Skip to main content

Advertisement

SpringerLink
Log in

To move or not to move? Analyzing motion cueing in vehicle simulators by means of massive simulations

  • Original Article
  • Published:
Virtual Reality Aims and scope Submit manuscript

Abstract

Motion platforms and motion cueing algorithms (MCA) have been included in virtual reality applications for several decades. They are necessary to provide suitable inertial cues in vehicle simulators. However, the great number of operational constraints that these devices and algorithms suffer, namely limited physical space, elevated costs, absence of sufficient power, difficulty of tuning and lack of standardized assessment methods, have hindered their widespread use. This work tries to clarify open questions in the field, such as: How important is MCA tuning? How much does size, number of DOF and power/latency matter? Can the absence of motion be better than poor motion cueing? What are the key factors that should be addressed to enhance the design of these devices? Although absolute certain answers cannot be given, this paper tries to clarify these research questions by performing massive experiments with simulated motion platforms of different types, sizes and powers. The information obtained from these experiments will be important to customize the design of real devices for this particular use. Ideally, subjective experiments with human experts would have been preferred. However, the use of simulated devices allows comparing many different motion platforms. In this paper, forty of these devices are simulated, optimized by means of a heuristic algorithm and compared with objective indicators in order to measure their relative performance using the classical MCA, something that would require an unreasonable amount of effort with real users and real devices. The obtained results show that MCA tuning is of the utmost importance in motion cueing. They also suggest that high power can usually compensate for lack of size and that a 6-DOF motion platform slightly improves the performance of a 3-DOF motion platform.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Advani S, Hosman R (2006) Towards standardising high-fidelity cost-effective motion cueing in flight simulation. In: Royal aeronautical society conference on: cutting costs in flight simulation. Balancing quality and capability, London

  • Advani S, Hosman R, Potter M (2007) Objective motion fidelity qualification in flight training simulators. In: AIAA modeling and simulation technologies conference and exhibit, Hilton Head, SC, USA

  • Arioui H, Hima S, Nehaoua L (2009) 2 DOF low cost platform for driving simulator: modeling and control. In: IEEE/ASME international conference on advanced intelligent mechatronics, AIM 2009

  • Arioui H, Hima S, Nehaoua L, Bertin RJ, Espié S (2011) From design to experiments of a 2-DOF vehicle driving simulator. IEEE Trans Veh Technol 60(2):357–368

    Google Scholar 

  • Asadi H, Mohamed S, Rahim Zadeh D, Nahavandi S (2015) Optimisation of nonlinear motion cueing algorithm based on genetic algorithm. Veh Syst Dyn 53(4):526–545

    Google Scholar 

  • Asadi H, Mohammadi A, Mohamed S, Lim CP, Khatami A, Khosravi A, Nahavandi S (2016) A particle swarm optimization-based washout filter for improving simulator motion fidelity. In: 2016 IEEE international conference on systems, man, and cybernetics (SMC). IEEE

  • Asadi H, Mohamed S, Lim CP, Nahavandi S (2017) Robust optimal motion cueing algorithm based on the linear quadratic regulator method and a genetic algorithm. IEEE Trans Syst Man Cybern Syst 47(2):238–254

    Google Scholar 

  • Bailey R, Knotts L, Horowitz S, Malone H (1987) Effect of time delay on manual flight control and flying qualities during in-flight and ground-based simulation. In: Flight simulation technologies conference

  • Berger DR, Schulte-Pelkum J, Bülthoff HH (2010) Simulating believable forward accelerations on a stewart motion platform. ACM Trans Appl Percept 7(1):5:1–5:27

    Google Scholar 

  • Bertollini G, Glase Y, Szczerba J, Wagner R (2014) The effect of motion cueing on simulator comfort, perceived realism, and driver performance during low speed turning. In: Proceedings of the driving simulator conference, Paris

  • Bruenger-Koch M (2005) Motion parameter tuning and evaluation for the DLR automotive simulator. In: Driving simulation conference North America (DSC-NA), Orlando, FL, USA

