12月30日，沪指低开上冲，几度翻红，维持水面附近窄幅震荡。硬科技震荡上行，截至11:08，机器人ETF基金（159213）涨0.67%，冲击五连阳，盘中资金 大举净申购2000万元，加上今日已经连续3个交易日强势吸金超6300万元。 | 【机器人ETF基金（159213）标的指数前十大成分股】 | | --- | | 序号 | 代码 | 名称 | 申万―级行业 | 涨跌幅 | 估算权重▼ | | --- | --- | --- | --- | --- | --- | | 1 | 002230 | 科大讯飞 | 计算机 | -0.18% | 9.96% | | 2 | 300124 | 汇川技术 | 机械设备 | 0.19% | 9.94% | | 3 | 601689 | 拓管集团 | 汽车 | 0.81% | 7.71% | | 4 | 002236 | 大华股份 | 计算机 | -0.16% | 4.59% | | 5 | 002008 | 大族激光 | 机械设备 | -0.81% | 4.27% | | 6 | 688169 | 石头科技 | 家用电器 | -0.74% | 3.86% | | 7 | ...

要理解这3万亿美元的宏大叙事，首先必须拆解华尔街投行惯用的分类加总估值法模型。在摩根士丹利和Ark Invest的激进模型中，传统的汽车销售业务在 总估值中的占比已经被压缩到了历史最低点，甚至不足30%。 当韦德布什分析师在2025年年末将特斯拉的牛市目标价推向3万亿美元市值的门槛时，华尔街的空气中弥漫着一种既贪婪又警惕的复杂气氛。这背后的逻 辑不再是钢铁与电池的堆叠，而是硅基智能对传统制造业估值体系的绞杀。 如果仅从汽车制造商的视角审视，特斯拉当下的市盈率不仅昂贵，简直到了荒谬的程度；但若将其置于"AI与机器人超级周期"的叙事框架中，那个看似遥 不可及的"3万亿"数字，似乎又成了通往未来的入场券。 这也正是马斯克最擅长的游戏，他成功地将一家年产数百万辆汽车的硬科技公司，通过FSD V13的迭代与Optimus机器人的量产预期，硬生生"格式化"为 一家拥有物理实体的人工智能巨头。 华尔街为什么敢喊出"3万亿"？ 这个支撑特斯拉每日现金流的"现金牛"，在资本眼中已沦为一张单纯的入场券，其存在的意义仅仅是为那个庞大的AI训练集群提供源源不断的资金输血。 为什么会发生这种视角的急剧转换？ 核心在于"边际成本"的魔法 ...

Investment Rating - The report maintains a "Positive" outlook on the Robotaxi industry, indicating a strong belief in its growth potential driven by technological advancements and supportive policies [1]. Core Insights - The Robotaxi sector is undergoing a transformation, leveraging L4 autonomous driving technology to replace human drivers, thereby reducing operational costs and enhancing profit margins. The industry is transitioning from a phase of technical validation to one of scalable operations, with significant growth expected in the coming years [2][3]. - The industry structure is evolving, comprising intelligent driving technology, hardware production, and terminal operations. Key players are focusing on data collection, vehicle manufacturing, and operational management to create a cohesive ecosystem [2][3]. - Policy frameworks are gradually improving, encouraging pilot programs while ensuring safety. This regulatory environment is facilitating the expansion of Robotaxi companies into international markets [2][3]. Summary by Sections 1. Robotaxi Background: Intelligent Driving Technology Reshaping the Mobility Service Industry - The demand for efficient, comfortable, and affordable travel experiences drives the evolution of the mobility service industry, with technological upgrades transforming supply models [15]. - The entry of autonomous driving technology is leading to a restructuring of the capacity value chain, moving from traditional taxi ownership to a more decentralized model [20][22]. - The feasibility of technology is improving, with leading companies demonstrating lower accident rates compared to human drivers, validating the safety and reliability of L4 systems [26][34]. 2. Industry Chain Structure: Intelligent Driving Technology + Hardware Production + Terminal Operations - The current industry participants are adopting a triangular cooperation model, where intelligent driving companies provide solutions, manufacturers supply vehicle chassis, and service platforms manage operations [47][48]. - The operational aspect is becoming increasingly important, with the efficiency of fleet management and scheduling emerging as new competitive barriers [2][3]. 3. Policy Guidance: Encouraging Pilot Programs While Ensuring Safety - Domestic policies are evolving to support pilot programs under safety assurances, while international markets are gradually opening up, allowing Robotaxi companies to expand their operations [2][3]. 4. Industry Growth Phase: A Trillion-Dollar Market with Potential for Billion-Dollar Enterprises - The industry is in a high-growth phase, with the penetration rate of autonomous driving services expected to rise significantly. Key catalysts in the coming years will include mass production of vehicles and global operational expansion [2][3]. - The market is anticipated to give rise to billion-dollar enterprises as leading companies optimize costs and scale operations [2][3].

