”问题、安全验证保障、以及大规模训练需求等。 \u003C\u002Fp>\n\u003Cp>Shashua教授阐述了Mobileye针对这些限制设计的架构方案，以及如何通过“快思考与慢思考”决策双系统、视觉-语言-语义-动作（VLSA）融合模型等创新，在算力和功耗受限的实时系统中，充分发挥现代人工智能的优势，同时保持极高的决策准确性。\u003C\u002Fp>\n\u003Ch3>\u003Cstrong>“快思考与慢思考”决策双系统\u003C\u002Fstrong>\u003C\u002Fh3>\n\u003Cp>\u003Cspan data-ccp-props=\"{"201341983":0,"335559740":257}\">这一架构的核心在于从“快思考”和“慢思考”两个维度看待驾驶任务：快思考系统负责高频率的“反射式”决策，包括安全层相关决策；慢思考系统则负责处理需要对整体场景进行推理的驾驶决策（不直接影响行车安全），因此可采用低频率运行模式。\u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan data-ccp-props=\"{"201341983":0,"335559740":257}\">\u003Cimg src=\"https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002F5a6ff54ec40f4d0803a26a2e566f688c_1767863258664.png\" alt=\"\" width=\"1200\" height=\"676\" \u002F>\u003C\u002Fspan>\u003C\u002Fp>\n\u003Ch3>\u003Cstrong>基于\u003C\u002Fstrong>\u003Cstrong>ACI\u003C\u002Fstrong>\u003Cstrong>的涌现式驾驶策略\u003C\u002Fstrong>\u003C\u002Fh3>\n\u003Cp>\u003Cspan data-ccp-props=\"{"201341983":0,"335559685":720,"335559740":257,"335559991":720}\">这一架构的核心支撑是人工群体智能（Artificial Community Intelligence，ACI），一种基于自博弈的驾驶策略框架，其驾驶策略训练依托于感知状态仿真，而非逼真图像。借助Mobileye路网智能技术生成的高清地图，ACI将车辆、行人、公交车等道路参与者模型置于真实道路场景中，并为每个模型配置数百万种可能的驾驶行为。这一设计可高密度注入低频但高风险的极端场景，并能在极短时间内生成数十亿小时级别的仿真驾驶数据。\u003C\u002Fspan>\u003C\u002Fp>\n\u003Ch3>\u003Cstrong>视觉-语言-语义-动作（VLSA）模型\u003C\u002Fstrong>\u003C\u002Fh3>\n\u003Cp>视觉-语言-语义-动作（VLSA）模型作为慢思考系统的核心，是一种基于视觉-语言的深层场景语义处理模型，其作用类似于在复杂驾驶场景中陪伴新手驾驶员的资深驾驶员。VLSA模型不直接控制车辆或输出行驶轨迹，而是提供结构化的语义指导，馈入规划系统，而安全关键控制仍由受正式安全层约束的快思考系统负责。\u003C\u002Fp>\n\u003Cp>通过快思考与慢思考的功能分离机制，结合大规模仿真训练，构建了一套无需将生成式模型置入安全环路、也无需依赖人工远程操控来解决所有边缘场景的自动驾驶规模化可拓展的路径。 \u003C\u002Fp>\n\u003Cp>\u003Ca href=\"https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fvideos\u002Fces_2026_slide28.mp4\">\u003Cspan data-ccp-props=\"{"201341983":0,"335559685":720,"335559740":257,"335559991":720}\">\u003Cimg src=\"https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fvideos\u002Fces_2026_slide28.mp4\" alt=\"\" \u002F>\u003Cvideo controls=\"controls\" width=\"100%\" height=\"100%\">\n\u003Csource src=\"https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fvideos\u002Fces_2026_slide28.mp4\" type=\"video\u002Fmp4\" \u002F>\u003C\u002Fvideo>\u003C\u002Fspan>\u003C\u002Fa>\u003C\u002Fp>\n\u003Ch3>\u003Cstrong>从“需注视”到“注意力脱离”：ADAS、自动驾驶与机器人技术的未来 \u003C\u002Fstrong>\u003C\u002Fh3>\n\u003Cp>Shashua教授展望了2030年代驾驶自动化行业在三大领域的进化路径，涵盖ADAS、消费级自动驾驶汽车、以及自动驾驶出租车（Robotaxi），核心围绕“智能安全扩展”展开。\u003Cspan data-ccp-props=\"{"201341983":0,"335559740":257}\">目前应用于高端消费级汽车的L2++“驾驶员运动脱离|需注视”系统，将持续进行成本优化，逐步成为更广泛车型的标配。\u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>消费级L3系统将从当前的“视觉脱离”向L4“注意力脱离”驾驶进化，降低人工干预频率，并扩展至现有高速公路设计运营范围之外的场景。已具备商业部署能力的自动驾驶出租车(Robotaxi)系统，将通过传感器优化、成本降低，以及车辆远程操控比例的大幅下降等实现加速发展。\u003C\u002Fp>\n\u003Cp>展望2030年，驾驶自动化领域的下一个重大变革将是从“视觉脱离”到“注意力脱离”的跨越，人工干预频率将降至足以支撑大规模部署的水平。  \u003C\u002Fp>\n\u003Cp>\u003Cspan data-ccp-props=\"{"201341983":0,"335559740":257}\">\u003Cimg src=\"https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002Fbad8d11c129052a484c41c13a57ae2e8_1767863433955.jpg\" alt=\"\" width=\"1200\" height=\"675\" \u002F>\u003C\u002Fspan>\u003C\u002Fp>\n\u003Ch3>\u003Cstrong>Mobileye 与 Mentee Robotics：物理AI的新疆域\u003C\u002Fstrong>\u003C\u002Fh3>\n\u003Cp>在主题演讲的尾声，Shashua教授宣布Mobileye将\u003Ca href=\"https:\u002F\u002Fwww.mobileyechina.com\u002Fnews\u002Fmobileye-to-acquire-mentee-robotics-to-accelerate-physical-ai-leadership\u002F\">\u003Cu>收购\u003C\u002Fu>\u003Cu>Mentee Robotics\u003C\u002Fu>\u003C\u002Fa>。Mentee专注于垂直整合的软硬件研发，核心技术包括仿真优先式学习、少样本泛化、高灵巧度人形机器人手部设计和零远程操控技术。\u003C\u002Fp>\n\u003Cp>通过此次收购，Mobileye的业务范围将从汽车领域拓展至更广泛的、同样面向现实世界构建的物理AI系统赛道。 \u003C\u002Fp>\n\u003Cp>此举反映了驾驶自动化与机器人技术的深度融合趋势，两大领域共享核心物理AI技术架构，涵盖多模态感知、环境建模、意图感知规划、精密控制，以及不确定性下的决策制定能力。\u003C\u002Fp>","2026-01-10T08:00:00.000Z",[6],{"id":161,"type":100,"url":162,"title":163,"description":164,"primary_tag":5,"author_name":131,"is_hidden":96,"lang":132,"meta_description":164,"image":165,"img_alt":166,"content":167,"download_doc":136,"download_title":136,"download_btn":136,"webinar_video":136,"thumbnail":136,"is_gated":96,"featured":96,"publish_date":168,"tags":169},309,"mobileye-announces-ces-2026-press-conference","Mobileye于CES 2026期间举行新闻发布会","Mobileye总裁兼首席执行官Amnon Shashua教授将于太平洋时间1月6日下午1:45发表年度演讲","https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002F697b5d8ccebf9baefdbc5c7fc1630189_1766064653115.png","Mobileye即将亮相CES 2026","\u003Cp>\u003Cstrong>耶路撒冷，\u003C\u002Fstrong>\u003Cstrong>2025\u003C\u002Fstrong>\u003Cstrong>年\u003C\u002Fstrong>\u003Cstrong>12\u003C\u002Fstrong>\u003Cstrong>月\u003C\u002Fstrong>\u003Cstrong>18\u003C\u002Fstrong>\u003Cstrong>日\u003C\u002Fstrong> - Mobileye（纳斯达克股票代码：MBLY）宣布，将于CES2026期间举办年度发布会 -“\u003Cstrong>Mobileye Live at CES 2026\u003C\u002Fstrong>”。该发布会将于 2026年1月6日（星期二）太平洋时间下午1:45（北京时间1 月 7 日上午 5:45）举行，Mobileye总裁兼首席执行官Amnon Shashua教授将发表年度主题演讲。\u003C\u002Fp>\n\u003Cp>Shashua教授将阐述Mobileye在实体人工智能（Physical AI）下一阶段的发展愿景，深入解读人工智能关键突破如何持续推动公司技术创新与产品规划的升级。演讲将重点介绍Mobileye高级驾驶辅助与自动驾驶全系列产品的最新技术进展，解析公司推动出行方式变革的核心战略，并前瞻性展望下一代芯片架构发展方向。\u003C\u002Fp>\n\u003Cp>本次发布会还将设有专题对话环节，Shashua教授将与大众汽车自动驾驶出行公司首席执行官Christian Senger进行深度交流，共同探讨双方在自动驾驶规模化落地领域的合作成果。\u003C\u002Fp>\n\u003Cp>本次发布会为邀请制活动，将在拉斯维加斯展会现场举行，并通过Mobileye官方\u003Ca href=\"https:\u002F\u002Fwww.youtube.com\u002Fmobileye\">YouTube频道\u003C\u002Fa>向全球同步直播。\u003C\u002Fp>\n\u003Cp>此外，1月6日至9日展会期间，Mobileye将在拉斯维加斯会展中心（LVCC）西馆设立专属贵宾体验区，接待客户、媒体、行业分析师及合作伙伴。体验区将通过动态演示，全面展示Mobileye全系列产品矩阵、人工智能驱动出行的核心技术，以及EyeQ芯片平台的发展演进。\u003C\u002Fp>\n\u003Cp>\u003Cstrong>\u003Cspan data-contrast=\"auto\">Mobileye Live at CES 2026\u003C\u002Fspan>\u003C\u002Fstrong> \u003Cbr \u002F>日期：2026年1月6日（星期二）\u003Cbr \u002F>时间：太平洋时间下午1:45\u003Cbr \u002F>\u003Cspan data-contrast=\"auto\">点击\u003Ca href=\"https:\u002F\u002Fwww.mobileye.com\u002Fces-2026\u002Fregister\u002Fb15158896608a49589967c544827f6f8\u002F\">此处\u003C\u002Fa>注册。\u003C\u002Fspan>\u003Cspan data-ccp-props=\"{"201341983":0,"335559740":240}\"> \u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan data-contrast=\"auto\">+++\u003C\u002Fspan>\u003Cspan data-ccp-props=\"{"201341983":0,"335559740":240}\"> \u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan data-contrast=\"auto\">Contacts\u003C\u002Fspan>\u003Cspan data-ccp-props=\"{"201341983":0,"335559740":240}\"> \u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan data-contrast=\"auto\">Dan Galves\u003C\u002Fspan> \u003Cbr \u002F>\u003Cspan data-contrast=\"auto\">Investor Relations\u003C\u002Fspan> \u003Cbr \u002F>\u003Ca href=\"mailto:investors@mobileye.com\">\u003Cspan data-contrast=\"auto\">investors@mobileye.com\u003C\u002Fspan>\u003C\u002Fa>\u003Cspan data-ccp-props=\"{"201341983":0,"335559740":240}\"> \u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan data-contrast=\"auto\">Justin Hyde\u003C\u002Fspan> \u003Cbr \u002F>\u003Cspan data-contrast=\"auto\">Media Relations\u003C\u002Fspan> \u003Cbr \u002F>\u003Ca href=\"mailto:justin.hyde@mobileye.com\">\u003Cspan data-contrast=\"auto\">justin.hyde@mobileye.com\u003C\u002Fspan>\u003C\u002Fa>\u003Cspan data-ccp-props=\"{"201341983":0,"335559740":240}\"> \u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cstrong>\u003Cspan data-contrast=\"auto\">About Mobileye\u003C\u002Fspan>\u003C\u002Fstrong>\u003Cspan data-ccp-props=\"{"201341983":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, mapping and integrated software and hardware. Since our founding in 1999, Mobileye has enabled the wide adoption of advanced driver-assistance systems that bolster driving safety, while pioneering such groundbreaking technologies as REM™ crowdsourced mapping, True Redundancy™ sensing, and Responsibility Sensitive Safety™ (RSS). 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 advanced driver-assistance systems. Through 2024, more than 200 million vehicles worldwide have been built with Mobileye’s EyeQ technology inside. 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=54373421&newsitemid=20251210319558&lan=en-US&anchor=https%3A%2F%2Fwww.mobileye.com&index=1&md5=da9421b7be8d0acc52dcbedbf7ed3380\">\u003Cspan data-contrast=\"auto\">https:\u002F\u002Fwww.mobileye.com\u003C\u002Fspan>\u003C\u002Fa>\u003Cspan data-contrast=\"auto\">.\u003C\u002Fspan>\u003Cspan data-ccp-props=\"{"201341983":0,"335557856":16777215,"335559739":360,"335559740":240}\"> \u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan data-contrast=\"auto\">“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\u002Fspan>\u003Cspan data-ccp-props=\"{"201341983":0,"335557856":16777215,"335559739":360,"335559740":240}\"> \u003C\u002Fspan>\u003C\u002Fp>","2025-12-18T08:00:00.000Z",[6],{"id":171,"type":100,"url":172,"title":173,"description":174,"primary_tag":5,"author_name":131,"is_hidden":96,"lang":132,"meta_description":174,"image":175,"img_alt":176,"content":177,"download_doc":136,"download_title":136,"download_btn":136,"webinar_video":136,"thumbnail":136,"is_gated":96,"featured":96,"publish_date":178,"tags":179},301,"prof-amnon-shashua-named-to-time100-ai-list","Amnon Shashua 教授入选《时代》周刊全球百大AI影响力人物","TIME100 AI 评选出全球人工智能领域最具影响力的 100 人。","https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002F74fa93cba8273c6b5416108a4d4bc2f2_1756382949215.jpg","Mobileye 创始人 Amnon Shashua 教授荣登2025年度《时代》周刊全球百大AI影响力人物榜单。 (Credit: Yanai Yechiel)","\u003Cp>Mobileye 创始人、首席执行官兼总裁Amnon Shashua教授近日入选\u003Ca href=\"http:\u002F\u002Ftime.com\u002Ftime100ai\">《时代》周刊全球百大AI影响力人物\u003C\u002Fa>榜单，该榜单旨在表彰全球人工智能（AI）领域最受瞩目的百位杰出代表。Shashua教授因其在驾驶自动化、机器人技术、语言模型与高级推理等人工智能现实应用领域的卓越贡献而获此殊荣。\u003C\u002Fp>\n\u003Cp>作为人工智能研究领域的先行者，Shashua教授持续推动Mobileye高级驾驶辅助系统与高等级驾驶自动化解决方案的商业化落地及发展。Mobileye成立25年来，始终引领并推动人工智能技术的创新与应用，致力于将尖端研究成果转化为挽救生命的汽车技术，其解决方案已部署于全球超过两亿辆汽车。\u003C\u002Fp>\n\u003Cp>驾驶自动化是“物理人工智能”最具前景的应用领域之一，此类智能系统必须能够与现实世界实时交互并带来实际影响。这项技术具有革新交通体系、挽救数百万人生命的潜力，而其实现取决于能否兼顾安全性与可扩展性，这一挑战正是Shashua教授二十余年以来的研究动力。在Shashua教授的领导下，Mobileye为驾驶自动化的实现定义了清晰的技术路径，将先进的人工智能技术与工程精密性相结合，以达到系统所需的安全性和规模化部署能力，并带来变革性影响。\u003C\u002Fp>\n\u003Cp>Shashua教授对系统安全性与透明度的执着追求是他对行业发展产生深远影响的关键所在。2017年，Mobileye 推出\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fpdf\u002F1708.06374\">责任敏感安全模型\u003C\u002Fa>（RSS），为人工智能驱动的高等级驾驶自动化确立了安全定义与保障框架。2024年，Shashua教授与Mobileye首席技术官Shai Shalev-Shwartz教授共同发布\u003Ca href=\"https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Ffiles\u002FSDS_Safety_Architecture.pdf\">《高等级驾驶自动化系统安全架构》\u003C\u002Fa>，提出了设计驾驶自动化系统、评估性能及消除不合理风险的核心原则，进一步完善了该体系。其种种贡献为监管机构、汽车制造商和公众评估驾驶自动化的安全性构建了更为清晰的路线图。\u003C\u002Fp>\n\u003Cp>作为\u003Ca href=\"https:\u002F\u002Fwww.menteebot.com\u002F\">Mentee Robotics\u003C\u002Fa>联合创始人，Shashua教授正在领导开发以人工智能为核心的人形机器人。此类机器人专为现实环境而设计，能够执行家庭与仓储场景中的高级任务。2025年亮相的Mentee原型机以量产为目标，展现出卓越的敏捷性、力量感知与持续运行能力，彰显了物理人工智能在工业与日常生活中的应用潜力。\u003C\u002Fp>\n\u003Cp>\u003Cspan data-ccp-props=\"{}\">此外，Shashua教授还与\u003Ca href=\"https:\u002F\u002Fdoubleai.com\u002F\">AAI Technologies\u003C\u002Fa>联手，力图攻克人工智能的下一个前沿领域：“破解超级智能的密码”。在其领导下，AAI正在开发深度推理与学习的新范式，模拟科学家探索发现的迭代过程，为构建超越人类科学、技术、工程、数学（STEM）专业能力的超级人工智能绘制技术蓝图。\u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>这些成果共同体现了Shashua教授在以下三大关键领域塑造人工智能未来的愿景：致力于更安全的驾驶自动化、构建感知物理世界的智能机器，以及攻克需要专家级推理能力的复杂科学难题。\u003C\u002Fp>\n\u003Cp>《时代》周刊编辑团队通过梳理过去一年内人工智能领域的重大进展，结合行业领袖与专家意见，最终遴选出百位影响力人物。入选的百位领袖、创新者、塑造者与思想者共同影响着人工智能的未来发展。\u003C\u002Fp>\n\u003Cp>\u003Cspan data-contrast=\"auto\">完整榜单请参阅: \u003C\u002Fspan>\u003Ca href=\"http:\u002F\u002Ftime.com\u002Ftime100ai\">\u003Cspan data-contrast=\"none\">time.com\u002Ftime100ai\u003C\u002Fspan>\u003C\u002Fa>\u003C\u002Fp>\n\u003Cp> \u003C\u002Fp>","2025-08-28T07:00:00.000Z",[6],{"id":181,"type":151,"url":182,"title":183,"description":184,"primary_tag":98,"author_name":131,"is_hidden":96,"lang":132,"meta_description":184,"image":185,"img_alt":186,"content":187,"download_doc":136,"download_title":136,"download_btn":136,"webinar_video":136,"thumbnail":136,"is_gated":96,"featured":96,"publish_date":188,"tags":189},287,"mobileyes-ces-2025-showing","Mobileye在CES 2025的亮点回顾","Mobileye在拉斯维加斯展示的最新技术引得众人驻足","https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002Fe8f3cdb7e19232218e35664f2bec3a8c_1737633952001.jpg","Mobileye在CES 2025的精彩表现！","\u003Cp> 尽管CES 2025已圆满落幕，但我们的CES之旅仍令人回味无穷。得益于深入浅出的演讲、引人入胜的展台展示以及关于交通出行和高等级驾驶自动化汽车未来的精彩讨论，Mobileye的出行解决方案在为期四天的展会中无疑成为了焦点。\u003C\u002Fp>\n\u003Cp>我们展出了搭载Mobileye Drive™的两款车型——大众ID Buzz和Holon Mover，以及由多层有机玻璃打造的3D汽车外形互动产品方案，吸引了大量观众驻足观看。与此同时，Mobileye的高管们参与了多场圆桌讨论，分享了对汽车行业关键议题的独到见解。\u003C\u002Fp>\n\u003Cp>欣赏Mobileye在CES 2025的精彩时刻！\u003Cbr \u002F>\u003Ciframe title=\"Mobileye at CES 2025: Highlights\" src=\"https:\u002F\u002Fplayer.vimeo.com\u002Fvideo\u002F1047443316?h=904d935a52&badge=0&autopause=0&player_id=0&app_id=58479\" width=\"600\" height=\"338\" frameborder=\"0\">\u003C\u002Fiframe>\u003C\u002Fp>\n\u003Ch3>Now. Next. Beyond.  \u003C\u002Fh3>\n\u003Cp> Mobileye总裁兼首席执行官Amnon Shashua教授在其一年一度的CES主题演讲“Mobileye: Now. Next. Beyond.”中，分享了他对高等级驾驶自动化行业未来的愿景，探讨了高等级驾驶自动化汽车如何从Demo阶段迅速迈向现实生活的实际应用。