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1.发明公开
公开(公告)号：EP3690761A1
公开(公告)日：2020-08-05
申请号：EP20153041.7
申请日：2020-01-22
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 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.-
公开(公告)号：EP3690745A1
公开(公告)日：2020-08-05
申请号：EP20153665.3
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 learning method for providing a functional safety by warning a driver about a potential dangerous situation by using an explainable AI which verifies detection processes of a neural network for an autonomous driving is provided. And the learning method includes steps of: (a) a learning device for verification, if at least one training image for verification is acquired, instructing a property extraction module to apply extraction operation to the training image for verification to extract property information on characteristics of the training image for verification to thereby generate a quality vector; (b) the learning device for verification instructing the neural network for verification to apply first neural network operations to the quality vector, to thereby generate predicted safety information; and (c) the learning device for verification instructing a loss module to generate a loss, and perform a backpropagation by using the loss, to thereby learn parameters included in the neural network for verification.
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公开(公告)号：EP3690718A1
公开(公告)日：2020-08-05
申请号：EP20152585.4
申请日：2020-01-20
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 training a main CNN by using a virtual image and a style-transformed real image is provided. And the method includes steps of: (a) a learning device acquiring first training images; and (b) the learning device performing a process of instructing the main CNN to generate first estimated autonomous driving source information, instructing the main CNN to generate first main losses and perform backpropagation by using the first main losses, to thereby learn parameters of the main CNN, and a process of instructing a supporting CNN to generate second training images, instructing the main CNN to generate second estimated autonomous driving source information, instructing the main CNN to generate second main losses and perform backpropagation by using the second main losses, to thereby learn parameters of the main CNN.
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公开(公告)号：EP3686802A1
公开(公告)日：2020-07-29
申请号：EP20151740.6
申请日：2020-01-14
申请人： StradVision, Inc.
发明人： KIM, Kye-Hyeon , KIM, Yongjoong , KIM, Insu , KIM, Hak-Kyoung , NAM, Woonhyun , BOO, SukHoon , SUNG, Myungchul , YEO, Donghun , RYU, Wooju , JANG, Taewoong , JEONG, Kyungjoong , JE, Hongmo , CHO, Hojin
摘要： A method for generating one or more test patterns and selecting optimized test patterns among the test patterns to verify an integrity of convolution operations is provided for fault tolerance, fluctuation robustness in extreme situations, functional safety of the convolution operations, and annotation cost reduction. The method includes steps of: a computing device (a) instructing a pattern generating unit to generate the test patterns by using a certain function such that saturation does not occur while at least one original CNN applies the convolution operations to the test patterns; (b) instructing a pattern evaluation unit to generate each of evaluation scores of each of the test patterns by referring to said each of the test patterns and one or more parameters of the original CNN; and (c) instructing a pattern selection unit to select the optimized test patterns among the test patterns by referring to the evaluation scores.
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公开(公告)号：EP3686794A1
公开(公告)日：2020-07-29
申请号：EP20151251.4
申请日：2020-01-10
申请人： StradVision, Inc.
发明人： KIM, Kye-Hyeon , KIM, Yongjoong , KIM, Insu , KIM, Hak-Kyoung , NAM, Woonhyun , BOO, SukHoon , SUNG, Myungchul , YEO, Donghun , RYU, Wooju , JANG, Taewoong , JEONG, Kyungjoong , JE, Hongmo , CHO, Hojin
摘要： A method for learning parameters of a CNN using a 1×K convolution operation or a K×1 convolution operation is provided to be used for hardware optimization which satisfies KPI. The method includes steps of: a learning device (a) instructing a reshaping layer to two-dimensionally concatenate features in each group comprised of corresponding K channels of a training image or its processed feature map, to thereby generate a reshaped feature map, and instructing a subsequent convolutional layer to apply the 1 xK or the Kx1 convolution operation to the reshaped feature map, to thereby generate an adjusted feature map; and (b) instructing an output layer to refer to features on the adjusted feature map or its processed feature map, and instructing a loss layer to calculate losses by referring to an output from the output layer and its corresponding GT.
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公开(公告)号：EP3686791A1
公开(公告)日：2020-07-29
申请号：EP19219877.8
申请日：2019-12-27
申请人： StradVision, Inc.
发明人： KIM, Kye-Hyeon , KIM, Yongjoong , KIM, Insu , KIM, Hak-Kyoung , NAM, Woonhyun , BOO, SukHoon , SUNG, Myungchul , YEO, Donghun , RYU, Wooju , JANG, Taewoong , JEONG, Kyungjoong , JE, Hongmo , CHO, Hojin
摘要： A method for learning parameters of an object detector based on a CNN adaptable to customers' requirements such as KPI by using an image concatenation and a target object merging network is provided. The CNN can be redesigned when scales of objects change as a focal length or a resolution changes depending on the KPI. The method includes steps of: a learning device instructing an image-manipulating network to generate n manipulated images; instructing an RPN to generate first to n-th object proposals respectively in the manipulated images, and instructing an FC layer to generate first to n-th object detection information; and instructing the target object merging network to merge the object proposals and merge the object detection information. In this method, the object proposals can be generated by using lidar. The method can be useful for multi-camera, SVM(surround view monitor), and the like, as accuracy of 2D bounding boxes improves.
