Abstract: A method for achieving better performance in an autonomous driving while saving computing powers, by using confidence scores representing a credibility of an object detection which is generated in parallel with an object detection process is provided. And the method includes steps of: (a) a computing device acquiring at least one circumstance image on surroundings of a subject vehicle, through at least one image sensor installed on the subject vehicle; (b) the computing device instructing a Convolutional Neural Network (CNN) to apply at least one CNN operation to the circumstance image, to thereby generate initial object information and initial confidence information on the circumstance image; and (c) the computing device generating final object information on the circumstance image by referring to the initial object information and the initial confidence information with a support of a Reinforcement Learning (RL) agent, and through V2X communications with at least part of surrounding objects.

Abstract: A method for face recognition by using a multiple patch combination based on a deep neural network is provided. The method includes steps of: a face-recognizing device, (a) if a face image with a 1-st size is acquired, inputting the face image into a feature extraction network, to allow the feature extraction network to generate a feature map by applying convolution operation to the face image with the 1-st size, and to generate multiple features by applying sliding-pooling operation to the feature map, wherein the feature extraction network has been learned to extract a feature using a face image for training having a 2-nd size and wherein the 2-nd size is smaller than the 1-st size; and (b) inputting the multiple features into a learned neural aggregation network, to allow the neural aggregation network to aggregate the multiple features and to output an optimal feature for the face recognition.

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 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 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 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.

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 training a CNN by using a camera and a radar together, to thereby allow the CNN to perform properly even when an object depiction ratio of a photographed image acquired through the camera is low due to a bad condition of a photographing circumstance is provided. And the method includes steps of: (a) a learning device instructing a convolutional layer to apply a convolutional operation to a multichannel integrated image, to thereby generate a feature map; (b) the learning device instructing an output layer to apply an output operation to the feature map, to thereby generate estimated object information; and (c) the learning device instructing a loss layer to generate a loss by using the estimated object information and GT object information corresponding thereto, and to perform backpropagation by using the loss, to thereby learn at least part of parameters in the CNN.

Abstract: A method for learning an automatic labeling device for auto-labeling a base image of a base vehicle using sub-images of nearby vehicles is provided. The method includes steps of: a learning device inputting the base image and the sub-images into previous trained dense correspondence networks to generate dense correspondences; and into encoders to output convolution feature maps, inputting the convolution feature maps into decoders to output deconvolution feature maps; with an integer k from 1 to n, generating a k-th adjusted deconvolution feature map by translating coordinates of a (k+1)-th deconvolution feature map using a k-th dense correspondence; generating a concatenated feature map by concatenating the 1-st deconvolution feature map and the adjusted deconvolution feature maps; and inputting the concatenated feature map into a masking layer to output a semantic segmentation image and instructing a 1-st loss layer to calculate 1-st losses and updating decoder weights and encoder weights.

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 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 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 switching driving modes of a subject vehicle to support the subject vehicle to perform a platoon driving by using platoon driving information is provided. And the method includes steps of: (a) a basement server, which interworks with the subject vehicle driving in a first mode, acquiring first platoon driving information, to N-th platoon driving information by referring to a real-time platoon driving information DB; (b) the basement server (i) calculating a first platoon driving suitability score to an N-th platoon driving suitability score by referring to first platoon driving parameters to N-th platoon driving parameters and (ii) selecting a target platoon driving group to be including the subject vehicle; (c) the basement server instructing the subject vehicle to drive in a second mode.

Abstract: A learning method for selecting specific information, to be used for updating an HD Map is provided. And the method includes steps of: (a) a learning device instructing a coordinate neural network to generate a local feature map and a global feature vector by applying a coordinate neural network operation to a coordinate matrix; (b) the learning device instructing a determination neural network to generate a first estimated suitability score to an N-th estimated suitability score by applying a determination neural network operation to the integrated feature map; (c) the learning device instructing a loss layer to generate a loss by referring to (i) the first estimated suitability score to the N-th estimated suitability score and (ii) a first Ground Truth(GT) suitability score to an N-th GT suitability score, and perform backpropagation by using the loss, to thereby learn parameters of the determination neural network and the coordinate neural 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 calibrating a physics engine of a virtual world simulator for learning of a deep learning-based device 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 method for remotely controlling at least one autonomous vehicle is provided. The method includes steps of: an autonomous driving control device, (a) on condition that the autonomous driving control device detects driving environment by referring to sensor information and allows the autonomous vehicle to travel on an autonomous driving mode or a manual driving mode, determining whether the autonomous driving control device fails to establish a driving plan by using the driving environment and whether the autonomous driving control device fails to change to the manual driving mode by using the driving environment; and (b) if the autonomous driving control device fails to establish the driving plan or fails to change to the manual driving mode, selecting a remote control mode and transmitting request information to a remote control service providing server, to allow a remote driver to control the autonomous vehicle by using a specific remote vehicle.

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 detecting driving events occurring during driving, to thereby detect driving scenarios including at least part of the driving events is provided. And the method includes steps of: (a) a learning device, if 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, is acquired, instructing a RNN to apply RNN operations to the specific components, of the specific enumerated event vector, in the specific order, 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 GT driving scenario, which has been acquired beforehand, and to perform a BPTT by using the RNN loss, to thereby learn at least part of parameters of the RNN.