Group 1 - The Ministry of Industry and Information Technology has approved the commercial operation of L3 autonomous driving for the first time in China, allowing vehicles to operate under specific conditions with the system taking over driving tasks [1] - The two models approved for L3 autonomous driving are Changan Deep Blue SL03 and Arcfox Alpha S6, marking a significant step towards the commercialization of L3 technology [1] - The year 2026 is anticipated to be the "mass production year" for L3 autonomous driving, with several companies aiming to launch L3 vehicles by then [3][4] Group 2 - The approval clarifies the responsibility division for L3 autonomous driving, indicating that if an accident occurs while the system is activated, the car manufacturer may bear primary responsibility [1] - The L3 level is seen as a crucial transition from "assisted driving" to "fully autonomous driving," with L4 expected to achieve greater breakthroughs [1][4] - Major automotive companies, including XPeng, Chery, and GAC, have set timelines for the mass production of L3 vehicles, with GAC planning to launch its first L3 model in Q4 of this year [3][4] Group 3 - The automotive industry is experiencing intensified competition in intelligent driving technologies, with companies like BYD, Geely, and Chery developing their own autonomous driving systems [9] - The integration of AI and data-driven technologies is becoming essential for enhancing autonomous driving capabilities, moving beyond traditional rule-based systems [9][12] - The VLA model is emerging as a key technology in the transition from L2 to L4 autonomous driving, offering improved scene reasoning and generalization capabilities [9][14] Group 4 - The shift towards L3 autonomous driving represents a new beginning for human-machine coexistence, with ongoing exploration in technology iteration and regulatory improvement [17] - Companies are increasingly focusing on in-house development of core technologies, such as battery technology and autonomous driving algorithms, to enhance brand competitiveness [16] - The balance between self-research and collaboration is crucial for companies to maintain technological leadership while managing costs [16][17]

Group 1 - The article discusses various directions in embodied intelligence, including VLN, VLA, reinforcement learning, and real2sim2real, highlighting the confusion among newcomers regarding which path to choose [1] - For those engaged in SLAM, both VLN and VLA are recommended as good entry points, especially if they have robotic arms, while low-cost hardware options like SO-100 can be utilized for experiments [1] - The importance of having a good idea is emphasized, as many new researchers face challenges in finding innovative topics, and the article offers a paper guidance service to assist them [1][2] Group 2 - The paper guidance service is led by a team of experts from top universities and leading companies, covering a range of prestigious conferences and journals [2] - The service provides a comprehensive support process from topic selection to publication strategy, aiming to help researchers produce high-quality results quickly [2][3] - The article also mentions a promotional offer where the first ten inquiries can receive a free matching with a dedicated mentor [5]

Core Viewpoint - The article discusses the technological anxiety in intelligent driving, particularly among mid-tier manufacturers, and highlights the anticipated growth in demand for end-to-end (E2E) and VLA (Vision-Language-Action) technologies in the coming year [2]. Group 1: Industry Trends - The mass production of cutting-edge technologies like end-to-end systems is expected to begin next year, with L2 technologies becoming more standardized and moving towards lower-tier markets [2]. - The total sales of passenger vehicles priced above 200,000 are around 7 million, but leading new forces account for less than one-third of this, indicating a slow adoption of end-to-end mass production models [2]. - The maturity of end-to-end technology is seen as a precursor to larger-scale production, with the advancement of L3 regulations necessitating urgent technological upgrades among mid-tier manufacturers [2]. Group 2: Recruitment and Training - There is a growing demand for positions related to end-to-end and VLA technologies, as many professionals are seeking to quickly learn these advanced skills [3]. - The article mentions the launch of specialized courses aimed at practical applications of end-to-end and VLA technologies, designed for individuals already working in the field [3][6]. - The courses will cover various modules, including navigation information application, reinforcement learning optimization, and production experiences related to diffusion and autoregressive models [3][6]. Group 3: Course Details - The end-to-end production course will focus on practical implementation, detailing key modules and offering seven practical exercises suitable for those looking to advance their careers [3][6]. - The VLA course will cover foundational algorithms and theories, including BEV perception and large language models, with practical applications based on diffusion models and VLA algorithms [6][11]. - The instructors for these courses are experienced professionals from top-tier companies and academic institutions, ensuring a high level of expertise in the training provided [5][8][13].

