Name: Pony.ai 首席執行官談自動駕駛和人工智能的未來 (Pony.ai CEO on the future of autonomous driving and AI)
Uploaded: 2025-07-20T18:35:56.000Z
Duration: 18 min 26 s

Let's talk about some of the challenges to drive this car.

讓我們來談談駕駛這輛車的一些挑戰。

反身代名詞

情態助動詞 B1

With any networking glitch, you may run into a safety issue.

任何網絡故障都可能導致安全問題。

如果我們少用電，電力就會減少。

Founded in 2016, Pony AI has become one of the world's leading autonomous driving companies.

Pony AI 成立於 2016 年，現已成為全球領先的自動駕駛公司之一。

It has operations across the US and China and recently carried out an IPO on the NASDAQ.

該公司的業務遍及美國和中國，最近在納斯達克進行了首次公開募股。

Pony AI creates the technology behind driverless cars and seeks to partner with automakers.

Pony AI 創造了無人駕駛汽車背後的技術，並尋求與汽車製造商合作。

While real-world trials of driverless cars are underway, mass adoption hasn't quite happened yet with challenges including public perception and regulation.

雖然無人駕駛汽車的實際測試正在進行中，但由於公眾認知和監管等方面的挑戰，大規模採用還沒有完全實現。

Let's get into it with James Peng, the CEO of Pony AI.

讓我們與 Pony AI 的首席執行官 James Peng 一起來探討這個問題。

James, thanks for joining me on this special episode of Beyond the Valley, recording in front of a live audience here at Converge Live in Singapore.

詹姆斯，感謝你參加我在新加坡 Converge Live 現場錄製的《超越山谷》特別節目。

It's great to have the opportunity to actually talk to you again.

Yeah, it's been a long time since I was living and working in Guangzhou.

是啊，我已經很久沒有在廣州生活和工作了。

Just talk to me about the driverless car journey so far since your founding in 2016 to where you are now.

請跟我談談無人駕駛汽車從 2016 年成立到現在的發展歷程。

Sure, probably I'll paint the picture of what's now.

當然，我可能會描繪一下現在的情況。

Actually, in several of the cities for us, on the open streets of Beijing, Guangzhou, Shenzhen, and Shanghai, the local residents can already use our app just called Pony AI, or they can use the third-party ride-hailing apps to actually hail in a ride, just like you are using an Uber or Grab.

事實上，在北京、廣州、深圳和上海等幾個城市的開放街道上，當地居民已經可以使用我們的應用程序 Pony AI，也可以使用第三方叫車應用程序叫車，就像使用 Uber 或 Grab 一樣。

And the local residents, while you hail in the vehicle, the vehicle will come in without the driver behind the wheel.

而當地居民，當你在車內呼喊時，車輛會在沒有司機駕駛的情況下進來。

And it can take you wherever you specify in your app.

它還能帶你到你在應用程序中指定的任何地方。

So I think the experience itself is already quite just like a typical taxi.

是以，我認為這種體驗本身就已經很像一般的計程車了。

On those streets, typically we have 20 to 25 paid customers, paid rides per day, per vehicle.

在這些街道上，我們通常每天每輛車有 20 到 25 名付費乘客。

So we can see it's already a popular service in the local area.

由此可見，這項服務在當地很受歡迎。

I think now the challenge is how we can quickly scale them up.

我認為，現在的挑戰是如何快速擴大規模。

So I think about three years ago, there was still a driver behind the wheel in the robo taxis in China in particular.

是以，我認為大約三年前，特別是在中國，自動駕駛計程車的方向盤上還有一名司機。

But now, like you said, you can hail an autonomous car via the app.

但現在，就像你說的，你可以通過應用程序叫到一輛自動駕駛汽車。

How did you go so quickly from having a safety driver through to completely driverless?

從有安全駕駛員到完全無人駕駛，你們是如何做到如此迅速的？

And specifically, I guess in China, you've got operations in Guangzhou, Beijing, Shanghai, and Shenzhen.

具體來說，我想在中國，你們在廣州、北京、上海和深圳都有業務。

What has happened in China that's allowed this rapid development?

中國究竟發生了什麼，讓發展如此迅速？

Yeah, it seems rapid from the get-go, from a thousand miles away, but actually it has took us six years from the first trial to become fully driverless.

