Dr Yue Xie
- Marie Sklodowska-Curie Future Roads Fellow
- Lecturer at the Loughborough University, UK
About
Dr. Yue Xie was a Marie Skłodowska-Curie Future Roads Fellow at the University of Cambridge, specializing in AI and multi-agent systems for future road automation. She earned her Ph.D. in computer science from the University of Adelaide in 2021 and has experience as a Postdoctoral Fellow at CSIRO and the University of Adelaide. Her research spans AI, bio-inspired optimization, mining engineering, public health, and soft robotics. With over 15 peer-reviewed papers, she actively contributes as a reviewer and guest editor for international journals. Her focus was on integrating AI and information theory for vehicle-road coordination.
Fellowship period: 15 June 2023 – 31 August 2025
Research
Strategic Themes
Innovation in AI applications: By utilizing AI for real-time decision-making, to revolutionise how urban infrastructures and robotic systems operate, making them more efficient, responsive, and adaptive to changing environments.
Bio-inspired methodologies: Solving complex engineering problems by bio-inspired, leading to more robust, adaptable, and efficient AI systems.
Project Title: Highway intelligent traffic control system based on vehicle-road coordination and multi-agent technology
Abstract:
Global traffic congestion continues to intensify, leading to increased travel times, excessive fuel consumption, and escalating emissions. Addressing these challenges requires innovative solutions in traffic management and sustainable energy use. In this project, we introduce an intelligence traffic system aimed at integrating autonomous vehicle technology, advanced multi-agent coordination, and real-time data analytics to optimize highway flow and reduce congestion. Our proposed system can improve traditional traffic routing but also addresses the complex electric vehicle (EV) routing and charging problem, offering a model for efficient energy distribution by integrating EV charging with delivery fleet operations.
Methodologically, we employ simulations across multiple traffic scenarios, including fully autonomous environments and mixed-traffic conditions where autonomous and traditional vehicles coexist. This approach allows for the testing of adaptive control algorithms in diverse settings, such as temporary road management and winter road maintenance, capturing the system’s response under both standard and adverse traffic conditions. Through intelligent vehicle-to-infrastructure (V2I) communication, the system dynamically allocates charging resources based on vehicle routing needs, minimizing wait times and maximizing energy efficiency.
The proposed system should be able to mitigates congestion and reduces travel durations but also optimizes EV charging station utilization and streamlines energy distribution across the vehicle network, particularly benefiting fleet-based logistics. By enabling vehicles to interact with the electricity grid, the system facilitates energy-saving measures, contributing to overall sustainability efforts. Additionally, the system’s strategic routing choices and optimized infrastructure use underscore its potential to support temporary and seasonal traffic requirements effectively. These outcomes underscore the promise of intelligent transport systems in delivering substantial societal, economic, and environmental benefits, and they lay a foundation for advancing adaptive, sustainable urban mobility.
Project TRL: TR3 (Experimental Proof of Concept)
Industry Secondment needs: National Highway for real-world scenarios of traffic management.
Dr. Yue Xie’s research interests lie broadly at the intersection of artificial intelligence, bio-inspired methodology and real-world applications. Her research departs form the bio-inspired methodology and AI, seeking to blend the intricate patterns observed in natural systems with AI techniques. This unique combination aims to address complex challenges in urban traffic management, automatic soft robots design. By integrating principles from biological systems into artificial intelligence, Dr. Xie's research not only contributes to the theoretical understanding of AI but also has significant practical implications for the development of efficient, sustainable, and intelligent traffic control solutions.