This project aims to address the safety and reliability problems in autonomous systems, which is also one of the biggest challenges in autonomous vehicles. The research outcome will unlock enhanced and adaptive safety-critical functionalities, including but not limited to failure recognition, fast criteria computation, and intelligent control.
The successful candidate will have a unique opportunity to develop intelligent safety-critical control algorithms, enabling improved sensing reliability and operational safety. Adaptive model predictive control and learning technologies will be adopted in the development. The effectiveness and efficiency of the algorithms will be tested in one of our autonomous vehicle testing platforms.
Primary supervisor: Dr Chengyuan Liu
Secondary supervisor: Prof Wen-Hua Chen
Entry requirements for United Kingdom
Students should have, or expect to achieve, at least a 2:1 honours degree (or equivalent international qualification) in a relevant subject. A relevant master's degree will be an advantage.
International applicants must meet the minimum English language requirements. Further details are available on the International website.
Knowledge in control theory is essential. Understanding in machine learning methods will be desired.
English language requirements
Applicants must meet the minimum English language requirements. Further details are available on the International website.
Find out more about research degree funding
How to apply
All applications should be made online and must include a research proposal. Under the programme name, select 'Aeronautical and Automotive Engineering'. Please quote the advertised reference number AACME-23-024 in your application.
To avoid delays in processing your application, please ensure that you submit the minimum supporting documents.