Obstacle Detection for Intelligent Transportation Systems Using Deep Stacked Autoencoder and k -Nearest Neighbor Scheme

Obstacle detection is an essential element for the development of intelligent transportation systems so that accidents can be avoided. In this paper, we propose a stereovision-based method for detecting obstacles in urban environment. The proposed method uses a deep stacked auto-encoders (DSA) model...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	IEEE sensors journal Jg. 18; H. 12; S. 5122 - 5132
Hauptverfasser:	Dairi, Abdelkader, Harrou, Fouzi, Ying Sun, Senouci, Mohamed
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	IEEE 15.06.2018
Schlagworte:	autonomous vehicles Clustering algorithms deep learning intelligent transportation systems Kernel Machine learning Machine learning algorithms Obstacle detection Partitioning algorithms Roads Sensors
ISSN:	1530-437X, 1558-1748
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!