Electronics and Telecommunications Research Institute

Hyundai Motor Company

Kia Motors Corporation

Samsung Electronics Co., Ltd.

Caterpillar Inc.

Yonsei University

Ajou University Industry-Academic Cooperation Foundation

Gm Global Technolgoy Operations, Inc.

Hyundai Transys Incorporated

Nautilus Hyosung Inc.

Pukyong National University

Nifco Korea Inc.

Aptiv Korea LLC

Hoon Ki Lee

Youn Ok Park

Seungjae Bahng

Seul Ki Han

Tae Whan Yoo

Uk Il Yang

Ji Eun Won

Hyung Sun Jang

Bo Young Yun

Jung Hak Kim

Young Jo Bang

Jung Pil Choi

Su Min Jo

Il Gyu Kim

Woo Young Lee

Sang Won Lee

Sung Woo Lee

Sang Bok Won

Bing Hui

Timothy A Lorentz

Junhyeong Kim

Jung Tae Kim

Jong-Hoon Lee

Euntai Kim

Beomseong Kim

MinkYun Yoo

Seung Eun Hong

Hyeong Ho Lee

Dong-Min Kim

Bong Seop Song

Hee Sang Chung

Bong Tae Kim

Yool Kwon

Sung-Hyun Kim

Byungho Lee

Min Kyun Yoo

Kyung Yeol Sohn

Jun Woo Kim

Hyeong Sook Park

Kyung-Hoon Lee

Mi Kyong Han

Sang-Kyu Kim

Young Ha Lee

Jae Pil Hwang

Jae Kwon Kim

Mathew D Guldan

Euihyun Paik

Jeong Jin Lee

Hyok Jin Chong

Seongkeun Park

Hyungsun Jang

Baehoon Choi

Jong Gwan Lee

HyunJu Kim

YoungWon Kim

Jun Beom Kim

Seok Hwan Park

Junghyun Kim

Hyun Kyu Chung

Youn-Ok Park

Jun Hwan Lee

Hyun Kyung Kim

Kwang Roh Park

Hoon Do Heo

Jun-Woo Kim

Sejun Kim

Seung Joon Lee

Kyoung Jun Lee

Seung Jae Bahng

Dong Gu Lee

Chang Wahn Yu

Sang Hark Lee

Sung Yeol Choi

Yoo Mee Min

Gosan Noh

Ji Soo Park

Hyun Seok Ko

Hoe Ju Chung

Min Ho Park

Eui Hyun Paik

Jeffrey A Morgan

Won Sang Cho

Kwangwoo Jeong

Young Ho Park

Uijung Kim

Il Min Kim

Hoo Young Ahn

Noh Sam Park

Bong Sob Song

Uk Song Kang

Hyun Myung Wu

Ki-Baek Kim

JuYun Ro

Hyunjin Yoon

Jae Geun Kim

Hyunju Kim

Youngwon Kim

Saehoon Ju

Brent G Drews

Seungjae Bang

The present disclosure discloses beamforming methods and apparatuses. According to an exemplary embodiment of the present disclosure, an antenna message generation deep neural network (DNN) and a beam characteristic generation DNN of a bipartite graph neural network (BGNN) having terminals as vertices on one side and APs as vertices on another side may be constructed.

Beamforming method and apparatus using deep neural network in wireless communication system

The present disclosure relates to an omnidirectional sensor fusion system and method and a vehicle including the same. The omnidirectional sensor fusion system includes a sensor track processing unit configured to receive recognition information from one or more sensors to generate a sensor track, a sensor track association determination unit configured to determine, based on the generated sensor track being located at an overlapping point of sensing regions of the one or more sensors, an association between a previous sensor fusion track and the sensor track, the sensor track association determination unit further configured to change sensor track information in response to the determined association and output a sensor fusion track, a sensor fusion track tracing unit configured to trace the output sensor fusion track, and a sensor fusion track maintenance unit configured to maintain the traced sensor fusion track.