Core Viewpoint - The recent announcement by Haomo Technology regarding a complete shutdown and holiday for all employees starting November 24, 2025, marks a significant downturn for the company, which has seen a drastic reduction in workforce and challenges in maintaining its position in the intelligent driving sector [2][3][22]. Company Overview - Haomo Technology, incubated by Great Wall Motors in 2019, was once a leading player in the intelligent driving industry, primarily supplying Great Wall's brands with its driving systems [2][3]. - The company had a peak workforce of nearly 800 employees, focusing on the development of intelligent driving technologies for passenger vehicles [2][3]. Recent Developments - In late 2023, Haomo lost a key contract with Great Wall's Weipai brand, which shifted to a competitor, Yuanrong Qixing, for its intelligent driving solutions due to delays in Haomo's product development [3][9]. - Despite retaining contracts with Great Wall for mid- and low-tier models in 2024, Haomo is not the sole supplier for other major automakers like Beijing Hyundai, Toyota, and BMW [8][9]. Strategic Challenges - Haomo's initial strategy involved a heavy investment in high-level talent and technology, but the company struggled to keep pace with competitors who adopted more advanced technological approaches [5][12]. - The company's reliance on Qualcomm chips limited its ability to compete effectively in the high-performance segment of the intelligent driving market, as its AI computing power was insufficient for urban driving applications [11][12]. Financial and Operational Issues - Haomo's financial health has deteriorated, with a significant drop in valuation from $1 billion in 2021 to approximately 900 million yuan in 2024, reflecting limited growth and investor confidence [20][22]. - The company has faced challenges in converting its technological advancements into cash flow, leading to a reliance on external financing to sustain operations [18][20]. Conclusion - The trajectory of Haomo Technology illustrates the complexities of navigating the intelligent driving landscape, where strong initial backing from Great Wall Motors ultimately constrained its ability to diversify partnerships and adapt to rapid technological changes [22][23].

Core Insights - The article discusses the advancements and challenges faced by Li Auto in the development of its autonomous driving technology, particularly focusing on the end-to-end model and VLA (Vision-Language-Action) integration [2][5][9]. Group 1: Model Performance and Data Utilization - The performance improvement of end-to-end models slows down after reaching a certain amount of training data, specifically after 10 million clips, where the model's MPI (Miles Per Interaction) only doubled in five months [5]. - To enhance model performance, Li Auto adjusted the training data mix, increasing the quantity of generated data, including corner cases, and implementing manual rules for safety and compliance in special scenarios [5][9]. Group 2: VLA Integration and Decision-Making - The introduction of VLA aims to enhance the decision-making capabilities of the end-to-end model, addressing issues such as illogical behavior, lack of deep thinking in decision-making, and insufficient preventive judgment based on scenarios [5][6]. - VLA incorporates spatial intelligence, linguistic intelligence, and action policy, allowing the model to understand and communicate spatial information effectively, and generate smooth driving trajectories using diffusion models [6][9]. Group 3: Simulation and Testing Efficiency - Li Auto upgraded its model evaluation methods by utilizing a world model for closed-loop simulation and testing, significantly reducing testing costs from 18.4 per kilometer to 0.53 per kilometer [9][11]. - The closed-loop training framework AD-R1 was introduced, allowing for efficient data management and reinforcement learning, with high-value data being processed through a series of steps back to the cloud platform [11][12]. Group 4: Computational Power and Resources - Li Auto's total computational power is 13 EFLOPS, with 3 EFLOPS dedicated to inference and 10 EFLOPS for training, utilizing 50,000 training and inference cards [13]. - The emphasis on inference power is crucial in the VLA era, as it is necessary for generating simulation training environments [13].

