Mobileye高級駕駛輔助系統（ADAS）
Mobileye is the global leader in the development of vision technology for Advanced Driver Assistance Systems (ADAS) and autonomous driving.
We have over 1,700 employees continuing our two-decade tradition of developing state-of-the-art technologies in support of automotive safety and autonomous driving solutions.
Mobileye是高級駕駛輔助系統（ADAS）和自動駕駛視覺技術開發的全球領導者。
擁有1,700多名員工，延續了兩個十年的傳統，即開發支持汽車安全和自動駕駛解決方案的最新技術。
Mobileye是Tier 2汽車供應商，與所有主要的Tier 1供應商合作，涵蓋了絕大多數的汽車市場（擁有25個以上OEM的計劃）。這些OEM選擇Mobileye的原因在于其先進的技術，創新的文化和敏捷性。直接的結果是，作為對安全性至關重要的汽車產品，進行嚴格驗證過程的一部分，技術的魯棒性和性能已在數百萬英里的行駛里程中經過了實戰測試。
Mobileye is a Tier 2 automotive supplier working with all major Tier 1 suppliers, covering the vast majority of the automotive market (programs with over 25 OEMs). These OEMs choose Mobileye, for its advanced technology, innovation culture, and agility. As a direct result, the robustness and performance of our technology have been battle-tested over millions of driving miles as part of the stringent validation processes of safety-critical automotive products.
From the beginning, Mobileye has developed hardware and software in-house. This has facilitated the strategic advantage of responsive and short development cycles of highly interdependent hardware, software and algorithmic stacks. This interdependence is key to producing high-performance and low power consumption products.
從一開始，Mobileye就內部開發硬件和軟件。這促進了高度相互依賴的硬件，軟件和算法堆棧的響應時間短，開發周期短的戰略優勢 。這種相互依賴關系是生產高性能和低功耗產品的關鍵。
Mobileye在的系統級芯片（SoC）- EyeQ ? 家族-提供處理能力，支持基于單個camera傳感器的ADAS功能全面的套件。第四代和第五代，EyeQ ? 將進一步支持半和完全自主駕駛，具有帶寬/吞吐量流和處理的全套環繞camera，雷達和激光雷達。
Mobileye’s system-on-chip (SoC) – the EyeQ? family – provides the processing power to support a comprehensive suite of ADAS functions based on a single camera sensor. In its fourth and fifth generations, EyeQ? will further support semi and fully autonomous driving, having the bandwidth/throughput to stream and process the full set of surround cameras, radars and LiDARs.
ADAS
Advanced Driver Assistance Systems (ADAS) systems range on the spectrum of passive/active.
A passive system alerts the driver of a potentially dangerous situation so that the driver can take action to correct it. For example, Lane Departure Warning (LDW) alerts the driver of unintended/unindicated lane departure; Forward Collision Warning (FCW) indicates that under the current dynamics relative to the vehicle ahead, a collision is imminent. The driver then needs to brake in order to avoid the collision.
高級駕駛員輔助系統（ADAS）系統的作用范圍是被動/主動。
被動系統會警告駕駛員潛在的危險情況，以便駕駛員可以采取措施進行糾正。例如，車道偏離警告（LDW）會警告駕駛員意外/意外的車道偏離；前向碰撞警告（FCW）表示在相對于前方車輛的當前動態情況下，即將發生碰撞。然后，駕駛員需要剎車以避免碰撞。
相反，主動安全系統會采取行動。自動緊急制動（AEB）可以識別即將發生的碰撞和剎車，而無需任何駕駛員干預。主動功能的其它示例，包括自適應巡航控制（ACC），車道保持輔助（LKA），車道居中（LC）和交通擁堵輔助（TJA）。
In contrast, active safety systems take action. Automatic Emergency Braking (AEB) identifies the imminent collision and brakes without any driver intervention. Other examples of active functions are Adaptive Cruise Control (ACC), Lane Keeping Assist (LKA), Lane Centering (LC), and Traffic Jam Assist (TJA).
ACC automatically adjusts the host vehicle speed from its pre-set value (as in standard cruise control) in case of a slower vehicle in its path. LKA and LC automatically steer the vehicle to stay within the lane boundaries. TJA is a combination of both ACC and LC under traffic jam conditions. It is these automated features which comprise the building blocks of semi/fully autonomous driving.
