Paper Info
Title
Robust Deep-Learning-Based Road-Prediction For Augmented Reality Navigation Systems At Night
Abstract
This paper proposes an approach that predicts the road course from camera sensors lever-aging deep learning techniques. Road pixels are identified by training a multi-scale convolutional neural network on a large number of full-scene-labeled night-time road images including adverse weather conditions. A framework is presented that applies the proposed approach to longer distance road course estimation, which is the basis for an augmented reality navigation application. In this framework long range sensor data (radar) and data from a map database are fused with short range sensor data (camera) to produce a precise longitudinal and lateral localization and road course estimation. The proposed approach reliably detects roads with and without lane markings and thus increases the robustness and availability of road course estimations and augmented reality navigation. Evaluations on an extensive set of high precision ground truth data taken from a differential GPS and an inertial measurement unit show that the proposed approach reaches state-of-the-art performance without the limitation of requiring existing lane markings.
Year
DOI
Venue
2016
10.1109/ITSC.2016.7795862
2016 IEEE 19TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC)
Field
DocType
Citations 
Computer vision,Image sensor,Computer science,Convolutional neural network,Augmented reality,Robustness (computer science),Ground truth,Inertial measurement unit,Artificial intelligence,Deep learning,Differential GPS
Conference
1
PageRank 
References 
Authors
0.37
0
6
Name
Order
Citations
PageRank
Matthias Limmer181.33
Julian Forster2122.60
Dennis Baudach310.37
florian schule431.09
Roland Schweiger5356.84
Hendrik P. A. Lensch6147196.59