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Correlations of subjective assessments and objective metrics for vehicle handling and steering: A walk through history
KTH, Skolan för teknikvetenskap (SCI), Farkost och flyg, Fordonsdynamik. Volvo Cars.ORCID-id: 0000-0002-6699-1965
KTH, Skolan för teknikvetenskap (SCI), Farkost och flyg, Fordonsdynamik.ORCID-id: 0000-0002-2265-9004
Volvo Cars.
KTH, Skolan för teknikvetenskap (SCI), Farkost och flyg, Fordonsdynamik.ORCID-id: 0000-0001-8928-0368
2016 (Engelska)Ingår i: International Journal of Vehicle Design, ISSN 0143-3369, E-ISSN 1741-5314, Vol. 72, nr 1, s. 17-67Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Achieving customer satisfaction concerning steering feel and vehicle handling requires subjective assessments and tuning of vehicle components by expert test drivers and engineers. Extensive subjective testing is expensive, time consuming and requires physical vehicles, which is in conflict with reduction of development time and cost. Objective testing and model-based development are constantly increasing but translating subjective requirements into objective ones is non-trivial. This paper summarises, discusses and classifies the methods, strategies and findings in previously published research regarding correlations of subjective assessments and objective metrics for vehicle handling and steering. The aim is twofold: (i) to identify key parameters of steering, handling and their preferred values and (ii) to compile and discuss the fundamental issues to deal with in the continued search for correlations between objective metrics and subjective assessments. The paper gives a comprehensive overview and insight of different aspects to take into account when conducting research in this field.

Ort, förlag, år, upplaga, sidor
Inderscience Enterprises , 2016. Vol. 72, nr 1, s. 17-67
Nyckelord [en]
steering feel, vehicle handling, driver preferences, objective metrics, subjective assessments, regression analysis, neural networks, fuzzy logic, vehicle steering, customer satisfaction, literature review
Nationell ämneskategori
Farkostteknik
Forskningsämne
Farkostteknik; SRA - Transport
Identifikatorer
URN: urn:nbn:se:kth:diva-169081DOI: 10.1504/IJVD.2016.079191ISI: 000391087600002Scopus ID: 2-s2.0-84989221515OAI: oai:DiVA.org:kth-169081DiVA, id: diva2:819650
Projekt
iCOMSA
Forskningsfinansiär
VINNOVA, 2012-04609
Anmärkning

QC 20170127

Tillgänglig från: 2015-06-11 Skapad: 2015-06-11 Senast uppdaterad: 2017-12-04Bibliografiskt granskad
Ingår i avhandling
1. Towards Efficient Vehicle Dynamics Evaluation using Correlations of Objective Metrics and Subjective Assessments
Öppna denna publikation i ny flik eller fönster >>Towards Efficient Vehicle Dynamics Evaluation using Correlations of Objective Metrics and Subjective Assessments
2015 (Engelska)Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
Ort, förlag, år, upplaga, sidor
KTH Royal Institute of Technology, 2015. s. xiv, 64
Serie
TRITA-AVE, ISSN 1651-7660 ; 2015:28
Nationell ämneskategori
Farkostteknik
Identifikatorer
urn:nbn:se:kth:diva-169085 (URN)
Presentation
2015-06-12, Vehicle Engineering Lab, Teknikringen 8, Stockholm, 10:00 (Engelska)
Opponent
Handledare
Anmärkning

QC 20150611

Tillgänglig från: 2015-06-11 Skapad: 2015-06-11 Senast uppdaterad: 2015-06-11Bibliografiskt granskad
2. Towards efficient vehicle dynamics development: From subjective assessments to objective metrics, from physical to virtual testing
Öppna denna publikation i ny flik eller fönster >>Towards efficient vehicle dynamics development: From subjective assessments to objective metrics, from physical to virtual testing
2017 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
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. 

Ort, förlag, år, upplaga, sidor
Stockholm: Kungliga tekniska högskolan, 2017. s. 72
Serie
TRITA-AVE, ISSN 1651-7660 ; 2017:12
Nyckelord
Steering feel, vehicle handling, driver preference, objective metrics, subjective assessments, regression analysis, artificial neural network
Nationell ämneskategori
Farkostteknik
Forskningsämne
Farkostteknik
Identifikatorer
urn:nbn:se:kth:diva-202348 (URN)978-91-7729-302-6 (ISBN)
Disputation
2017-03-17, D3, Lindstedsvägen 5, Stockholm, 10:00 (Engelska)
Opponent
Handledare
Projekt
iCOMSA
Forskningsfinansiär
VINNOVA, 2012-04609
Anmärkning

QC 20170223

Tillgänglig från: 2017-02-23 Skapad: 2017-02-21 Senast uppdaterad: 2017-02-23Bibliografiskt granskad

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