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BME AutomatedDrive Vehicle Dynamics and Control Research Group

Dr. Ádám Bárdos

Research fellow


Stoczek u. 6., building J, H-1111 Budapest, Hungary

+36 1 463 1882


Introduction of the Research Group

The Vehicle Dynamics and Control Research Team establishment was led by our vision to develop automated driving algorithms that outperform even a professional human driver by means of driving skills. Accordingly, our research and development focus on motion and behavior planning and on the connecting motion control. 

We are convinced that academic research should be rooted in industrial and social needs. Hence, application-oriented work, implementation, and demonstration of our findings on real vehicles and in relevant environments are key for us. According to it, the team has the opportunity to use a beautiful BMW M2 Competition vehicle as a test platform converted to drive-by-wire and equipped with all the relevant sensor systems, rapid control prototyping units, and automotive ECUs to experiment, test, and demonstrate our results. Our symbiotic relationship with ZalaZONE Automotive Proving Ground is also a competitive advantage. Additionally, our team members can show up with not only an academic background but proven experience in the automotive R&D.

We are agnostic for the use of methods and tools, which means we are experimenting and competing different technologies and algorithms (including modern control theory, machine learning, model-based design, etc.) to find the appropriate solution. Our unique findings are related to automated vehicle driving at handling limits that can significantly enhance road safety and can additionally be used in autonomous off-road missions and motorsport scenarios. We are looking forward to finding partners for the technology transfer and development of our know-how and patent.

Watch our 3-minute introductory video:


- Demonstration of automated vehicle drifting. https://youtu.be/saFgazF2bcs - Performing automated evasive maneuvers at handling limits with machine learning https://automateddrive.bme.hu/blog-posts/towards-beating-a-human-drivers-performance-with-ai

- Lap time optimization and control 

- Vehicle cloud control, https://automateddrive.bme.hu/blog-posts/cloudcontrol-measurement-at-zalazone-proving-ground 


Implementation and experimental evaluation of a MIMO drifting controller on a test vehicle
Bardos, A., Domina, A., Tihanyi, V., Szalay, Z., Palkovics, L.
IEEE Intelligent Vehicles Symposium, Proceedings, 2020, pp. 1472–1478, 9304820

Pneumatic Tire Aquaplaning: an Experimental Investigation on Manifestations and Influences of Appearance
Fichtinger, A., Bárdos, Á., Szalay, Z., Edelmann, J., Plöchl, M.
Acta Polytechnica Hungarica, 2022, 19(9), pp. 45–65

Autonomous Vehicle Drift With a Soft Actor-critic Reinforcement Learning Agent
Hunor Toth, S., Janos Viharos, Z., Bardos, A.
SAMI 2022 - IEEE 20th Jubilee World Symposium on Applied Machine Intelligence and Informatics, Proceedings, 2022, pp. 15–20

Model predictive controller design for vehicle motion control at handling limits in multiple equilibria on varying road surfaces
Czibere, S., Domina, Á., Bárdos, Á., Szalay, Z.
Energies, 2021, 14(20), 6667

LTV-MPC Approach for Automated Vehicle Path Following at the Limit of Handling
Domina, Á., Tihanyi, V.
Sensors (Basel, Switzerland), 2022, 22(15)




Test vehicles (BMW M2), access to ZalaZONE Automotive Proving Ground, Automotive sensors and programmable ECUs, rapid prototyping units


Autonomous Systems National Laboratory