Research
Research at the Frontier of Intelligent and Connected Transportation
My research lies at the intersection of intelligent transportation, artificial intelligence, wireless communications, sensing, localisation, data analytics and networked control.
I am currently a Chair Professor at Southeast University and Director of the Research Centre for Frontier Technologies in Intelligent and Connected Transportation. My research and student supervision span the School of Transportation, the School of Telecommunications and the School of Information Science and Engineering, creating an interdisciplinary environment in which transportation systems, communication networks, artificial intelligence and control are studied as an integrated whole.
Our objective is to develop both the fundamental theories and enabling technologies required for future intelligent mobility systems. We address scientific questions in communications, information fusion, networked systems and decision-making, while also developing technologies for connected and automated vehicles, vehicle–road–cloud cooperation, smart infrastructure and large-scale transportation systems.
Research Vision
Future transportation systems will not achieve their full potential by improving individual vehicles, sensors or communication networks in isolation. Safe and efficient intelligent transportation requires the coordinated design of:
* sensing, to understand vehicles, roads, infrastructure and the surrounding environment;
* communication, to exchange information reliably and with low latency;
* computing, to process distributed and multimodal data;
* artificial intelligence, to extract knowledge and support decision-making; and
* control, to translate information into safe and efficient actions.
A central theme of our current research is therefore the integration of sensing, communication, computing and control. Rather than treating communication networks merely as data pipes and transportation systems merely as communication users, we investigate how the information system and physical transportation system can be jointly modelled, designed and optimised.
This philosophy builds on my earlier research into wireless localisation, distributed estimation, graph theory, large-scale dynamic networks, vehicular communications and multi-agent systems, and extends it towards intelligent and connected transportation and autonomous mobility. The current research page documents this progression from wireless networks and localisation to vehicular networks, smart roads and integrated transportation systems.
Current Research Priorities
1. Intelligent Connected Vehicles and Vehicle–Road Cooperation
Connected and automated vehicles must operate safely in environments where onboard sensors may be affected by occlusion, adverse weather, limited visibility, sensor failure or localisation uncertainty. We investigate how vehicles, roadside infrastructure, communication networks and traffic-management systems can cooperate to improve perception, decision-making and control.
Current topics include:
cooperative perception and infrastructure-assisted sensing;
vehicle-to-everything communication;
vehicle–road integrated architectures;
coordinated driving and vehicle platooning;
intelligent road infrastructure;
mixed traffic involving automated and human-driven vehicles; and
system-level evaluation of safety, efficiency and reliability.
The objective is not simply to connect vehicles, but to use connectivity to improve the performance of the entire transportation system.
2. Multisource Information Fusion and High-Accuracy Localisation
Accurate and reliable positioning remains a fundamental requirement for intelligent vehicles, mobile robots and transportation infrastructure. My earlier research developed distributed localisation algorithms, geometric methods and analytical tools for wireless sensor networks. Current work extends these foundations to multimodal positioning for intelligent connected vehicles.
We investigate the integration of information from GNSS, inertial sensors, cameras, LiDAR, radar, wireless signals, vehicle motion, digital maps and roadside infrastructure. Particular attention is given to challenging environments such as tunnels, urban canyons, underground facilities and areas with weak or unavailable satellite signals.
Our research addresses:
multimodal integrated navigation and positioning;
infrastructure-assisted cooperative localisation;
uncertainty modelling and propagation;
detection and resolution of conflicting sensor information; and
robust localisation for autonomous vehicles, robots and intelligent infrastructure in challenging environments.
Rather than assuming that adding more sensors automatically improves reliability, we study when information sources should be trusted, how contradictions should be handled and how uncertainty should influence subsequent control decisions.
3. Artificial Intelligence and Foundation Models for Transportation
Artificial intelligence is transforming traffic perception, prediction, management and control. However, transportation systems differ from conventional AI applications because they are safety-critical, spatially distributed, continuously changing and closely coupled with human behaviour and physical infrastructure.
Our research explores:
transportation foundation models and large models;
multimodal traffic-data representation and learning;
intelligent transportation decision-making;
graph neural networks for road and mobility networks;
knowledge- and mechanism-informed machine learning;
uncertainty-aware and trustworthy AI; and
AI-assisted transportation simulation and digital twins.
We are particularly interested in combining data-driven methods with transportation theory, communication-network models and physical constraints. The aim is to develop AI systems that are not only accurate, but also interpretable, reliable and suitable for deployment in real transportation environments.
4. Integrated Sensing, Communication, Computing and Control
Future intelligent vehicles and infrastructure will compete for communication, sensing and computing resources. A vehicle facing uncertain perception may require additional roadside sensing, communication bandwidth or edge-computing capacity; a safety-critical manoeuvre may require different network guarantees from routine traffic information.
