Core Insights - The recent National Day holiday saw a significant increase in traffic, with an average of 12.5 million new energy vehicles on the road daily, marking a 30% increase year-on-year and a 70% increase compared to regular days [2] - Unlike previous years where companies showcased their intelligent driving (智驾) capabilities during the holiday, this year saw a notable silence from major brands, with only Huawei and Xiaomi releasing relevant reports [3][4] - Major personnel changes occurred in the intelligent driving teams of companies like Xiaopeng and NIO, indicating a shift in strategy from the "Intelligent Driving Year" to a "Universal Intelligent Driving Year" [4][5] Industry Trends - The transition from "Intelligent Driving Year" to "Universal Intelligent Driving Year" suggests a focus on technological advancements rather than mere market penetration [5] - Data from Huawei indicates that during the recent holiday, their intelligent driving models achieved a total driving distance of 294 million kilometers, with 90.8% of users actively utilizing the assisted driving feature [6][8] - The challenge for new energy vehicle companies lies in achieving reliable L3 and L4 level intelligent driving in urban environments, as opposed to highways where conditions are more favorable [8][10] Technological Developments - The limitations of traditional end-to-end models have prompted a demand for innovative approaches in intelligent driving technology [10][11] - Three evolutionary strategies have emerged among leading brands: the "Improvement School" represented by Momenta, focusing on enhancing learning processes; the "Practical School" represented by Li Auto and Xiaopeng, emphasizing optimization of driving details; and the "World Model" (WA) approach, which simulates a virtual world for learning [11][13][17] - The WA model, which aims to provide a deeper understanding of driving logic, is seen as a more advanced but costly alternative to the VLA model, which is already integrated into products like Li Auto's i8 and Xiaopeng's G7 Ultra [21][17] Competitive Landscape - The intelligent driving sector is entering a more competitive phase, likened to a knockout tournament where brands must demonstrate their technological capabilities and ecosystem collaboration [22][24] - Smaller companies face significant challenges due to high costs and the need for integrated capabilities, with many struggling to keep up with the leading players [24][26] - The long-term outlook suggests that while VLA and WA represent different approaches, both are essential for the future of intelligent driving, with companies like Xiaopeng betting on both strategies to attract users and investors [26]

Core Insights - The rapid commercialization of Vision-Language-Action (VLA) models is redefining the technical threshold for advanced intelligent driving [1] - The competition surrounding VLA will significantly influence the future competitive landscape of Chinese automotive companies and may lead to a reshuffling of the entire intelligent driving industry [7] Group 1: VLA Model Development - Li Auto has launched its VLA driver model with the flagship electric vehicle i8, while Yuanrong Qixing released its self-developed VLA model, DeepRoute IO 2.0, on August 26, covering approximately 200,000 vehicles [2] - XPeng Motors introduced a new generation VLA architecture at the P7 launch on August 27, claiming a latency of less than 100 ms and a planning frame rate of 20 Hz, setting a new benchmark for mass production [3] - The VLA model's ability to abstract and categorize real-world scenarios through language enhances its generalization capabilities compared to traditional end-to-end models [8][12] Group 2: Technical and Economic Challenges - The VLA model requires significant computational power, with Li Auto and XPeng utilizing cloud clusters of 13 EFLOPS and 8 EFLOPS, respectively, while many smaller companies are limited to 0.2-0.6 EFLOPS [14] - The data requirements for VLA are substantial, necessitating the collection and annotation of visual-language-action triplets, with 90% of the training data sourced from 2.93 billion kilometers of real vehicle logs [13] - The cost of training a VLA model can reach 12-15 million RMB per session, which is a significant portion of the annual R&D budget for smaller companies [15] Group 3: Industry Restructuring - The high costs associated with VLA models create survival challenges for smaller automotive companies, which may struggle to compete against larger firms with established technological advantages [19][29] - The transition from rule-based algorithms to VLA models requires a gradual and systematic approach, making it difficult for many second-tier brands to replicate the success of leading companies [21][23] - The VLA model's emergence may lead to a significant industry reshuffle, with many mid-tier companies potentially becoming "outsourcing providers" or low-end manufacturers [24][30] Group 4: Competitive Landscape - The VLA model's introduction is expected to alter the competitive dynamics, with companies like Huawei and Momenta currently holding a dominant market share in intelligent driving [45] - The VLA model's multi-modal learning and reasoning capabilities allow companies like Li Auto and XPeng to achieve performance levels close to those of larger competitors in long-tail scenarios [48] - The year 2025 could mark a pivotal moment for both leading companies and VLA practitioners, potentially leading to a reversal of fortunes in the intelligent driving market [51]