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GNSS-shortages-resistant and self-Adaptive rear axle kinematic parameter estimator (SA-RAKPE)

Brunker, Alexander 1; Wohlgemuth, Thomas; Frey, Michael ORCID iD icon 1; Gauterin, Frank ORCID iD icon 1
1 Institut für Fahrzeugsystemtechnik (FAST), Karlsruher Institut für Technologie (KIT)

Abstract (englisch):

This paper investigates the improvements from an intelligent self-adaptive modification to a Global Navigation Satellite System (GNSS)-Based Rear Axle Kinematic Parameter Estimator (SA-RAKPE) for an automatic-driving-system in a passenger vehicle. The required highly accurate dead-reckoning localization can be achieved by a well-calibrated kinematic odometry model. For this purpose, the presented Extended Kalman filter approach combines a Differential-Velocity system model and a GNSS measurement model. Subsequently, the intelligent self-adaptive modifications are introduced to allow the SA-RAKPE to work even under difficult conditions. The self-adaptive modifications include a GNSS-Delay-Finder- Module that calculates variable delays of the signals used in complex vehicle architectures. The newly developed SA-RAPKE deals with changes in the system and measurement model accuracies and even works during interruptions caused by GNSS-shortages. To do this, it changes the update equations and fills the interruptions with virtual parameter measurements to avoid estimation inaccuracies from observability loss and even to store the level of learned parameters. ... mehr

DOI: 10.1109/IVS.2017.7995760
Zitationen: 20
Zitationen: 19
Zugehörige Institution(en) am KIT Institut für Fahrzeugsystemtechnik (FAST)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2017
Sprache Englisch
Identifikator ISBN: 978-1-5090-4804-5
KITopen-ID: 1000073837
Erschienen in 28th IEEE Intelligent Vehicles Symposium, IV 2017, Los Angeles, California, USA, 11th - 14th June 2017
Verlag IEEE Computer Society
Seiten 456-461
Nachgewiesen in Scopus
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