是的，從一開始，從千里之外看，這似乎很快，但實際上，從第一次試驗到實現完全無人駕駛，我們花了六年時間。

Our fully driverless cars first rolled out at the end of 2022.

我們的完全無人駕駛汽車將於 2022 年底首次推出。

So it's about a bit more than two years now.

Of course, behind the scene, it has been countless miles of testing, the improvements of AI algorithm, and of course, most importantly, a deep collaboration with OEM so we can fit the vehicles with sensors and AI algorithms and make them safe on the road.

當然，在這背後，是無數英里的測試、人工智能算法的改進，當然，最重要的是與原始設備製造商的深度合作，這樣我們才能為車輛安裝傳感器和人工智能算法，使其安全上路。

Our record actually show that the average safety of our autonomous driving vehicles is about a magnitude, almost 10x safer than a typical human drivers in the same place.

事實上，我們的記錄顯示，自動駕駛車輛的平均安全性比同一地點的普通人類駕駛員高出約 10 倍。

What do you think are the main differences between China's approach at this point to autonomous driving and robo-taxis versus what's happening in the US?

您認為目前中國在自動駕駛和機器人計程車方面的做法與美國的主要區別是什麼？

我認為總的來說，它們非常相似。

They're the same vehicles with sensors and AI algorithms.

它們是裝有傳感器和人工智能算法的相同車輛。

But I think there are two major differences.

One is the driving condition is quite different.

I think in most of the major cities in China, the streets are typically more crowded and there's more pedestrians than the cyclists.

我認為，在中國大多數大城市，街道通常比較擁擠，行人比騎車人多。

So I think the predictability of the road conditions is a bit harder on the roads in China.

是以，我認為中國的道路路況更難預測。

Another major difference, I think it's the supply chain of all the sensors and the chips and the vehicles itself, especially the battery-powered electrical vehicles.

另一個主要區別，我認為是所有傳感器、芯片和車輛本身的供應鏈，尤其是電池驅動的電動汽車。

I think because of the ecosystem is maturing, the cost of autonomous driving vehicles in China is generally much cheaper than what's in the US.

我認為，由於生態系統日趨成熟，中國自動駕駛汽車的成本普遍比美國便宜得多。

So I think those are the two major differences.

是以，我認為這是兩個主要區別。

James, the world's been talking about AI for the last two years nonstop.

詹姆斯，過去兩年全世界都在不停地談論人工智能。

What we're seeing with your technology is the application of AI in action, right?

我們從你們的技術中看到的是人工智能在行動中的應用，對嗎？

Just run us through, you're not making cars, to be clear.

You're providing the technology that allows these cars to be driverless.

你們提供的技術可以讓這些汽車實現無人駕駛。

請向我們介紹一下你們的技術堆棧。

What is it, the technology you're providing, and also how is AI enabling the car effectively to drive itself?

你們提供的技術是什麼？人工智能是如何讓汽車實現自動駕駛的？

We are equipping the vehicles with a virtual driver.

我們正在為車輛配備虛擬駕駛員。

那麼，我們在開車時該做些什麼呢？

And then we need to predict their behavior.

然後，我們需要預測他們的行為。

And then we also need to figure out our actions.

然後，我們還需要弄清楚我們的行動。

So in short, the driving itself is an iteration of perceiving, prediction, and then making the decision planning.

是以，簡而言之，駕駛本身就是一個感知、預測和決策規劃的反覆過程。

What's behind all those is we modify the vehicle with sensors.

這一切的背後，是我們用傳感器對車輛進行改裝。

We equipped with computing power and also AI algorithm.

我們配備了計算能力和人工智能算法。

That's like our brain to make the decision.

這就像是我們的大腦在做決定。

So with all those, and then we let the vehicles on the road continue to practice.

有了這些，我們就可以讓路上的車輛繼續練習了。

So it's like ourselves learning how to drive.

And with all this, it's gradually improving.

And one of the key developments over the past two years has been the rise of generative AI, foundational AI models, large language models.

而過去兩年的主要發展之一就是生成式人工智能、基礎人工智能模型、大型語言模型的興起。

Do any of these new advances in models have an impact on your tech stack and the way your cars and your technology can approach autonomous driving?

