Advertisement

A High Fidelity Driving Simulation Platform for the Development and Validation of Advanced Driver Assistance Systems

  • Marco Grottoli
  • Anne van der Heide
  • Yves LemmensEmail author
Chapter

Abstract

New vehicle designs with advanced driver assistance systems need to be validated with respect to human perceptions of comfort and risk. Therefore, human-in-the-loop simulations are used to evaluate a wide range of scenarios in driving simulators. In order to improve human-in-the-loop simulation, the chapter begins by reporting solver advancements that enable the real-time simulation of complex mechatronic systems using high fidelity multibody and multi-physics simulation models. A driving simulator setup is then presented that makes use of the high-fidelity vehicle models and can simulate vehicles with advanced driver assistance systems. The essential components of the simulator are outlined and initial results of a comparison study between high fidelity model and equivalent low fidelity models. Finally, two test cases are described that use respectively an adaptive cruise control function and an autonomous intersection crossing function.

List of Abbreviations

ABS

Anti-Lock Braking System

ACC

Adaptive Cruise Control

ADAS

Advanced Driver-Assistance Systems

ADF

Automated Driving Functionality

AIC

Autonomous Intersection Crossing

CFD

Computational Fluid Dynamics

DoF

Degree(s) of Freedom

DTM

Double Track Model

EPS

Electric Power Steering

ESP

Electronic Stability Program

HiL

Hardware in the Loop

HuiL

Human in the Loop

ICE

Internal Combustion Engine

MBS

Multibody Simulation

MCA

Motion Cueing Algorithm

OEM

Original Equipment Manufacturer

SiL

Software in the Loop

STM

Single Track Model

Notes

Acknowledgements

The authors would like to acknowledge the ENABLE-S3 project that has received funding from the ECSEL Joint Undertaking under grant agreement No. 692455. This joint undertaking receives support from the European Union’s HORIZON 2020 research and innovation programme and from the governments of Spain, Portugal, Poland, Ireland, Belgium, France, Netherlands, United Kingdom, Slovakia, Norway.

References

  1. 1.
    Prescott, W., Heirman, G., Furman, M., De Cuyper, J., Lippeck, A., & Brauner, H. (2012). Using high-fidelity multibody vehicle models in real-time simulations. In SAE 2012 World Congress & Exhibition, Detroit, Technical Paper No. 2012-01-0927.Google Scholar
  2. 2.
    Garcia de Jalon, J., & Bayo, E. (1994). Kinematic and dynamic simulation of multibody systems: The real-time challenge. New York: Springer.CrossRefGoogle Scholar
  3. 3.
    Arnold, M., Burgermeister, B., & Eichberger, A. (2007). Linearly implicit time integration methods in real-time applications: DAEs and stiff ODEs. Multibody System Dynamics, 17(2–3), 99–117.Google Scholar
  4. 4.
    Prescott, W. (2011). Parallel processing of multibody systems for real-time analysis. In Proceedings of the 2011 ECCOMAS Thematic Conference on Multibody Dynamics, Brussels.Google Scholar
  5. 5.
    Shabana, A. A. (1997). Flexible multibody dynamics: Review of past and recent developments. Multibody System Dynamics, 1, 189–222.MathSciNetCrossRefGoogle Scholar
  6. 6.
    Shabana, A. A. (2013). Dynamics of multibody systems. Cambridge: Cambridge University Press.Google Scholar
  7. 7.
    Hu, Y., Zhan, W., & Tomizuka, M. (2018). Probabilistic prediction of vehicle semantic intention and motion. In IEEE Intelligent Vehicles Symposium (pp. 307–313).Google Scholar
  8. 8.
    Stanger, T., & del Re, L. (2013). A model predictive cooperative adaptive cruise control approach. In Proceedings of the American Control Conference (pp. 1374–1379).Google Scholar
  9. 9.
    Naranjo, J. E., González, C., García, R., & de Pedro, T. (2006). ACC + Stop&Go maneuvers with throttle and brake fuzzy control. IEEE Transactions on Intelligent Transportation Systems, 7(2), 213–225.CrossRefGoogle Scholar
  10. 10.
    Coulter, R. (1990). Implementation of the pure pursuit path tracking algorithm. Carnegie Mellon University Robotics Inst., Report CMU-RI-TR-92-01.Google Scholar
  11. 11.
    Del Vecchio, D., Malisoff, M., & Verma, R. (2009). A separation principle for hybrid automata on a partial order. In Proceedings of the American Control Conference (pp. 3638–3643).Google Scholar
  12. 12.
    Hafner, M. R., Cunningham, D., Caminiti, L., & Del Vecchio, D. (2013). Cooperative collision avoidance at intersections: Algorithms and experiments. IEEE Transactions on Intelligent Transportation Systems, 14(3), 1162–1175.CrossRefGoogle Scholar
  13. 13.
    Schmidt, S. F., & Conrad, B. (1970). Motion drive signals for piloted flight simulators. NASA Technical Report @ ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/19700017803.pdf. Accessed May 20, 2019.Google Scholar
  14. 14.
    Reid, L. D., & Nahon, M. A. (1985). Flight simulator motion-base drive algorithms: Part 1—Developing and testing the equations. Technical Report, University of Toronto Institute for Aerospace Studies.Google Scholar
  15. 15.
    Dagdelen, M., Reymond, G., Kemeny, A., Bordier, M., & Maïzi, N. (2004). MPC-based motion cueing algorithm: Development and application to the ULTIMATE driving simulator. In Driving Simulation Conference (DSC), Paris (pp. 221–233).Google Scholar
  16. 16.
    Fang, Z., & Kemeny, A. (2012). Motion cueing algorithms for a real-time automobile driving simulator. In Driving Simulation Conference (DSC), Paris (pp. 1–12).Google Scholar
  17. 17.
    en.wikipedia.org/wiki/List_of_self-driving_car_fatalities. Accessed May 20, 2019.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Marco Grottoli
    • 1
  • Anne van der Heide
    • 1
  • Yves Lemmens
    • 1
    Email author
  1. 1.Siemens PLM SoftwareLouvainBelgium

Personalised recommendations