TAS Team Meets with SF Express(Hong Kong) and Rino AI to Discuss Smart Logistics and Autonomous Campus Delivery Collaboration

HONG KONG, China – January 29, 2026 – The Hong Kong Polytechnic University’s Trustworthy AI and Autonomous Systems Laboratory (TAS LAB) held a strategic cooperation meeting with SF Express Hong Kong and Rino AI to explore collaborative opportunities in smart logistics and autonomous campus delivery vehicles . This meeting underscores PolyU’s commitment to advancing industry-academia partnerships and developing practical solutions for next-generation logistics systems that address real-world challenges in urban delivery and campus mobility.

The meeting brought together key representatives from TAS LAB, SF Express(Hong Kong), and Rino AI to discuss innovative approaches to intelligent logistics and autonomous delivery technologies. The discussions centered on leveraging cutting-edge AI, robotics, sensor fusion, and IoT solutions to address modern logistics challenges, particularly in campus and urban environments where safety, efficiency, and sustainability are paramount concerns.

Attendees from PolyU included Prof. Weisong WEN from TAS Lab and research team members. Representing SF Express Hong Kong were senior executives from the logistics operations and technology innovation divisions, while Rino AI was represented by their leadership team specializing in artificial intelligence solutions and autonomous systems development.

The three-party collaboration will focus on several key technological domains and application scenarios:

1. Smart Logistics Systems: Developing comprehensive AI-powered solutions for intelligent logistics management, including dynamic route optimization algorithms, real-time package tracking systems, predictive demand forecasting, and intelligent warehouse management. The collaboration aims to integrate machine learning models with SF Express(Hong Kong)’s extensive operational data to create adaptive logistics systems that can respond to changing conditions in real-time. This includes developing advanced algorithms for fleet management, delivery scheduling optimization, and resource allocation that can significantly enhance operational efficiency while reducing energy consumption and environmental impact. The system will leverage big data analytics and cloud computing infrastructure to process vast amounts of logistics data, enabling predictive maintenance, demand forecasting, and intelligent decision-making across the entire supply chain.

2. Autonomous Campus Delivery Vehicles: Designing and implementing intelligent unmanned delivery vehicles specifically tailored for university campus environments. The project will address unique challenges associated with campus delivery, including pedestrian detection and avoidance, navigation in mixed-use environments with students and faculty, compliance with campus safety regulations, and integration with existing campus infrastructure. The autonomous vehicles will leverage advanced perception systems, including LiDAR, cameras, and radar sensors, combined with sophisticated path planning algorithms to ensure safe and efficient delivery operations. Special attention will be given to human-robot interaction design to ensure the vehicles can operate seamlessly in crowded campus settings, with features such as audio-visual alerts, intuitive gesture recognition, and emergency stop mechanisms. The vehicles will be designed to handle various weather conditions, navigate complex terrain including stairs and ramps, and operate efficiently during peak hours when campus foot traffic is highest.

3. Technology Integration and System Architecture: Combining PolyU TAS LAB’s cutting-edge research expertise in autonomous systems, localization, and navigation with SF Express(Hong Kong)’s extensive logistics experience and operational insights, alongside Rino AI’s artificial intelligence capabilities and machine learning infrastructure. The collaboration will focus on developing a comprehensive technology stack that integrates perception, planning, and control systems with logistics management platforms. This includes creating robust communication protocols between autonomous vehicles and central dispatch systems, implementing edge computing solutions for real-time decision-making, and developing fail-safe mechanisms to ensure system reliability. The system architecture will incorporate multi-layer redundancy, cybersecurity measures to protect against potential threats, and scalable cloud infrastructure to support future expansion. Advanced sensor fusion techniques will combine data from multiple sources to create accurate environmental models, while deep learning algorithms will enable the vehicles to learn from experience and continuously improve their performance.

4. Pilot Programs and Field Testing: Exploring opportunities to establish comprehensive testbed environments at PolyU campus for real-world validation and demonstration of autonomous delivery technologies. The pilot programs will serve as living laboratories where researchers can collect operational data, test new algorithms, and refine system performance under actual operating conditions. The testbed will enable iterative development and validation of technologies before broader deployment, allowing the team to address challenges related to weather conditions, varying terrain, obstacle avoidance, and user acceptance. Data collected from these pilot programs will inform future system improvements and provide valuable insights for scaling the technology to other campuses and urban environments. The pilot phase will include controlled experiments to test specific capabilities, followed by gradual expansion to full operational deployment. User feedback mechanisms will be integrated to gather insights from students, faculty, and staff about their experiences with the autonomous delivery system, helping to refine user interfaces and improve overall service quality.

5. Safety and Regulatory Compliance: Developing comprehensive safety protocols and working toward compliance with local regulations governing autonomous vehicle operations in Hong Kong. This includes establishing safety standards for autonomous campus delivery, conducting thorough risk assessments, implementing redundant safety systems with multiple fail-safe mechanisms, and collaborating with regulatory bodies to ensure that autonomous delivery vehicles meet all necessary requirements for operation in public spaces. The collaboration will work closely with university safety departments, local transportation authorities, and industry standards organizations to develop best practices for autonomous delivery operations. Safety features will include emergency braking systems, collision avoidance algorithms, remote monitoring and intervention capabilities, and comprehensive logging of all operational data for incident investigation and continuous improvement.