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.

Abstract: A learning method for acquiring at least one personalized reward function, used for performing a Reinforcement Learning (RL) algorithm, corresponding to a personalized optimal policy for a subject driver is provided. And the method includes steps of: (a) a learning device performing a process of instructing an adjustment reward network to generate first adjustment rewards, by referring to the information on actual actions and actual circumstance vectors in driving trajectories, a process of instructing a common reward module to generate first common rewards by referring to the actual actions and the actual circumstance vectors, and a process of instructing an estimation network to generate actual prospective values by referring to the actual circumstance vectors; and (b) the learning device instructing a first loss layer to generate an adjustment reward and to perform backpropagation to learn parameters of the adjustment reward network.

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.

Abstract: A method for achieving better performance in autonomous driving while saving computing power, by using confidence scores representing a credibility of an object detection which is generated in parallel with an object detection process is provided.

Abstract: A learning method for calculating collision probability, to be used for determining whether it is appropriate or not to switch driving modes of a vehicle capable of an autonomous driving, by analyzing a recent driving route of a driver is provided. And the method includes steps of: (a) a learning device, on condition that a status vector and a trajectory vector are acquired, performing processes of (i) instructing a status network to generate a status feature map and (ii) instructing a trajectory network to generate a trajectory feature map; (b) the learning device instructing a safety network to calculate a predicted collision probability representing a predicted probability of an accident occurrence; and (c) the learning device instructing a loss layer to generate a loss by referring to the predicted collision probability and a GT collision probability, which have been acquired beforehand, to learn at least part of parameters.

Abstract: A learning method for performing a seamless parameter switch by using a location-specific algorithm selection for an optimized autonomous driving is provided. And the method includes steps of: (a) a learning device instructing a K-th convolutional layer to apply a convolution operation to K-th training images, to thereby generate K-th feature maps; (b) the learning device instructing a K-th output layer to apply a K-th output operation to the K-th feature maps, to thereby generate K-th estimated autonomous driving source information; (c) the learning device instructing a K-th loss layer to generate a K-th loss by using the K-th estimated autonomous driving source information and its corresponding GT, and then to perform backpropagation by using the K-th loss, to thereby learn K-th parameters of the K-th CNN; and (d) the learning device storing the K-th CNN in a database after tagging K-th location information to the K-th CNN.

Abstract: A learning method for transforming a virtual video on a virtual world to a more real-looking video is provided. And the method includes steps of: (a) a learning device instructing a generating CNN to apply a convolutional operation to an N-th virtual training image, N-th meta data and (N-K)-th reference information to generate an N-th feature map; (b) the learning device instructing the generating CNN to apply a deconvolutional operation to the N-th feature map to generate an N-th transformed image; (c) the learning device instructing a discriminating CNN to apply a discriminating CNN operation to the N-th transformed image to generate a category score vector; (d) the learning device instructing the generating CNN to generate a generating CNN loss by referring to the category score vector and its corresponding GT, and to perform backpropagation by referring to the generating CNN loss to learn parameters of the generating CNN.

Abstract: A method for planning an autonomous driving by using a V2X communication and an image processing under a road circumstance where both vehicles capable of the V2X communication and vehicles incapable of the V2X communication exist is provided. And the method includes steps of: (a) a computing device, corresponding to a subject autonomous vehicle, instructing a planning module to acquire recognition information on surrounding vehicles including (i) first vehicles capable of a V2X communication and (ii) second vehicles incapable of the V2X communication; (b) the computing device instructing the planning module to select an interfering vehicle among the surrounding vehicles; and (c) the computing device instructing the planning module to generate a potential interference prediction model, and to modify current optimized route information in order to evade a potential interfering action, to thereby generate updated optimized route information of the subject autonomous vehicle.

Abstract: A method for delivering a steering intention of an autonomous driving module to a steering apparatus more accurately by using a reference map is provided. And the method includes steps of: (a) a computing device, if a subject intended steering signal inputted by the autonomous driving module at a current timing is acquired, instructing a signal adjustment module to select, by referring to the reference map, specific reference steering guide values corresponding to the subject intended steering signal; (b) the computing device (i) adjusting the subject intended steering signal by referring to the specific reference steering guide values, in order to generate a subject adjusted steering signal, and (ii) transmitting the subject adjusted steering signal to the steering apparatus, to thereby support the steering apparatus to rotate the subject vehicle by a specific steering angle corresponding to the subject intended steering signal.

Abstract: A method for providing an autonomous driving service platform for autonomous vehicles by using a competitive computing and information fusion is provided.

Abstract: A learning method for providing a functional safety by warning a driver about a potential dangerous situation by using an explainable AI which verifies detection processes of a neural network for an autonomous driving is provided.

Abstract: A learning method for detecting driving events occurring during driving, to thereby detect driving scenarios including at least part of the driving events is provided. The method includes: (a) in response to a specific enumerated event vector including each of pieces of information on each of specific driving events as its specific components in a specific order being acquired, a learning device instructing a recurrent neural network (RNN) to apply RNN operations to the specific components of the specific enumerated event vector, to thereby detect a specific predicted driving scenario including the specific driving events; (b) the learning device instructing a loss module to generate an RNN loss by referring to the specific predicted driving scenario and a specific ground-truth (GT) driving scenario and to perform a backpropagation through time (BPTT) by using the RNN loss, to thereby learn at least part of the parameters of the RNN.