\u003Cbr \u002F>\u003Ciframe title=\"Mobileye: Now. Next. Beyond CES 2025 Press Conference with Prof. Amnon Shashua\" src=\"https:\u002F\u002Fplayer.vimeo.com\u002Fvideo\u002F1047296511?h=a4dba090ba&badge=0&autopause=0&player_id=0&app_id=58479\" width=\"600\" height=\"338\" frameborder=\"0\">\u003C\u002Fiframe> \u003C\u002Fp>\n\u003Ch3>引擎盖下的秘密 \u003C\u002Fh3>\n\u003Cp>在我们展台上，高耸的时尚几何结构展示了Mobileye的最新产品，吸引了无数参观者前来了解我们的技术。展台内，除了展示最新的电子控制单元（ECU）套件外，还展示了Mobileye令人印象深刻的成像雷达及其他先进技术。\u003Cbr \u002F>\u003Ciframe title=\"Mobileye at CES 2025 Booth Walkthrough\" src=\"https:\u002F\u002Fplayer.vimeo.com\u002Fvideo\u002F1047295510?h=b1bfab4cd2&badge=0&autopause=0&player_id=0&app_id=58479\" width=\"600\" height=\"338\" frameborder=\"0\">\u003C\u002Fiframe> \u003C\u002Fp>\n\u003Ch3>Mobileye 主舞台\u003C\u002Fh3>\n\u003Cp>“安全”是Mobileye展台演示的核心主题。关于冗余、人工智能驱动的平台，与多方协作的讨论都涉及了提高平均故障间隔时间（MTBF）对安全出行的重要性。\u003C\u002Fp>\n\u003Cp>我们回顾了Mobileye从驾驶辅助系统到完全自动驾驶出租车的演变，探讨了人工智能驱动的高等级驾驶自动化技术路线，并阐释了在我们的系统中如何利用冗余机制提升可靠性。此外，Mobileye执行副总裁Johann (JJ) Jungwirth、大众汽车ADMT高级副总裁Sebastian Lasek和MOIA首席执行官Sascha Meyer还在展台主舞台进行了一场主题为“开启全球高等级驾驶自动化出行”的圆桌讨论。三位高管深入交流了Mobileye与大众汽车集团在无人驾驶服务和大众汽车ID Buzz AD方面的紧密合作，探讨了在不同地区实施项目时所采用的软硬件技术，以及对未来出行蓝图的共同愿景。\u003Cbr \u002F>\u003Ciframe title=\"Unlocking Autonomous Mobility Globally with Volkswagen, MOIA and Mobileye\" src=\"https:\u002F\u002Fplayer.vimeo.com\u002Fvideo\u002F1047297083?h=60431b5f7a&badge=0&autopause=0&player_id=0&app_id=58479\" width=\"600\" height=\"338\" frameborder=\"0\">\u003C\u002Fiframe>\u003C\u002Fp>\n\u003Ch3>高管活动 \u003C\u002Fh3>\n\u003Cp>Mobileye首席技术官Shai Shalev-Shwartz教授参加了一场关于高等级驾驶自动化汽车技术现状的圆桌讨论。在展望高等级驾驶自动化技术的现状和未来时，Shalev-Shwartz教授表示，未来几年内，私家车搭载的“驾驶员视觉脱离”驾驶系统，以及完全自动驾驶出租车服务成本的降低，极有可能改变我们目前对出行的看法。他认为“这是一个具有革命意义的时刻，创新产品将改变人们对通勤的认知”。\u003Cbr \u002F>\u003Ciframe title=\"CTA & PAVE Autonomous Vehicle Roundtable at CES 2025\" src=\"https:\u002F\u002Fplayer.vimeo.com\u002Fvideo\u002F1047299158?h=32a75cd4b6&badge=0&autopause=0&player_id=0&app_id=58479\" width=\"600\" height=\"338\" frameborder=\"0\">\u003C\u002Fiframe>\u003Cbr \u002F>Mobileye高等级驾驶自动化业务执行副总裁Johann (JJ) Jungwirth与行业领袖共同探讨了高等级驾驶自动化汽车融入社区、赢得消费者信任以及利用人工智能提升高等级驾驶自动化技术等话题。\u003Cbr \u002F>\u003Ciframe title=\"JJ at Autonomous Vehicles - The Future is Finally Here Panel (CES 2025)\" src=\"https:\u002F\u002Fplayer.vimeo.com\u002Fvideo\u002F1049234229?h=ed2b30df34&badge=0&autopause=0&player_id=0&app_id=58479\" width=\"600\" height=\"338\" frameborder=\"0\">\u003C\u002Fiframe>\u003C\u002Fp>\n\u003Ch3>展会互动\u003C\u002Fh3>\n\u003Cp>我们十分高兴与同样对未来出行充满热情的志同道合者们交流。\u003Cbr \u002F>\u003Ciframe title=\"Man On The Street at CES 2025\" src=\"https:\u002F\u002Fplayer.vimeo.com\u002Fvideo\u002F1051080523?h=a875a35f16&badge=0&autopause=0&player_id=0&app_id=58479\" width=\"600\" height=\"338\" frameborder=\"0\">\u003C\u002Fiframe>\u003C\u002Fp>","2025-01-23T08:00:00.000Z",[64,99,6,21,84],{"id":191,"type":151,"url":192,"title":193,"description":194,"primary_tag":98,"author_name":131,"is_hidden":96,"lang":132,"meta_description":194,"image":195,"img_alt":196,"content":197,"download_doc":136,"download_title":136,"download_btn":136,"webinar_video":136,"thumbnail":136,"is_gated":96,"featured":96,"publish_date":198,"tags":199},285,"prof-amnon-shashua-at-ces-2025","CES 2025: Amnon Shashua 教授畅谈未来出行变革","在CES 2025主题演讲 “Mobileye: Now. Next. Beyond.”中，Mobileye 总裁兼首席执行官Amnon Shashua教授向听众提出了一个核心问题 – 颠覆出行的关键是什么？","https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002F7d850b79bb7c52216b36173a9f6bcfb3_1736363040927.jpg","通过融合尖端技术与智能路网专长，兼顾成本效益，Mobileye正在塑造未来出行蓝图","\u003Cp>这是Mobileye创始人兼首席执行官Amnon Shashua教授连续第十年分享他对驾驶自动化行业未来的愿景，探讨了在人工智能的突破性进展推动下，高等级驾驶自动化汽车迅速从实验阶段走向日常现实中。尽管完全自动驾驶出租车已经在北美和欧洲开始投入使用，但Mobileye认为高等级驾驶自动化技术还将在消费级出行领域引发一场革命。\u003C\u002Fp>\n\u003Cp>点击此处观看主题演讲。\u003C\u002Fp>\n\u003Cp>\u003Ciframe title=\"YouTube video player\" src=\"https:\u002F\u002Fwww.youtube.com\u002Fembed\u002FMQnkqXoMEOc?si=VayQlJneUY5nIrwU\" width=\"560\" height=\"315\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\">\u003C\u002Fiframe>\u003C\u002Fp>\n\u003Ch3>精确度 vs. 召回率\u003C\u002Fh3>\n\u003Cp>Shashua教授提到了实现可扩展的完全自动驾驶所需的两个关键维度：召回率（可用性）和精确度（安全性）。召回率可确保系统处理多样化的场景、地理环境和条件，而精确度则通过最大限度地减少错误来确保安全，更侧重于安全性。\u003C\u002Fp>\n\u003Cp>\u003Cimg src=\"https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002F3c584de8c954aee7d8eadcd75d1953b5_1736363079618.jpg\" alt=\"Precision vs. Recall\" width=\"600\" height=\"389\" \u002F>\u003C\u002Fp>\n\u003Ch3>创建安全架构\u003C\u002Fh3>\n\u003Cp>在谈到Mobileye的安全架构及其如何应对不合理风险时，Shashua教授解释道，Mobileye的方法可以处理四种错误类型。他还详细阐述了Mobileye系统如何通过一系列软件和硬件冗余设计来减少这些错误的发生。随后，他介绍了Mobileye的创新融合方法 – 主系统-监护系统-备用系统（\u003Cstrong>Primary-Guardian-Fallback (PGF)\u003C\u002Fstrong>\u003Cstrong>）\u003C\u002Fstrong>，这种分层决策模型将多数法则扩展至非二元决策，从而增强系统在复杂场景下的可靠性和适应能力。\u003C\u002Fp>\n\u003Cp>\u003Cimg src=\"https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002Fced2c26cc06d0aeed8104fdf72d06dd3_1736363132926.jpg\" alt=\"\" width=\"600\" height=\"389\" \u002F>\u003C\u002Fp>\n\u003Ch3>工程创新：开辟全新视觉感知方式\u003C\u002Fh3>\n\u003Cp>Shashua教授还介绍了用于分层视觉的独特感知技术。这种先进的处理技术由Mobileye最新的系统集成芯片EyeQ6™驱动，可生成3D感知，从而构建一种冗余且可靠的环境理解。\u003C\u002Fp>\n\u003Cp>Mobileye在精度方面不断取得突破，尤其是在成像雷达技术的应用上，这项技术已经取得了显著进展。预计于2026年投产的成像雷达技术可提供高分辨率，其感知能力能够弥补摄像头存在的盲区和感知弱点。在Mobileye Drive™和Mobileye Chauffeur™中，这项技术发挥着关键作用，受到了客户的广泛关注。\u003C\u002Fp>\n\u003Ch3>展望未来\u003C\u002Fh3>\n\u003Cp>最后，他还谈及了产品从演示到规模化量产所面临的挑战，强调可购性是普及先进系统的关键。通过融合尖端技术与智能路网专长，兼顾成本效益，Mobileye正在塑造未来出行蓝图。\u003C\u002Fp>\n\u003Cp> \u003C\u002Fp>","2025-01-08T08:00:00.000Z",[6,21,78,64,99],{"id":201,"type":92,"url":202,"title":203,"description":204,"primary_tag":5,"author_name":131,"is_hidden":96,"lang":132,"meta_description":204,"image":205,"img_alt":206,"content":207,"download_doc":136,"download_title":136,"download_btn":136,"webinar_video":136,"thumbnail":136,"is_gated":96,"featured":96,"publish_date":208,"tags":209},281,"mobileye-at-ces-2025","Mobileye 闪耀 CES 2025","在展会期间，您可访问我们的新闻工具库，获取最新的活动、新闻、多媒体内容等信息。","https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002F1833572c15ccfb76fcd471355eedb899_1734044782739.png","Amnon Shashua 教授将于 1 月 7 日上午 11 点举行年度新闻发布会，拉开 Mobileye CES 2025 序幕。","\u003Cp>\u003Cstrong>Mobileye 亮相 CES 2025\u003C\u002Fstrong>\u003C\u002Fp>\n\u003Cp>Mobileye 将加入并出席 CES 2025，展示推动道路安全与自动驾驶出行的创新技术与解决方案。从高级驾驶辅助系统（ADAS）到完全自动驾驶汽车（AV），了解我们如何引领智能驾驶的出行未来。\u003C\u002Fp>\n\u003Cp>欲了解更多关于新闻发布会、展位演示和重点活动的信息，请访问 \u003Ca href=\"https:\u002F\u002Fwww.mobileye.com\u002Fces-2025\u002F\">mobileye.com\u002Fces-2025\u003C\u002Fa>。\u003C\u002Fp>\n\u003Cp>\u003Cstrong>新闻\u003C\u002Fstrong>\u003C\u002Fp>\n\u003Cp>\u003Cstrong>\u003Ca href=\"https:\u002F\u002Fwww.mobileye.com\u002Fnews\u002Fmobileye-announces-ces-2025-press-conference\u002F\" target=\"_blank\" rel=\"noopener\">Mobileye CES 2025 新闻发布会\u003C\u002Fa>\u003C\u002Fstrong>\u003C\u002Fp>\n\u003Cp>在展会开幕之际，Mobileye 将举行年度 CES 新闻发布会“Mobileye: Now. Next. Beyond.”，创始人，总裁兼 CEO Amnon Shashua 教授将重点介绍多个平台和出行应用领域的创新解决方案，并分享我们对 2025 年及出行未来的愿景。\u003C\u002Fp>\n\u003Cp>点击\u003Ca href=\"https:\u002F\u002Fwww.mobileye.com\u002Fnews\u002Fmobileye-announces-ces-2025-press-conference\u002F\">这里\u003C\u002Fa>，了解更多关于我们的新闻发布会和 CES 2025 活动的咨询。\u003C\u002Fp>\n\u003Cp>\u003Cstrong>Mobileye 技术与解决方案\u003C\u002Fstrong>\u003C\u002Fp>\n\u003Cp>[**]gallery:mobileye-technology-and-solutions[**]\u003C\u002Fp>\n\u003Cp>\u003Cstrong>Mobileye SuperVision \u003C\u002Fstrong>\u003C\u002Fp>\n\u003Cp>[**]gallery:mobileyes-advanced-platforms-in-the-drivers-seat[**]\u003C\u002Fp>\n\u003Cp>\u003Cstrong>由 Mobileye 所驱动\u003C\u002Fstrong>\u003C\u002Fp>\n\u003Cp>[**]gallery:driven-by-mobileye[**]\u003C\u002Fp>\n\u003Cp>\u003Cstrong>Amnon Shashua 教授\u003C\u002Fstrong>\u003C\u002Fp>\n\u003Cp>[**]gallery:professor-amnon-shashua[**]\u003C\u002Fp>\n\u003Cp>\u003Cstrong>图例\u003C\u002Fstrong>\u003C\u002Fp>\n\u003Cp>[**]gallery:infographics[**]\u003C\u002Fp>\n\u003Cp>\u003Cstrong>视频\u003C\u002Fstrong>\u003C\u002Fp>\n\u003Cp>[**]vimeo-press:1031870114[**]\u003C\u002Fp>\n\u003Cp> \u003C\u002Fp>\n\u003Cp> \u003C\u002Fp>\n\u003Cp> \u003C\u002Fp>","2024-12-08T08:00:00.000Z",[6,21,13,27],{"id":211,"type":151,"url":212,"title":213,"description":214,"primary_tag":5,"author_name":131,"is_hidden":96,"lang":132,"meta_description":214,"image":215,"img_alt":216,"content":217,"download_doc":136,"download_title":136,"download_btn":136,"webinar_video":136,"thumbnail":136,"is_gated":96,"featured":96,"publish_date":218,"tags":219},280,"driving-ai","Mobileye Driving AI Day 活动的5大要点","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>","2024-11-27T08:00:00.000Z",[6,21,27],{"id":221,"type":151,"url":222,"title":223,"description":224,"primary_tag":113,"author_name":225,"is_hidden":96,"lang":132,"meta_description":224,"image":226,"img_alt":227,"content":228,"download_doc":136,"download_title":136,"download_btn":136,"webinar_video":136,"thumbnail":136,"is_gated":96,"featured":96,"publish_date":229,"tags":230},279,"the-mobileye-safety-methodology-for-fully-autonomous-driving","Mobileye 高等级驾驶自动化系统安全架构方法论","我们的高等级驾驶自动化系统安全架构方法论以基本原则和行业标准为基础，旨在降低不同类型的风险。","Prof. Shai Shalev-Shwartz and Prof. Amnon Shashua","https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002F114f82911350ead2e8d23f09ea3935a9_1732626715488.jpg","图片来源：大众汽车集团","\u003Cp>就自动驾驶而言，怎样才算足够安全？\u003C\u002Fp>\n\u003Cp>迄今为止，即使全球已有数以千计的自动驾驶汽车上路行驶，且自动驾驶汽车的商业化也指日可待，自动驾驶的安全问题仍然悬而未决。目前的一个关键基准指标是平均故障间隔时间（MTBF）- 从根本上说，该指标可确定在事故或伤害发生的频率方面，自动驾驶系统的水平是否优于普通人类？这一指标极易掌握和测量，但仔细研究就会发现其局限性——违规和粗心驾驶严重影响人类的统计数据，而自动驾驶系统不可能酒驾，也不会在开车时向他人发短信。更重要的是，衡量人类驾驶的标准不仅是事故，而且应该包括避免鲁莽行为 – 即：对自己和其他道路使用者承担谨慎驾驶的责任，避免不合理的风险。因此，我们认为，虽然足够高的平均故障间隔时间至关重要，但它并不足以证明自动驾驶系统的安全性。\u003C\u002Fp>\n\u003Cp>经过多年的突破性发展，如今，Mobileye及整个行业对完全自动驾驶的安全要求有了更清晰的认识。在发布的一篇新论文中，我们介绍了一个旨在大规模部署安全自动驾驶系统的框架。该框架基于两大关键原则：\u003C\u002Fp>\n\u003Cul>\n\u003Cli>\n\u003Cp>系统的总体平均故障间隔时间应至少与针对人类的数据一样好。\u003C\u002Fp>\n\u003C\u002Fli>\n\u003Cli>\n\u003Cp>系统应消除不合理风险，同时使自动驾驶系统提供商明确界定合理与不合理风险的边界。\u003C\u002Fp>\n \u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>第一项要求使用平均故障间隔时间作为指标，解决了底线问题 —— 自动驾驶车辆所造成的伤害不得超过人类驾驶车辆所造成的伤害。然而，这还不够，因此我们补充了第二项要求 —— 消除不合理风险。该要求使用调整后的平均故障间隔时间指标，其中包含了透明度、问责制和遵守严格安全标准等原则。\u003C\u002Fp>\n\u003Cp>第二项要求的难点在于：如何严格界定“合理”与“不合理”风险之间的边界，以及如何制定消除不合理风险的方法论。有关我们具体实施的技术细节，请阅读我们的\u003Ca href=\"https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Ffiles\u002FSDS_Safety_Architecture.pdf\">论文\u003C\u002Fa>。\u003C\u002Fp>\n\u003Cp> \u003C\u002Fp>","2024-11-26T08:00:00.000Z",[6,21,114],{"id":232,"type":151,"url":233,"title":234,"description":235,"primary_tag":5,"author_name":236,"is_hidden":96,"lang":132,"meta_description":235,"image":237,"img_alt":238,"content":239,"download_doc":136,"download_title":136,"download_btn":136,"webinar_video":136,"thumbnail":136,"is_gated":96,"featured":96,"publish_date":240,"tags":241},261,"autonomous-decisions-the-bias-variance-tradeoff-in-self-driving-technology","自主决策：自动驾驶技术中的偏差-方差权衡","大语言模型和自动驾驶应用中，单一人工智能系统与复合人工智能系统的比较","Prof. Amnon Shashua and Prof. Shai Shalev-Shwartz","https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002Ff8167f28d4d800bd54b3e04c9fda5a5d_1715717822589.jpg","Monolithic versus compound AI systems in LLMs and autonomous driving.","\u003Cp>Back in November 2022, the release of ChatGPT garnered widespread attention, not only for its versatility but also for its end-to-end design. This design involved a single foundational component, the GPT 3.5 large language model, which was enhanced through both supervised learning and reinforcement learning from human feedback to support conversational tasks. This holistic approach to AI was highlighted again with the launch of Tesla’s latest FSD system, described as an end-to-end neural network that processes visual data directly “from photons to driving control decisions,\" without intermediary steps or “glue code.\" \u003C\u002Fp>\n\u003Cp>While AI models continue to evolve, the latest generation of ChatGPT has moved away from the monolithic E2E approach. Consider the following example of a conversation with ChatGPT:\u003C\u002Fp>\n\u003Cp>\u003Cimg src=\"https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002Fchatgpt_blog2.png\" alt=\"\" width=\"746\" height=\"448\" \u002F>\u003C\u002Fp>\n\u003Cp>When asked to compute \"what is 3456 * 3678?,\" the system first translates the question into a short Python script to perform the calculation, and then formats the output of the script into a coherent natural language text. This demonstrates that ChatGPT does not rely on a single, unified process. Instead, it integrates multiple subsystems—including a robust deep learning model (GPT LLM) and separately coded modules. Each subsystem has its defined role, interfaces, and development strategies, all engineered by humans. Additionally, 'glue code' is employed to facilitate communication between these subsystems. This architecture is referred to as “\u003Ca href=\"https:\u002F\u002Fbair.berkeley.edu\u002Fblog\u002F2024\u002F02\u002F18\u002Fcompound-ai-systems\u002F\" target=\"_blank\" rel=\"noopener\">Compound AI Systems\u003C\u002Fa>\u003Ca href=\"..\u002F..\u002Fblog\u002Fautonomous-decisions-the-bias-variance-tradeoff-in-self-driving-technology\u002F#_edn1\">\u003Csup>1\u003C\u002Fsup>\u003C\u002Fa>” (CAIS).