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Depth丨 Autonomous Driving Industry in 2021 — Part 1

China’s Autonomous Driving “Blowout” in 2021

Having seen too many “first years” and “bubbles”, here is a question: Is the “blowout” of the autonomous driving commercialization real and reliable in 2021?

Frankly speaking, the emergence of the entire industry’s inflection point may be too early. According to a research report released by McKinsey, the cost per kilometer of autonomous driving is roughly the same as the cost of traditional cars. But at present, the cost of autonomous driving cannot be compared with that of traditional cars.

However, as more and more self-driving companies and technology companies achieve large-scale mass production, enterprises that can solve the problem of mass production will accelerate forward, while those which can’t will be under constant pressure in the uncertain external environment and white-hot market competition.

From this perspective, 2021 has paved the way for the future pattern of the autonomous driving industry. There are several factors to consider:

1. “Road construction” is completed, and the basic software and hardware construction is mature

In 2021, with smart travel, carbon neutrality, and digital new infrastructure being adopted as urban strategies in many provinces and cities. Moreover, computing centers, 5G, edge computing, vehicle-road collaboration, high-precision geographic data, and unmanned vehicle road testing and commercial practice were vigorously promoted, making the software and hardware systems on which autonomous driving depends tend to be perfected.

2. As it is “opening to traffic”, and a large number of autonomous driving products began to be delivered on the road

In 2021, all types of L2-L4 autonomous vehicles will begin to walk out of the closed road test site and embark on real urban roads. JD.com, Alibaba, Meituan, Wumart, White Rhino, Haomo.AI, etc. have real-time unmanned delivery vehicles, which have landed in industrial parks and business districts on a large scale; the mass production and delivery plans of Tesla, Xpeng, and Great Wall’s self-driving passenger vehicles continue to accelerate.While the commercial operation is realized, real road condition information is continuously delivered to the back-end algorithm for iterative optimization and upgrading.

Earlier mass production means greater data and algorithm advantages. The “Matthew Effect” of autonomous driving is emerging.

3. The affinity of technology is unprecedented

With the popularization of digitalization and the habit of reducing contact in the post-epidemic era, self-driving has gradually become a service that the public is familiar with. It is reported that more than 20,000 users take a robotic taxi at least 10 times a month. In some parks, unmanned delivery vehicles built by JD.com, Alibaba, and Haomo.AI in cooperation with Wumart and Meituan have become standard delivery options for residents’ purchases. It is fair to say that autonomous driving has never been so close to the lives of ordinary people, which also lays a mental foundation for the subsequent exploration of business models.

China, where autonomous driving is rolling out on a large scale, has the potential to be the world’s largest autonomous driving market.

The Industrialization Focus of Autonomous Driving

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.

End

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source:https://www.163.com/dy/article/GVCKRLFT053823M4.html

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