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公开(公告)号：US20230058530A1
公开(公告)日：2023-02-23
申请号：US17575317
申请日：2022-01-13
Inventor： Jung-Gyu KANG , Kyoung-Wook MIN , Jae-Hyuck PARK , Doo-Seop CHOI , Jeong-Dan CHOI
Abstract: Disclosed herein is a method for deidentifying a driver image dataset. The method includes generating a combination dataset having a preset size based on a driver image dataset, extracting face shape information from each of pieces of driver image data forming the driver image dataset, and generating a deidentified dataset using the combination dataset and the face shape information.
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Patent Agency Ranking
公开(公告)号：US20230008458A1
公开(公告)日：2023-01-12
申请号：US17857352
申请日：2022-07-05
Inventor： Kyoung-Wook MIN , Jae-Hyuck PARK
Abstract: Disclosed herein are an autonomous driving method for avoiding a stopped vehicle and an apparatus for the same. The autonomous driving method for avoiding a stopped vehicle is performed by an autonomous driving control apparatus provided in an autonomous vehicle, and includes obtaining taillight recognition information for a stopped vehicle identified ahead of the autonomous vehicle, determining whether the stopped vehicle is to be avoided in consideration of the taillight recognition information, when it is determined that the stopped vehicle is to be avoided, setting an avoidance method in consideration of whether lane returning is to be performed, which is determined based on an autonomous driving task, and setting an avoidance time point corresponding to the avoidance method and controlling the autonomous vehicle to avoid the stopped vehicle by traveling along an avoidance path generated in conformity with the avoidance time point.
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公开(公告)号：US20230186645A1
公开(公告)日：2023-06-15
申请号：US17993402
申请日：2022-11-23
Inventor： Do Wook KANG , Jae-Hyuck PARK , Kyoung-Wook MIN , Kyung Bok SUNG , Yoo-Seung SONG , Dong-Jin LEE , Jeong Dan CHOI
CPC classification number: G06V20/584 , B60W40/04 , B60W2554/4046 , B60W2420/52
Abstract: Disclosed is a system performing a method for detecting intersection traffic light information including a traffic light detection module including an image sensor for generating first signal data based on traffic light image data in which a traffic light is included, a communication module that receives second signal data for communication with a surrounding object and an external device, an object information collection module that collects dynamic data of the surrounding object, and a signal information inference module that infers third signal data based on the dynamic data. The dynamic data of the surrounding object includes at least one information of whether the surrounding object moves, a moving direction of the surrounding object, and whether the surrounding object accelerates or decelerates. Each of the signal data includes pieces of information about a type of the traffic light and a signal direction of the traffic light.
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公开(公告)号：US20240233157A9
公开(公告)日：2024-07-11
申请号：US18491916
申请日：2023-10-23
Inventor： Jae-Hyuck PARK , Kyoung-Wook MIN , Jeong Dan CHOI
CPC classification number: G06T7/55 , G06V10/7715 , G06T2207/20221
Abstract: Disclosed is a processor which includes a camera image feature extractor that extracts a camera image feature based on a camera image, a LIDAR image feature extractor that extracts a LIDAR image feature based on a LIDAR image, a sampling unit that performs a sampling operation based on the camera image feature and the LIDAR image feature and generates a sampled LIDAR image feature, a fusion unit that fuses the camera image feature and the sampled LIDAR image feature and generates a fusion map, and a decoding unit that decodes the fusion map and generates a depth map. The sampling operation includes back-projecting a pixel location of the camera image feature on a camera coordinate system to generate a back-projection point, and projecting the back-projection point on a plane of the LIDAR image to calculate sampling coordinates.
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公开(公告)号：US20240135564A1
公开(公告)日：2024-04-25
申请号：US18491916
申请日：2023-10-22
Inventor： Jae-Hyuck PARK , Kyoung-Wook MIN , Jeong Dan CHOI
CPC classification number: G06T7/55 , G06V10/7715 , G06T2207/20221
Abstract: Disclosed is a processor which includes a camera image feature extractor that extracts a camera image feature based on a camera image, a LIDAR image feature extractor that extracts a LIDAR image feature based on a LIDAR image, a sampling unit that performs a sampling operation based on the camera image feature and the LIDAR image feature and generates a sampled LIDAR image feature, a fusion unit that fuses the camera image feature and the sampled LIDAR image feature and generates a fusion map, and a decoding unit that decodes the fusion map and generates a depth map. The sampling operation includes back-projecting a pixel location of the camera image feature on a camera coordinate system to generate a back-projection point, and projecting the back-projection point on a plane of the LIDAR image to calculate sampling coordinates.
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公开(公告)号：US20220164609A1
公开(公告)日：2022-05-26
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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