Will cars really be able to drive themselves without human operators? Should they? And are they good business investments? Everyone is searching for answers.
汽车真的能够实现无人驾驶吗？应该实现吗？汽车是好的商业投资吗？每个人都在寻找答案。

Autonomous vehicle technology has reached a point where no automaker can ignore it. Every major auto maker is racing to develop and perfect autonomous vehicles, believing that the market for them could one day reach trillions of dollars. Companies such as Ford, General Motors, Nissan, Mercedes, Tesla, and others have invested billions in autonomous technology research and development. GM bought a self-driving car startup called Cruise. Ride-hailing companies like Uber and Lyft believe driverless cars that eliminate labor costs are key to their long-term profitability. (A study conducted by UBS shows that the cost per mile of a selfdriving “robo-taxi” will be about 80 percent less than that of a traditional taxi.) Cars that drive themselves have been on the road in select locations in California, Arizona, Michigan, Paris, London, Singapore, and Beijing. Marketing firm ABI predicts that roughly 8 million vehicles with some level of self-driving capabilities will be shipped in 2025. In December 2018, Waymo, a subsidiary of Google Alphabet, launched a commercial self-driving taxi service called “Waymo One” in the Phoenix metropolitan area.
自动驾驶汽车技术已发展到任何汽车制造商都无法忽视的地步。各大汽车制造商都在竞相开发和完善自动驾驶汽车，并相信其市场规模终有一天可能达到数万亿美元。福特、通用、日产、梅赛德斯、特斯拉等公司已在自动驾驶技术研发上投入了数十亿美元。通用汽车收购了一家名为 Cruise 的自动驾驶汽车初创公司。Uber 和 Lyft 等网约车公司认为，能够消除劳动力成本的无人驾驶汽车是其长期盈利的关键。（瑞银的一项研究表明，自动驾驶“机器人出租车”的每英里成本将比传统出租车低约 80%。）目前，自动驾驶汽车已在加州、亚利桑那州、密歇根州、巴黎、伦敦、新加坡和北京等地的部分地区上路行驶。营销公司 ABI 预测，2025 年将有大约 800 万辆具备一定程度自动驾驶功能的汽车出货。2018 年 12 月，谷歌 Alphabet 旗下的 Waymo 在凤凰城大都会区推出了名为“Waymo One”的商业自动驾驶出租车服务。

A car that is supposed to take over driving from a human requires a powerful computer system that must process and analyze large amounts of data generated by myriad sensors, cameras, and other devices to control and adjust steering, accelerating, and braking in response to real-time conditions. Key technologies include:
一辆旨在取代人类驾驶的汽车需要一个强大的计算机系统，该系统必须处理和分析由无数传感器、摄像头和其他设备产生的大量数据，以便根据实时情况控制和调整转向、加速和制动。关键技术包括：

Sensors: Self-driving cars are loaded with sensors of many different types. Sensors on car wheels measure the car’s velocity as it drives and moves through traffic. Ultrasonic sensors measure and track positions of line curbs, sidewalks, and objects close to the car.
传感器：自动驾驶汽车配备了各种类型的传感器。车轮上的传感器可以测量汽车行驶和穿越交通时的速度。超声波传感器可以测量并追踪路缘线、人行道以及汽车附近物体的位置。

Cameras: Cameras are needed for spotting things like lane lines on the highway, speed signs, and traffic lights. Windshield-mounted cameras create a 3-D image of the road ahead. Cameras behind the rearview mirror focus on lane markings. Infrared cameras pick up infrared beams emitted from headlamps to extend vision for night driving.
摄像头：摄像头用于识别高速公路上的车道线、限速标志和交通信号灯等。安装在挡风玻璃上的摄像头可以创建前方道路的 3D 图像。后视镜后面的摄像头则专注于车道标记。红外摄像头可以拾取前照灯发出的红外光束，从而扩大夜间驾驶的视野。

Lidars: Lidars are light detection and ranging devices that sit on top of most self-driving cars. A lidar fires out millions of laser beams every second, measuring how long they take to bounce back. The lidar takes in a 360-degree view of a car’s surroundings, identifying nearby objects with an accuracy up to 2 centimeters. Lidars are very expensive and not yet robust enough for a life of potholes, extreme temperatures, rain, or snow.
激光雷达：激光雷达是安装在大多数自动驾驶汽车顶部的光探测和测距设备。激光雷达每秒发射数百万束激光，并测量其反射所需的时间。激光雷达可以 360 度全方位观察汽车周围环境，识别附近物体的精度高达 2 厘米。激光雷达价格昂贵，而且目前还不够坚固，无法适应坑洼路面、极端温度、雨雪天气。
GPS: A global positioning system (GPS) pinpoints the car’s macro location, and is accurate to within 1.9 meters. Combined with reading from tachometers, gyroscopes, and altimeters, it provides initial positioning.
GPS：全球定位系统 (GPS) 可精确定位车辆的宏观位置，精度可达 1.9 米以内。结合转速表、陀螺仪和高度计的读数，可提供初始定位。