作者 | candywisdom 编辑 | 自动驾驶之心 原文链接： https://zhuanlan.zhihu.com/p/1970953355355469364 点击下方 卡片 ，关注" 自动驾驶之心 "公众号 戳我-> 领取 自动驾驶近30个 方向 学习 路线 >>自动驾驶前沿信息获取 → 自动驾驶之心知识星球 本文只做学术分享，如有侵权，联系删文 从自动驾驶转到具身智能已经有一年的时间了，之前在自动驾驶上一系列工作和一些个人思考还一直没有好好的做个总结。（Ps: 虽然广义来说，自动驾驶属于具身智 能的子领域，但是现阶段二者所面临的问题和解决问题的具体方式还是存在较大差异，所以还是算是进入了一个转向了一个新的方向。） 可预期的短时间内，主要精力投入应该不会放在自动驾驶上了，但总觉得该给自动驾驶的这段经历留个记录。倒不是说这些工作多"惊天动地"，反而有些是"关注度不 高但挺实在"的探索，它们可能没上过热搜，但个人认为其确确实实解决过实际问题，希望可以给做相关方向的朋友提供点参考。 1. 从目标检测开始逐步往端到端planning拓展，构建一个强有力的端侧policy； 2. 针对端到端模型的闭环评测和训 ...

Group 1 - The core relationship between active safety and assisted driving is that both rely on similar underlying technologies to enhance user driving experience, with active safety focusing on preventing collisions regardless of who is driving [2][3] - Active safety aims to prevent accidents by providing alerts and taking control of the vehicle when necessary, while assisted driving systems follow navigation to transport users safely and efficiently [2][3] - The necessity of LiDAR in active safety is emphasized, as it significantly enhances safety by compensating for human limitations in various driving conditions [5][6] Group 2 - The active safety field has been expanding to cover high-frequency and high-risk driving scenarios over the past decade, but there are concerns about whether the current enumeration of accident scenarios is sufficient [7][8] - The complexity of real-world driving scenarios poses challenges for rule-based systems, which may struggle to account for unpredictable events [10][11] - The transition to model-based approaches in active safety could address these challenges by providing more effective responses to complex situations [15] Group 3 - The concept of "the death of active safety" is introduced, suggesting that as driving becomes safer through optimization and the advent of higher-level autonomous driving, the need for active safety may diminish [16] - Despite these challenges, the industry remains committed to improving active safety technologies, with a belief that advancements will lead to significant changes in the next few years [18] - The focus is shifting from competition to collaboration in creating a safer future, with ongoing efforts to reduce the probability and severity of accidents [18]

Core Insights - The article discusses the differences between VLM (Visual Language Model) and VLA (Visual Language Action) in the context of autonomous driving, particularly focusing on scenarios like blind spot deceleration [1][2]. Group 1: VLM and VLA Differences - VLM operates by perceiving scenarios such as uncontrolled intersections and outputs a deceleration request to the E2E (End-to-End) model, which then reduces speed to 8-12 km/h, creating a sense of disconnection in the response [2]. - VLA, on the other hand, utilizes a self-developed base model to understand the scene directly, allowing for a more nuanced approach to blind spot deceleration, resulting in a smoother and more contextually appropriate response based on various road conditions [2]. Group 2: Action Mechanism - The action generated by VLA is described as a more native deceleration action rather than a dual-system command, indicating a more integrated approach to scene understanding and response [3]. - There are concerns raised in the comments regarding VLM's reliability as an external module, questioning its ability to accurately interpret 3D space and the stability of its triggering mechanisms [3].