Abstract: A method for adjusting a position of a driver assistance device according to a driver state is provided. The method includes steps of: a position adjusting device, (a) inputting an upper and a lower body images of a driver, acquired after the driver sits and starts a vehicle, into a pose estimation network, to acquire body keypoints, calculate body part lengths, and adjust a driver's seat position; and (b) while the vehicle is traveling, inputting the upper body image into a face detector to detect a facial part, inputting the facial part into an eye detector to detect an eye part, and inputting the adjusted driver's seat position and 2D coordinates of an eye into a 3D coordinates transforming device, to generate 3D coordinates of the eye referring to the 2D coordinates and the driver's seat position, and adjust a mirror position of the vehicle referring to the 3D coordinates.

Abstract: A method for providing a dynamic adaptive deep learning model other than a fixed deep learning model, to thereby support at least one specific autonomous vehicle to perform a proper autonomous driving according to surrounding circumstances is provided. And the method includes steps of: (a) a managing device which interworks with autonomous vehicles instructing a fine-tuning system to acquire a specific deep learning model to be updated; (b) the managing device inputting video data and its corresponding labeled data to the fine-tuning system as training data, to thereby update the specific deep learning model; and (c) the managing device instructing an automatic updating system to transmit the updated specific deep learning model to the specific autonomous vehicle, to thereby support the specific autonomous vehicle to perform the autonomous driving by using the updated specific deep learning model other than a legacy deep learning model.

Abstract: A learning method for supporting a safer autonomous driving through a fusion of information acquired from images and communications is provided. And the method includes steps of: (a) a learning device instructing a first neural network and a second neural network to generate an image-based feature map and a communication-based feature map by using a circumstance image and circumstance communication information; (b) the learning device instructing a third neural network to apply a third neural network operation to the image-based feature map and the communication-based feature map to generate an integrated feature map; (c) the learning device instructing a fourth neural network to apply a fourth neural network operation to the integrated feature map to generate estimated surrounding motion information; and (d) the learning device instructing a first loss layer to train parameters of the first to the fourth neural networks.

Abstract: A learning method for generating integrated object detection information by integrating first object detection information and second object detection information is provided. And the method includes steps of: (a) a learning device instructing a concatenating network to generate one or more pair feature vectors; (b) the learning device instructing a determining network to apply FC operations to the pair feature vectors, to thereby generate (i) determination vectors and (ii) box regression vectors; (c) the learning device instructing a loss unit to generate an integrated loss by referring to the determination vectors, the box regression vectors and their corresponding GTs, and performing backpropagation processes by using the integrated loss, to thereby learn at least part of parameters included in the DNN.

Abstract: A method for calculating exact location of a subject vehicle by using information on relative distances is provided. And the method includes steps of: (a) a computing device, if a reference image is acquired through a camera on the subject vehicle, detecting reference objects in the reference image; (b) the computing device calculating image-based reference distances between the reference objects and the subject vehicle, by referring to information on reference bounding boxes, corresponding to the reference objects, on the reference image; (c) the computing device (i) generating a distance error value by referring to the image-based reference distances and coordinate-based reference distances, and (ii) calibrating subject location information of the subject vehicle by referring to the distance error value.

Abstract: A method for learning an automatic parking device of a vehicle for detecting an available parking area is provided. The method includes steps of: a learning device, (a) if a parking lot image of an area nearby the vehicle is acquired, (i) inputting the parking lot image into a segmentation network to output a convolution feature map via an encoder, output a deconvolution feature map by deconvoluting the convolution feature map via a decoder, and output segmentation information by masking the deconvolution feature map via a masking layer; (b) inputting the deconvolution feature map into a regressor to generate relative coordinates of vertices of a specific available parking region, and generate regression location information by regressing the relative coordinates; and (c) instructing a loss layer to calculate 1-st losses by referring to the regression location information and an ROI GT, and learning the regressor via backpropagation using the 1-st losses.

Abstract: A method for detecting emergency vehicles in real time, and managing subject vehicles to support the emergency vehicles to drive without interferences from the subject vehicles by referring to detected information on the emergency vehicles is provided. And the method includes steps of: (a) a management server generating metadata on the specific emergency vehicle by referring to emergency circumstance information; (b) the management server generating a circumstance scenario vector by referring to the emergency circumstance information and the metadata, comparing the circumstance scenario vector with reference scenario vectors, to thereby find a specific scenario vector whose similarity score with the circumstance scenario vector is larger than a threshold, and acquiring an emergency reaction command by referring to the specific scenario vector; (c) the management server transmitting the emergency reaction command to each of the subject vehicles.

Abstract: A method for integrating images from vehicles performing a cooperative driving is provided. The method includes steps of: a main driving image integrating device on one main vehicle (a) inputting one main driving image into a main object detector to (1) generate one main feature map by applying convolution operation via a main convolutional layer, (2) generate main ROIs via a main region proposal network, (3) generate main pooled feature maps by applying pooling operation via a main pooling layer, and (4) generate main object detection information on the main objects by applying fully-connected operation via a main fully connected layer; (b) inputting the main pooled feature maps into a main confidence network to generate main confidences; and (c) acquiring sub-object detection information and sub-confidences from sub-vehicles, and integrating the main object detection information and the sub-object detection information using the main & the sub-confidences to generate object detection result.