Core Viewpoint - The article highlights a significant milestone in autonomous driving, marking the first successful "zero intervention" drive across the United States using Tesla's Full Self-Driving (FSD) technology, specifically version 14.2, achieved by driver David Moss [1][2][25]. Group 1: Achievement Details - David Moss completed a journey of 2,732.4 miles (approximately 4,397 kilometers) from Los Angeles to Myrtle Beach, South Carolina, taking 2 days and 20 hours without any human intervention [23][18]. - This journey involved navigating through 24 states, showcasing the FSD's capability to handle complex driving conditions, including busy urban streets and adverse weather [20][22]. - The successful completion of this trip is seen as a validation of Tesla's FSD technology, demonstrating that Level 4 autonomous driving is achievable even in real-world scenarios [25][70]. Group 2: Technological Significance - The transition to an end-to-end neural network approach in FSD, moving away from traditional programming methods, has allowed the AI to learn driving from millions of hours of video data [16][53]. - The FSD version 14.2 integrates navigation and path planning into the neural network, enabling the system to understand and react to real-time road conditions like a human driver [64][66]. - This achievement counters skepticism regarding the viability of a purely visual-based autonomous driving system, proving that it can operate effectively without expensive lidar or high-definition maps [71][70]. Group 3: Historical Context - Elon Musk's promise of achieving a fully autonomous coast-to-coast drive by the end of 2017 has been fulfilled eight years later, highlighting the long journey and technological evolution that led to this moment [15][31]. - The article reflects on the initial skepticism and challenges faced by Tesla in the autonomous driving space, particularly against competitors like Waymo [50][57]. - The successful drive is seen as a culmination of years of development and a significant leap towards the future of autonomous vehicles, where human intervention may become obsolete [10][75].

来源丨新智元（ID：AI_era） 编辑丨 Aeneas KingHZ 图 源丨X@ David Moss 2026年第一天， 特斯拉FSD，完成全球首个完全自动驾驶的横穿美国。 从今天起，人类的自动驾驶，到达了全新的里程碑！ 就在2025年的最后一天，当全世界都在准备倒数跨年时，车主David Moss静悄悄地扔出了一枚深水 炸弹—— 他驾驶搭载 FSD V14.2的Model 3，完成了全球首次、经由第三方数据验证的「零接管」横贯美国之 旅。 从美国西海岸开到东海岸，2天20小时，人类0次接管。 物理世界的「自动驾驶奇点」，终于降临！ 这条推特，也彻底引爆了全球科技圈和AI圈。 由此，他也成为全世界第一个全程凭借自动驾驶横穿美国的人。 可以说，这是特斯拉正式通过了公路上的图灵测试。 这场AI主导的公路旅行，直接震撼了全球特斯拉车主。 前特斯拉AI总监Karpathy兴奋高呼：这一刻终于来了，这是端到端神经网络的胜利，这是「软件 2.0」在物理世界的完全接管，不再需要人类写下的规则！ 特斯拉官方账号，表扬了这次壮举。 一位特斯拉车主赞叹：「我们 已步入自动驾驶穿越美洲大陆的时代。」 特斯拉掌门人马斯克，也激 ...

特斯拉的完全自动驾驶系统(FSD)正在跨越一个关键门槛。 最近，一辆搭载FSD v14的Model 3，从美国西海岸洛杉矶出发，横穿整个大陆，在 2天20小时内抵达东海岸南卡罗来纳州 。 全程2732英里，100%依赖FSD ，覆盖高速公路、城市道路、夜间驾驶及多次进出超级充电站等复杂场景，整个行程未出现任何人工接管。 马斯克本人在第一时间转发祝贺。值得一提的是，这条路线，正是马斯克自2016 年 Autopilot 2.0发布以来反复提及、却始终未能兑现的目标。当年他曾预 计，特斯拉可以在2017年实现"海岸到海岸"的自动驾驶。现在回头看，这个目标并非不可能。 特斯拉社区对这一成就反响热烈，因为零接管的横跨海岸驾驶一直被视为自动驾驶技术成熟度的重要标志。特斯拉北美官方账号在社交媒体上确认："首辆使用 FSD Supervised从海岸到海岸自主驾驶的特斯拉，零接管，全程FSD。" 这不是官方演示，也不是实验室测试，而是一位普通车主在真实交通环境下完成的实跑记录。 对自动驾驶行业而言，这趟旅程的意义，远不止"跑得远"，而在于它第一次让一个问题变得现实而具体： FSD 是否已经可以完全代替人类驾驶员？ 零接管横 ...

