Abstract: A method of classifying an object according to an embodiment includes extracting a first feature by transforming rectangular coordinates of points included in the box of the object, obtained from a point cloud acquired using a LiDAR sensor, into complex coordinates and performing Fast Fourier Transform (FFT) on the complex coordinates, obtaining an average and a standard deviation as a second feature, the average and the standard deviation being parameters of a Gaussian model for the points included in the box of the object, and classifying the type of object based on at least one of the first feature or the second feature.

Abstract: The present disclosure may relate to a vehicle control apparatus and a method thereof. The vehicle control apparatus with a sensor and processor may generate a first virtual box based on sensor data and first points representing an external vehicle and protruding object and create a second virtual box from second points excluding the object portion. Validity of the second virtual box is determined using criteria including location, angle, and distance relative to the first virtual box. If valid, the second virtual box is mapped onto the external vehicle, and a signal is generated indicating this mapping.

Abstract: A vehicle control apparatus may comprise a sensor, memory, and processor. The processor may determine, from unreliable track information indicating a second vehicle ahead of a detected first vehicle, conditions such as the second vehicle's motion state, type, or initial heading direction. It then may adjust virtual track data accordingly, considering factors like a virtual track path, a virtual box orientation, or a virtual box position. The vehicle control apparatus may output signals indicating this adjusted data, guided by criteria such as the second vehicle's motion, alignment with a host vehicle, type, a number of stored pieces of virtual track information, a virtual track path length, a virtual box position, or lane changes of the host vehicle. The vehicle control apparatus may control, based on the signals, autonomous operation of the host vehicle.

Abstract: A vehicle control apparatus and a method thereof are provided. The vehicle control apparatus includes a sensor (e.g., light detection and ranging (LiDAR)), a communication circuit, and a processor. The processor receives a signal indicating that a vehicle has entered a specified space that is identified via a sensor, divides the specified space into a lower space and an upper space based on a vertical axis, generates a lower box corresponding to a first external object in the lower space, generates an upper box corresponding to a second external object in the upper space, determines whether the upper box at least partially overlaps with the lower box, and outputs a signal indicating that the second external object and the first external object are a same object.

Abstract: In a vehicle control apparatus and a method thereof, the vehicle control apparatus may include a light detection and ranging (LiDAR), a memory, and a processor including at least one core. The processor may receive, from the LiDAR, one of driving information associated with driving of an external vehicle, type information associated with a type of the external vehicle, or a first signal indicating an error occurring in the LiDAR, and may switch a mode associated with driving to an emergency mode different from a normal mode for outputting first information, which corresponds to the external vehicle and which is generated by use of first data associated with the driving of the external vehicle stored in the memory, second data associated with the type of the external vehicle stored in the memory, the driving information, and the type information, based on the driving information or the type information, which is incapable of occupying the at least one core, or receiving the first signal.

Abstract: An apparatus for recognizing an object and a method thereof are provided. The apparatus may comprise a sensor, a memory configured to store reference grid maps representing an appearance of a vehicle, and a processor configured to project a plurality of points indicating an external object, obtained by the sensor, onto a first surface to obtain a first grid map including a first plurality of grids, project the plurality of points indicating the external object onto a second surface to obtain a second grid map including a second plurality of grids, determine, based on at least one of the reference grid maps, the first grid map, or the second grid map, a type of the external object, and output a signal indicating the type of the external object.

Abstract: A method of tracking an object using a LiDAR sensor includes clustering LiDAR data that includes a plurality of points for an object detected by the LiDAR sensor, generating information on a plurality of segment boxes for each channel using a result of the clustering, and selecting, among the segment boxes, an associated segment box at a current time for a target object that is being tracked. The selecting includes calculating a correlation index between a current representative point and each of a tracking representative point and a previous representative point of each of the segment boxes at the current time, selecting candidate segment boxes for the associated segment box using the correlation index, and selecting the associated segment box at the current time.

Abstract: A vehicle control apparatus and a method thereof are provided. The vehicle apparatus includes light control detection and ranging (LiDAR), a memory, and a processor. The processor generates a virtual meta object, based on receiving information associated with a first plurality of points, identifies whether a converted virtual track and the virtual meta data are associated with each other, based on converting a virtual track into a time point when the virtual meta object is generated, identifies whether there is the virtual meta object, determines whether the virtual track is identified in a first area formed at a distance spaced apart at a first specified distance or more from the front of a vehicle, and activates a function for determining whether to remove at least one virtual box obtained from a second plurality of points, using the virtual track stored in the second space.

Abstract: The vehicle control apparatus may comprise a sensor and a processor configured to determine, based on frames obtained by using the sensor, a plane formed by a first axis and a second axis, the second axis corresponding to a driving direction of a vehicle, and the first axis being perpendicular to the second axis, detect objects in regions of interest of the plane, determine virtual boxes corresponding to the objects, generate a first field of view (FOV) based on the virtual boxes, determine ground points, generate a second FOV based on the ground points, generate a third FOV based on at least one of the first FOV, the second FOV, or angle information included in specification information of the sensor, determine a state of a boundary region of the sensor based on the third FOV, and output a signal indicating the determined state of the boundary region of the sensor.

