全自动驾驶汽车还有多久可以上路？面临哪些挑战？

朝阳e站译制小组以国际化视野，为您分享海外媒体在“成品油、新能源、加油站以及便利店”等领域最新动态，以全球第一手素材制作纯正的原版译文，定期发布，敬请期待。本期将推出汽车新闻网站（Electrek）和麦肯锡公司网站(Mckinsey)关于“全自动驾驶汽车的未来发展前景”的相关报道。

发布媒体：汽车新闻网站（Electrek）和麦肯锡公司网站(Mckinsey)。

一分钟速读：本文简述当前全自动驾驶汽车发展情况及部分软件工作原理，鉴于目前该技术的发展趋势和开发软件所面临的障碍，认为实现高水平的自动驾驶可能还需要5-10年。

Tesla Autopilot applied brakes in Model 3 crash that resulted in explosions.

特斯拉Model 3驾驶辅助系统在撞车事故中刹车导致爆炸。

自动驾驶汽车何时上路？

As cars achieve initial self-driving thresholds, some supporters insist that fully autonomous cars are around the corner. But the technology tells a (somewhat) different story.

随着汽车跨过了自动驾驶的门槛，一些支持者坚定地认为全自动汽车就在眼前，但事实却并非如此。

The most recent people targeted for replacement by robots? Car drivers. Automotive players face a self-driving-car disruption driven largely by the tech industry, and the associated buzz has many consumers expecting their next cars to be fully autonomous. But a close examination of the technologies required to achieve advanced levels of autonomous driving suggests a significantly longer timeline; such vehicles are perhaps five to ten years away.

哪些人最容易被机器人取代呢？汽车司机。汽车厂商面临着一场主要由科技行业推动的自动驾驶汽车革命，并且相关消息让许多消费者认为下一代汽车能够完全自动驾驶。但对实现自动驾驶所需技术的研究表明，实现高水平的自动驾驶可能还需要5到10年的时间。

The first attempts to create autonomous vehicles (AVs) concentrated on assisted-driving technologies. These advanced driver-assistance systems (ADAS)—including emergency braking, backup cameras, adaptive cruise control, and self-parking systems—first appeared in luxury vehicles. Eventually, industry regulators began to mandate the inclusion of some of these features in every vehicle, accelerating their penetration into the mass market. By 2016, the proliferation of ADAS had generated a market worth roughly $15 billion.

制造自动驾驶汽车首先要将精力集中在辅助驾驶技术上。这些先进的驾驶辅助系统（ADAS）：包括紧急刹车、备用摄像头、自适应巡航控制和自动停车系统，这些系统首先应用在豪华汽车上。后来，行业监管机构开始强制要求每一款汽车都必须具备其中一些功能，从而加快了它们进入大众市场的步伐。到2016年，ADAS的广泛应用创造了一个价值约150亿美元的市场。

Around the world, the number of ADAS systems (for instance, those for night vision and blind-spot vehicle detection) rose from 90 million units in 2014 to about 140 million in 2016—a 50 percent increase in just two years.

在全球范围内，使用ADAS系统(例如用于夜视和盲点车辆检测的系统)车辆的数量从2014年的9000万台增加到2016年的1.4亿台，仅用了两年时间就增长了50%。

Both the customer’s willingness to pay and declining prices have contributed to the technology’s proliferation. A recent McKinsey survey finds that drivers, on average, would spend an extra $500 to $2,500 per vehicle for different ADAS features. Although at first they could be found only in luxury vehicles, many original-equipment manufacturers (OEMs) now offer them in cars in the $20,000 range. Many higher-end vehicles not only autonomously steer, accelerate, and brake in highway conditions. Some commercial passenger vehicles driving limited distances can even park themselves in extremely tight spots.

强烈的客户购买意愿和不断下降的价格都促进了这项技术的普及。麦肯锡(McKinsey)最近的一项调查发现，对于不同的ADAS功能，驾驶员平均每辆车要多花500至2500美元。虽然最初只能在豪华车中发现ADAS的身影，但现在许多汽车装配厂 （OEMs）对2万美元范围内的汽车中也提供这种产品。许多高端汽车不仅能在高速公路上自动驾驶、加速和刹车，一些短途的商用客车甚至可以自行停在非常狭窄的地方。

But while headway has been made, the industry hasn’t yet determined the optimum technology archetype for semiautonomous vehicles and consequently remains in the test-and-refine mode. So far, three technology solutions have emerged.

