Trustworthy Embodied Autonomous Vehicles Design Through Foundation Model - PhD

About the Project

We invite applications for a self-funded PhD to advance trustworthy embodied autonomous vehicle systems through the integration of foundation models and human-centred design. This research will explore how autonomous ground, aerial, or swarm agents can collaborate effectively with human operators by sensing human trust in real time and adapting their behaviour accordingly. Candidates may focus on multimodal human trust estimation, trust-adaptive decision-making, or cognitive human–machine interfaces that enhance safety and performance in complex environments. This project offers a unique opportunity to contribute to next-generation autonomous technologies that prioritise human trust, transparency, and reliable human–machine teaming.

This project aims to advance trustworthy embodied autonomous vehicle systems by integrating foundation models with human-centred design. The overarching goal is to enable autonomous ground, aerial, or swarm systems to collaborate closely with human operators while ensuring confidence, safety, and reliable shared decision-making. To achieve this, the programme offers two closely connected research directions.

The first area focuses on real-time multimodal human trust sensing. The candidate will investigate how human trust can be continuously inferred from behavioural and physiological indicators such as gaze patterns, response timing, speech cues, and interaction behaviours. By leveraging foundation models trained on diverse human signals, the research will create an adaptive trust estimation framework that generalises across individuals and operational contexts. This work will provide the essential human-state awareness required for trustworthy autonomy.

The second area centres on trust-adaptive autonomous behaviour design. Here, the candidate will develop embodied vehicles capable of dynamically adjusting their collaboration strategy—such as autonomy level, motion behaviours, and information transparency—based on real-time human trust. By aligning vehicle behaviour with operator needs and cognitive state, the autonomy can prevent both under-trust (excessive intervention) and over-trust (misuse), supporting safer and more effective human-swarm teaming.

Students undertaking this project will be embedded within Cranfield’s world-leading research ecosystem, working at the Human-Machine-X Collaboration (HUMAX) Lab and the Aerospace Integration Research Centre (AIRC)—a £65M national facility equipped with advanced simulation environments, autonomous vehicle platforms, and human-in-the-loop experimentation suites. They will have the opportunity to collaborate with major industrial partners across aerospace, defence, and transport sectors, benefiting from Cranfield’s strong industry networks and applied research culture. Throughout the programme, the student will be fully supported to produce high-quality scientific outputs and develop a strong international profile.

Entry requirements

Applicants should have a first or second class UK honours degree or equivalent in a related discipline. This project would suit candidates with a sound background in engineering, computer science, or related disciplines.

Cranfield Doctoral Network

Research students at Cranfield benefit from being part of a dynamic, focused and professional study environment and all become valued members of the Cranfield Doctoral Network. This network brings together both research students and staff, providing a platform for our researchers to share ideas and collaborate in a multi-disciplinary environment. It aims to encourage an effective and vibrant research culture, founded upon the diversity of activities and knowledge. A tailored programme of seminars and events, alongside our Doctoral Researchers Core Development programme (transferable skills training), provide those studying a research degree with a wealth of social and networking opportunities.

How to apply

For further information please contact:

Name:Dr Yang Xing

Email: yang.x@cranfield.ac.uk

If you are eligible to apply for this studentship, please complete the online application form.

Please note that applications will be reviewed as they are received. Therefore, we encourage early submission, as the position may be filled before the stated deadline.