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公开(公告)号：EP3690731A3
公开(公告)日：2020-10-28
申请号：EP20153637.2
申请日：2020-01-24
申请人： StradVision, Inc.
发明人： Kim, Kye-Hyeon , Kim, Yongjoong , Kim, Hak-Kyoung , Nam, Woonhyun , Boo, SukHoon , Sung, Myungchul , Shin, Dongsoo , Yeo, Donghun , Ryu, Wooju , Lee, Myeong-Chun , Lee, Hyungsoo , Jang, Taewoong , Jeong, Kyungjoong , Je, Hongmo , Cho, Hojin
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：EP3690725A1
公开(公告)日：2020-08-05
申请号：EP20152973.2
申请日：2020-01-21
申请人： Stradvision, Inc.
发明人： Kim, Kye-Hyeon , Kim, Yongjoong , Kim, Hak-Kyoung , Nam, Woonhyun , Boo, SukHoon , Sung, Myungchul , Shin, Dongsoo , Yeo, Donghun , Ryu, Wooju , Lee, Myeong-Chun , Lee, Hyungsoo , Jang, Taewoong , Jeong, Kyungjoong , Je, Hongmo , Cho, Hojin
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：EP3699886A2
公开(公告)日：2020-08-26
申请号：EP20152435.2
申请日：2020-01-17
申请人： StradVision, Inc.
发明人： Kim, Kye-Hyeon , Kim, Yongjoong , Kim, Hak-Kyoung , Nam, Woonhyun , Boo, SukHoon , Sung, Myungchul , Shin, Dongsoo , Yeo, Donghun , Ryu, Wooju , Lee, Myeong-Chun , Lee, Hyungsoo , Jang, Taewoong , Jeong, Kyungjoong , Je, Hongmo , Cho, Hojin
摘要： A method for giving a warning on a blind spot of a vehicle based on V2V communication is provided. The method includes steps of: (a) if a rear video of a first vehicle is acquired from a rear camera, a first blind-spot warning device transmitting the rear video to a blind-spot monitor, to determine whether nearby vehicles are in the rear video using a CNN, and output first blind-spot monitoring information of determining whether the nearby vehicles are in a blind spot; and (b) if second blind-spot monitoring information of determining whether a second vehicle is in the blind spot, is acquired from a second blind-spot warning device of the second vehicle, over the V2V communication, the first blind-spot warning device warning that one of the second vehicle and the nearby vehicles is in the blind spot by referring to the first and the second blind-spot monitoring information.
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公开(公告)号：EP3699814A1
公开(公告)日：2020-08-26
申请号：EP20153516.8
申请日：2020-01-24
申请人： StradVision, Inc.
发明人： Kim, Kye-Hyeon , Kim, Yongjoong , Kim, Hak-Kyoung , Nam, Woonhyun , Boo, SukHoon , Sung, Myungchul , Shin, Dongsoo , Yeo, Donghun , Ryu, Wooju , Lee, Myeong-Chun , Lee, Hyungsoo , Jang, Taewoong , Jeong, Kyungjoong , Je, Hongmo , Cho, Hojin
IPC分类号： G06K9/00
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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公开(公告)号：EP3690817A1
公开(公告)日：2020-08-05
申请号：EP20153532.5
申请日：2020-01-24
申请人： StradVision, Inc.
发明人： Kim, Kye-Hyeon , Kim, Yongjoong , Kim, Hak-Kyoung , Nam, Woonhyun , Boo, SukHoon , Sung, Myungchul , Shin, Dongsoo , Yeo, Donghun , Ryu, Wooju , Lee, Myeong-Chun , Lee, Hyungsoo , Jang, Taewoong , Jeong, Kyungjoong , Je, Hongmo , Cho, Hojin
IPC分类号： G06T7/80
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：EP3690731A2
公开(公告)日：2020-08-05
申请号：EP20153637.2
申请日：2020-01-24
申请人： StradVision, Inc.
