The objectives are to determine which quantities are important to measure to determine how drivers perceive vehicle stability, and to develop a regression model to predict which induced external disturbances drivers are able to feel.
Background
Driver experience of a vehicle’s dynamic performance is important to auto manufacturers. Test engineers and test drivers perform several on-road assessments to evaluate the vehicle’s dynamic performance before sign-off for production. The presence of external disturbances such as aerodynamic forces and moments play a significant role in the overall vehicle assessment. As a result, it is important to understand the relation between the subjective experience of the drivers and these external disturbances acting on the vehicle.
Method
A sequence of external yaw and roll moment disturbances of varying amplitudes and frequencies is added to a straight-line high-speed stability simulation test in a driving simulator. The tests are performed with both common and professional test drivers, and their evaluations to these external disturbances are recorded. The sampled data from these tests are used to generate the needed regression model.
Results
A model is derived for predicting which disturbances drivers can feel. It quantifies difference in sensitivity between driver types and between yaw and roll disturbances.
Conclusion
The model shows a relationship between steering input and driver sensitivity to external disturbances in a straight-line drive. Drivers are more sensitive to yaw disturbance than roll disturbance and increased steering input lowers sensitivity.
Application
Identify the threshold above which unexpected disturbances such as aerodynamic excitations can potentially create unstable vehicle behaviour.
BakerC. J. (1993). The behaviour of road vehicles in unsteady cross winds. Journal of Wind Engineering and Industrial Aerodynamics, 49(1), 439–448. https://doi.org/10.1016/0167-6105(93)90038-P
BruzeliusF.FernandezJ. G.AugustoB. (2013). A Basic Vehicle Dynamics Model for Driving Simulators. International Journal of Vehicle Systems Modelling and Testing, 8(4), 364–385, https://doi.org/10.1504/ijvsmt.2013.057530
5.
CarbonneL.WinklerN.EfraimssonG. (2016). Use of Full Coupling of Aerodynamics and Vehicle Dynamics for Numerical Simulation of the Crosswind Stability of Ground Vehicles. SAE International Journal of Commercial Vehicles, 9(2), 359–370. https://doi.org/10.4271/2016-01-8148
FainelloM.MinenD. (2014). Active vehicle ride and handling development by using integrated SIL/HIL techniques in a highperformance driving simulator. In PfefferP. E. (Ed.), 5th International Munich Chassis Symposium 2014 (pp. 183–184). Springer Fachmedien. https://doi.org/10.1007/978-3-658-05978-1_15
8.
FreedmanD., 1938-2008. (2009). Statistical models: Theory and practice. Cambridge University Press.
9.
HeiderichM. (2017). Vehicle Handling Optimization and Control – New approach for improvement of the vehicle performance by using a simulation-based optimization and evaluation method (Neuer Ansatz zur Verbesserung des Fahrverhaltens durch Verwendung einer simulationsbasierte. In PfefferP. E. (Ed.), 7th International Munich Chassis Symposium 2016 (pp. 279–293). Springer Fachmedien. https://doi.org/10.1007/978-3-658-14219-3_21
10.
HowellJ.Baden FullerJ. (2010). A Relationship between Lift and Lateral Aerodynamic Characteristics for Passenger Cars. 2010-01–1025. https://doi.org/10.4271/2010-01-1025
11.
HuemerJ.StickelT.SaganE.SchwarzM.WallW. A. (2014). Influence of unsteady aerodynamics on driving dynamics of passenger cars. Vehicle System Dynamics, 52(11), 1470–1488. https://doi.org/10.1080/00423114.2014.944191
12.
JanssonJ.SandinJ.AugustoB.FischerM.BlissingB.KällgrenL. (2014). DESIGN AND PERFORMANCE OF THE VTI SIM IV. 04, 7.
13.
KawakamiM.MurataO.MaedaK. (2015). Improvement in Vehicle Motion Performance by Suppression of Aerodynamic Load Fluctuations. SAE International Journal of Passenger Cars - Mechanical Systems, 8(1), 205–216. https://doi.org/10.4271/2015-01-1537
14.
