Autonomous Driving, Who Is Making Money in Silence?
Tesla: Achieving True Self-driving Is More Difficult Than Imagined
Recently, Tesla CEO Elon Musk said in an interview with the media: “Achieving true autonomous driving is much more difficult than imagined.” Because “the entire traffic system is actually designed for the human eye and brain. To truly develop a car that can replace human drivers, AI must restore the visual imaging of the human eye and the processing function of the neural network for vision.”
Autonomous Driving Takes off in China
Last year, the autonomous driving industry showed a good development trend. First-tier cities such as Beijing, Shanghai, Guangzhou, and Shenzhen have continued to expand the scale of autonomous driving, and some new first-tier cities have also begun to launch a large number of autonomous driving products.
In terms of sub-scenarios, RoboTaxi has been launched in many places across the country, and trial operations have been opened in Beijing. In closed parks, unmanned logistics vehicles, unmanned delivery vehicles, and even unmanned sales vehicles have begun to work; and on ordinary roads, unmanned sweepers are going to be launched gradually.
How to Commercialize Autonomous Driving Technology?
This is also a conundrum facing all self-driving companies.
At present, most autonomous driving companies are actively launching products, and various products such as RoboTaxi , unmanned delivery vehicles, unmanned sweepers, unmanned shuttle vehicles and other self-driving products have begun to enter the life of the public, allowing ordinary people to feel the charm.
On the other hand, for autonomous driving companies, although the technology has been implemented, they are still unable to rely on these products to support themselves. External financing is still needed to support the company's growth, which has also become a “headache for many self-driving companies.”.
Public data reveals that the financing scale of the autonomous driving industry in 2021 is billions of dollars, which also approves that autonomous driving companies are still unable to obtain self-hematopoiesis ability and need to rely on financing to transfuse blood.
However, some companies are breaking the predicament of autonomous driving commercialization, believing that airports, factories, parks, and other scenarios can achieve large-scale commercialization earlier. Therefore, it has taken the lead in this field and reached the forefront of autonomous driving commercialization at this stage.
The direction that is easier to achieve commercialization: use unmanned vehicles to get income and data first, and continue to expand and refine the technology. When the technology is improved, further expand to RoboTaxi and more fields. We should solve borderless issues and implement unmanned technology in parks, airports, factories, and other scenarios. At the same time, unmanned delivery vehicles and RoboTaxi fleets are put into trial operation on open roads in cities. We will gradually realize commercialization by the strategy： from carrying objects to people, from closed, semi-closed to open roads.
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.
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