Core Viewpoint - The article discusses a new investigation by the National Highway Traffic Safety Administration (NHTSA) into Tesla's Full Self-Driving (FSD) system, raising concerns about its potential to cause traffic safety violations and accidents, particularly the risk of "diminished intelligence" with prolonged use of the system [1][6]. Investigation Details - NHTSA has opened an investigation into FSD, prompted by user complaints and media reports, focusing on traffic safety violations while FSD is engaged [2]. - The investigation covers approximately 2,882,566 Tesla vehicles equipped with FSD, which could lead to a recall if issues are confirmed [2][10]. Types of Violations - The investigation highlights two main types of violations: 1. Ignoring red lights, with 18 complaints confirmed, including 4 incidents resulting in injuries [2][3]. 2. Incorrect lane usage, such as entering oncoming traffic lanes or ignoring road signs, with another 18 complaints reported [3][10]. Incident Reports - A total of 58 reports of FSD-related traffic safety violations have been documented, resulting in 23 injuries [3][8]. - Notably, a testing agency found that while FSD performed well in initial tests, issues arose after extended use, leading to dangerous situations [6][11]. System Evaluation - NHTSA's review will assess the FSD system's ability to warn users of upcoming actions, response times, and its recognition of traffic signals and lane markings [10]. - The investigation will also evaluate whether over-the-air updates affect FSD's compliance with traffic laws [10]. Historical Context - This investigation is part of a series of ongoing inquiries into Tesla's FSD, with previous investigations addressing various incidents and compliance issues [13]. - The typical duration for such investigations is at least 18 months, indicating a slow regulatory response to rapidly evolving AI technology [15]. Future Implications - The outcome of the investigation may not significantly impact Tesla, as the company has historically navigated regulatory challenges effectively [11][15]. - The evolving nature of AI technology poses challenges for traditional regulatory frameworks, which may struggle to keep pace with advancements in systems like FSD [15].

Core Insights - The article discusses the current state and future prospects of autonomous driving technology, emphasizing the importance of AI and various modeling approaches in achieving higher levels of automation [4][6][9]. Group 1: Industry Development - The autonomous driving industry is rapidly evolving, with significant advancements expected in the next few years, particularly in AI and related fields [4]. - Companies like Waymo and Tesla are leading the way in achieving Level 4 (L4) automation, while Level 5 (L5) may take at least five more years to realize [4][6]. - The integration of Vision-Language Models (VLA) is seen as a key to enhancing decision-making capabilities in autonomous vehicles, addressing long-tail problems that pure end-to-end models may struggle with [6][9]. Group 2: Technical Approaches - The article outlines different modeling approaches in autonomous driving, including end-to-end models and the emerging VLA paradigm, which combines language processing with visual data to improve reasoning and decision-making [5][9]. - The effectiveness of current autonomous driving systems is still limited, with many challenges remaining in achieving full compliance with traffic regulations and safety standards [10][14]. - The discussion highlights the importance of data and cloud computing capabilities in narrowing the performance gap between domestic companies and leaders like Tesla [14][15]. Group 3: Talent and Education - There is a recognized talent gap in the autonomous driving sector, with a strong recommendation for students to pursue AI and computer science to prepare for future opportunities in the industry [4][6]. - The article suggests that practical experience in larger autonomous driving companies may provide better training and growth opportunities compared to smaller robotics firms [16][20].