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商业 - 科技

特斯拉:让汽车教你怎么开车

Katie Fehrenbacher 2015年12月03日

目前仍是汽车自动驾驶技术的早期阶段。“未来10年还需要完成,这方面将有大量的工作会完成。”

特斯拉最新发布的自动驾驶系统引起了很多人的兴趣,但人们更应该关注的,其实是该系统背后的若干新型技术。

特斯拉CEO埃隆•马斯克在一个活动上介绍说,这套最新的自动驾驶系统,之所以具备了持续学习和改进的能力,是得于机器学习算法、汽车以及特斯拉车型的无线联网功能,以及加上特斯拉所采集的详细的测绘和传感器数据。

机器学习算法是计算机科技领域的最新进展,计算机可以对海量数据进行分析,并利用它做出越来越精准的预测。简言之,就是机器具有了学习功能。现在像谷歌、Facebook和特斯拉等企业现在都在利用机器学习技术“训练”软件,为客户提供帮助或向他们销售新的服务。

机器学习技术是计算机走向人工智能的方法,同时也是人工智能的一种形式。虽然马斯克本人就人工智能的危险曾经发表过不少耸人听闻的言论,不过他在上述活动上澄清说,他只是担心那些被用作邪恶用途的人工智能。

在媒体提问环节,有位记者问道,特斯拉的自动驾驶服务与市面上其他基于计算机技术的驾驶辅助功能有什么区别时,马斯克强调了特斯拉自动驾驶系统的学习功能。

他表示:“整个特斯拉车队是在同一个网络上操作的。当一辆车子学到了什么东西,所有的车子也就都学到了。这是就超越了其他汽车公司做不到所做的。”在讲解自动驾驶软件时,马斯克解释道,每名利用自动驾驶系统进行驾驶的驾驶员,实质上都变成了一名“教自动驾驶系统应该如何工作”的专家培训师。

Tesla’s new autopilot system is relying on the cutting edge of machine learning, connectivity and mapping data.

While Tesla’s new hands-free driving is drawing a lot of interest this week, it’s the technology behind-the-scenes of the company’s newly-enabled autopilot service that should be getting more attention.

At an event on Wednesday Tesla’s CEO Elon Musk explained that the company’s new autopilot service is constantly learning and improving thanks to machine learning algorithms, the car’s wireless connection, and detailed mapping and sensor data that Tesla collects.

Tesla’s cars in general have long been using data, and over-the-air software updates, to improve the way they operate.

Machine learning algorithms are the latest in computer science where computers can take a large data set, analyze it and use it to make increasingly accurate predictions. In short, they are learning. Companies like Google GOOG 0.07% , Facebook FB 1.63% and now Tesla TSLA 2.58% are using machine learning as a way to train software to help customers or sell them new services.

Machine learning is the way that computers can become artificially intelligent, and the technology is a form of AI. While Musk has taken a sort of alarmist stance against the dangers of AI, he clarified during the event on Wednesday that he’s only concerned with artificial intelligence that is meant for nefarious purposes.

When a reporter asked Musk during the media Q&A what made his company’s autopilot service different than other computer-based driving assistance features that competing big auto makers are working on, Musk emphasized learning.

“The whole Tesla fleet operates as a network. When one car learns something, they all learn it. That is beyond what other car companies are doing,” said Musk. When it comes to the autopilot software, Musk explained that each driver using the autopilot system essentially becomes an “expert trainer for how the autopilot should work.”

特斯拉CEO埃隆•马斯克展示最新自动驾驶软件的功能

也许大多数汽车公司都没有打造自己的机器学习系统,但谷歌的自动驾驶汽车技术却与特斯拉有异曲同工之妙。

从某种角度上看,特斯拉出品的汽车相比于传统汽车,特斯拉汽车倒是更像是一种更类似于智能联网产品,比如说智能家居产品公司Nest的学习型恒温器(现在由谷歌母公司旗下的Alphabet公司所有)。Nest公司的恒温器采用了传感器和计算机算法,能够逐渐学习使用者的行为模式,并能通过软件升级提供更加有用的服务,甚至能影响Nest 生产什么样的下一代硬件的生产决定。

