In the future competition of the automotive industry, AI large models are becoming a new focus. On November 11, 2024, Leapmotor announced at its third-quarter earnings conference that it is developing an end-to-end AI large-scale intelligent driving system, which is scheduled to be mass-produced in 2025. This news not only marks the important layout of Leapmotor on the intelligent track, but also indicates that the intelligent transformation of the 10-200,000 yuan new energy passenger car market is accelerating.
1. AI model: a new engine for intelligence
The application of AI large models in the automotive field is driving the rapid progress of intelligent driving technology. Industry leaders such as Xpeng Motors, Li Auto, and NIO have also announced their own intelligent driving development routes and set up departments dedicated to end-to-end large-scale model research and development. Behind these actions is the rapid increase in the penetration rate of intelligent driving systems in the market of 100,000~200,000 yuan. According to the data of the Passenger Association Branch, in the first half of the year, the penetration rate of L2 (including L2+) intelligent driving assistance systems in the 100,000~150,000 yuan market exceeded 30%, and the penetration rate in the 150,000~200,000 yuan market reached 60%. This trend shows that intelligent driving is gradually becoming the standard configuration of new energy vehicles.
Figure: Leapmotor end-to-end intelligent driving AI, lowering the threshold for intelligent driving
2. Technological breakthroughs and market reshaping
The introduction of AI large models is not only a breakthrough at the technical level, but also a reshaping of the market structure. In the 10-200,000 yuan market, the rapid penetration of intelligence will make the proportion of independent brands and foreign brands in China's auto market alternate. In the future, the market share of independent brands is expected to continue to expand. Behind this change is the potential of large AI models to improve production efficiency and reduce costs.
3. Challenges: data, adaptability, regulations
Although the AI model has great prospects, its application in the automotive industry still faces three major challenges. The first is the challenge of data acquisition and processing capacity. AI technology requires the acquisition and processing of large amounts of data, especially high-quality annotated data. The second is the problem of adaptability to complex scenes. Vehicle-road cloud AI technology needs to adapt to various complex traffic scenarios and weather changes. Finally, there are legal, regulatory and ethical issues. The application of AI technology still faces challenges in terms of privacy protection, data security, and responsibility for autonomous driving.
4. Industry cooperation and data sharing
In the face of these challenges, industry collaboration and data sharing are all the more important. Li Dan, deputy director of China FAW R&D Institute, pointed out that at present, each car company's own data is limited and is a data island. If the entire automotive industry joins forces to put data in a safe place and everyone uses it together, it will be of great help to the entire Chinese automotive industry. For this reason, the China Association of Automobile Manufacturers, the China Academy of Information and Communications Technology and other institutions and car companies have jointly launched the construction of a credible data space in the automotive industry.
5. Construction of norms and standards
In order to standardize automotive data processing activities, the China Association of Automobile Manufacturers (CAAM) has also formulated a set of specifications for the evaluation of automotive privacy protection capabilities with the Automotive Cybersecurity Working Committee of the China Cyber Security Industry Alliance, and officially released the "Automotive Privacy Protection" logo at the "2024 China Automotive Software Conference". This indicates that the industry is developing in the direction of standardization and standardization.
6. Conclusions
The application of AI large models in the automotive industry indicates that the intelligent transformation of the 10-200,000 yuan market is accelerating. This transformation will not only reshape the market structure, but also promote the market share expansion of domestic brands. However, to achieve this transformation, OEMs need to overcome challenges in terms of data acquisition, scenario adaptability, and legal and regulatory requirements. Industry cooperation and data sharing will be the key, and the construction of norms and standards will provide support for the healthy development of AI large models. With the advancement of technology and the deepening of industry cooperation, we have reason to believe that AI models will play a greater role in the future automotive industry and bring users a smarter and safer driving experience.