发明人： Kim, Kye-Hyeon , Kim, Yongjoong , Kim, Hak-Kyoung , Nam, Woonhyun , Boo, SukHoon , Sung, Myungchul , Shin, Dongsoo , Yeo, Donghun , Ryu, Wooju , Lee, Myeong-Chun , Lee, Hyungsoo , Jang, Taewoong , Jeong, Kyungjoong , Je, Hongmo , Cho, Hojin
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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7.发明公开
公开(公告)号：EP3690729A1
公开(公告)日：2020-08-05
申请号：EP20153495.5
申请日：2020-01-24
申请人： StradVision, Inc.
发明人： Kim, Kye-Hyeon , Kim, Yongjoong , Kim, Hak-Kyoung , Nam, Woonhyun , Boo, SukHoon , Sung, Myungchul , Shin, Dongsoo , Yeo, Donghun , Ryu, Wooju , Lee, Myeong-Chun , Lee, Hyungsoo , Jang, Taewoong , Jeong, Kyungjoong , Je, Hongmo , Cho, Hojin
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：EP3699886A3
公开(公告)日：2020-11-04
申请号：EP20152435.2
申请日：2020-01-17
申请人： StradVision, Inc.
发明人： Kim, Kye-Hyeon , Kim, Yongjoong , Kim, Hak-Kyoung , Nam, Woonhyun , Boo, SukHoon , Sung, Myungchul , Shin, Dongsoo , Yeo, Donghun , Ryu, Wooju , Lee, Myeong-Chun , Lee, Hyungsoo , Jang, Taewoong , Jeong, Kyungjoong , Je, Hongmo , Cho, Hojin
摘要： A method for giving a warning on a blind spot of a vehicle based on V2V communication is provided. The method includes steps of: (a) if a rear video of a first vehicle is acquired from a rear camera, a first blind-spot warning device transmitting the rear video to a blind-spot monitor, to determine whether nearby vehicles are in the rear video using a CNN, and output first blind-spot monitoring information of determining whether the nearby vehicles are in a blind spot; and (b) if second blind-spot monitoring information of determining whether a second vehicle is in the blind spot, is acquired from a second blind-spot warning device of the second vehicle, over the V2V communication, the first blind-spot warning device warning that one of the second vehicle and the nearby vehicles is in the blind spot by referring to the first and the second blind-spot monitoring information.
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公开(公告)号：EP3690844A1
公开(公告)日：2020-08-05
申请号：EP20153534.1
申请日：2020-01-24
申请人： StradVision, Inc.
发明人： Kim, Kye-Hyeon , Kim, Yongjoong , Kim, Hak-Kyoung , Nam, Woonhyun , Boo, SukHoon , Sung, Myungchul , Shin, Dongsoo , Yeo, Donghun , Ryu, Wooju , Lee, Myeong-Chun , Lee, Hyungsoo , Jang, Taewoong , Jeong, Kyungjoong , Je, Hongmo , Cho, Hojin
IPC分类号： G08G1/00 , B60W30/165 , 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：EP3690726A1
公开(公告)日：2020-08-05
申请号：EP20152976.5
申请日：2020-01-21
申请人： StradVision, Inc.
发明人： Kim, Kye-Hyeon , Kim, Yongjoong , Kim, Hak-Kyoung , Nam, Woonhyun , Boo, SukHoon , Sung, Myungchul , Shin, Dongsoo , Yeo, Donghun , Ryu, Wooju , Lee, Myeong-Chun , Lee, Hyungsoo , Jang, Taewoong , Jeong, Kyungjoong , Je, Hongmo , Cho, Hojin
摘要： A learning method for transforming a virtual video on a virtual world to a more real-looking video is provided. And the method includes steps of: (a) a learning device instructing a generating CNN to apply a convolutional operation to an N-th virtual training image, N-th meta data and (N-K)-th reference information to generate an N-th feature map; (b) the learning device instructing the generating CNN to apply a deconvolutional operation to the N-th feature map to generate an N-th transformed image; (c) the learning device instructing a discriminating CNN to apply a discriminating CNN operation to the N-th transformed image to generate a category score vector; (d) the learning device instructing the generating CNN to generate a generating CNN loss by referring to the category score vector and its corresponding GT, and to perform backpropagation by referring to the generating CNN loss to learn parameters of the generating CNN.
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