Core Viewpoint - MogoMind, launched by MOGOX, is the first physical world cognitive model that aims to enhance urban traffic management through real-time data integration and intelligent decision-making [1][8]. Group 1: MogoMind's Functionality and Role - MogoMind serves three primary roles: central decision-maker for urban traffic, multi-functional assistant for vehicle operation, and invisible foundation for autonomous driving [2]. - The model utilizes an integrated sensing and computing network to capture and analyze vast amounts of heterogeneous data, enabling real-time perception and decision-making [1][4]. Group 2: Improvements Over Traditional Systems - Traditional traffic perception systems rely on isolated devices, leading to information silos and limited coverage, which hampers effective traffic management [3]. - MogoMind's multi-modal sensor collaboration combines LiDAR, high-definition cameras, and millimeter-wave radar to create a continuous sensing network, addressing compatibility issues and enhancing data accuracy [4]. Group 3: Limitations and Challenges - The effectiveness of MogoMind decreases in suburban areas due to deployment and maintenance costs, resulting in a significant drop in data accuracy and update frequency [5]. - The model's reliance on sample vehicle data for road condition estimation presents challenges during low traffic periods, leading to data sparsity and reduced model performance [5]. Group 4: Societal and Technical Implications - MogoMind's focus on efficiency may overlook safety and equity concerns in specific areas, highlighting the need to quantify social values within the model [6]. - The model exposes critical issues in the industry, such as the need for improved physical data collection, human behavior modeling, and balancing multiple objectives [6][7]. Group 5: Future Directions - Addressing the identified challenges requires interdisciplinary collaboration among traffic engineers, sociologists, and policymakers to develop innovative solutions [7]. - MogoMind's development signifies a step towards integrating intelligent transportation systems with urban planning and social governance [7][8].

作者 | 华卫 当大模型的聚光灯照向实体经济，一个"必答题"浮出水面：数字世界里惊艳的大模型技术，怎样才能变成现实世界中实实在在的生产力？ 在 2025 世界人工智能大会（WAIC 2025）期间，蘑菇车联（MOGOX）发布首个物理世界 AI 大模型——MogoMind。在蘑菇车联展区，MogoMind 作 为首个深度理解物理世界 AI 大模型，成为本届大会最受关注的人工智能技术应用之一。 通过深度整合实时、海量的多模态交通数据，MogoMind 能够从物理世界的复杂数据中抽取意义、从经验中学习规则、在不同场景中灵活决策，形成对 交通环境的全局感知、深度认知和实时推理决策能力，可以为多类型智能体提供实时数字孪生与深度理解服务，成为城市和交通高效运行的"AI 数字基 座"。 依托 MogoMind 大模型能力，蘑菇车联推出多款 L4 级前装量产自动驾驶车辆，包括 RoboBus、RoboSweeper 和 RoboTaxi，深度融入全局感知、深 度认知和实时推理决策能，推动自动驾驶技术在公共交通、城市环卫、无人零售等多场景应用。 其中，自动驾驶巴士 MOGOBUS 搭载端到端"MogoAutoPilot+Mog ...

Investment Rating - The industry investment rating is "Outperform the Market," indicating that the industry is expected to perform better than the market benchmark over the next 12 months [56]. Core Insights - The WAIC conference highlighted key trends in technology innovation, emphasizing the acceleration of intelligent agent applications by companies like Tencent and JD.com, the rapid development of open-source ecosystems supporting AI, and the focus on world models and embodied intelligence [5][4]. - The report recommends companies with strong technological capabilities and broad application scenarios, including Alibaba, Tencent, Kuaishou, Baidu, Horizon Robotics, Li Auto, and Xpeng, as they are expected to benefit from the increasing demand for large model applications [5][4]. Summary by Sections AI Development and Applications - Tencent has launched over 10 intelligent agents across various verticals, while JD.com has open-sourced its JoyAgent intelligent agent [5][4]. - Alibaba's Tongyi Qianwen has surpassed 400 million downloads, with over 140,000 derivative models created [5][4]. - The report notes that Tencent's mixed 3D world model 1.0 significantly simplifies the 3D scene construction process, enhancing efficiency in game development and digital content creation [9][4]. Autonomous Driving - The report identifies a dual inflection point in the autonomous driving sector, with improved regulatory environments and the introduction of Tesla's advanced Full Self-Driving (FSD) technology in China expected to boost competition [4][5]. - The penetration rate of L2+ autonomous driving vehicles in China is estimated to be around 30-35%, with projections indicating it could exceed 50% by 2026 [4][5]. Company Recommendations - The report recommends investing in companies with robust technological foundations and diverse application scenarios, specifically highlighting Alibaba, Tencent, Kuaishou, Baidu, Horizon Robotics, Li Auto, and Xpeng [5][4]. - The anticipated growth in cloud business driven by the increasing demand for large model applications is expected to support the stock performance of these companies [5][4].