尽管已经取得了进展，但该行业尚未确定半自动车辆的最佳技术原型，目前仍处于测试和改进阶段。到目前为止，已经出现了三种技术解决方案。

1

Camera over radar relies predominantly on camera systems, supplementing them with radar data.

基于雷达的摄像机方案主要依赖于摄像机系统，用雷达数据补充它们。

2

Radar over camera relies primarily on radar sensors, supplementing them with information from cameras.

基于摄像机的雷达方案主要依靠雷达传感器，从摄像机上获取信息来补充他们。

3

The hybrid approach combines light detection and ranging (lidar), radar, camera systems, and sensor-fusion algorithms to understand the environment at a more granular level.

结合了光探测和测距（激光雷达）、雷达、摄像机系统和传感器融合算法的组合方案，可以更加精确地了解周围环境。

The cost of these systems differs; the hybrid approach is the most expensive one. However, no clear winner is yet apparent. Each system has its advantages and disadvantages. The radar-over-camera approach, for example, can work well in highway settings, where the flow of traffic is relatively predictable. The combined approach, on the other hand, works better in heavily populated urban areas, where accurate measurements and granularity can help vehicles navigate narrow streets and identify smaller objects of interest.

这些系统的成本不同，组合方案是最昂贵的。然而，每个系统都有其优点和缺点，到目前为止，还不能确定哪个方案是最优的。例如，基于摄像机的雷达系统方案，可以在高速公路环境中很好地工作，因为在高速公路上，交通流量是相对可预测的。从另一方面来说，组合方案在人口密集的城市地区效果更好，精确的测量可以帮助车辆在狭窄的街道上行驶，并能识别较小的物体。

解决自动驾驶车辆的技术挑战

AVs will undoubtedly usher in a new era for transportation, but the industry still needs to overcome some challenges before autonomous driving can be practical. We have already seen ADAS solutions ease the burdens of driving and make it safer. Yet in some cases, humans trust or rely on these new systems too much. This is not a new phenomenon. When airbags moved into the mainstream, in the 1990s, some drivers and passengers took this as a signal that they could stop wearing their seatbelts. This illusion resulted in additional injuries and deaths.

自动驾驶车辆无疑将开启交通运输的新纪元，但在实现自动驾驶之前，汽车行业还需要克服一些困难。我们都知道ADAS一定程度减轻了驾驶员的负担，使行驶过程更加安全。然而，人类有时过于信任或依赖这些新系统。20世纪90年代，当安全气囊开始作为汽车标配的时候，一些司机和乘客认为他们可以停止系安全带，而这种错觉导致了更多的伤亡。

There remains something of a safety conundrum. In 2015, accidents involving distracted drivers in the United States killed nearly 3,500 people and injured 391,000 more in conventional cars, with drivers actively controlling their vehicles. Unfortunately, experts expect that the number of vehicle crashes initially will not decline dramatically after the introduction of AVs that offer significant levels of autonomous control but nonetheless require drivers to remain fully engaged in a backup, fail-safe role.

安全难题仍然存在。2015年，在美国因驾驶员分心而发生的交通事故造成近3500人死亡，39.1万人受伤。不幸的是，专家们预计，在引入自动驾驶系统后，汽车碰撞事故的数量在初期不会大幅下降。自动驾驶车辆系统提供了较高程度的自动控制，但仍要求驾驶员充分发挥主观能动性，防止故障发生。

Safety experts worry that drivers in semiautonomous vehicles could pursue activities such as reading or texting and thus lack the required situational awareness when asked to take control. As drivers reengage, they must immediately evaluate their surroundings, determine the vehicle’s place in them, analyze the danger, and decide on a safe course of action. At 65 miles an hour, cars take less than four seconds to travel the length of a football field, and the longer a driver remains disengaged from driving, the longer the reengagement process could take. Automotive companies must develop a better human–machine interface to ensure that the new technologies save lives rather than contributing to more accidents.

