About the Project
This PhD project focuses on the development of next-generation autonomous technologies that combine vehicle control with advanced environmental perception. You will explore how real-time sensing, multi-sensor fusion, world modelling, spatial intelligence, and intelligent algorithms can jointly enable safer, greener, and smarter vehicle operations or asset management.
Key research topics include environment cooperative perception, multi-sensor calibration, visual localisation, and AI-based vehicle control. By leveraging onboard and infrastructure-supported sensors (such as camera, radar, and LiDAR), your work will enhance a vehicle's ability to perceive its environment and respond optimally in dynamic operating conditions.
A particular research direction may involve the development of world models capable of learning the spatial dynamics of transport environments, including the prediction of future states and behaviours of vehicles, pedestrians, and other objects. These models could support scene understanding, decision-making, and trajectory planning in complex and previously unseen situations.
You may also explore multimodal foundation models, vision-language models, or other emerging AI approaches for combining visual and contextual information to improve perception and autonomous decision-making. Meanwhile, you will also develop intelligent control strategies that minimise energy use while ensuring punctuality, operational efficiency, and safety.
Alternatively, you could investigate how perception and vision-based localisation systems can support accurate vehicle positioning, particularly in GNSS-denied environments (e.g. tunnels). By using perception technologies, the research could enable reliable localisation where satellite-based positioning is unavailable, supporting safer autonomous operation and navigation.
Core research themes include:
- Cooperative perception and infrastructure-assisted sensing
- Multi-sensor calibration and real-time sensor fusion
- World models, dynamic scene understanding and future-state prediction
- Visual localisation, spatial intelligence and environment mapping
- Eco-driving and optimal control for energy-efficient vehicle operation
- AI-driven modelling and intelligent control in transport environments
- Autonomous vehicle decision-making and trajectory planning
Our group already has perception system developed and a large amount of first-hand, self-collected multimodal datasets available for use. You will work closely with industrial partners, gaining access to experimental platforms, real-world data, and test environments.
Funding Notes
This PhD is ideal for candidates with a background in computer vision and intelligent transportation. Experience with tools such as Python, MATLAB, or other machine learning frameworks is highly desirable.
We offer a range of funding opportunities for both UK and international students, including University Scholarships and Industrial Scholarships.
Project Supervisors
Dr. Ning Zhao
Career overview
Dr Ning Zhao is an Assistant Professor in Railway Systems Engineering at the University of Birmingham's Centre for Railway Research and Education. He holds a PhD in Electronic, Electrical and Computer Engineering from the University of Birmingham, obtained in 2013, and an MSc in Communication Engineering from the same institution in 2009. Dr Zhao became a Chartered Engineer (CEng) in 2020 and was awarded Fellowship of the Higher Education Academy (FHEA) in 2019. Dr Zhao has extensive experience in research and teaching within the field of railway system engineering, with a focus on areas such as autonomous train systems, traction systems, hydrogen systems, and railway control and signalling systems. He collaborates with various industrial partners globally, including Edinburgh Tram, SMRT (Singapore), Network Rail, Nottingham Tram, Siemens, and Guangzhou Metro. His research has been supported by several grants from EPSRC, Innovate UK, and industry partners. He has published over 40 papers in high-impact journals and conferences, including IEEE Transactions on Intelligent Transportation Systems and Transportation Research Part C: Emerging Technologies. Dr Zhao has also played a significant role in developing the DriveSmart product in collaboration with Ricardo Rail, which received a Highly Recommended Commendation at the Global Light Rail Awards. In addition to his research, Dr Zhao is actively involved in teaching, leading several MSc modules related to railway systems, including Railway Control and Digital Systems, Principles of Railway Control Systems, and Applications of Railway Control Systems. He has also provided supervision and support to more than 80 MSc, PhD, MEng, and BEng students. Furthermore, he manages relationships between the University of Birmingham and overseas universities concerning undergraduate and postgraduate exchange programmes and research cooperation.
Research interests
Dr Ning Zhao's research focuses on railway systems engineering, with specific interests in autonomous train systems, traction systems, hydrogen systems, and railway control and signalling systems. He engages in multi-disciplinary research collaborating with various industrial partners globally, including Edinburgh Tram, SMRT (Singapore), Network Rail, Nottingham Tram, Siemens, and Guangzhou Metro. Dr Zhao has been awarded several grants from EPSRC, Innovate UK, and industry partners. His work includes the development of the DriveSmart product in collaboration with Ricardo Rail, which received a Highly Recommended Commendation at the Global Light Rail Awards. He has published over 40 papers in high-impact journals and conferences, such as IEEE Transactions on Intelligent Transportation Systems and Transportation Research Part C: Emerging Technologies. His research also encompasses operation and timetable optimisation, energy-saving technologies, and system modelling related to railway networks.