Advanced Teleoperator Assistance System (ATAS) for Safe Vehicle Teleoperation Under Network Latency

Eligibility: UK, EU, and international graduates with the required entry requirements

Duration: Full-Time – between three and three and a half years fixed term

Application deadline: 27 May 2023

Interview date: Will be confirmed to shortlisted candidates

Start date: September 2023

For further details contact: Dr Qian Lu


Coventry University (CU) is inviting applications from suitably qualified graduates for a fully funded PhD studentship. The successful candidate will join this cutting-edge research project ‘Advanced Teleoperator Assistance System (ATAS) for Safe Vehicle Teleoperation Under Network Latency’.

Project details

Vehicle teleoperation is an essential fallback solution for connected and autonomous vehicles (CAV) to cope with situations CAVs cannot handle, for example, unplanned situations or system failures. A remote human operator (i.e., teleoperator) will take over control and drive the vehicle to a safe place using driver perspective video streams transmitted from the vehicle. Teleoperation requires wireless communication between teleoperators and vehicles (e.g., cellular network), therefore teleoperators need to overcome significant network delays. Most existing approaches applied for addressing network latencies are from robotics teleoperation, hence cannot meet real-time and human factor requirements for teleoperation of road vehicles.

This research aims to develop innovative solutions suitable for vehicle teleoperation and assist teleoperators in driving safely under delayed network conditions. To achieve this aim, the following objectives will be considered:

Objective 1: Develop a low compute latency visualisation method to assist the teleoperator to account for the effect of video streaming latency

Objective 2: Develop an initiative teleoperator control prediction to mitigate the effects of control latency by applying the predicted teleoperator control actions ahead of latency using deep-learning-based prediction approaches.

Funding details

Tuition fees and bursary.


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 College and Centre for Research Capability and Development, which provides support with high-quality training and career development activities. 

Candidate specification

Entry criteria for applicants to PhD 

  • A bachelor’s (honours) degree in Engineering, Automotive, Computer Science or a related discipline with a minimum classification of 2:1 and a minimum mark of 60% in the project element (or equivalent), or an equivalent award from an overseas institution.


  • the potential to engage in innovative research and to complete the PhD within 3.5 years
  • An adequate proficiency in English must be demonstrated by applicants whose first language is not English. The general requirement is a minimum overall IELTS Academic score of 7.0 with a minimum of 6.5 in each of the four sections, or the TOEFL iBT test with a minimum overall score of 95 with a minimum of 21 in each of the four sections.

 For further details please visit:

Additional requirements
  • Strong experience in machine learning and mathematical modelling based on data as well as physics.
  • Strong experience in control engineering, control theory as well as vehicle dynamics
  • Experience in vehicular user experience and human factor is preferred.
  • Competent programming skills (in Matlab, Python, C++) and
  • Enthusiastic to work on an inter-disciplinary research project.


How to apply

To find out more about the project please contact Dr Qian Lu at

All applications require full supporting documentation, and a cover letter plus an up-to 2000-word supporting statement is required showing how the applicant’s expertise and interests are relevant to the project. 

Apply to Coventry University
 Queen’s Award for Enterprise Logo
University of the year shortlisted
QS Five Star Rating 2020