Stradvision, Inc.

Hyundai Motor Company

Kia Motors Corporation

Kia Corporation

Dongsoo Shin

Hongmo Je

Wooju Ryu

Yongjoong Kim

Hojin Cho

Woonhyun Nam

Taewoong Jang

SukHoon Boo

Kyungjoong Jeong

Donghun Yeo

Myungchul Sung

Kye-Hyeon Kim

Hak-Kyoung Kim

Hyungsoo Lee

Myeong-Chun Lee

Jeong-Eom Lee

Seona Kim

Donghee Seok

HeeJin Ro

Kye Yoon Kim

Ga Hee Kim

Jung Mi Park

Sihyun Joo

Jungsang Min

Jongmin Oh

Sukhoon Boo

Heejin Ro

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. 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, thereby to 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) reinforcement learning (RL) agent, and through V2X communications with at least part of surrounding objects.

Method and device for attention-driven resource allocation by using reinforcement learning and V2X communication to thereby achieve safety of autonomous driving

A dialogue system for a vehicle may include: an input processor configured to receive a dialogue among occupants of the vehicle including a driver and at least one passenger, to detect vehicle operation information, to identify the at least one passenger based on the dialogue among the occupants or the vehicle operation information, to generate passenger number information which estimates a change in a number of passengers in the vehicle based on the dialogue among the occupants when the vehicle arrives at a stop-over point, and to acquire a pre-utterance message according to the passenger number information; and a result processor configured to output a pre-utterance according to the pre-utterance message.

Dialogue system and dialogue processing method

A dialogue system for a vehicle may include: a storage storing context information including at least one of vehicle state information indicating a vehicle state, user information indicating a driver of the vehicle, or driving environment information indicating a driving environment of the vehicle; an input processor determining whether a context of the vehicle is a pre-utterance context, in which the dialogue system firstly outputs an utterance, based on the stored context information, determining whether a task related to the pre-utterance context is a duplicate task which has already processed or is currently processing when the context of the vehicle is the pre-utterance context, and obtaining at least one of an action corresponding to the pre-utterance context or a pre-utterance message to be output by the dialogue system when the task related to the pre-utterance context is not a duplicate task; a dialogue manager obtaining, from the storage, a parameter value of an action parameter used to perform the action corresponding to the pre-utterance context when the input processor obtains the action corresponding to the pre-utterance context; and a result processor generating a dialogue response.

Dialogue system, and dialogue processing method

A dialogue system for a vehicle may include: an input processor configured to receive a user's utterance, to acquire an utterance text by recognizing the user's utterance, to recognize a dialogue subject based on the acquired utterance text, and to identify the user; and a dialogue manager including a memory storing program instructions and a processor configured to execute the stored program instructions, the dialogue manager configured to verify whether a chat room related to the dialogue subject is present, and to determine whether to add the identified user as a participant of the chat room based on a result of the verification.

Disclosed herein is a dialogue system capable of identifying preliminary indications related to the failure of the vehicle through a dialogue with a user, to check a part that may not be measured by a sensor and to inhibit accidents so as to provide user safety, a vehicle having the same and a dialogue processing method.

Dialogue system, vehicle having the same and dialogue processing method

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.

Learning method and learning device for determining whether to switch mode of vehicle from manual driving mode to autonomous driving mode by performing trajectory-based behavior analysis on recent driving route

A dialogue system for a vehicle may include: a storage configured to store vehicle state information and driving environment information; an input processor configured to acquire an utterance from a user and to extract an action corresponding to the utterance when it is identified that the utterance includes user state information; a dialogue manger configured to acquire a parameter value of a condition determination parameter used to determine whether the action corresponding to the utterance is executable from the storage, to determine an action to be executed based on the parameter value of the condition determination parameter, and to acquire a parameter value of an action parameter used to execute the determined action from the storage; and a result processor configured to generate a response to execute the determined action using the acquired parameter value of the action parameter.

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.

Method and device for short-term path planning of autonomous driving through information fusion by using V2X communication and image processing

A method for providing an autonomous driving service platform for autonomous vehicles by using a competitive computing and information fusion is provided. And the method includes steps of: (a) a service server acquiring individual sensor data and individual driving data through sensors installed on at least part of the autonomous vehicles including a subject vehicle; (b) the service server performing (i) a process of acquiring autonomous driving source information for the subject vehicle by inputting specific sensor data of specific autonomous vehicles among the autonomous vehicles and subject sensor data of the subject vehicle to data processing servers and (ii) a process of acquiring circumstance-specific performance information on the data processing servers from a circumstance-specific performance DB; and (c) the service server transmitting the autonomous driving source information and the circumstance-specific performance information to the subject vehicle, to thereby instruct the subject vehicle to perform the autonomous driving.

Method for providing autonomous driving service platform to be used for supporting autonomous driving of vehicles by using competitive computing and information fusion, and server using the same

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. And the learning method includes steps of: (a) a learning device for verification, if at least one training image for verification is acquired, instructing a property extraction module to apply extraction operation to the training image for verification to extract property information on characteristics of the training image for verification to thereby generate a quality vector; (b) the learning device for verification instructing the neural network for verification to apply first neural network operations to the quality vector, to thereby generate predicted safety information; and (c) the learning device for verification instructing a loss module to generate a loss, and perform a backpropagation by using the loss, to thereby learn parameters included in the neural network for verification.

Learning method and learning device for providing functional safety by warning driver about potential dangerous situation by using explainable AI which verifies detection processes of autonomous driving network, and testing method and testing device using the same

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.

Method and device for providing information for evaluating driving habits of driver by detecting driving scenarios occurring during driving

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.

Learning method for supporting safer autonomous driving without danger of accident by estimating motions of surrounding objects through fusion of information from multiple sources, learning device, testing method and testing device using the same

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.

Method and device for adjusting driver assistance apparatus automatically for personalization and calibration according to driver's status

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.

Method and device for providing personalized and calibrated adaptive deep learning model for the user of an autonomous vehicle

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.

Growing community of inventors

Gyeonggi-do, South Korea

Dongsoo Shin