The Commercialization of the Autonomous Driving Market Is Accelerating, How Can Start-up Teams Break Through? — Part1
Whether it’s the driverless buses in parks or unmanned delivery vehicles in parks, autonomous driving technology has gradually penetrated all aspects of human life.
The mature autonomous driving technology has been widely used in airports, trunk line logistics, ports, mines, and other business scenarios, helping enterprises to address the issues of labor shortage and low efficiency in emergencies such as epidemics.
How long will it take for autonomous driving solutions to fully mature? How do startup teams differentiate themselves when giants rush into the market?
Three Elements of Autonomous Driving’s Penetration: Computing Power, Productivity, and Core Components
Autonomous driving is a natural outcome after human technological and engineering capabilities reach a certain level.
The reason behind the autonomous driving boom involves the enhancement of the comprehensive capabilities of algorithms, computing power, the automotive industry, and the sensor industry.
In terms of algorithm and computing power, machine learning algorithms are gradually becoming mature, but the reason why artificial intelligence has not made breakthroughs is that the computing power at that time cannot support the needs of artificial intelligence.
With the development of Moore’s Law, the improvement of chip computing power has made autonomous driving possible.
In the automotive industry, the productivity of the automotive industry’s production line was limited 10 years ago, and it often pays a huge price to make autonomous vehicles; now if you want to get a pioneer car, there are a large number of suppliers that can meet your demands. Productivity has greatly improved and the industrial chain is also maturing.
In terms of sensors, the cost of lidar was once higher than a car in 2017. In this case, it is too expensive to achieve autonomous driving.
Today, lidar is no longer the barrier that hinders the realization of autonomous driving.
When it comes to autonomous driving, Chinese and overseas technology is often compared together. So is there a gap?
From algorithmic point of view, the gap is not big at all.
At present, the RoboTaxi we see is not really unmanned, and most of them need to be equipped with safety officers to take over the vehicle at any time.
As of March this year, Trend Micro, a Chinese autonomous driving startup, has officially surpassed 1.4 million kilometers of commercial operation mileage of “really unmanned” autonomous driving, emerging as the first autonomous driving company in the world to create this milestone.
From the perspective of specific categories, in terms of commercial vehicles, Trend Micro has placed a huge amount of overseas orders in categories such as logistics, patrol cars, minibuses, midibuses, and distribution vehicles. In these fields, most are the domestic competing products, few are the overseas. In fact, there are a lot of scenarios in the commercial field that have not been done.
The unsatisfied customer’s demand means that as long as you do it, you can eat the cake, and the competitive pressure is low. While when it comes to passenger cars, overseas Tesla is indeed far ahead.
AI Data: Helping Artificial Intelligence Break New Barriers
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 that can be learned by artificial intelligence.
In the artificial intelligence system, data has an important role. Thus, all developers from Google and Microsoft to ordinary individual developers are paying a lot of attention to the high-quality labeled data.
In the current practice of artificial intelligence applications, different level of data quality demonstrates an obvious gap in the value of artificial intelligence solutions.
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.
Previously, media reported that a user had a car accident while riding in a smart driving vehicle. After the investigation, it was discovered that the smart driving system failed to distinguish the difference between the white vehicle and the cloud and did not identify obstacles. The vehicle failed to brake in time, which in turn triggered tragic consequences.
In this case, the lack of accurate data on the distinction between white vehicles and the cloud is the direct factor leading to the tragedy.
Therefore, the measures to provide high-quality AI data for different scenarios and different needs have gradually become the consensus of artificial intelligence solutions.
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