Radar: Radar bounces radio waves off objects to help see a car’s surroundings, including blind spots, and is especially helpful for spotting big metallic objects, such as other vehicles.
雷达：雷达将无线电波从物体上反射回来，以帮助观察汽车周围环境，包括盲点，并且对于发现其他车辆等大型金属物体尤其有用。
Computer: All the data generated by these technologies needs to be combined, analyzed, and turned into a robot-friendly picture of the world, with instructions on how to move through it, requiring almost supercomputer-like processing power. Its software features obstacle avoidance algorithms, predictive modeling, and “smart” object discrimination (for example, knowing the difference between a bicycle and a motorcycle) to help the vehicle follow traffic rules and navigate obstacles.
计算机：所有这些技术产生的数据都需要整合、分析，并转化为机器人能够理解的世界图景，并指导机器人如何在其中移动，这需要近乎超级计算机般的处理能力。其软件具备避障算法、预测模型和“智能”物体识别（例如，识别自行车和摩托车之间的区别），以帮助机器人遵守交通规则并避开障碍物。
Machine Learning, Deep Learning, and Computer Vision Technology: The car’s computer system has to be “trained” using machine intelligence and deep learning to do things like detect lane lines and identify cyclists, by showing it millions of examples of the subject at hand. Because the world is too complex to write a rule for every possible scenario, cars must be able to “learn” from experience and figure out how to navigate on their own.
机器学习、深度学习和计算机视觉技术：汽车的计算机系统必须通过机器学习和深度学习进行“训练”，通过向其展示数百万个相关示例，才能完成诸如检测车道线和识别骑行者等任务。由于世界过于复杂，我们无法为所有可能的情况制定规则，因此汽车必须能够从经验中“学习”，并自行找到导航方法。

Maps: Before an autonomous car takes to the streets, its developers use cameras and lidars to map its territory in extreme detail. That information helps the car verify its sensor readings, and it is key for any vehicle to know its own location. Self-driving car companies are notorious for overhyping their progress. Should we believe them? At this point, the outlook for them is clouded.
地图：在自动驾驶汽车上路之前，其开发人员会使用摄像头和激光雷达对其行驶区域进行极其详细的绘制。这些信息有助于汽车验证传感器读数，而了解自身位置对于任何车辆来说都至关重要。自动驾驶汽车公司以夸大其词而臭名昭著。我们应该相信他们吗？目前，他们的前景一片黯淡。

In March 2018, a self-driving Uber Volvo XC90 operating in autonomous mode struck and killed a woman in Tempe, Arizona. Uber suspended autonomous vehicle testing for a period of time. Even before the accident, Uber’s self-driving cars were having trouble driving through construction zones and next to tall vehicles like big truck rigs. Uber’s drivers had to intervene far more frequently than drivers in other autonomous car projects.
2018 年 3 月，一辆处于自动驾驶模式的 Uber 沃尔沃 XC90 在亚利桑那州坦佩撞倒了一名女子，导致其死亡。Uber 暂停了一段时间的自动驾驶汽车测试。即使在事故发生之前，Uber 的自动驾驶汽车在穿越施工区域以及与大型卡车等高大车辆并排行驶时也遇到了困难。与其他自动驾驶汽车项目相比，Uber 的司机不得不更频繁地进行干预。

