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Toward automated vehicle control beyond the stability limits via active drifting control

Professional drivers in drifting competitions demonstrate accurate control over a car's position and sideslip while operating in an open-loop unstable region of state space. Could similar approaches help autonomous cars contend with excursions past the stable handling limits, thereby improving overall safety outcomes?

Developing controllers for automated drifting could provide great insight into the general problem of fully utilizing the entire state space and ensuring that the widest possible range of maneuvers is available to an autonomous vehicle, should the need arise. When drifting, a standard assumption is removed - the orientation of the vehicle's velocity vector no longer follows that of the vehicle body. Although this at first seems daunting, the controller derivation instead reveals an opportunity: the rotation rate of the vehicle's velocity vector can now be used directly to track the path.

Then, by yawing the vehicle body faster or slower than its velocity vector, we can simultaneously control the sideslip of the vehicle.

The study will address the following objectives:

  1. Vehicle dynamic modelling and simulation with the tire force saturation
  2. Road-tire friction coefficient identification via vehicle state estimation
  3. Precise vehicle drifting control beyond the stability limits. Improve the vehicle path tracking performance under limited driving conditions


Mohanty, A., Zawislak, R., Bhamidipati, S., & Gao, G. (2021, September). Precise relative positioning for tandem drifting cars. In Proceedings of the 34th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2021) (pp. 113-124).

Zhao, T., Yurtsever, E., Chladny, R., & Rizzoni, G. (2021, September). Collision Avoidance with Transitional Drift Control. In 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) (pp. 907-914). IEEE.

Subosits, J. K., & Gerdes, J. C. (2021). Impacts of model fidelity on trajectory optimization for autonomous vehicles in extreme maneuvers. IEEE Transactions on Intelligent Vehicles, 6(3), 546-558.

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 would 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: 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%.