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公开(公告)号：US20200250514A1
公开(公告)日：2020-08-06
申请号：US16724302
申请日：2019-12-22
申请人： STRADVISION, INC.
发明人： Kye-Hyeon KIM , Yongjoong KIM , Hak-Kyoung KIM , Woonhyun NAM , SukHoon BOO , Myungchul SUNG , Dongsoo SHIN , Donghun YEO , Wooju RYU , Myeong-Chun LEE , Hyungsoo LEE , Taewoong JANG , Kyungjoong JEONG , Hongmo JE , Hojin CHO
摘要： A learning method for generating integrated object detection information by integrating first object detection information and second object detection information is provided. And the method includes steps of: (a) a learning device instructing a concatenating network to generate one or more pair feature vectors; (b) the learning device instructing a determining network to apply FC operations to the pair feature vectors, to thereby generate (i) determination vectors and (ii) box regression vectors; (c) the learning device instructing a loss unit to generate an integrated loss by referring to the determination vectors, the box regression vectors and their corresponding GTs, and performing backpropagation processes by using the integrated loss, to thereby learn at least part of parameters included in the DNN.
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公开(公告)号：US10733511B1
公开(公告)日：2020-08-04
申请号：US16724994
申请日：2019-12-23
申请人： StradVision, Inc.
发明人： Kye-Hyeon Kim , Yongjoong Kim , Hak-Kyoung Kim , Woonhyun Nam , SukHoon Boo , Myungchul Sung , Dongsoo Shin , Donghun Yeo , Wooju Ryu , Myeong-Chun Lee , Hyungsoo Lee , Taewoong Jang , Kyungjoong Jeong , Hongmo Je , Hojin Cho
摘要： A learning method for selecting specific information, to be used for updating an HD Map is provided. And the method includes steps of: (a) a learning device instructing a coordinate neural network to generate a local feature map and a global feature vector by applying a coordinate neural network operation to a coordinate matrix; (b) the learning device instructing a determination neural network to generate a first estimated suitability score to an N-th estimated suitability score by applying a determination neural network operation to the integrated feature map; (c) the learning device instructing a loss layer to generate a loss by referring to (i) the first estimated suitability score to the N-th estimated suitability score and (ii) a first Ground Truth(GT) suitability score to an N-th GT suitability score, and perform backpropagation by using the loss, to thereby learn parameters of the determination neural network and the coordinate neural network.
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公开(公告)号：US20200241526A1
公开(公告)日：2020-07-30
申请号：US16731089
申请日：2019-12-31
申请人： STRADVISION, INC.
发明人： Kye-Hyeon KIM , Yongjoong KIM , Hak-Kyoung KIM , Woonhyun NAM , SukHoon BOO , Myungchul SUNG , Dongsoo SHIN , Donghun YEO , Wooju RYU , Myeong-Chun LEE , Hyungsoo LEE , Taewoong JANG , Kyungjoong JEONG , Hongmo JE , Hojin CHO
摘要： A method for remotely controlling at least one autonomous vehicle is provided. The method includes steps of: an autonomous driving control device, (a) on condition that the autonomous driving control device detects driving environment by referring to sensor information and allows the autonomous vehicle to travel on an autonomous driving mode or a manual driving mode, determining whether the autonomous driving control device fails to establish a driving plan by using the driving environment and whether the autonomous driving control device fails to change to the manual driving mode by using the driving environment; and (b) if the autonomous driving control device fails to establish the driving plan or fails to change to the manual driving mode, selecting a remote control mode and transmitting request information to a remote control service providing server, to allow a remote driver to control the autonomous vehicle by using a specific remote vehicle.
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公开(公告)号：US10728461B1
公开(公告)日：2020-07-28
申请号：US16740165
申请日：2020-01-10
申请人： Stradvision, Inc.