The Uber accident raised questions about whether autonomous vehicles were even ready to be tested on public roads and how regulators should deal with this. Autonomous vehicle technology’s defenders pointed out that nearly 40,000 people die on U.S. roads every year, and human error causes more than 90 percent of crashes. But no matter how quickly selfdriving proliferates, it will be a long time before the robots can put a serious dent in those numbers and convince everyday folks that they’re better off letting the cars do the driving. Uber has revised its approach to autonomous driving and plans to launch its selfdriving cars in pockets of cities where weather, demand, and other conditions are most favorable. While proponents of self-driving cars like Tesla’s Elon Musk envision a self-driving world where almost all traffic accidents would be eliminated, and older adults and those with disabilities could travel freely, most Americans think otherwise. A Pew Research Center survey found that most people did not want to ride in self-driving cars and were unsure if they would make roads more dangerous or safer. Eighty-seven percent wanted a person always behind the wheel, ready to take over if something went wrong.
优步事故引发了人们对自动驾驶汽车是否已准备好在公共道路上进行测试以及监管机构应如何应对的质疑。自动驾驶汽车技术的捍卫者指出，美国每年有近4万人死于道路交通事故，人为失误导致的事故占90%以上。但无论自动驾驶技术如何快速普及，机器人都还需要很长时间才能真正减少这些死亡人数，并让普通人相信让汽车来驾驶更好。优步已经修改了其自动驾驶策略，并计划在天气、需求和其他条件最有利的部分城市推出其自动驾驶汽车。虽然像特斯拉的埃隆·马斯克这样的自动驾驶汽车支持者设想一个几乎所有交通事故都将被消除、老年人和残疾人可以自由出行的自动驾驶世界，但大多数美国人却不这么认为。皮尤研究中心的一项调查发现，大多数人不愿乘坐自动驾驶汽车，也不确定它们会让道路变得更危险还是更安全。87% 的人希望方向盘始终有人控制，以便在出现问题时随时接管。

There’s still plenty that needs to be improved before self-driving vehicles could safely take to the road. Autonomous vehicles are not yet able to operate safely in all weather conditions. Heavy rain or snow can confuse current car radar and lidar systems-autonomous vehicles can’t operate on their own in such weather conditions. These vehicles also have trouble when tree branches hang too low or bridges and roads have faint lane markings. On some roads, self-driving vehicles will have to make guidance decisions without the benefit of white lines or clear demarcations at the edge of the road, including Botts’ Dots (small plastic markers that define lanes). Botts’ Dots are not believed to be effective lane-marking for autonomous vehicles.
在自动驾驶汽车能够安全上路之前，仍有许多方面需要改进。自动驾驶汽车目前尚无法在所有天气条件下安全行驶。暴雨或暴雪会使现有的车载雷达和激光雷达系统产生干扰——自动驾驶汽车无法在这样的天气条件下自行行驶。当树枝垂得太低或桥梁和道路的车道标线模糊时，这些车辆也会遇到麻烦。在某些道路上，自动驾驶汽车必须在没有白线或路边清晰分界线（包括 Botts' Dots（用于划分车道的小型塑料标记）的情况下做出导航决策。Botts' Dots 被认为不是自动驾驶汽车有效的车道标记。

Computer vision systems are able to reliably recognize objects. What remains challenging is “scene understanding”-for example, the ability to determine
计算机视觉系统能够可靠地识别物体。剩下的挑战是“场景理解”，例如，确定
whether a bag on the road is empty or is hiding bricks or heavy objects inside. Although autonomous vehicle vision systems are now capable of picking out traffic lights reliably, they are not always able to make correct decisions if traffic lights are not working. This requires experience, intuition, and knowing how to cooperate among multiple vehicles. Autonomous vehicles must also be able to recognize a person moving alongside a road, determine whether that person is riding a bicycle, and predict how that person is likely to respond and behave. All of that is still difficult for an autonomous vehicle to do right now. Chaotic environments such as congested streets teeming with cars, pedestrians, and cyclists are especially difficult for self-driving cars to navigate.
路上的袋子是空的还是里面藏着砖块或重物。虽然自动驾驶汽车视觉系统现在能够可靠地识别交通信号灯，但如果交通信号灯不工作，它们并不总是能够做出正确的决定。这需要经验、直觉，以及如何在多辆车之间协作。自动驾驶汽车还必须能够识别在路边移动的人，确定此人是否骑自行车，并预测此人可能的反应和行为。所有这些对于自动驾驶汽车来说现在仍然很难做到。拥挤的街道等混乱的环境，挤满了汽车、行人和骑自行车的人，对于自动驾驶汽车来说尤其难以导航。

Driving a car to merge into rapidly flowing lanes of traffic is an intricate task that often requires eye contact with oncoming drivers. How can autonomous vehicles communicate with humans and other machines to let them know what they want to do? Researchers are investigating whether electronic signs and car-to-car communication systems would solve this problem. There’s also what’s called the “trolley problem”: In a situation where a crash is unavoidable, how does a robot car decide whom or what to hit? Should it hit the car coming up on its left or a tree on the side of the road?
驾驶汽车并入快速行驶的车道是一项复杂的任务，通常需要与对面驶来的驾驶员进行眼神交流。自动驾驶汽车如何与人类和其他机器进行通信，让它们知道它们想要做什么？研究人员正在研究电子标志和车对车通信系统能否解决这个问题。此外，还有一个所谓的“电车难题”：在无法避免碰撞的情况下，自动驾驶汽车如何决定撞向谁或撞向什么？是撞向左侧的车辆，还是撞向路边的树木？

