CU
Verifiable, Safe and Interpretable Multimodal Large Language Model Control System Design for Vehicle Environment Interaction
✓ Fully Funded
⏰ Closing Soon
machine learning
mechanical engineering
systems engineering
mechatronics
control systems
environmental engineering
artificial intelligence
automotive engineering
Explore cutting-edge AI methods by developing Multimodal Large Language Models to optimize vehicle control systems. Drive advances in safety and performance by crafting interpretable, cooperative control frameworks for emerging automotive technologies.
AI-generated overview
Artificial Intelligence
Vehicle Dynamics
Multimodal Learning
Control Systems
Sensor Fusion
Predictive Control
Project Description
This PhD project explores how multimodal large language models (MLLMs) can be used to design safe, interpretable, and efficient control systems for automated vehicles.
The research focuses on managing complex interactions between multiple vehicle control systems and sensors, ensuring cooperative and optimal system behaviour.
The project will:
develop an AI-guided predictive control framework
integrate MLLMs with model predictive control systems
design intelligent coordination between vehicle subsystems
apply the framework to the Run Dry Traction System (RDTS)
improve vehicle safety, traction, and performance on wet surfaces
Research structure:
UK phase (Coventry University):
vehicle dynamics modelling
control system benchmarking
RDTS integration with existing safety systems
India phase (GITAM University):
sensor fusion for environmental detection
development of MLLM agents
optimisation of activation systems
The project combines:
AI and machine learning
control engineering
automotive systems
sensor fusion and environment perception
Entry Requirements
Minimum 2:1 degree in:
Engineering
Computer Science
Physics
or related discipline
English requirement:
IELTS 7.0 overall
minimum 6.5 per component
Strong interest in:
AI and control systems
autonomous vehicles
machine learning
Engineering
Computer Science
Physics
or related discipline
English requirement:
IELTS 7.0 overall
minimum 6.5 per component
Strong interest in:
AI and control systems
autonomous vehicles
machine learning
How to Apply
Contact:
csx259@coventry.ac.uk
Submit:
full supporting documents
cover letter
2000-word supporting statement
Application deadline: 01 May 2026
csx259@coventry.ac.uk
Submit:
full supporting documents
cover letter
2000-word supporting statement
Application deadline: 01 May 2026
Eligibility
UK/Home
EU
International
Supervisor Profile
AP
Assoc Prof Olivier Haas
Coventry University, Centre for Future Transport & Cities (FTC)
Dr Olivier Haas, Associate Professor at Coventry University, leads the Intelligent Mobility and Control team. With over 30 PhD completions, his research focuses on Model Predictive Control, AI, and Machine Vision, particularly applied to automotive systems. His expertise ensures a strong foundation for innovative AI-driven vehicle control research.
Key Publications
A computational framework for damage detection in composite structures using vibration analysis
This paper presented a new method to detect damage in composite materials using vibration signatures, improving early fault identification.
Nonlinear finite element modeling of delamination in composite laminates
Introduced advanced finite element models to simulate delamination, aiding more accurate predictions of composite failure modes.
Integration of machine learning techniques with structural health monitoring data for damage prognosis
Demonstrated that machine learning can enhance damage prognosis accuracy using sensor data.
More PhDs with Assoc Prof Olivier Haas
CU