  • Bruenger-Koch M, Briest S, Vollrath M (2006) Do you feel the difference? A motion assessment study. In: Driving simulation conference Asia/Pacific. Tsukuba, Japan

  • Bruschetta M, Maran F, Beghi A (2017) A fast implementation of MPC-based motion cueing algorithms for mid-size road vehicle motion simulators. Veh Syst Dyn 55(6):802–826. https://doi.org/10.1080/00423114.2017.1280173

    Article  Google Scholar 

  • Burki-Cohen J, Go TH, Longridge T (2001) Flight simulator fidelity considerations for total air line pilot training and evaluation. In: Proceedings of the AIAA modeling and simulation technologies conference

  • Calabro F, Soto-Faraco S, Vaina LM (2011) Acoustic facilitation of object movement detection during self-motion. Proc R Soc B Biol Sci 278(1719):2840–2847

    Google Scholar 

  • Casas S, Coma I, Riera JV, Fernández M (2015) Motion-cuing algorithms: characterization of users’ perception. Hum Factors 57(1):144–162

    Google Scholar 

  • Casas S, Coma I, Portalés C, Fernández M (2016) Towards a simulation-based tuning of motion cueing algorithms. Simul Model Pract Theory 67:137–154

    Google Scholar 

  • Casas S, Coma I, Portalés C, Fernández M (2017a) Optimization of 3-DOF parallel motion devices for low-cost vehicle simulators. J Adv Mech Des Syst Manuf 11(2):JAMDSM0023

    Google Scholar 

  • Casas S, Olanda R, Dey N (2017b) Motion cueing algorithms: a review—algorithms, evaluation and tuning. Int J Virtual Augment Real 1(1):90–106

    Google Scholar 

  • Casas S, Portalés C, Rueda S, Fernández M (2017c) On the simulation of robotic motion platforms with digital filters for virtual reality applications. Rev Iberoam Autom Inform Ind: RIAI 14(4):455–466

    Google Scholar 

  • Casas S, Portalés C, Gimeno J, Fernández M (2018a) Simulation of parallel mechanisms for motion cueing generation in vehicle simulators using AM-FM bi-modulated signals. Mechatronics 53:251–261

    Google Scholar 

  • Casas S, Portalés C, Morillo P, Fernández M (2018b) A particle swarm approach for tuning washout algorithms in vehicle simulators. Appl Soft Comput 68:125–135

    Google Scholar 

  • Colombet F, Dagdelen M, Reymond G, Pere C, Merienne F, Kemeny A (2008) Motion cueing: what’s the impact on the driver’s behaviour? In: Driving simulator conference, Monte-Carlo, Monaco

  • Conrad B, Schmidt SF (1971) A study of techniques for calculating motion drive signals for flight simulators

  • Dagdelen M, Reymond G, Kemeny A, Bordier M, Maizi N (2009) Model-based predictive motion cueing strategy for vehicle driving simulators. Control Eng Pract 17(19):995–1003

    Google Scholar 

  • DNV (2011) Standard for certification maritime simulator systems No. 2.14. D. N. Veritas. https://rules.dnvgl.com/docs/pdf/DNV/stdcert/2011-01/Standard2-14.pdf

  • Draper MH, Viirre ES, Furness TA, Gawron VJ (2001) Effects of image scale and system time delay on simulator sickness within head-coupled virtual environments. Hum Factors 43(1):129–146

    Google Scholar 

  • Fischer M (2007) A survey of state-of-the-art motion platform technology and motion cueing algorithms. In: 2nd motion simulator conference. Braunschweig, Germany

  • Fischer M (2009) Motion-cueing-algorithmen für eine realitätsnahe bewegungssimulation. Berichte aus dem DLR-Institut für Verkehrssystemtechnik 5

  • Fischer M, Sehammer H, Palmkvist G (2010) Motion cueing for 3-, 6- and 8-degrees-of-freedom motion systems. In: Driving simulation conference Europe, Paris, France

  • Frank LH, Casali JG, Wierwille WW (1988) Effects of visual display and motion system delays on operator performance and uneasiness in a driving simulator. Hum Factors 30(2):201–217