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公开(公告)号：EP3686790A1
公开(公告)日：2020-07-29
申请号：EP19219455.3
申请日：2019-12-23
申请人： StradVision, Inc.
发明人： KIM, Kye-Hyeon , KIM, Yongjoong , KIM, Insu , KIM, Hak-Kyoung , NAM, Woonhyun , BOO, SukHoon , SUNG, Myungchul , YEO, Donghun , RYU, Wooju , JANG, Taewoong , JEONG, Kyungjoong , JE, Hongmo , CHO, Hojin
摘要： A method for learning parameters of a CNN for image recognition is provided to be used for hardware optimization which satisfies KPI. The method includes steps of: a learning device (1) instructing a first transposing layer or a pooling layer to generate an integrated feature map by concatenating each of pixels, per each of ROIs, in corresponding locations on pooled ROI feature maps; and (2) (i) instructing a second transposing layer or a classifying layer to divide an adjusted feature map, whose volume is adjusted from the integrated feature map, by each of the pixels, and instructing the classifying layer to generate object information on the ROIs, and (ii) backpropagating object losses. Size of a chip can be decreased as convolution operations and fully connected layer operations are performed by a same processor. Accordingly, there are advantages such as no need to build additional lines in a semiconductor manufacturing process.
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公开(公告)号：EP3686781A1
公开(公告)日：2020-07-29
申请号：EP19219842.2
申请日：2019-12-27
申请人： StradVision, Inc.
发明人： KIM, Kye-Hyeon , KIM, Yongjoong , KIM, Insu , KIM, Hak-Kyoung , NAM, Woonhyun , BOO, SukHoon , SUNG, Myungchul , YEO, Donghun , RYU, Wooju , JANG, Taewoong , JEONG, Kyungjoong , JE, Hongmo , CHO, Hojin
摘要： A method for learning parameters of an object detector with hardware optimization based on a CNN for detection at distance or military purpose using an image concatenation is provided. The CNN can be redesigned when scales of objects change as a focal length or a resolution changes depending on the KPI. The method includes steps of: (a) concatenating n manipulated images which correspond to n target regions; (b) instructing an RPN to generate first to n-th object proposals in the n manipulated images by using an integrated feature map, and instructing a pooling layer to apply pooling operations to regions, corresponding to the first to the n-th object proposals, on the integrated feature map; and (c) instructing an FC loss layer to generate first to n-th FC losses by referring to the object detection information, outputted from an FC layer.
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公开(公告)号：EP3657382A1
公开(公告)日：2020-05-27
申请号：EP19195508.7
申请日：2019-09-05
申请人： Stradvision, Inc.
发明人： KIM, Kye-Hyeon , KIM, Yongjoong , KIM, Insu , KIM, Hak-Kyoung , NAM, Woonhyun , BOO, SukHoon , SUNG, Myungchul , YEO, Donghun , RYU, Wooju , JANG, Taewoong , JEONG, Kyungjoong , JE, Hongmo , CHO, Hojin
IPC分类号： G06K9/00
摘要： A method for warning a vehicle of a risk of lane change is provided. The method includes steps of: (a) an alarm device, if at least one rear image captured by a running vehicle is acquired, segmenting the rear image by using a learned convolutional neural network (CNN) to thereby obtain a segmentation image corresponding to the rear image; (b) the alarm device checking at least one free space ratio in at least one blind spot by referring to the segmentation image, wherein the free space ratio is determined as a ratio of a road area without an object in the blind spot to a whole area of the blind spot; and (c) the alarm device, if the free space ratio is less than or equal to at least one predetermined threshold value, warning a driver of the vehicle of the risk of lane change.
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10.发明公开
公开(公告)号：EP3633558A1
公开(公告)日：2020-04-08
申请号：EP19195517.8
申请日：2019-09-05
申请人： Stradvision, Inc.
发明人： KIM, Kye-Hyeon , KIM, Yongjoong , KIM, Insu , KIM, Hak-Kyoung , NAM, Woonhyun , BOO, SukHoon , SUNG, Myungchul , YEO, Donghun , RYU, Wooju , JANG, Taewoong , JEONG, Kyungjoong , JE, Hongmo , CHO, Hojin
IPC分类号： G06N3/04 , G06N3/08 , G06N5/00 , G08G1/16 , G06K9/00 , G06K9/32 , G06K9/46 , G06K9/62 , B60W40/00
摘要： A learning method of a CNN (Convolutional Neural Network) for monitoring one or more blind spots of a monitoring vehicle is provided. The learning method includes steps of: a learning device instructing a detector to output class information and location information on a monitored vehicle in a training image; instructing a cue information extracting layer to output cue information on the monitored vehicle by using the outputted information, and instructing an FC layer to determine whether the monitored vehicle is located on the blind spots by neural-network operations with the cue information or its processed values; and learning parameters of the FC layer and parameters of the detector, by backpropagating loss values for the blind spots by referring to the determination and its corresponding GT and backpropagating loss values for the vehicle detection by referring to the class information and the location information and their corresponding GT, respectively.
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