CU
Drone-assisted Connected and Autonomous Vehicles for Enhanced Road Safety and Traffic Efficiency
✓ Fully Funded
⏰ Closing Soon
🎓 Business and Management
🎓 Mechanical Engineering
🎓 Transport Geography
AI
autonomous vehicles
drones
reinforcement learning
smart cities
traffic systems
Fully funded PhD developing AI-driven drone-assisted systems to improve road safety and traffic efficiency.
Project Description
This PhD project explores the integration of drones and Connected and Autonomous Vehicles (CAVs) to enhance road safety and traffic efficiency.
The research will develop a cooperative AI framework that combines aerial and ground sensor data using advanced techniques such as multi-agent reinforcement learning and transformer-based models. The project aims to improve environmental awareness, coordination, and decision-making in complex traffic systems.
It will also investigate large-scale deployment strategies and real-world applications of drone-assisted CAV systems, contributing to the future of smart mobility and intelligent transport systems.
Entry Requirements
Minimum 2:1 degree in Computer Science or related field
• Strong programming and mathematical skills
• Knowledge of AI, machine learning, or reinforcement learning
• Experience with federated learning or multi-agent systems (preferred)
• IELTS 7.0 overall (if required)
• Strong programming and mathematical skills
• Knowledge of AI, machine learning, or reinforcement learning
• Experience with federated learning or multi-agent systems (preferred)
• IELTS 7.0 overall (if required)
How to Apply
Contact supervisor and submit application with supporting documents and 2000-word statement.
Eligibility
UK/Home
EU
International
Supervisor Profile
PS
Prof Soufiene Djahel
Coventry University, Centre for Future Transport & Cities (FTC)
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