谈及春节期间的保障工作，赵龙飞认为关键在于防患于未然。他最关注的是一份不断迭代的安全任务清单：首要环节 是安全员对车辆的日常技术检查与维护，坚决杜绝"带病上路"；其次是与车队长、运营人员加强跟车巡检，针对节日 交通特点组织专项安全培训，并密切关注安全员状态、科学优化班次以预防疲劳；最后则是通过节前专项应急演练， 确保流程熟练，并配齐应急物资。 作为一线运营者，赵龙飞深切感受到行业的发展。近年来，政策支持持续加码与央地协同构建起完善的发展环境，正 推动自动驾驶巴士从概念产品走向实地验证。如今，蘑菇车联的MOGOBUS已在全国十余个城市落地，更成功中标新 加坡首个L4级自动驾驶巴士项目，迈出中国智驾"出海"新步伐。 对于2026年的工作，赵龙飞有着清晰的规划。他将持续深耕服务品质，做好人员培训、车辆巡检和数据复盘，提升乘 客满意度；公司层面，将加速国际化落地，深化国内场景覆盖，同时迭代自研技术，推动"前装量产＋视觉与固态激 光雷达融合"技术路线的规模化落地。 "春节假期的路况，是自动驾驶全年中最大的'考场'之一。"蘑菇车联运营负责人赵龙飞说。节前最后一周，他和 团队已进入全天候"备考"状态。 谈及未来五年的行业变 ...

Group 1 - The Land Transport Authority (LTA) of Singapore announced that a consortium consisting of MKX Technologies, MOGOX, and BYD has won the bid for Singapore's first Level 4 autonomous bus service pilot project, marking a significant recognition of MOGOX's full-stack autonomous driving technology in the international market [1][3] - The pilot project, initiated by LTA in January this year, aims to deploy autonomous buses on two key routes by the second half of 2026, connecting major commercial and leisure centers, including the Marina Bay Cruise Centre and MRT stations, with a plan for autonomous buses to operate alongside regular buses within three years [3] - MOGOX's MOGOBUS secured the bid due to its leading Level 4 autonomous driving technology, mature implementation experience, and robust delivery capabilities, as stated by MOGOX's Vice President, Lü Bin, who emphasized the collaboration with partners MKX and BYD for the successful implementation of the project in Singapore [3] Group 2 - In recent years, MOGOX has integrated AI capabilities deeply with autonomous driving technology, utilizing end-to-end perception models and hybrid architectures to significantly enhance vehicle adaptability and generalization in urban scenarios, while also employing its self-developed AI model, MogoMind, to ensure high safety and reliability of the autonomous driving system in real-world conditions [5] - MOGOBUS has achieved regular operations in over 10 provinces in China, accumulating over 2 million kilometers of safe driving and serving more than 200,000 passengers, while also providing autonomous shuttle services at various international events [5]

Core Insights - The Land Transport Authority (LTA) of Singapore has awarded a pilot project for autonomous bus services to a consortium consisting of MKX Technologies, MOGOX, and BYD, marking Singapore's first official L4-level autonomous bus project [1] - This initiative represents a significant step for Chinese autonomous driving technology in expanding its services globally [1] Group 1: Project Details - The pilot operation will commence in the second half of 2026, utilizing autonomous buses on the 191 route in the One-North area and the 400 route along Marina Bay and Shenton Way [1] - These routes connect major commercial and leisure centers, integrating autonomous buses into Singapore's public transport system for the first time [1] Group 2: Company Background - MOGOBUS, the autonomous bus developed by MOGOX, has already achieved regular operations on open roads, scenic areas, and parks across more than ten provinces in China, accumulating over 2 million kilometers of safe driving distance [1]

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]