发明人： Kye-Hyeon Kim , Yongjoong Kim , Hak-Kyoung Kim , Woonhyun Nam , SukHoon Boo , Myungchul Sung , Dongsoo Shin , Donghun Yeo , Wooju Ryu , Myeong-Chun Lee , Hyungsoo Lee , Taewoong Jang , Kyungjoong Jeong , Hongmo Je , Hojin Cho
摘要： A method for correcting an incorrect angle of a camera is provided. And the method includes steps of: (a) a computing device, generating first reference data or second reference data according to circumstance information by referring to a reference image; (b) the computing device generating a first angle error or a second angle error by referring to the first reference data or the second reference data with vehicle coordinate data; and (c) the computing device instructing a physical rotation module to adjust the incorrect angle by referring to the first angle error or the second angle error.
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公开(公告)号：US10699192B1
公开(公告)日：2020-06-30
申请号：US16262985
申请日：2019-01-31
申请人： Stradvision, Inc.
发明人： Kye-Hyeon Kim , Yongjoong Kim , Insu Kim , Hak-Kyoung Kim , Woonhyun Nam , SukHoon Boo , Myungchul Sung , Donghun Yeo , Wooju Ryu , Taewoong Jang , Kyungjoong Jeong , Hongmo Je , Hojin Cho
摘要： A method for optimizing a hyperparameter of an auto-labeling device performing auto-labeling and auto-evaluating of a training image to be used for learning a neural network is provided for computation reduction and achieving high precision. The method includes steps of: an optimizing device, (a) instructing the auto-labeling device to generate an original image with its auto label and a validation image with its true and auto label, to assort the original image with its auto label into an easy-original and a difficult-original images, and to assort the validation image with its own true and auto labels into an easy-validation and a difficult-validation images; and (b) calculating a current reliability of the auto-labeling device, generating a sample hyperparameter set, calculating a sample reliability of the auto-labeling device, and optimizing the preset hyperparameter set. This method can be performed by a reinforcement learning with policy gradient algorithms.
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公开(公告)号：US10657396B1
公开(公告)日：2020-05-19
申请号：US16731087
申请日：2019-12-31
申请人： STRADVISION, INC.
发明人： Kye-Hyeon Kim , Yongjoong Kim , Hak-Kyoung Kim , Woonhyun Nam , SukHoon Boo , Myungchul Sung , Dongsoo Shin , Donghun Yeo , Wooju Ryu , Myeong-Chun Lee , Hyungsoo Lee , Taewoong Jang , Kyungjoong Jeong , Hongmo Je , Hojin Cho
摘要： A method for detecting passenger statuses by analyzing a 2D interior image of a vehicle is provided. The method includes steps of: a passenger status-detecting device (a) inputting the 2D interior image taken with a fisheye lens into a pose estimation network to acquire pose points corresponding to passengers; and (b) (i) calculating location information on the pose points relative to a preset reference point by referring to a predetermined pixel-angle table, if a grid board has been placed in the vehicle, the pixel-angle table has been created such that vertical angles and horizontal angles, formed by a first line and second lines, correspond to pixels of grid corners, in which the first line connects a camera and a top center of the grid board and the second lines connects the corners and the camera and (ii) detecting the passenger statuses by referring to the location information.
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公开(公告)号：US10482584B1
公开(公告)日：2019-11-19
申请号：US16262996
申请日：2019-01-31
申请人： Stradvision, Inc.
发明人： Kye-Hyeon Kim , Yongjoong Kim , Insu Kim , Hak-Kyoung Kim , Woonhyun Nam , SukHoon Boo , Myungchul Sung , Donghun Yeo , Wooju Ryu , Taewoong Jang , Kyungjoong Jeong , Hongmo Je , Hojin Cho
摘要： A method for detecting jittering in videos generated by a shaken camera to remove the jittering on the videos using neural networks is provided for fault tolerance and fluctuation robustness in extreme situations. The method includes steps of: a computing device, generating each of t-th masks corresponding to each of objects in a t-th image; generating each of t-th object motion vectors of each of object pixels, included in the t-th image by applying at least one 2-nd neural network operation to each of the t-th masks, each of t-th cropped images, each of (t−1)-th masks, and each of (t−1)-th cropped images; and generating each of t-th jittering vectors corresponding to each of reference pixels among pixels in the t-th image by referring to each of the t-th object motion vectors. Thus, the method is used for video stabilization, object tracking with high precision, behavior estimation, motion decomposition, etc.
