Abstract: An anode active material for a secondary battery includes a carbon-based active material, and silicon-based active material particles doped with magnesium. At least some of the silicon-based active material particles include pores, and a volume ratio of pores having a diameter of 50 nm or less among the pores is 2% or less based on a total volume of the silicon-based active material particles.

Abstract: The present disclosure relates to an organic electroluminescent compound, a plurality of host materials, and an organic electroluminescent device comprising the same. By comprising the compound according to the present disclosure or by comprising a specific combination of compounds according to the present disclosure as a plurality of host materials, it is possible to produce an organic electroluminescent device having improved driving voltage, luminous efficiency, and/or lifetime properties compared to the conventional organic electroluminescent devices.

Abstract: A method for manufacturing an electronic device including a semiconductor memory may include forming a first carbon electrode material, surface-treating the first carbon electrode material to decrease a surface roughness of the first carbon electrode material, and forming a second carbon electrode material on the treated surface of the first carbon electrode material. The second carbon electrode material may have a thickness that is greater than a thickness of the first carbon electrode material.

Abstract: A method of automatically detecting a dynamic object recognition error in an autonomous vehicle is provided. The method includes parsing sensor data obtained by frame units from a sensor device equipped in an autonomous vehicle to generate raw data by using a parser, analyzing the raw data to output a dynamic object detection result by using a dynamic object recognition model, determining that detection of a dynamic object recognition error succeeds by using an error detector when the dynamic object detection result satisfies an error detection condition, and storing the raw data and the dynamic object detection result by using a non-volatile memory when the detection of the dynamic object recognition error succeeds.

Abstract: There is provided a method for providing driver's driving information of a user terminal device, including accessing a driving information providing server storing data generated in a driving recording device for a vehicle, receiving driving record data including event record data corresponding to a driving-related event of a driver from the driving information providing server, and displaying a driving-related event occurrence location on a map using the received driving record data. The driving-related event may include at least two or more of a lane departure event, a forward collision possibility event, a rear side collision possibility event, a sudden deceleration event, a sudden acceleration event, a sudden stop event, a sudden start event, and a speeding event.

Abstract: Provided is a method of generating and controlling a driving path for an autonomous vehicle, the method including generating a driving path that matches a driving intention on the basis of sensing data acquired from a sensing module of the autonomous vehicle, determining steering angle information corresponding to the generated driving path, and controlling a steering angle of the autonomous vehicle.

Abstract: The present disclosure relates to an apparatus and a method for predicting future trajectories of various types of objects using an artificial neural network trained by a method for training an artificial neural network to predict future trajectories of various types of moving objects for autonomous driving. The apparatus for predicting future trajectories includes a shared information generation module configured to: collect location information of one or more objects around an autonomous vehicle for a predetermined time, generate past movement trajectories for the one or more objects based on the location information, and generate a driving environment feature map for the autonomous vehicle based on road information around the autonomous vehicle and the past movement trajectories; and a future trajectory prediction module configured to generate future trajectories for the one or more objects based on the past movement trajectories and the driving environment feature map.

Abstract: Disclosed is a system for executing a traffic light recognition model learning and inference method, includes a data collection platform including a camera for collecting image data, and a first processor that samples traffic light image data including a traffic light among the image data, generates annotation data based on the traffic light image data, and generates a traffic light data set using the traffic light image data and the annotation data, wherein the traffic light data set includes information on a location of the traffic light, a type of the traffic light, traffic light on/off, and a traffic signal direction of the traffic light.

Abstract: Disclosed herein are an object recognition apparatus of an automated driving system using error removal based on object classification and a method using the same. The object recognition method is configured to train a multi-object classification model based on deep learning using training data including a data set corresponding to a noise class, into which a false-positive object is classified, among classes classified by the types of objects, to acquire a point cloud and image data respectively using a LiDAR sensor and a camera provided in an autonomous vehicle, to extract a crop image, corresponding to at least one object recognized based on the point cloud, from the image data and input the same to the multi-object classification model, and to remove a false-positive object classified into the noise class, among the at least one object, by the multi-object classification model.

Abstract: Disclosed are a method and an apparatus for generating a map for autonomous driving and recognizing a location based on the generated map. When generating a map, a spherical range image is obtained by projecting 3D coordinate information corresponding to a 3D space onto a 2D plane, and semantic segmentation is performed on the spherical range image to generate a semantic segmented image. Then, map data including a spherical range image, a semantic segmented image, and lane attribute information are generated.

