Domestic News - The State Administration for Market Regulation released the "Guidelines for Compliance with Pricing Behavior in the Automotive Industry (Draft for Comments)" to unify regulatory rules and clarify legal boundaries for automotive production and sales enterprises [7] - Three departments announced a notification to appropriately reduce penalties for early loan settlements during the vehicle trade-in process, aiming to boost consumer spending [8] - The "15th Five-Year Plan" proposal from Shenyang emphasizes strengthening the automotive and parts industry, enhancing the supply chain, and promoting the development of smart connected new energy vehicles [9] - FAW-Volkswagen has commenced trial production of new models in Tianjin, with plans for multiple new models to be launched between 2026 and 2027 [10] - NIO has established 500 charging and battery swap stations in Shanghai, with a daily capacity of over 60,000 kWh and a coverage rate of 95% for users within 3 kilometers of a swap station [11] - The world's largest automotive safety testing center has opened in Ningbo, Zhejiang, featuring various world records and advanced testing capabilities [12] - Changan Automobile is expanding into Italy and Spain with two new electric vehicle models, with plans to introduce plug-in hybrid versions next year [13] - XPeng Motors is negotiating with Malaysian partner EPMB to start large-scale electric vehicle production in Malaysia by 2026 [15] International News - In Indonesia, new car sales fell by 1% year-on-year in November, totaling 74,252 units [16] - Volkswagen has begun testing autonomous vehicles in Germany, focusing on its Gen.Urban model capable of driverless operation in urban conditions [17] - Nissan has signed a final agreement with Wayve to integrate AI driving software into its next-generation ProPILOT system [18] - Tesla has initiated unmanned Robotaxi road tests in Austin, Texas, aiming for fully autonomous operation without safety personnel [19] Commercial Vehicles - Dongfeng launched the "Fourth Generation High-Energy Small Card" - Dongfeng T7, which aims to redefine the small truck market with enhanced features [20] - FAW Jiefang unveiled its "Blue Path 3.0" platform for new energy products, featuring four basic models and 165 series products, with significant market interest [21] - BYD has become the leading exporter of new energy buses in November, capturing nearly 25% of the market share, with a total of 3,933 units exported from January to November, a year-on-year increase of 19.25% [22][24] - The Jinbei Haise King 2026 model has been launched in Beijing, showcasing its capabilities as a "comfortable all-purpose business cabin" [25]

Core Insights - The article discusses the transition of AI from model capability competition to execution capability in the physical world, highlighting the challenges and opportunities in this domain [2][3]. Company Summaries - Zhi Bian Liang is focused on developing embodied intelligence foundational models and general-purpose robots, emphasizing the need for a physical model that operates in the real world, distinct from virtual models [4]. - Yuan Rong Qi Xing has been involved in autonomous driving, witnessing the industry's evolution from high-precision mapping to end-to-end models, and has successfully deployed 200,000 vehicles with their driving assistance systems, with a projection of reaching one million vehicles next year [5]. Challenges in AI Implementation - The transition from simulation to real-world application presents significant challenges, including the need for extensive pre-training based on real-world data, which is not easily replicated in simulated environments [6][7]. - The physical world introduces complexities that are not present in simulations, such as the need for precise manipulation and the impact of minor errors on outcomes [9][10]. Importance of Data and Training - The collection of vast amounts of real-world data is crucial for effective pre-training, and the integration of language models can enhance learning efficiency [7][18]. - The current data generation from 200,000 vehicles is substantial, necessitating careful selection and quality control to optimize model performance [18]. Future of Commercialization - The commercialization of embodied intelligence is expected to gain momentum by 2026, with predictions of significant advancements in practical applications and return on investment [21][22]. - The industry is currently in a phase similar to early autonomous driving, with many companies still in the demo stage, but there is optimism about achieving scalable commercial applications soon [19][20]. Role of Language Models - Language models are seen as essential for providing supervisory information during training, aiding in the rapid learning of complex tasks [12][13]. - However, there is debate about the necessity of language in physical AI, with some arguing that while it enhances understanding, it may not be critical for all applications [15][26]. Technical Considerations - The development of physical AI models requires overcoming significant engineering challenges, including the need for real-time feedback and the limitations of current computational resources [25][26]. - The scaling laws in AI suggest that with sufficient data and resources, it is feasible to train models that can operate effectively in the physical world within a reasonable timeframe [24][26].

