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公开(公告)号：US10890916B2
公开(公告)日：2021-01-12
申请号：US16731083
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 learning method for performing a seamless parameter switch by using a location-specific algorithm selection for an optimized autonomous driving is provided. And the method includes steps of: (a) a learning device instructing a K-th convolutional layer to apply a convolution operation to K-th training images, to thereby generate K-th feature maps; (b) the learning device instructing a K-th output layer to apply a K-th output operation to the K-th feature maps, to thereby generate K-th estimated autonomous driving source information; (c) the learning device instructing a K-th loss layer to generate a K-th loss by using the K-th estimated autonomous driving source information and its corresponding GT, and then to perform backpropagation by using the K-th loss, to thereby learn K-th parameters of the K-th CNN; and (d) the learning device storing the K-th CNN in a database after tagging K-th location information to the K-th CNN.
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公开(公告)号：US10824151B2
公开(公告)日：2020-11-03
申请号：US16738320
申请日：2020-01-09
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 providing a dynamic adaptive deep learning model other than a fixed deep learning model, to thereby support at least one specific autonomous vehicle to perform a proper autonomous driving according to surrounding circumstances is provided. And the method includes steps of: (a) a managing device which interworks with autonomous vehicles instructing a fine-tuning system to acquire a specific deep learning model to be updated; (b) the managing device inputting video data and its corresponding labeled data to the fine-tuning system as training data, to thereby update the specific deep learning model; and (c) the managing device instructing an automatic updating system to transmit the updated specific deep learning model to the specific autonomous vehicle, to thereby support the specific autonomous vehicle to perform the autonomous driving by using the updated specific deep learning model other than a legacy deep learning model.
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3.发明授权公开(公告)号：US10796434B1
公开(公告)日：2020-10-06
申请号：US16739168
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
IPC分类号： G06K9/00 , G06T7/11 , G08G1/14 , G06K9/62 , B60R11/04 , G06N3/08 , B62D15/02 , B60Q5/00 , B60Q9/00
摘要： A method for learning an automatic parking device of a vehicle for detecting an available parking area is provided. The method includes steps of: a learning device, (a) if a parking lot image of an area nearby the vehicle is acquired, (i) inputting the parking lot image into a segmentation network to output a convolution feature map via an encoder, output a deconvolution feature map by deconvoluting the convolution feature map via a decoder, and output segmentation information by masking the deconvolution feature map via a masking layer; (b) inputting the deconvolution feature map into a regressor to generate relative coordinates of vertices of a specific available parking region, and generate regression location information by regressing the relative coordinates; and (c) instructing a loss layer to calculate 1-st losses by referring to the regression location information and an ROI GT, and learning the regressor via backpropagation using the 1-st losses.
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公开(公告)号：US10779139B2
公开(公告)日：2020-09-15
申请号：US16731953
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
IPC分类号： H04W4/46 , G05D1/00 , G01S13/931 , G01S13/00
摘要： A method for a V2V communication by using a radar module used for detecting objects nearby is provided. And the method includes steps of: (a) a computing device performing (i) a process of instructing the radar module to transmit 1-st transmitting signals by referring to at least one 1-st schedule and (ii) a process of generating RVA information by using (1-1)-st receiving signals, corresponding to the 1-st transmitting signals; and (b) the computing device performing a process of instructing the radar module to transmit 2-nd transmitting signals by referring to at least one 2-nd schedule.
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公开(公告)号：US10768638B2
公开(公告)日：2020-09-08
申请号：US16724513
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
IPC分类号： G05D1/02
摘要： A method for switching driving modes of a subject vehicle to support the subject vehicle to perform a platoon driving by using platoon driving information is provided. And the method includes steps of: (a) a basement server, which interworks with the subject vehicle driving in a first mode, acquiring first platoon driving information, to N-th platoon driving information by referring to a real-time platoon driving information DB; (b) the basement server (i) calculating a first platoon driving suitability score to an N-th platoon driving suitability score by referring to first platoon driving parameters to N-th platoon driving parameters and (ii) selecting a target platoon driving group to be including the subject vehicle; (c) the basement server instructing the subject vehicle to drive in a second mode.
