Human-centric Autonomous Driving in an AV-Pedestrian Interactive Environment Using SVO

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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Unsupervised Monocular Depth Estimation for Autonomous Driving

Proceedings of the International Display Workshops ◽

10.36463/idw.2019.3dsap2_3dp2-2 ◽

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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Cloning Safe Driving Behavior for Self-Driving Cars using Convolutional Neural Networks

Recent Patents on Computer Science ◽

10.2174/2213275911666181106160002 ◽

2019 ◽

Vol 12 (2) ◽

pp. 120-127 ◽

Cited By ~ 5

Author(s):

Wael Farag

Keyword(s):

Gradient Descent ◽

Autonomous Driving ◽

Driving Behavior ◽

Training Data ◽

Stochastic Gradient Descent ◽

Data Set ◽

Safe Driving ◽

Processing Pipeline ◽

Self Driving Cars ◽

And Training

Background: In this paper, a Convolutional Neural Network (CNN) to learn safe driving behavior and smooth steering manoeuvring, is proposed as an empowerment of autonomous driving technologies. The training data is collected from a front-facing camera and the steering commands issued by an experienced driver driving in traffic as well as urban roads. Methods: This data is then used to train the proposed CNN to facilitate what it is called “Behavioral Cloning”. The proposed Behavior Cloning CNN is named as “BCNet”, and its deep seventeen-layer architecture has been selected after extensive trials. The BCNet got trained using Adam’s optimization algorithm as a variant of the Stochastic Gradient Descent (SGD) technique. Results: The paper goes through the development and training process in details and shows the image processing pipeline harnessed in the development. Conclusion: The proposed approach proved successful in cloning the driving behavior embedded in the training data set after extensive simulations.

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