Safety is a prerequisite for the practical application of autonomous driving. Reliable driving perception is essential for achieving safe autonomous driving.
This project aims to develop a series of sensing-enhanced methods enabling improved reliability and safety of autonomous driving. The impacts of natural noise on the intelligent vehicle-sensing system will be studied, including but not limited to raindrops, rain streaks, and fog.
The sensing-enhanced methods will be based on the combination of computationally physical modelling and artificial intelligence for ensuring high-quality perception while satisfying safe autonomous driving. The sensing-enhanced performance of the methods will be analysed and tested in our virtual driving simulator and test platform.
Supervisors
Primary supervisor: Jingjing Jiang
Secondary supervisor: Yuanjian Zhang
Entry requirements for United Kingdom
Applicants should have or expect to achieve at least a 2:1 honours degree (or equivalent international qualifications) in engineering, mathematics or science. A relevant master’s degree and/or experience in advanced AI techniques such as Image recognition, Semantic segmentation, or Object detection would be an advantage.
Experience with Programming using Python, C++, or MATLAB programming is requisite. At the same time, we warmly welcome applicants with experience in virtual traffic scenario simulation and construction.
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-23-030 in your application.
To avoid delays in processing your application, please ensure that you submit the minimum supporting documents.
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