Mobileye supports a comprehensive suite of ADAS functions – AEB, LDW, FCW, LKA, LC, TJA, Traffic Sign Recognition (TSR), and Intelligent High-beam Control (IHC) – using a single camera mounted on the windshield, processed by a single EyeQ? chip.
如果行駛中的車輛速度較慢，ACC會根據其預設值，自動調整本車速度（如標準巡航控制）。LKA和LC會自動引導車輛停留在車道邊界內。在交通擁堵情況下，TJA是ACC和LC的組合。 這些自動化功能構成了半自動駕駛/全自動駕駛的基礎。
Mobileye支持安裝在擋風玻璃上的單個攝像頭，由ABS，LDW，FCW，LKA，LC，TJA，交通標志識別（TSR）和智能遠光燈控制（IHC）等全面的ADAS功能套件支持。單EyeQ ? 芯片。
除了通過與汽車原始設備制造商集成來交付這些ADAS產品之外，Mobileye還提供售后預警系統，可以將其改裝到任何現有車輛上。Mobileye售后產品在一個捆綁包中提供了許多救生警告，從而保護駕駛員免受分心和疲勞的危險。
In addition to the delivery of these ADAS products through integration with automotive OEMs, Mobileye offers an aftermarket warning-only system that can be retrofitted onto any existing vehicle. The Mobileye aftermarket product offers numerous life-saving warnings in a single bundle, protecting the driver against the dangers of distraction and fatigue.
Computer Vision
From the outset, Mobileye’s philosophy has been that if a human can drive a car based on vision alone – so can a computer. Meaning, cameras are critical to allow an automated system to reach human-level perception/actuation: there is an abundant amount of information (explicit and implicit) that only camera sensors with full 360 degree coverage can extract, making it the backbone of any automotive sensing suite.
It is this early recognition – nearly two decades ago – of the camera sensor superiority and investment in its development, that led Mobileye to become the global leader in computer vision for automotive.
從一開始，Mobileye的哲學就是， 如果人類可以僅憑視覺駕駛汽車，那么計算機也可以。 這意味著，相機對于使自動化系統達到人類水平的感知/致動至關重要：只有大量的傳感器信息（顯式和隱式）才可以提取具有完整360度覆蓋范圍的相機傳感器，從而使其成為任何汽車的骨干感應套件。
正是在將近二十年前的早期認可中，攝像機傳感器的優越性和對其開發的投資使Mobileye成為了汽車計算機視覺領域的全球領導者。
Mobileye開發camera功能的方法，始終是首先生產經過優化并經過驗證的，可滿足所有功能需求的最佳，獨立的僅相機產品。作為展示，演示車僅從耶路撒冷自動駕駛到特拉維夫，而后僅依靠攝像頭傳感器，而批量生產的自動駕駛車融合了附加的傳感器，以提供基于多種模式（主要是雷達和LiDAR）的強大，冗余的解決方案。
Mobileye’s approach to the development of camera capabilities has always been to first produce optimal, self-contained camera-only products, demonstrated and validated to serve all functional needs. As a showcase, our demonstration vehicle drives autonomously from Jerusalem to Tel Aviv and back relying on camera sensors alone, while series-production autonomous vehicles fuse-in additional sensors for delivering a robust, redundant solution based on multiple modalities (mainly radar and LiDAR).
From ADAS to Autonomous
The road from ADAS to full autonomy depends on mastering three technological pillars:
? Sensing: robust and comprehensive human-level perception of the vehicle’s environment, and all actionable cues within it.
? Mapping: as a means of path awareness and foresight, providing redundancy to the camera’s real-time path sensing.
? Driving Policy: the decision-making layer which, given the Environmental Model – assesses threats, plans maneuvers, and negotiates the multi-agent game of traffic.
Only the combination of these three pillars will make fully autonomous driving a reality.
