Classification of Road Surface and Weather-Related Condition Using Deep Convolutional Neural Networks
- verfasst von
- Alexander Busch, Daniel Fink, Max Heinrich Laves, Zygimantas Ziaukas, Mark Wielitzka, Tobias Ortmaier
- Abstract
In order to achieve the goal of autonomous driving, a precise perception of the vehicle’s environment is required. In particular, the weather-related road condition has a major influence on vehicle dynamics and thus on driving safety. In this paper, we compare Deep Convolutional Neural Networks of different computational effort, namely Inception-v3, GoogLeNet and the much smaller SqueezeNet, for classification of road surface and its weather-related condition. Previously, different regions of interest were compared in order to provide the networks with optimal input data.
- Organisationseinheit(en)
-
Institut für Mechatronische Systeme
- Typ
- Aufsatz in Konferenzband
- Seiten
- 1042-1051
- Anzahl der Seiten
- 10
- Publikationsdatum
- 13.02.2020
- Publikationsstatus
- Veröffentlicht
- Peer-reviewed
- Ja
- ASJC Scopus Sachgebiete
- Fahrzeugbau, Luft- und Raumfahrttechnik, Maschinenbau, Fließ- und Transferprozesse von Flüssigkeiten
- Elektronische Version(en)
-
https://doi.org/10.1007/978-3-030-38077-9_121 (Zugang:
Geschlossen)