近日，法国总统马克龙访华之际，《回声报》、《费加罗报》、法新社等法国媒体均聚焦中国，其中， 中国在科技创新和绿色发展领域取得的成就成为关注焦点。事实上，最近，《金融时报》《经济学人》 等多家外媒关注中国成为创新强国，并纷纷探究其中原因。报道称，中国已从"世界工厂"变身，西方国 家需要在竞争中迎头赶上。 中国作为技术强国的崛起已无可置疑 "创新"已成为中国经济社会发展的关键词。不久前党的二十届四中全会通过的"十五五"规划建议中，有 61次提及"创新"。近年来，中国重大科创成果密集涌现，全球创新指数排名从2012年的第34位升至2025 年的第10位。 "中国几乎在所有领域都占据优势地位"。这是法国外交及中国问题专家伊曼纽尔·林科特近日接受《费 加罗报》专访时做出的判断。文章指出，在当前全球技术格局中，特别是在电动汽车、人工智能等关键 领域，中国作为技术强国的崛起已无可置疑。中国近年来主动引导全球标准设定、技术创新推进，已进 入价值链高端。 澳大利亚广播公司12月8日文章引用澳大利亚智库最新发布的一份报告指出，中国在8个人工智能类别中 的7个，全部13个先进材料和制造技术类别，所有7个国防、太空、机器人和交通类别 ...

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 世界模型，近一年自动驾驶学术界和工业界的热词。很多小伙伴咨询柱哥，有没有一门系统讲解世界模型和自动驾驶的精品课程，筹备了很久终于和大家见面！ 我们联合 工业界大佬 共同开展，先前的《端到端与VLA自动驾驶小班课》备受大家好评，因此我们进一步推出这门世界模型小班课， 课程聚焦于通用世界模型、 视频生成、OCC生成等世界模型算法，涵盖特斯拉世界模型、李飞飞团队Marble等。欢迎大家加入学习~ 早鸟优惠！开课即止~ 讲师介绍 Jason：C9本科+QS50 PhD，已发表CCF-A论文2篇，CCF-B论文若干。现任国内TOP主机厂算法专家，目前从事端到端、大模型、世界模型等前沿算法的预研和量 产，并已主持和完成多项自动驾驶感知和端到端算法的产品量产交付，拥有丰富的端到端算法研发和实战经验。 课程大纲 这门课程讲如何展开 第一章：世界模型介绍 第一章主要针对自动驾驶世界模型概括性的内容讲解。 这一章老师会先复盘世界模型和端到端自动驾驶的联系，接着讲解世界模型的发展历史以及当下的应用案 例。然后介绍世界模型有哪些流派 ...

当前，自动驾驶发展进入深水区，干线物流因其场景相对规范、市场规模巨大，被公认为可能是最快实 现商业化的"黄金赛道"。 12月12日，旨在加速干线物流自动驾驶商业化落地的干线物流自动驾驶商业化路径研讨会暨四川省港投 集团干线物流自动驾驶"挑战赛"招募发布会在清华大学举办。该活动由四川省港航投资集团有限责任公 司（以下简称四川港投集团）、西部科学城智能网联汽车创新中心（以下简称西部智联）、清华大学智 能绿色车辆与交通全国重点实验室共同主办。 中国公路学会理事长翁孟勇会上表示，发展干线物流自动驾驶是解决行业结构性痛点的必由之路。目 前，在试验区、封闭场地开展试验的意义已经有限，应当在相对开放的场景中推动自动驾驶实践落地。 为深化战略布局与合作，四川港投经济技术研究有限公司与西部智联在会上签署战略合作协议，双方在 干线物流自动驾驶领域建立起紧密的生态合作伙伴关系。 清华大学车辆与运载学院教授、智能绿色车辆与交通全国重点实验室主任、西部科学城智能网联汽车创 新中心首席科学家李克强表示，交通系统智能化成为趋势，高速公路干线物流自动驾驶是技术进步和真 实需求共同驱动的成果，能带来巨大经济和社会效应。他认为，自动驾驶不是孤立 ...