Group 1 - Tesla showcased its Cybercab, a fully autonomous electric vehicle, at the China International Import Expo, highlighting the arrival of the autonomous driving era supported by "end-to-end neural network" technology [1] - The Cybercab features no steering wheel or pedals and utilizes Tesla Vision and end-to-end neural network for autonomous driving without expensive hardware like LiDAR, catering to the 92% of travel scenarios that involve 1-2 passengers [1] - Alongside the Cybercab, Tesla also presented two humanoid robots, Tesla Bot, which share technology with electric vehicles, including cameras and neural network systems, benefiting from the vast visual data accumulated by the vehicles for training [1] Group 2 - Tesla displayed a range of energy products, including Solar Roof, Powerwall, V4 Supercharger, Cybervault, and Megapack, establishing a circular ecosystem for utilizing, storing, and reusing clean energy across various applications [2] - The exhibition featured a futuristic neighborhood setup where Solar Roof converts sunlight into clean electricity, stored in Powerwall, with the entire power supply for the neighborhood coming from the Megapack commercial energy storage system [2] - The display included advanced electric vehicles like the Cybertruck, Model 3, and Model Y, alongside robots performing household tasks, emphasizing a blend of technology and everyday life [2]

Core Insights - Tesla showcased its Cybercab at the China International Import Expo, marking its first appearance in the Asia-Pacific region, alongside humanoid robots, emphasizing a future-oriented business model that integrates vehicles, energy, and robotics [1][2] Group 1: Cybercab and Robotaxi - The Cybercab features no steering wheel or pedals, utilizing TeslaVision and an end-to-end neural network for autonomous driving, eliminating the need for expensive lidar technology [2] - The Cybercab is set to launch mass production in Q2 2026 and will serve Tesla's Robotaxi fleet, which has already accumulated over 600,000 kilometers in Austin and the Bay Area [2] - The operational cost of the Robotaxi service is projected to be lower than all existing transportation options, with costs per kilometer expected to be just a few cents [2][3] Group 2: Humanoid Robots - Tesla's humanoid robots share technology with its vehicles, including cameras and neural networks, and are currently capable of performing various tasks autonomously [4] - The company plans to start production of humanoid robots by the end of 2026, aiming for an annual production capacity of 1 million units by 2030 [4] - The development of humanoid robots is expected to significantly increase the human-robot ratio globally, with applications in manufacturing, home services, and healthcare [4] Group 3: Supply Chain and Industry Impact - The transition of automotive supply chains to humanoid robotics is anticipated to lower component costs significantly, addressing the industry's supply chain maturity challenges [5][6] - Domestic suppliers in China are becoming globally competitive in key components like batteries and precision gearboxes, which could lead to substantial value creation in the supply chain [6]

Core Viewpoint - Artificial intelligence is a global focus, with Tesla showcasing its Cybercab autonomous vehicle and Tesla Bot at the China International Import Expo, igniting public expectations for AI technology implementation [2] Group 1: Tesla's Innovations - Tesla's Cybercab, featuring a golden streamlined body and no steering wheel or pedals, represents the reality of fully autonomous driving supported by "end-to-end neural network" technology [2] - The Cybercab's debut at the expo marks its first appearance in the Asia-Pacific region, showcasing Tesla's pure visual end-to-end neural network capabilities [2] - Tesla has accumulated 60 billion miles of driving data by the end of Q3, enhancing its end-to-end neural network capabilities [3] Group 2: Future Plans and Developments - Tesla plans to start mass production of the Cybercab in Q2 2026, aiming to serve its Robotaxi autonomous ride-hailing fleet [3] - The deployment of Robotaxi is expected to be cautious, with operations anticipated in 8 to 10 urban areas by the end of 2025 [3] - The third-generation Tesla Bot is set to be released in Q1 2026, with production lines being installed to achieve an annual capacity of 1 million units by the end of 2026 [5] Group 3: Strategic Shift and Market Potential - Tesla's strategic shift emphasizes AI and humanoid robots, with the humanoid robot business projected to account for 80% of Tesla's value in the future [5] - The potential market size for humanoid robots could reach $26 trillion, according to ARK Invest's Cathie Wood, who remains optimistic about Tesla's long-term prospects [5]