Omnidirectional sensor fusion system and method and vehicle including the same

Disclosed is a beamforming method using a deep neural network. The deep neural network may include an input layer, L hidden layers, and an output layer, and the beamforming method may include: obtaining channel information h between a base station and K terminals and a transmit power limit value P of the base station, and inputting h and P into the input layer; and performing beamforming on signals to be transmitted to the K terminals using beamforming vectors derived using the output layer and at least one activation function, wherein the base station transmits the signals to the K terminals using M transmit antennas. Here, the output layer may be configured in a direct beamforming learning (DBL) scheme, a feature learning (FL) scheme, or a simplified feature learning (SFL) scheme.

Deep learning based beamforming method and apparatus

A cup holder for a vehicle may include a holder body slidably coupled to a cup holder mounting portion in the vehicle, a finger elastically and pivotably mounted to the holder body, and configured to press and support a side surface of a cup inserted into the cup holder together with the holder body, a support connected to the holder body by a link such that the support is vertically movable in accordance with pivotal movement of the link, the support being configured to support a bottom portion of the cup, and a locking member disposed between the finger and the link, to lock the link, for prevention of pivotal movement of the link, or to unlock the link in linkage with pivotal operation of the finger.

Cup holder for vehicle

An apparatus for predicting a concurrent lane change of multiple vehicles to prevent or minimize collision, and a vehicle using the same. The apparatus includes a subject vehicle state estimator for estimating a state of a subject vehicle based on subject vehicle information and surrounding lane state information, a surrounding vehicle state estimator for estimating a state of a surrounding vehicle based on the subject vehicle state information and surrounding vehicle information, a lane state estimator for estimating a state of a surrounding lane based on the estimated subject vehicle state information and surrounding lane information, and a lane change predictor for predicting concurrent lane change of the subject vehicle and the surrounding vehicle based on the estimated subject vehicle state information, the estimated surrounding vehicle state information and the estimated surrounding lane state information.

Apparatus and method for predicting concurrent lane change and vehicle including the same

The present invention discloses apparatus for tracking a target vehicle which tracks the target vehicle by identifying a plurality of sensor tracks output from a plurality of different kinds of sensors. The apparatus for tracking a target vehicle incudes a sensor track processing unit, a sensor track association determination unit, a sensor fusion track tracking unit, and a sensor fusion track management unit.

Apparatus and method for tracking target vehicle and vehicle including the same

The present disclosure provides an apparatus for associating sensor data in a vehicle. The apparatus includes a plurality of sensors configured to sense an external object and a processor configured to be electrically connected with the plurality of sensors. The processor is configured to obtain a sensor data set for a target located around the vehicle using the plurality of sensors, select at least a portion of sensor data included in the sensor data set based on at least one of target information, a curvature of a road where the vehicle or the target is traveling, or a type of a sensor configured to sense the target, and generate track data for the target by associating at least the portion of the sensor data.

Apparatus and method for associating sensor data in vehicle

Disclosed are an occupancy distance map (ODM) information reliability determination system and method and a vehicle using the same. The system includes a signal conversion unit configured to receive a plurality of sensing signals and to perform signal processing, a calculation unit configured to calculate state information of a nearby vehicle detected from processed signals, a sensor fusion track output unit configured to output sensor fusion track information based on the calculated state information of the nearby vehicle, an ODM output unit configured to output ODM information based on the calculated state information of the nearby vehicle, an ODM fusion unit configured to generate sensor fusion ODM information by performing sensor fusion with respect to the ODM information, and an ODM information reliability determination unit configured to determine reliability of the sensor fusion ODM information by confirming association between the sensor fusion track information and the sensor fusion ODM information.

ODM information reliability determination system and method and vehicle using the same

An apparatus and method for identifying a short cut-in vehicle, and a vehicle using the same are disclosed. The apparatus includes a signal conversion unit configured to receive and signal-process a plurality of sensing signals, a computation unit configured to compute state information of a surrounding vehicle detected from the signal-processed signal, a sensor fusion track output unit configured to output a sensor fusion track based on the computed state information of the surrounding vehicle, an occupancy distance map (ODM) output unit configured to output ODM information including a grid map corresponding to a vehicle detection region and an ODM object including a plurality of detection points based on the computed state information of the surrounding vehicle, and a cut-in vehicle identification unit configured to identify a cut-in vehicle based on the output sensor fusion track and ODM information.