安全专家担心，驾驶半自动汽车的驾驶员可能会进行阅读或发短信等活动，因此当被要求控制车辆时，他们会措手不及。当驾驶员重新操控车辆时，他们必须立即评估周围环境，确定车辆在其中的位置，分析危险，并决定安全的行动路线。设想一下，汽车以每小时65英里的速度，穿越足球场只用不到4秒钟时间，驾驶员离开驾驶的时间越长，重新回到驾驶状态的过程可能需要的时间就越长。汽车公司必须开发更好的人机界面，以确保新技术能够拯救生命，而不是造成更多的事故。

In the next five years, vehicles that adhere to SAE’s high-automation level-4 designation will probably appear. These will have automated-driving systems that can perform all aspects of dynamic mode-specificity AVs, even if human drivers don’t respond to requests for intervention. While the technology is ready for testing at a working level in limited situations, validating it might take years because the systems must be exposed to a significant number of uncommon situations. Engineers also need to achieve and guarantee reliability and safety targets.

在未来五年内，符合SAE高自动化第四级设计的车辆可能会出现。它们将拥有自动驾驶系统，即使人类驾驶员不响应干预请求，该系统也能动态处理遇到的各种问题。虽然该技术可以在特定的情况下进行工作测试，但是验证它可能需要数年时间，因为系统必须要测试大量不常见的情况。工程师还需要实现并保证可靠性和安全性。

The challenge at SAE’s levels 4 and 5 centers on operating vehicles without restrictions in any environment—for instance, unmapped areas or places that don’t have lanes or include significant infrastructure and environmental features. Building a system that can operate in (mostly) unrestricted environments will therefore require dramatically more effort, given the exponentially increased number of use cases that engineers must cover and test. In the absence of lane markings or on unpaved roads, for example, the system must be able to guess which areas are appropriate for moving vehicles. This can be a difficult vision problem, especially if the road surface isn’t significantly different from its surroundings (for example, when roads are covered with snow).

SAE第四级和第五级的挑战集中于可以在任何环境下不受限制地驾驶车辆——例如，没有车道或包含重要基础设施和环境特征的未映射区域或地方。鉴于工程师必须覆盖和测试的用例数量呈指数级增长，构建一个能够在（大多数）不受限制的环境下运行的系统将需要付出极大的努力。例如，在没有车道标志或未铺设路面的情况下，系统必须能够分辨出哪些区域适合车辆运行。这可能是一个困难的视觉问题，特别是当路面与其周围环境没有明显的不同时（例如，当道路被雪覆盖时）。

全自动驾驶汽车发展进程仍较缓慢

iven current development trends, fully autonomous vehicles won’t be available in the next ten years. The main stumbling block is the development of the required software.

鉴于目前的发展趋势，和开发所需的软件面临的障碍，未来十年全自动驾驶汽车发展依然缓慢。

In fact, hardware capabilities are already approaching the levels needed for well-optimized AV software to run smoothly.

实际上，硬件功能已接近优化良好的AV（自动驾驶汽车）软件平稳运行所需的水平。

Daunting software issues remain：The software to complement and utilize the full potential of autonomous-vehicle hardware still has a way to go.

软件难题：完善更新软件和利用好自动驾驶汽车硬件的全部潜力仍有很长的路要走。

One issue: AVs must learn how to negotiate driving patterns involving both human drivers and other AVs. Localizing vehicles with a very high degree of accuracy using error-prone GPS sensors is another complexity that needs to be addressed. Solving these challenges requires not only significant upfront R&D but also long test and validation periods.

AVS(无人驾驶汽车）必须学习如何应对涉及人类驾驶员和其他AVS的驾驶模式。GPS定位系统易出错，如何精准的定位车辆是另一个待解决的复杂问题。解决这些挑战性的问题不仅需要的前期研发时间长，而且测试和验证的周期也很长。

延伸阅读

自动驾驶汽车（Autonomous vehicles）又称无人驾驶汽车，是一种通过电脑系统实现无人驾驶的智能汽车。无人驾驶技术可以拆解为“环境感知-决策与规划-控制与执行”过程的理解、学习和记忆的物化。

No.1

环境感知

Three types of issues illustrate the software problem more specifically. First, object analysis, which detects objects and understands what they represent, is critical for autonomous vehicles. The system, for example, should treat a stationary motorcycle and a bicyclist riding on the side of the street in different ways and must therefore capture the critical differences during the object-analysis phase.

