[{"data":1,"prerenderedAt":310},["ShallowReactive",2],{"tags-opinion":3,"tag-posts-opinion":126,"header-links":154,"corporate-footer":241,"footer-year":309},[4,12,20,27,33,41,48,55,63,70,77,84,90,98,106,113,120],{"id":5,"tag":6,"url":7,"list_order":5,"tag_title":8,"tag_description":9,"tag_image":10,"is_visible":11},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",true,{"id":13,"tag":14,"url":15,"list_order":16,"tag_title":17,"tag_description":18,"tag_image":19,"is_visible":11},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":21,"tag":22,"url":23,"list_order":21,"tag_title":24,"tag_description":25,"tag_image":26,"is_visible":11},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":16,"tag":28,"url":29,"list_order":13,"tag_title":30,"tag_description":31,"tag_image":32,"is_visible":11},"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":34,"tag":35,"url":36,"list_order":37,"tag_title":38,"tag_description":39,"tag_image":40,"is_visible":11},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":42,"tag":43,"url":44,"list_order":34,"tag_title":45,"tag_description":46,"tag_image":47,"is_visible":11},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":49,"tag":50,"url":51,"list_order":42,"tag_title":52,"tag_description":53,"tag_image":54,"is_visible":11},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":56,"tag":57,"url":58,"list_order":59,"tag_title":60,"tag_description":61,"tag_image":62,"is_visible":11},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":64,"tag":65,"url":66,"list_order":56,"tag_title":67,"tag_description":68,"tag_image":69,"is_visible":11},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":37,"tag":71,"url":72,"list_order":73,"tag_title":74,"tag_description":75,"tag_image":76,"is_visible":11},"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":78,"tag":79,"url":80,"list_order":78,"tag_title":81,"tag_description":82,"tag_image":83,"is_visible":11},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":59,"tag":85,"url":86,"list_order":49,"tag_title":87,"tag_description":88,"tag_image":89,"is_visible":11},"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":91,"tag":92,"url":93,"list_order":64,"tag_title":94,"tag_description":95,"tag_image":96,"is_visible":97},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",false,{"id":99,"tag":100,"url":101,"list_order":102,"tag_title":103,"tag_description":104,"tag_image":105,"is_visible":97},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":107,"tag":108,"url":109,"list_order":91,"tag_title":110,"tag_description":111,"tag_image":112,"is_visible":11},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":114,"tag":115,"url":116,"list_order":99,"tag_title":117,"tag_description":118,"tag_image":119,"is_visible":11},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":121,"tag":122,"url":123,"list_order":107,"tag_title":124,"tag_description":125,"tag_image":62,"is_visible":11},19,"Physical AI","physical-ai","Mobileye Blog | Physical AI","Read the latest news and updates about Mobileye's Physical AI technologies",[127,142],{"id":128,"type":58,"lang":129,"primary_tag":57,"url":130,"title":131,"short_title":132,"subtitle":132,"description":133,"meta_description":133,"image":134,"img_alt":135,"author_name":136,"download_doc":132,"download_title":132,"download_btn":132,"webinar_video":132,"thumbnail":132,"author_id":132,"content":137,"publish_date":138,"featured":139,"is_gated":132,"is_infographic":132,"is_hidden":97,"is_active":11,"tags":140,"updated_at":141},254,"en","mobileye-dxp-as-a-novel-approach","采用全新的Mobileye DXP平台解决定制化需求",null,"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.","Prof. Shai Shalev-Shwartz and Prof. Amnon Shashua","\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>","2024-02-21T06:00:00.000Z",0,[57,6,65,115,22],"2026-08-04T08:00:37.472Z",{"id":143,"type":58,"lang":129,"primary_tag":6,"url":144,"title":145,"short_title":132,"subtitle":132,"description":146,"meta_description":146,"image":147,"img_alt":148,"author_name":149,"download_doc":132,"download_title":132,"download_btn":132,"webinar_video":132,"thumbnail":132,"author_id":132,"content":150,"publish_date":151,"featured":139,"is_gated":132,"is_infographic":132,"is_hidden":97,"is_active":11,"tags":152,"updated_at":153},199,"defining-a-new-taxonomy-for-consumer-autonomous-vehicles","Defining a New Taxonomy for Consumer Autonomous Vehicles","Mobileye’s CEO and CTO detail the new