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Depth丨 Autonomous Driving Industry in 2021 — Part 3

Technology companies and OEMs patted themselves in the early self-driving industry. While in 2021, most of the mass production was delivered systematically in ecological alliances.

For example, Tesla has cooperated with charging pile companies to set up a network in the United States. Not long ago, on the “1024 Xpeng Automobile Technology Day”, Xpeng emphasized that AI and manufacturing must be well developed before building their own ecology system. Meanwhile, Haomo.AI’s main strategy is to grow together with its partners.

In addition to Great Wall’s manufacturing capacity and vehicle business, Haomo.AI also gets support from hundreds of partners such as Meituan, Wumart, Tencent, Qualcomm, Baidu, and Thundersoft. They work together to jointly explore key stages of commercialization, like R&D, manufacturing, implementation, iteration, and operation.

In the context of global “chip shortage”, it is naturally inseparable from the support of chip partners in order to achieve mass production. Domestic enterprises such as Xpeng, NIO, and GAC have maintained cooperation with overseas chip manufacturers Mobileye and Qualcomm.

From the above-mentioned realities, it can be found that the market is becoming more pragmatic and prudent in chasing the fancy concept of leapfrog technology. So it takes a virtuous path of incremental development and pays closer attention to the sustainable business cycle.

What Does Autonomous Driving Rely on to Support Industrial Logic and Business Norms?

1. Security Supports the Real Needs of the Market

With the large-scale implementation, whether passenger vehicles or commercial vehicles, the safety and stability of self-driving will determine business efficiency and personal safety. The public will inevitably have stricter demands and higher expectations on safety issues.

It can be seen that Robotaxi regards safety as the first priority. After a long-term road test, Baidu Apollo has achieved “zero” accidents so far in the 18 million-kilometer test mileage. Haomo.AI’s business covers passenger cars, low-speed unmanned vehicles, and smart hardware. Under the three processes “from low speed to high, carrying things to carrying people, and business to civilian use”, it collects data and iterates system step by step, and tries to be smart on the basis of absolute safety.

2. Data Nurtures Leading Technological Innovations

What matters most is to ensure the safety.The leading self-driving companies across the world, all take the accumulation of real data as a prerequisite for technological innovation.

Based on the diverse real road data, Tesla makes its system perform better than those based on test and simulated data. The intricate real road scenes in China are the best technology testing ground for autonomous driving firms. For example, Haomo.AI is expected to carry 1 million passenger cars within 3 years, which means creating tens billions real road mileage data every year. This platform-based data advantage can form barriers and construct leading algorithms to execute a reliable autopilot strategy in different traffic scenarios.

Behind Autonomous Driving: High-quality Labeled Data

The three essential elements for artificial intelligence to operate are computing power, algorithms, and data. Together, they form the whole of artificial intelligence.

Among these three elements, computing power is the ability of technical facilities, the algorithm is the working method, and data is the basis for optimizing the algorithm. In other words, the first two are equipment and capabilities. Data is the knowledge material, which plays an important role. High-quality training data will maximize the efficiency of artificial intelligence, while low-quality AI data will be not only impossible to improve efficiency, but also will hinder the evolution of artificial intelligence to a certain extent.

End

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

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