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 Viewpoint - The launch of the new XPeng P7 is seen as a strategic move to regain a top position in the electric sedan market priced above 200,000 yuan, with a focus on advanced technology and design [1][2]. Group 1: Product Launch and Market Positioning - The new XPeng P7 was launched with four Ultra versions priced between 219,800 and 301,800 yuan, aiming to compete with models like the Xiaomi SU7 and Tesla Model 3 [1][5]. - The target for the new P7 is to achieve sales that place it among the top three in the competitive 200,000 to 250,000 yuan electric sedan market, which has seen a 60% year-on-year sales increase [2][5]. - The P7 received over 10,000 pre-orders within 7 minutes of its launch, indicating strong market interest [5]. Group 2: Production and Profitability - XPeng is ramping up production capacity for the P7, with a focus on quality control and a rapid production pace, aiming for a monthly sales target of around 4,200 units to secure a top market position [5][6]. - The P7 is expected to enhance the company's overall gross margin, with analysts suggesting it could act as a profitability accelerator for XPeng [5][6]. Group 3: Technological Advancements - The P7 features three Turing AI chips, enhancing its intelligent driving capabilities, with a significant update expected by the end of the year [6][7]. - The company has invested nearly 5 billion yuan in the VLA (Vision-Language-Action) technology, indicating a strong commitment to AI development in the automotive sector [10]. Group 4: Future Outlook - XPeng's CEO anticipates that the automotive industry will see higher profit margins in the AI era, with a potential shift from small to significant profits as technology and production capabilities improve [11]. - Despite current challenges, XPeng aims to close the valuation gap with leading competitors like Tesla, with expectations of improved market recognition in the near future [11].

Group 1 - The establishment of the Embodied Intelligence Heart Technology Exchange Group focuses on various advanced technologies including VLA, VLN, remote operation, Diffusion Policy, reinforcement learning, VLA+RL, sim2real, multimodal large models, simulation, motion control, target navigation, mapping and localization, and navigation [1] - Interested individuals can add the assistant's WeChat AIDriver005 to join the community [2] - To expedite the group entry process, it is advised to include a note with the institution/school, name, and research direction [3]

Core Viewpoint - The article emphasizes the recruitment of business partners for the autonomous driving sector, highlighting the need for expertise in various advanced technologies and offering attractive incentives for potential candidates [2][3][5]. Group 1: Recruitment Details - The company plans to recruit 10 outstanding partners for autonomous driving-related course development, research paper guidance, and hardware development [2]. - Candidates with expertise in large models, multimodal models, diffusion models, and other advanced technologies are particularly welcome [3]. - Preferred qualifications include a master's degree or higher from universities ranked within the QS200, with priority given to candidates with significant conference contributions [4]. Group 2: Incentives and Opportunities - The company offers resource sharing related to autonomous driving, including job recommendations, PhD opportunities, and study abroad guidance [5]. - Attractive cash incentives are part of the compensation package for successful candidates [5]. - Opportunities for collaboration on entrepreneurial projects are also available [5].

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]

Group 1 - The article announces the recruitment of teachers for embodied intelligence training, targeting both B-end (business) and C-end (consumer) training services, with compensation above industry standards [1] - The training covers various advanced topics including VLA, VLN, remote operation, Diffusion Policy, reinforcement learning, sim2real, multimodal large models, simulation, motion control, and target navigation [2] - B-end training is aimed at enterprises, universities, and research institutions, while C-end training focuses on students and job seekers, with responsibilities including curriculum design and material preparation [3] Group 2 - Candidates are required to have a doctoral degree or higher (including those currently enrolled), with a preference for those who have published two papers in A-level or Q1 journals/conferences, or have two years of industry experience [3] - Interested individuals can add a specified WeChat contact for further inquiries [4]

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]

