Drone-assisted Connected and Autonomous Vehicles for Enhanced Road Safety and Traffic Efficiency
Eligibility: UK/International (including EU) graduates with the required entry requirements
Duration: Full-Time – between three and three and a half years fixed term
Application deadline: 1st May
Interview date: Will be confirmed to shortlisted candidates
Start date: September 2026
For further details contact: Professor Soufiene Djahel
Introduction
Recent research in cooperative perception and control for Connected and Autonomous Vehicles (CAVs) has moved rapidly into AI-driven frameworks. For perception, transformer-based models such as V2VFormer enhance vehicle-to-vehicle (V2V) cooperative sensing by dynamically weighting spatial and channel features across agents. On the decision-making and control side, multi-agent reinforcement learning (MARL) methods are being applied to CAVs to address safety, efficiency and coordination under mixed motives and shared information. At the same time, leveraging the advantages of drones in speed, flexibility, and wide field of view, recent studies recognized that aerial support through drones has significant potential in improving road safety and traffic efficiency, paving the way for drones and CAVs cooperation and large-scale deployment of drone-assisted CAV systems. Therefore, this PhD project advances CAV research by introducing a novel drone-assisted cooperative intelligence framework. Unlike existing V2V or V2X approaches, which rely on ground-based perception, this project leverages aerial sensing and AI-driven heterogeneous data fusion to improve environmental awareness and decision-making. From an industry standpoint, the proposed framework will enable new business models for stakeholders in the drone community, and further advance the development, deployment, and widespread adoption of CAV technologies.
Project details
This PhD project aims to answer the following research question:
“How can cooperative AI systems efficiently integrate heterogeneous aerial and ground sensor data, multi-agent learning, and realistic drone-CAVs interactions to optimize traffic environments at scale?
Funding
Tuition fees and bursary
The studentship will include one return economy airfare to GITAM/Coventry University, visa and overseas healthcare to cover the mobility period
Benefits
The successful candidate will receive comprehensive research training including technical, personal and professional skills. All researchers at Coventry University (from PhD to Professor) are part of the Doctoral and Researcher College, which provides support with high-quality training and career development activities.
Entry requirements
- A minimum of a 2:1 first degree in a relevant discipline/subject area with a minimum 60% mark in the project element or equivalent with a minimum 60% overall module average.
PLUS
- The potential to engage in innovative research and to complete the PhD within 3.5 years.
- A minimum of English language proficiency (IELTS academic overall minimum score of 7.0 with a minimum of 6.5 in each component).
Additional requirements
We are looking for highly skilled applicants, from any nationality, who have demonstrated outstanding academic performance in Computer Science or closely related disciplines at both undergraduate and Master’s levels (a distinction is essential). In particular, the successful applicants must possess a strong foundation in programming, mathematics, and the core principles of AI, together with a solid understanding of advanced topics such as Federated Learning (FL) and Multi‑Agent Deep Reinforcement Learning (MADRL). Applicants must be able to rapidly master new simulation environments, analytical evaluation techniques, and experimental frameworks.
How to apply
To find out more about the project, please contact Professor Soufiene Djahel
All applications require full supporting documentation, a covering letter, plus a 2000-word supporting statement showing how the applicant’s expertise and interests are relevant to the project.
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