Core Insights - The article highlights the launch of the first 24-hour fully autonomous Robotaxi service in Guangzhou's Huangpu District by WeRide, marking a significant milestone in the commercialization of autonomous driving in urban settings [2][4]. Group 1: Robotaxi Features and Technology - The WeRide GXR Robotaxi is designed specifically for urban travel, featuring Level 4 (L4) autonomous driving capabilities for public road operations [4]. - It is equipped with the NVIDIA DRIVE Thor X chip, enabling over 20 trillion calculations per second, and the Sensor Suit 5.6, which allows it to operate in adverse weather conditions [4][5]. - The vehicle includes a robust redundancy system across its architecture, control chassis, and braking systems, ensuring safe and reliable operation in complex traffic environments [5]. Group 2: User Experience and Design - The GXR offers a spacious interior designed for five passengers, with features such as a dedicated luggage area, VIP seating, voice-controlled sliding doors, and remote climate control via an app [5]. - User feedback indicates that the vehicle's design enhances the overall travel experience, making it convenient and comfortable for passengers [5]. Group 3: Operational Milestones and Expansion - WeRide's transition from human-driven to fully autonomous operations took only 11 months, from the launch of the new GXR model to obtaining necessary operational licenses [5]. - The Robotaxi service covers approximately 145 square kilometers in Huangpu, with plans for expansion to key landmarks and transportation hubs in Guangzhou by May 2025 [7]. - The company has been operating its Robotaxi service for over 2,200 days, establishing a "Guangzhou model" for global expansion in autonomous vehicle commercialization [7]. Group 4: Industry Context and Future Outlook - The autonomous driving industry is shifting from a focus on technological competition to a phase of commercial validation, with WeRide's cost reductions and real-world data accumulation being critical to its business model [7].