Nerd For Tech
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Nerd For Tech

Self-driving Truck, is it the Next Battlefield?


What Do Smart Cars Look Like in the Future?

The Chinese market probably already has the answer.

Starting from the debut of the Polar Fox Alpha S Huawei HI version at the Shanghai Auto Show, major manufacturers have placed high-level autonomous driving technology as the core of the competition. This driverless cars war has made almost all manufacturers struggling.

Self-driving Truck

With the boom of smart cars getting more intensifying, heavy-duty trucks are bound to become the next battlefield for autonomous driving technology. At the end of last month, Pony.Ai, which has been deeply involved in the field of self-driving passenger vehicles, officially announced its self-driving truck brand “Pony. Tron”.

At present, the development of autonomous driving technology has reached a tipping point. Hong Rong, vice chairman of the Shanghai Pudong Federation of Industry and Commerce, told the reporter of China Times that now is a good opportunity for the automobile industry to overtake on a curve. “The friendly and supportive attitude of policies promotes the rapid development of autonomous vehicles. Many car companies can take advantage of the development of new energy vehicles to pave the path for smart cars. But right now the track is too crowded and there indeed exist bubbles.”

Undoubtedly, as competition intensifies, the entire automotive industry has also entered into a reshuffle period, and more and more start-ups have been eliminated on the autonomous driving track. Wang Chao, a researcher at WeRide, told reporters that autonomous driving is undoubtedly the right choice, but many start-ups have fallen on the road before they see the break of the dawn.

So, is Pony.Ai, known as the “ domestic self-driving startup with the highest estimated valuation feasible”?

Pony.AI and Its Robotaxi

When it comes to Pony.AI, many people are not very familiar with it.
In fact, this “newborn” of autonomous driving has already received investment from many leading international institutional investors and automakers.

Pony.Ai has established cooperation with Toyota, Hyundai, GAC, FAW, and other well-known international and domestic automakers and completed 7 rounds of financing in just four years, with a total financing amount of more than 1.1 billion US dollars, The valuation after the latest round of financing reached 5.3 billion U.S. dollars.

Ever since its establishment, Pony.Ai has developed rapidly. In 2018, Pony.Ai landed the L4 Robotaxi operating fleet in California, USA, and then landed a limited area of Robotaxi trial operation project in Nansha District, Guangzhou.

Users Can Use the Mini-apps to Take Self-driving Taxis. In 2020, it obtained Beijing’s self-driving manned test license and became the first start-up company to obtain this license. Next, Pony.Ai will also start the normalized operation of self-driving taxis in Shanghai.

Will “Pony. Tron” be the First One in Commercial Landing?

In terms of commercial vehicles, Pony.Ai started its layout in the field of autonomous driving trucks in 2018. In 2020, after receiving Toyota’s support, Pony.Ai also increased its investment in research and development of autonomous truck technology. In December of the same year, Guangzhou City granted Pony. Tron a test license for self-driving trucks and approved it to carry out public road tests. Three months later, Pony.Ai announced the brand name of its truck business- Pony. Tron. Some people say that this is the “Pony. Tron speed”.

Scalable Labeled Data is Indispensable in Boosting Self-driving Truck

The mainstream algorithm model of autonomous driving is mainly based on supervised deep learning. It is an algorithm model that derives the functional relationship between known variables and dependent variables. A large amount of structured labeled data is required to train and tune the model.

On this basis, if you want to make self-driving cars more “intelligent”, and form a closed loop of the business model for self-driving applications that can be replicated in different vertical landing scenarios, the model needs to be supported by massive and high-quality real road data.


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