Previously in this space we examined the roles of radar and LiDAR (Light Detection and Ranging) in developing self-driving vehicles. Now we will look at the third member of the Sensor Trifecta, machine vision.
提供识别对象（例如行人）或来自相机图像的模式的能力涉及一种称为深度学习的人工智能。深度学习的基本概念是使用神经网络来检查数百或数千个示例的正确答案，然后使用该经验来创建算法在新情况下解决类似的问题（在自主驾驶的情况下，是左转还是左转或正确，加速或制动）。
同样，通过深度学习可以开发另一种算法，以将停止标志的图像分解为其必不可少的部分：标志的形状（八角形），颜色（红色）和“停止”一词，以正确解释提供的信息。在这两种情况下，在培训期间都建立了功能和动作之间的映射。
To give you an idea of the complexity of the Deep Learning task for Level 4 or 5 autonomous vehicles, consider the Imagenet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in object category location (for 1,000 categories) and detection (for 200 fully labeled categories) using millions of images. A Top-5 error rate—the fraction of test images for which the correct label is not among the five labels considered most probable by the model—is on the order of 10%.
所以,正确解释的难度camera images you might now be wondering which sensor technology among Radar, Laser, or Machine Vision will win out. The answer is all three. Use of all three sensor technologies is needed to give the autonomous car the redundancy to: 1) develop an accurate map of its immediate surroundings; 2) provide the necessary situational awareness without error; and 3) determine a safe, drivable path, maneuver in traffic, and avoid collisions.
花一秒钟让它安顿下来。好的，让我们继续前进
除了为自动驾驶汽车提供成功的深度学习算法的困难外，是现实世界中的事实，即在路上有和没有其他汽车的驾驶是两个完全不同的问题。前者必须考虑不可预测的动议。抛出需要制定“政策”的需求，即在四路停止或在红色上右转时该怎么做，您可以快速理解这如何使已经具有挑战性的机器学习任务复杂化。本能表明，还有很多工作要做，实际上，政策可能是机器视觉算法的致命弱点，因为人类驾驶员的行为方式不可预测。
还有另一种方式吗？也许。Mobileye是一家位于以色列的公司，为汽车行业开发相机，硬件和软件是如此。Recently acquired by Intel for $15.3 billion, Mobileye is pinning its hopes on a system that employs eight cameras spaced around the vehicle, along with processing chips. Key to the Mobileye effort, starting in 2018, is crowdsourcing data to produce high-definition maps, as well as the driving policy intelligence underlying driving decisions.
当前的3D映射是通过激光扫描进行的，该扫描机队在美国道路上行驶。但这是一个非常时间和人力密集型操作。美国的道路网络有400万英里的道路，因此即使有大量汽车，这也可能需要一段时间。Mobileye称其答案的道路体验管理（REM），该管理可实现众包的实时数据，以进行精确本地化和高清车道数据，以支持完全自动驾驶。
Key to the Mobileye effort, starting in 2018, is crowdsourcing data to produce high-definition maps, as well as the driving policy intelligence underlying driving decisions.
Whereas a normal digital map would show roads, intersections and geophysical landmarks; these maps would have a precision measured in centimeters.
Here’s how: With millions of front-facing cameras about to be installed in vehicles the idea is to use these cameras with some built-in AI to locate key road markers—lanes, signposts and other objects—and send the results up to the cloud, analyze it there and send back updated maps about the road just traveled.
根据Mobileye的说法，仅需在给定的道路上行驶的9辆车即可获得所需的高清地图准确性（10厘米）。Mobileye声称，向云的数据传输速率不会像您想象的那样高：每公里的道路10kb或100公里的1MB。相机的AI将解析不重要的数据，使传输速率易于管理。
Transforming the imagery captured into useful data will require a huge amount of computing power. Mobileye’s current chip, EyeQ4, will be ready for volume production in 2018 and is aimed at supporting Level 3 automation, the ability of a car to drive itself with only occasional human intervention. For fully autonomous vehicles being prepared for launch in the 2020-21 time frame the next version of Mobileye’s computer vision chip, known as EyeQ5, will need to perform 12-15 Tera (trillion) operations per second to process the visual data, while maintaining power consumption of 5W or less, according to the company.
技术特定的标准，IEEE P2020（2020项目），用于汽车相机图像质量和通信协议正在开发中。该项目旨在指定测量和测试汽车图像质量的方法和指标，以确保一致性并创建跨行业参考点。一个主要目标是定义一套标准化的客观和主观测试方法，以测量OEM和1 Tier 1系统集成商和组件供应商的汽车相机图像质量，通信和比较。
IEEE’s Standards Association Working Group on Automotive System Image Quality reports strong support from participants including Daimler, Ford, GM, Intel, Jaguar Land Rover, LG, ON Semiconductor, OmniVision, Panasonic, PSA, Robert Bosch, Samsung, Sony, Valeo, and many more.