Less advanced versions of autonomous vehicle technology are already on the market. No current production car in the United States can drive while you sleep, read, or tweet, but many systems can maintain following distance with the vehicle ahead or keep your car centered in its lane, even down to a stop in bumper-to-bumper traffic. In some cases these systems allow the “driver” behind the wheel to take hands off the wheel, provided that person keeps paying attention and is ready to take control if needed.
市场上已经出现了一些不太先进的自动驾驶汽车技术。目前，美国还没有一款量产汽车能够在你睡觉、阅读或发推文时行驶，但许多系统可以与前车保持车距，或让你的车保持在车道中央，即使在拥堵的交通中也能保持停车。在某些情况下，这些系统允许“驾驶员”将手从方向盘上移开，前提是该驾驶员保持注意力集中，并随时准备在必要时接管控制权。

These less-advanced systems can’t see things like stopped fire trucks or traffic lights. But humans haven’t made good driving backups because their attention tends to wander. At least two Tesla drivers in the United States have died using the system. (One hit a truck in 2016, another hit a highway barrier in 2018.) There is what is called a “handoff problem.” A semiautonomous car needs to be able to determine what its human “driver” is doing and how to get that person to take the wheel when needed.
这些不太先进的系统无法识别停下的消防车或交通信号灯等情况。但人类驾驶员的注意力容易分散，因此无法做出有效的倒车预警。在美国，至少有两名特斯拉驾驶员在使用该系统时丧生。（其中一人在2016年撞上了一辆卡车，另一人在2018年撞上了高速公路护栏。）这其中存在所谓的“交接问题”。半自动驾驶汽车需要能够判断其人类“驾驶员”在做什么，并在需要时如何让其接管方向盘。

And let’s not forget security. A self-driving car is essentially a collection of networked computers and sensors linked wirelessly to the outside world, and it is no more secure than other networked systems. Keeping
别忘了安全问题。自动驾驶汽车本质上是一组联网的计算机和传感器，通过无线方式连接到外界，它的安全性并不比其他联网系统高。
systems safe from intruders who want to crash or weaponize cars may prove to be the greatest challenge confronting autonomous vehicles in the future.
保护系统免受那些想要撞毁汽车或将其武器化的入侵者的侵害，可能是未来自动驾驶汽车面临的最大挑战。

A computer-driven car that can handle any situation as well as a human under all conditions is decades away at best. Researchers at Cleveland State University estimate that only 10 to 30 percent of all vehicles will be fully self-driving by 2030. PwC analysts estimate that 12 percent of all vehicles will be fully autonomous by then, but they will only work in geographically constrined areas under good weather conditions, as does Waymo’s fleet of self-driving vans in Phoenix. Truly autonomous cars are still science fiction.
一辆能够在所有条件下像人类一样处理任何情况的计算机驱动汽车，至少也需要几十年的时间才能实现。克利夫兰州立大学的研究人员估计，到 2030 年，只有 10%到 30%的汽车能够实现完全自动驾驶。普华永道的分析师估计，到那时，所有汽车中将有 12%能够实现完全自动驾驶，但它们只能在天气条件良好、地理位置受限的区域内行驶，就像 Waymo 在凤凰城的自动驾驶货车车队一样。真正的自动驾驶汽车仍然只是科幻小说。

What is more likely is that self-driving technology will be incorporated into human-driven cars. Current auto models are being equipped with technologies such as advanced object recognition, radar-and-laser detection, some capability to take control of driving if the driver has made a mistake, and ultradetailed highway maps that were originally developed for self-driving vehicles. By 2022, nearly all new vehicles in the United States will have automatic emergency braking, which reduces rear-end crashes by 50 percent and crashes with injuries by 56 percent. Once emergency braking technology has been fully deployed, it could reduce fatalities and injuries from rear-end crashes by 80 percent. Human-driven vehicles with some level of self-driving technology will become safer at a rate that completely autonomous vehicles may have trouble matching. This makes the need for fully self-driving cars less compelling.
更有可能的是，自动驾驶技术将被融入人类驾驶的汽车。目前的汽车车型配备了先进的物体识别、雷达和激光探测等技术，在驾驶员犯错时能够接管驾驶，以及最初为自动驾驶汽车开发的超详细公路地图。到2022年，美国几乎所有新车都将配备自动紧急制动系统，这将使追尾事故减少50%，致伤事故减少56%。紧急制动技术一旦全面部署，可将追尾事故的死亡和受伤人数减少80%。配备一定程度自动驾驶技术的人类驾驶汽车将以完全自动驾驶汽车难以匹敌的速度提升安全性。这使得对完全自动驾驶汽车的需求不再那么迫切。

