Core Algorithm - The industry has shifted towards end-to-end solutions, moving away from modular approaches, at least in public discourse [1] - The introduction of world models is prevalent, with some companies using them to generate training data, while others incorporate them into end-to-end models to enhance performance [1][8] - There is a divergence in opinions regarding the necessity of language models (VLA) in autonomous driving, with some companies arguing that language is not essential for driving tasks [1][11] Simulation and Infrastructure - The closed-loop systems have evolved from data-driven to simulation testing and training loops [2] - 3DGS is highlighted as a crucial technology for building simulation environments, as emphasized by Tesla at CVPR 2025 [5] - Infrastructure is critical, with companies like Xiaomi and Li Auto noting its benefits for development efficiency [3][14] Organizational Capability - Organizational ability is vital, as large autonomous driving teams face significant management challenges [4] - Team culture and collaboration are emphasized as essential for overcoming complex technical and management issues [5] Technical Choices Comparison - A comparison of various companies' technical choices reveals differing approaches to core technologies and the role of world models and simulation tools [9] - Companies like Li Auto advocate for a training loop that evolves from imitation to self-learning, while NVIDIA emphasizes interpretability and reasoning in AI [9] Key Non-Core Factors - R&D infrastructure and engineering efficiency are crucial for the success of autonomous driving technologies [14] - Simulation and synthetic data are becoming essential for addressing corner cases that real-world data cannot cover [14] - The scale of computing power and chip adaptation is critical, as autonomous driving is not just a software issue but also a hardware challenge [15] User Experience and Safety - User experience and safety are paramount, with companies like Xiaomi stressing the importance of balancing advanced technology with user concerns [17] - The need for a dual-stack safety mechanism is highlighted, ensuring that even aggressive end-to-end models have a fallback to traditional rule-based systems for safety [19]

Core Insights - Tesla's Full Self-Driving (FSD) technology has demonstrated its capability by completing a 4,397 km journey across the U.S. without human intervention, showcasing its stability in complex driving conditions [1] - The competition in the autonomous driving sector is intensifying, particularly in China, where several companies are facing significant challenges, leading to a consolidation of players [1] - The industry consensus is that by 2026, only two to three companies will emerge as leaders in the autonomous driving space [1] Group 1: Tesla's Technological Advancements - Tesla's FSD V12 and V14 represent critical turning points, with V12 proving the feasibility of a model-driven end-to-end approach, prompting the industry to shift towards this model [2] - FSD V14 addresses the limitations of previous versions by integrating a reasoning capability, leading to a potential unification of L2 and L4 development paradigms [2] Group 2: Competitive Landscape in China - Companies like Horizon Robotics, Zhaojun Technology, and WeRide are emerging as strong competitors, with Horizon completing a significant technology architecture switch and launching its HSD model [3][4] - WeRide has shifted focus from L4 Robotaxi to L2+ solutions, achieving rapid development and production timelines [3] - Zhaojun Technology has adopted an aggressive strategy by completely overhauling its previous technology framework to focus on end-to-end solutions [4] Group 3: Industry Trends and Challenges - The industry is witnessing a shift from rule-based to model-driven approaches, with VLA (Vision-Language-Action) models gaining traction among manufacturers like Xpeng and Li Auto [5][6] - Huawei is taking a different approach by rejecting VLA in favor of WA (World Action) models, emphasizing the need for a more streamlined process [6] - The competition is expected to intensify as companies strive to secure sufficient data and funding to support their autonomous driving technologies [10][11] Group 4: Future Outlook - The autonomous driving sector is entering a phase of stricter regulations and increased competition, with a focus on L2+ and urban navigation assistance (NOA) as immediate priorities for many companies [12] - By 2026, the market is anticipated to narrow down to a few key players, with Huawei currently leading the pack, followed by Horizon, Momenta, and WeRide [12][13]