從ADAS到完全自主的道路取決于掌握三個技術支柱：
? 感應：對車輛環境及其內部所有可行線索的全面，全面的人類感知。
? 映射：作為路徑感知和預見的一種手段，為camera的實時路徑感應提供冗余。
? 駕駛策略：決策層根據環境模型–評估威脅，計劃演習并協商交通的多主體博弈。
只有將這三個支柱結合起來，才能實現完全自動駕駛。
The Sensing Challenge
Perception of a comprehensive Environmental Model breaks down into four main challenges:
? Freespace: determining the drivable area and its delimiters
? Driving Paths: the geometry of the routes within the drivable area
? Moving Objects: all road users within the drivable area or path
? Scene Semantics: the vast vocabulary of visual cues (explicit and implicit) such as traffic lights and their color, traffic signs, turn indicators, pedestrian gaze direction, on-road markings, etc.
傳感挑戰
全面環境模型的認知可分為四個主要挑戰：
? 自由空間：確定可驅動區域及其定界符
? 行駛路線：可駕駛區域內路線的幾何形狀
? 移動物體：可駕駛區域或路徑內的所有道路使用者
? 場景語義：大量的視覺線索（顯性和隱式），例如交通信號燈及其顏色，交通標志，轉向指示器，行人注視方向，道路標記等。
The Mapping Challenge
The need for a map to enable fully autonomous driving stems from the fact that functional safety standards require back-up sensors – “redundancy” – for all elements of the chain – from sensing to actuation. Within sensing, this applies to all four elements mentioned above.
While other sensors such as radar and LiDAR may provide redundancy for object detection – the camera is the only real-time sensor for driving path geometry and other static scene semantics (such as traffic signs, on-road markings, etc.). Therefore, for path sensing and foresight purposes, only a highly accurate map can serve as the source of redundancy.
In order for the map to be a reliable source of redundancy, it must be updated with an ultra-high refresh rate to secure its low Time to Reflect Reality (TTRR) qualities.
制圖挑戰
需要地圖以實現全自動駕駛的原因是，功能安全標準要求從感測到執行到后備鏈的所有元素，需要備用傳感器-“冗余”。在感測中，適用于上述所有四個元素。
盡管其它傳感器（例如雷達和LiDAR）可能會為對象檢測提供冗余，但攝像頭是唯一用于行駛路徑幾何形狀和其它靜態場景語義（例如交通標志，道路標記等）的實時傳感器。出于路徑檢測和預見目的，只有高度精確的映射才能用作冗余的來源。
為了使地圖成為可靠的冗余源，必須使用超高的刷新率對其進行更新，確保其較低的反映現實時間（TTRR）質量。
為了應對這一挑戰，Mobileye為利用集群的力量鋪平了道路：利用基于攝像頭的ADAS系統的泛濫，以近乎實時的方式建立和維護準確的環境圖。
Mobileye的道路體驗管理（REM TM）是完全自治的端到端映射和本地化引擎。 解決方案由三層組成：收集代理（任何配備攝像頭的車輛），地圖聚合服務器（云）和使用地圖的代理（自動車輛）。
To address this challenge, Mobileye is paving the way for harnessing the power of the crowd: exploiting the proliferation of camera-based ADAS systems to build and maintain in near-real-time an accurate map of the environment.
Mobileye’s Road Experience Management (REMTM) is an end-to-end mapping and localization engine for full autonomy. The solution is comprised of three layers: harvesting agents (any camera-equipped vehicle), map aggregating server (cloud), and map-consuming agents (autonomous vehicle).
The harvesting agents collect and transmit data about the driving path’s geometry and stationary landmarks around it. Mobileye’s real-time geometrical and semantic analysis, implemented in the harvesting agent, allows it to compress the map-relevant information – facilitating very small communication bandwidth (less than 10KB/km on average).
The relevant data is packed into small capsules called Road Segment Data (RSD) and sent to the cloud. The cloud server aggregates and reconciles the continuous stream of RSDs – a process resulting in a highly accurate and low TTRR map, called “Roadbook.”