\u003C\u002Fp>\n\u003Cp>Before we proceed, it is crucial to dispel misconceptions about which system architecture is \"new\" or \"traditional\". Despite the hype, the E2E approach in autonomous driving is not a novel concept; it dates back to \u003Ca href=\"https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F1988\u002Ffile\u002F812b4ba287f5ee0bc9d43bbf5bbe87fb-Paper.pdf\" target=\"_blank\" rel=\"noopener\">the Alvinn project (Pomeraleau, 1989)\u003C\u002Fa>\u003Ca href=\"..\u002F..\u002Fblog\u002Fautonomous-decisions-the-bias-variance-tradeoff-in-self-driving-technology\u002F#_edn2\">\u003Csup>2\u003C\u002Fsup>\u003C\u002Fa>. The CAIS approach is also not new, but as we have shown above, it has been adopted by the most recent versions of ChatGPT.\u003C\u002Fp>\n\u003Cp>\u003Cem>This blog aims to explore the nuances between E2E systems and CAIS, by drawing a deep connection to the \u003C\u002Fem>\u003Ca href=\"https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FBias–variance_tradeoff\" target=\"_blank\" rel=\"noopener\">\u003Cem>bias-variance tradeoff\u003C\u002Fem>\u003C\u002Fa>\u003Ca href=\"..\u002F..\u002Fblog\u002Fautonomous-decisions-the-bias-variance-tradeoff-in-self-driving-technology\u002F#_edn3\">\u003Cem>\u003Csup>3\u003C\u002Fsup>\u003C\u002Fem>\u003C\u002Fa>\u003Cem> in machine learning and statistics. For concreteness, we focus the discussion on self-driving systems, but the connection is applicable more generally to the design of any AI-based system.\u003C\u002Fem>\u003C\u002Fp>\n\u003Ch3>\u003Cstrong>The bias-variance tradeoff\u003C\u002Fstrong>\u003C\u002Fh3>\n\u003Cp>The grand question, driving any school of thought for building a data-driven system, is the \"bias\u002Fvariance\" tradeoff. Bias, also known as “approximation error,\" means that our learning system cannot reflect the full richness of reality. Variance, also known as “generalization error,\" means that our learning system overfits to the observed data, and fails to generalize to unseen examples.\u003C\u002Fp>\n\u003Cp>The total error of the learned model is the sum of the approximation and generalization errors, so in order to reach a sufficiently small error, we need to delicately control both. There is a tradeoff between the two terms since we can decrease the generalization error by restricting the learned model to come from a specific family of models, but this might introduce a bias if the chosen family of models cannot reflect the full richness of reality.\u003C\u002Fp>\n\u003Cp>Based on this background, we can formalize the two approaches for building a self-driving system. A CAIS, or an “engineered system,\" deliberately puts architectural restrictions on the self-driving system for the sake of reducing the generalization error. This introduces some bias. For example, going from \"photons\" to a “sensing state,\" which is a model of reality surrounding the host vehicle—location and measurements of road users, roadway structures, drivable paths, obstacles and so forth—and from there to control decisions introduces bias. The reason for this bias is that the sensing state might not be rich enough to reflect reality to its fullest and therefore the capacity of the system is constrained. In contrast, an E2E network skipping the sensing state step and instead mapping incoming videos directly to vehicle control decisions would not suffer from this bias of the “sensing state abstraction.\" On the other hand, the E2E approach will have a higher generalization error, and the approach advocated by the E2E proponents is to compensate for this error with huge amounts of data, which in turn necessitates a huge investment in compute, storage, and data engines.\u003C\u002Fp>\n\u003Cp>To recap, the E2E approach – in the simplistic form being communicated to the public – is to define a system with zero bias while incrementally reducing variance through volumes of data for training the system with the purpose of gradually eliminating all “corner cases\". In an engineered approach, on the other hand, the system starts with a built-in bias due to the abstraction of the sensing state and driving policy while (further) reducing variance through data fed into separate subsystems with a high-level fusion glue-code. The amount of data required for each subsystem is exponentially smaller than the amount of data required for a single monolithic system.\u003C\u002Fp>\n\u003Ch3>\u003Cstrong>The devil is in the AI details\u003C\u002Fstrong>\u003C\u002Fh3>\n\u003Cp>The story, however, is more delicate than the above dichotomy. Starting from the bias element, it’s not that E2E systems will have zero bias—since the neural network resides on an on-board computer in the car, its size is constrained by the available compute and memory. Therefore, the limited compute available in a car introduces bias. In addition, the approximation error of a well-engineered approach is not necessarily excessively large for several reasons. For one, Waymo is clear evidence that the bias of an engineered system is sufficiently small for building a safe autonomous car. Moreover, in a well-engineered system we can add a subsystem that skips some of the abstractions (e.g., the sensing state abstraction) and thus further reduce the bias in the overall system.\u003C\u002Fp>\n\u003Cp>The variance element is also more nuanced. In an \"engineered\" system, the variance is reduced through abstractions (such as sensing state) as well as through the high-level fusion of multiple subsystems. In a pure E2E system, the variance should be reduced only through more and more data. But this process of reducing variance through a data pipeline deserves more scrutiny. Take the notion of mean time between failures (MTBF) and let's assume that failures are measured by critical interventions. Let's take MTBF as a measure of readiness of a self-driving vehicle to operate in an \"eyes-off\" manner. In Mobileye's engagement with car makers the MTBF target is 10\u003Csup>7\u003C\u002Fsup> hours of driving. Just for reference, \u003Ca href=\"https:\u002F\u002Fwww.teslafsdtracker.com\u002Fhome\" target=\"_blank\" rel=\"noopener\">public data\u003C\u002Fa>\u003Ca href=\"..\u002F..\u002Fblog\u002Fautonomous-decisions-the-bias-variance-tradeoff-in-self-driving-technology\u002F#_edn4\">\u003Csup>4\u003C\u002Fsup>\u003C\u002Fa> on Tesla's recent V12.3.6 version of FSD stands around 300 miles per critical intervention which amounts to an MTBF of roughly 10 hours – which is 6 orders of magnitude away from the target MTBF. Let's assume that somehow the MTBF has reached 10\u003Csup>6\u003C\u002Fsup> hours and we wish merely to improve it by one order of magnitude in order to reach 10\u003Csup>7\u003C\u002Fsup> hours. How much data would we need to collect? This is a question about the nature of the long tail of a distribution. To make things concrete, assume we have 1 million vehicles on the road driving one hour per day and the data in question is event-driven—i.e., when an intervention of the human driver occurs then a recording of some time around the event is being made and sent to the car maker for further training. An intervention event represents a \"corner case\" and the question of the long tail is how those corner cases are distributed. Consider the following scenario where B is the set of “bad” corner cases. An example of a heavy tail distribution is when B = {b_1,...,b_{1000}} and the probability of b_i to occur is 10\u003Csup>-9\u003C\u002Fsup> for every i. Even if we assume that when a corner case is discovered then we can somehow retrain the network and fix that corner case, without creating any new corner cases, we must knock off around 900 corner cases so that P(B) = 10\u003Csup>-7\u003C\u002Fsup>. Because the MTBF is 10\u003Csup>6\u003C\u002Fsup> then we encounter one corner case per day. It follows that we will need around 3 years to get this done. The point here is that no one knows how the long tail is structured - we gave one possible long tail scenario but in reality it could be worse or it could be better. As mentioned previously, while there is a precedent that the bias of an engineered system is sufficient for building a safe autonomous car (e.g. Waymo), there is still no precedent that reducing variance solely by a recurring data engine is sufficient for building a safe autonomous car.\u003C\u002Fp>\n\u003Cp>It follows that to solely double-down on a data pipeline might be too risky. What else can be done in the E2E approach for reducing variance? To lead into it lets ask ourselves:\u003C\u002Fp>\n\u003Cp>\u003Cbr \u002F>  (i) Why does Tesla FSD have a sensing state in their display? The idea of an E2E system is you go from \"photons to control\" while skipping the need to build a sensing state. Are they doing that solely for the purpose of notifying the driver what the system “sees?\" Or does it have a more tacit purpose?\u003C\u002Fp>\n\u003Cp>\u003Cbr \u002F>  (ii) Why has Tesla \u003Ca href=\"https:\u002F\u002Fwww.theverge.com\u002F2024\u002F5\u002F7\u002F24151497\u002Ftesla-lidar-bought-luminar-elon-musk-sensor-autonomous\" target=\"_blank\" rel=\"noopener\">purchased 2,000 lidars\u003C\u002Fa>\u003Ca href=\"..\u002F..\u002Fblog\u002Fautonomous-decisions-the-bias-variance-tradeoff-in-self-driving-technology\u002F#_edn5\">\u003Csup>5\u003C\u002Fsup>\u003C\u002Fa> from Luminar? Presumably for creating ground truth (GT) data for a supervised training. But why?\u003C\u002Fp>\n\u003Cp>The two riddles are of course related.\u003C\u002Fp>\n\u003Cp>Imagine an E2E network comprising of a backbone and two heads - one for outputting vehicle control and the other for outputting the sensing state. Such a network is still technically E2E (from photons to control) but also has a branch for sensing state. The sensing state branch needs to be trained in a supervised manner from GT data, hence the need for 2,000 lidars. The real question is whether the GT data can be created automatically without manual labeling. The answer is definitely yes because Mobileye does that. We have an \"auto-GT\" pipeline for training for sensing state. And, the reason why you would want a branch outputting the sensing state is not merely for displaying the sensing state to the driver. The real purpose of the sensing state branch is to reduce the variance (and more importantly the sample complexity of the system which is the amount of data needed for training) of the system through the \"multi-tasking\" principle. We addressed this \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fpdf\u002F1604.06915\" target=\"_blank\" rel=\"noopener\">back in 2016\u003C\u002Fa>\u003Ca href=\"..\u002F..\u002Fblog\u002Fautonomous-decisions-the-bias-variance-tradeoff-in-self-driving-technology\u002F#_edn6\">\u003Csup>6\u003C\u002Fsup>\u003C\u002Fa> and gave as an example an agricultural vehicle on the side of the road. The probability of observing a rare vehicle type somewhere in the image is much higher than the probability of observing such a vehicle immediately in front of us. Therefore, without a sensing state head (that detects the rare vehicle type on the shoulder even if it is not affecting the control of the host vehicle) one would need much more data in order to see such a vehicle immediately in front us, which affects the control of the host vehicle. What this comes to show is that the sensing state abstraction is important as it hints to the neural network that a good approach for giving correct control commands may need to understand the concept of vehicles and to detect all of the vehicles around us. Importantly, this abstraction is learned through supervised learning, using GT data which is created automatically (by a well-engineered offline system).\u003C\u002Fp>\n\u003Cp>Another component of the popular E2E narrative is \"no glue-code\" in the system. No glue-code means no bugs being entered by careless engineers. But this too is a misconception. There is glue-code – not in the neural network but in the process of preparing the data for the E2E training. One clear example is the automatic creation of GT data. Elon Musk gave such an example – that of human drivers not respecting stop signs as they should and instead perform a \u003Ca href=\"https:\u002F\u002Fwww.teslarati.com\u002Ftesla-rolling-stop-markey-blumenthal-letter-elon-musk\u002F\" target=\"_blank\" rel=\"noopener\">rolling stop\u003C\u002Fa>\u003Ca href=\"..\u002F..\u002Fblog\u002Fautonomous-decisions-the-bias-variance-tradeoff-in-self-driving-technology\u002F#_edn7\">\u003Csup>7\u003C\u002Fsup>\u003C\u002Fa>. The rolling stop events had to be taken out of the training data so that the E2E system would not adopt bad behavior (as it is supposed to imitate humans). In other words, the glue-code (and bugs) are shifting from the system code to data curation code. Actually, it may be easier to detect bugs in system code (at least there are existing methodologies for that) than to detect bugs in data curation.