Core Insights - Tesla has unveiled its "World Simulator," a neural network that ingests 500 years of human driving experience daily to evolve in an infinite virtual environment, which can also be utilized by its humanoid robot, Optimus [1][2][3] Group 1: Technology Overview - The "World Simulator" generates various driving scenarios, including rare situations like pedestrians crossing the road and vehicles cutting in, allowing AI to simulate and test responses in a controlled environment [2][3] - Tesla employs an "end-to-end" neural network for autonomous driving, processing raw data from multiple cameras and other inputs to directly generate driving commands without separate modules for perception, prediction, and planning [6][7][9] - This approach allows the AI to learn human-like decision-making and reduces information loss between modules, enhancing overall system performance [13][16] Group 2: Data Utilization - Tesla's fleet generates a vast amount of data, equivalent to 500 years of human driving experience daily, which is filtered to extract high-quality learning samples for the AI [25][27] - The AI's ability to generalize from complex scenarios, such as predicting vehicle behavior in adverse weather, is attributed to exposure to diverse driving conditions [30] Group 3: Simulation Capabilities - The "World Simulator" can evaluate new AI models in a closed-loop environment, recreate real-world dangerous scenarios for testing, and generate extreme situations to challenge the AI's limits [46] - This simulator serves as a foundational AI engine that extends beyond automotive applications, also being applicable to Tesla's humanoid robot project, Optimus [47][48]

Core Viewpoint - The article discusses the evolution of Li Auto's autonomous driving technology from following Tesla to referencing Tesla, highlighting original innovations made by Li Auto that Tesla has not publicly addressed [2][3]. Group 1: Development Line of Li Auto's Autonomous Driving - Initially, Li Auto's autonomous driving was considered to be following Tesla, but after the introduction of VLM, it transitioned to a reference model, showcasing original innovations not mentioned by Tesla [2]. - The core innovation of Li Auto's VLA is at the DeepSeek MoE level, which is lower than the DeepSeek MLA innovation level [2]. - During the V10-11 period, it was acceptable to say Li Auto was following Tesla, but from V12 onwards, the extent of following has significantly decreased [2]. Group 2: Ashok's Presentation at ICCV 2025 - Ashok Elluswamy discussed Tesla's shift to a single, large end-to-end neural network that directly generates control actions from sensor data, eliminating explicit perception modules [4]. - The reasons for this shift include the difficulty of encoding human values into code, poor interface definitions between traditional perception, prediction, and planning, and the need for scalability to handle real-world complexities [5]. - Key challenges in learning from pixels to control include the curse of dimensionality, interpretability and safety guarantees, and evaluation [6]. Group 3: Solutions to Challenges - To address the curse of dimensionality, Tesla utilizes extensive data from its fleet and employs complex data collection methods to extract valuable corner case data [7]. - For interpretability, end-to-end models can be prompted to predict auxiliary outputs for debugging and safety assurance, with the main focus being on control actions [8]. - The evaluation challenge is addressed through a neural network closed-loop simulator that allows for comprehensive testing and performance assessment [10]. Group 4: Comparison with Li Auto - The article argues that Li Auto's prior announcements on natural language processing and 3D Gaussian representation predate Ashok's presentation, indicating that Li Auto is not merely following Tesla [13]. - The discussion highlights that Ashok's concepts lack groundbreaking ideas, suggesting that Li Auto's innovations are leading rather than following [13]. - The article also notes that Tesla's potential adoption of a VLA-based solution aligns with Li Auto's previously published architecture [16].