三类问题更具体地说明了软件目前需突破的难点。

首先，对象分析（环境感知）对于自动驾驶汽车来说是至关重要的，它可以发现目标并理解它们所代表的内容。例如，该系统应以不同的方式处理在街道边上分别处于静止状态的摩托车和运动状态的自行车，因此必须在对象分析阶段捕获关键差异。

No.2

决策与规划

Decision-making systems are the second issue. To mimic human decision making, they must negotiate a plethora of scenarios and undergo intensive, comprehensive “training.” Understanding and labeling the different scenarios and images collected is a nontrivial problem for an autonomous system, and creating comprehensive “if-then” rules covering all possible scenarios of door-to-door autonomous driving generally isn’t feasible. However, developers can build a database of if-then rules and supplement it with an artificial-intelligence (AI) engine that makes smart inferences，Creating such an engine is an extremely difficult task that will require significant development, testing, and validation.

其次，决策系统：为了模拟人类决策，他们必须全方位地综合大量的场景进行认知训练，理解和标记所收集的不同场景和图像对于系统来说非常重要。创建全面的“if-then”规则覆盖所有可能的驾驶场景通常来说很难做到。虽然，开发人员可以构建if-then规则的数据库，并使用人工智能（AI）引擎对其进行补充。但是这仍然是一项非常困难的任务，因为需要进行大量的开发，测试和验证。

No.3

控制与执行

The system also needs a fail-safe mechanism that allows a car to fail without putting its passengers and the people around it in danger. It would be daunting even to build safeguards to ensure against the worst outcomes and control vehicles so they can stop safely. Redundancies and long test times will be required.

最后，该系统还需要一个故障安全保护机制，使汽车能够在发生故障的情况下不使乘客和周围人员陷入危险。因为无法检查和控制所有可能的软件状态和结果。即使建立安全保障机制以防止最坏的结果，并控制车辆使其安全停车，仍面临着不少的困难，还需要较长的时间测试。

为全自动驾驶开辟道路

As companies push the software envelope in their attempts to create the first fully autonomous vehicle.

当公司试图制造第一辆全自动驾驶汽车时，他们还带动了软件的发展。

To perfect self-driving cars, companies in the AV space are now working on different approaches, focused on perception, mapping, and localization.

为了完善自动驾驶汽车，AV（自动驾驶汽车）领域的公司现在正致力于不同的方法，专注于环境感知技术、精准定位和高精地图。

No.1

环境感知技术

Perception. The goal—to achieve reliable levels of perception with the smallest number of test and validation miles needed.

环境感知技术，目标是以最少的测试和有效里程数达到可靠的感知水平。

1

Radar, sonar, and cameras.

雷达，声呐和摄像机

2

Lidar augmentation.

激光雷达增强

It requires more data-processing and computational power but is more robust in various environments—especially tight, traffic-heavy ones.

它需要更多的数据处理和计算能力，但在各种环境中更好用，特别是在交通拥挤的环境。

No.2

精准定位技术

Localization. By identifying a vehicle’s exact position in its environment, localization is a critical prerequisite for effective decisions about where and how to navigate. A couple of approaches are common.

精准定位技术，通过识别车辆在其环境中的确切位置，精准定位技术是有效决定导航位置和运行方式的关键先决条件，有两种方法很常见：

1

HD mapping.

高清地图

2

GPS localization without HD maps.

GPS定位

Both approaches also rely heavily on inertial navigation systems and odometry data. Experience shows that the first approach is generally much more robust and enables more accurate localization, while the second is easier to implement, since HD maps are not required.

这两种方法也严重依赖于惯性导航系统和测距数据。经验表明，第一种方法通常更加强大，可以实现更精准的定位，而第二种方法则更容易实现，因为不需要HD地图。

No.3

决策与规划技术

Fully autonomous cars can make thousands of decisions for every mile traveled.

全自动驾驶的汽车每行驶一英里可做出上千个决定。

1

Neural networks.

神经网络

2

Rule-based decision making.

