People | Locations | Statistics |
---|---|---|
Naji, M. |
| |
Motta, Antonella |
| |
Aletan, Dirar |
| |
Mohamed, Tarek |
| |
Ertürk, Emre |
| |
Taccardi, Nicola |
| |
Kononenko, Denys |
| |
Petrov, R. H. | Madrid |
|
Alshaaer, Mazen | Brussels |
|
Bih, L. |
| |
Casati, R. |
| |
Muller, Hermance |
| |
Kočí, Jan | Prague |
|
Šuljagić, Marija |
| |
Kalteremidou, Kalliopi-Artemi | Brussels |
|
Azam, Siraj |
| |
Ospanova, Alyiya |
| |
Blanpain, Bart |
| |
Ali, M. A. |
| |
Popa, V. |
| |
Rančić, M. |
| |
Ollier, Nadège |
| |
Azevedo, Nuno Monteiro |
| |
Landes, Michael |
| |
Rignanese, Gian-Marco |
|
Brace, Christian
University of Bath
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (7/7 displayed)
- 2017Chassis Dynamometer Testing
- 2017Distance Estimation by Fusing Radar and Monocular Camera with Kalman Filter
- 2012The effect of advance combustion control features on the performance of a highly downsized gasoline engine
- 2011Spatially resolved heat flux measurements from a HSDI engine over NEDC
- 2007Automated data processing and metric generation for driveability analysiscitations
- 2006A Study of Natural Neighbour Interpolation and its application to automotive engine test data
- 2002Investigation of 'sweep' mapping approach on engine testbedcitations
Places of action
Organizations | Location | People |
---|
article
Automated data processing and metric generation for driveability analysis
Abstract
This paper describes automated data processing and manoeuvre detection techniques developed as partof a suite of tools used for the prediction of longitudinal vehicle driveability. The core task is toidentify events of interest in recorded objective driveability time-series data and generate metricsdescribing these manoeuvres, which can then be correlated with subjective evaluations provided by thevehicle test drivers. The objective events of particular interest are the start of transients and gear-shiftevents. As a necessary precursor to the generation of the objective metrics, procedures were designedto check data integrity and to automatically replace data that were found to be faulty, ensuring that aslittle 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 furthermanual attention following the automated processing. Of these, 64\% proved to be irrecoverable due todata problems and were rejected. The processing and generation of metrics takes approximately onesecond per set of test data, producing a time saving of approximately 95\%. This makes it possible toperform real-time processing and metric generation as part of a continuous testing scheme or real-timeevaluation.