taxonomy revealed at CES 2023 for deploying eyes-off\u002Fhands-off self-driving consumer vehicles.","https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002F848b0bf9c27dbd2df10eaa07a2a2c790_1675702827035.png","Mobileye's new taxonomy for autonomous vehicles is based on defining the interaction between man and machine.","Prof. Amnon Shashua and Prof. Shai Shalev-Shwartz","\u003Cp>Tech and auto companies had a very turbulent 2022 in almost every aspect. In spite of great turmoil, the automobile industry has shifted gears in its pursuit to adopt and deploy consumer-level autonomy in the near future. A number of the industry conventions around autonomous driving have again become unclear and ambiguous as a result of this development. The confusion threatens to obscure the real benefits of autonomy in terms of safety, convenience, and efficiency.  \u003C\u002Fp>\n\u003Cp>We see a growing need for a new way of talking and thinking about consumer AVs (CAV) that recognizes how they will work in the real world, and ensures the usefulness, safety, and scalability of consumer-level AVs. To achieve that, Mobileye has laid down a new taxonomy alongside a set of basic requirements for CAV, which was presented in our yearly address at \u003Ca href=\"https:\u002F\u002Fwww.mobileye.com\u002Fblog\u002Fces-2023-recap\u002F\">the last CES\u003C\u002Fa>.\u003C\u002Fp>\n\u003Ch3>The Need for Clarity\u003C\u002Fh3>\n\u003Cp>Autonomous driving is viewed mainly through the prism of SAE Levels of autonomy, also known as J3016, which is widely accepted as the industry standard. When introduced in 2014, the SAE J3016 provided a very good reference for AV development and regulation while everybody was still wrapping their minds around the question of “what is autonomous driving, exactly?”. However, as we move forward with the productization of autonomous and highly automated systems, it is evident that the current Level 1-5 taxonomy cannot form a basis for a product-oriented description that is clear for both the engineer and the end customer.\u003C\u002Fp>\n\u003Cp>When looking at the current industry discourse, we see two issues that need to be addressed. The first issue is vague and unclear definitions from an end-user perspective. Second and more important is the unnecessary distinction between Level 3 and Level 4. According to J3016, Level 3 and Level 4 differ in the Minimum Risk Maneuver (MRM) requirements and the vigilance level of the human driver. This may lead to “\u003Cem>failures by design\u003C\u002Fem>” of the autonomous system, as demonstrated in an \u003Ca href=\"https:\u002F\u002Famnon-shashua.medium.com\u002Fon-black-swans-failures-by-design-and-safety-of-automated-driving-systems-1401076e9027\">opinion paper\u003C\u002Fa> we published in 2021.\u003C\u002Fp>\n\u003Ch3>Simpler Language\u003C\u002Fh3>\n\u003Cp>To deal with the deficiencies depicted above, we propose a simplified language that defines the levels of autonomy based on four axes: (i) Eyes-on\u002FEyes-off, (ii) Hands-on\u002FHands-off, (iii) Driver versus No-driver, and (iv) MRM requirement. As seen in the chart below, this creates four product categories covering the entire spectrum of automated driving.\u003C\u002Fp>\n\u003Cp>\u003Ca href=\"https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002F84fb9e3a75b5cc9da909240510812dc9_1675683237023.png\" target=\"_blank\" rel=\"noopener\">\u003Cimg src=\"https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002F84fb9e3a75b5cc9da909240510812dc9_1675683237023.png\" alt=\"Mobileye's new taxonomy for driving automation, based on the respective roles of man and machine.\" width=\"1650\" height=\"928\" \u002F>\u003C\u002Fa>\u003C\u002Fp>\n\u003Cp>\u003Cstrong>1) Eyes-on\u002FHands-on\u003C\u002Fstrong>: this category covers all the basic driver-assist functions, such as Autonomous Emergency Braking (AEB) and Lane Keep Assist (LKA). The driver is still responsible for the entire driving task while the system monitors the human driver (Level 1-2 according to SAE).