Core Viewpoint - The smart driving industry is experiencing a "term overload" phenomenon, with various factions emerging around different models such as VLA (Vision Language Action), VA (Vision Action), and WA (World Action) [2] Group 1: Industry Trends - The industry is divided between proponents of VLA, like Li Auto and Yuanrong Qixing, and opponents like Huawei and Xiaopeng, who prefer WA [2] - Xiaomi is focusing on end-to-end development, showcasing significant potential in this area, despite starting later than competitors like Li Auto and NIO [3][6] - Xiaomi's end-to-end algorithm has evolved rapidly, with multiple versions released within a year, indicating a fast-paced development cycle [6] Group 2: Technological Development - Xiaomi's latest version of its HAD (Highly Automated Driving) system incorporates world models and reinforcement learning, enhancing its cognitive capabilities [3][4] - The introduction of world models and reinforcement learning is seen as a necessary evolution from simple data-driven approaches to more complex cognitive-driven methodologies [9][10] - Xiaomi's approach emphasizes maximizing the model's intelligence density within limited computational resources [8][15] Group 3: Team Structure and Strategy - Xiaomi's smart driving team has grown to over 1,800 members, reflecting a rapid scaling compared to competitors [6][12] - The team is divided into three groups focusing on different technological routes, including end-to-end, VLA, and other exploratory research [4][13] - Xiaomi's strategy is characterized by a gradual introduction of new technologies, prioritizing user experience over merely adopting the latest advancements [5][10] Group 4: Challenges and Responses - The integration of reinforcement learning faces challenges, such as ensuring the fidelity of world models and managing computational efficiency [4][33] - Xiaomi's team has encountered external criticism, which they view as a necessary part of their growth and development process [25][26] - The company aims to balance the introduction of new technologies with the need for practical, user-friendly solutions [10][11]

Core Insights - The domestic market for passenger cars priced above 200,000 yuan accounts for 30% of the market share, while those below 130,000 yuan hold a significant 50% share, indicating a vast opportunity for companies like Horizon and Momenta to capture market share in the autonomous driving sector [1][13] - Horizon has launched its Horizon SuperDrive (HSD) solution based on the Journey 6 series chip, entering mass production with significant activation numbers shortly after the launch of new models [1][14] - The company aims to make urban assisted driving technology accessible to vehicles priced at 100,000 yuan, targeting a production scale of 10 million units within the next 3-5 years [2][14] Market Dynamics - The market for vehicles priced below 130,000 yuan is largely untapped in terms of urban assisted driving features, attracting various autonomous driving companies to accelerate their market strategies [1][13] - Horizon's HSD solution has seen rapid adoption, with over 12,000 activations within two weeks of launching two new models, indicating strong market demand [1][14] Technological Development - Horizon is focusing 90% of its R&D resources on end-to-end technology, which is seen as crucial for the future of autonomous driving [2][14] - The company believes that a solid end-to-end foundation is essential for integrating new modalities and enhancing product performance [15][21] Competitive Landscape - Companies lacking chip development capabilities are increasingly collaborating with Horizon, highlighting the company's strong position in the market [2][14] - Horizon's commitment to an end-to-end approach distinguishes it from competitors who are exploring various models, such as VLA [2][21] Technical Insights - The end-to-end system developed by Horizon is one of the few complete systems available, with a focus on seamless information transfer and high-dimensional feature integration [16][17] - The distinction between one-stage and two-stage end-to-end systems is critical, with the former providing a more cohesive and intuitive driving experience [18][19] Future Directions - Horizon plans to enhance its product experience and safety, emphasizing the importance of market acceptance over new terminologies and concepts [11][22] - The company is open to integrating VLA technology in the future but maintains that a robust end-to-end system is foundational for success [24]

