Automated data processing and metric generation for driveability analysis

S. G. Pickering, Christopher J. Brace

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

This paper describes automated data processing and manoeuvre detection techniques developed as part of a suite of tools used for the prediction of longitudinal vehicle driveability. The core task is to identify events of interest in recorded objective driveability time-series data and generate metrics describing these manoeuvres, which can then be correlated with subjective evaluations provided by the vehicle test drivers. The objective events of particular interest are the start of transients and gear-shift events. As a necessary precursor to the generation of the objective metrics, procedures were designed to check data integrity and to automatically replace data that were found to be faulty, ensuring that as little data and testing time were wasted as is possible. Of 741 tests analysed (average of 12 drivers for each of 5 vehicles), only 11\% of tests needed further manual attention following the automated processing. Of these, 64\% proved to be irrecoverable due to data problems and were rejected. The processing and generation of metrics takes approximately one second per set of test data, producing a time saving of approximately 95\%. This makes it possible to perform real-time processing and metric generation as part of a continuous testing scheme or real-time evaluation.
Original languageEnglish
Pages (from-to)429-441
Number of pages13
JournalProceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering
Volume221
Issue number4
Early online date6 Dec 2006
DOIs
Publication statusPublished - 1 Apr 2007

Keywords

  • driveability
  • data processing
  • metric generation
  • instrumentation

Fingerprint Dive into the research topics of 'Automated data processing and metric generation for driveability analysis'. Together they form a unique fingerprint.

Cite this