Name: Dr Yue Xie
Academic Division: Civil Engineering
Research Group: Bio-Inspired Robotics Lab
Fellowship period: 15 June 2023 – 14 June 2026
Email: yx388@cam.ac.uk
https://orcid.org/0000-0002-7959-4563
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.