那么,特斯拉的自动驾驶系统乃至车子本身是如何学习的?一切都从数据开始。

构建这种类型的驾驶员辅助系统的企业,包括像谷歌这样正在设计完全自动驾驶技术的公司,都需要“教”会计算机如何利用数据传感器系统,而不是人类的感观,来接管汽车驾驶的核心部分。要达到这个目标,这些企业首先要利用大量的数据来对计算机算法进行“训练”。

英伟达公司(Nvidia)的汽车部门高级总监丹尼•夏皮罗在接受《财富》(Fortune)采访时表示,你可以把它想象成一个孩子通过经验和重复来不断学习的过程。针对自动驾驶汽车和驾驶员辅助应用,英伟达推出了一系列能让电脑处理海量数据的高性能芯片,最近还推出了一个名叫Drive PX的计算机系统。

为了构建一辆自动驾驶汽车,汽车公司需要把长达几十万甚至几百万英里的驾驶视频和数据输入计算机的数据模型,从基础上搭建一个庞大的驾驶“词库语境”。计算机算法会利用虚拟技术分析和理解这些视频。其这样做的目的是,当意外事件发生时,——比如一个球滚到了路上,——汽车可以自动识别当前模式,并采取相应规避措施(减速行驶,因为可能会有小孩跑到马路上捡球)。

英伟达所做的,则是把这个庞大的“驾驶辞典”载入一个强大而小巧的计算机硬件里,使它能够被用到汽车上。此后,包括谷歌和特斯拉在内的一些公司则会通过多种来源继续添加各种类型的数据,让模型继续进行学习。

要使汽车计算机能够在马路上做出更智能的决策,汽车公司会尽可能收集更多的数据,其中既包括顾客的驾驶数据,也包括GPS和地图数据,以及企业员工驾驶测试车时产生的数据。

While most car companies might not be building learning systems, Google’s self-driving cars operate in a similar manner.

In that way, Tesla’s cars are more similar to smart connected gadgets like Nest’s learning thermostat (now owned by Google’s Alphabet), than they are to traditional cars. Nest’s thermostat, using sensors and algorithms, learns its owner’s behavior over time, and through software updates offers increasingly useful services, or even informs Nest’s decisions about its next-generation of hardware.

So, how does Tesla’s autopilot system, and its cars in general, learn? It all starts with data.

Companies building these types of driver-assistance services, as well as full-blown self-driving cars like Google’s, need to teach a computer how to take over key parts (or all) of driving using digital sensor systems instead of a human’s senses. To do that companies generally start out by training algorithms using a large amount of data.

You can think of it how a child learns through constant experiences and replication, explained Nvidia’s Senior Director of Automotive, Danny Shapiro in an interview with Fortune. Nvidia NVDA 1.57% sells high performance chips that enable computers to process large amounts of data, and more recently started selling a computing system, called Drive PX, for self-driving cars and driver-assist applications.

To create a self-driving car, companies feed hundreds of thousands, or even millions, of miles of driving videos and data into a computer’s data model to basically create a massive vocabulary around driving. The algorithms use visual techniques to break down the videos and to understand them. The goal is that when something unexpected happens — a ball rolls into the street — the car can recognize the pattern and react accordingly (slow down because a child could be running into the street after it).

For Nvidia, the company loads this “driving dictionary,” as Shapiro calls it, onto powerful but compact computing hardware that can be used on the car. After that, companies like Google and Tesla add various types of data from different sources to continue to inform the model over time.

Companies try to gather as much data as possible to help a car’s computer make smarter and better decisions on the roads. This includes data from customers driving, data from GPS and maps, and data from company employees driving research cars.

特斯拉正在制作高精度地图信息来指导其自动驾驶仪
 

为了更好地让自动驾驶系统进行学习,特斯拉正在制作详细的高清地图。另外,借助汽车归功于其车型的硬件配置,特斯拉还能采集驾驶员的数据。去年生产的所有特斯拉汽车均生产的所有轿车在底部都安装了12个颗传感器,后视镜附近装有一个颗前视摄像头,车底前部还安装了一套雷达系统。这些传感系统源源不断地收集着数据,既帮助了特斯拉的自动驾驶技术走向成熟,也使特斯拉未来的驾驶体验能变得更好。

另外,所有特斯拉汽车都配备的车子都安装了常年开启的无线连接装置。在这些装置的帮助下,,因而自动驾驶系统的驾驶信息和使用信息经收集后,会自动发送到云端,然后被软件分析。此外特斯拉还收集了那些搭载新型自动驾驶技术和车道转换系统的汽车的信息,并用这些信息对算法进行训练。然后,特斯拉会对这些算法进行路试,并将它们整合到即将推出的软件里。