\u003C\u002Fp>\n\u003Ch3>\u003Cstrong>Don’t get into the way of analytic solutions\u003C\u002Fstrong>\u003C\u002Fh3>\n\u003Cp>Finally, we would like to point out that when we have an analytical solution for a problem, it is certainly not better to use a purely E2E machine learning method. For example, the long multiplication exercise mentioned at the start of this blog highlights the fact that ChatGPT+, rightfully so, uses a good old calculator for the task and does not attempt to \"learn\" how to do long multiplication from a massive amount of data. In the driving policy stack (determining the actions the host vehicle should do and outputting the vehicle control commands) there are numerous analytical calculations. Knowing when to replace \"learning\" with analytical calculations is crucial for variance reduction but also for transparency, explainability and \u003Ca href=\"https:\u002F\u002Fwww.mobileye.com\u002Fopinion\u002Fmobileye-dxp-as-a-novel-approach\u002F\">tuning\u003C\u002Fa>\u003Ca href=\"..\u002F..\u002Fblog\u002Fautonomous-decisions-the-bias-variance-tradeoff-in-self-driving-technology\u002F#_edn8\">\u003Csup>8\u003C\u002Fsup>\u003C\u002Fa> (imitating humans is somewhat problematic because many of them are not good drivers).\u003C\u002Fp>\n\u003Ch3>\u003Cstrong>Final words\u003C\u002Fstrong>\u003C\u002Fh3>\n\u003Cp>AI is progressing at a remarkable pace. The revolution of foundation models of the like of ChatGPT began as a monolithic E2E model (back in 2022) whereas today it evolved into \u003Ca href=\"https:\u002F\u002Fopenai.com\u002Fchatgpt\u002Fpricing\" target=\"_blank\" rel=\"noopener\">ChatGPT+\u003C\u002Fa>\u003Ca href=\"..\u002F..\u002Fblog\u002Fautonomous-decisions-the-bias-variance-tradeoff-in-self-driving-technology\u002F#_edn9\">\u003Csup>9\u003C\u002Fsup>\u003C\u002Fa> which represents an engineered solution built on top of AI components including the base LLM, plugins for retrieval, code interpreter and image generation tools. There is much to be said about the claims that the future of AI is \u003Ca href=\"https:\u002F\u002Fbair.berkeley.edu\u002Fblog\u002F2024\u002F02\u002F18\u002Fcompound-ai-systems\u002F\" target=\"_blank\" rel=\"noopener\">shifting\u003C\u002Fa>\u003Ca href=\"..\u002F..\u002Fblog\u002Fautonomous-decisions-the-bias-variance-tradeoff-in-self-driving-technology\u002F#_edn10\">\u003Csup>10\u003C\u002Fsup>\u003C\u002Fa> from monolithic models to compound AI systems. We believe that this trend is even more important given the extremely high accuracy requirement for, and safety-critical aspect of, autonomous driving.\u003C\u002Fp>\n\u003Cp> \u003C\u002Fp>\n\u003Cp>\u003Cu>References:\u003C\u002Fu>\u003C\u002Fp>\n\u003Cp style=\"font-size: 1.3rem; margin-top: 1.4rem;\">\u003Ca id=\"_edn1\" href=\"#_ednref1\" name=\"_edn1\">\u003C\u002Fa>\u003Csup class=\"ref\">1\u003C\u002Fsup> Compound AI Systems\u003Cbr \u002F>\u003Ca href=\"https:\u002F\u002Fbair.berkeley.edu\u002Fblog\u002F2024\u002F02\u002F18\u002Fcompound-ai-systems\u002F\">https:\u002F\u002Fbair.berkeley.edu\u002Fblog\u002F2024\u002F02\u002F18\u002Fcompound-ai-systems\u002F\u003C\u002Fa>\u003C\u002Fp>\n\u003Cp style=\"font-size: 1.3rem; margin-top: 1.4rem;\">\u003Ca id=\"_edn2\" href=\"#_ednref2\" name=\"_edn2\">\u003C\u002Fa>\u003Csup class=\"ref\">2\u003C\u002Fsup> the Alvinn project (Pomeraleau, 1989)\u003Cbr \u002F>\u003Ca style=\"word-wrap: break-word;\" href=\"https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F1988\u002Ffile\u002F812b4ba287f5ee0bc9d43bbf5bbe87fb-Paper.pdf\">https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F1988\u002Ffile\u002F812b4ba287f5ee0bc9d43bbf5bbe87fb-Paper.pdf\u003C\u002Fa>\u003C\u002Fp>\n\u003Cp style=\"font-size: 1.3rem; margin-top: 1.4rem;\">\u003Ca id=\"_edn3\" href=\"#_ednref3\" name=\"_edn3\">\u003C\u002Fa>\u003Csup class=\"ref\">3\u003C\u002Fsup> Bias-variance tradeoff\u003Cbr \u002F>\u003Ca href=\"https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FBias–variance_tradeoff\">https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FBias–variance_tradeoff\u003C\u002Fa>\u003C\u002Fp>\n\u003Cp style=\"font-size: 1.3rem; margin-top: 1.4rem;\">\u003Ca id=\"_edn4\" href=\"#_ednref4\" name=\"_edn4\">\u003C\u002Fa>\u003Csup class=\"ref\">4\u003C\u002Fsup> Public data on Tesla's recent V12.3.6 version of FSD\u003Cbr \u002F>\u003Ca href=\"https:\u002F\u002Fwww.teslafsdtracker.com\u002Fhome\">https:\u002F\u002Fwww.teslafsdtracker.com\u002Fhome\u003C\u002Fa>\u003C\u002Fp>\n\u003Cp style=\"font-size: 1.3rem; margin-top: 1.4rem;\">\u003Ca id=\"_edn5\" href=\"#_ednref5\" name=\"_edn5\">\u003C\u002Fa>\u003Csup class=\"ref\">5\u003C\u002Fsup> Tesla purchased 2000 Lidars from Luminar\u003Cbr \u002F>\u003Ca href=\"https:\u002F\u002Fwww.theverge.com\u002F2024\u002F5\u002F7\u002F24151497\u002Ftesla-lidar-bought-luminar-elon-musk-sensor-autonomous\">https:\u002F\u002Fwww.theverge.com\u002F2024\u002F5\u002F7\u002F24151497\u002Ftesla-lidar-bought-luminar-elon-musk-sensor-autonomous\u003C\u002Fa>\u003C\u002Fp>\n\u003Cp style=\"font-size: 1.3rem; margin-top: 1.4rem;\">\u003Ca id=\"_edn6\" href=\"#_ednref6\" name=\"_edn6\">\u003C\u002Fa>\u003Csup class=\"ref\">6\u003C\u002Fsup> On the Sample Complexity of End-to-end Training vs. Semantic Abstraction Training \u003Cbr \u002F>\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fpdf\u002F1604.06915\">https:\u002F\u002Farxiv.org\u002Fpdf\u002F1604.06915\u003C\u002Fa>\u003C\u002Fp>\n\u003Cp style=\"font-size: 1.3rem; margin-top: 1.4rem;\">\u003Ca id=\"_edn7\" href=\"#_ednref7\" name=\"_edn7\">\u003C\u002Fa>\u003Csup class=\"ref\">7\u003C\u002Fsup> Human drivers not respecting stop signs as they should and instead perform a rolling stop\u003Cbr \u002F>\u003Ca href=\"https:\u002F\u002Fwww.teslarati.com\u002Ftesla-rolling-stop-markey-blumenthal-letter-elon-musk\u002F\">https:\u002F\u002Fwww.teslarati.com\u002Ftesla-rolling-stop-markey-blumenthal-letter-elon-musk\u002F\u003C\u002Fa>\u003C\u002Fp>\n\u003Cp style=\"font-size: 1.3rem; margin-top: 1.4rem;\">\u003Ca id=\"_edn8\" href=\"#_ednref8\" name=\"_edn8\">\u003C\u002Fa>\u003Csup class=\"ref\">8\u003C\u002Fsup> Mobileye DXP as a novel approach \u003Cbr \u002F>\u003Ca href=\"https:\u002F\u002Fwww.mobileye.com\u002Fopinion\u002Fmobileye-dxp-as-a-novel-approach\u002F\">https:\u002F\u002Fwww.mobileye.com\u002Fopinion\u002Fmobileye-dxp-as-a-novel-approach\u002F\u003C\u002Fa>\u003C\u002Fp>\n\u003Cp style=\"font-size: 1.3rem; margin-top: 1.4rem;\">\u003Ca id=\"_edn9\" href=\"#_ednref9\" name=\"_edn9\">\u003C\u002Fa>\u003Csup class=\"ref\">9\u003C\u002Fsup> ChatGPT+\u003Cbr \u002F>\u003Ca href=\"https:\u002F\u002Fopenai.com\u002Fchatgpt\u002Fpricing\">https:\u002F\u002Fopenai.com\u002Fchatgpt\u002Fpricing\u003C\u002Fa>\u003C\u002Fp>\n\u003Cp style=\"font-size: 1.3rem; margin-top: 1.4rem;\">\u003Ca id=\"_edn10\" href=\"#_ednref10\" name=\"_edn10\">\u003C\u002Fa>\u003Csup class=\"ref\">10\u003C\u002Fsup> Future of AI is shifting from monolithic models to compound AI systems\u003Cbr \u002F>\u003Ca href=\"https:\u002F\u002Fbair.berkeley.edu\u002Fblog\u002F2024\u002F02\u002F18\u002Fcompound-ai-systems\u002F\">https:\u002F\u002Fbair.berkeley.edu\u002Fblog\u002F2024\u002F02\u002F18\u002Fcompound-ai-systems\u002F\u003C\u002Fa>\u003C\u002Fp>","2024-05-15T07:00:00.000Z",[6,21],{"id":243,"type":57,"url":244,"title":245,"description":246,"primary_tag":55,"author_name":225,"is_hidden":96,"lang":132,"meta_description":246,"image":247,"img_alt":248,"content":249,"download_doc":136,"download_title":136,"download_btn":136,"webinar_video":136,"thumbnail":136,"is_gated":136,"featured":96,"publish_date":250,"tags":251},254,"mobileye-dxp-as-a-novel-approach","采用全新的Mobileye DXP平台解决定制化需求","Mobileye DXP 解决了定制需求与上市时间和性能风险之间的矛盾","https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002Fd3e61ddfe04ec9f19f25187580a6c1f7_1708003321461.jpg","Mobileye's DXP addresses the tension between the need to customize and time-to-market and performance risks.","\u003Cp>Over the past decade, as we have embarked on the gradual transition from driver assist towards autonomous driving, there have been several crossroads along the way where the conventional industry approach has overlooked an inconvenient reality and thus limited the potential for safer and ubiquitous autonomous vehicles – the shared goal in the industry. The path of least resistance often leads to a choice of what seems doable now, but isn’t practical or scalable later.\u003C\u002Fp>\n\u003Cp>We saw this when it was understood that AVs needed HD maps on a level that regular navigation maps could not support, and the common approach was to use dedicated lidar-equipped vehicles to manually map an area for AV driving. This was slow, costly, geographically limited, and generated maps that were quickly out of date. This was when we came up with the idea of REM crowdsourced mapping using cameras already onboard ADAS-equipped vehicles travelling on their typical routes.\u003C\u002Fp>\n\u003Cp>We also saw this when the industry came to a consensus that cameras, radars, and lidars were all necessary for redundant sensing for AVs, but overlooked how equipping vehicles with that many sensors would make them highly expensive, dramatically limiting profitable business models. This is why we are investing in developing imaging radar with lidar-like output, to reduce the number of lidars included and thereby significantly lowering the hardware costs per vehicle. We even see today that our then-radical early 2000s decision to design our own chip and tightly couple hardware with software is proving central to cost-effective high-performance delivery, while others are only now considering this route.\u003C\u002Fp>\n\u003Cp>The industry is now at another crossroads, where we must not overlook an important reality, even if it is inconvenient or hard to overcome, and it has to do with the inherent tension between, on one hand, automakers' desire to customize their offerings and, on the other hand, the time-to-market and performance risks involved in full development of automated driving systems from scratch.\u003C\u002Fp>\n\u003Ch3>\u003Cstrong>The differentiability-scalability-risk tradeoff \u003C\u002Fstrong>\u003C\u002Fh3>\n\u003Cp>For automated driving to become commonplace, automakers will want to customize their driving experience for their particular consumers' expectations. How to do this optimally must take into consideration three key factors: the ability for the automaker to differentiate among other brands, the ability for the supplier to scale to many automakers, and a need to minimize the risk of not executing to the desired performance, cost, and timeline. Given the numerous announcements about ambitious projects in recent years that never came, or have yet to come, to fruition, this execution risk cannot be overlooked.  \u003C\u002Fp>\n\u003Cp>  \u003Cimg src=\"https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002F1381038603fa2dbc89d8fcbcd240c2ef_1708001820543.jpg\" alt=\"\" width=\"650\" height=\"300\" \u002F>\u003C\u002Fp>\n\u003Ch3>\u003Cstrong>Where to draw the line between sense, plan, and act?\u003C\u002Fstrong>\u003C\u002Fh3>\n\u003Cp>The foundation of all robotics is sense-plan-act: perceive the environment, make a plan, and execute the plan. If differentiability is desired, the automaker must control some part of this stack, but the hard question is where to draw the line between what the supplier controls and what the automaker controls, with all three aspects of the tradeoff in mind. If the supplier provides part of the perception and the automaker develops the rest, differentiation is obtained, but the risk to the automaker is very high; development is difficult, time-consuming, and costly. Integrating everything into one complete and robust system is extremely hard.\u003C\u002Fp>\n\u003Cp>\u003Cimg src=\"https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002F945570dd6474b8083bf5dfaf1dbcdcd6_1708002027160.jpg\" alt=\"\" width=\"650\" height=\"337\" \u002F>\u003C\u002Fp>\n\u003Cp>On the other hand, if the supplier provides all of the driving policy and the automaker handles only the actions taken by the vehicle, either not enough differentiation is achieved on the part of the automaker, \u003Cem>or\u003C\u002Fem> the supplier needs to handle all of the automaker’s driving experience requirements, which is a challenge to scalability.  \u003C\u002Fp>\n\u003Cp>\u003Cimg src=\"https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002Ff23a6c4da27193d5421b3baceea322c2_1708002148211.jpg\" alt=\"\" width=\"650\" height=\"332\" \u002F>\u003C\u002Fp>\n\u003Cp>So then the answer seems easy – draw the line between perception and planning. Get all your sensing from the supplier, and the automaker can build their own driving policy and actuation stack.