    Google Scholar 

  • Garrett NJI, Best MC (2010) Driving simulator motion cueing algorithms—a survey of the state of the art. In: Proceedings of the 10th international symposium on advanced vehicle control (AVEC), Loughborough, UK

  • Garrett NJI, Best MC (2013) Model predictive driving simulator motion cueing algorithm with actuator-based constraints. Veh Syst Dyn 51(8):1151–1172. https://doi.org/10.1080/00423114.2013.783219

    Article  Google Scholar 

  • Gouverneur B, Mulder JA, van Paassen MM, Stroosma O, Field EJ (2003) Optimisation of the SIMONA research simulator’s motion filter settings for handling qualities experiments. In: AIAA modeling and simulation technologies conference and exhibit, Austin, TX, USA

  • Grant PR (1996) The development of a tuning paradigm for flight simulator motion drive algorithms. Ph.D, University of Toronto

  • Grant PR, Reid LD (1997a) Motion washout filter tuning: rules and requirements. J Aircr 34(2):145–151

    Google Scholar 

  • Grant PR, Reid LD (1997b) PROTEST: an expert system for tuning simulator washout filters. J Aircr 34(2):152–159

    Google Scholar 

  • Grant PR, Blommer M, Artz B, Greenberg J (2009) Analysing classes of motion drive algorithms based on paired comparison techniques. Veh Syst Dyn 47(9):1075–1093

    Google Scholar 

  • Groen EL, Bles W (2004) How to use body tilt for the simulation of linear self motion. J Vestib Res 14(5):375–385

    Google Scholar 

  • Hosman R, Advani S (2016) Design and evaluation of the objective motion cueing test and criterion. Aeronaut J 120(1227):873–891

    Google Scholar 

  • Huang ARW, Chen C (2003) A low-cost driving simulator for full vehicle dynamics simulation. IEEE Trans Veh Technol 52(1):162–172

    Google Scholar 

  • Icao S (2009) Manual of criteria for the qualification of flight simulation training devices, volume I—aeroplanes. International Civil Aviation Organization, Montreal

    Google Scholar 

  • Izaguirre E, Hernandez L, Rubio E, Prieto PJ, Hernandez A (2011) Decoupled control of a 3-DOF pneumatic platform used as motion simulator. Rev Iberoam Autom Inform Ind 8(4):345–356

    Google Scholar 

  • Jakobović D, Jelenković L (2002) The forward and inverse kinematics problems for Stewart parallel mechanisms. In: 8th international scientific conference on production engineering (CIM 2002)

  • Jansson J, Sandin J, Augusto B, Fischer M, Blissing B, Källgren L (2014) Design and performance of the VTI Sim IV. In: Driving simulation conference

  • Jones M (2017) Motion cueing optimisation applied to rotorcraft flight simulation. CEAS Aeronaut J 8(3):523–539

    Google Scholar 

  • Kemeny A (1999) Simulation and perception. In: Introduction de la Conférence sur la Simulation de Conduite

  • Klüver M, Herrigel C, Preuß S, Schöner HP, Hecht H (2015) Comparing the incidence of simulator sickness in five different driving simulators. In: Proceedings of driving simulation conference, Tübingen, Germany

  • Kurosaki M (1978) Optimal washout for control of a moving base simulator. In: Proceedings of the seventh triennial World Congress of IFAC (International Federation of Automatic Control), Helsinki, Finland

    Google Scholar 

  • Laurens J, Droulez J (2007) Bayesian processing of vestibular information. Biol Cybern 96:389–404

    MathSciNet  MATH  Google Scholar 

  • MacNeilage PR, Banks MS, Berger DR, Bulthoff HH (2007) A Bayesian model of the disambiguation of gravitoinertial force by visual cues. Exp Brain Res 179(2):263–290

    Google Scholar 

  • Mauro S, Gastaldi L, Pastorelli S, Sorli M (2016) Dynamic flight simulation with a 3 d of parallel platform. Int J Appl Eng Res 11(18):9436–9442