Apparatus and method for identifying short cut-in vehicle and vehicle using the same

A method of fusing a surrounding vehicle-to-vehicle (V2V) signal and a sensing signal of an ego vehicle includes calculating a position of a surrounding target vehicle and a velocity of the target vehicle based on the ego vehicle using global positioning system (GPS)/vehicle velocity information detected in the ego vehicle and GPS/vehicle velocity information included in a V2V signal detected in the target vehicle, extracting a sensor signal within a predetermined specific distance error from a position of the target vehicle relative to the ego vehicle of the V2V signal as a sensor information candidate group corresponding to the V2V signal, adaptively using a constant velocity model, a kinematic model or a dynamic model according to the extracted situation, and comparing the V2V signal with information on an object tracked using both the sensing signal and the V2V signal to estimate a position and velocity error of the V2V signal.

System and method for fusing surrounding V2V signal and sensing signal of ego vehicle

A close cut-in vehicle identification apparatus includes an information generator configured to generate sensor fusion information, estimated vehicle state information, and risk determination information based on surrounding vehicle information upon receiving the surrounding vehicle information, a feature combination generator configured to extract a plurality of features based on the sensor fusion information, the estimated vehicle state information, and the risk determination information and to combine the extracted features to generate a plurality of feature combinations, a classification learner configured to classify and learn the plurality of feature combinations depending on a weight factor, and a vehicle intention determination device configured to determine intention of a surrounding vehicle based on a classification learning result value to select an optimum feature combination corresponding to identification of the close cut-in vehicle and configured to determine a situation of the close cut-in vehicle based on the selected optimum feature combination.

Apparatus and method for identifying close cut-in vehicle and vehicle including apparatus

Disclosed is a sensory information providing apparatus. The sensory information providing apparatus may comprise a learning model database storing a plurality of learning models related to sensory effect information with respect to a plurality of videos; and a video analysis engine generating the plurality of learning models by extracting sensory effect association information by analyzing the plurality of videos and sensory effect meta information of the plurality of videos, and extracting sensory information corresponding to an input video stream by analyzing the input video stream based on the plurality of learning model.

Sensory information providing apparatus, video analysis engine, and method thereof

An apparatus and a method are capable of accurately deciding a maneuver of a surrounding vehicle. The apparatus includes a first surrounding vehicle information detector configured to obtain first surrounding vehicle information for a surrounding vehicle of a vehicle by using a front radar device. The apparatus further includes a second surrounding vehicle information detector configured to obtain second surrounding vehicle information for the surrounding vehicle by using a corner radar device. The apparatus also includes a processor configured to decide a motion of the surrounding vehicle by using the first surrounding vehicle information and the second surrounding vehicle information. The processor is also configured to decide a maneuver of the surrounding vehicle by using maneuver decision logic derived by a mechanical training technique. The processor is further configured to decide a final maneuver of the surrounding vehicle by using the two decision results.

Apparatus and method for deciding a maneuver of a nearby vehicle

Disclosed are a random access method and a random access apparatus in a wireless communication system. An operation method of a terminal in a communication system, according to the present invention, comprises receiving system information including configuration information on a physical random access channel (PRACH) composed of a plurality of access slots from a first base station; selecting one access slot among the plurality of access slots based on the configuration information; and transmitting a random access preamble to the first base station through the one access slot. Therefore, the capacity of the random access channel can be increased without further occupation of radio resources.

Method for random access in wireless communication system and apparatus for the same

A high speed moving terminal including first and second antennas each communicating with first and second radio units (RUs) of a base station distributedly installed in a mobile wireless backhaul network communicates with the first and second RUs, respectively, with different uplink-downlink configuration of the first RU and the second RU, and transmits control information of the corresponding downlink signal in an uplink subframe that comes first in the time domain after processing latency of a downlink signal received from the first RU among uplink frames with the uplink-downlink configurations of the first RU and the second RU if the downlink signal is received from the first RU through the first antenna.

High speed moving terminal and method for transmitting control information thereof, and method for receiving control information of base station in mobile wireless backhaul network

Growing community of inventors

Daejeon, South Korea

Hoon Ki Lee