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International Journal of Automotive Technology > Volume 15(6); 2014 > Article
International Journal of Automotive Technology 2014;15(6): 893-907.
doi: https://doi.org/10.1007/s12239-014-0094-7
LINKS BETWEEN SUBJECTIVE ASSESSMENTS AND OBJECTIVE METRICS FOR STEERING
M. NYBACKA1, X. HE2, G. GÓMEZ2, E. BAKKER2, L. DRUGGE1
1KTH Royal Institute of Technology
2Volvo Car Corporation
ABSTRACT
The characteristics of steering perception are decisive factors for overall driver preference and for vehicle safety. Car manufacturers are continuously required to tune the characteristics of the vehicle and have a strong need to be more effective in the design and evaluation of cars. Using only objective metrics (OM) can result in unwanted steering feel and using only subjective assessments (SA) is time-consuming, costly and non-repetitive. Before a tool can be built to predict the steering feel in front-end development and to improve design knowledge from the full vehicle level to the component level, links between subjective assessments and objective metrics must be found and analysed. The data collected for the study presented in this paper include subjective ratings from expert drivers and objective measurements made with steering robots, involving twelve expert drivers and over twenty vehicles across four different vehicle classes. Linear regression and neural network analysis (NN) have been used to explore reliable subjective-objective links. The tools and methods used in this research showed promising results. Most of the links found concern response and torque feedback. The preferred ranges of some crucial objective metrics leading to more desirable steering feel have been defined and presented. The results indicate that it would be possible for car manufacturers to develop new vehicles more effectively with a steering feel in line with the design criteria by using the tools and methods investigated in this paper.
Key Words: Steering feel, Driver preference, Objective metrics, Subjective assessments, Regression analysis, Neural network
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