Impact of Neutron-induced SEU in FPGA CRAM on Image-based Lane Tracking for Autonomous Driving: from Bit Upset to SEFI and Erroneous Behavior

2021 ◽  
pp. 1-1
Author(s):  
Tomonari Tanaka ◽  
Wang Liao ◽  
Masanori Hashimoto ◽  
Yukio Mitsuyama
Keyword(s):  
Autonomous Driving ◽  
Lane Tracking

scholarly journals Autonomous Driving Implementation in an Experimental Environment

2021 ◽  
Author(s):  
Namig Aliyev ◽  
Mehmet Turan Guzel ◽  
Oguzhan Sezer
Keyword(s):  
Autonomous Systems ◽  
Network Models ◽  
Autonomous Driving ◽  
Corner Detection ◽  
Neural Network Models ◽  
Traffic Lights ◽  
Lane Tracking ◽  
System A ◽  
Experimental Environment ◽  
Expansion Time

Autonomous systems require identifying the environment and it has a long way to go before putting it safely into practice. In autonomous driving systems, the detection of obstacles and traffic lights are of importance as well as lane tracking. In this study, an autonomous driving system is developed and tested in the experimental environment designed for this purpose. In this system, a model vehicle having a camera is used to trace the lanes and avoid obstacles to experimentally study autonomous driving behavior. Convolutional Neural Network models were trained for Lane tracking. For the vehicle to avoid obstacles, corner detection, optical flow, focus of expansion, time to collision, balance calculation, and decision mechanism were created, respectively.

scholarly journals A Color Vision-Based Lane Tracking System for Autonomous Driving on Unmarked Roads

2004 ◽  
Vol 16 (1) ◽  
pp. 95-116 ◽  
Author(s):  
Miguel Angel Sotelo ◽  
Francisco Javier Rodriguez ◽  
Luis Magdalena ◽  
Luis Miguel Bergasa ◽  
Luciano Boquete
Keyword(s):  
Color Vision ◽  
Tracking System ◽  
Autonomous Driving ◽  
Lane Tracking

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.

F1_Tenth_Course_Report_Group_C

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.

Unsupervised Monocular Depth Estimation for Autonomous Driving

2019 ◽  
pp. 128
Author(s):  
Chih-Shuan Huang ◽  
Wan-Nung Tsung ◽  
Wei-Jong Yang ◽  
Chin-Hsing Chen
Keyword(s):  
Depth Estimation ◽  
Autonomous Driving ◽  
Monocular Depth
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