This PhD project focuses on the development of next-generation autonomous train technologies that combine energy-efficient control with advanced environmental perception. You will explore how real-time sensing, multi-sensor fusion, and intelligent algorithms can jointly enable safer, greener, and smarter rail operations.
Key research topics include eco-driving, environment cooperative perception, multi-sensor calibration, and AI-based train control. By leveraging onboard and infrastructure-supported sensors (such as camera, radar, and LiDAR), your work will enhance a train’s ability to perceive its environment and respond optimally in dynamic operating conditions. Meanwhile, you will also develop intelligent control strategies that minimise energy use while ensuring punctuality and safety.
Core research themes include:
- Environment cooperative perception and infrastructure-assisted sensing
- Multi-sensor calibration and real-time sensor fusion
- Eco-driving and optimal control for energy-efficient rail operation
- Autonomous vehicle decision-making and trajectory planning
- AI-driven modelling and intelligent control in rail environments
You will be based at the Birmingham Centre for Railway Research and Education (BCRRE) and work closely with industrial partners, gaining access to experimental platforms, real-world data, and test environments.
This PhD is ideal for candidates with a background in computer vision and intelligent transportation. Experience with tools such as MATLAB, Python or machine learning frameworks is highly desirable.
Supervisor: Dr Ning Zhao (N.Zhao@bham.ac.uk ).
Funding: We offer a range of funding opportunities for both UK and international students, including University Scholarships (the current application deadline is in May) and Industrial Scholarships. Funding will be awarded on a rolling basis, so early application is encouraged to ensure full consideration.