Many analysts expect the first deployment of self-driving technology will be robot taxi services operating in limited conditions and areas, so their operators can avoid particularly tricky intersections and make sure everything is mapped in fine detail.
许多分析师预计，自动驾驶技术的首次部署将是在有限条件和区域内运行的机器人出租车服务，因此其操作员可以避开特别棘手的路口，并确保一切都得到详细绘制。

The Boston Consulting Group predicts that 25 percent of all miles driven in the United States by 2030 may be by shared self-driving vehicles. To take a ride, you’d probably have to use predetermined pickup and drop-off points, so your car can always pull over safely and legally. The makers of self-driving cars will be figuring out how much to charge so they can recoup their research and development costs, but not so much as to dissuade potential riders. They’ll struggle with regulators and insurance companies over what to do in the inevitable event of a crash.
波士顿咨询集团预测，到2030年，美国25%的行驶里程可能来自共享自动驾驶汽车。要搭乘共享汽车，你可能需要使用预先设定的上下车地点，这样你的汽车才能安全合法地停靠。自动驾驶汽车制造商将研究收费标准，以便收回研发成本，但又不能高到吓跑潜在的用户。他们将与监管机构和保险公司就不可避免的事故处理方案展开斗争。

Sources: Kelsey Mays, “Which Cars Have Self-Driving Features for 2020?” Cars.com, March 4, 2020; “2020 Autonomous Vehicle Technology Report,” wevolver.com, accessed February 22, 2020; Matthew Beedham, “Autonomous Driving Still Isn’t a Reality in 2020-and It’s Getting People Killed,” thenextweb.com, accessed April 22, 2020; Sameepa Shetty, “Uber’s Self -Driving Cars Are a Key to Its Path to Profitability,” CNBC, January 28, 2020; Christopher Mims, “Self-Driving Cars Have a Problem: Safer Human-Driven Ones,” Wall Street Journal, June 15, 2019 and “Driverless Hype Collides with Merciless Reality,” Wall Street Journal, September 13, 2018; Matt McCall, “Why Autonomous Vehicles Are Such a Game-Changer for Uber and Lyft,” InvestorPlace.com, March 25, 2019; Alex Davies, “The WIRED Guide to Self-Driving Cars,” WIRED, May 17, 2018; and Daisuke Wakabashai, “Uber’s Self-Driving Cars Were Struggling Before Arizona Crash,” New York Times, March 23, 2018.
资料来源：Kelsey Mays，“2020 年哪些汽车具备自动驾驶功能？” Cars.com ，2020 年 3 月 4 日；“2020 年自动驾驶汽车技术报告”， wevolver.com ，2020 年 2 月 22 日访问；Matthew Beedham，“自动驾驶在 2020 年仍未实现，而且还会造成人员死亡”， thenextweb.com ，2020 年 4 月 22 日访问；Sameepa Shetty，“优步的自动驾驶汽车是其盈利之路的关键”，CNBC，2020 年 1 月 28 日；Christopher Mims，“自动驾驶汽车存在问题：更安全的人类驾驶汽车”，华尔街日报，2019 年 6 月 15 日以及“无人驾驶汽车炒作与无情现实相撞”，华尔街日报，2018 年 9 月 13 日； Matt McCall，“自动驾驶汽车为何会改变 Uber 和 Lyft 的命运”， InvestorPlace.com ，2019 年 3 月 25 日；Alex Davies，“连线杂志自动驾驶汽车指南”，连线杂志，2018 年 5 月 17 日；Daisuke Wakabashai，“优步自动驾驶汽车在亚利桑那州车祸前举步维艰”，纽约时报，2018 年 3 月 23 日。

11-13 What are the management, organizational, and technology challenges posed by selfdriving car technology?
11-13 自动驾驶汽车技术带来了哪些管理、组织和技术挑战？
11-14 Are self-driving cars good business investments? Explain your answer.
11-14 自动驾驶汽车是好的商业投资吗？请解释你的答案。
11-15 What ethical and social issues are raised by self-driving car technology?
11-15 自动驾驶汽车技术引发了哪些伦理和社会问题？

11-16 Will cars really be able to drive themselves without human operators? Should they?
11-16 汽车真的能够实现无人驾驶吗？应该吗？