Core Viewpoint - Tesla is shifting to a single, large end-to-end neural network that directly generates control actions from pixel and sensor data, eliminating explicit perception modules [1][34]. Group 1: Reasons for Transition to End-to-End Neural Networks - Integrating human values (like driving smoothness and risk assessment) into code is extremely challenging [3]. - Poor interface definitions between traditional perception, prediction, and planning can lead to information loss [4]. - The end-to-end approach is easier to scale for handling long-tail problems in the real world [5]. - It allows for homogeneous computation with deterministic latency, which is crucial for real-time systems [6]. Group 2: Challenges in Learning "Pixel to Control" - The primary challenges include the curse of dimensionality, interpretability and safety guarantees, and evaluation [7][8][9]. - The input context can be extensive, with a 30-second window potentially reaching 2 billion tokens [10][49]. - Tesla leverages its vast fleet data to extract valuable corner case data through complex, trigger-based data collection methods [11][51][56]. Group 3: Solutions to Challenges - For the curse of dimensionality, Tesla refines its extensive driving data to ensure the right correlations are captured [51][56]. - Interpretability is addressed by prompting the end-to-end model to predict various auxiliary outputs for debugging and safety assurance [12][60]. - Evaluation challenges are tackled by creating a neural network-based world simulator that can generate consistent video streams from multiple cameras [19][79]. Group 4: Future Developments - The next step involves the Cyber Cab, a next-generation vehicle designed specifically for robotaxi services, utilizing the same neural network technology [25][83]. - The technology developed for autonomous driving is also being adapted for humanoid robots, such as Optimus [26][86].

Core Insights - The Chinese humanoid robot industry is rapidly advancing, leveraging manufacturing advantages and significantly reducing costs, making robots more accessible to consumers [1][2][4] - While China excels in hardware production and cost control, the U.S. maintains an edge in software ecosystems and AI capabilities, particularly in developing intelligent robots that can understand and interact with their environment [10][11][59] - The competition between Chinese and American humanoid robots is intensifying, with both sides focusing on different aspects of development: China on market penetration and cost reduction, and the U.S. on advanced AI and software integration [13][15][60] Industry Overview - The humanoid robot market is projected to experience explosive growth, with estimates suggesting that the global market could reach 1.1 trillion yuan by 2035, and the Chinese market alone could achieve 300 billion yuan [23][24] - As of mid-2025, over 220 humanoid robot companies exist globally, with Chinese firms accounting for more than half of this total [27] - The Chinese humanoid robot sector is witnessing a surge in enterprise registrations, with over 105 new companies established in the first half of 2025, reflecting a significant shift towards commercialization [42] Technological Developments - Chinese companies are focusing on specific industrial applications for humanoid robots, such as quality inspection in automotive manufacturing and precision tasks in agriculture [5][6][60] - Despite advancements, Chinese humanoid robots still face challenges in basic capabilities like motion control and autonomy, indicating a need for further technological development [31][34] - The U.S. is making strides in creating humanoid robots with advanced AI capabilities, such as Tesla's Optimus, which is designed to perform complex tasks and adapt to various environments [38][50][55] Market Dynamics - The competition is characterized by a divergence in strategies: Chinese firms prioritize cost-effective production and market capture, while American firms emphasize software innovation and AI integration [15][62] - Significant investments are flowing into the humanoid robot sector, with over 14 billion yuan raised globally in the first half of 2025, and Chinese companies securing a substantial portion of this funding [24][66] - The regional concentration of humanoid robot companies is notable, with the Yangtze River Delta region housing a significant share of enterprises, indicating a trend towards industrial clustering [43] Future Outlook - The humanoid robot industry is at a critical juncture, transitioning from basic mobility to functional task execution, with the potential for widespread application in various sectors due to labor shortages in aging societies [37][66] - The ultimate competition will hinge on the development of "embodied intelligence," which combines advanced AI with humanoid robotics, determining which country can produce robots that not only move but also think and adapt [19][64]