We investigate the dynamic allocation and coordination of these resources according to the state of the transportation system and the requirements of individual tasks. Topics include:
joint sensing and communication;
edge intelligence and distributed computing;
task-oriented communication;
communication-aware perception and control;
multi-timescale and hierarchical control; and
performance and safety guarantees under imperfect communication and sensing.
This direction draws on our previous work in network resource management, distributed sensing and estimation, ultra-dense networks and 6G communications.
5. Networked Control, Cooperative Intelligence and Provable Safety
Wireless communication and artificial intelligence can improve coordination, but communication delays, packet losses, sensing errors and model uncertainty can also introduce new risks. Our research therefore examines how cooperative systems can remain safe and stable when information is incomplete or unreliable.
Current interests include:
distributed control of connected vehicles and mobile agents;
cooperative vehicle and robot formations;
control under communication delay and packet loss;
risk-aware decision-making;
resilience to sensor, communication and infrastructure failures; and
coordination of large-scale heterogeneous multi-agent systems.
The objective is to move beyond average-case performance and develop methods capable of quantifying and controlling risk in safety-critical transportation systems.
6. Smart Infrastructure, Digital Twins and Complex Transportation Systems
Intelligent transportation requires infrastructure that can sense, communicate, compute and interact with vehicles. We investigate smart-road and intelligent-infrastructure technologies for traffic monitoring, cooperative perception, road-condition assessment and transportation-system management.
Research topics include:
intelligent roads and roadside sensing;
traffic simulation and large-scale system modelling;
monitoring of roads, bridges, tunnels and underground infrastructure;
autonomous inspection and mobile robotic systems;
low-altitude and ground transportation coordination; and
resilient operation of complex transportation systems.
This research connects fundamental work with major engineering applications, including intelligent highways, autonomous freight corridors, heavy-truck platooning and infrastructure inspection.
Distinctive Academic Contributions
Wireless Localisation and Distributed Estimation
My group has conducted long-term research into localisation in wireless and sensor networks, including distributed algorithms, geometric constraints, flip ambiguities, path-loss calibration, mobile-agent localisation and estimation over networked systems. This work has provided theoretical and algorithmic foundations for subsequent research in cooperative positioning and intelligent connected vehicles.
Graph Theory and Large-Scale Dynamic Networks
We have applied graph theory, particularly random geometric graphs, to analyse connectivity, capacity, routing, delay, interference and localisation in wireless multi-hop networks. We have also studied the architectures and communication strategies required by large-scale, highly dynamic vehicular and multi-agent networks.
Communication Networks for Intelligent Transportation
A continuing contribution of our work has been to challenge the conventional separation between communication networks and transportation systems. We have investigated communication-network design that explicitly accounts for vehicle mobility, transportation dynamics, traffic-control objectives and the operational requirements of intelligent transportation systems.
From Fundamental Theory to Large-Scale Deployment
Our research connects mathematical analysis and algorithm design with field systems and major engineering projects. Applications have included wireless localisation, distributed sensing, railway passenger information, intelligent highways, vehicle–road cooperation, autonomous freight transportation and intelligent heavy-truck platooning.
Selected Major Research Programmes
Autonomous Heavy-Truck Platooning and Demonstration, 2025–2028
Subproject leader for the Ordos major “open competition” project on the industrial development and demonstration of new-energy autonomous heavy-truck platooning systems. The project addresses coordinated driving, communication, perception, control and operational deployment.
Multimodal High-Accuracy Positioning for Intelligent Connected Vehicles, 2022–2025
Principal Investigator of a National Natural Science Foundation of China key project, with funding of RMB 2.68 million. The project investigates accurate and reliable integrated positioning in connected-vehicle environments.
Integrated Highway Vehicle–Road Cooperation Systems, 2019–2022
Project Leader of the National Key R&D Program project “Integration and Application of Highway Intelligent Vehicle–Road Cooperative Systems.” The project had total funding of RMB 117.31 million, including RMB 26.81 million in central-government funding, and developed integrated technologies for intelligent highways, connected vehicles and cooperative transportation systems.
National Key R&D Programme on 6G Communications
Our team participated in a National Key R&D programme addressing key technologies for all-scenario, on-demand 6G services. The programme investigated intelligent network architectures and resource coordination for heterogeneous and dynamically changing service requirements.
iMOVE Cooperative Research Centre, Australia
As the University of Technology Sydney academic leader for intelligent transportation systems, I contributed to the development and organisation of the successful iMOVE CRC bid. The programme received A$55 million in Australian Government funding and A$178.78 million in participant cash and in-kind contributions, giving a total programme value of approximately A$234 million.
Autonomous Freight Corridor
I served in a senior technical role for vehicle–road cooperation in the planning and design of the approximately 428-kilometre Jiuquan–Mingshui autonomous freight corridor in Gansu. The corridor was conceived as a commercially oriented highway for autonomous heavy trucks and as an important demonstration of large-scale intelligent freight transportation.