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Objective metrics for vehicle handling and steering and their correlations with subjective assessments
Volvo Cars.ORCID iD: 0000-0002-6699-1965
KTH, School of Engineering Sciences (SCI), Aeronautical and Vehicle Engineering, Vehicle Dynamics.ORCID iD: 0000-0002-2265-9004
Volvo Cars.
KTH, School of Engineering Sciences (SCI), Aeronautical and Vehicle Engineering, Vehicle Dynamics.ORCID iD: 0000-0001-8928-0368
2016 (English)In: International Journal of Automotive Technology, ISSN 1229-9138, E-ISSN 1976-3832, Vol. 17, no 5, 777-794 p.Article in journal (Refereed) Published
Abstract [en]

This paper focuses on increasing the available knowledge about correlations between objective metrics and subjective assessments in steering feel and vehicle handling. Linear and non-linear correlations have been searched for by means of linear regression and neural network training, complemented by different statistical tools. For example, descriptive statistics, the t-distribution and the normal distribution have been used to define the 95% confidence interval for expected subjective assessments and their mean, which makes it possible to predict the subjective rating related to a given objective metric and its area of confidence. Single- and multi-driver correlations have been investigated, as well as how the use of different databases and different vehicle classes affects the results. A method for automatizing the search for correlations when using the driver-by-driver strategy is also explained and evaluated. Ranges of preferred objective metrics for vehicle dynamics have been defined. Vehicles with characteristics within these ranges of values are expected to receive a higher subjective rating when evaluated. Finally, linear correlations between objective metrics have been studied, linear dependency between objective metrics has been identified and its consequences have been presented.

Place, publisher, year, edition, pages
Korean Society of Automotive Engineers , 2016. Vol. 17, no 5, 777-794 p.
Keyword [en]
Driver preference; Neural network; Objective metrics; Regression analysis; Steering feel; Subjective assessments; Vehicle handling
National Category
Vehicle Engineering
Identifiers
URN: urn:nbn:se:kth:diva-169083DOI: 10.1007/s12239-016-0077-yISI: 000379040800005Scopus ID: 2-s2.0-84977119468OAI: oai:DiVA.org:kth-169083DiVA: diva2:819653
Note

QC 20160819

Available from: 2015-06-11 Created: 2015-06-11 Last updated: 2017-12-04Bibliographically approved
In thesis
1. Towards Efficient Vehicle Dynamics Evaluation using Correlations of Objective Metrics and Subjective Assessments
Open this publication in new window or tab >>Towards Efficient Vehicle Dynamics Evaluation using Correlations of Objective Metrics and Subjective Assessments
2015 (English)Licentiate thesis, comprehensive summary (Other academic)
Place, publisher, year, edition, pages
KTH Royal Institute of Technology, 2015. xiv, 64 p.
Series
TRITA-AVE, ISSN 1651-7660 ; 2015:28
National Category
Vehicle Engineering
Identifiers
urn:nbn:se:kth:diva-169085 (URN)
Presentation
2015-06-12, Vehicle Engineering Lab, Teknikringen 8, Stockholm, 10:00 (English)
Opponent
Supervisors
Note

QC 20150611

Available from: 2015-06-11 Created: 2015-06-11 Last updated: 2015-06-11Bibliographically approved
2. Towards efficient vehicle dynamics development: From subjective assessments to objective metrics, from physical to virtual testing
Open this publication in new window or tab >>Towards efficient vehicle dynamics development: From subjective assessments to objective metrics, from physical to virtual testing
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Vehicle dynamics development is strongly based on subjective assessments (SA) of vehicle prototypes, which is expensive and time consuming. Consequently, in the age of computer- aided engineering (CAE), there is a drive towards reducing this dependency on physical test- ing. However, computers are known for their remarkable processing capacity, not for their feelings. Therefore, before SA can be computed, it is required to properly understand the cor- relation between SA and objective metrics (OM), which can be calculated by simulations, and to understand how this knowledge can enable a more efficient and effective development process.

The approach to this research was firstly to identify key OM and SA in vehicle dynamics, based on the multicollinearity of OM and of SA, and on interviews with expert drivers. Sec- ondly, linear regressions and artificial neural network (ANN) were used to identify the ranges of preferred OM that lead to good SA-ratings. This result is the base for objective require- ments, a must in effective vehicle dynamics development and verification.

The main result of this doctoral thesis is the development of a method capable of predicting SA from combinations of key OM. Firstly, this method generates a classification map of ve- hicles solely based on their OM, which allows for a qualitative prediction of the steering feel of a new vehicle based on its position, and that of its neighbours, in the map. This prediction is enhanced with descriptive word-clouds, which summarizes in a few words the comments of expert test drivers to each vehicle in the map. Then, a second superimposed ANN displays the evolution of SA-ratings in the map, and therefore, allows one to forecast the SA-rating for the new vehicle. Moreover, this method has been used to analyse the effect of the tolerances of OM requirements, as well as to verify the previously identified preferred range of OM.

This thesis focused on OM-SA correlations in summer conditions, but it also aimed to in- crease the effectiveness of vehicle dynamics development in general. For winter conditions, where objective testing is not yet mature, this research initiates the definition and identifica- tion of robust objective manoeuvres and OM. Experimental data were used together with CAE optimisations and ANOVA-analysis to optimise the manoeuvres, which were verified in a second experiment. To improve the quality and efficiency of SA, Volvo’s Moving Base Driving Simulator (MBDS) was validated for vehicle dynamics SA-ratings. Furthermore, a tablet-app to aid vehicle dynamics SA was developed and validated.

Combined this research encompasses a comprehensive method for a more effective and ob- jective development process for vehicle dynamics. This has been done by increasing the un- derstanding of OM, SA and their relations, which enables more effective SA (key SA, MBDS, SA-app), facilitates objective requirements and therefore CAE development, identi- fies key OM and their preferred ranges, and which allow to predict SA solely based on OM. 

Place, publisher, year, edition, pages
Stockholm: Kungliga tekniska högskolan, 2017. 72 p.
Series
TRITA-AVE, ISSN 1651-7660 ; 2017:12
Keyword
Steering feel, vehicle handling, driver preference, objective metrics, subjective assessments, regression analysis, artificial neural network
National Category
Vehicle Engineering
Research subject
Vehicle and Maritime Engineering
Identifiers
urn:nbn:se:kth:diva-202348 (URN)978-91-7729-302-6 (ISBN)
Public defence
2017-03-17, D3, Lindstedsvägen 5, Stockholm, 10:00 (English)
Opponent
Supervisors
Projects
iCOMSA
Funder
VINNOVA, 2012-04609
Note

QC 20170223

Available from: 2017-02-23 Created: 2017-02-21 Last updated: 2017-02-23Bibliographically approved

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Gil Gómez, GasparNybacka, MikaelDrugge, Lars

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