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公开(公告)号：US11132607B1
公开(公告)日：2021-09-28
申请号：US17211123
申请日：2021-03-24
申请人： Stradvision, Inc.
发明人： Kye-Hyeon Kim , Hongmo Je , Yongjoong Kim , Wooju Ryu
摘要： A method for explainable active learning, to be used for an object detector, by using a deep autoencoder is provided. The method includes steps of an active learning device (a) (i) inputting acquired test images into the object detector to detect objects and output bounding boxes, (ii) cropping regions, corresponding to the bounding boxes, in the test images, (iii) resizing the test images and the cropped images into a same size, and (iv) inputting the resized images into a data encoder of the deep autoencoder to output data codes, and (b) (i) confirming reference data codes corresponding to the number of the resized images less than a counter threshold by referring to a data codebook, (ii) extracting specific data codes from the data codes, (iii) selecting specific test images as rare samples, and (iv) updating the data codebook by referring to the specific data codes.
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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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公开(公告)号：US10919543B2
公开(公告)日：2021-02-16
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：US10776647B2
公开(公告)日：2020-09-15
申请号：US16732053
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 achieving better performance in an autonomous driving while saving computing powers, by using confidence scores representing a credibility of an object detection which is generated in parallel with an object detection process is provided. And the method includes steps of: (a) a computing device acquiring at least one circumstance image on surroundings of a subject vehicle, through at least one panorama view sensor installed on the subject vehicle; (b) the computing device instructing a Convolutional Neural Network (CNN) to apply at least one CNN operation to the circumstance image, to thereby generate initial object information and initial confidence information on the circumstance image; and (c) the computing device generating final object information on the circumstance image by referring to the initial object information and the initial confidence information.
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公开(公告)号：US20200250526A1
公开(公告)日：2020-08-06
申请号：US16738749
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 achieving better performance in an autonomous driving while saving computing powers, by using confidence scores representing a credibility of an object detection which is generated in parallel with an object detection process is provided. And the method includes steps of: (a) a computing device acquiring at least one circumstance image on surroundings of a subject vehicle, through at least one image sensor installed on the subject vehicle; (b) the computing device instructing a Convolutional Neural Network (CNN) to apply at least one CNN operation to the circumstance image, to thereby generate initial object information and initial confidence information on the circumstance image; and (c) the computing device generating final object information on the circumstance image by referring to the initial object information and the initial confidence information with a support of a Reinforcement Learning (RL) agent, and through V2X communications with at least part of surrounding objects.
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公开(公告)号：US20200250470A1
公开(公告)日：2020-08-06
申请号：US16724617
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 method for enhancing an accuracy of object distance estimation based on a subject camera by performing pitch calibration of the subject camera more precisely with additional information acquired through V2V communication is provided. And the method includes steps of: (a) a computing device, performing (i) a process of instructing an initial pitch calibration module to apply a pitch calculation operation to the reference image, to thereby generate an initial estimated pitch, and (ii) a process of instructing an object detection network to apply a neural network operation to the reference image, to thereby generate reference object detection information; (b) the computing device instructing an adjusting pitch calibration module to (i) select a target object, (ii) calculate an estimated target height of the target object, (iii) calculate an error corresponding to the initial estimated pitch, and (iv) determine an adjusted estimated pitch on the subject camera by using the error.
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公开(公告)号：US20200250468A1
公开(公告)日：2020-08-06
申请号：US16731990
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 training a CNN by using a camera and a radar together, to thereby allow the CNN to perform properly even when an object depiction ratio of a photographed image acquired through the camera is low due to a bad condition of a photographing circumstance is provided. And the method includes steps of: (a) a learning device instructing a convolutional layer to apply a convolutional operation to a multichannel integrated image, to thereby generate a feature map; (b) the learning device instructing an output layer to apply an output operation to the feature map, to thereby generate estimated object information; and (c) the learning device instructing a loss layer to generate a loss by using the estimated object information and GT object information corresponding thereto, and to perform backpropagation by using the loss, to thereby learn at least part of parameters in the CNN.
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公开(公告)号：US20200250442A1
公开(公告)日：2020-08-06
申请号：US16739767
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 achieving better performance in an autonomous driving while saving computing powers, by using confidence scores representing a credibility of an object detection which is generated in parallel with an object detection process is provided. And the method includes steps of: (a) a computing device acquiring at least one circumstance image on surroundings of a subject vehicle, through at least one panorama view sensor installed on the subject vehicle; (b) the computing device instructing a Convolutional Neural Network (CNN) to apply at least one CNN operation to the circumstance image, to thereby generate initial object information and initial confidence information on the circumstance image; and (c) the computing device generating final object information on the circumstance image by referring to the initial object information and the initial confidence information, with a support of an RL agent.
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9.发明申请公开(公告)号：US20200249675A1
公开(公告)日：2020-08-06
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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公开(公告)号：US20200247321A1
公开(公告)日：2020-08-06
申请号：US16738579
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 adjusting a position of a driver assistance device according to a driver state is provided. The method includes steps of: a position adjusting device, (a) inputting an upper and a lower body images of a driver, acquired after the driver sits and starts a vehicle, into a pose estimation network, to acquire body keypoints, calculate body part lengths, and adjust a driver's seat position; and (b) while the vehicle is traveling, inputting the upper body image into a face detector to detect a facial part, inputting the facial part into an eye detector to detect an eye part, and inputting the adjusted driver's seat position and 2D coordinates of an eye into a 3D coordinates transforming device, to generate 3D coordinates of the eye referring to the 2D coordinates and the driver's seat position, and adjust a mirror position of the vehicle referring to the 3D coordinates.
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