Core Viewpoint - The article discusses the evolution of autonomous driving technology, highlighting the competitive landscape among major tech companies, automakers, and startups, and how advancements are reshaping transportation methods [2][3]. Group 1: Tesla's Development - Tesla is recognized as a pioneer in autonomous driving, with its aggressive data-driven approach that discards traditional methods like LiDAR and high-definition maps in favor of pure visual perception [6]. - The development path includes the transition from modular designs to end-to-end neural networks, aiming to make AI think and drive like humans [6]. - Key technologies introduced include BEV (Bird's Eye View) and Occupancy Network, enhancing spatial awareness and reducing reliance on high-definition maps [8][12]. Group 2: Huawei's Progress - Huawei's ADS technology has evolved from multi-sensor fusion and high-definition map reliance to a "mapless" approach, enhancing perception algorithms and ultimately leading to end-to-end model applications [23]. - The ADS 1.0 version relied on multiple sensors and high-definition maps, while ADS 2.0 marked a breakthrough in "mapless" driving [25][26]. - The latest ADS 3.0 aims for full scene intelligent driving, integrating advanced perception networks and optimizing hardware for better performance [28]. Group 3: Momenta's Strategy - Momenta employs a dual strategy of data-driven algorithms and mass production of autonomous driving products, creating a feedback loop for continuous improvement [33]. - The company focuses on low-cost automated mapping and crowd-sourced map updates, enhancing its capabilities in complex environments [35]. Group 4: Horizon's Path - Horizon has developed a unique path from automotive-grade AI chips to full-stack solutions, emphasizing software and hardware collaboration for efficiency [47]. - The company has progressively advanced from early ADAS prototypes to L2+ and L3 capabilities, with plans for broader applications in 2025 [49][50]. Group 5: Xiaopeng's Evolution - Xiaopeng's autonomous driving journey reflects a shift from multi-sensor fusion and high-definition maps to a "mapless" approach, driven by AI large models [79]. - The XPILOT series has evolved from basic parking assistance to advanced highway and urban navigation capabilities, with significant improvements in system generalization [81][90]. Group 6: NIO's Development - NIO's approach is characterized by a cautious evolution from collaborative development to full-stack self-research, focusing on safety and reliability [98]. - The introduction of the World Model NWM in 2025 signifies a new phase in NIO's autonomous driving capabilities, enhancing cognitive and reasoning abilities [110].

"世界模型能够从根本上解决VLA系统对数据的依赖，是伪命题。" 柱哥这两天和星球大佬讨论VLA和WA的路线之争，分享给大家。 2025年的自动驾驶赛道正分裂为两大阵营：小鹏、理想、元戎启行押注 VLA路线，华为、蔚来则力 推世界行为模型（WA）。后者认为WA才是能真正实现自动驾驶的终极方案。然而血淋淋的现实 是：这不过是个套壳的数据依赖论。 VLA依赖海量数据训练得到的VLM进一步扩展Action的能力，但工业界最得天独厚的优势就是有海 量的数据，这给模型研发提供了无限可能。在普通场景大家都已经做到99.9%的能力下，长尾场景才 是既分高下也决生死的所在。 世界模型为什么会被吹捧，生成式的方法理论上可以无限扩展corner case，但生成的前提是用海量真 实数据训练物理规则认知框架。 你去生成一个卡车在马路上打篮球的场景，理论上虽然可以，但实际上VLA也好，WA也好，都未必 能真正理解。 『自动驾驶之心知识星球』目前集视频 + 图文 + 学习路线 + 问答 + 求职交流为一体，是一个综合类 的自驾社区，已经超过4000人了。 我们期望未来2年内做到近万人的规模。给大家打造一个交流+技 术分享的聚集地，是许多 ...

