- Patent Title: Learning method and learning device of recurrent neural network for autonomous driving safety check for changing driving mode between autonomous driving mode and manual driving mode, and testing method and testing device using them
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Application No.: US16723753Application Date: 2019-12-20
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Publication No.: US11042780B2Publication Date: 2021-06-22
- Inventor: Kye-Hyeon Kim , Yongjoong Kim , Hak-Kyoung Kim , Woonhyun Nam , SukHoon Boo , Myungchul Sung , Dongsoo Shin , Donghun Yeo , Wooju Ryu , Myeong-Chun Lee , Hyungsoo Lee , Taewoong Jang , Kyungjoong Jeong , Hongmo Je , Hojin Cho
- Applicant: Stradvision, Inc.
- Applicant Address: KR Pohang-si
- Assignee: Stradvision, Inc.
- Current Assignee: Stradvision, Inc.
- Current Assignee Address: KR Pohang-si
- Agency: Kaplan Breyer Schwarz, LLP
- Main IPC: G06K9/00
- IPC: G06K9/00 ; G06K9/62 ; G05D1/00 ; G05D1/02 ; G06N3/04 ; G06N3/08

Abstract:
A method for learning a recurrent neural network to check an autonomous driving safety to be used for switching a driving mode of an autonomous vehicle is provided. The method includes steps of: a learning device (a) if training images corresponding to a front and a rear cameras of the autonomous vehicle are acquired, inputting each pair of the training images into corresponding CNNs, to concatenate the training images and generate feature maps for training, (b) inputting the feature maps for training into long short-term memory models corresponding to sequences of a forward RNN, and into those corresponding to the sequences of a backward RNN, to generate updated feature maps for training and inputting feature vectors for training into an attention layer, to generate an autonomous-driving mode value for training, and (c) allowing a loss layer to calculate losses and to learn the long short-term memory models.
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