這些車型的新進展是否會對您的技術堆棧以及汽車和技術實現自動駕駛的方式產生影響？

Actually, a lot of the similar algorithms and the similar approach, we actually have adopted them even before the general AI becomes a buzzword.

實際上，很多類似的算法和方法，在人工智能成為流行詞之前，我們就已經採用了。

For example, some of the algorithms we use, the end-to-end training, where we use a data-driven approach to feed the vehicles to learn how to drive.

例如，我們使用的一些算法、端到端訓練，我們使用數據驅動的方法，讓車輛學習如何駕駛。

Another example is we use reinforcement learning to help the vehicles.

另一個例子是，我們利用強化學習來幫助車輛。

So the general or the basic idea is quite simple because driving self is not a rule.

是以，總體或基本概念非常簡單，因為自我駕駛不是一種規則。

The hardest part of driving is how the vehicle interact with the other cars and the pedestrians and the cyclists.

駕駛最難的部分是車輛如何與其他車輛、行人和騎自行車的人互動。

So the driving self is really an interaction.

是以，自我駕駛實際上是一種互動。

That's really hard, but the interaction self actually can be trained with reinforcement learning.

這確實很難，但實際上可以通過強化學習來訓練交互自我。

The vehicle self is playing a game, how we can ensure the safety of ourselves and everything around us in a mutual understandable way by signaling our intention with other vehicles.

車輛自我正在進行一場遊戲，我們如何通過向其他車輛發出我們的意圖信號，以相互理解的方式確保自己和周圍一切事物的安全。

So that iterative process and that approach and that thinking is in general shared with all the other AI applications.

是以，這種迭代過程、方法和思維總體上與所有其他人工智能應用相同。

Yeah, and just on the sort of algorithm and model side, one of the key developments has been the improving efficiency of those models.

是的，就算法和模型而言，關鍵的發展之一是提高了這些模型的效率。

When you have that conversation, of course, deep seek always comes up in that, but it's about efficiency.

當然，當你進行這樣的對話時，深層次的尋求總是會被提及，但這與效率有關。

You know, a key part of your technology stack is the way that the AI on board is also interacting with the cloud and the network.

要知道，技術堆棧的一個關鍵部分就是人工智能與雲和網絡的交互方式。

But there is also a push for more and more AI applications to become on device, processed on board for more efficiency, greater safety in terms of data, et cetera.

但是，人們也在推動越來越多的人工智能應用成為設備上的應用，在船上進行處理，以提高效率和數據安全性等。

How are the more efficient models getting you to that stage?

效率更高的機型是如何讓您達到這一階段的？

Actually for the autonomous driving, it has always been focusing on the edge computing because I think that it's a real time application that requires almost 100% safety requirements.

實際上，對於自動駕駛來說，它一直專注於邊緣計算，因為我認為這是一個實時應用，幾乎需要 100% 的安全要求。

That means anything relying on communication with the server means that with any networking glitch, you may run into a safety issue.

這就意味著，任何依賴與服務器通信的東西都意味著，一旦出現任何網絡故障，你都可能遇到安全問題。

So it has always been on board computing, on board algorithm needs to be the one that ensures the final safety.

所以一直以來，機載計算、機載算法都是確保最終安全的關鍵。

So the optimization and improving efficiency has always been our focus for the last eight years.

是以，優化和提高效率一直是我們過去八年的工作重點。

So the key challenge is really how do we use the limited computing power within the vehicle and then have the AI algorithms to really compute as efficient as possible?

是以，關鍵的挑戰其實是我們如何利用車內有限的計算能力，然後讓人工智能算法真正實現儘可能高效的計算？

Because it's not just about computing power, it's also about if we use less electricity, less power on our AI algorithm, that means we can give more range into the vehicle because we share the same battery pack with the vehicle.

因為這不僅關係到計算能力，還關係到如果我們使用更少的電力，更少的人工智能算法功率，這意味著我們可以為車輛提供更長的續航里程，因為我們與車輛共用同一個電池組。

James, let's talk about some of the challenges to driverless car adoption as it stands.

詹姆斯，讓我們來談談目前無人駕駛汽車應用所面臨的一些挑戰。

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Pony.ai 首席執行官談自動駕駛和人工智能的未來 (Pony.ai CEO on the future of autonomous driving and AI)

episode

perception

approach

critical