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The Commercialization of the Autonomous Driving Market Is Accelerating, How Can Start-up Teams Break Through? — Part3

There Are Many Challenges in Commercialization, and It Is Necessary to Use Engineering Capabilities to Break the Situation

Although the needs of many industries have awakened, the commercialization of autonomous driving is still a hard nut to crack.

In the stage of commercialization, the challenges we face often do not come from the autonomous driving technology itself, but from the scenarios of customers in thousands of industries, requiring manufacturers to quickly adapt to a variety of models and scenarios. Technically, everyone is working on making the system adaptable to any car model, so that it has basic capabilities such as obstacle avoidance and autonomous driving.

But this is not a real product, a real product has the ability to tackle all kinds of engineering problems.

For instance, the complexity of the tasks handled by the logistics AI driver and the car AI driver is different. The logistics AI driver not only needs to realize unmanned driving, but also hand over goods.

And in extreme environments such as heavy rain, snow, and fog, how can we achieve remote escape in airports and factory logistics where our products cannot stop operating? How to ensure safe operation in extreme situations? At this time, the test is the engineering ability of the product.

Only by addressing engineering issues can we truly generate value for clients.

This is also the first core problem that manufacturers need to solve when commercializing the technology.

After the engineering problem is handled, there is also the management problem.

For example, when unmanned forklifts and manual forklifts work together, how to formulate new rules?

In the park, there are no traffic police command rules, but it still needs to operate in an orderly manner.

This requires the establishment of a set of management processes, and only the strict implementation of agreements with customers can improve the efficiency of product use.

The reasons why commercialization is complicated are:

In this process, it is not only to solve a technical problem but also an engineering one, such as management problems and process problems, so that it can be used. Otherwise, this product actually has no commercial value.

To Seize the Market, It Is Crucial to Know About Customer Needs

There are many friends and businessmen in the autonomous driving track, and almost all of them are equipped with a strong capital background.

Many people believe that grabbing the market requires strong sales capabilities. While some think that business logic should be established if we want to seize the market opportunity.

The criteria for measuring the establishment of business logic include whether it meets customers' demand, understands customers and brings value to customers and whether it is customer-centric. Our experience is that when your product is sufficient to meet customer needs, it is very easy to enter a certain market, and many contracts do not require the sales staff to spend much effort to expand.

On the contrary, some manufacturers sell their products to customers with the advantage of strong sales capabilities but fail to bring value to customers. It is like parking their cars to gather dust at customer sites. It is also difficult to continue cooperation with customers in the future, and sustainable development cannot be realized.

When customers choose a driverless solution provider, the core indicator considered is whether the company can really tackle problems for customers.

Therefore, in the process of commercialization, self-driving manufacturers need to deeply satisfy customer needs, so as to bring a perfect experience to customers. Autopilot manufacturers should realize that they need to earn less short-term quick money and form a sustainable development path in order to stand out in the long rung.

In the next 5 years, there is no doubt that the autonomous driving capacity will be stronger.

With the maturity of management and regulations, more scenarios are expected to be opened in the future.

On the enterprise side, industrialization capabilities will become more mature and practical, which can meet the customer demands in specific industries and scenarios. Everyone is also expected to experience the beautiful life brought by driverless cars.

High-quality Training Data Is 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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