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Erschienen in:
2024 | OriginalPaper | Buchkapitel
KPI-Based Attribute Development of Lane Keeping Assist Systems
verfasst von : Daniel Neidlein, Matthias Becker, Sebastian Wrodartschik, Bernhard Schick
Erschienen in: 13th International Munich Chassis Symposium 2022
Verlag: Springer Berlin Heidelberg
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Abstract
The present contribution introduces a systematic approach to the development of optimal attributes of Lane Keeping Assist Systems. An Active Lane Departure Warning (ALDW) with corrective steering interventions was the specific use case.
Firstly, a subjective evaluation baseline was created from a proband study involving five different ALDW implementations and a catalogue of attribute related subjective rating criteria. Afterwards the respective vehicles were characterized by conducting designated driving maneuvers and computing objective performance indicators from the measurements taken. By correlating the subjective ratings with the performance indicators, a set of descriptive key performance indicators (KPIs) with corresponding target values was identified.
Based on the objective targets an optimized calibration variant was prepared and implemented in a test vehicle. By repeating and evaluating the initial driving maneuvers, the coverage of targets was ensured. Conclusively proving the effectiveness of the introduced approach, a verification study under comparable conditions delivered significant subjective improvement as well.