\u003C\u002Fp>\n\u003Cp>\u003Cstrong>2) Eyes-on\u002FHands-off\u003C\u002Fstrong>: this is a driver-assistance function where the driver’s hands can be off the steering wheel while the system takes control of the driving and the driver supervises the system (hence, Eyes-on) within a specified Operational Design Domain (ODD). With a proper driving monitoring system (DMS), one can create a very useful human\u002Fmachine synergetic interaction (analogous to pilots supervising the auto-pilot system in an aircraft) and increase the overall safety of driving. This is usually referred to as Level 2+, a term that was first coined by Mobileye and not formally defined by SAE. Due to the absence of the Eyes-on\u002FHands-off category from the SAE taxonomy, it is usually wrongly classified as Level 3-4. It is important to emphasize that the role of the “supervisor” changes from (1) to (2): in an Eyes-on\u002FHands-on system it is the \u003Cem>system\u003C\u002Fem> that is supervising the driver and intervening (rarely) to avoid an accident (like applying the brakes to avoid collision). It is important that \u003Cem>interventions\u003C\u002Fem> by the system happen rarely and in emergency situations rather than a continuous interference with the human driver. In an Eyes-on\u002FHands-off system it is the \u003Cem>human driver\u003C\u002Fem> who is supervising the system. To be effective, the interventions should happen rarely, and in order to keep the human driver vigilant, a proper DMS should be in place. An Eyes-on\u002FHands-off setting increases safety since the failure modes of the human and the system are very different: human failures are mostly concentrated on lack of attention and distraction that can happen in good weather and mundane road conditions whereas system failures mostly occur in challenging environments (weather, road types, and traffic maneuvers).\u003C\u002Fp>\n\u003Cp>\u003Cstrong>3) Eyes-off\u002FHands-off\u003C\u002Fstrong>: the system controls the driving function within a specified ODD (say, highways with on\u002Foff ramp transitions) without the human driver needing to supervise the driving (hence, Eyes-off). Once the ODD comes to an end, and if the driver does not take back control, the system is able to conduct a full MRM and stop safely on the shoulder of the road. This category can be classified as either Level 3 or Level 4 according to SAE J3016. It is worth noting that J3016 exempts the Level 3 system from performing full MRM by allowing it to stop in-lane, but we argue that a \u003Ca href=\"https:\u002F\u002Ftheintercept.com\u002F2023\u002F01\u002F10\u002Ftesla-crash-footage-autopilot\u002F\">stop-in-lane emergency maneuver is not safe\u003C\u002Fa>. In addition, the Eyes-off\u002FHands-off system still requires a qualified driver sitting in the driver’s seat so he\u002Fshe will be able to take control in non-safety-related situations happening at zero velocity in order not to jeopardize the flow of traffic (e.g., deadlocks, policeman directing traffic, etc.).\u003C\u002Fp>\n\u003Cp>\u003Cstrong>4) No Driver\u003C\u002Fstrong>: when there is no human driver present, say in a Robotaxi, the role of the human driver is replaced by a \u003Cem>teleoperator\u003C\u002Fem> who can intervene to resolve non-safety situations like those mentioned above.\u003C\u002Fp>\n\u003Cp>In the simplified language we propose above, the requirements from the driver are well defined, so there are no ambiguities from the end-user perspective. The human driver is either supervised by the system or is supervising the system or is allowed to disconnect attention entirely without being bothered by the system. The Eyes-off category translates to a full MRM capability of safely stopping on the shoulder of the road without blocking traffic. The value proposition of an Eyes-off setting is \u003Cstrong>time\u003C\u002Fstrong>, i.e., the human in the driving seat can legally attend to non-driving matters, within the prescribed ODD, without the need to supervise the system.\u003C\u002Fp>\n\u003Ch3>Usefulness, Safety, Scalability\u003C\u002Fh3>\n\u003Cp>\u003Cimg src=\"https:\u002F\u002Fstatic.mobileye.com\u002Fwebsite\u002Fus\u002Fcorporate\u002Fimages\u002F4fae36a27d981a211498c92aaf56312a_1675683373282.png\" alt=\"The process of moving from human operation to autonomous driving in an ODD should be clear.\" width=\"1650\" height=\"704\" \u002F>\u003C\u002Fp>\n\u003Cp>We contend that an Eyes-off system should be governed by three principles: (i) usefulness, (ii) safety, and (iii) scalability.