新闻荐读 特斯拉首席执行官埃隆・马斯克（Elon Musk）于周日证实，公司已在得克萨斯州奥斯汀启动无人驾驶 Robotaxi 路 测，测试车辆内未配备任何乘员。 马斯克表示，Robotaxi不仅是一项服务，更是"个人交通的终极形式"，能让出行更安全、高效、便宜。Robotaxi将 为特斯拉带来巨大收益，是"万亿美元级机会"。 据媒体此前报道，当地时间6月22日，马斯克在社交平台上宣布"推出自动驾驶出租车"。他表示，这是十年辛勤工 作的成果，特斯拉AI芯片和软件团队都是在特斯拉内部从零开始组建的。 和之前宣传的无人驾驶不同，每辆车都在前排配备了安全员。 马斯克在社交媒体上表示，特斯拉自动驾驶出租车服务将固定收取4.20美元的统一费用。他强调，公司在安全方 面"格外谨慎"，特斯拉将配备专业团队实施远程监控干预，保障试运营的安全性。 两辆特斯拉 Model Y Robotaxi 被目击在奥斯汀公共道路上行驶，车内空无一人。 马斯克还表示，Robotaxi车队可实现高利用率（每车每周运行超40小时），毛利率可能高达70%—80%，远超传统 汽车业务。马斯克称，特斯拉的目标是让无人驾驶车辆比人类驾驶安全10倍以上。他 ...

（市场有风险，投资需谨慎。本文为AI基于第三方数据生成，仅供参考，不构成个人投资建议。） 来源：市场资讯 这位曾是特斯拉"忠实信徒"的投资者，近年来已转变为公司的公开批评者。他批评公司首席执行官埃隆·马斯克将精力分散于其他事务，例如此前领导美国 政府效率部以及收购社交媒体平台X。格伯认为，马斯克离开公司管理层的那段时间，让特斯拉在与竞争对手的自动驾驶竞赛中损失了宝贵时间。 在技术路线上，格伯对特斯拉坚持采用纯视觉（AI和摄像头）方案、拒绝使用激光雷达的做法表示担忧。他指出，主要竞争对手Waymo凭借激光雷达等技 术，在美国主要城市持续扩张其自动驾驶服务，并建立了强大的市场影响力。格伯认为，特斯拉在基础设施建设和规模化部署方面已经落后。 特斯拉早期投资者、Gerber Kawasaki财富投资管理公司CEO罗斯·格伯（Ross Gerber）近期表示，2026年对特斯拉而言将是决定成败的关键一年。他认为， 公司的未来很大程度上取决于其自动驾驶技术能否兑现承诺，否则将面临市场的严格审视。 格伯在接受采访时指出，特斯拉的牛市叙事高度依赖自动驾驶汽车的成功。尽管该公司已在美国多个城市推出无人驾驶汽车服务，但格伯认为实 ...

Core Insights - The performance of robots from Zhiyuan and Yushu at the GDPS 2025 marks a significant step towards the commercialization of embodied intelligence robots, with 2025 being viewed as the year of mass production [1] - The current state of the embodied intelligence industry is characterized by a "three hot, three cold" phenomenon, indicating a disparity between technological advancements and practical applications [6] Industry Development - The embodied intelligence industry in China is in its early stages, with a projected market size of 400 billion yuan by 2030 and over 1 trillion yuan by 2035 [3] - Over 100 financing events occurred in the humanoid robot sector in 2025, with total investments exceeding 20 billion yuan, pushing the valuations of leading companies like Zhiyuan close to 20 billion yuan [3] Challenges and Opportunities - The industry faces challenges such as incompatible hardware interfaces and data formats, leading to data silos, despite advancements in technology [6] - There is a pressing need for standardized safety certifications and performance evaluation metrics to build user trust in robotic products [6] Standardization and Collaboration - Industry experts emphasize the necessity of establishing standards to unify efforts and facilitate the growth of the trillion-yuan market [6][7] - A flexible standardization approach is advocated, allowing for both rigid safety protocols and adaptable guidelines for innovation [7] Market Dynamics - The future of the embodied intelligence sector may mirror the trajectory of the autonomous driving industry, with potential for a few dominant players to emerge alongside numerous startups in the supply chain [9] - The industry is expected to see significant investment opportunities in high-barrier core component sectors, while the downstream market may diversify [10] Data and Technology - High-quality, large-scale data is identified as a critical competitive advantage for training embodied intelligence systems [10] - The industry must transition from imitation-based methods to reasoning and planning frameworks, enhancing the capabilities of robots [10]

