Foto von Robert Jacumet

M.Sc. Robert Jacumet

Technische Universität München


Theresienstr. 90
80333 München

Model Predictive Control based Motion Cueing Algorithms for Driving Simulators

Driving simulators are increasingly used for research, development, and testing in fields such as autonomous driving, human driving behavior, new UI/UX concepts, and driving dynamics. The simulators offer a safer and measurable testing environment, with further advantages in their controllability and reproducibility. Essentially, the road is brought into the lab, allowing for shorter development cycles of new vehicles and accelerated research on drivers and vehicles. In addition to car manufacturers, driving simulators are also utilized by universities, research institutions, and traffic authorities.

For results found in driving simulator studies to be valid and therefore capable of replacing real-world vehicle tests, the simulators must provide the impression of a real drive. To achieve this, visual, vestibular, acoustic, and haptic stimuli are generated. The motion of the driving simulator generates forces that act on the driver's body within the simulator. These motion stimuli are referred to as "Motion Cues". Algorithms that calculate the control signals for the motion system of the simulator are called "Motion Cueing Algorithms" (MCAs).

MCAs must create a highly realistic motion experience from the perspective of the test subject (driver) while being limited to the motion space of the simulator, which is orders of magnitude smaller than the driving maneuvers on real roads. If perceived accelerations and angular velocities in the simulator deviate too much from those that would be experienced in a real vehicle, it reduces realism and can lead to motion sickness.

As part of my research, I am investigating Model Predictive Control (MPC)-based Motion Cueing Algorithms, which show great potential for controlling large and complex driving simulators. Challenges lie in formulating and efficiently solving optimization problems in real time for nonlinear simulator systems with up to nine non-independent degrees of freedom. The presence of the human driver in the system introduces two additional challenges. Firstly, we must predict driver inputs in the simulator over a long enough time horizon to calculate the optimal motion of the simulator, i.e., the motion that the driver expects based on their inputs. Secondly, it is an open question of how we can directly optimize for the test subjects' subjective rating metrics, i.e., how to design MCAs that directly optimize for the best rating according to the unknown subjective human driver in the system. Therefore, machine learning and learning-based control approaches also play a role in my work alongside MPC.

Short Biography

Since 12/2022

Ph.D. Candidate

Research New Technologies, BMW Group

Chair of Automatic Control Engineering

Technical University Munich (TUM)

10/2020 – 11/2022

Master of Science, Electrical Engineering and Information Technology

Focus: Control Theory, Signal Processing, Machine Learning

Technical University Munich (TUM)

10/2017 – 10/2020

Bachelor of Science, Electrical Engineering and Information Technology

Focus: Control Theory, Signal Processing

Technical University Munich (TUM)

Student Projects

I supervise research internships (FP), bachelor's and master's theses.

For this, I am always looking for good and motivated students. In these projects, I pay attention to providing adequate training time so that you can, of course, also succeed without prior knowledge in the area of Motion Cueing. On top, the scientific topics usually have an interesting practical relevance due to the research theme of my external Ph.D.

Below is a regularly updated list of open student projects. If you are interested in any of the topics, don't hesitate to contact me via email. Briefly describe your motivation and let me know the desired topic and intended start date. Please attach a current academic record and your current CV.

In addition to the currently advertised projects, I always have other open topics for future investigations. Therefore, if there is nothing suitable for you among the advertised projects, send me an email with the same documents. If you are a suitable candidate, I will suggest a fitting topic in my research area.

The supervision is the same as for internal doctoral candidates.


R. Jacumet, C. Rathgeber and V. Nenchev, "Analytical Safety Bounds for Trajectory Following Controllers in Autonomous Vehicles," 2023 9th International Conference on Control, Decision and Information Technologies (CoDIT), Rome, Italy, 2023, (accepted)

T. Brüdigam, R. Jacumet, D. Wollherr and M. Leibold, "Safe Stochastic Model Predictive Control," 2022 IEEE 61st Conference on Decision and Control (CDC), Cancun, Mexico, 2022, pp. 1796-1802, doi: 10.1109/CDC51059.2022.9992772.