Electronics and Telecommunications Research Institute

Doo-Seop Choi

Kyoung-Wook Min

Jeong-Dan Choi

Joo-Young Kim

Yong-Woo Jo

Jae-Hyuck Park

Yoo-Seung Song

Kyung-Bok Sung

Jungyu Kang

Dong-Jin Lee

Do-Wook Kang

Jung-Gyu Kang

Taeg-Hyun An

Seung-Jun Han

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.

Method and apparatus for deidentifying driver image dataset

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.

Deep network learning method using autonomous vehicle and apparatus for the same

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.

Apparatus for recognizing object of automated driving system using error removal based on object classification and method using the same

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Sejong-si, South Korea

Doo-Seop Choi