由于车企的具体目标不同,他们往往会依赖不同类型的数据。比如谷歌在自家的自动驾驶汽车上采用了又大又昂贵的LIDAR(激光雷达)系统。马斯克认为,就对于特斯拉自动驾驶汽车的需求而言来说,LIDAR系统有点杀鸡用牛刀的感觉。

不过马斯克也表示,特斯拉的自动驾驶和车道转换技术需要比标准导航技术更详细的高清地图数据。为了满足这一需求,特斯拉已经开始打造自己的高清地图——其精细度要高于标准导航系统100倍。它的数据大多数来自路面上行驶的特斯拉轿车,但也有一些是特斯拉员工驾驶测试车所获得的数据。

这些新的服务也为企业带来了意想不到的业务模式。马斯克表示,特斯拉将来可能会有兴趣把这些地图数据卖给其他汽车公司。

特斯拉并不是唯一一家研究汽车辅助和自动驾驶技术的公司。谷歌在未来科技方面一直走在前面,奥迪(Audi)也有自己的交通拥堵辅助软件。英伟达公司的夏皮罗表示,目前大多数汽车制造商都在研究这些技术。

英伟达的Drive PX系统已于今年夏天开始出货。夏皮罗表示,英伟达已经与50多家公司和研究机构进行了接洽。特斯拉Model S的17寸大屏和仪表盘就使用了的就是英伟达的芯片。,目前也有人推测称,特斯拉在Model X SUV的未来版本中可能将使用Drive PX系统。不过夏皮罗并没有详细讨论英伟达与特斯拉或奥迪等公司的关系的细节(奥迪的在其交通拥堵辅助系统里也使用了英伟达的技术)。

夏皮罗最后指出,虽然有些公司已经开始采用这些技术,但目前仍是汽车自动驾驶技术的早期阶段。“未来10年还需要完成,这方面将有大量的工作会完成。”(财富中文网)

译者:朴成奎

审校:任文科

The data from Tesla drivers was enabled by the hardware choices that Tesla has made. All Tesla cars built in the past year have 12 sensors on the bottom of the vehicle, a front-facing camera next to the rear-view mirror, and a radar system under the nose. These sensing systems are constantly collecting data to help the autopilot work on the road today, but also to amass data that can make Tesla’s operate better in the future.

Because all of Tesla’s cars have an always-on wireless connection, data from driving and using autopilot is collected, sent to the cloud, and analyzed with software. For autopilot, Tesla takes the data from cars using the new automated steering or lane change system, and uses it to train its algorithms. Tesla then takes these algorithms, tests them out and incorporates them into upcoming software.

Companies will rely on different types of data depending on what they’re trying to do with the cars. For example, Google has used large and expensive LIDAR (light-based radar) sensors on its self-driving cars. But Tesla’s Musk said that LIDAR was basically overkill for what Tesla’s autopilot cars need.

But Musk said that Tesla wanted much more detailed high-precision mapping data for its automated steering and lane change applications than was available through the standard navigation tech. To meet its needs, Tesla has started to build high-precision maps —that have 100 times the level of granularity compared to standard navigation systems — using mostly data from Tesla cars driving on roads, but also some data from Tesla employees driving research cars.

These new services could provide unexpected business models for companies. Musk said that Tesla might be interested in selling the mapping data to other car companies down the road.

Tesla isn’t the only car maker working on driver-assist and self-driving car tech. Google is blazing ahead on its futuristic tech, while Audi has traffic jam assist software. Nvidia’s Shapiro says that most automakers are investigating these technologies.

Nvidia started shipping Drive PX this summer, and Shapiro says that it’s engaged with over 50 companies and researchers. Tesla uses Nvidia chips in the 17-inch screen and the instrument cluster for its Model S and there has been speculation around whether Tesla might use the Drive PX system in future versions of the Model X SUV. Shapiro wouldn’t discuss the specifics of its relationships with Tesla or Audi, which uses Nvidia’s tech in its traffic jam system.

Shapiro cautioned that despite some companies already deploying these technologies, it’s still early days for self-driving car tech. “A huge amount of work will be done on this over the next decade,” he said.

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