\u003C\u002Fp>\n\u003Cp>\u003Cstrong>Usefulness\u003C\u002Fstrong>: A good Eyes-off system should operate in an ODD that enables prolonged and continuous periods of use such that going in\u002Fout from an ODD does not happen frequently. As depicted in the figure below, if we look at the average person’s commute, going in\u002Fout the ODD should happen only once. According to this requirement, we set the minimum useful ODD threshold to be freeways up to 80 mph, including the ability to navigate on-ramps and off-ramps. Anything below that is considered not useful. For example, a system with an ODD of freeways up to 40 mph would require the driver to take control every time the lead vehicle exceeds 40 mph. This also poses a safety risk as it increases the friction between the human and the machine.\u003C\u002Fp>\n\u003Cp>\u003Cstrong>Safety\u003C\u002Fstrong>: A safe Eyes-off solution should have no systematic errors, i.e., an error that can be reproduced in a certain emergency situation that is within the system’s ODD. Trying to hide behind statistics that the particular scenario will only happen rarely leads to unacceptable compromises in system design. \u003C\u002Fp>\n\u003Cp>To better articulate the point, let's look at the ODD of current Level 3 systems coming to market – freeways-only up to 40 mph with no lane changes. Because of the low operating speed, the system should allegedly be able to cope with any in-lane emergency braking. However, out-of-lane emergency maneuvers – responding to dangerous cut-ins, for example – are considered rare and not supported by the sensor configuration, the sensing state, and driving policy algorithms. For example, a sensor configuration that does not include high-resolution 360-degree coverage cannot support a lane change under all traffic densities. Failing to do so creates a “reproducible error.”\u003C\u002Fp>\n\u003Cp>The result of our requirement for no reproducible errors is that the system ODD should be much larger than the customer ODD. In other words, it is possible to limit the ODD to the customer to not allow lane changes in a regular mode of operations, but still, the system needs to be able to change lanes in an emergency maneuver. This notion should translate directly to the system’s design –high-resolution surround sensors, driving policy, high-definition maps, controllability, and so forth.\u003C\u002Fp>\n\u003Cp>\u003Cstrong>Scalability\u003C\u002Fstrong>: We believe the operational domain of a full Eyes-off\u002FHands-off vehicle can best be thought of as a stack of ODDs – starting from highways, then adding arterial roads, signaled intersections, unprotected turns and so forth, that eventually add up to autonomy everywhere. We call those ODDs “autonomous blades,” and through this, we have built an evolutionary path of incremental steps in our products from Eyes-on\u002FHands-off systems to full AVs.\u003C\u002Fp>\n\u003Cp>Without this approach, AV development simply doesn’t scale. Every blade of operation requires a “moonshot” of validation and engineering, and if that moonshot is successful, it doesn’t guarantee success in the next blade. Just because a prototype AV can manage city streets in daylight hours doesn’t mean it can easily adapt to multilane highways at 80 mph at night, and vice versa. Instead, we have focused our “moonshot” efforts on the Eyes-on\u002FHands-off system with full ODD as a baseline for eyes-off blades.  \u003C\u002Fp>\n\u003Ch3>The Bridge to Consumer AVs\u003C\u002Fh3>\n\u003Cp>Our \u003Ca href=\"https:\u002F\u002Fwww.mobileye.com\u002Fsolutions\u002Fsuper-vision\u002F\">Mobileye SuperVision™\u003C\u002Fa> camera-only Eyes-on\u002FHands-off system already contains the entire technological backbone needed to enable hands-off driving, such that the transition to eyes-off blades only adds active sensors as redundant components to the perception system. All the heavy lifting of detailed sensing, the driving policy required to maneuver the car in any traffic scenario, and the requirement for HD maps covering all types of roads are all done in the SuperVision system. The redundancies to the perception system then become the only incremental work needed to make the leap from eyes-on to eyes-off.\u003C\u002Fp>\n\u003Cp>Today more than ever, we believe in the potential for autonomous technology to transform the world and how we travel daily. We at Mobileye know many share that belief, and as we put our technologies like SuperVision on the road, we will see those benefits come to life – especially if our industry can clearly share what that future looks like and disambiguate as many uncertainties as we 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