Abstract: Disclosed herein are an object recognition apparatus of an automated driving system using error removal based on object classification and a method using the same. The object recognition method is configured to train a multi-object classification model based on deep learning using training data including a data set corresponding to a noise class, into which a false-positive object is classified, among classes classified by the types of objects, to acquire a point cloud and image data respectively using a LiDAR sensor and a camera provided in an autonomous vehicle, to extract a crop image, corresponding to at least one object recognized based on the point cloud, from the image data and input the same to the multi-object classification model, and to remove a false-positive object classified into the noise class, among the at least one object, by the multi-object classification model.

Abstract: Provided is a method of generating and controlling a driving path for an autonomous vehicle, the method including generating a driving path that matches a driving intention on the basis of sensing data acquired from a sensing module of the autonomous vehicle, determining steering angle information corresponding to the generated driving path, and controlling a steering angle of the autonomous vehicle.

Abstract: Provided is a driving guide system, and more specifically, a system for guiding a vehicle occupant in driving through linguistic description. One embodiment of the present invention is an apparatus for guiding driving with linguistic description of a destination, which is installed on a vehicle and guides driving by outputting a linguistic description of a destination building, wherein the apparatus sets a destination according to a command or input, receives appearance information of a building of the destination from a server, and represents and outputs the appearance information in a linguistic form.

Abstract: Provided is a driving guide system, and more specifically, a system for guiding a vehicle occupant in driving through linguistic description. One embodiment of the present invention is an apparatus for guiding driving with linguistic description of a destination, which is installed on a vehicle and guides driving by outputting a linguistic description of a destination building, wherein the apparatus sets a destination according to a command or input, receives appearance information of a building of the destination from a server, and represents and outputs the appearance information in a linguistic form.

Abstract: Provided is an autonomous driving technology in which the autonomous driving method includes planning global travelling such that guidance information of global node points is acquired, determining a location of a subject vehicle, generating a first local high-definition map such that the first local high-definition map is generated for at least one section in a global-travelling planned route included in the planning of the global travelling using at least one of a road view and an aerial view provided from a map server, planning a local route for autonomous driving using the first local high-definition map, and controlling the subject vehicle according to the planning of the local route to perform the autonomous driving.

Abstract: Provided is an autonomous driving method. The autonomous driving method includes a global driving planning operation in which global guidance information for global node points are acquired, a host vehicle location determination operation, an information acquisition operation in which information regarding an obstacle and a road surface marking within a preset distance ahead is acquired, a local precise map generation operation in which a local precise map for a corresponding range is generated using the information acquired within the preset distance ahead, a local route planning operation in which a local route plan for autonomous driving within at least the preset distance is established using the local precise map, and an operation of controlling a host vehicle according to the local route plan to perform autonomous driving.

Abstract: Provided are an apparatus and method for extracting a salient line for an information sign. The apparatus includes an image input portion configured to receive an image including information indication lines to be detected, a signal calculator configured to generate a symmetric function signal consistent with a thickness and a direction of an information indication line having a preset thickness in the received image and perform a convolution of the information indication line and the generated symmetric function signal, a highest value enhancement portion configured to enhance highest values in results of the convolution using a signal quality improvement filter, and a local highest value extractor configured to find local highest values among the enhanced highest values and extract a line connecting the local highest values as a salient line connecting center points in the information indication line.

Abstract: Provided are an apparatus and method for sharing and learning driving environment data to improve the decision intelligence of an autonomous vehicle. The apparatus for sharing and learning driving environment data to improve the decision intelligence of an autonomous vehicle includes a sensing section which senses surrounding vehicles traveling within a preset distance from the autonomous vehicle, a communicator which transmits and receives data between the autonomous vehicle and another vehicle or a cloud server, a storage which stores precise lane-level map data, and a learning section which generates mapping data centered on the autonomous vehicle by mapping driving environment data of a sensing result of the sensing section to the precise map data, transmits the mapping data to the other vehicle or the cloud server through the communicator, and performs learning for autonomous driving using the mapping data and data received from the other vehicle or the cloud server.

Abstract: Provided is a technology for a vehicle sensor calibration, in which a vehicle sensor calibration apparatus according to an embodiment includes a sensor device installed inside a calibration room that is a space in which a vehicle having a vehicle sensor mounted thereon is positioned, and configured to obtain basic position information and basic orientation information of the vehicle, a calibration execution module installed in the vehicle and configured to execute calibration on the vehicle sensor on the basis of information received from the outside, and a control module configured to generate calibration position information and calibration orientation information for calibration of the vehicle by analyzing the basic position information and the basic orientation information obtained by the sensor device.

Abstract: Provided herein is a device and a method for recognizing an obstacle and a parking slot to support an unmanned autonomous parking function. The device includes a motion measurement unit measuring a vehicle motion using an in-vehicle sensor, an inverse perspective transform unit performing inverse perspective transformation of an image, which is obtained using a wide-angle camera, to obtain an inverse perspective image, and an obstacle detection unit detecting the obstacle using the inverse perspective image.