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公开(公告)号：US11010668B2
公开(公告)日：2021-05-18
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 autonomous driving while saving computing power, 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, thereby to 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) reinforcement learning (RL) agent, and through V2X communications with at least part of surrounding objects.
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公开(公告)号：US10824947B2
公开(公告)日：2020-11-03
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：US10821897B2
公开(公告)日：2020-11-03
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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4.发明授权公开(公告)号：US10803333B2
公开(公告)日：2020-10-13
申请号：US16725181
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 calculating exact location of a subject vehicle by using information on relative distances is provided. And the method includes steps of: (a) a computing device, if a reference image is acquired through a camera on the subject vehicle, detecting reference objects in the reference image; (b) the computing device calculating image-based reference distances between the reference objects and the subject vehicle, by referring to information on reference bounding boxes, corresponding to the reference objects, on the reference image; (c) the computing device (i) generating a distance error value by referring to the image-based reference distances and coordinate-based reference distances, and (ii) calibrating subject location information of the subject vehicle by referring to the distance error value.
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公开(公告)号：US10796571B2
公开(公告)日：2020-10-06
申请号：US16739349
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： G08G1/0965 , G08G1/04 , G01C21/36 , G05D1/02
摘要： A method for detecting emergency vehicles in real time, and managing subject vehicles to support the emergency vehicles to drive without interferences from the subject vehicles by referring to detected information on the emergency vehicles is provided. And the method includes steps of: (a) a management server generating metadata on the specific emergency vehicle by referring to emergency circumstance information; (b) the management server generating a circumstance scenario vector by referring to the emergency circumstance information and the metadata, comparing the circumstance scenario vector with reference scenario vectors, to thereby find a specific scenario vector whose similarity score with the circumstance scenario vector is larger than a threshold, and acquiring an emergency reaction command by referring to the specific scenario vector; (c) the management server transmitting the emergency reaction command to each of the subject vehicles.
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6.发明申请公开(公告)号：US20200250982A1
公开(公告)日：2020-08-06
申请号：US16738484
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 warning by detecting an abnormal state of a driver of a vehicle based on deep learning is provided. The method includes steps of: a driver state detecting device (a) inputting an interior image of the vehicle into a drowsiness detecting network, to detect a facial part of the driver, detect an eye part from the facial part, detect a blinking state of an eye to determine a drowsiness state, and inputting the interior image into a pose matching network, to detect body keypoints of the driver, determine whether the body keypoints match one of preset driving postures, to determine the abnormal state; and (b) if the driver is in a hazardous state referring to part of the drowsiness state and the abnormal state, transmitting information on the hazardous state to nearby vehicles over vehicle-to-vehicle communication to allow nearby drivers to perceive the hazardous state.
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公开(公告)号：US20200250492A1
公开(公告)日：2020-08-06
申请号：US16739201
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 learning an automatic labeling device for auto-labeling a base image of a base vehicle using sub-images of nearby vehicles is provided. The method includes steps of: a learning device inputting the base image and the sub-images into previous trained dense correspondence networks to generate dense correspondences; and into encoders to output convolution feature maps, inputting the convolution feature maps into decoders to output deconvolution feature maps; with an integer k from 1 to n, generating a k-th adjusted deconvolution feature map by translating coordinates of a (k+1)-th deconvolution feature map using a k-th dense correspondence; generating a concatenated feature map by concatenating the 1-st deconvolution feature map and the adjusted deconvolution feature maps; and inputting the concatenated feature map into a masking layer to output a semantic segmentation image and instructing a 1-st loss layer to calculate 1-st losses and updating decoder weights and encoder weights.
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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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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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