Semantic Concept Testing in Autonomous Driving by Extraction of Object-Level Annotations from CARLA

Bottom-up saliency models identify the salient regions of an image based on features such as color, intensity and orientation. These models are typically used as predictors of human visual behavior and for computer vision tasks. In this paper, we conduct a systematic evaluation of the saliency maps computed with four selected bottom-up models on images of urban and highway traffic scenes. Saliency both over whole images and on object level is investigated and elaborated in terms of the energy and the entropy of the saliency maps. We identify significant differences with respect to the amount, size and shape-complexity of the salient areas computed by different models. Based on these findings, we analyze the likelihood that object instances fall within the salient areas of an image and investigate the agreement between the segments of traffic participants and the saliency maps of the different models. The overall and object-level analysis provides insights on the distinctive features of salient areas identified by different models, which can be used as selection criteria for prospective applications in autonomous driving such as object detection and tracking.

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

CICTP 2020 ◽

10.1061/9780784483053.047 ◽

2020 ◽

Author(s):

Kun Jiang ◽

Yunlong Wang ◽

Shengjie Kou ◽

Diange Yang

Keyword(s):

Autonomous Driving

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Single-Frequency GNSS Positioning for Assisted, Cooperative and Autonomous Driving

Proceedings of the 30th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2017) ◽

10.33012/2017.15151 ◽

2017 ◽

Cited By ~ 1

Author(s):

Peter F. de Bakker ◽

Christian C.J.M. Tiberius

Keyword(s):

Autonomous Driving ◽

Single Frequency ◽

Gnss Positioning

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Centimeter-Accurate GNSS Corrections and Integrity Information via ISO 26262 Certified Modules for Autonomous Driving Applications

Proceedings of the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019) ◽

10.33012/2019.16994 ◽

2019 ◽

Author(s):

Philip Kreikenbohm ◽

Adrian Kipka ◽

Markus Brandl ◽

Herbert Landau ◽

Fabian Pastor ◽

...

Keyword(s):

Autonomous Driving ◽

Iso 26262

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Trend of an Autonomous Driving Control Vehicle

The Journal of the Institute of Electrical Engineers of Japan ◽

10.1541/ieejjournal.133.595 ◽

2013 ◽

Vol 133 (9) ◽

pp. 595-598

Author(s):

Kenji SUZUKI ◽

Hisaaki ISHIDA ◽

Hirofumi INOSE ◽

Rui KOBAYASHI

Keyword(s):

Autonomous Driving

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Imaging through Scattering Media with a Learning Based Prior

Electronic Imaging ◽

10.2352/issn.2470-1173.2020.14.coimg-306 ◽

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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Deep Traffic Light Perception with Spatiotemporal Analysis for Autonomous Driving

2020 European Control Conference (ECC) ◽

10.23919/ecc51009.2020.9143756 ◽

2020 ◽

Author(s):

Lixiao Yang ◽

Xiaonian Wang ◽

Jun Wang

Keyword(s):

Autonomous Driving ◽

Spatiotemporal Analysis ◽

Light Perception ◽

Traffic Light

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F1_Tenth_Course_Report_Group_C

10.31224/osf.io/68bvh ◽

2018 ◽

Author(s):

Yi Chen ◽

Sagar Manglani ◽

Roberto Merco ◽

Drew Bolduc

Keyword(s):

Operating System ◽

Autonomous Driving ◽

Software Tools ◽

Simulation Environment ◽

Robot Operating System ◽

Available Information

In this paper, we discuss several of major robot/vehicle platforms available and demonstrate the implementation of autonomous techniques on one such platform, the F1/10. Robot Operating System was chosen for its existing collection of software tools, libraries, and simulation environment. We build on the available information for the F1/10 vehicle and illustrate key tools that will help achieve properly functioning hardware. We provide methods to build algorithms and give examples of deploying these algorithms to complete autonomous driving tasks and build 2D maps using SLAM. Finally, we discuss the results of our findings and how they can be improved.

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