在信息爆炸的时代，消费者的注意力被分散在各个媒介平台之上。对于企业而言，单一的媒介推广方式已难以满足品牌传播 与业务增长的需求，从"单点突破"迈向"全网渗透"成为媒介推广的必然趋势。那么，如何实现这一转变，让媒介推广发挥最 大效能呢？ 单点突破是企业媒介推广的起始阶段，如同在战场上集中优势兵力攻克一个关键据点。企业需要深入分析自身产品或服务的 特点、目标受众的特征以及市场竞争态势，精准选择一个最具潜力的媒介平台作为突破口。 例如，一款针对年轻女性的时尚美妆产品，在初期可以选择小红书平台进行重点推广。小红书以其庞大的年轻女性用户群 体、浓厚的美妆分享氛围和强大的种草能力，成为该产品的理想推广阵地。企业可以与美妆领域的知名博主合作，通过精美 的图片、详细的试用分享和真实的用户评价，迅速吸引目标受众的关注，打开产品知名度，实现单点销量的快速增长。 抖音、B站等视频平台以生动直观的视频内容吸引着大量用户。企业可以制作高质量的产品宣传视频、使用教程视频或品牌 故事视频，在视频中巧妙融入产品信息和品牌理念，让用户在沉浸式的观看体验中产生共鸣和购买欲望。同时，利用视频平 台的算法推荐机制，将视频精准推送给目标受众，提高曝光 ...

本届泰达汽车论坛上，来自政产学研界的嘉宾回顾"十四五"汽车业取得的业绩，共同为"十五五"产业发展建言献策。本报梳理了四个热点话题，以飨读 者。 13 William the state 3 3 2 1 2 1.85 Kiss 17 1-57 1 150 are 1 20 B - 4/12/2 194 P 12 t 2015 A I 195 Children y W 东风汽车集团有限公司首席专家、东风汽车研发总院副院长陈涛表示，AI技术正推动汽车从"功能机"向"智能体"转变，通过多模态大模型实时捕捉驾驶员生 理和情绪状态，自动调节车内环境，实现"共情式"出行体验。在企业端，AI能力体系已应用于研发、制造、供应链和服务全链条，显著提升生产效率和质量 控制水平。 智能化转型成为本届泰达汽车论坛重点讨论的话题，在多场关于智能化的专场论坛中，技术创新、安全底线、政策法规等问题被反复提起，成为汽车智 能化进程的重要挑战。 数据驱动产业生态重构 北京理工大学副教授、新能源汽车国家大数据联盟副秘书长刘鹏指出，数据作为新生产要素，正深刻改变汽车产业的研发、生产与服务模式。数据已成为驱 动产业全面升级的新动力，如何系统性挖掘数据 ...

Core Viewpoint - The article highlights the transformation of agriculture in Shandong province through the integration of technology, moving from traditional farming methods to smart, data-driven agricultural practices, thereby enhancing productivity and sustainability [1][12]. Group 1: Technological Advancements in Agriculture - Smart agricultural machinery is reshaping production scenes, transitioning from "human-led" to "cloud-based" farming [1]. - The use of drones for fertilization and pest control, along with AI systems for monitoring, is becoming standard in Shandong's agricultural practices [1][4]. - As of now, Shandong has established over 1,000 smart agriculture scenarios, including more than 20,000 acres of intelligent greenhouses and 1.8 million agricultural drones, covering over 170 million acres of operational area annually [6][12]. Group 2: Data-Driven Management - A comprehensive digital management system is being developed, allowing for real-time data collection and analysis, which enhances decision-making in agricultural practices [7][11]. - The integration of satellite navigation and AI technology enables precise operations in farming, such as autonomous harvesting and planting [8][11]. - The "Qilu Agricultural Cloud" platform consolidates agricultural data resources, totaling 2.94 billion entries, facilitating a data-driven management approach [11]. Group 3: Industry Collaboration and Ecosystem Development - Shandong is promoting a collaborative ecosystem that integrates technology, industry, and talent to enhance agricultural productivity [12][13]. - The province is breaking down silos in the agricultural supply chain, ensuring a seamless connection from production to market [12]. - Initiatives like the "High Tang Agricultural Brain" project digitize local agricultural knowledge, providing farmers with easy access to expert advice and resources [9][12]. Group 4: Future Outlook - Future plans include expanding the application of "space-ground" technology in agriculture and promoting AI models across various crop types [14]. - Continued investment in digital agriculture is expected to enhance connectivity between provincial and local systems, making smart agriculture more accessible [14].

