蘑菇车联的MOGOBUS在与多家企业的激烈竞争中，凭借领先的L4级自动驾驶全栈技术、成熟的落地经验及稳健的交付能力中标。蘑菇车联副总裁 吕斌表示："感谢我们的联合体合作伙伴MKX和比亚迪的通力合作，未来我们将携手一起做好自动驾驶项目在新加坡的落地和持续推进。" 近日，新加坡陆路交通管理局（LTA）宣布，由MKX Technologies、蘑菇车联（MOGOX）与比亚迪组成的联合体，中标新加坡自动驾驶巴士服务试 点项目，这是新加坡首个L4级自动驾驶巴士官方项目，标志着蘑菇车联自动驾驶全栈技术解决方案已获得国际市场认可，迈出服务全球的关键一步。 今年1月，LTA启动了自动驾驶巴士服务试点项目招标， 计划2026年下半年，投入自动驾驶巴士用于纬壹科技城191号线路，以及滨海湾和珊顿大道 的400号线路进行试点运营。两条路线连接主要的商业和休闲中心，如滨海湾邮轮中心、滨海湾花园、地铁站等，三年内实现自动驾驶巴士与普通巴士一 同运营。 近年来，蘑菇车联持续将AI能力与自动驾驶技术深度融合，采用端到端感知模型及混合架构，大幅提升车辆在城市场景中的适应能力与泛化能力， 同时结合自研的物理世界AI大模型MogoMind，赋予 ...

本报讯（记者孙奇茹）新加坡陆路交通管理局（LTA）近日宣布，由MKX Technologies、蘑菇车联 （MOGOX）与比亚迪组成的联合体中标新加坡自动驾驶巴士服务试点项目。这是新加坡首个L4级自动 驾驶巴士官方项目，也标志着中国自动驾驶迈出服务全球的关键一步。 此次MOGOBUS的中标，已不同于此前新加坡在小范围落地的商业区的封闭式或半封闭式场景自动驾驶 接驳，而是新加坡首次将自动驾驶巴士引入新加坡公共交通系统，应用于城市日常公交线路。 据悉，蘑菇车联自动驾驶巴士MOGOBUS已在中国十余个省份的开放道路、景区及园区实现常态化运 营，累计安全行驶里程突破200万公里。 2026年下半年，LTA将投入自动驾驶巴士用于纬壹科技城191号线路，以及滨海湾和珊顿大道的400号线 路进行试点运营。两条路线连接主要的商业和休闲中心，这是自动驾驶巴士首次被纳入海外公共交通系 统，应用于城市日常公交线路之中。 ...

Core Viewpoint - The article discusses the launch of MogoMind, the first AI model designed to deeply understand the physical world, which aims to transform advanced AI technology into practical productivity in the real economy [2][4]. Group 1: MogoMind Overview - MogoMind integrates real-time, massive multimodal traffic data to extract meaning from complex physical world data, enabling global perception, deep cognition, and real-time decision-making capabilities [4][9]. - The model features 7 billion parameters, ensuring centimeter-level perception and millisecond-level response times, optimized for real-time traffic scenarios [6][7]. - MogoMind serves as a real-time search engine for the physical world, differentiating itself from traditional language models by enabling real-time interaction with dynamic physical environments [8][9]. Group 2: Key Capabilities - MogoMind possesses six key capabilities: real-time global perception of traffic data, real-time understanding of physical information, real-time reasoning for traffic capacity, optimal path planning, real-time digital twin of traffic environments, and real-time risk alerts [10][11]. - The model can predict traffic flow and assess road capacity dynamically, utilizing reinforcement learning to uncover patterns and trends in traffic data [13]. Group 3: Applications and Impact - MogoMind acts as a decision-making hub for urban traffic management, providing comprehensive insights for traffic flow regulation and emergency response [14][16]. - In the autonomous driving sector, MogoMind enhances safety and reliability by continuously learning from diverse data sources and scenarios [16][19]. - The platform is designed to be open, allowing car manufacturers to integrate their data without concerns over data sovereignty [18]. Group 4: Cross-Scenario Adaptability - MogoMind is positioned as a core engine for AI networks that interact with the physical world, capable of supporting various intelligent agents beyond traffic scenarios [19][20]. - Its capabilities and features allow for seamless integration with different types of intelligent systems, including drones and robots, facilitating collaborative decision-making across various domains [20].

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]