Core Insights - MogoMind, an AI model launched by Mushroom Car Union, aims to provide comprehensive technical support for traffic intelligence by integrating real-time, all-encompassing, and platform-based capabilities [1][3] - The model functions as a real-time search engine for the physical world, capturing vast amounts of heterogeneous data related to vehicle trajectories, speed changes, traffic flow, and pedestrian dynamics [1] - MogoMind's ability to understand physical information in real-time allows it to identify road conditions, traffic signs, and obstacles, transforming complex traffic environment data into actionable intelligent decision-making suggestions [1] Traffic Flow Prediction - MogoMind employs traffic flow prediction models and traffic capacity assessment algorithms to dynamically calculate road capacity in real-time, considering various factors such as traffic volume, vehicle types, road geometry, and traffic signal timing [3] - The model utilizes reinforcement learning techniques to uncover patterns and trends in traffic data, predicting future traffic flow changes [3] - MogoMind offers services such as real-time route planning, digital twin technology, and warning alerts, seamlessly integrating with various traffic devices and systems from different manufacturers for unified data management and collaborative processing [3]

Core Insights - The MogoMind AI model developed by Mushroom Car Union is introduced as a real-time search engine for the physical world, enhancing capabilities beyond traditional digital models [1] - MogoMind integrates various devices to create a comprehensive perception network for real-time understanding of physical information, including road conditions and vehicle statuses [4] Group 1: MogoMind Capabilities - MogoMind can process multimodal information and real-time data from the physical world, addressing limitations of traditional language models that only handle static text [1] - The model supports emergency response to road incidents, provides over-the-horizon traffic alerts, and enhances real-time risk perception in blind spots for both drivers and autonomous vehicles [3] Group 2: Applications and Impact - Mushroom Car Union has launched multiple L4 level mass-produced autonomous vehicles utilizing MogoMind, integrating global perception, deep cognition, and real-time decision-making capabilities [3] - The autonomous buses equipped with MogoMind have successfully operated in 10 provinces across China, covering over 2 million kilometers and serving more than 200,000 passengers [3]

专题：2025世界人工智能大会 新浪科技讯 7月27日中午消息，近日全球人工智能领域年度盛会——2025世界人工智能大会暨人工智能 全球治理高级别会议（WAIC 2025）在上海举行。 大会期间，蘑菇车联围绕AI大模型在交通领域的应用，展示深度理解物理世界的AI大模型MogoMind、 智能体与物理世界实时交互的AI网络等多项核心技术产品。 在蘑菇车联展区，MogoMind作为首个深度理解物理世界AI大模型备受关注。据悉，该模型参数规模达 到了70亿，感知精度、认知准确度超90%，多模态推理准确率超88%，能推演超800个交通场景，目前 已在北京、上海、浙江等8个城市落地应用。 相比数字世界中的大模型，MogoMind可以视为物理世界的实时搜索引擎，通过接入物理世界实时动态 数据，MogoMind形成全局感知、深度认知和实时推理决策能力，能够从数据中抽取意义、从经验中学 习规则、在场景中灵活决策。 责任编辑：王翔 MogoMind依托交通数据流实时全局感知、物理信息实时认知理解、通行能力实时推理计算、最优路径 实时自主规划、交通环境实时数字孪生、道路风险实时预警提醒六大关键能力，解决了当前AI缺乏物 理世界实 ...

Ground-based Perception and Computing Network - The breakthrough of 5G-A technology represents a significant leap in communication speed, latency, and positioning accuracy, with peak download rates reaching 10Gbps and latency reduced to milliseconds [2][4] - 5G-A technology supports various applications, including extended reality (XR), cloud gaming, and industrial internet, showcasing its transformative potential in sectors like autonomous driving and smart cities [4][5] - Major Chinese telecom operators are accelerating the deployment of 5G-A networks, with China Mobile planning to complete the smart transformation of 400,000 base stations by 2025, covering over 300 cities [4][5] AI-Driven Smart Traffic Practices - Companies like Mushroom Car Union are building AI-driven perception and computing networks for smart traffic, utilizing roadside intelligent units and cloud AI models for real-time traffic optimization [5][6] - The system enhances traffic efficiency by synchronizing vehicle intentions at intersections, improving traffic flow by over 30% [6][8] - The integration of AI perception networks significantly reduces traffic accident rates, demonstrating the technology's value in public safety [8] Space-based Perception and Computing Network - The recent launch of the "Three-body Computing Constellation" marks a major breakthrough in space-based perception and computing networks, enabling on-orbit data processing and real-time analysis [9][11] - The constellation consists of 12 satellites with a total computing power of 1000POPS, allowing for rapid disaster warning and environmental monitoring [11][12] - The integration of AI into space infrastructure enables autonomous scheduling and execution of multi-source data fusion tasks, enhancing the efficiency of various applications [12][14] Future Outlook: Integrated Perception and Computing Network - The complementary nature of ground-based and space-based networks allows for seamless integration, ensuring continuous navigation and communication in various scenarios [15][17] - Challenges such as standardization, resource allocation, and security need to be addressed, but they also present new opportunities in chip development and software innovation [17][18] - The true value of AI perception networks lies in driving technology integration through scenario-based approaches, enhancing capabilities in autonomous driving and other applications [18][20] Conclusion - The transition from ground-based 5G-A to the space-based "Three-body Constellation" signifies a shift towards an integrated AI perception and computing network, reshaping communication, perception, and computation boundaries [20][21] - China's strategic positioning in 5G-A and space computing networks places it at the forefront of this technological evolution, paving the way for a new era of digital civilization [20]