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Can AI based robot car win the race

A modern Formula 1 race is a breathtaking display of engineering precision. Yet the popularity of the sport arguably has less to do with the performance of the cars than with the skill and daring displayed by the drivers as they push those cars to the limit. Success on the race track has been a celebrated human achievement for more than a century. Will it now become a similar triumph for artificial intelligence (AI)?

The objective of this PhD research study is to develop an AI based robot driver that can achieve the most optimal laptime in the real racing tracks.

The objective in racing is easily defined: if you complete the circuit in less time than your competitors, you win. However, achieving this goal involves a complicated battle with physics, because negotiating the track requires careful use of the frictional force between the tire and the road, and this force is limited. Using some of that friction for braking, for instance, leaves less force available for rounding a corner. Brake too early going into a turn and your car is slow, losing time. Brake too late and you won’t have enough cornering force to hold your desired racing line as you near the tightest part of the turn. Brake too hard and you might induce a spin. Professional racing drivers are eerily good at finding and maintaining the limits of their car, lap after lap, for an entire race.

The study will address the following objectives:

  1. The handling limits of a car should be well described by physics, and it, therefore, stands to reason that they could be calculated or learnt by AI.
  2. To win the race, the AI based robot driver must choose trajectories that allow the car to stay within these ever-changing friction limits as much as it physically can.
  3. Using AI based controller for finding and maintaining the limits of the race car, lap after lap, for an entire race.
  4. 4. Demontrate the propsed AI based robot car can be well employed to extended racing tracks to achieve the best laptime than any human drivers.

There are potential collabration opportunities with the Formula car companies and racing teams to guide their racing car design and racing driver operations from the proposed AI based robot driver.

How to apply

If you are interested in applying for the above PhD topic please follow the steps below:

  1. Contact the supervisor by email or phone to discuss your interest and find out if you woold be suitable. Supervisor details can be found on this topic page. The supervisor will guide you in developing the topic-specific research proposal, which will form part of your application.
  2. Click on the 'Apply here' button on this page and you will be taken to the relevant PhD course page, where you can apply using an online application.
  3. Complete the online application indicating your selected supervisor and include the research proposal for the topic you have selected.

Good luck!

This is a self funded topic

Brunel offers a number of funding options to research students that help cover the cost of their tuition fees, contribute to living expenses or both. See more information here: https://www.brunel.ac.uk/research/Research-degrees/Research-degree-funding. The UK Government is also offering Doctoral Student Loans for eligible students, and there is some funding available through the Research Councils. Many of our international students benefit from funding provided by their governments or employers. Brunel alumni enjoy tuition fee discounts of 15%.

Meet the Supervisor(s)


Dong Zhang - Dr. Dong Zhang is a Lecturer in Automotive Design in the department of Mechanical and Aerospace Engineering. He leads the research on intelligent vehicle & transportation control systems, and founds the Brunel Racing Formula Student AI group.  He obtained his Ph.D. degree in vehicle dynamic control from University of Lincoln in October 2019. His PhD thesis was titled with ''A systematic approach to cooperative driving systems based on optimal control allocation''. After this, he worked as a research fellow in Centre for System Intelligence and Efficiency; and the Department of Mechanical and Aerospace Engineering, at Nanyang Technological University, Singapore from 2019 to 2020. During this period, his research was focusing to provide novel solutions to address the security challenges raised by intelligent and connected vehicles; and to develop a novel, integrated vehicle chassis control system for intelligent and automated electric vehicles (iAEVs). Before joining Brunel University London in 2021, he worked as a senior research project manager in scientific collaborative projects with car industry for developing the next-generation vehicle chassis control system. Dr. Zhang is welcoming and putting a special emphasis on the collaboration with car companies for developing human-centered automotive control systems and Advanced Driver Assistance Systems (ADAS).   Dr. Zhang's research interests strongly reside in the area of human-centered automotive control systems, intelligent vehicle/transportation control, game theory based driver-vehicle shared control systems, vehicle dynamic and safety control, adaptive vehicle motion control, and A.I based autonomous vehicle control. His research has resulted in more than thirty peer-reviewed papers in top journals and conferences, and about ten patents for invention.  Dr. Zhang has experience in co-supervising PhD students to completion. Please feel free to contact if you are seeking PhD opportunities in vehicle dynamic/safety control, intelligent vehicle /transportation control systems, A.I based autonomous vehicle control and driver-vehicle interaction.