KawamuraT.OgawaA. (2014). Effect of unsteady lift force on vehicle dynamics in heave and pitch motion. 2014-01–0576. https://doi.org/10.4271/2014-01-0576
15.
KeeJ. D.RhoJ. H.KimK. H.LeeD. H. (2014). High speed driving stability of passenger car under crosswind effects. International Journal of Automotive Technology, 15(5), 741–747. https://doi.org/10.1007/s12239-014-0077-8
16.
KimD.JeongM.BaeB.AhnC. (2019). Design of a Human Evaluator Model for the Ride Comfort of Vehicle on a Speed Bump Using a Neural Artistic Style Extraction. Sensors, 19(24), 5407, Article 24. https://doi.org/10.3390/s19245407
17.
KrantzW.PitzJ.-O.StollD.NguyenM.-T. (2014). Simulation des Fahrens unter instationärem Seitenwind. ATZ - Automobiltechnische Zeitschrift, 116(2), 64–68. https://doi.org/10.1007/s35148-014-0046-6
18.
KumarA.SebbenS.SällströmE.JacobsonB. J. H.BroniewiczA. (2019). Analysis of Subjective Qualitative Judgement of Passenger Vehicle High Speed Drivability due to Aerodynamics. Energies, 12(14), 2839. https://doi.org/10.3390/en12142839
19.
KusachovA. (2016). Motion perception and tire models for winter conditions in driving simulators(Depending on RTAC solution). Department of Applied Mechanics, Chalmers University of Technology.
MacadamC. C.SayersM. W.PointerJ. D.GleasonM. (1990). Crosswind sensitivity of passenger cars and the influence of chassis and aerodynamic properties on driver preferences. Vehicle System Dynamics, 19(4), 201–236. https://doi.org/10.1080/00423119008968942
22.
NguyenM.-T.PitzJ.KrantzW.NeubeckJ.WiedemannJ. (2017). Subjective perception and evaluation of driving dynamics in the virtual test drive. SAE International Journal of Vehicle Dynamics, Stability, and NVH, 1(2), 247–252. https://doi.org/10.4271/2017-01-1564
OkadaY.NouzawaT.NakamuraT.OkamotoS. (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 flowcharacteristics that stabilize vehicle behavior. SAE International Journal of Passenger Cars - Mechanical Systems, 2(1), 138–156. https://doi.org/10.4271/2009-01-0004
25.
SchroeckD.KrantzW.WiddeckeN.WiedemannJ. (2011). Unsteady aerodynamic properties of a vehicle model and their effect on driver and vehicle under side wind conditions. SAE International Journal of Passenger Cars - Mechanical Systems, 4(1), 108–119. https://doi.org/10.4271/2011-01-0154
ThiffaultP.BergeronJ. (2003). Monotony of road environment and driver fatigue: A simulator study. Accident; analysis and prevention, 35(3), 381–391. https://doi.org/10.1016/S0001-4575(02)00014-3
WalkerS. H.DuncanD. B. (1967). Estimation of the probability of an event as a function of several independent variables. Biometrika, 54(1/2), 167–179. https://doi.org/10.2307/2333860
32.
WangY.ZhangQ.ZhangL.HuY. (2019). A method to automatic measuring riding comfort of autonomous vehicles: based on passenger subjective rating and vehicle parameters. In MarcusA.WangW. (Eds.), Design, User Experience, and Usability. Application Domains (pp. 130–145). Springer International Publishing. https://doi.org/10.1007/978-3-030-23538-3_10
33.
WeirD. H.McRuerD. T. (1973). Measurement and Interpretation of Driver/Vehicle System Dynamic Response. Human Factors: The Journal of Human Factors and Ergonomics Society, 15(4), 367–378. https://doi.org/10.1177/001872087301500408
34.
WilliamsonA. M.FeyerA.-M.FriswellR. (1996). The impact of work practices on fatigue in long distance truck drivers. Accident; analysis and prevention, 28(6), 709–719. ISSN 0001-4575. https://doi.org/10.1016/s0001-4575(96)00044-9