\u003C\u002Fp>\n\u003Cp>\u003Cimg src=\"https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002Fbce90c5eac3727c9ca12d4f1ab1a58e3_1708002252931.jpg\" alt=\"\" width=\"650\" height=\"331\" \u002F>\u003C\u002Fp>\n\u003Cp>While tantalizing, this too is highly problematic, and where a key reality of AV development gets overlooked: perception is never perfect, and a driving policy must be robust enough to anticipate those imperfections, requiring an intimate integration of perception and planning. Many attempts have been made with this approach and are at various stages of failure, due to what we call the \"underestimation plague\" – a tendency to vastly underestimate how hard driving policy really is.\u003C\u002Fp>\n\u003Cp>Driving policy must deal with predictions, intentions, uncertainties, and risks of decision-making errors and is therefore highly complex. This approach is therefore also not scalable for the automaker, as driving policy is intimately integrated with perception and must be continually adapted and revalidated as perception changes. \u003C\u002Fp>\n\u003Ch3>\u003Cstrong>Separating the universal from the unique \u003C\u002Fstrong>\u003C\u002Fh3>\n\u003Cp>Given the above, the better path to enable differentiation while also minimizing execution risk and enabling scalability is to draw the line \u003Cspan style=\"font-style: normal !msorm;\">\u003Cem>in the middle of\u003C\u002Fem>\u003C\u002Fspan> the policy stack, with the goal of keeping the perception and sensing integrated, while offering space to the automaker to define behavioral elements of the vehicle.\u003C\u002Fp>\n\u003Cp>\u003Cimg src=\"https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002Fd694ea364ba1e8f44b1ca1fb90b6eaff_1708003677023.jpg\" alt=\"\" width=\"650\" height=\"366\" \u002F>\u003C\u002Fp>\n\u003Cp>In order to do this, we must define which aspects are universal and should be the same for all systems, and which aspects are unique – where differentiation can and should be possible. Perception is clearly universal, and the way the vehicle performs actions is clearly unique. Driving policy, however, is partially universal and partially unique. On the one hand, it must be tailored to sensing, and on the other hand, it is responsible for the look and feel of the driving experience. The art is to find the right granularity of abstractions that will enable us to make the separation between the universal content (which we want to standardize) and the unique content (which we want to customize around).\u003C\u002Fp>\n\u003Cp>\u003Cimg src=\"https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002Fc7d9afa73d33066de2b13baa1b7d31be_1708003813431.jpg\" alt=\"\" width=\"650\" height=\"360\" \u002F>\u003C\u002Fp>\n\u003Ch3>\u003Cstrong>Mobileye DXP: When, what, and how\u003C\u002Fstrong>\u003C\u002Fh3>\n\u003Cp>Mobileye Driving Experience Platform, or DXP, is a programming language that separates between the universal and the unique, by organizing the decision-making according to when, what, and how. The \"when\" and \"what\" are universal, and the \"how\" is unique. For example, \"when\" approaching a stop sign, the vehicle must brake to stop (the \"what\"). But \"how\" each vehicle model brakes is unique – later and stronger, or earlier and milder, for example. Or, when approaching a roundabout, the vehicle must decide whether to yield or to proceed in front of another vehicle. Assuming both are safe, what should the vehicle do? This is the “how” category, and different automakers will want to tune their systems differently.\u003C\u002Fp>\n\u003Cp>\u003Cimg src=\"https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002Fa9661f51c4a3e05dadefe3c330c7d06e_1708003943206.jpg\" alt=\"\" width=\"650\" height=\"367\" \u002F>\u003C\u002Fp>\n\u003Ch3>\u003Cstrong>How it works inside DXP\u003C\u002Fstrong>\u003C\u002Fh3>\n\u003Cp>Within a given scenario – the \"what\" and \"when\" – the automaker defines packages or families of \"how\" – a variety of implementations of how to brake to stop for example. The automaker constructs packages of “how instances” out of the platform’s “how families\". The platform provides online and offline tools for creating these packages. The platform also offers reference designs for required packages, in order to reduce execution risk, such that the automaker doesn't need to implement all packages from day one, but can focus on where it specifically wants to provide differentiation.\u003C\u002Fp>\n\u003Cp>Automakers then create code that selects the appropriate package during online driving, based on application parameters like locality, road type, regulation, driving mode, and weather conditions. This solves the differentiability-scalability-risk tradeoff by offering differentiability without breaking the intimate integration between sensing and policy, by using the right abstractions, and, accordingly, reducing execution risk, because the platform will be based on a working product out of the box, and all efforts can focus on differentiation. Automakers can also make post-production tweaks to the driving experience in response to consumer feedback.\u003C\u002Fp>\n\u003Cp>The backbone of this platform is based on redundancy in perception engines, and driving policy using Responsibility-Sensitive Safety combined with analytical calculations and intentions (you can learn more about that in \u003Ca href=\"https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=_z3qBZ6vQL8&t=1349s\" target=\"_blank\" rel=\"noopener\">this video)\u003C\u002Fa>.\u003C\u002Fp>\n\u003Cp>We see great potential in DXP, and in the short time since we unveiled it at CES, several automakers have expressed interest in learning more. The tangible benefits of automated driving systems should help an automaker further define its brand. DXP offers a better roadmap to that future.\u003C\u002Fp>","2024-02-21T08:00:00.000Z",[56,6,64,114,21],{"id":253,"type":100,"url":254,"title":255,"description":256,"primary_tag":5,"author_name":136,"is_hidden":96,"lang":132,"meta_description":256,"image":257,"img_alt":258,"content":259,"download_doc":136,"download_title":136,"download_btn":136,"webinar_video":136,"thumbnail":136,"is_gated":136,"featured":96,"publish_date":260,"tags":261},250,"prof-amnon-shashua-at-ces-2024","Amnon Shashua 教授 CES 2024: Now. Next. Beyond 新闻发布会","CES 2024首日，Mobileye首席执行官Amnon Shashua教授主持了备受瞩目的年度新闻发布会，分析了Mobileye辅助驾驶和自动驾驶系统的现状与未来","https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002F62e61593274d34538d7719105558a450_1705490954008.jpg","Mobileye CEO Prof. Amnon Shashua speaking at his annual CES press conference.","\u003Cp>Mobileye’s annual CES press conference was again an industry focal point, with Mobileye CEO Professor Amnon Shashua presenting his insights into key issues.\u003C\u002Fp>\n\u003Cp>During his presentation, Prof. Shashua discussed the current state of self-driving car technologies and future possibilities for the industry, zeroing in on two main issues: (1) creating a safer eyes-off system, and (2) providing a platform for carmakers that allows them to customize their driving experience, which was unveiled as Mobileye’s new DXP platform.\u003C\u002Fp>\n\u003Cp>Prof. Shashua also introduced new and exciting collaborations between Mobileye and several OEMs, including Mobileye securing a series of \u003Ca href=\"https:\u002F\u002Fwww.mobileye.com\u002Fnews\u002Fmobileye-reveals-new-wins-for-key-tech-platforms-with-large-global-automaker\u002F\">production design wins\u003C\u002Fa> from a major Western automaker, Chery's approaching launch of Mobileye’s Cloud-Enhanced ADAS on its Exeed VX model.\u003C\u002Fp>\n\u003Cp>During the event, held in Las Vegas, Prof. Shashua highlighted breakthroughs in AI and the opportunities to apply them to autonomous driving. He also updated the expected timeline for Mobileye’s EyeQ™ 6H SoC and noted the next generation of Mobileye's imaging radars.\u003C\u002Fp>\n\u003Cp>Watch the video below to see the full press conference and learn about Mobileye's bridge from hands-on to no-driver systems.\u003C\u002Fp>\n\u003Cp>\u003Ciframe src=\"https:\u002F\u002Fwww.youtube.com\u002Fembed\u002Fuco1z54FAdA?si=CaV3jgH9LqWAiXUT&amp\" width=\"560\" height=\"314\" allowfullscreen=\"allowfullscreen\">\u003C\u002Fiframe>\u003C\u002Fp>","2024-01-17T08:00:00.000Z",[84,6,99,21,13],{"id":263,"type":100,"url":264,"title":265,"description":266,"primary_tag":136,"author_name":136,"is_hidden":96,"lang":132,"meta_description":266,"image":267,"img_alt":268,"content":269,"download_doc":136,"download_title":136,"download_btn":136,"webinar_video":136,"thumbnail":136,"is_gated":136,"featured":96,"publish_date":270,"tags":271},244,"mobileye-announces-ces-2024-press-conference-with-prof-amnon-shashua","Mobileye Announces CES 2024 Press Conference with Prof. Amnon Shashua  ","Mobileye: Now. Next. Beyond. and additional events presented live from Las Vegas  ","https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002F70e29fa9085f2151107ae35044c5a59b_1703020097934.jpg","President & CEO Prof. Amnon Shashua will deliver his annual CES address on January 9, 2024, at 11:00 a.m. PT.","\u003Cp>\u003Cspan data-contrast=\"none\">Mobileye (Nasdaq: MBLY) will present its 2024 CES Press Conference, \u003C\u002Fspan>\u003Cem>\u003Cspan data-contrast=\"none\">Mobileye: \u003C\u002Fspan>\u003C\u002Fem>\u003Cem>\u003Cspan data-contrast=\"none\">Now. Next. Beyond. \u003C\u002Fspan>\u003C\u002Fem>\u003Cspan data-contrast=\"none\">with President & CEO Prof. Amnon Shashua, on January 9, 2024, at 11:00 a.m. PT.  \u003C\u002Fspan>\u003Cspan data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\"> \u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan data-contrast=\"none\">The annual CES address will explore the state of self-driving, highlighting Mobileye’s advancements in delivering an evolutionary vision of autonomy, including a spectrum of scalable solutions from hands-off\u002Feyes-on ADAS to eyes-off autonomous vehicles. Prof. Shashua will also introduce a new technological breakthrough that unlocks automakers’ ability to configure the driving experience in ways that align with their brand and consumer tastes.\u003C\u002Fspan>\u003Cspan data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\"> \u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan data-contrast=\"none\">As a follow up to\u003C\u002Fspan>\u003Cem>\u003Cspan data-contrast=\"none\"> Now. Next. Beyond., \u003C\u002Fspan>\u003C\u002Fem>\u003Cspan data-contrast=\"none\">Mobileye CTO Prof. Shai Shalev-Shwartz will present \u003C\u002Fspan>\u003Cem>\u003Cspan data-contrast=\"none\">Mobileye's Driving Experience Platform: Architecture, Abstractions, and APIs \u003C\u002Fspan>\u003C\u002Fem>\u003Cspan data-contrast=\"none\">on Wednesday, January 10 at 1:00 p.m. PT.\u003C\u002Fspan> \u003Cspan data-contrast=\"none\">Autonomous driving demands weaving a complex AI tapestry with the precision vital for safety-focused systems. This talk will delve into Mobileye's specialized programming platform engineered specifically to meet this challenge. Prof. Shalev-Shwartz will showcase how the platform's APIs provide automakers with the tools to program the AI's functionality to the unique desired driver characteristics of each car model.\u003C\u002Fspan>\u003Cspan data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\"> \u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan data-contrast=\"none\">Also on Wednesday, January 10, Mobileye Senior Vice President of AV Johann “JJ” Jungwirth will appear on the CES-presented Conference Session panel titled \u003C\u002Fspan>\u003Cem>\u003Cspan data-contrast=\"none\">The Middle Lane: Self-Driving Cars Today \u003C\u002Fspan>\u003C\u002Fem>\u003Cspan data-contrast=\"none\">at 9:00 a.m. PT.