    Google Scholar 

  • Mohammadi A, Asadi H, Mohamed S, Nelson K, Nahavandi S (2016) MPC-based motion cueing algorithm with short prediction horizon using exponential weighting. 2016 IEEE international conference on systems, man, and cybernetics (SMC), IEEE

  • Mohammadi A, Asadi H, Mohamed S, Nelson K, Nahavandi S (2018) Optimizing model predictive control horizons using genetic algorithm for motion cueing algorithm. Expert Syst Appl 92:73–81

    Google Scholar 

  • Nahon MA, Reid LD (1990) Simulator motion-drive algorithms—a designer’s perspective. J Guid Control Dyn 13(2):356–362

    Google Scholar 

  • Nahon MA, Reid LD, Kirdeikis J (1992) Adaptive simulator motion software with supervisory control. J Guid Control Dyn 15(2):376–383

    MATH  Google Scholar 

  • Nehaoua L, Amouri A, Arioui H (2005) Classic and adaptive washout comparison for a low cost driving simulator. In: Proceedings of the 13th mediterranean conference on control and automation, Limassol, Cyprus

  • Nehaoua L, Arioui H, Espie S, Mohellebi H (2006) Motion cueing algorithms for small driving simulator. In: ICRA 2006. Proceedings of 2006 IEEE international conference on robotics and automation, Orlando, FL, USA

  • Nehaoua L, Hima S, Arioui H, Seguy N, Espié S (2007) Design and modeling of a new motorcycle riding simulator. In: American control conference, 2007. ACC’07, IEEE

  • Nehaoua L, Mohellebi H, Amouri A, Arioui H, Espié S, Kheddar A (2008) Design and control of a small-clearance driving simulator. IEEE Trans Veh Technol 57(2):736–746

    Google Scholar 

  • Optitrack (2018) http://optitrack.com/. Motion Capture Systems—Optitrack. http://www.naturalpoint.com/optitrack/. Retrieved 15/04/2018

  • Page LR (2000) Brief history of flight simulation. In: SimTecT 2000 proceedings, Sydney, NSW, Australia

  • Parrish RV, Dieudonne JE, Bowles RL, Martin DJ (1973) Coordinated adaptive washout for motion simulators. In: AIAA visual and motion simulation conference, Palo Alto, CA, USA, American Institute of Aeronautics and Astronautics

  • Parrish RV, Dieudonne JE, Martin DJ Jr (1975) Coordinated adaptive washout for motion simulators. J Aircr 12(1):44–50

    Google Scholar 

  • Pham D, Röttgermann S, Flores FG, Kecskeméthy A (2015) Optimal motion cueing algorithm selection and parameter tuning for sickness-free robocoaster ride simulations. Mechanisms, transmissions and applications. Springer, Berlin, pp 127–135

    Google Scholar 

  • Pouliot NA, Gosselin CM, Nahon MA (1998) Motion simulation capabilities of three-degree-of-freedom flight simulators. J Aircr 35(1):9–17

    Google Scholar 

  • Reid LD, Nahon MA (1985) Flight simulation motion-base drive algorithms: part 1—developing and testing the equations. University of Toronto, UTIAS, Toronto, p 296

    Google Scholar 

  • Reid LD, Nahon MA (1986a) Flight simulation motion-base drive algorithms: part 2—selecting the system parameters. University of Toronto, UTIAS, Toronto, p 307

    Google Scholar 

  • Reid LD, Nahon MA (1986b) Flight simulation motion-base drive algorithms: part 3—pilot evaluations. University of Toronto, UTIAS, Toronto

    Google Scholar 

  • Reid LD, Nahon MA (1988) Response of airline pilots to variations in flight simulator motion algorithms. J Aircr 25(7):639–646

    Google Scholar 

  • Reymond G, Kemeny A (2000) Motion cueing in the renault driving simulator. Veh Syst Dyn 34:249–259

    Google Scholar 

  • Romano RA, Park GD, Paul V, Allen RW (2016) Motion cueing evaluation of off-road heavy vehicle handling, SAE Technical Paper

