Why Labeled Data is Important to Self-building Autonomous Car Companies?
Rebuilding Momentum of Automatic Driving
In 2021, one of the biggest news should be the cross-border construction of self-driving cars. In the beginning, Apple invested 4 trillion WON in Kia for cooperation in the field of electric vehicles and plans to launch Apple cars in 2024. Followed by Xiaomi’s announcement of cross-border car building in April, Lei Jun, CEO of Xiaomi said that this was his last venture, which attracted a burst of sigh and one-sided support from internet users. The participation of giant internet or technology companies became the hottest topic in the first half of the year.
In fact, whether autonomous driving companies or Internet enterprises, their car building actually looks more like a “last resort”.
There are two reasons:
First, the main engine factory will not provide 100% cooperation. The most famous is SAIC chairman Chen Hong, who said that he does not accept the overall solution of automatic driving provided by any suppliers, including Huawei, “it seems that they are the soul and we are the body.”
Second, Huawei, Dajiang, and other hardware giants entering, the competition in the industry is intensified. To break through the bottleneck, self-building a car may be the best way out.
However, although car building seems to be a feasible direction, the threshold for auto-driving companies is not low. We should know that automobile design and production need to involve at least hundreds of parts and components.
Moreover, parts are not combined like building blocks. Therefore, it is difficult to integrate. In other words, after more than a hundred years of development, the automobile industry has formed a rigorous and complete production process and system and has already derived a set of industrial civilizations based on safety, which can not be subverted by the latecomers in just a few years.
In addition to making cars independently, there is actually a way to go
Just like many automatic driving companies abroad, they rely on the main engine factory, like Cruise — the combination of car factory + technology company”. In 2021, Cruise surpassed Waymo for the first time after obtaining Microsoft’s investment, which is enough to prove that this road is the most feasible one. In this mode, enterprises can also focus more on algorithm research and development, so as to maximize time efficiency.
Acceleration of Commercialization
In the first half of the year, the optimistic point was that the commercialization process had been greatly accelerated, and Robotaxi was the first one.
In January this year, Autox was officially opened to the public in Pingshan District, Shenzhen, China, and completely driverless Robotaxi was commercialized. Followed by Pony.ai, expanding the Robotaxi services to five major cities in China and the United States. In foreign countries, Waymo and Cruise have already submitted applications, hoping to start charging self-driving vehicles in San Francisco.
Robotaxi is recognized as the largest market for automatic driving landing fields. Domestic and foreign giants also made efforts in the first half of the year, hoping to seize the first opportunity.
Read more: Robotaxi Seems to be Getting Closer
The Impact of the Epidemic Continues
The epidemic has brought a new landing scene for automatic driving. The epidemic broke out in Guangzhou a few months ago. In this emergency, unmanned distribution has become a safe and reliable method of material transportation in control areas. Under the order of the government, autonomous enterprises in Guangzhou began to send driverless vehicles to support material transportation. The local staff directly received the materials to avoid the risk of cross-infection.
After reviewing the development of automatic driving in the first half of the year, it is not difficult to see that with the gradual progress of technical reserves and the gradual improvement of supporting facilities, automatic driving is getting closer and closer to our life. At the same time, we should not be too optimistic. Whether in terms of technical reliability or the perfection of laws and regulations, the real manned automatic driving still needs to wait.
High-quality Data is the Future of the Industry
From the perspective of the research direction of artificial intelligence technology, whether in the field of traditional machine learning or deep learning, supervised learning based on training data is still a major model training method. Especially in the field of deep learning, more labeled data is needed to improve the effectiveness of the model.
What we need to be clear is for AI companies and the entire industry, data annotation is an important part of the realization of artificial intelligence. The accuracy and efficiency of the labeled data affect the final result of the artificial intelligence algorithm 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.
In the field of autonomous driving, data annotation scenes usually include changing lanes and overtaking, passing intersections, unprotected left and right turn without traffic light control, and some complex long-tail scenes such as vehicles running red lights, pedestrians crossing the road, and roadsides as well as illegally parked vehicles, etc.
End
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Source: https://www.163.com/dy/article/GFRA3TRU0538NEII.html
