Optical Memory and Neural Networks (Information Optics), volume 28, issue 4, pages 283-295
Object Detection with Deep Neural Networks for Reinforcement Learning in the Task of Autonomous Vehicles Path Planning at the Intersection
Publication type: Journal Article
Publication date: 2019-10-01
Quartile SCImago
Q3
Quartile WOS
—
Impact factor: 0.8
ISSN: 1060992X, 19347898
Electronic, Optical and Magnetic Materials
Electrical and Electronic Engineering
General Computer Science
Abstract
Among a number of problems in the behavior planning of an unmanned vehicle the central one is movement in difficult areas. In particular, such areas are intersections at which direct interaction with other road agents takes place. In our work, we offer a new approach to train of the intelligent agent that simulates the behavior of an unmanned vehicle, based on the integration of reinforcement learning and computer vision. Using full visual information about the road intersection obtained from aerial photographs, it is studied automatic detection the relative positions of all road agents with various architectures of deep neural networks (YOLOv3, Faster R-CNN, RetinaNet, Cascade R-CNN, Mask R-CNN, Cascade Mask R-CNN). The possibilities of estimation of the vehicle orientation angle based on a convolutional neural network are also investigated. Obtained additional features are used in the modern effective reinforcement learning methods of Soft Actor Critic and Rainbow, which allows to accelerate the convergence of its learning process. To demonstrate the operation of the developed system, an intersection simulator was developed, at which a number of model experiments were carried out.
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Citations by publishers
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1 publication, 3.57%
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RIOR Publishing Center
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1 publication, 3.57%
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- We do not take into account publications that without a DOI.
- Statistics recalculated only for publications connected to researchers, organizations and labs registered on the platform.
- Statistics recalculated weekly.
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Yudin D. A. et al. Object Detection with Deep Neural Networks for Reinforcement Learning in the Task of Autonomous Vehicles Path Planning at the Intersection // Optical Memory and Neural Networks (Information Optics). 2019. Vol. 28. No. 4. pp. 283-295.
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Yudin D. A., Skrynnik A., Krishtopik A., Belkin I., Panov A. I. Object Detection with Deep Neural Networks for Reinforcement Learning in the Task of Autonomous Vehicles Path Planning at the Intersection // Optical Memory and Neural Networks (Information Optics). 2019. Vol. 28. No. 4. pp. 283-295.
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TY - JOUR
DO - 10.3103/S1060992X19040118
UR - https://doi.org/10.3103%2FS1060992X19040118
TI - Object Detection with Deep Neural Networks for Reinforcement Learning in the Task of Autonomous Vehicles Path Planning at the Intersection
T2 - Optical Memory and Neural Networks (Information Optics)
AU - Yudin, D A
AU - Skrynnik, A
AU - Belkin, I
AU - Panov, A I
AU - Krishtopik, A
PY - 2019
DA - 2019/10/01 00:00:00
PB - Pleiades Publishing
SP - 283-295
IS - 4
VL - 28
SN - 1060-992X
SN - 1934-7898
ER -
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@article{2019_Yudin,
author = {D A Yudin and A Skrynnik and I Belkin and A I Panov and A Krishtopik},
title = {Object Detection with Deep Neural Networks for Reinforcement Learning in the Task of Autonomous Vehicles Path Planning at the Intersection},
journal = {Optical Memory and Neural Networks (Information Optics)},
year = {2019},
volume = {28},
publisher = {Pleiades Publishing},
month = {oct},
url = {https://doi.org/10.3103%2FS1060992X19040118},
number = {4},
pages = {283--295},
doi = {10.3103/S1060992X19040118}
}
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Yudin, D. A., et al. “Object Detection with Deep Neural Networks for Reinforcement Learning in the Task of Autonomous Vehicles Path Planning at the Intersection.” Optical Memory and Neural Networks (Information Optics), vol. 28, no. 4, Oct. 2019, pp. 283-295. https://doi.org/10.3103%2FS1060992X19040118.
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