  • Royal-Aeronautical-Society (1979) 50 years of flight simulation, conference proceedings, London, UK

  • Salisbury IG, Limebeer DJ (2016) Optimal motion cueing for race cars. IEEE Trans Control Syst Technol 24(1):200–215

    Google Scholar 

  • Schmidt SF, Conrad B (1969) The calculation of motion drive signals for piloted flight simulators. NASA, Palo Alto

    Google Scholar 

  • Schmidt SF, Conrad B (1970) Motion drive signals for piloted flight simulators. NASA, Palo Alto

    Google Scholar 

  • Schroeder JA (1999) Helicopter flight simulation motion platform requirements. Moffett Field, California, National Aeronautics and Space Administration. NASA/TP–1999-208766

  • Schroeder JA, Chung WWY, Hess RA (2000) Evaluation of a motion fidelity criterion with visual scene changes. J Aircr 37(4):580–587

    Google Scholar 

  • Schwarz CW (2007) Two mitigation strategies for motion system limits in driving and flight simulators. IEEE Trans Syst Man Cybern Part A Syst Hum 37(4):562–568

    Google Scholar 

  • Sinacori JB (1977) The determination of some requirements for a helicopter flight research simulation facility, vol 7805. Moffet Field, CA, USA

  • Sivan R, Ish-Shalom J, Huang JK (1982) An optimal control approach to the design of moving flight simulators. IEEE Trans Syst Man Cybern 12(6):818–827

    Google Scholar 

  • Slob JJ (2008) State-of-the-art driving simulators, a literature survey. Eindhoven University of Technology, Eindhoven

    Google Scholar 

  • Stahl K, Abdulsamad G, Leimbach K, Vershinin YA (2014) State of the art and simulation of motion cueing algorithms for a six degree of freedom driving simulator. In: 17th international conference on intelligent transportation systems (ITSC), Qingdao, China, IEEE

  • Stewart D (1965) A platform with six degrees of freedom. In: Proceedings of the institution of mechanical engineers

  • Stroosma O, Van Paassen MM, Mulder M, Hosman R, Advani S (2013) Applying the objective motion cueing test to a classical washout algorithm. In: AIAA modeling and simulation technologies (MST) conference, Boston, MA, USA

  • Telban RJ, Cardullo FM (2005) Motion cueing algorithm development: human-centered linear and nonlinear approaches. Langley Research Center, Hampton

    Google Scholar 

  • Teufel HJ, Nusseck HG, Beykirch KA, Butler JS, Kerger M, Bülthoff HH (2007) MPI motion simulator: development and analysis of a novel motion simulator. In: AIAA modeling and simulation technologies conference and exhibit, Hilton Head, SC, USA, pp 1–11

  • Thöndel E (2012) Design and optimisation of a motion cueing algorithm for a truck simulator. In: European simulation and modelling conference—ESM, Essen, Germany

  • Valente Pais AR, Wentink M, van Paassen MM, Mulder M (2009) Comparison of three motion cueing algorithms for curve driving in an urban environment. Presence 18(3):200–221

    Google Scholar 

  • Wiskemann CM, Drop FM, Pool DM, Van Paassen MM, Mulder M, Bülthoff HH (2014) Subjective and objective metrics for the evaluation of motion cueing fidelity for a roll-lateral reposition maneuver. American Helicopter Society 70th Annual Forum, Montreal, Quebec

Download references

Author information

Authors and Affiliations

  1. University of Valencia, Valencia, Spain

    Sergio Casas, Cristina Portalés, Pedro Morillo & Marcos Fernández

Authors
  1. Sergio Casas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cristina Portalés
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pedro Morillo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Marcos Fernández
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sergio Casas.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Casas, S., Portalés, C., Morillo, P. et al. To move or not to move? Analyzing motion cueing in vehicle simulators by means of massive simulations. Virtual Reality 24, 93–108 (2020). https://doi.org/10.1007/s10055-019-00387-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10055-019-00387-9

Keywords

Search

Navigation