\u003C\u002Fspan>\u003Cspan data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\"> \u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan data-contrast=\"none\">For updated information on Mobileye’s CES news and events, visit: \u003C\u002Fspan>\u003Ca href=\"https:\u002F\u002Fwww.mobileye.com\u002Fces-2024\u002F\">www.mobileye.com\u002Fces-2024\u002F\u003C\u002Fa>\u003Cspan data-contrast=\"none\">. \u003C\u002Fspan>\u003Cspan data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\"> \u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cstrong>\u003Cspan data-contrast=\"none\">\u003Cu>Mobileye CES 2024 Events\u003C\u002Fu>\u003C\u002Fspan>\u003C\u002Fstrong>\u003Cspan data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\"> \u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cstrong>\u003Cspan data-contrast=\"none\">CES 2024 Press Conference, \u003C\u002Fspan>\u003C\u002Fstrong>\u003Cstrong>\u003Cem>\u003Cspan data-contrast=\"none\">Mobileye: \u003C\u002Fspan>\u003C\u002Fem>\u003C\u002Fstrong>\u003Cstrong>\u003Cem>\u003Cspan data-contrast=\"none\">Now. Next. Beyond. \u003C\u002Fspan>\u003C\u002Fem>\u003C\u002Fstrong>\u003Cstrong>\u003Cspan data-contrast=\"none\">w\u003C\u002Fspan>\u003C\u002Fstrong>\u003Cstrong>\u003Cspan data-contrast=\"none\">ith Prof. Amnon Shashua\u003C\u002Fspan>\u003C\u002Fstrong>\u003Cspan data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":240}\"> \u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan data-contrast=\"none\">Tuesday, January 9, 2024, 11:00 a.m. PT\u003C\u002Fspan>\u003Cspan data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":240}\"> \u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Ca href=\"https:\u002F\u002Fwww.mobileye.com\u002Fces-2024\u002F\">Visit here for registration and livestream details\u003C\u002Fa>\u003Cspan data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":240}\"> \u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":240}\"> \u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cstrong>\u003Cem>\u003Cspan data-contrast=\"none\">M\u003C\u002Fspan>\u003C\u002Fem>\u003C\u002Fstrong>\u003Cstrong>\u003Cem>\u003Cspan data-contrast=\"none\">obileye's Driving\u003C\u002Fspan>\u003C\u002Fem>\u003C\u002Fstrong>\u003Cstrong>\u003Cem> \u003C\u002Fem>\u003C\u002Fstrong>\u003Cstrong>\u003Cem>\u003Cspan data-contrast=\"none\">Experience\u003C\u002Fspan>\u003C\u002Fem>\u003C\u002Fstrong>\u003Cstrong>\u003Cem> \u003C\u002Fem>\u003C\u002Fstrong>\u003Cstrong>\u003Cem>\u003Cspan data-contrast=\"none\">Platform: Architecture, Abstractions, and \u003C\u002Fspan>\u003C\u002Fem>\u003C\u002Fstrong>\u003Cstrong>\u003Cem>\u003Cspan data-contrast=\"none\">APIs \u003C\u002Fspan>\u003C\u002Fem>\u003C\u002Fstrong>\u003Cstrong>\u003Cspan data-contrast=\"none\">with CTO Shai Shalev-Shwartz\u003C\u002Fspan>\u003C\u002Fstrong>\u003Cspan data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":240}\"> \u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan data-contrast=\"none\">Wednesday, Jan 10, 2024: 1:00-1:40 p.m. PT\u003C\u002Fspan>\u003Cspan data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":240}\"> \u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Ca href=\"https:\u002F\u002Fwww.mobileye.com\u002Fces-2024\u002F\">Visit here for registration and livestream details\u003C\u002Fa>\u003Cspan data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":240}\"> \u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":240}\"> \u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cstrong>\u003Cspan data-contrast=\"auto\">CES Session \u003C\u002Fspan>\u003C\u002Fstrong>\u003Cstrong>\u003Cem>\u003Cspan data-contrast=\"auto\">The Middle Lane: Self-Driving Cars\u003C\u002Fspan>\u003C\u002Fem>\u003C\u002Fstrong>\u003Cspan data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":240}\"> \u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan data-contrast=\"auto\">Featuring Mobileye’s JJ Jungwirth\u003C\u002Fspan>\u003Cspan data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":240}\"> \u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan data-contrast=\"none\">Wednesday, January 10, 9:00-9:40 a.m. PT\u003C\u002Fspan>\u003Cspan data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":240}\"> \u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Ca href=\"https:\u002F\u002Fwww.ces.tech\u002Fsessions-events\u002Fauto\u002Fauto01.aspx\">Visit here for more information\u003C\u002Fa>\u003Cspan data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":240}\"> \u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\"> \u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan data-contrast=\"none\">+++\u003C\u002Fspan>\u003Cspan data-ccp-props=\"{"201341983":0,"335551550":2,"335551620":2,"335559739":160,"335559740":259}\"> \u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cstrong>\u003Cspan data-contrast=\"none\">Contacts\u003C\u002Fspan>\u003C\u002Fstrong>\u003Cspan data-contrast=\"none\"> \u003C\u002Fspan>\u003Cspan data-ccp-props=\"{"201341983":0,"335559739":0,"335559740":259}\"> \u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan data-contrast=\"none\">Dan Galves \u003C\u002Fspan>\u003Cspan data-ccp-props=\"{"201341983":0,"335559739":0,"335559740":259}\"> \u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan data-contrast=\"none\">Investor Relations \u003C\u002Fspan>\u003Cspan data-ccp-props=\"{"201341983":0,"335559739":0,"335559740":259}\"> \u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Ca href=\"mailto:investors@mobileye.com\">\u003Cspan data-contrast=\"none\">investors@mobileye.com\u003C\u002Fspan>\u003C\u002Fa>\u003Cspan data-contrast=\"none\"> \u003C\u002Fspan>\u003Cspan data-ccp-props=\"{"201341983":0,"335559739":0,"335559740":259}\"> \u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp> \u003C\u002Fp>\n\u003Cp>\u003Cspan data-contrast=\"none\">Justin Hyde \u003C\u002Fspan>\u003Cspan data-ccp-props=\"{"201341983":0,"335559739":0,"335559740":259}\"> \u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan data-contrast=\"none\">Media Relations \u003C\u002Fspan>\u003Cspan data-ccp-props=\"{"201341983":0,"335559739":0,"335559740":259}\"> \u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Ca href=\"mailto:justin.hyde@mobileye.com\">\u003Cspan data-contrast=\"none\">justin.hyde@mobileye.com\u003C\u002Fspan>\u003C\u002Fa>\u003Cspan data-ccp-props=\"{"201341983":0,"335559739":0,"335559740":259}\"> \u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan data-ccp-props=\"{"201341983":0,"335559737":-6,"335559739":160,"335559740":259}\"> \u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cstrong>\u003Cspan data-contrast=\"none\">About Mobileye\u003C\u002Fspan>\u003C\u002Fstrong>\u003Cspan data-ccp-props=\"{"201341983":0,"335559737":-6,"335559739":160,"335559740":259}\"> \u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan data-contrast=\"none\">Mobileye (Nasdaq: MBLY) leads the mobility revolution with its autonomous driving and driver-assistance technologies, harnessing world-renowned expertise in computer vision, artificial intelligence, mapping, and data analysis. Since its founding in 1999, Mobileye has pioneered such groundbreaking technologies as REM™ mapping, True Redundancy™ sensing, and Responsibility Sensitive Safety (RSS). These technologies are driving the ADAS and AV fields towards the future of mobility – enabling self-driving vehicles and mobility solutions, powering industry-leading advanced driver-assistance systems and delivering valuable intelligence to optimize mobility infrastructure. To date, more than 150 million vehicles worldwide have been built with Mobileye technology inside. In 2022 Mobileye listed as an independent company separate from Intel (Nasdaq: INTC), which retains majority ownership. For more information, visit \u003C\u002Fspan>\u003Ca href=\"https:\u002F\u002Fwww.mobileye.com\u002F\">\u003Cspan data-contrast=\"none\">https:\u002F\u002Fwww.mobileye.com\u003C\u002Fspan>\u003C\u002Fa>\u003Cspan data-contrast=\"none\">.\u003C\u002Fspan>\u003Cspan data-ccp-props=\"{"201341983":0,"335559737":-6,"335559739":160,"335559740":259}\"> \u003C\u002Fspan>\u003C\u002Fp>","2023-12-20T08:00:00.000Z",[21,6,13,27,78,99],{"id":273,"type":100,"url":274,"title":275,"description":276,"primary_tag":5,"author_name":136,"is_hidden":96,"lang":132,"meta_description":276,"image":277,"img_alt":278,"content":279,"download_doc":136,"download_title":136,"download_btn":136,"webinar_video":136,"thumbnail":136,"is_gated":136,"featured":96,"publish_date":280,"tags":281},240,"mobileye-ceo-amnon-shashua-named-2023-automotive-all-star","Mobileye CEO Professor Amnon Shashua Named 2023 Automotive News All-Star","Mobileye is honored that CEO Professor Amnon Shashua has been recognized as a 2023 Automotive News All-Star.","https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002Fc3ece88ac9b3ddf800b4db0127698987_1702298590764.png","Professor Amnon Shashua, Founder and CEO of Mobileye","\u003Cp>Professor Amnon Shashua has been recognized as a 2023 \u003Cem>Automotive News \u003C\u002Fem>All-Star, marking the second time he has received this award from \u003Cem>Automotive News\u003C\u002Fem>. The award recognizes automotive industry leaders who are \"making a significant impact on their companies and the industry at large\".\u003C\u002Fp>\n\u003Cp>The award, in the Automated Vehicles category, highlights Shashua’s leadership as Mobileye incrementally builds the bridge towards autonomous driving with products like Mobileye SuperVision™ and Mobileye Chauffeur™. Having contracts in place with automakers representing 34 percent of global auto production, Mobileye is at the forefront of the industry's evolution.\u003C\u002Fp>\n\u003Cp>In the context of his receiving this award, Shashua sat down with Pete Bigelow from the \u003Cem>Automotive News\u003C\u002Fem> podcast \u003Cem>Shift\u003C\u002Fem> and discussed the broader challenges of the autonomous vehicle industry, including regulatory considerations, market-specific adaptations, and the delicate balance between safety, efficiency, and cost.\u003C\u002Fp>\n\u003Cp>\u003Cem>\u003Ca href=\"https:\u002F\u002Fsoundcloud.com\u002Fuser-383952226\u002Fmobileyes-amnon-shashua-eyes-profits-on-road-to-full-autonomy?si=3efb4a875a2141c0acda67e3fe1886cf&utm_source=clipboard&utm_medium=text&utm_campaign=social_sharing\">Listen to the podcast now\u003C\u002Fa>\u003C\u002Fem>\u003C\u002Fp>","2023-12-11T08:00:00.000Z",[6,64,99,70],{"id":283,"type":100,"url":284,"title":285,"description":286,"primary_tag":5,"author_name":136,"is_hidden":96,"lang":132,"meta_description":286,"image":287,"img_alt":288,"content":289,"download_doc":136,"download_title":136,"download_btn":136,"webinar_video":136,"thumbnail":136,"is_gated":136,"featured":96,"publish_date":290,"tags":291},236,"shanghai-jiaotong-university-names-amnon-honorary-professor","Shanghai Jiaotong University Names Prof. Amnon Shashua Honorary Professor","STJU recognized Amnon’s outstanding contributions to the field of AI before his lecture to 300 students and faculty members","https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002Ff9dbf90e71827e17f021f3d17a64e829_1696158764985.jpg","Amnon Shashua Receives Honorary Professorship From SJTU","\u003Cp>In a ceremony held on Tuesday, September 12, Professor Amnon Shashua was appointed as an honorary professor at the Global Institute of Future Technology and a guest professor at Shanghai Jiaotong University (SJTU).\u003C\u002Fp>\r\n\u003Cp>SJTU, renowned for its expertise in nurturing top engineers and scientists, welcomed Amnon to its campus, where he delivered a lecture, titled, \"The State of AI: Opportunities, Limitations and Dangers\" to an overflowing room of approximately 300 students, faculty, and experts.\u003C\u002Fp>\r\n\u003Cp>During the lecture, Professor Shashua spoke about the emergence of reasoning and abstraction in large language models (LLMs). While acknowledging advancements in reasoning, he explained the formidable challenge of LLMs to abstract from data, a capability inherent to human cognition. He provided examples, from joint work with Prof. Shai Shalev Shwartz, demonstrating the ongoing limitations in LLMs' ability to perform abstraction, indicating that this aspect of development does not look like a natural evolution in the field and may necessitate more substantial breakthroughs.\u003C\u002Fp>\r\n\u003Cp>The lecture concluded with an exploration of the pressing issue of AI alignment, by going over some recent results from his university research lab and from work with Shai, and the ongoing endeavors to establish safeguards for AI systems.\u003C\u002Fp>\r\n\u003Cp>You can watch the full lecture below:\u003Cbr>\u003Cbr>\u003C\u002Fp>\r\n\u003Ciframe title=\"YouTube video player\" src=\"https:\u002F\u002Fwww.youtube.com\u002Fembed\u002FOiN9Jz27NKE?si=3QzG332h1KXqUmaC\" width=\"560\" height=\"315\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\">\u003C\u002Fiframe>","2023-10-02T07:00:00.000Z",[6,99],{"id":293,"type":57,"url":294,"title":295,"description":296,"primary_tag":5,"author_name":297,"is_hidden":96,"lang":132,"meta_description":296,"image":298,"img_alt":299,"content":300,"download_doc":136,"download_title":136,"download_btn":136,"webinar_video":136,"thumbnail":136,"is_gated":136,"featured":96,"publish_date":301,"tags":302},233,"are-we-on-the-edge-of-a-chat-gpt-moment-for-autonomous-driving","Are we on the edge of a “ChatGPT moment” for autonomous driving?","Prof. Shai Shalev-Shwartz and Prof. Amnon Shashua discuss the end-to-end approach for solving self-driving vehicle problems, asking if it is both sufficient and necessary.","Prof. Shai Shalev-Shwartz and Prof. Amnon Shashua ","https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002Ff25b156deca6571a0ee6b607a893ba29_1694719403818.jpg","     ","\u003Cp>Large Language Models (LLMs) such as ChatGPT have revolutionized the world of natural language understanding and generation. Building such models generally has two stages: a general unsupervised pre-training step, and a specific Reinforcement Learning from Human Feedback (RLHF) step. In the pre-training phase, the system “reads” a large portion of the internet (trillions of words) and aims at predicting the next word given previous words, therefore learning the distribution of text over the internet. In the RLHF phase, humans are asked to rank the quality of different answers from the model to given questions, and the neural network is fine-tuned to prefer better answers.