Core Viewpoint - Rivian has launched its first custom AI chip, the Rivian Autonomy Processor 1 (RAP1), which aims to replace Nvidia products in future models, boasting performance four times that of previous Nvidia systems [2][3] Group 1: Rivian's AI Chip Development - Rivian's RAP1 chip is designed to be integrated into the upcoming R2 SUV, marking a strategic shift towards in-house chip development to enhance its autonomous driving capabilities [3] - The RAP1 chip utilizes TSMC's 5nm process and features a memory bandwidth of 205GB per second, with two RAP1 chips capable of processing 5 billion pixels per second [3] - Rivian claims that the self-developed chip is a critical turning point for achieving Level 4 autonomous driving, moving beyond the Level 2 capabilities previously supported by Nvidia [3] Group 2: Competitive Landscape - Tesla is also advancing its chip development with the AI5 chip, set for mass production in 2027, which will utilize a 3nm process and offer 2000-2500 TOPS of computing power, five times that of the current HW4 chip [4] - The global automotive industry is witnessing a surge in self-developed chip initiatives, driven by the need for supply chain security, cost efficiency, and differentiated competition [5][6] - American automakers, particularly Tesla, are leading in chip development, with Tesla's FSD chip achieving a computing power of 1000 TOPS, enhancing its autonomous driving capabilities [5] Group 3: Strategic Implications - The shift towards self-developed chips is seen as a necessary strategy for automakers to maintain competitive advantages in the evolving automotive landscape [6][7] - Rivian's approach aims to create a highly integrated smart ecosystem where the chip serves as the core, processing data from sensors to enhance vehicle intelligence [8] - The introduction of the Autonomy Plus subscription service represents a new revenue stream for Rivian, aligning with the trend of combining hardware sales with software profitability [8] Group 4: Industry Transformation - The automotive industry is transitioning from a reliance on Tier 1 suppliers to a model that integrates hardware and software, with self-developed chips being a key breakthrough [7] - The competitive landscape is shifting towards a focus on technological capabilities and strategic safety, making self-developed chips a survival necessity for automakers [9] - The ongoing "chip war" among automakers is expected to shape the profitability and market positioning of companies in the smart vehicle era [9]

Core Viewpoint - Tesla's future is seen as increasingly uncertain, with 2026 identified as a critical year for the company to demonstrate substantial progress in its ambitious goals, particularly in autonomous driving technology [1][2]. Group 1: Concerns About Autonomous Driving - The primary concern for Tesla is its autonomous driving technology, which if it fails to meet market expectations, could undermine the bullish investment logic surrounding the company [1]. - Ross Gerber, a long-time investor, has shifted from being a supporter to a critic, emphasizing that 2026 will be a year where Tesla must show tangible results in its plans [2][9]. - Gerber attributes Tesla's struggles in the autonomous driving sector to its refusal to adopt lidar technology, which has allowed competitors like Waymo to gain a significant market advantage [7][10]. Group 2: Competitive Landscape - Waymo is currently leading the autonomous driving market, having established a strong presence in major U.S. cities, while Tesla is just beginning to deploy its autonomous taxi service [10]. - Gerber believes that Alphabet, Waymo's parent company, is a better investment choice due to its resources and established market position, which puts Tesla at a disadvantage [10]. Group 3: Leadership and Public Perception - Gerber has criticized Elon Musk for being distracted by other ventures, which he believes has negatively impacted Tesla's stock performance and operational focus [5][6]. - The public perception of Musk has also become a concern, as his political involvement has polarized consumer opinions, potentially affecting Tesla's market appeal even if it catches up technologically [10].