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公开(公告)号：US10325352B1
公开(公告)日：2019-06-18
申请号：US16255197
申请日：2019-01-23
申请人： Stradvision, Inc.
发明人： Kye-Hyeon Kim , Yongjoong Kim , Insu Kim , Hak-Kyoung Kim , Woonhyun Nam , SukHoon Boo , Myungchul Sung , Donghun Yeo , Wooju Ryu , Taewoong Jang , Kyungjoong Jeong , Hongmo Je , Hojin Cho
摘要： There is provided a method for transforming convolutional layers of a CNN including m convolutional blocks to optimize CNN parameter quantization to be used for mobile devices, compact networks, and the like with high precision via hardware optimization. The method includes steps of: a computing device (a) generating k-th quantization loss values by referring to k-th initial weights of a k-th initial convolutional layer included in a k-th convolutional block, a (k−1)-th feature map outputted from the (k−1)-th convolutional block, and each of k-th scaling parameters; (b) determining each of k-th optimized scaling parameters by referring to the k-th quantization loss values; (c) generating a k-th scaling layer and a k-th inverse scaling layer by referring to the k-th optimized scaling parameters; and (d) transforming the k-th initial convolutional layer into a k-th integrated convolutional layer by using the k-th scaling layer and the (k−1)-th inverse scaling layer.
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公开(公告)号：US10229346B1
公开(公告)日：2019-03-12
申请号：US16120664
申请日：2018-09-04
申请人： Stradvision, Inc.
发明人： Kye-Hyeon Kim , Yongjoong Kim , Insu Kim , Hak-Kyoung Kim , Woonhyun Nam , SukHoon Boo , Myungchul Sung , Donghun Yeo , Wooju Ryu , Taewoong Jang , Kyungjoong Jeong , Hongmo Je , Hojin Cho
摘要： A learning method for detecting a specific object based on convolutional neural network (CNN) is provided. The learning method includes steps of: (a) a learning device, if an input image is obtained, performing (i) a process of applying one or more convolution operations to the input image to thereby obtain at least one specific feature map and (ii) a process of obtaining an edge image by extracting at least one edge part from the input image, and obtaining at least one guide map including information on at least one specific edge part having a specific shape similar to that of the specific object from the obtained edge image; and (b) the learning device reflecting the guide map on the specific feature map to thereby obtain a segmentation result for detecting the specific object in the input image.
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公开(公告)号：US10223614B1
公开(公告)日：2019-03-05
申请号：US16121084
申请日：2018-09-04
申请人： Stradvision, Inc.
发明人： Kye-Hyeon Kim , Yongjoong Kim , Insu Kim , Hak-Kyoung Kim , Woonhyun Nam , SukHoon Boo , Myungchul Sung , Donghun Yeo , Wooju Ryu , Taewoong Jang , Kyungjoong Jeong , Hongmo Je , Hojin Cho
摘要： A learning method for detecting at least one lane based on a convolutional neural network (CNN) is provided. The learning method includes steps of: (a) a learning device obtaining encoded feature maps, and information on lane candidate pixels in a input image; (b) the learning device, classifying a first parts of the lane candidate pixels, whose probability scores are not smaller than a predetermined threshold, as strong line pixels, and classifying the second parts of the lane candidate pixels, whose probability scores are less than the threshold but not less than another predetermined threshold, as weak lines pixels; and (c) the learning device, if distances between the weak line pixels and the strong line pixels are less than a predetermined distance, classifying the weak line pixels as pixels of additional strong lines, and determining that the pixels of the strong line and the additional correspond to pixels of the lane.
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