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Feel the Tire - Tire Influence on Driver’s Handling Assessment

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.advisor Pauwelussen, Joop, Prof., HAN University of Applied Sciences, The Netherlands
dc.contributor.author Monsma, Saskia
dc.date.accessioned 2015-11-26T10:03:11Z
dc.date.available 2015-11-26T10:03:11Z
dc.date.issued 2015
dc.identifier.isbn 978-952-60-6548-9 (electronic)
dc.identifier.isbn 978-952-60-6547-2 (printed)
dc.identifier.issn 1799-4942 (electronic)
dc.identifier.issn 1799-4934 (printed)
dc.identifier.issn 1799-4934 (ISSN-L)
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/18823
dc.description.abstract A key question in the development of a tire is "How can this tire improve vehicle handling?" Good handling tires contribute not only to active safety of vehicles, but also to the pleasure of driving. Handling performance is largely determined by the driver. Therefore, the final and most important handling assessment of tires is done by professional test drivers driving on a handling circuit and giving their subjective opinion. This provides the tire manufacturer with the important tire handling performance, but it gives limited information on what the driver perceives as good and how his opinion is formed; the driver is still a 'black box'. Three methods, all based on field experiments, for gaining this knowledge about subjective assessments were chosen for this research. They have in common that they predict the driver's subjective assessment of tire handling, based on vehicle dynamics measurements. The differences lie in the way they derive and utilize these measurements. For method 1, the prediction is done with a General Regression Neural Network based on vehicle dynamics measurements. With this method, several limitations for using regression for tire handling can be circumvented.Method 2 focuses on the driver's workload as an indication for his subjective assessment. This method derives from the fact that the driver adapts to changing vehicle handling behavior. Method 3 also focuses on the driver but not by looking at measures from 'outside' the driver, like workload measures, but by modeling the driver behavior during closed-loop driver-vehicle simulations and looking at driver parameters 'inside' the driver (model).  The results show that all three methods can predict the driver's opinion about tire handling, based on vehicle dynamics measures. Analysis of the relevant measures for the prediction of methods 1 and 2, provides information on the 'what'-question. Likewise, method 3 provides information on the 'how'-question. In addition, drivers adapting behavior, e.g., compensating for less good handling tires by investing more effort, can be quantified with the mental workload measures. This makes them good indicators of driver's perceived tire handling behavior, even when the performance measures do not show differences. For implementation of one or more methods, only a subset of the vehicle dynamics measurements used for this research is needed. During use, the methods can be adapted to changing tire testing methods, with different measurements, handling aspects or maneuvers. When vehicle and tire models are available, these methods can also be used for virtual testing, predicting driver's opinion on tire handling. This research provides a first step in opening up the 'black box' of the driver by quantifying the driver's tire feeling.  en
dc.format.extent 211 + app. 65
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Aalto University en
dc.publisher Aalto-yliopisto fi
dc.relation.ispartofseries Aalto University publication series DOCTORAL DISSERTATIONS en
dc.relation.ispartofseries 202/2015
dc.subject.other Transport engineering en
dc.title Feel the Tire - Tire Influence on Driver’s Handling Assessment en
dc.type G4 Monografiaväitöskirja fi
dc.contributor.school Insinööritieteiden korkeakoulu fi
dc.contributor.school School of Engineering en
dc.contributor.department Koneenrakennustekniikan laitos fi
dc.contributor.department Department of Engineering Design and Production en
dc.subject.keyword tire characteristics en
dc.subject.keyword tire en
dc.subject.keyword handling en
dc.subject.keyword assessment en
dc.subject.keyword driver modelling en
dc.subject.keyword driver mental workload en
dc.subject.keyword general regression neural network en
dc.subject.keyword vehicle en
dc.subject.keyword dynamics en
dc.identifier.urn URN:ISBN:978-952-60-6548-9
dc.type.dcmitype text en
dc.type.ontasot Doctoral dissertation (monograph) en
dc.type.ontasot Väitöskirja (monografia) fi
dc.contributor.supervisor Juhala, Matti, Prof., Aalto University, Department of Engineering Design and Production, Finland
dc.contributor.supervisor Tammi, Kari, Prof., Aalto University, Department of Engineering Design and Production, Finland
dc.opn Stensson Trigell, Annika, Prof., KTH Royal Institute of Technology, Sweden
dc.opn Lappi, Otto, Dr., Helsinki University, Finland
dc.contributor.lab Automotive Engineering en
dc.rev Stensson Trigell, Annika, Prof., KTH Royal Institute of Technology, Sweden
dc.rev Brookhuis, Karel, Prof., University of Groningen, The Netherlands
dc.date.defence 2015-11-27
local.aalto.formfolder 2015_11_26_klo_10_12
local.aalto.archive yes

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