Core Viewpoint - He Xiaopeng, CEO of XPeng Motors, has placed pressure on the autonomous driving team by betting that the second-generation VLA will match Tesla's FSD V14.2 capabilities by August 30, 2026, or face a humorous consequence for the team leader [2][3][21]. Group 1: Autonomous Driving Development - XPeng Motors plans to officially release the second-generation VLA in the first quarter of 2026, with a full rollout to Ultra models [5][23]. - He Xiaopeng expressed confidence that the second-generation VLA could potentially reach L4 capabilities, and possibly L5 with additional time [6][24]. - The VLA concept integrates visual, language, and action capabilities, aiming for a seamless execution of tasks without breaking them into steps [8][26]. Group 2: Technological Advancements - The second-generation VLA has been trained using nearly 100 million video clips, equating to the driving experience of a human driver over 65,000 years [8][26]. - XPeng's self-developed Turing AI chip has a computing power of 750 TOPS per chip, totaling 2250 TOPS for the vehicle, significantly surpassing the industry standard [11][29]. - The model utilizes a cloud computing cluster with 30,000 cards and plans to expand to 50,000 cards, ensuring ample computational resources for development [8][26]. Group 3: Competitive Landscape - Tesla's FSD has a significant advantage with over 600,000 test vehicles generating 1.6 billion frames of image data daily, accumulating over 9.6 billion kilometers of driving distance [14][32]. - In practical tests, Tesla's FSD V13.2.9 required 5 interventions over a 20-kilometer complex route, while XPeng's second-generation VLA only needed 1 [16][33]. - The latest FSD V14.2 has improved system performance and addressed over 95% of hesitation and abnormal braking issues from the previous version, enhancing the driving experience [17][34].

Core Viewpoint - He Xiaopeng, CEO of XPeng Motors, has placed pressure on the autonomous driving team by betting that the second-generation VLA will match Tesla's FSD V14.2 capabilities by August 30, 2026, or face a humorous consequence for the team leader [2][4]. Group 1: Autonomous Driving Development - XPeng Motors plans to officially launch the second-generation VLA in the first quarter of 2026, with a full rollout to Ultra models [4]. - He Xiaopeng expressed confidence that the second-generation VLA could potentially reach L4 capabilities, and possibly L5 with additional time [6]. - The VLA concept integrates visual, language, and action capabilities, aiming for a seamless execution of tasks without breaking them into multiple steps [8]. Group 2: Technological Advancements - The second-generation VLA will eliminate the language translation step, allowing for direct generation of action commands from visual signals [9]. - The model has been trained using nearly 100 million video clips, equating to the driving experience of a human driver over 65,000 years [9]. - XPeng's self-developed Turing AI chip boasts a computing power of 750 TOPS per chip, with a total of 2250 TOPS across three chips, significantly surpassing the industry standard [12]. Group 3: Competitive Landscape - Tesla's FSD has a significant advantage due to its extensive global testing network, with over 600,000 vehicles generating 1.6 billion frames of data daily and accumulating over 9.6 billion kilometers of driving experience [13]. - The latest FSD V14.2 version has improved system performance and resolved over 95% of hesitation and abnormal braking issues from previous versions [13]. - XPeng's second-generation VLA is still in development, and its actual performance remains to be seen [14].

Core Viewpoint - The article discusses the technological anxiety in intelligent driving, particularly among mid-tier manufacturers, and highlights the anticipated growth in demand for end-to-end (E2E) and VLA (Vision-Language-Action) technologies in the coming year [2]. Group 1: Industry Trends - The mass production of cutting-edge technologies like end-to-end systems is expected to begin next year, with L2 technology becoming more standardized and moving towards lower-tier markets [2]. - The total sales of passenger vehicles priced above 200,000 are around 7 million, but leading new forces account for less than one-third of this, indicating a slow adoption of end-to-end mass production models [2]. - The maturity of end-to-end technology is seen as a precursor to larger-scale production, with the advancement of L3 regulations prompting urgent upgrades among mid-tier manufacturers [2]. Group 2: Recruitment and Training - There is a growing demand for positions related to end-to-end and VLA technologies, as many professionals are seeking to quickly learn these advanced skills [3]. - The article mentions the launch of specialized courses aimed at practical applications of end-to-end and VLA technologies, designed for individuals already working in the field [3][6]. - The courses will cover various modules, including navigation information application, reinforcement learning optimization, and production experiences related to diffusion and autoregressive models [3][6]. Group 3: Course Details - The end-to-end production course will focus on practical implementation, including seven major practical applications, making it suitable for those looking to advance their careers [3][6]. - The VLA course will cover foundational algorithms and theories, including BEV perception and large language models, with practical projects based on diffusion models and VLA algorithms [6][11]. - The instructors for these courses are experienced professionals from top-tier companies and academic institutions, ensuring a high-quality learning experience [5][8][13].