基于规则的决策

Hybrid approach. Many experts view a hybrid approach that employs both neural networks and rule-based programming as the best solution. The hybrid approach, especially combined with statistical-inference models, is the most popular one today.

混合方法，许多专家认为混合采用神经网络和基于规则的决策两种方法作为最佳解决方案。特别当混合方法是与统计推断模型相结合，是当今最流行的方法。

No.4

测试与验证技术

he automotive industry has significant experience with test-and-validation techniques. Here are some of the typical approaches used to develop AVs.

汽车行业在测试和验证技术方面拥有丰富的经验。以下是一些用于开发AV（自动驾驶汽车）的典型方法：

1

Brute force.

直接测试

2

Software-in-the-loop or model-in-the loop simulations.

模型在环仿真

3

Hardware-in-the-loop (HIL) simulations.

硬件在环(HIL)仿真

延伸阅读

模型在环（Model in the Loop，简称MIL）是用模型驱动进行嵌入式系统的开发时，在开发阶段初期及建模阶段中进行的仿真方式。嵌入式系统需和其运作的环境互动，一般会预期有合理的传感器信号为其输入，也会依输入及系统设计来驱动实体系统。若嵌入式系统模型和环境模型连接，一起进行仿真，则即称为模型在环模拟。

硬件在环（Hardware-in-the-loop ，简称HIL)是计算机专业术语，也即是硬件在回路。通过使用“硬件在环”(HiL) ，可以显著降低开发时间和成本。在过去，开发电气机械元件或系统时,使用计算机仿真和实际的实验就已经彼此独立开来。

Ultimately, companies will probably implement a hybrid approach that involves all of these methods to achieve the required confidence levels in the least amount of time.Speeding up the process.While current assessments indicate that the introduction of fully autonomous vehicles is probably over a decade away, the industry could compress that time frame in several ways.

最终，公司可能会采用一种混合方法，以在最短的时间内达到所需的水平。加快这个过程，虽然目前的评估表明，全自主的汽车的推出可能还要十年以上，但汽车行业可以通过多种方式加快研发进程。

1

First, Specifically, they could link up with nontraditional industry participants, such as technology start-ups and OEMs.

他们可以与非传统的行业参与者建立联系，例如技术初创企业和原始设备制造商。

2

Next, proprietary solutions may be prohibitively expensive to develop and validate, As a result, interoperable components will encourage a modular, plug-and-play system-development framework.

专有解决方案的开发和验证成本可能非常昂贵，因此，鼓励生产模块化可互相操作的组件和即插即用的系统开发框架。

3

Another way to speed up the process would be to make the shift to integrated system development. Instead of the current overwhelming focus on components with specific uses, the industry needs to pay more attention to developing actual (system of) systems, especially given the huge safety issues surrounding AVs.

转向集成系统开发，业界需要把关注点更多地放在集成系统开发，而不是过多地关注只具有特定用途的组件，特别是考虑到围绕AV（自动驾驶汽车）的重大安全问题。

The arrival of fully autonomous cars might be some years in the future. How will autonomous cars make decisions, sense their surroundings, and safeguard the people they transport? companies seeking a piece of this pie need to position themselves strategically to capture it now, and regulators need to play catch-up to ensure the safety of the public without hampering the race for innovation.

全自动驾驶汽车时代的到来可能还要几年。自动驾驶汽车如何做决定、如何精准地感知周围环境以及如何更好地保护他们的使用者等，都是值得考虑的问题。同时，进入该领域的公司应做好战略定位把握好这个极具竞争力的行业，而监管者需要跟上步伐，确保公众安全的同时不妨碍创新竞争。

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参考资料：

1. 汽车新闻网站Electrek关于特斯拉3在莫斯科爆炸细节的介绍：

https://electrek.co/2019/08/12/tesla-autopilot-brakes-model-3-crash-explosions/

2. 麦肯锡公司网站关于自动驾驶汽车技术何时实现的介绍：

https://www.mckinsey.com/industries/automotive-and-assembly/our-insights/self-driving-car-technology-when-will-the-robots-hit-the-road

刊期：第1074期 6903篇

值班主编：徐迎 译制: 广东 古美龄 江苏 张加杏

美编：江苏 张娟 浙江 伍一声 校阅：江苏 俞婧

统筹：新疆 李飞