Camera-Based Lane Marking Detection for ADAS and Autonomous Driving

2015 ◽  
pp. 514-519 ◽  
Author(s):  
Yasamin Alkhorshid ◽  
Kamelia Aryafar ◽  
Gerd Wanielik ◽  
Ali Shokoufandeh
Keyword(s):  
Autonomous Driving ◽  
Lane Marking

scholarly journals Towards HD Maps from Aerial Imagery: Robust Lane Marking Segmentation Using Country-Scale Imagery

2018 ◽  
Vol 7 (12) ◽  
pp. 458 ◽  
Author(s):  
Peter Fischer ◽  
Seyed Majid Azimi ◽  
Robert Roschlaub ◽  
Thomas Krauß
Keyword(s):  
Remote Sensing ◽  
Autonomous Driving ◽  
Quality Parameters ◽  
Random Forest Classifier ◽  
Data Sources ◽  
Aerial Imagery ◽  
Remote Sensing Technology ◽  
Current State ◽  
Lane Marking ◽  
Sensing Technology

The upraise of autonomous driving technologies asks for maps characterized bya broad range of features and quality parameters, in contrast to traditional navigation maps which in most cases are enriched graph-based models. This paper tackles several uncertainties within the domain of HD Maps. The authors give an overview about the current state in extracting road features from aerial imagery for creating HD maps, before shifting the focus of the paper towards remote sensing technology. Possible data sources and their relevant parameters are listed. A random forest classifier is used, showing how these data can deliver HD Maps on a country-scale, meeting specific quality parameters.

Lane Marking Detection Techniques for Autonomous Driving

2021 ◽  
pp. 217-226
Author(s):  
Ahmed N. Ahmed ◽  
Ali Anwar ◽  
Sven Eckelmann ◽  
Toralf Trautmann ◽  
Steven Latré ◽  
...  
Keyword(s):  
Autonomous Driving ◽  
Detection Techniques ◽  
Lane Marking

Self-Localization Based on Visual Lane Marking Maps: An Accurate Low-Cost Approach for Autonomous Driving

2018 ◽  
Vol 19 (2) ◽  
pp. 582-597 ◽  
Author(s):  
Rafael Peixoto Derenzi Vivacqua ◽  
Massimo Bertozzi ◽  
Pietro Cerri ◽  
Felipe Nascimento Martins ◽  
Raquel Frizera Vassallo
Keyword(s):  
Low Cost ◽  
Autonomous Driving ◽  
Cost Approach ◽  
Lane Marking

A Lightweight and Multi-OS Compatible Middleware Designed for Autonomous Driving

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Kun Jiang ◽  
Yunlong Wang ◽  
Shengjie Kou ◽  
Diange Yang
Keyword(s):  
Autonomous Driving

Trend of an Autonomous Driving Control Vehicle

2013 ◽  
Vol 133 (9) ◽  
pp. 595-598
Author(s):  
Kenji SUZUKI ◽  
Hisaaki ISHIDA ◽  
Hirofumi INOSE ◽  
Rui KOBAYASHI
Keyword(s):  
Autonomous Driving

Imaging through Scattering Media with a Learning Based Prior

2020 ◽  
Vol 2020 (14) ◽  
pp. 306-1-306-6
Author(s):  
Florian Schiffers ◽  
Lionel Fiske ◽  
Pablo Ruiz ◽  
Aggelos K. Katsaggelos ◽  
Oliver Cossairt
Keyword(s):  
Optimization Problem ◽  
Autonomous Driving ◽  
Scattering Media ◽  
Photon Scattering ◽  
Point Spread ◽  
Spread Function ◽  
Radiative Transport Equation ◽  
Spatio Temporal ◽  
Transient Imaging

Imaging through scattering media finds applications in diverse fields from biomedicine to autonomous driving. However, interpreting the resulting images is difficult due to blur caused by the scattering of photons within the medium. Transient information, captured with fast temporal sensors, can be used to significantly improve the quality of images acquired in scattering conditions. Photon scattering, within a highly scattering media, is well modeled by the diffusion approximation of the Radiative Transport Equation (RTE). Its solution is easily derived which can be interpreted as a Spatio-Temporal Point Spread Function (STPSF). In this paper, we first discuss the properties of the ST-PSF and subsequently use this knowledge to simulate transient imaging through highly scattering media. We then propose a framework to invert the forward model, which assumes Poisson noise, to recover a noise-free, unblurred image by solving an optimization problem.

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