\u003C\u002Fp>\n\u003Cp>Recently, Tesla has \u003Ca href=\"https:\u002F\u002Fwww.cnbc.com\u002F2023\u002F09\u002F09\u002Fai-for-cars-walter-isaacson-biography-of-elon-musk-excerpt.html\" target=\"_blank\" rel=\"noopener\">indicated\u003C\u002Fa> they will adopt this approach for end-to-end solving of the self-driving problem. The premise is to switch from a well-engineered system comprised of data-driven components interconnected by many lines of codes to a pure data-driven approach comprised of a single end-to-end neural network. When examining a technological solution for a given problem, the two questions one should ask is whether the solution is \u003Cstrong>sufficient\u003C\u002Fstrong> and whether it is \u003Cstrong>necessary\u003C\u002Fstrong>:\u003C\u002Fp>\n\u003Cul style=\"padding-left: 43px;\">\n\u003Cli>Sufficiency: does this approach tackle all of the requirements of self-driving?\u003C\u002Fli>\n\u003Cli>Necessity: is this the best approach or is it an over-kill (trying to kill a fly with a rocket)?\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>Some critical requirements of self-driving systems are transparency, controllability and performance:\u003C\u002Fp>\n\u003Col>\n\u003Cli>Transparency and Explainability: Driving systems must perceive and plan. Perception means creating a factual description of reality (the location of lanes, vehicles, pedestrians and so forth). Planning involves leveraging perception into driving decisions that must balance a tradeoff between usefulness and safety. For example, what speed should the car drive at a residential road with parked vehicles on its sides? Driving slower will be safer; say, in case a child runs into the street from between parked cars. But driving too slow will compromise the usefulness of the function and impede other vehicles. We believe how a self-driving vehicle balances such tradeoffs must be transparent so that society, through regulation, should have a say in decisions that affect all road users.\u003C\u002Fli>\n\u003Cli>Controllability: Reproducible system mistakes should be captured and fixed immediately, while not compromising the overall performance of the system. Furthermore, while human drivers take bad decisions from time to time, like not yielding properly or driving while impaired, society will not tolerate “lapses of judgement” of a self-driving system and every decision should be controllable.\u003C\u002Fli>\n\u003Cli>Performance: The Mean-Time-Between-Failures (MTBF) must be extremely high and non-reproducible errors (“black swans”) should be extremely rare.\u003C\u002Fli>\n\u003C\u002Fol>\n\u003Cp>Now, let us judge the end-to-end solution in light of the above requirements.\u003C\u002Fp>\n\u003Cp>For transparency, while it may be possible to steer an end-to-end system towards satisfying some regulatory rules, it is hard to see how to give regulators the option to dictate the exact behavior of the system in all situations. In fact, the most recent trend in LLMs is to combine them with symbolic reasoning elements – also known as good, old fashion coding. See for example \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.10601\" target=\"_blank\" rel=\"noopener\">Tree of Thoughts\u003C\u002Fa>, \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2308.09687\" target=\"_blank\" rel=\"noopener\">Graph-of-Thoughts\u003C\u002Fa> and \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2211.10435\" target=\"_blank\" rel=\"noopener\">PAL\u003C\u002Fa>.\u003C\u002Fp>\n\u003Cp>For controllability, end-to-end approaches are an engineering nightmare. Evidence shows that the performance of GPT-4 over time \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.09009\" target=\"_blank\" rel=\"noopener\">deteriorates\u003C\u002Fa> as a result of attempts to keep improving the system. This can be attributed to phenomena like catastrophic forgetfulness and other \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2212.09251\" target=\"_blank\" rel=\"noopener\">artifacts of RLHF\u003C\u002Fa>. Moreover, there is no way to guarantee “no lapse of judgement” for a fully neuronal system. The trend in LLMs is to combine LLMs with external, code-based, tools in order to have guarantees on elements of the systems (e.g. “calculator” in \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.04761\" target=\"_blank\" rel=\"noopener\">Toolformer\u003C\u002Fa> and \u003Ca href=\"https:\u002F\u002Fwww.ai21.com\u002Fblog\u002Fjurassic-x-crossing-the-neuro-symbolic-chasm-with-the-mrkl-system\" target=\"_blank\" rel=\"noopener\">Jurrasic-x neuro-symbolic system\u003C\u002Fa>).\u003C\u002Fp>\n\u003Cp>Regarding performance (i.e., the high MTBF requirement), while it may be possible that with massive amounts of data and compute an end-to-end approach will converge to a sufficiently high MTBF, the current evidence does not look promising. Even the most advanced LLMs make embarrassing mistakes quite often. Will we trust them for making safety critical decisions? It is well known to machine learning experts that the most difficult problem of statistical methods is the long tail. The end-to-end approach might look very promising to reach a mildly large MTBF (say, of a few hours), but this is orders of magnitude smaller than the requirement for safe deployment of a self-driving vehicle, and each increase of the MTBF by one order of magnitude becomes \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F1604.06915\" target=\"_blank\" rel=\"noopener\">harder and harder\u003C\u002Fa>. It is not surprising that the recent live demonstration of Tesla’s latest FSD by Elon Musk shows an \u003Ca href=\"https:\u002F\u002Fnypost.com\u002F2023\u002F08\u002F29\u002Felon-musk-almost-runs-red-light-livestreaming-tesla-software\u002F\" target=\"_blank\" rel=\"noopener\">MTBF of roughly one hour\u003C\u002Fa>.\u003C\u002Fp>\n\u003Cp>Taken together, we see many concerns regarding the ability of an end-to-end approach to fully tackle the self-driving challenge. What we would further argue is that an end-to-end approach is an over-kill. The premise of a fully end-to-end approach is “no lines of code, everything should be done by a single gigantic neural network.” Such a system requires maintaining a huge model, with every single update carefully balanced - yet this approach goes against current trends in utilizing LLMs as components within real systems. One such trend is the neural-symbolic approach, in which the fully neuronal LLM is one component within a larger system that uses code-based tools (for example \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.04761\" target=\"_blank\" rel=\"noopener\">Toolformer\u003C\u002Fa>). Another trend is the expert approach, in which LLMs are fine tuned to specific, well defined tasks; and the evidence so far is that small dedicated models outperform significantly larger models (e.g. the \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2308.12950\" target=\"_blank\" rel=\"noopener\">code LLaMa project\u003C\u002Fa>). These trends have implications on the data and compute requirements, showing that quality of data, architecture, and system design may be far more important than sheer quantity.\u003C\u002Fp>\n\u003Cp>In summary, we argue that an end-to-end approach is neither necessary nor sufficient for self-driving systems. There is no argument that data-driven methods including convolutional networks and transformers are crucial elements of self-driving systems, however, they must be carefully embedded within a well-engineered architecture.\u003C\u002Fp>","2023-09-14T07:00:00.000Z",[21,6],{"id":304,"type":100,"url":305,"title":306,"description":307,"primary_tag":5,"author_name":136,"is_hidden":96,"lang":132,"meta_description":307,"image":308,"img_alt":309,"content":310,"download_doc":136,"download_title":136,"download_btn":136,"webinar_video":136,"thumbnail":136,"is_gated":136,"featured":96,"publish_date":311,"tags":312},206,"israel-prize-awarded-to-mobileye-founder-prof-amnon-shashua","Prof. Amnon Shashua Awarded Israel Prize","The nation's highest civilian honor, the recognition for lifetime achievement cites our CEO's contruibutions to philanthropy, industry, and automotive safety.","https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002F94c089545fd9312bb2e582198a23aecd_1679518410534.jpg","Mobileye president and CEO Prof. Amnon Shashua","\u003Cp>JERUSALEM, March 22 — Today, the State of Israel’s Ministry of Education named Prof. Amnon Shashua, Mobileye’s founder, president and CEO, as the recipient of the Israel Prize for Lifetime Achievement, the nation’s highest civilian honor.\u003C\u002Fp>\n\u003Cp>The Israel Prize, now in its 70\u003Csup>th\u003C\u002Fsup> year, celebrates excellence in arts, culture, business and sciences, along with recognizing those who have made lifelong contributions to Israeli society. Shashua was honored for his groundbreaking contributions to the tech industry in Israel, his global impact on automotive safety and applied artificial intelligence, and his philanthropy.\u003C\u002Fp>\n\u003Cp>“I am deeply honored to be recognized with this lifetime achievement award from the country I love,” said Shashua. “I have the privilege of working with thousands of people in Israel and around the world, and together we have achieved scientific and technological innovations with tremendous impact.\u003C\u002Fp>\n\u003Cp>“This moment also offers a chance to emphasize an important mission for me – fostering social cohesion across the communities that make Israel unique, a cause that my family pursues through our foundation’s work in several fields. Congratulations to the other recipients of the Israel Prize on their achievements.”\u003C\u002Fp>\n\u003Cp>Shashua is a world-renowned expert in AI, computer vision, natural language processing, and other related fields. He is a 2020 Dan David Prize laureate in the field of artificial intelligence and was selected as the 2022 Mobility Innovator by the Automotive Hall of Fame. Shashua has founded and actively leads four companies using applied AI in various fields from automotive to assisted wearables to fintech: Mobileye, OrCam, AI21 Labs, and \"One Zero,\" the first digital bank in Israel.\u003C\u002Fp>\n\u003Cp>Shashua and the other two recipients of this year’s lifetime achievement prizes will receive their award in a special ceremony on April 26.\u003C\u002Fp>\n\u003Cp>___________________________________\u003C\u002Fp>\n\u003Cp>Mobileye (Nasdaq: MBLY) leads the mobility revolution with its autonomous driving and driver-assistance technologies, harnessing world-renowned expertise in computer vision, artificial intelligence, mapping, and data analysis. Since its founding in 1999, Mobileye has pioneered such groundbreaking technologies as REM™ crowdsourced mapping, True Redundancy™ sensing, and Responsibility Sensitive Safety (RSS). These technologies are driving the ADAS and AV fields towards the future of mobility – enabling self-driving vehicles and mobility solutions, powering industry-leading advanced driver-assistance systems and delivering valuable intelligence to optimize mobility infrastructure. To date, more than 130 million vehicles worldwide have been built with Mobileye technology inside. In 2022 Mobileye listed as an independent company separate from Intel (Nasdaq: INTC), which retains majority ownership. For more information, visit \u003Ca href=\"https:\u002F\u002Fwww.mobileye.com\">https:\u002F\u002Fwww.mobileye.com\u003C\u002Fa>.\u003C\u002Fp>\n\u003Cp>“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\u002Fp>","2023-03-22T07:00:00.000Z",[70,6],{"id":314,"type":151,"url":315,"title":316,"description":317,"primary_tag":5,"author_name":136,"is_hidden":96,"lang":132,"meta_description":317,"image":318,"img_alt":319,"content":320,"download_doc":136,"download_title":136,"download_btn":136,"webinar_video":136,"thumbnail":136,"is_gated":96,"featured":96,"publish_date":321,"tags":322},225,"now-next-beyond-prof-amnon-shashua-at-ces-2023","Now, Next, Beyond: Prof. Amnon Shashua at CES 2023","At CES this year, Prof. Amnon Shashua, President and CEO of Mobileye, presented our roadmap and progress towards fully autonomous vehicles.","https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002F5a27679c9f345c771979c316d73ec7d5_1692801152226.png","Mobileye: Now, Next, Beyond - CES 2023 Press Conference with Prof. Amnon Shashua","\u003Cp>The address delivered each year at CES by our CEO stands among the most eagerly anticipated events in automotive tech, and this year more viewers signed up and tuned in than ever before. Professor Amnon Shashua presented a new driving automation taxonomy, using three axes to characterize degrees of autonomous capability: eyes on or eyes off, hands on or hands off, driver or no driver. He gave insight into our most advanced technologies and solutions, outlined our business strategy, discussed the path of validating an eyes-off system, and much more.