Electronics and Telecommunications Research Institute

Jae-Hyuck Park

Kyoung-Wook Min

Jeong-Dan Choi

Doo-Seop Choi

Joo-Young Kim

Yoo-Seung Song

Kyung-Bok Sung

Yong-Woo Jo

Jungyu Kang

Dong-Jin Lee

Do-Wook Kang

Jung-Gyu Kang

Taeg-Hyun An

Seung-Jun Han

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.

Autonomous driving method for avoiding stopped vehicle and apparatus for the same

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 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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Daejeon, South Korea

Jae-Hyuck Park