9月13日，国家建筑绿色低碳技术创新中心建筑运维智慧化方向联合实验室建设合作协议签约仪式、科技项目与成果发布、"绿色医院与智慧运营"中日国际 合作揭榜挂帅项目签约仪式暨建筑智慧运维与节能低碳技术交流会在北京成功举办。 本次会议由国家建筑绿色低碳技术创新中心、建科公共设施运营管理有限公司主办，中国建设科技集团中央研究院建筑智慧运维研究中心等多家单位联合主 办，中国建筑一局（集团）有限公司等协办，《暖通空调》杂志社承办，新华网、光明网等媒体支持报道。 开幕式上，国家建筑绿色低碳技术创新中心主任、中国建设科技集团党委书记、董事长孙英，中国建筑西南设计研究院有限公司党委书记、董事长陈勇等致 辞，建科公共设施运营管理有限公司副总经理刘志国主持。大会举行"国家建筑绿色低碳技术创新中心建筑智能感知与自主运维装备联合实验室""国家建筑 绿色低碳技术创新中心建筑运维大数据技术联合实验室"建设合作协议签约仪式，还进行了"绿色医院与智慧运营"中日国际合作揭榜挂帅项目签约及特聘专 家证书颁发。 下GB|国家建筑绿色低碳技术创新中心 "绿色医院与智慧运营"中日国际合作揭榜挂帅项目签约仪式暨特聘专家证书颁发仪式 医院智慧运维与绿色发展科 ...

"汽车行业正从马力与真皮转向算力与体验，从依赖品牌溢价转向追求技术溢价。"中汽创智科技有 限公司首席执行官谈民强，在近日举办的2025泰达汽车论坛上表示，自主品牌冲击高端必须摆脱"以价 换量"的路径依赖，转向以软硬协同驱动全链创新的高附加值模式。 近年来，中国汽车品牌在新能源与智能网联领域成功实现换道超车，涌现出多家高端新能源品 牌。"但我们必须始终牢记汽车的本质是交通工具，安全与可靠是不可逾越的底线，应避免过度宣传和 误导用户。"谈民强提醒道。 反观国际传统汽车巨头，凭借数十年甚至上百年的技术、资金和人才积累正在全力反击。近期，传 统厂商如奔驰、宝马、大众等与博世等企业成立软硬件联盟，并邀请英伟达、高通等企业加入，旨在打 造"芯片+操作系统"联盟以构建技术壁垒。 在谈民强看来，当前汽车产业正经历百年未有之大变局，其本质是由技术革命驱动的价值链重塑。 智能网联、自动驾驶、三电系统等原属于豪华汽车的先进技术，正以前所未有的速度普及至主流市场。 谈民强指出，真正领先不仅在于市场规模，更在于是否在芯片、算法、操作系统等核心技术上实现 自主突破，是否能够发挥新型举国体制优势，打通数据壁垒、强化技术软件，协同产业链上下 ...

