Robust lane recognition for autonomous driving

Publikation: Beitrag zu Konferenzen › Paper › Beigetragen › Begutachtung

Beitragende

Details

OriginalspracheEnglisch
Seiten1-6
Seitenumfang6
PublikationsstatusVeröffentlicht - 2017
Peer-Review-StatusJa

Konferenz

Titel2017 Conference on Design and Architectures for Signal and Image Processing
KurztitelDASIP 2017
Dauer27 - 29 September 2017
StadtDresden
LandDeutschland

Externe IDs

Scopus 85044295283
ORCID /0000-0003-2571-8441/work/142240423

Schlagworte

Forschungsprofillinien der TU Dresden

DFG-Fachsystematik nach Fachkollegium

Schlagwörter

  • automobiles, image sensors, mobile robots, object detection, traffic cones, road, driving simulator, robust lane recognition, autonomous cars, time 0.55 ms, Cameras, Robustness, Object detection, Automobiles, Roads, Sensors, Viola-Jones, Lane Recognition, road vehicles, traffic engineering computing, Viola-Jones method, emergency situation, high detection rate, Viola-Jones object detection method, robust autonomous driving algorithm, Algorithm design and analysis, Autonomous Driving, Object Detection, Machine Learning