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The Aggregation of Digital Twin Technology and Multi-sensor Fusion in Highly Automated Vehicles


   Faculty of Environment

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  Dr Mahdi Rezaei, Prof N Merat  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

About the Project

Highly automated vehicles known as Level 4 vehicles, can operate in self-driving mode in most circumstances; and the driver is allowed to involve in various types of non-driving activities, including sleeping. However, the automated driving mode is only expected to perform in limited geofenced areas called the operational design domain (ODD). Therefore, a human driver still has to be present with the option of resuming the driving control whenever required.

Utilising computer vision, machine learning, and the aggregation of multi-sensor data fusion with the hot concept of the digital twin technology, this research focuses on enhancing the level of vehicle automation, extending the ODD, increasing the comfort of the driver and passengers, and promoting the public trust in autonomous vehicles (AVs).

Using camera and LiDAR point cloud data, the research percepts the traffic and road environment condition, as well as assessing the driver readiness for a safe and swift response to a takeover request (TOR). The research involves driver behaviour monitoring (eye, head pose, hand, and body pose modelling), traffic perception and understanding (vehicle, pedestrian and cyclist detection), and tracking.

Background

Institute for Transport Studies (ITS) at the University of Leeds (one of the World’s Top 100 Universities) is the leading centre in Autonomous Vehicles research in the United Kingdom and one of the top 10 global pioneers in the field. The centre currently has 80+ senior academic staff, researchers, and PhD students in multi-disciplinary areas of computer vision, AI and machine learning, engineering, behaviour modelling, human factors, psychology, and transportation safety.

Aims and objectives

The research performs studies in both driver behaviour monitoring and 360° environment perception using multiple sensors such as cameras, LiDAR, RADAR, and communication with digital twin information/infrastructures for safer and swift transition between human driving mode and autonomous mode, whenever required. The in-cabin situation awareness research involves head pose, eye gaze, and body pose monitoring and modelling, and the environment perception involves understanding the road/traffic condition, including road users detection (vehicles, pedestrians, cyclists), tracking, and trajectory estimation. Although the student is encouraged to work on both in-cabin and road understanding and monitoring, the main focus would be on traffic environment perception. While we put more focus on computer vision and machine learning-based research; the PhD candidate has the option of performing joint research within our great interdisciplinary team with various backgrounds, from the human factor to mathematics, robotics, and civil engineering.

How to apply

Formal applications for research degree study should be made online through the University's website. Please state clearly in the research information section that the research degree you wish to be considered for is Aggregation of Digital Twin Technology and Multi-sensor Fusion Towards Highly Automated Vehicles as well as Dr Mahdi Rezaei as your proposed supervisor.

The programme code that you need to apply for is PHP-ENVE-FT which is EPSRC DTP Environment.

IMPORTANT: Please ONLY apply via the university website here and follow the above instructions carefully. We will NOT consider applications submitted via direct email or via the FindaPhD email enquiry section.

If English is not your first language, you must provide evidence that you meet the University's minimum English language requirements (below).

We welcome applications from all suitably-qualified candidates from all countries around the world, but UK black and minority ethnic (BME) researchers are currently under-represented in our Postgraduate Research community, and we would therefore particularly encourage applications from UK BME candidates. All scholarships will be awarded on the basis of merit.

Essential Qualifications and Skills

  • A master’s degree in a relevant discipline (e.g. Computer Science, Artificial Intelligence, Data Analytics, Machine Learning, Robotics, or Mathematics) with a minimum grade of Merit or equivalent degree from an overseas university
  • In-depth understanding and practical experience in digital image/video processing, computer vision, machine learning, linear algebra, and statistics
  • Programming proficiency (e.g. in Python, C++, or R)
  • Submission of a research proposal for the PhD topic, including introduction, related works, research gaps, aims and objectives, methodology, outcomes, timetable, and references.  More info and guidelines on writing a research proposal: here.

Desired Qualifications

  • A track record of previous publication(s) in the field of computer vision and digital image/video processing in well-known conference venues/journals 
  • Graduation with a 1st class bachelor and a distinction (A/A+) masters degree
  • Practical experience with LiDAR point cloud and multi-sensor data fusion/aggregations solutions

Entry requirements

Applicants to research degree programmes should normally have at least a first-class or an upper second class British Bachelors Honours degree (or equivalent) in an appropriate discipline. Please also see further information under 'How to apply'.

English language requirements

The minimum English language entry requirement for research postgraduate research study is an IELTS of 6.0 overall with at least 5.5 in each component (reading, writing, listening and speaking) or equivalent. The test must be dated within two years of the start date of the course in order to be valid. Some schools and faculties have higher requirements.

Contact details

For any further information about the application procedure please contact the Graduate School Office

e: [Email Address Removed], t: +44 (0)113 343 1314.

Any enquiries about the project can be forwarded to Dr Mahdi Rezaei via [Email Address Removed]


Funding Notes

This 3.5-year EPSRC DTP award will provide full tuition fees, a stipend at the UK research council rate (UK Sterling £15,840 for 2022/23), and a research training and support grant.

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