特斯拉最新發(fā)布的自動駕駛系統(tǒng)引起了很多人的興趣,，但人們更應(yīng)該關(guān)注的,，其實(shí)是該系統(tǒng)背后的若干新型技術(shù),。

特斯拉CEO埃隆?馬斯克在一個(gè)活動上介紹說，這套最新的自動駕駛系統(tǒng),，之所以具備了持續(xù)學(xué)習(xí)和改進(jìn)的能力,，是得于機(jī)器學(xué)習(xí)算法、汽車以及特斯拉車型的無線聯(lián)網(wǎng)功能,，以及加上特斯拉所采集的詳細(xì)的測繪和傳感器數(shù)據(jù),。

機(jī)器學(xué)習(xí)算法是計(jì)算機(jī)科技領(lǐng)域的最新進(jìn)展，計(jì)算機(jī)可以對海量數(shù)據(jù)進(jìn)行分析,，并利用它做出越來越精準(zhǔn)的預(yù)測,。簡言之，就是機(jī)器具有了學(xué)習(xí)功能?，F(xiàn)在像谷歌,、Facebook和特斯拉等企業(yè)現(xiàn)在都在利用機(jī)器學(xué)習(xí)技術(shù)“訓(xùn)練”軟件，為客戶提供幫助或向他們銷售新的服務(wù),。

機(jī)器學(xué)習(xí)技術(shù)是計(jì)算機(jī)走向人工智能的方法,，同時(shí)也是人工智能的一種形式。雖然馬斯克本人就人工智能的危險(xiǎn)曾經(jīng)發(fā)表過不少聳人聽聞的言論,，不過他在上述活動上澄清說,，他只是擔(dān)心那些被用作邪惡用途的人工智能。

在媒體提問環(huán)節(jié),，有位記者問道,，特斯拉的自動駕駛服務(wù)與市面上其他基于計(jì)算機(jī)技術(shù)的駕駛輔助功能有什么區(qū)別時(shí)，馬斯克強(qiáng)調(diào)了特斯拉自動駕駛系統(tǒng)的學(xué)習(xí)功能,。

他表示：“整個(gè)特斯拉車隊(duì)是在同一個(gè)網(wǎng)絡(luò)上操作的,。當(dāng)一輛車子學(xué)到了什么東西，所有的車子也就都學(xué)到了,。這是就超越了其他汽車公司做不到所做的,。”在講解自動駕駛軟件時(shí),，馬斯克解釋道,，每名利用自動駕駛系統(tǒng)進(jìn)行駕駛的駕駛員，實(shí)質(zhì)上都變成了一名“教自動駕駛系統(tǒng)應(yīng)該如何工作”的專家培訓(xùn)師,。

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.”

也許大多數(shù)汽車公司都沒有打造自己的機(jī)器學(xué)習(xí)系統(tǒng),，但谷歌的自動駕駛汽車技術(shù)卻與特斯拉有異曲同工之妙。

從某種角度上看,，特斯拉出品的汽車相比于傳統(tǒng)汽車,，特斯拉汽車倒是更像是一種更類似于智能聯(lián)網(wǎng)產(chǎn)品,，比如說智能家居產(chǎn)品公司Nest的學(xué)習(xí)型恒溫器（現(xiàn)在由谷歌母公司旗下的Alphabet公司所有）。Nest公司的恒溫器采用了傳感器和計(jì)算機(jī)算法,，能夠逐漸學(xué)習(xí)使用者的行為模式,，并能通過軟件升級提供更加有用的服務(wù)，甚至能影響Nest 生產(chǎn)什么樣的下一代硬件的生產(chǎn)決定,。

那么,，特斯拉的自動駕駛系統(tǒng)乃至車子本身是如何學(xué)習(xí)的？一切都從數(shù)據(jù)開始,。

構(gòu)建這種類型的駕駛員輔助系統(tǒng)的企業(yè),，包括像谷歌這樣正在設(shè)計(jì)完全自動駕駛技術(shù)的公司，都需要“教”會計(jì)算機(jī)如何利用數(shù)據(jù)傳感器系統(tǒng),，而不是人類的感觀,，來接管汽車駕駛的核心部分。要達(dá)到這個(gè)目標(biāo),，這些企業(yè)首先要利用大量的數(shù)據(jù)來對計(jì)算機(jī)算法進(jìn)行“訓(xùn)練”,。

英偉達(dá)公司（Nvidia）的汽車部門高級總監(jiān)丹尼?夏皮羅在接受《財(cái)富》（Fortune）采訪時(shí)表示，你可以把它想象成一個(gè)孩子通過經(jīng)驗(yàn)和重復(fù)來不斷學(xué)習(xí)的過程,。針對自動駕駛汽車和駕駛員輔助應(yīng)用,，英偉達(dá)推出了一系列能讓電腦處理海量數(shù)據(jù)的高性能芯片，最近還推出了一個(gè)名叫Drive PX的計(jì)算機(jī)系統(tǒng),。

