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Identification of the friction potential for the application in an automated emergency braking system

: Lex, Cornelia

Postprint urn:nbn:de:0011-n-2302769 (500 KByte PDF)
MD5 Fingerprint: ef3544c812d14367808018a2bf595647
Erstellt am: 09.03.2014

Forschungsinstitut für Kraftfahrwesen und Fahrzeugmotoren, Stuttgart:
13th Stuttgart International Symposium Automotive and Engine Technology 2013. Vol.2 : 26 and 27 February 2013, Stuttgart, Germany. Documentation
Wiesbaden: ATZlive, 2013
Stuttgart International Symposium "Automotive and Engine Technology" <13, 2013, Stuttgart>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IAIS ()
automated emergency braking; friction potential; sensor data; echo state networks

The capabilities of Automated Emergency Braking Systems (AEB) can be significantly improved when the actual friction between tires and road is known. In this work, it is investigated whether an estimation of the friction potential based on sensor data is feasible with an accuracy sufficient for an AEB. Recurrent neural networks trained by Echo State Networks (ESNs) are used to estimate friction potential from sensor data. Measurements have been conducted on a proving ground with three different tire types, two different surfaces, different driving manoeuvres and different tire inflation pressures. Standard on-board sensors of the vehicle and advanced measurement equipment have been used to measure the vehicle reaction. Based on this work, a rough understanding is gained on how well the fri ction potential can be estimated in certain situations.