智能驾驶行业专题：Robo-X 的产业趋势、市场空间和产 业链拆解 20251222 摘要 L4 级自动驾驶市场潜力巨大，预计 2030 年全球市场空间达万亿级别， 国内 Robot Taxi 和 Robot Van 潜在替代市场规模分别为 2,360 亿元 和 1,645 亿元，无人卡车、公交车和环卫车等细分赛道亦具潜力。 全球多地政府放宽自动驾驶限制，明确监管框架，推动智能驾驶发展。 中国北京、上海、广州、深圳等城市已开启 ROS 服务，武汉、重庆等城 市也在开放相关服务。 强化学习和世界模型是 L4 级自动驾驶底层技术，解决了传统模仿学习的 数据稀缺和模块依赖问题，提高了系统泛化决策能力，有效应对辅助驾 驶需要改进的重要场景。 Robotaxi 运营成本优势显著，无安全员情况下每公里运营成本仅 0.81 元，低于传统燃油和电动网约车。当运营车辆规模达 1,000 台时，有望 实现营业利润转正。 Robotaxi 商业模式多样，主机厂、自动驾驶公司和出行服务商合作是 主流。国内外企业加速布局，如特斯拉已在德州上线无人驾驶出租车， 累计行程超 40 万公里。 Q&A 目前 ROS 行业的整体趋势和市场空间如 ...

Core Insights - The 2025 World Artificial Intelligence Conference (WAIC 2025) was recently held in Shanghai, focusing on advancements in AI technology and governance in the transportation sector [1] Group 1: MogoMind AI Model - MogoMind is the first AI model designed for deep understanding of the physical world, featuring 7 billion parameters, with perception accuracy and cognitive accuracy exceeding 90%, and multi-modal reasoning accuracy over 88% [3] - The model can simulate over 800 traffic scenarios and has been implemented in 8 cities including Beijing, Shanghai, and Zhejiang [3] - MogoMind functions as a real-time search engine for the physical world, integrating real-time dynamic data to enhance global perception, deep cognition, and real-time decision-making capabilities [3][4] Group 2: Key Capabilities - MogoMind utilizes six key capabilities: real-time global perception of traffic data, physical information understanding, real-time traffic capacity reasoning, optimal path planning, digital twin of traffic environments, and real-time risk alerts [4] - The model captures vast amounts of heterogeneous data such as vehicle trajectories, speed changes, traffic flow, and pedestrian dynamics, providing a data foundation for intelligent analysis and precise decision-making [4] Group 3: Applications in Transportation Management - In traffic management, MogoMind enables managers to grasp the overall operation of urban traffic systems and make informed decisions based on real-time data analysis [5] - The model enhances travel safety and efficiency by providing real-time information understanding and planning services, including advanced warnings for blind spots and optimal route planning [5] Group 4: Autonomous Driving Integration - MogoMind supports the training of autonomous driving models through multi-source data fusion and continuous learning from diverse scenarios [5] - The company has launched several L4 level mass-produced autonomous vehicles, including RoboBus, RoboSweeper, and RoboTaxi, which integrate MogoMind's capabilities for various applications in public transport, urban sanitation, and unmanned retail [5] - The MOGOBUS, equipped with the "MogoAutoPilot+MogoMind" system, has successfully operated in 10 provinces, covering over 2 million kilometers and serving more than 200,000 passengers [5]