Core Viewpoint - The article discusses the contrasting approaches of two leading companies in the humanoid robotics industry, Starry Era and Yuzhu Technology, highlighting their differing philosophies on how to enhance robot capabilities and their respective paths towards commercialization [8][10][49]. Group 1: Company Strategies - Starry Era focuses on a "soft and hard integration" approach, emphasizing the importance of combining hardware and software to create a cohesive system for humanoid robots [30][32]. - Yuzhu Technology adopts a "hardware-first" strategy, prioritizing the development of hardware capabilities before integrating software solutions [31][32]. - Both companies have distinct views on the viability of the VLA (Vision-Language-Action) paradigm, with Starry Era seeing it as a broad framework for integrating various modalities, while Yuzhu expresses skepticism about its practical application [12][16]. Group 2: Technical Development - Starry Era has developed an end-to-end VLA model, ERA-42, which integrates reinforcement learning and world models, showcasing their commitment to advancing robot intelligence [15][39]. - Yuzhu Technology is concentrating on building reusable data and model resources, focusing on the engineering aspects of distributed computing to enhance their robots' capabilities [22][27]. - Both companies recognize the necessity of a closed-loop system that combines perception, decision-making, and execution to achieve effective humanoid robot performance in complex environments [34][54]. Group 3: Market Positioning - Starry Era is currently deploying its robots in B-end industrial scenarios, achieving over 70% efficiency in real-world applications, with plans to reach around 90% efficiency next year [23][36]. - Yuzhu Technology is primarily focusing on entertainment and demonstration scenarios, acknowledging that their robots are not yet ready for complex tasks, thus adopting a strategy of gradual market entry [26][27]. - Both companies anticipate a significant shift in the humanoid robotics market, with predictions of a "ChatGPT moment" within the next few years, where robots will be capable of understanding and executing complex instructions in unfamiliar environments [50][56]. Group 4: Future Outlook - The industry is expected to see parallel advancements in various technical paths, including end-to-end VLA and world models, with leading companies validating commercial viability in specific industrial applications [56]. - In the mid-term, a unified technical standard may emerge, expanding applications from industrial to logistics, healthcare, and retail sectors [56]. - Long-term aspirations include humanoid robots becoming household companions, necessitating advancements in safety, reliability, and natural interaction [56].

Core Insights - The article discusses the advancements of the Li Auto VLA driver model, highlighting its improved capabilities in understanding semantics, reasoning, and trajectory planning, which are crucial for autonomous driving [1][3][5] Group 1: VLA Model Capabilities - The VLA model demonstrates enhanced semantic understanding through multimodal input, improved reasoning via thinking chains, and a closer approximation to human driving intuition through trajectory planning [1] - Four core abilities of the VLA model are showcased: spatial understanding, reasoning ability, communication and memory capability, and behavioral ability [1][3] Group 2: Research and Development Trends - The VLA model has evolved from VLM+E2E, integrating various cutting-edge technologies such as end-to-end learning, trajectory prediction, visual language models, and reinforcement learning [5] - While traditional perception and planning tasks are still being optimized in the industry, the academic community is increasingly shifting focus towards large models and VLA, indicating a wealth of subfields still open for exploration [5] Group 3: VLA Research Guidance Program - A VLA research paper guidance program has been initiated, receiving positive feedback, aimed at helping participants systematically grasp key theoretical knowledge and develop their own research ideas [6] - The program includes a structured curriculum over 14 weeks, covering topics from traditional end-to-end autonomous driving to writing methodologies for research papers [9][11][30] Group 4: Course Structure and Requirements - The course is designed for a maximum of 8 participants per session, targeting individuals with a background in VLA and autonomous driving at various academic levels [12][15] - Participants are expected to have a foundational understanding of deep learning, Python programming, and familiarity with PyTorch, with specific hardware requirements suggested for optimal performance [21][22] Group 5: Expected Outcomes - Participants will gain insights into classic and cutting-edge research papers, coding skills, and methodologies for writing and submitting research papers, culminating in the production of a draft paper [20][34] - The program aims to enhance participants' understanding of algorithms, their advantages and disadvantages, and to stimulate their research ideas through structured guidance [20][34]