Provided is a method for learning a recurrent neural network (RNN) to check an autonomous driving safety, which is used for switching a driving mode of an autonomous vehicle. The method performed by a learning device includes: a step (a) of inputting, if training images corresponding to front and rear cameras of the autonomous vehicle are acquired, each pair of the training images into corresponding CNNs to concatenate the training images and generate feature maps for training; a step (b) of inputting the feature maps for training into long short-term memory (LSTM) models corresponding to sequences of a forward RNN, and into LSTMs 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 a step (c) of allowing a loss layer to calculate losses and learn the LSTM models. According to the present invention, the number of sensors can be minimized compared to a conventional method.
RNN 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