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公开(公告)号：US12154033B2
公开(公告)日：2024-11-26
申请号：US17886198
申请日：2022-08-11
Inventor： Joo-Young Kim , Kyoung-Wook Min , Yong-Woo Jo , Doo-Seop Choi , Jeong-Dan Choi
Abstract: Disclosed herein are a deep network learning method using an autonomous vehicle and an apparatus for the same. The deep network learning apparatus includes a processor configured to select a deep network model requiring an update in consideration of performance, assign learning amounts for respective vehicles in consideration of respective operation patterns of multiple autonomous vehicles registered through user authentication, distribute the deep network model and the learning data to the multiple autonomous vehicles based on the learning amounts for respective vehicles, and receive learning results from the multiple autonomous vehicles, and memory configured to store the deep network model and the learning data.
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公开(公告)号：US11507783B2
公开(公告)日：2022-11-22
申请号：US17380663
申请日：2021-07-20
Inventor： Dong-Jin Lee , Do-Wook Kang , Jungyu Kang , Joo-Young Kim , Kyoung-Wook Min , Jae-Hyuck Park , Kyung-Bok Sung , Yoo-Seung Song , Taeg-Hyun An , Yong-Woo Jo , Doo-Seop Choi , Jeong-Dan Choi , Seung-Jun Han
Abstract: Disclosed herein are an object recognition apparatus of an automated driving system using error removal based on object classification and a method using the same. The object recognition method is configured to train a multi-object classification model based on deep learning using training data including a data set corresponding to a noise class, into which a false-positive object is classified, among classes classified by the types of objects, to acquire a point cloud and image data respectively using a LiDAR sensor and a camera provided in an autonomous vehicle, to extract a crop image, corresponding to at least one object recognized based on the point cloud, from the image data and input the same to the multi-object classification model, and to remove a false-positive object classified into the noise class, among the at least one object, by the multi-object classification model.
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公开(公告)号：US11940814B2
公开(公告)日：2024-03-26
申请号：US17532089
申请日：2021-11-22
Inventor： Yoo-Seung Song , Joo-Young Kim , Kyoung-Wook Min , Yong-Woo Jo , Jeong-Dan Choi
CPC classification number: G05D1/0291 , B60W60/0015 , G05D1/0289 , H04W4/44 , H04W4/46 , B60W2556/50 , B60W2556/65 , G05D2201/0213
Abstract: Disclosed herein are a cooperative driving method based on driving negotiation and an apparatus for the same. The cooperative driving method is performed by a cooperative driving apparatus for cooperative driving based on driving negotiation, and includes determining whether cooperative driving is possible in consideration of a driving mission of a requesting vehicle that requests cooperative driving with neighboring vehicles, when it is determined that cooperative driving is possible, setting a responding vehicle from which cooperative driving is to be requested among the neighboring vehicles, performing driving negotiation between the requesting vehicle and the responding vehicle based on a driving negotiation protocol, and when the driving negotiation is completed, performing cooperative driving by providing driving guidance information for vehicle control to at least one of the requesting vehicle and the responding vehicle.
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公开(公告)号：US20230053134A1
公开(公告)日：2023-02-16
申请号：US17886198
申请日：2022-08-11
Inventor： Joo-Young KIM , Kyoung-Wook Min , Yong-Woo Jo , Doo-Seop Choi , Jeong-Dan Choi
Abstract: Disclosed herein are a deep network learning method using an autonomous vehicle and an apparatus for the same. The deep network learning apparatus includes a processor configured to select a deep network model requiring an update in consideration of performance, assign learning amounts for respective vehicles in consideration of respective operation patterns of multiple autonomous vehicles registered through user authentication, distribute the deep network model and the learning data to the multiple autonomous vehicles based on the learning amounts for respective vehicles, and receive learning results from the multiple autonomous vehicles, and memory configured to store the deep network model and the learning data.
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