Abstract: Embodiments of the present disclosure relate to a method for producing refined hydrocarbons from waste plastics, the method including: a pretreatment process of pretreating waste plastics; a pyrolysis process of producing pyrolysis gas by introducing the waste plastics pretreated in the pretreatment process into a pyrolysis reactor; a lightening process of producing pyrolysis oil by introducing the pyrolysis gas into a hot filter; and a distillation process of distilling the pyrolysis oil to obtain refined hydrocarbons, wherein a liquid condensed in the hot filter is re-introduced into the pyrolysis reactor, and a system for producing refined hydrocarbons from waste plastics.

Abstract: Disclosed herein are a deep network learning method using an autonomous vehicle and an apparatus for the same. The deep network learning apparatus includes a processor configured to select a deep network model requiring an update in consideration of performance, assign learning amounts for respective vehicles in consideration of respective operation patterns of multiple autonomous vehicles registered through user authentication, distribute the deep network model and the learning data to the multiple autonomous vehicles based on the learning amounts for respective vehicles, and receive learning results from the multiple autonomous vehicles, and memory configured to store the deep network model and the learning data.

Abstract: Disclosed herein are a cooperative driving method based on driving negotiation and an apparatus for the same. The cooperative driving method is performed by a cooperative driving apparatus for cooperative driving based on driving negotiation, and includes determining whether cooperative driving is possible in consideration of a driving mission of a requesting vehicle that requests cooperative driving with neighboring vehicles, when it is determined that cooperative driving is possible, setting a responding vehicle from which cooperative driving is to be requested among the neighboring vehicles, performing driving negotiation between the requesting vehicle and the responding vehicle based on a driving negotiation protocol, and when the driving negotiation is completed, performing cooperative driving by providing driving guidance information for vehicle control to at least one of the requesting vehicle and the responding vehicle.

Abstract: Disclosed herein are a method and apparatus for processing a driving cooperation message. The method for processing a driving cooperation message includes receiving multiple first driving cooperation messages from neighboring autonomous vehicles, adjusting cooperation classes of the multiple first driving cooperation messages, creating driving strategies corresponding to the adjusted cooperation classes in descending order of priorities of the adjusted cooperation classes, generating second driving cooperation messages including the adjusted cooperation classes and the driving strategies corresponding to the adjusted cooperation classes, and sending the second driving cooperation messages to the neighboring autonomous vehicles requiring cooperative driving.

Abstract: Disclosed herein are a cooperative driving method based on driving negotiation and an apparatus for the same. The cooperative driving method is performed by a cooperative driving apparatus for cooperative driving based on driving negotiation, and includes determining whether cooperative driving is possible in consideration of a driving mission of a requesting vehicle that requests cooperative driving with neighboring vehicles, when it is determined that cooperative driving is possible, setting a responding vehicle from which cooperative driving is to be requested among the neighboring vehicles, performing driving negotiation between the requesting vehicle and the responding vehicle based on a driving negotiation protocol, and when the driving negotiation is completed, performing cooperative driving by providing driving guidance information for vehicle control to at least one of the requesting vehicle and the responding vehicle.

Abstract: The autonomous driving device including a communication circuit configured to communicate with an unmanned aerial vehicle, a plurality of sensors disposed in the autonomous vehicle to monitor all directions of the autonomous vehicle, and a processor, wherein the processor is configured to: control the unmanned aerial vehicle to hover at each of a plurality of waypoints of a designated flight path by controlling a relative position of the unmanned aerial vehicle through the communication circuit, change a posture angle of the unmanned aerial vehicle to a plurality of posture angles corresponding to the waypoints of the flight path, generate a plurality of images including the checkerboard and corresponding to the plurality of waypoints and the plurality of posture angles through the plurality of sensors, and calibrate the plurality of sensors on the basis of a relationship between matching points of the plurality of images.

Abstract: Disclosed herein are a deep network learning method using an autonomous vehicle and an apparatus for the same. The deep network learning apparatus includes a processor configured to select a deep network model requiring an update in consideration of performance, assign learning amounts for respective vehicles in consideration of respective operation patterns of multiple autonomous vehicles registered through user authentication, distribute the deep network model and the learning data to the multiple autonomous vehicles based on the learning amounts for respective vehicles, and receive learning results from the multiple autonomous vehicles, and memory configured to store the deep network model and the learning data.

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 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 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: The autonomous driving device including a communication circuit configured to communicate with an unmanned aerial vehicle, a plurality of sensors disposed in the autonomous vehicle to monitor all directions of the autonomous vehicle, and a processor, wherein the processor is configured to: control the unmanned aerial vehicle to hover at each of a plurality of waypoints of a designated flight path by controlling a relative position of the unmanned aerial vehicle through the communication circuit, change a posture angle of the unmanned aerial vehicle to a plurality of posture angles corresponding to the waypoints of the flight path, generate a plurality of images including the checkerboard and corresponding to the plurality of waypoints and the plurality of posture angles through the plurality of sensors, and calibrate the plurality of sensors on the basis of a relationship between matching points of the plurality of images.

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 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: A system for measuring displacement of an accelerating tube by using a micro-alignment telescope, which includes a vacuum chamber; a hollow accelerating tube in the vacuum chamber; a sighting target attached to a surface of the accelerating tube while protruding from the surface of the accelerating tube; the micro-alignment telescope spaced apart from one side surface of the vacuum chamber; a first lens device interposed between the micro-alignment telescope and the vacuum chamber; and a second lens device spaced apart from an opposite side surface of the vacuum chamber by a distance, wherein the vacuum chamber includes first and second viewports placed on the surfaces of the vacuum chamber in correspondence with each other, and the micro-alignment telescope, the first lens device, the first viewport, the sighting target, the second viewport and the second lens device are aligned on a same axis in one direction.

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: A system for measuring displacement of an accelerating tube by using a micro-alignment telescope, which includes a vacuum chamber; a hollow accelerating tube in the vacuum chamber; a sighting target attached to a surface of the accelerating tube while protruding from the surface of the accelerating tube; the micro-alignment telescope spaced apart from one side surface of the vacuum chamber; a first lens device interposed between the micro-alignment telescope and the vacuum chamber; and a second lens device spaced apart from an opposite side surface of the vacuum chamber by a distance, wherein the vacuum chamber includes first and second viewports placed on the surfaces of the vacuum chamber in correspondence with each other, and the micro-alignment telescope, the first lens device, the first viewport, the sighting target, the second viewport and the second lens device are aligned on a same axis in one direction.