智驾的遮羞布被掀开

Core Insights - The automotive industry is transitioning towards more advanced autonomous driving technologies, moving beyond the simplistic "end-to-end" models that have been prevalent [2][3][25] - Companies are exploring new architectures and models, such as VLA and world models, to address the limitations of current systems and enhance safety and reliability in autonomous driving [4][14][25] Group 1: Industry Trends - Major players like Huawei, Li Auto, and Xpeng are developing unique software architectures to improve autonomous driving capabilities, indicating a shift towards more complex systems [4][5][14] - The introduction of new terminologies and models reflects a diversification in approaches to autonomous driving, with no clear standard emerging [4][25] - The industry is witnessing a split in technological pathways, with some companies focusing on L3 capabilities while others remain at L2, leading to a potential widening of the technology gap [25][26] Group 2: Data Challenges - The demand for high-quality data is critical for training large models in the new phase of autonomous driving, but companies face challenges in acquiring and annotating sufficient real-world data [15][22] - Companies are increasingly turning to simulation and AI-generated data to overcome data scarcity, with some suggesting that simulated data may become more important than real-world data in the future [22][23] Group 3: Competitive Landscape - The competition is intensifying as companies with self-developed capabilities advance towards more complex technologies, while others may rely on suppliers, leading to a concentration of orders among a few capable suppliers [26][27] - The shift towards L3 capabilities will require companies to focus not only on technology but also on operational aspects, as the responsibility for safety and maintenance will shift from users to manufacturers [25][26]