Driving simulators have been extensively used in the last decade by the major automotive industries and racing teams, mostly for activities related to vehicle development and driver training. In recent years, the availability of smaller scale, less expensive dynamic simulators has opened the path toward the use of such tools for studying the interaction between vehicle, driver, and environment in a wider perspective, that includes psychological, medical, ergonomic, and safety issues. Static and dynamic driving simulators are nowadays deployed in dedicated research centers where university scholars, public organizations, and vehicle manufacturers can lead joint research activities ranging from driving safety, human factors, and roadway design studies to assessing the effects of health conditions and medications on task performance, cognitive development and functioning, decision-making, peer influences, and many other areas. Simulator data, which provide a detailed assessment of driving performance, can be coupled with other data, such as physiological activity, brain function, or hormone levels, to test a broad variety of hypotheses in a safe and reproducible environment. All of the aforementioned studies are based on the hypothesis that driving feelings are faithfully reproduced in the simulated environment. For dynamic driving simulators, this capability depends on the so-called Motion Cueing algorithm (MCA), that aims at creating realistic driving sensation via motion cues close to that in a real drive, despite the limited capabilities of the motion platform. Quality of the MCA is essential to guarantee a satisfactory reproduction of the driving feelings, as well as to avoid the onset of false motion cues that may induce, in the worst case, driver motion sickness.

The use of driving simulators in new research areas, such as those regarding the impact of medical conditions on driving capabilities, requires the design of a new generation of MCAs. In fact, it is of fundamental importance to design the MCA on the basis of models of the human sensorial systems, so that the platform movements give theappropriate sensorial cues for the given experiment at hand. In this perspective, the MCA algorithms may turn out to be quite different with respect to the classical ones, whose aim is mainly the reproduction of vehicle accelerations. The aim of the research contract is to develop effective motion cueing algorithms for advanced dynamic simulators, as well as virtual drivers for vehicle simulation environments. Focus of the research will be on the use of Model Predictive Control techniques. Tools will be developed for Vi-DriveSim (, an advanced, 9 degree-of-freedom driving simulator.
VI-grade is the leading provider of best-in-class software products and services for advanced applications in the field of system level simulation. VI-grade, established in 2005, delivers innovative solutions to streamline the development process from concept to sign-off in the transportation industry, mainly automotive, aerospace, motorcycle, motorsports and railway

Related publications:
- M. Bruschetta, F. Maran, and A. Beghi. A non-linear MPC based motion cueing implementation for a 9 DOFs dynamic simulator platform. In Proceedings of the 53rd IEEE Conference on Decision and Control, CDC 2014, 2014
- M. Bruschetta, F. Maran, and A. Beghi. An MPC approach to the design of motion cueing algorithms for a high performance 9 DOFs driving simulator. In Proceedings of the 2014 Driving Simulation Conference, 2014
- A. Beghi, M. Bruschetta, and F. Maran. A real-time implementation of an MPC-based motion cueing strategy with time-varying prediction. In Proceedings of the 2013 IEEE Conference on Systems, Man and Cybernetics, pages 4149-4154, 2013.
- A. Beghi, M. Bruschetta, and F. Maran. A real time implementation of mpc based motion cueing strategy for driving simulators. In Proceedings of the 51st IEEE Conference on Decision and Control, CDC 2012 , pages 6340-6345, 2012





Sponsor: Vi-Grade GmbH
Beginning Date: February, 2015
HIT project coordinator: Alessandro Beghi
HIT Involved Staff: TBD - Mattia Buschetti 
Contact e-mail: beghi[at]