Abstract
In vehicle development, rating vehicle reactions to external disturbances such as aerodynamic excitations are important for improving safety and comfort of passengers. Vehicle motion reactions under such conditions are dependent on both disturbance and drivers’ steering actions. A predictive model has been developed to correctly anticipate the drivers’ ability to identify unexpected external disturbances for straight-line, high-speed driving in a driving simulator. The measured variables were band-pass filtered to desired frequency ranges. Excess yaw and roll velocities, defined as the difference between actual rotations and rotations predicted with a dynamic model from the cause of actual steering, were calculated. The standard deviations of the measured variables in a time window around disturbances were used in a regression model to predict the driver responses. Replacing actual rotations with excess rotations reduced the importance of steering input as a predictor by approximately 2/3, thus improving the accuracy of the predictive model. The model showed the change in driver sensitivity to rotations at different frequency intervals. It also showed that only driver input in around 1 ∼ 2 Hz reduces driver sensitivity and that drivers are not necessarily sensitive to high rotational accelerations, but rather to large differences between actual vehicle yaw and roll and expected vehicle yaw and roll responses from the steering input The result from this study were later compared to succeeding on-road tests which confirmed that the predictive model was improved with the use of excess motion variables.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Abbreviations
- a y :
-
lateral acceleration, m/s2
- v x :
-
longitudinal velocity, m/s
- c DT :
-
driver type,-
- ω i :
-
rotational rate, °/s
- ω excess i :
-
excess rotational rate, °/s
- ω steer i :
-
predicted rotational rate due to steering, °/s
- ω x :
-
roll rate, °/s
- ω x,std :
-
standard deviation value of roll rate, °/s
- ω z :
-
yaw rate, °/s
- ω z,std :
-
standard deviation value of yaw rate, °/s
- δ sw :
-
steering angle, °
- δ sw,std :
-
standard deviation value of steering angle,°
- τ sw :
-
steering torque, Nm
- τ sw,std :
-
standard deviation value of steering torque, Nm
- VTI:
-
Statens väg- och transportforskninginstitut (Swedish National Road and Transport Research Institute)
- IMU:
-
inertia measurement unit
References
Bosetti, P., Da Lio, M. and Saroldi, A. (2014). On the human control of vehicles: An experimental study of acceleration. European Transport Research Review, 6, 157–170.
Brandt, A. (2021). Driving Stability of Passenger Vehicles under Crosswinds. Ph.D. Dissertation. Chalmers University of Technology. Göteborg, Sweden.
Brandt, A., Jacobson, B. and Sebben, S. (2022). High speed driving stability of road vehicles under crosswinds: An aerodynamic and vehicle dynamic parametric sensitivity analysis. Vehicle System Dynamics 60, 7, 2334–2357.
Bruzelius, F., Gomez Fernandez, J. and Augusto, B. (2013). A basic vehicle dynamics model for driving simulators. Int. J. Vehicle Systems Modelling and Testing 8, 4, 364–385.
Carbonne, L., Winkler, N. and Efraimsson, G. (2016). Use of full coupling of aerodynamics and vehicle dynamics for numerical simulation of the crosswind stability of ground vehicles. SAE Int. J. Commercial Vehicles 9, 2016-01-8148, 359–370.
Chadwick, A., Garry, K. and Howell, J. (2000). Transient aerodynamic characteristics of simple vehicle shapes by the measurement of surface pressures. SAE Paper No. 2000-01-0876.
DEWESoft. (2021). GPS and Inertial Navigation Systems (INS and EMU), https://dewesoft.com/products/interfaces-and-sensors/gps-and-imu-devices (Accessed June 16, 2022)
Fainello, M. and Minen, D. (2014). Active vehicle ride and handling development by using integrated SIL/HIL techniques in a highperformance driving simulator. 5th Int. Munich Chassis Symp., Munich, Germany.
Favre, T. and Efraimsson, G. (2011). An assessment of detached-eddy simulations of unsteady crosswind aerodynamics of road vehicles. Flow, Turbulence and Combustion, 87, 133–163.
Freedman, D. A. (2009). Statistical Models: Theory and Practice (2nd edn). Cambridge University Press. Cambridge, UK.
Gemou, M. (2013). Transferability of driver speed and lateral deviation measurable performance from semi-dynamic driving simulator to real traffic conditions. European Transport Research Review, 5, 217–233.
Howell, J. and Fuller, J. B. (2010). A relationship between lift and lateral aerodynamic characteristics for passenger cars. SAE Paper No. 2010-01-1025.
Howell, J. and Le Good, G. (1999). The influence of aerodynamic lift on high speed stability. SAE Trans., 1008–1015.
Huang, T., Li, S., Wan, Z. and Gu, Z. (2019). Investigation of vehicle stability under crosswind conditions based on coupling methods. Proc. Institution of Mechanical Engineers, Part D: J. Automobile Engineering 233, 13, 3305–3317.
Huemer, J., Stiekel, T., Sagan, E., Schwarz, M. and Wall, W. A. (2014). Influence of unsteady aerodynamics on driving dynamics of passenger cars. Vehicle System Dynamics 52, 11, 1470–1488.
Jansson, J., Sandin, J., Augusto, B., Fischer, M., Blissing, B. and Källgren, L. (2014). Design and performance of the VTI Sim IV. Driving Simulation Conf. (DSC), Paris, France.