Core Viewpoint - The article emphasizes the importance of end-to-end technology in the development of autonomous driving solutions, highlighting Horizon's commitment to this approach as a foundation for future advancements in the industry. Market Overview - In the first three quarters of this year, the market share for passenger cars priced above 200,000 yuan accounted for 30%, while those below 130,000 yuan reached 50%, with many lower-priced models lacking urban auxiliary driving features [1]. - This gap in the market is attracting companies like Horizon and Momenta to accelerate their strategies to capture market opportunities [1]. Product Development - Horizon launched its Horizon SuperDrive (HSD) solution based on the Journey 6 series chips in April, entering mass production by November with the launch of the Exeed ET5 and Deep Blue L06 models, achieving over 12,000 activations within two weeks [1][2]. - The company aims to make urban auxiliary driving features available in vehicles priced around 100,000 yuan, targeting a production scale of ten million units in the next 3-5 years [2]. Technological Strategy - Horizon is one of the few companies firmly committed to the end-to-end approach in autonomous driving, believing that a solid end-to-end foundation is essential for integrating new modalities and enhancing product performance [3][7]. - The company has invested 90% of its R&D resources into developing and implementing end-to-end technology since the end of 2024 [2]. Technical Insights - Horizon's end-to-end system is described as a complete solution, contrasting with two-stage systems that may lose information during processing [4][5]. - The company believes that a robust end-to-end model is crucial for achieving high performance and seamless driving experiences, akin to human driving instincts [6][9]. Future Directions - Horizon's future plans include enhancing its end-to-end technology while exploring the integration of world models and reinforcement learning as auxiliary components to improve overall system performance [9][10]. - The focus remains on product experience and safety, with an emphasis on market acceptance rather than getting caught up in new terminologies or concepts [9].

Core Viewpoint - Huawei's automotive business has achieved significant milestones, including 1 million vehicles equipped with its driving technology and over 100 million units of laser radar shipped, showcasing its long-term strategic vision in the automotive sector [3][4]. Group 1: Achievements and Strategy - As of July, 1 million vehicles have been equipped with Huawei's QianKun intelligent driving system, and the cumulative mileage for assisted driving has reached 4 billion kilometers [3]. - Huawei's automotive business has been investing since 2014, focusing on R&D rather than immediate commercialization, which has led to current profitability [4][5]. - The company has launched 28 models in collaboration with various brands, indicating a strong market presence [3]. Group 2: Technology Approach - Huawei prefers the World Action (WA) model over the Video Language Action (VLA) model for achieving true autonomous driving, believing WA is a more direct and effective approach [5][13]. - The WA model processes information directly from various inputs like vision, sound, and touch, bypassing the need to convert data into language [5][14]. - Huawei has developed the WEWA model based on the WA architecture, which will be deployed in ADS 4.0 [6]. Group 3: Business Model and Pricing - Huawei's CEO emphasizes that there is no such thing as a free service in the automotive industry; costs are often hidden or transferred [7][17]. - The company believes charging for assisted driving systems is justified due to ongoing costs for updates and maintenance throughout the vehicle's lifecycle [8][18]. - Huawei's approach to lifecycle management ensures that users receive continuous upgrades, enhancing their experience over time [18]. Group 4: Future Plans - Huawei aims to achieve L3 capabilities for highway driving and L4 pilot capabilities in urban areas by 2026, with plans for large-scale commercial use by 2028 [11]. - The company is also working on transforming the intelligent cockpit into a "digital nanny," integrating AI to enhance user experience [11]. Group 5: Safety and Technology Enhancements - Huawei's increase in sensor configurations, such as additional laser radars, is driven by a commitment to safety rather than merely increasing product pricing [19][20]. - The company focuses on enhancing the precision of its systems to prevent accidents and improve user safety in various driving scenarios [20][22].

Core Insights - Huawei's automotive business has achieved significant milestones, including 1 million vehicles equipped with Huawei's QianKun intelligent driving system and over 1 million units of laser radar shipped as of July this year [1] - The company emphasizes a long-term strategic vision, having invested in the automotive sector since 2014, which has led to current profitability without setting explicit commercialization goals [1][4] - Huawei's CEO of the Intelligent Automotive Solutions BU, Jin Yuzhi, believes that focusing solely on commercialization can be counterproductive, advocating for a commitment to technology development and user needs [1] Automotive Business Performance - As of August, 28 models have been launched in collaboration with Huawei, including brands like Audi and Avita [1] - Cumulative mileage for assisted driving has reached 4 billion kilometers [1] - The company has adopted a full lifecycle management approach for its products, ensuring continuous upgrades and maintenance for users [5][16] Technology Strategy - Huawei prefers the World Action (WA) model over the Video Language Action (VLA) model for autonomous driving, believing WA is the ultimate solution for achieving true autonomous driving [3][10] - The WA model processes information directly through vision inputs, eliminating the need to convert data into language, which is seen as a shortcut [3][11] - Huawei has developed the WEWA model based on the WA architecture, which will be deployed in ADS 4.0 [4] Future Plans - Huawei aims to achieve Level 3 (L3) autonomous driving capabilities on highways and Level 4 (L4) pilot capabilities in urban areas by 2026, with plans for large-scale commercial use of L4 by 2028 [9] - The company is also working to transform smart cockpits into "digital nannies," integrating AI as an AI Agent [9] Pricing and Business Model - Jin Yuzhi asserts that there is no such thing as free services in the automotive industry, as costs are often transferred in different forms [4][15] - The pricing for assisted driving systems is justified due to the ongoing costs of iteration, maintenance, and over-the-air updates [5][15] - Users who initially purchase the first version of ADS benefit from continuous upgrades, making the long-term cost of ownership more favorable [16] Safety and Sensor Technology - Huawei's increase in sensor configurations, such as additional laser radars, is driven by a commitment to safety rather than merely increasing product pricing [17][18] - The company aims to enhance safety in various driving scenarios, including parking and urban driving, by improving system precision through advanced sensor technology [17][18]