Core Viewpoint - The article discusses the challenges and advancements in the VLA (Variable Learning Algorithm) field, emphasizing the importance of real machine data and practical experience in achieving effective results in embodied intelligence applications. Group 1: Data Collection - Data collection methods for VLA primarily include imitation learning and reinforcement learning, with remote operation, VR, and full-body motion capture being key techniques [8][9] - The quality of data collected is crucial, and methods like real2sim2real are highlighted as important for effective data acquisition [8] Group 2: VLA Training - Before deploying models in real machines, simulation debugging is essential, especially when real machine data is insufficient [10] - Training techniques are critical, with challenges in fine-tuning models and achieving good results with small data sets [10] - Some algorithms, like ACT, are easier to train, while others, such as π0 and π0.5, require more intricate techniques and experience [10] Group 3: VLA Deployment - After training, models often need to be "slimmed down" due to their large parameter sizes, which poses challenges for deployment on edge chips [12] - Techniques like quantization and distillation are necessary to minimize parameter size while maintaining performance [12] Group 4: Educational Initiatives - The article introduces a practical course aimed at helping individuals learn VLA effectively, covering various aspects such as hardware, data collection, algorithms, and deployment [13][16] - The course is designed for a wide audience, including students and professionals looking to transition into the embodied intelligence field [27]

Core Viewpoint - The article discusses the challenges and advancements in the field of VLA (Vision-Language Alignment) models, emphasizing the importance of real machine data and practical applications in robotics and embodied intelligence. Group 1: Challenges in VLA Implementation - Many students struggle with the transition from theoretical knowledge to practical application, often finding it difficult to achieve satisfactory results without hands-on experience [2][6] - The reliance on real machine data for effective training and deployment of VLA models is highlighted, with a focus on the limitations of simulation data [2][8] Group 2: Data Collection and Training - Data collection methods for VLA include imitation learning and reinforcement learning, with a particular emphasis on remote operation and VR techniques [8] - The training of VLA models requires careful tuning and optimization, with specific challenges noted for models like π0 and π0.5, which demand a high level of expertise [10][12] Group 3: Deployment and Optimization - Post-training, VLA models often require optimization techniques such as quantization and distillation to reduce parameter size while maintaining performance [12] - The deployment of VLA models on edge devices presents significant challenges due to their typically large parameter sizes [12] Group 4: Educational Initiatives - The article introduces a practical course aimed at helping individuals learn about VLA, covering various aspects such as hardware, data collection, algorithm implementation, and real-world applications [14][30] - The course is designed for a diverse audience, including students and professionals looking to transition into the field of embodied intelligence [27][30]

Core Viewpoint - The article discusses the challenges and advancements in the VLA (Variable Learning Algorithm) field, emphasizing the importance of real machine data collection and the complexities involved in training and deploying VLA models. Group 1: Data Collection - Real machine data collection is crucial for VLA models, with methods including remote operation, VR, and full-body motion capture [2][8] - The effectiveness of data collection methods and ensuring high-quality data are significant challenges, particularly in the context of real-to-sim-to-real transitions [8] Group 2: VLA Training - Training VLA models typically requires simulation debugging before real machine deployment, especially when real machine data is insufficient [10] - Techniques for fine-tuning models and achieving good results with small data sets are critical, as many students struggle with training models effectively [10] Group 3: VLA Model Deployment - After training, VLA models often require "slimming" due to their large parameter sizes, which poses challenges for deployment on edge chips [12] - Lightweight operations such as quantization and distillation are essential to minimize parameter size while maintaining performance [12] Group 4: Educational Initiatives - The article introduces a practical course aimed at helping students effectively learn about VLA, covering hardware, data collection, algorithms, and deployment [14][16] - The course is designed for various audiences, including those seeking jobs in the field, beginners looking to advance, and researchers in embodied intelligence [27]

Core Viewpoint - The article discusses the challenges and advancements in the VLA (Variable Learning Algorithm) field, emphasizing the importance of real machine data collection and the complexities involved in model training and deployment. Group 1: Data Collection - Real machine data collection is crucial for VLA models, with methods including remote operation, VR, and full-body motion capture being highlighted as effective approaches [2][8]. - The article stresses the need for high-quality data and the significance of the real2sim2real process in ensuring effective data collection [8]. Group 2: Model Training - Training VLA models typically requires simulation debugging before real machine deployment, especially when real machine data is insufficient [10]. - The article notes that many beginners struggle with model training, particularly with advanced models like π0 and π0.5, which require specific techniques and experience to achieve good results [6][10]. Group 3: Model Deployment - After training, VLA models often need to undergo a "slimming" process due to their large parameter sizes, which poses challenges for deployment on edge devices [12]. - Techniques such as quantization and distillation are essential to minimize parameter size while maintaining performance [12]. Group 4: Educational Initiatives - The article introduces a practical course aimed at helping individuals learn about VLA, covering various aspects such as hardware, data collection, algorithms, and deployment [14][16]. - The course is designed for a wide audience, including students and professionals looking to transition into the VLA field, and includes hands-on experience with hardware [27][30].