Core Insights - The automotive industry is undergoing a significant transformation driven by data, which is reshaping the ecosystem and enhancing R&D, production, service, and management [3][4][12] - The need for collaboration among government, industry, academia, and research institutions is emphasized to establish data standards and a secure, shareable data ecosystem [3][4] Group 1: Data as a Driving Force - Data is identified as a new engine for the automotive industry, driving comprehensive upgrades across various sectors [3] - The transition from a "product-driven" to a "user-driven" era is highlighted, necessitating a shift in product definition, marketing strategies, and service models [4] - Companies are urged to leverage big data analytics to better understand user behavior and enhance product and service offerings [4] Group 2: Breaking Down Data Silos - The issue of data fragmentation and siloed systems in the bus industry is addressed, with a focus on creating a comprehensive big data system that connects all aspects of operations [6] - A case study from Xiamen King Long Bus Company illustrates how data-driven approaches have reduced order delivery times from 60 days to under 35 days and improved operational efficiency [6] Group 3: Emerging Data Infrastructure - The concept of a "data space" is introduced as a new infrastructure that allows for secure and efficient data sharing across enterprises and industries [7] - The data space is characterized by its ability to maintain data sovereignty while facilitating trusted data flow, particularly in sensitive scenarios [7] Group 4: AI Applications in the Industry - AI is being utilized to enhance efficiency across the battery lifecycle, addressing challenges in research, manufacturing, and recycling [8] - The development of an integrated platform for intelligent product design and real-time monitoring of battery health is highlighted as a significant advancement [8] Group 5: Data Security and Governance - The importance of establishing a collaborative governance framework for data security and compliance is stressed, particularly in the context of smart connected vehicles [11] - A report on data governance in the automotive industry outlines current challenges and offers recommendations for improving data security and cross-border data flow [12] Group 6: Industry Consensus and Future Directions - The forum reached a consensus that data-driven approaches are essential for the digital transformation of the automotive industry [12] - Emphasis is placed on breaking down data barriers and enhancing cross-industry collaboration to maximize data resource utilization while ensuring security and privacy [12]

Core Insights - JD Supermarket celebrated its 11th anniversary in Beijing, gathering over 400 representatives from the fast-moving consumer goods (FMCG) industry to discuss new trends and opportunities in retail [1] - Coca-Cola's collaboration with JD has lasted for 14 years, with JD being a crucial partner in understanding Chinese consumers and driving localized innovation [1] Group 1: Strategic Collaboration - Gilles Leclerc emphasized that the partnership will deepen in three areas: scenario marketing, data-driven strategies, and a comprehensive ecosystem [3] - In scenario marketing, the focus will be on creating impactful brand activities around major national events like the Spring Festival and FIFA, leveraging JD's PLUS membership system to enhance customer loyalty and repurchase rates [3] - The data-driven approach will utilize JD's real-time data insights and AI technology to predict consumer demand and improve business decision-making efficiency [3] Group 2: Ecosystem Development - The collaboration will extend beyond online retail into areas like instant delivery and dining, enhancing channel coverage and consumer reach [5] - Gilles Leclerc expressed confidence in JD Supermarket's user-centric approach and its retail innovation capabilities, aiming to provide consumers with a more convenient and personalized experience [6] - The deepened strategic cooperation between JD Supermarket and Coca-Cola sets a benchmark for collaboration between FMCG brands and retail platforms, promoting sustainable growth in a complex market environment [6]

Core Insights - Content marketing is essential for brands to resonate with users, but many fail due to a lack of effective techniques. Mastering five core skills can transform content marketing into a growth driver for brands [1]. Group 1: Key Techniques - Precise Positioning: Effective content marketing requires understanding user needs rather than self-promotion. Brands should create user personas to identify target demographics and focus on unmet needs, such as the challenges faced by new mothers [3]. - Content Quality: High-quality content should be useful, interesting, and emotionally resonant. It should address real problems, engage with current trends, and reflect brand values, like the storytelling approach of Nongfu Spring [4]. - Channel Matching: The effectiveness of content is influenced by its alignment with the appropriate channels. Different platforms require tailored content formats, such as short videos for TikTok and in-depth articles for WeChat [5]. Group 2: Data and Iteration - Data-Driven Strategy: Content marketing should be treated as a scientific process, utilizing metrics like completion rates and interaction rates to refine strategies. A/B testing can help determine the most effective content approaches [6]. - Continuous Iteration: The ultimate goal of content marketing is to build a content asset library. Brands should create series content and repurpose existing materials while establishing a feedback loop to enhance future content creation [7]. Conclusion - The essence of content marketing lies in exchanging value for trust, which in turn drives user action. By mastering precise positioning, prioritizing quality, matching channels, leveraging data, and iterating continuously, brands can shift from self-centered promotion to becoming a user-centric flow engine [9].