Optimization of the braking strategy for an emergency braking system by the application of machine learning

Markus Schratter, Sabine Amler, Paul Daman, Daniel Watzenig

Publikation: KonferenzbeitragPaperForschungBegutachtung

Originalspracheenglisch
Seitenumfang6
PublikationsstatusVeröffentlicht - 2018
Veranstaltung29th IEEE Intelligent Vehicles Symposium - Chinese flagship Intelligent Vehicle Proving Center, Changshu, Suzhou, China
Dauer: 26 Jun 201830 Jun 2018
http://www.2018iv.org/

Konferenz

Konferenz29th IEEE Intelligent Vehicles Symposium
KurztitelIV2018
LandChina
OrtChangshu, Suzhou
Zeitraum26/06/1830/06/18
Internetadresse

Dies zitieren

Schratter, M., Amler, S., Daman, P., & Watzenig, D. (2018). Optimization of the braking strategy for an emergency braking system by the application of machine learning. Beitrag in 29th IEEE Intelligent Vehicles Symposium, Changshu, Suzhou, China.

Optimization of the braking strategy for an emergency braking system by the application of machine learning. / Schratter, Markus; Amler, Sabine; Daman, Paul; Watzenig, Daniel.

2018. Beitrag in 29th IEEE Intelligent Vehicles Symposium, Changshu, Suzhou, China.

Publikation: KonferenzbeitragPaperForschungBegutachtung

Schratter, M, Amler, S, Daman, P & Watzenig, D 2018, 'Optimization of the braking strategy for an emergency braking system by the application of machine learning' Beitrag in 29th IEEE Intelligent Vehicles Symposium, Changshu, Suzhou, China, 26/06/18 - 30/06/18, .
Schratter M, Amler S, Daman P, Watzenig D. Optimization of the braking strategy for an emergency braking system by the application of machine learning. 2018. Beitrag in 29th IEEE Intelligent Vehicles Symposium, Changshu, Suzhou, China.
Schratter, Markus ; Amler, Sabine ; Daman, Paul ; Watzenig, Daniel. / Optimization of the braking strategy for an emergency braking system by the application of machine learning. Beitrag in 29th IEEE Intelligent Vehicles Symposium, Changshu, Suzhou, China.6 S.
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