為了構(gòu)建一輛自動駕駛汽車,，汽車公司需要把長達(dá)幾十萬甚至幾百萬英里的駕駛視頻和數(shù)據(jù)輸入計(jì)算機(jī)的數(shù)據(jù)模型，從基礎(chǔ)上搭建一個(gè)龐大的駕駛“詞庫語境”,。計(jì)算機(jī)算法會利用虛擬技術(shù)分析和理解這些視頻,。其這樣做的目的是，當(dāng)意外事件發(fā)生時(shí),，——比如一個(gè)球滾到了路上,，——汽車可以自動識別當(dāng)前模式，并采取相應(yīng)規(guī)避措施（減速行駛,，因?yàn)榭赡軙行『⑴艿今R路上撿球）。

英偉達(dá)所做的,，則是把這個(gè)龐大的“駕駛辭典”載入一個(gè)強(qiáng)大而小巧的計(jì)算機(jī)硬件里,，使它能夠被用到汽車上。此后,，包括谷歌和特斯拉在內(nèi)的一些公司則會通過多種來源繼續(xù)添加各種類型的數(shù)據(jù),，讓模型繼續(xù)進(jìn)行學(xué)習(xí)。

要使汽車計(jì)算機(jī)能夠在馬路上做出更智能的決策,，汽車公司會盡可能收集更多的數(shù)據(jù),，其中既包括顧客的駕駛數(shù)據(jù),，也包括GPS和地圖數(shù)據(jù)，以及企業(yè)員工駕駛測試車時(shí)產(chǎn)生的數(shù)據(jù),。

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.

為了更好地讓自動駕駛系統(tǒng)進(jìn)行學(xué)習(xí),，特斯拉正在制作詳細(xì)的高清地圖。另外,，借助汽車歸功于其車型的硬件配置,，特斯拉還能采集駕駛員的數(shù)據(jù)。去年生產(chǎn)的所有特斯拉汽車均生產(chǎn)的所有轎車在底部都安裝了12個(gè)顆傳感器,，后視鏡附近裝有一個(gè)顆前視攝像頭,，車底前部還安裝了一套雷達(dá)系統(tǒng)。這些傳感系統(tǒng)源源不斷地收集著數(shù)據(jù),，既幫助了特斯拉的自動駕駛技術(shù)走向成熟,，也使特斯拉未來的駕駛體驗(yàn)?zāi)茏兊酶谩?/p>

另外，所有特斯拉汽車都配備的車子都安裝了常年開啟的無線連接裝置,。在這些裝置的幫助下,，，因而自動駕駛系統(tǒng)的駕駛信息和使用信息經(jīng)收集后,，會自動發(fā)送到云端,，然后被軟件分析。此外特斯拉還收集了那些搭載新型自動駕駛技術(shù)和車道轉(zhuǎn)換系統(tǒng)的汽車的信息,，并用這些信息對算法進(jìn)行訓(xùn)練,。然后，特斯拉會對這些算法進(jìn)行路試,，并將它們整合到即將推出的軟件里,。

由于車企的具體目標(biāo)不同，他們往往會依賴不同類型的數(shù)據(jù),。比如谷歌在自家的自動駕駛汽車上采用了又大又昂貴的LIDAR（激光雷達(dá)）系統(tǒng),。馬斯克認(rèn)為，就對于特斯拉自動駕駛汽車的需求而言來說,，LIDAR系統(tǒng)有點(diǎn)殺雞用牛刀的感覺,。

不過馬斯克也表示，特斯拉的自動駕駛和車道轉(zhuǎn)換技術(shù)需要比標(biāo)準(zhǔn)導(dǎo)航技術(shù)更詳細(xì)的高清地圖數(shù)據(jù),。為了滿足這一需求,，特斯拉已經(jīng)開始打造自己的高清地圖——其精細(xì)度要高于標(biāo)準(zhǔn)導(dǎo)航系統(tǒng)100倍。它的數(shù)據(jù)大多數(shù)來自路面上行駛的特斯拉轎車,，但也有一些是特斯拉員工駕駛測試車所獲得的數(shù)據(jù),。

這些新的服務(wù)也為企業(yè)帶來了意想不到的業(yè)務(wù)模式。馬斯克表示,，特斯拉將來可能會有興趣把這些地圖數(shù)據(jù)賣給其他汽車公司,。

特斯拉并不是唯一一家研究汽車輔助和自動駕駛技術(shù)的公司,。谷歌在未來科技方面一直走在前面，奧迪（Audi）也有自己的交通擁堵輔助軟件,。英偉達(dá)公司的夏皮羅表示,，目前大多數(shù)汽車制造商都在研究這些技術(shù)。

英偉達(dá)的Drive PX系統(tǒng)已于今年夏天開始出貨,。夏皮羅表示,，英偉達(dá)已經(jīng)與50多家公司和研究機(jī)構(gòu)進(jìn)行了接洽。特斯拉Model S的17寸大屏和儀表盤就使用了的就是英偉達(dá)的芯片,。,，目前也有人推測稱，特斯拉在Model X SUV的未來版本中可能將使用Drive PX系統(tǒng),。不過夏皮羅并沒有詳細(xì)討論英偉達(dá)與特斯拉或奧迪等公司的關(guān)系的細(xì)節(jié)（奧迪的在其交通擁堵輔助系統(tǒng)里也使用了英偉達(dá)的技術(shù)）,。

夏皮羅最后指出，雖然有些公司已經(jīng)開始采用這些技術(shù),，但目前仍是汽車自動駕駛技術(shù)的早期階段,。“未來10年還需要完成,，這方面將有大量的工作會完成,。”（財(cái)富中文網(wǎng)）

譯者：樸成奎

審校：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.