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Objective measurements and computer-aided engineering simulations cannot be exploited to their full potential because of the high importance of driver feel in vehicle development. Furthermore, despite many studies, it is not easy to identify the relationship between objective metrics (OM) and subjective assessments (SA), a task further complicated by the fact that SA change between drivers and geographical locations or with time. This paper presents a method which uses two artificial neural networks built on top of each other that helps to close this gap. The first network, based solely on OM, generates a map that groups together similar vehicles, thus allowing a classification of measured vehicles to be visualised. This map objectively demonstrates that there exist brand and vehicle class identities. It also foresees the subjective characteristics of a new vehicle, based on its requirements, simulations and measurements. These characteristics are described by the neighbourhood of the new vehicle in the map, which is made up of known vehicles that are accompanied by word-clouds that enhance this description. This forecast is also extended to perform a sensitivity analysis of the tolerances in the requirements, as well as to validate previously published preferred range of steering feel metrics. The results suggest a few new modifications. Finally, the qualitative information given by this measurement-based classification is complemented with a second superimposed network. This network describes a regression surface that enables quantitative predictions, for example the SA of the steering feel of a new vehicle from its OM. [ABSTRACT FROM PUBLISHER]

This paper investigates subjective assessments (SA) of vehicle handling and steering feel tests, both numerical and verbal, to understand drivers’ use of judgement scales, rating tendencies and spread. Two different test methods are compared: a short multi-vehicle first-impression test with predefined-driving vs the standard extensive single-vehicle free-driving tests, both offering very similar results but with the former saving substantial testing time. Rating repeatability is evaluated by means of a blind test. Key SA questions are identified by numerical subjective assessment autocorrelations and by generating word clouds from the most used terms in verbal assessments, with both methods leading to similar key parameters. The results exposed in this paper enable better understanding of SA, allowing improving the overall subjective testing and evaluation process, and improving the data collection and analysis process needed before identifying correlations between SA and objective metrics. [ABSTRACT FROM AUTHOR]

During the development of new vehicles, finding correlation links between subjective assessments (SA) and objective metrics (OM) is an important part of the vehicle evaluation process. Studying different correlation links is important in that the knowledge gained can be used at the front end of development, during testing and when creating new systems. Both SA from expert drivers using a rating scale of 1–10 and OM from different tests measured by a steering robot were collected using standard testing protocols at an automotive manufacturer. The driver ratings were evaluated and the correlations were analysed using regression analysis and neural networks through a case study approach. Links were identified and were compared with related research. [ABSTRACT FROM AUTHOR]