Nerd For Tech
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Nerd For Tech

Autonomous Driving is the Best Investment Opportunity


Recently, the reporter interviewed many investors in random groups and asked them what their most promising investment track/opportunity was. Many people gave their answers. The autonomous driving track is also among them. The reason is that as users, with the gradual increase in the demand for assisted driving/intelligent driving, domestic suppliers have the opportunity to thrive on the soil of huge consumers.

But what is more consensus in the industry is that the overall track of autonomous driving is quite evident at present: trucks in high-speed scenes, sub-industry autonomous driving in closed scenes, including mines, ports, etc., as well as urban settings. Autonomous driving includes Robo-Taxi, Robo-Bus, etc.

A self-driving track think tank report pointed out that from the perspective of the operating environment, the simpler the road environment and the more standard the operation process, the easier to implement the automated driving system in batches, bringing cost and scale benefits. From the perspective of the driving subject, the more it can replace the scenes of human drivers’ fatigue driving behavior and high-risk operations, the more valuable it will be.

An autonomous driving founder said: “We have come into contact with a large number of industry engineering cases. It can be seen that Robo-Taxi will take a long time to land. Still, autonomous driving has achieved better results in a limited environment such as environmental sanitation and urban distribution. After starting the business in 2016, it is now focusing on the areas of smart sanitation, smart logistics, and smart travel, and is doing autonomous driving in L4 urban scenarios.”

Municipal Service Items

Many local governments have launched bidding projects for municipal services based on autonomous driving and intelligent network connection. The bidding requirements specify that the participating companies have similar functions for autonomous driving and sanitation operations. The subversion is already in progress.

In addition, the company and strategic partners have deployed nearly a thousand self-driving sanitation vehicles across the country. Regardless of the scale of deployment or the area covered, it has formed a “hardware joint development + software platform self-research” product model. At present, the company has also launched many so-called pilot dedicated line services. The purpose is not to make a profit but to open up the business model of the entire product.

Building Barriers

“We are working on solutions for the autonomous driving industry. Although we do not make publicity in the financing, we maintain a normal financing rhythm of about 1.5 times a year. The main purpose is to maintain research and development competitiveness. Our sales scale triples each year. However, our R&D growth rate is still higher than the market’s growth rate in order to build a high R&D barrier in the short term.” One founder said.

The Demand for Data Labeling Continues to Increase

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.

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.

The current artificial intelligence is also called data intelligence. At this stage of development, the more layers of the neural network, the larger amount of labeled data is needed.

For deep learning, data is meaningful only if it is well labeled.


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