Core Viewpoint - Huawei's automotive business is rapidly expanding, particularly in the field of assisted driving, with various collaboration models with car manufacturers being explored [2][3]. Group 1: Collaboration Models - Huawei's automotive business unit (BU) collaborates with car manufacturers through multiple models, including component supply, single intelligence (either smart cockpit or assisted driving), dual intelligence (both smart cockpit and assisted driving), and full-stack solutions [2]. - The collaboration process involves Huawei supporting car manufacturers throughout the entire lifecycle, from product definition and design to manufacturing and marketing [2][7]. Group 2: Technology Approach - Huawei's approach to assisted driving does not align with the Vision-Language-Action (VLA) model favored by some car manufacturers; instead, it emphasizes the World and Action (WA) model, which directly controls the vehicle through sensory inputs [3][9]. - The WA model is considered by Huawei to be the ultimate solution for achieving true autonomous driving, bypassing the language processing step [9]. Group 3: Commercialization and Market Strategy - Huawei does not have a specific short-term commercialization goal for its assisted driving technology, focusing instead on long-term user-centered strategies and sustainable investment [7]. - The company believes that the market for assisted driving features will evolve, and that pricing strategies should reflect the ongoing development and maintenance costs associated with these technologies [12]. Group 4: Industry Trends and Future Outlook - The number of players in the autonomous driving space is expected to decrease as the industry consolidates, with future success relying heavily on data-driven approaches [10]. - The differentiation in assisted driving technology is minimal, as the primary goal remains zero accidents and fatalities, with pricing determined by perceived value to consumers [11].

Core Insights - Huawei's automotive business is rapidly expanding its assisted driving solutions and collaborating with various car manufacturers, including Baojun, Leap Motor, and Hongqi, indicating a growing presence in the industry [2][3] - The company emphasizes a diverse cooperation model with car manufacturers, ranging from component supply to full-stack solutions, enhancing their capabilities from product definition to marketing [2][9] - Huawei's approach to assisted driving technology diverges from the prevalent Vision-Language-Action (VLA) model, focusing instead on a World and Action (WA) model that utilizes direct sensory inputs for vehicle control [3][10] Cooperation Models - Huawei's cooperation with car manufacturers includes multiple models: component supply, single intelligence (either smart cockpit or assisted driving), dual intelligence (both), and full-stack solutions [2][9] - The collaboration process is designed to deepen over time, with Huawei supporting car manufacturers throughout the entire product lifecycle, from design to marketing [2][9] Technology Perspective - Huawei does not endorse the VLA approach, believing it is not the ultimate solution for autonomous driving; instead, it prioritizes the WA model, which aims for direct control through sensory inputs [3][10] - The company acknowledges the rapid development of assisted driving technology and anticipates a consolidation of players in the market, driven by data, computing power, and algorithms [11] Commercial Strategy - Huawei does not have a specific short-term profitability target for its automotive business, focusing instead on long-term user-centered investments and sustainable growth [8] - The company argues that there is no such thing as a free service in the automotive sector, as costs are often hidden in vehicle pricing or future service fees [13]