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公开(公告)号：US20200250541A1
公开(公告)日：2020-08-06
申请号：US16738680
申请日：2020-01-09
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 supporting a safer autonomous driving through a fusion of information acquired from images and communications is provided. And the method includes steps of: (a) a learning device instructing a first neural network and a second neural network to generate an image-based feature map and a communication-based feature map by using a circumstance image and circumstance communication information; (b) the learning device instructing a third neural network to apply a third neural network operation to the image-based feature map and the communication-based feature map to generate an integrated feature map; (c) the learning device instructing a fourth neural network to apply a fourth neural network operation to the integrated feature map to generate estimated surrounding motion information; and (d) the learning device instructing a first loss layer to train parameters of the first to the fourth neural networks.
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公开(公告)号：US20200247469A1
公开(公告)日：2020-08-06
申请号：US16740213
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 delivering a steering intention of an autonomous driving module to a steering apparatus more accurately by using a reference map is provided. And the method includes steps of: (a) a computing device, if a subject intended steering signal inputted by the autonomous driving module at a current timing is acquired, instructing a signal adjustment module to select, by referring to the reference map, specific reference steering guide values corresponding to the subject intended steering signal; (b) the computing device (i) adjusting the subject intended steering signal by referring to the specific reference steering guide values, in order to generate a subject adjusted steering signal, and (ii) transmitting the subject adjusted steering signal to the steering apparatus, to thereby support the steering apparatus to rotate the subject vehicle by a specific steering angle corresponding to the subject intended steering signal.
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公开(公告)号：US20200242408A1
公开(公告)日：2020-07-30
申请号：US16723753
申请日：2019-12-20
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 learning a recurrent neural network to check an autonomous driving safety to be used for switching a driving mode of an autonomous vehicle is provided. The method includes steps of: a learning device (a) if training images corresponding to a front and a rear cameras of the autonomous vehicle are acquired, inputting each pair of the training images into corresponding CNNs, to concatenate the training images and generate feature maps for training, (b) inputting the feature maps for training into long short-term memory models corresponding to sequences of a forward RNN, and into those corresponding to the sequences of a backward RNN, to generate updated feature maps for training and inputting feature vectors for training into an attention layer, to generate an autonomous-driving mode value for training, and (c) allowing a loss layer to calculate losses and to learn the long short-term memory models.
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公开(公告)号：US20200239029A1
公开(公告)日：2020-07-30
申请号：US16724833
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 calculating collision probability, to be used for determining whether it is appropriate or not to switch driving modes of a vehicle capable of an autonomous driving, by analyzing a recent driving route of a driver is provided. And the method includes steps of: (a) a learning device, on condition that a status vector and a trajectory vector are acquired, performing processes of (i) instructing a status network to generate a status feature map and (ii) instructing a trajectory network to generate a trajectory feature map; (b) the learning device instructing a safety network to calculate a predicted collision probability representing a predicted probability of an accident occurrence; and (c) the learning device instructing a loss layer to generate a loss by referring to the predicted collision probability and a GT collision probability, which have been acquired beforehand, to learn at least part of parameters.
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公开(公告)号：US11074480B2
公开(公告)日：2021-07-27
申请号：US16740135
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 learning method for acquiring at least one personalized reward function, used for performing a Reinforcement Learning (RL) algorithm, corresponding to a personalized optimal policy for a subject driver is provided. And the method includes steps of: (a) a learning device performing a process of instructing an adjustment reward network to generate first adjustment rewards, by referring to the information on actual actions and actual circumstance vectors in driving trajectories, a process of instructing a common reward module to generate first common rewards by referring to the actual actions and the actual circumstance vectors, and a process of instructing an estimation network to generate actual prospective values by referring to the actual circumstance vectors; and (b) the learning device instructing a first loss layer to generate an adjustment reward and to perform backpropagation to learn parameters of the adjustment reward network.
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