A new competitive landscape for smart mobility: Tesla and Chinese automakers are racing for the commanding heights of autonomous driving technology
Smart mobility is reshaping the competitive landscape of the entire automotive industry. In this revolutionary change, autonomous driving technology is regarded as the "commanding height" of intelligent mobility. Whoever is the first to overcome this technological height will dominate the direction of future mobility. At present, Tesla and Chinese car companies are competing fiercely in this field.
Tesla relies on its FSD (Full Self-Driving) system to maintain its leading position in the field of autonomous driving. The system uses 8 surround-view cameras and 12 ultrasonic sensors to collect data, process them through a neural network and output control commands to achieve autonomous driving. With a large amount of actual road test data and strong computing power, Tesla's FSD system has realized autonomous driving on urban roads in the region. There are also some controversies about the FSD system, such as the reliability, privacy, and security of its vision-only algorithm.
Different from Tesla's pure vision algorithm route, Chinese car companies are more likely to adopt the technology route of sensor fusion. Taking Xpeng Motors as an example, its NGP autonomous driving system integrates multiple sensors such as lidar, millimeter-wave radar, and vision cameras to achieve high-precision environmental perception through perception fusion algorithms. Although this solution is more costly, reliability and security are more guaranteed. Car companies such as Li and NIO have also increased their investment in the field of autonomous driving, forming a new competitive landscape with Tesla.
Regardless of the technical route, the realization of true autonomous driving faces enormous challenges. Computing power support. Autonomous driving requires real-time processing of a large number of high-resolution images and point cloud data, which requires extremely high computing power. Tesla's Dojo supercomputer may give it some advantages. Data accumulation. Training autonomous driving algorithms requires a large amount of annotated real-world road data, which is a long and expensive process. Finally, there is algorithmic innovation. The generalization ability of existing deep learning algorithms in complex urban scenarios is still limited, and new algorithm breakthroughs are needed.
In addition to vehicle companies, artificial intelligence and Internet companies are also joining the competition. For example, Baidu's "Apollo" program and Goldman Sachs' driverless company Cruise, etc., are all deploying autonomous driving technology. In the future, a new cross-border competition pattern may be formed. Traditional car companies need to cooperate more with technology companies to get support in terms of computing power and algorithms.
Autonomous driving is regarded as the "commanding heights" of intelligent travel, and whoever can be the first to realize autonomous driving in urban areas will gain a huge advantage. Tesla and Chinese automakers are competing to the death in this field, with the former relying on the FSD system and computing power advantages, and the latter catching up through technology routes such as sensor fusion. In addition to vehicle companies, the entry of artificial intelligence and Internet companies will also reshape the competitive landscape of the industry. The competition for smart travel will become more intense, and who can overcome the height of autonomous driving technology will eventually dominate the development direction of smart travel.