Abstract: Disclosed is, inter alia, a method of producing a platinum alloy catalyst using a carbon protective layer. The method includes depositing a transition metal precursor on a Pt/C catalyst including a platinum component and a carbon carrier, placing carbon at the bottom of a reactor separately from the transition metal precursor-deposited Pt/C catalyst by a separation membrane; performing heat treatment on the inside of the; forming a Pt-M/C catalyst coated with a carbon protective layer by passing a gas product generated through thermal decomposition of the placed carbon through the separation membrane, and removing the carbon protective layer from the Pt-M/C catalyst by performing acid treatment on the carbon protective layer coated on the Pt-AMC catalyst.

Abstract: An electroless silver plating solution is provided. The electroless silver plating solution includes a silver complex solution and a reducing agent solution. The silver complex solution includes a silver carboxylate represented by Formula 1: R1—COOAg, a nitrogen-containing compound as a complexing agent, and a solvent. The reducing agent solution includes a reducing agent selected from the group consisting of glucose, hydrazine, hydroquinone, and derivatives thereof and a solvent. The electroless silver plating is based on a silver mirror reaction between the silver complex solution and the reducing agent solution.

Abstract: The present invention pertains to a genome editing method based on a CRISPR/CAS9 system, and a use thereof. A CRISPR system using oligonucleotide-induced mutagenesis and mismatch guide RNA (sgRNA) according to the present invention achieves a significant genome editing effect on target DNA. Thus, it is expected that the CRISPR system of the present invention will be able to be used in a wide range of fields, such as compositions for gene editing using genetic scissors, genome level screening, therapeutic agents for treating various diseases including cancer, the development of compositions for disease diagnosis or imaging, and the development of transgenic plants and animals.

Abstract: The present disclosure relates to an object perception method and an object perception apparatus. An object perception method may include detecting candidate objects by using at least one sensor; determining objects from a vehicle among the candidate objects; determining preceding objects among the vehicle objects from the vehicle; and determining the first closest preceding object and the second closest preceding object among the preceding objects.

Abstract: A method for recognizing small-traffic-equipment based on a LiDAR may include determining by acquiring LiDAR data of a surrounding environment and determining objects through data processing, selecting candidate objects of a size equal to or smaller than a predetermined size among the objects, determining at least one object cluster by grouping the candidate objects according to a predetermined position condition, and outputting information on at least one first cluster from the at least one object cluster.

Abstract: There is provided an object tracking apparatus comprising a light detection and ranging (lidar) sensor, one or more processors, and memory storing instructions. The object tracking apparatus receives lidar data from the lidar sensor and track an object using the received lidar data, determine, based on a predetermined criterion and information about the object, at least one of: a first score indicating a likelihood that the object is moving, or a second score indicating a likelihood that the object is stationary, output a comparison result value of a comparison between the first score and the second score, and determine, based on the comparison result value, whether the object is moving or stationary.

Abstract: A multilayer capacitor includes a capacitor body including a dielectric layer and first and second internal electrodes and having first and second surfaces opposing each other, third and fourth surfaces connected to the first and second surfaces and opposing each other, and fifth and sixth surfaces connected to the first and second surfaces, connected to the third and fourth surfaces, and opposing each other, first and second side portions disposed on the fifth and sixth surfaces of the capacitor body, respectively, and first and second external electrodes disposed on the third and fourth surfaces of the capacitor body, respectively, and connected to the first and second internal electrodes, respectively. The first and second internal electrodes have protrusions at one-side edge in a direction perpendicular to the fifth and sixth surfaces of the capacitor body.

Abstract: A method for verifying tracking information may include updating verification information of each of the tracking channels based on output information corresponding to object tracking information of each of the tracking channels, generating reliability of the verification information of each of the tracking channels, generating a verification protocol for verifying an output of at least one tracking function of the first tracking channel among the tracking channels based on the reliability of the verification information, and performing output verification of at least one tracking function of the first tracking channel.

Abstract: An embodiment device for detecting a boundary of a road in a 3D point cloud using a cascade classifier includes a rule-based classifier configured to determine whether a received LiDAR cluster has a likelihood of becoming a candidate for the boundary of the road using a box parameter surrounding a point cloud constituting the LiDAR cluster and a point parameter, and a learning-based classifier configured to apply a machine-learning scheme to the LiDAR cluster selected as the candidate for the boundary of the road by the rule-based classifier to determine the LiDAR cluster to be the boundary of the road or an object other than the boundary of the road.

Abstract: A method for detecting an object includes: generating an exhaust gas meta object having the same data format as a track box for tracking a target object based on an information of a first object box corresponding to an exhaust gas included in a box information indicating a shape of an object; verifying a validity of the information of the first object box based on an association operation between the exhaust gas meta object and pre-stored track boxes; and generating and storing an exhaust gas virtual track based on the information of the first object box when it is determined that the information of the first object box is valid according to the verification.

Abstract: A method for determining reliability of a LiDAR track includes determining reliability of tracking input information for tracking a target object represented by the LiDAR track based on the reliability of each predetermined features related to the LiDAR track; determining reliability of function processing of a system based on processing reliability of each of predetermined functions of the system for tracking the target object; and determining reliability of the LiDAR track based on the reliability of the tracking input information and the reliability of the function processing and output reliability information.