收取代理收集并傳輸有關行駛路徑的幾何形狀和周圍固定路標的數據。 在收取代理中實施的Mobileye實時幾何和語義分析使它能夠壓縮與地圖有關的信息-促進非常小的通信帶寬（平均小于10KB / km）。
相關數據被打包到稱為路段數據（RSD）的小型capsules膠囊中，并發送到云中。云服務器聚合并協調連續的RSD流-此過程產生了高度準確且低TTRR的地圖，稱為“ Roadbook”。
映射鏈中的最后一個鏈接是本地化：為了使自動駕駛汽車可以使用任何地圖，該車輛必須能夠在其中定位自己。在地圖使用代理（自動駕駛汽車）中運行的Mobileye軟件，通過實時檢測存儲在其中的所有地標，自動在“Roadbook道路手冊”中對車輛進行定位。
此外， REM TM提供了跨行業信息共享的技術和商業渠道。 REM TM旨在使不同的OEM可以參與此AD關鍵資產（Roadbook）的建設，同時為其RSD貢獻獲得適當和成比例的補償。
The last link in the mapping chain is localization: in order for any map to be used by an autonomous vehicle, the vehicle must be able to localize itself within it. Mobileye software running within the map-consuming agent (the autonomous vehicle) automatically localizes the vehicle within the Roadbook by real-time detection of all landmarks stored in it.
Further, REMTM provides the technical and commercial conduit for cross-industry information sharing. REMTM is designed to allow different OEMs to take part in the construction of this AD-critical asset (Roadbook) while receiving adequate and proportionate compensation for their RSD contributions.
Driving Policy
Where sensing detects the present, driving policy plans for the future. Human drivers plan ahead by negotiating with other road users mainly using motion cues – the “desires” of giving-way and taking-way are communicated to other vehicles and pedestrians through steering, braking and acceleration. These “negotiations” take place all the time and are fairly complicated – which is one of the main reasons human drivers take many driving lessons and need an extended period of training until we master the art of driving. Moreover, the “norms” of negotiation vary from region to region as the code of driving in Massachusetts, for example, is quite different from that of California, even though the rules are identical.
駕駛策略
傳感可以檢測到當前情況，并為未來制定政策計劃。駕駛員主要通過使用運動線索與其它道路使用者進行協商，從而制定了提前調度，即通過轉向，制動和加速，將“讓步”和“讓步”的“愿望”傳達給其它車輛和行人。這些“協商”一直在發生，而且相當復雜，這是人類駕駛員上許多駕駛課程并需要長期訓練，直到掌握駕駛技術的主要原因之一。此外，協商的“規范”因地區而異，例如，馬薩諸塞州的駕車守則與加利福尼亞州的駕車守則大不相同，
使機器人系統控制汽車的挑戰在于，在可預見的未來，“其它”道路使用者很可能是人為驅動的。為了不妨礙交通，機器人汽車應表現出與人協商的技巧，同時時間保證功能安全。 換句話說，希望自動駕駛汽車安全駕駛，但要符合該地區的駕駛規范。Mobileye認為，對于手工制定的基于規則的決策而言，駕駛環境過于復雜。取而代之的是，采用機器學習來通過暴露于數據來“學習”決策過程。
The challenge behind making a robotic system control a car is that for the foreseeable future the “other” road users are likely to be human-driven, therefore in order not to obstruct traffic, the robotic car should display human negotiation skills but at the same time guarantee functional safety. In other words, we would like the robotic car to drive safely, yet conform to the driving norms of the region. Mobileye believes that the driving environment is too complex for hand-crafted rule-based decision making. Instead we adopt the use of machine learning to “learn” the decision making process through exposure to data.
Mobileye’s approach to this challenge is to employ what is called reinforcement learning algorithms trained through deep networks. This requires training the vehicle system through increasingly complex simulations by rewarding good behavior and punishing bad behavior. Our proprietary reinforcement learning algorithms add human-like driving skills to the vehicle system, in addition to the super-human sight and reaction times that our sensing and computing platforms provide. It also allows the system to negotiate with other human-driven vehicles in complex situations. Knowing how to do this well is one of the most critical enablers for safe autonomous driving.
Mobileye應對這一挑戰的方法，采用通過深度網絡訓練的所謂的強化學習算法。需要通過獎勵良好行為和懲罰不良行為，通過日益復雜的模擬來訓練車輛系統。除了感知和計算平臺提供的超人視覺和反應時間外，專有的強化學習算法還為車輛系統增加了類似人的駕駛技能。允許系統在復雜情況下與其它人為駕駛的車輛進行協商。理解如何做到這一點是安全自動駕駛的最關鍵因素之一。

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