\u003C\u002Fp>\n\u003Cp> \u003C\u002Fp>\n\u003Cp>\u003Ciframe title=\"YouTube video player\" src=\"https:\u002F\u002Fwww.youtube.com\u002Fembed\u002FUCBlR4QFQCA\" width=\"560\" height=\"315\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\">\u003C\u002Fiframe>\u003C\u002Fp>","2023-01-06T08:00:00.000Z",[6,78],{"id":324,"type":92,"url":325,"title":326,"description":327,"primary_tag":90,"author_name":136,"is_hidden":96,"lang":132,"meta_description":327,"image":328,"img_alt":329,"content":330,"download_doc":136,"download_title":136,"download_btn":136,"webinar_video":136,"thumbnail":136,"is_gated":136,"featured":96,"publish_date":331,"tags":332},192,"mobileye-at-ces-2023","Mobileye at CES 2023 Complete Press Kit","All the information you're looking for about Mobileye at CES 2023 can be found here in our online press kit, including Prof. Amnon Shashua's press conference.","https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002Fdcbdebad96fd1149f00e9a93eee90eb9_1672863216143.jpg","Mobileye at CES 2023 ","\u003Cp>\u003Cspan style=\"color: #000000;\">At CES 2023, Mobileye showcased innovation and progress on the road to autonomy. Through a series of events and presentations, including the Mobileye: Now, Next, Beyond Press Conference with Professor Amnon Shashua; demonstrations at the Mobileye booth; and exciting partner initiatives; Mobileye brought to CES 2023 an insider’s view into the autonomous vehicle revolution it is driving.\u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cstrong>Mobileye: Now, Next, Beyond - CES 2023 Press Conference with CEO Prof. Amnon Shashua (Replay)\u003C\u002Fstrong>\u003C\u002Fp>\n\u003Cp> \u003C\u002Fp>\n\u003Cp>\u003Ciframe class=\"ql-video\" src=\"https:\u002F\u002Fwww.youtube.com\u002Fembed\u002FUCBlR4QFQCA\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\">\u003C\u002Fiframe>\u003C\u002Fp>\n\u003Ch2>\u003Cstrong>Mobileye CES 2023 news:\u003C\u002Fstrong>\u003C\u002Fh2>\n\u003Cp>\u003Ca href=\"https:\u002F\u002Fwww.mobileye.com\u002Fnews\u002Fmobileye-growth-pipeline-fueled-with-supervision-and-future-av-wins\u002F\" target=\"_blank\" rel=\"noopener noreferrer\">Mobileye Growth Pipeline Fueled with SuperVision™ and Future AV Wins\u003C\u002Fa>\u003C\u002Fp>\n\u003Cp>\u003Ca style=\"color: #000000;\" href=\"https:\u002F\u002Fwww.mobileye.com\u002Fnews\u002Fmobileye-and-wnc-collaborate-on-imaging-radar-production\u002F\" target=\"_blank\" rel=\"noopener noreferrer\">Mobileye and WNC collaborate on imaging radar production\u003C\u002Fa>\u003C\u002Fp>\n\u003Cp>\u003Ca style=\"color: #000000;\" href=\"https:\u002F\u002Fwww.mobileye.com\u002Fnews\u002Fmobileye-kicks-off-av-pilot-in-germany\u002F\" target=\"_blank\" rel=\"noopener\">Mobileye kicks off AV pilot in Germany | Mobileye Blog\u003C\u002Fa>\u003Cspan style=\"color: #000000;\"> \u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Ca style=\"color: #000000;\" href=\"https:\u002F\u002Fwww.mobileye.com\u002Fnews\u002Fmobileye-announces-ces-press-conference-with-prof-amnon-shashua\u002F\" target=\"_blank\" rel=\"noopener noreferrer\">Mobileye Announces CES 2023 Press Conference with Prof. Amnon Shashua\u003C\u002Fa>\u003C\u002Fp>\n\u003Ch3>\u003Cstrong style=\"color: #000000;\">Mobileye \u003C\u002Fstrong>\u003Cstrong style=\"color: #404040; background-color: white;\">SuperVision™:\u003C\u002Fstrong>\u003C\u002Fh3>\n\u003Cp>\u003Ca style=\"background-color: #ffffff; color: #242424;\" href=\"https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fcommon\u002Ffiles\u002FSuperVision%20one%20pager.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">SuperVision Backgrounder\u003C\u002Fa> \u003Cspan style=\"color: #242424; background-color: #ffffff;\">(download)\u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>[**]gallery:mobileye-supervision[**]\u003C\u002Fp>\n\u003Ch3>\u003Cstrong>CES 2023 Photos:\u003C\u002Fstrong>\u003C\u002Fh3>\n\u003Cp>[**]gallery:ces-2023[**]\u003C\u002Fp>\n\u003Ch3>\u003Cstrong style=\"color: #000000;\">Mobileye Visual Assets:\u003C\u002Fstrong>\u003C\u002Fh3>\n\u003Cp>[**]gallery:mobileye-at-ces-2023[**]\u003C\u002Fp>\n\u003Cp>\u003Ca href=\"https:\u002F\u002Fvimeo.com\u002F763958794\" target=\"_blank\" rel=\"noopener noreferrer\">Mobileye: Autonomous Driving and Technology Development \u003C\u002Fa>(Broll)\u003C\u002Fp>","2023-01-03T08:00:00.000Z",[78,6,91],{"id":334,"type":100,"url":335,"title":336,"description":337,"primary_tag":5,"author_name":136,"is_hidden":96,"lang":132,"meta_description":337,"image":338,"img_alt":339,"content":340,"download_doc":136,"download_title":136,"download_btn":136,"webinar_video":136,"thumbnail":136,"is_gated":136,"featured":96,"publish_date":341,"tags":342},130,"shashua-mobility-innovator-award-automotive-hall-of-fame","Automotive Hall of Fame Awards Prof. Shashua for Innovation","“Entering the Automotive Hall of Fame is an incredible honor,” said Mobileye CEO Prof. Amnon Shashua at induction ceremony in Detroit.","https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002Fbd9ade080d4e388cd6434dc69ed9dce1_1658827956505.jpg","Mobileye CEO Prof. Amnon Shashua receives the 2022 Mobility Innovator Award during the Automotive Hall of Fame Induction and Awards Ceremony in Detroit.","\u003Cp>As mobility technology plays an increasingly important role in the advancement of the automobile, industry organizations have sought new ways to identify the most significant new developments and the people behind them.\u003C\u002Fp>\n\u003Cp>The Mobility Innovator Award, introduced last year by the \u003Ca href=\"https:\u002F\u002Fwww.automotivehalloffame.org\u002F\" target=\"_blank\" rel=\"noopener noreferrer\">Automotive Hall of Fame\u003C\u002Fa>, seeks to recognize “the outstanding work individuals have accomplished introducing new technologies and services that are redefining mobility.” This year, the esteemed honor went to our chief executive, \u003Ca href=\"https:\u002F\u002Fwww.mobileye.com\u002Famnon-shashua\u002F\" target=\"_blank\" rel=\"noopener noreferrer\">Professor Amnon Shashua\u003C\u002Fa>.\u003C\u002Fp>\n\u003Cp>“\u003Cspan style=\"color: black;\">I am truly overwhelmed. \u003C\u002Fspan>\u003Ca href=\"https:\u002F\u002Fwww.automotivehalloffame.org\u002Fhonoree\u002Famnon-shashua\u002F\" target=\"_blank\" rel=\"noopener noreferrer\">Entering the Automotive Hall of Fame\u003C\u002Fa> and being awarded the Mobility Innovator of the Year is an incredible honor,” Prof. Shashua said during the induction and awards ceremony in Detroit on Thursday. “I am privileged to have the chance to work in and influence such an exciting industry.”\u003C\u002Fp>\n\u003Cp>Shashua founded Mobileye in 1999. At the time, he said, “no one in the automotive industry believed that a single front-facing camera could achieve the level of performance and robustness needed for a system that would prevent or mitigate collisions and other safety functions. I must say that it gave me great pleasure to do something that everybody said could not be done.”\u003C\u002Fp>\n\u003Cp> \u003C\u002Fp>\n\u003Cp>\u003Ciframe class=\"ql-video\" src=\"https:\u002F\u002Fwww.youtube.com\u002Fembed\u002Fo1FmAg1OTGA\" width=\"560\" height=\"315\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\">\u003C\u002Fiframe>\u003C\u002Fp>\n\u003Cp>“Amnon’s biggest contribution to the automotive industry is actually laying the groundwork for a safer, greener, and data-driven future – decades before such a vision would be deemed anything but a fantasy,” Mobileye’s chief legal officer and general counsel Liz Cohen-Yerushalmi attested in the tribute video above. “I don’t think it would be an exaggeration to say that he has revolutionized the automotive industry forever.”\u003C\u002Fp>\n\u003Cp>\u003Cstrong>An Honorable History\u003C\u002Fstrong>\u003C\u002Fp>\n\u003Cp>Since its establishment in 1939, the Automotive Hall of Fame has bestowed more than 750 awards upon industry leaders and innovators. Its long list of inductees is a veritable \u003Cem>Who's Who\u003C\u002Fem> of notable names, identifiable with their eponymous brands to this day, such as W.O. Bentley, Ettore Bugatti, David D. Buick, Louis Chevrolet, Walter P. Chrysler, André Citroën, Enzo Ferrari, Soichiro Honda, Armand Peugeot, Ferdinand Porsche, Louis Renault, and Ratan Tata – as well as brothers Horace E. and John F. Dodge, three generations of Fords, three Toyodas [\u003Cem>sic\u003C\u002Fem>], and five Opels.\u003C\u002Fp>\n\u003Cp>This year’s new inductees include the late sportscar magnate Ferruccio Lamborghini, Chinese entrepreneur Lu Guanqiu, pioneering production engineer Taiichi Ohno, trailblazing female racing driver Lyn St. James, and Alma and Victor Green (authors of \u003Cem>The Green Book\u003C\u002Fem>). Alongside Prof. Shashua, the organization also honored \u003Ca href=\"https:\u002F\u002Fyoutu.be\u002FJ0SVWiDienk\" target=\"_blank\" rel=\"noopener noreferrer\">Ford CEO Jim Farley\u003C\u002Fa> as Industry Leader of the Year.\u003C\u002Fp>\n\u003Cp>\u003Cimg src=\"https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002Fd113ad90ae299d3079c796724eb0c864_1658474431123.png\" alt=\"Mobileye CEO Prof. Amnon Shashua with Ford CEO Jim Farley at the 2022 Automotive Hall of Fame induction and awards ceremony.\" \u002F>\u003C\u002Fp>\n\u003Cp>“The automotive industry is experiencing revolutionary change driven by innovators who are shaping the future of mobility. The Mobility Innovator Award celebrates individuals and their impact,” noted Automotive Hall of Fame president Sarah Cook. “We are thrilled to recognize Amnon Shashua for his industry-leading contributions to advanced driving assist systems and other autonomous driving solutions.”\u003C\u002Fp>\n\u003Cp>This award is the latest in a string of citations bestowed upon our founder and chief executive. Late last year, Shashua was named \u003Ca href=\"https:\u002F\u002Fwww.mobileye.com\u002Fnews\u002Famnon-shashua-automated-driving-executive-of-the-year-automotive-news\u002F\" target=\"_blank\" rel=\"noopener\">Automated Driving Executive of the Year by \u003Cem>Automotive News\u003C\u002Fem>\u003C\u002Fa>. He received the \u003Ca href=\"https:\u002F\u002Fwww.mobileye.com\u002Fnews\u002Fprof-amnon-shashua-wins-the-dan-david-prize\u002F\" target=\"_blank\" rel=\"noopener\">Dan David Prize\u003C\u002Fa> in 2020, and was named \u003Ca href=\"https:\u002F\u002Fwww.imaging.org\u002Fsite\u002FIST\u002FIST\u002FConferences\u002FEI\u002FEI_Scientist_of_the_Year.aspx\" target=\"_blank\" rel=\"noopener noreferrer\">Electronic Imaging Scientist of the Year\u003C\u002Fa> in 2019 by the Society for Imaging Science and Technology.\u003C\u002Fp>\n\u003Cp>In addition to his role as president and CEO of Mobileye, Prof. Shashua is a senior vice president at Intel. He holds the Sachs Chair in Computer Science at the Hebrew University of Jerusalem, has published more than 160 scientific papers, and holds over 90 patents. \u003C\u002Fp>\n\u003Cp>\u003Cspan style=\"background-color: white; color: #242424;\">A pioneering innovator in the field of artificial intelligence, Prof. Shashua leads multiple ventures to apply AI to tackle real-world challenges. In parallel to Mobileye, \u003C\u002Fspan>\u003Ca style=\"background-color: white; color: #4f52b2;\" href=\"https:\u002F\u002Fwww.orcam.com\u002F\" target=\"_blank\" rel=\"noopener noreferrer\">OrCam Technologies\u003C\u002Fa>\u003Cspan style=\"background-color: white; color: #242424;\"> develops wearable devices to assist the visually and hearing impaired; the \u003C\u002Fspan>\u003Ca style=\"background-color: white; color: #4f52b2;\" href=\"https:\u002F\u002Fwww.onezerobank.com\u002F\" target=\"_blank\" rel=\"noopener noreferrer\">One Zero Digital Bank\u003C\u002Fa>\u003Cspan style=\"background-color: white; color: #242424;\"> harnesses AI to enable smarter management of our finances; and \u003C\u002Fspan>\u003Ca style=\"background-color: white; color: #4f52b2;\" href=\"https:\u002F\u002Fwww.ai21.com\u002F\" target=\"_blank\" rel=\"noopener noreferrer\">AI21 Labs\u003C\u002Fa>\u003Cspan style=\"background-color: white; color: #242424;\"> is working to revolutionize how we read and write, unlocking new forms of communication and expression. We look forward to seeing Shashua’s groundbreaking work yield tangible breakthroughs in wearables, fintech, natural language processing, and more in the years to come – much as he has in the automotive industry.\u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cimg src=\"https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002F6bbb4abd3c4dab2e911ded29725e62b2_1658828020200.jpg\" alt=\"Mobileye CEO Prof. Amnon Shashua with his fellow award-recipients and inductees to the Automotive Hall of Fame.\" \u002F>\u003C\u002Fp>","2022-07-22T07:00:00.000Z",[6,70,99],1784714290372]