Kawakami, M., Murata, O. and Maeda, K. (2015). Improvement in vehicle motion performance by suppression of aerodynamic load fluctuations. SAE Int. J. Passenger Cars-Mechanical Systems 8, 2015-01-1537, 205–216.
Kawamura, T. and Ogawa, A. (2014). Effect of unsteady lift force on vehicle dynamics in heave and pitch motion. SAE Paper No. 2014-01-0576.
Kee, J. D., Rho, J. H., Kim, K. H. and Lee, D. H. (2014). High speed driving stability of passenger car under crosswind effects. Int. J. Automotive Technology 15, 5, 741–747.
Krantz, W., Pitz, J. O., Stoll, D. and Nguyen, M. T. (2014). Simulation des Fahrens unter instationärem Seitenwind. ATZ-Automobiltechnische Zeitschrift 116, 2, 64–68.
Kumar, A., Sallstrom, E., Sebben, S. and Jacobson, B. (2023a). Predictive model of driver’s perception of vehicle stability under aerodynamic excitation. SAE Paper No. 2023-01-0903.
Kumar, A., Sällström, E., Sebben, S., Jacobson, B. and Amiri, K. (2023b). Prediction of drivers’ subjective evaluation of vehicle reaction under aerodynamic excitations. Human Factors, 00187208231157935.
Kumar, A., Sebben, S., Sällström, E., Jacobson, B. J. H. and Broniewicz, A. (2019). Analysis of subjective qualitative judgement of passenger vehicle high speed drivability due to aerodynamics. Energies 12, 14, 2839.
Lal, S. K. and Craig, A. (2001). A critical review of the psychophysiology of driver fatigue. Biological Psychology 55, 3, 173–194.
Nguyen, M. T., Pitz, J., Krantz, W., Neubeck, J. and Wiedemann, J. (2017). Subjective perception and evaluation of driving dynamics in the virtual test drive. SAE Int. J. Vehicle Dynamics, Stability, and NVH 1, 2017-01-1564, 247–252.
Obialero, E. (2013). A Refined Vehicle Dynamic Model for Driving Simulators. M.S. Thesis. Chalmers University of Technology. Göteborg, Sweden.
Okada, Y., Nouzawa, T., Nakamura, T. and Okamoto, S. (2009). Flow structures above the trunk deck of sedan-type vehicles and their influence on high-speed vehicle stability 1st report: On-road and wind-tunnel studies on unsteady flow characteristics that stabilize vehicle behavior. SAE Int. J. Passenger Cars-Mechanical Systems 2, 2009-01-0004, 138–156.
Ranney, T. A., Simmons, L. A. and Masalonis, A. J. (1999). Prolonged exposure to glare and driving time: Effects on performance in a driving simulator. Accident Analysis & Prevention 31, 6, 601–610.
Sims-Williams, D. B., Dominy, R. G. and Howell, J. P. (2001). An investigation into large scale unsteady structures in the wake of real and idealized hatchback car models. SAE Trans., 1197–1208.
Sirkin, R. M. (2006). Statistics for the Social Sciences (3rd edn). Sage Publications. Thousand Oaks, California, USA.
Statens väg-och transportforskninginstitut (VTI, Swedish National Road and Transport Research Institute) (2021). Simulator facilities, https://www.vti.se/en/research/vehicle-technology-and-driving-simulation/driving-simulation/ simulator-facilities (Accessed June 16, 2022)
Sterken, L., Sebben, S., Lofdahl, L., Walker, T. and Wölken, T. (2015). Wake and unsteady surface-pressure measurements on an SUV with rear-end extensions. SAE Paper No. 2015-01-1545.
Thiffault, P. and Bergeron, J. (2003). Monotony of road environment and driver fatigue: A simulator study. Accident Analysis & Prevention 35, 3, 381–391.
VI-Grade. (2021a). A single vehicle model from concept to sign-off accelerates your vehicle development process. VI-CarRealTime. https://www.vi-grade.com/en/products/vi-carrealtime/ (Accessed June 16, 2022)
VI-Grade. (2021b). The family of turn-key, yet open driver-in-the-loop simulators. DiM150 & DiM250 DYNAMIC Simulator, https://www.vi-grade.com/en/products/diml50_dim250_dynamic_simulator/ (Accessed June 16, 2022)
Wagner, A. and Wiedemann, J. (2002). Crosswind behavior in the driver’s perspective. SAE Paper No. 2002-01-0086.
Walker, S. H. and Duncan, D. B. (1967). Estimation of the probability of an event as a function of several independent variables. Biometrika 54, 1–2, 167–179.
Williamson, A. M., Feyer, A. M. and Friswell, R. (1996). The impact of work practices on fatigue in long distance truck drivers. Accident Analysis & Prevention 28, 6, 709–719.
Acknowledgment
The authors would like to thank VTI for their support and valuable comments to this work. The authors are also very grateful to the Volvo Cars Driving Simulator Group, vehicle test engineers and fellow drivers for their contribution. Special thanks to VI grade for providing support and resources to work on CarReal Time.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Kumar, A., Sällström, E., Sebben, S. et al. Improved Predictive Model of Drivers’ Subjective Perception of Vehicle Reaction under Aerodynamic Excitations. Int.J Automot. Technol. 24, 1655–1664 (2023). https://doi.org/10.1007/s12239-023-0133-3
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12239-023-0133-3