Abstract: A method of filtering dynamic objects in radar-based ego-motion estimation includes converting measurement value at current time, measured by radar sensor, into point cloud, classifying the point cloud into points of a first object predicted as static object and points of a second object predicted as dynamic object, based on position value of dynamic object tracked at previous time, classifying the points of the first object into the points of the static object predicted as normal value and the points of the dynamic object predicted as outlier, based on outlier filtering algorithm, classifying the points of the second object into points of a candidate static object and points of a candidate dynamic object, based on velocity model of the static object, and tracking a position value of the dynamic object at current time, based on the points of the dynamic object and the points of the candidate dynamic object.

Abstract: Disclosed is a processor which includes a camera image feature extractor that extracts a camera image feature based on a camera image, a LIDAR image feature extractor that extracts a LIDAR image feature based on a LIDAR image, a sampling unit that performs a sampling operation based on the camera image feature and the LIDAR image feature and generates a sampled LIDAR image feature, a fusion unit that fuses the camera image feature and the sampled LIDAR image feature and generates a fusion map, and a decoding unit that decodes the fusion map and generates a depth map. The sampling operation includes back-projecting a pixel location of the camera image feature on a camera coordinate system to generate a back-projection point, and projecting the back-projection point on a plane of the LIDAR image to calculate sampling coordinates.

Abstract: A method for changing a route when an error occurs in an autonomous driving AI includes collecting error information of the AI when an error of the AI has occurred, extracting, from a storage, past error information about a same kind of AI as that of the AI based on the error information of the AI, generating an error analysis result based on the past error information, generating an error analysis result message based on the error analysis result, and determining whether the driving of the autonomous driving vehicle needs to be stopped based on the error analysis result message.

Abstract: Disclosed herein are an apparatus and method for automatic parking based on recognition of a parking area environment. The method may include searching for an available parking space, determining whether reverse parking is possible by recognizing the environment of the available parking space, recognizing at least one additional vehicle located in the vicinity of the available parking space, and setting a parking destination based on the determination of whether reverse parking is possible and the result of recognition of the at least one additional vehicle.

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: 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 system for infrastructure dynamic object recognition information convergence processing in an autonomous vehicle according to an embodiment of the present invention includes a recognition unit configured to receive object information collected by a sensor of the autonomous vehicle to recognize an environment, a V2X reception message processing unit configured to receive object information collected by an InfraEdge system, and a determination and control unit configured to use the object information collected by the sensor and the object information collected by the InfraEdge system and perform a determination for autonomous driving in consideration of a convergence delay time which is a time difference between a time recorded according to message reception of the V2X reception message processing unit and a current time.

Abstract: Disclosed herein is a method of controlling an autonomous vehicle driving in a lane of a main line. The method may include determining whether the autonomous vehicle is driving in a target lane to accommodate merging traffic, determining whether a merge request message is received from a merging vehicle when the autonomous vehicle is determined to drive in the target lane, determining whether a collision with the merging vehicle will occur based on the merge request message when the merge request message is received, and sending a merge approval message to the merging vehicle when the collision with the merging vehicle is expected.

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 herein are an apparatus and method for providing a seamless network service based on multiple heterogeneous networks. The method for providing a seamless network service based on multiple heterogeneous networks includes providing, by a multi-network management apparatus, a service transmitted from a server to a terminal over a primary network, synchronizing, by the multi-network management apparatus, service data transmitted over the primary network with a secondary network maintained in a standby state, changing, by the multi-network management apparatus, the secondary network to the primary network as a communication failure between the primary network and the terminal occurs, and providing, by the multi-network management apparatus, the service transmitted from the server to the terminal over the changed primary network.

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 an apparatus and method for providing a customized traffic guidance service. The method may include acquiring data about one or more nearby objects, detecting surrounding traffic conditions based on the data about the nearby objects, determining whether to provide a customized traffic guidance service, selecting one or more target objects to which the customized traffic guidance service is to be provided and guidance information to be provided to each of the target objects, generating a customized traffic guidance message for the selected guidance information, and transmitting the customized traffic guidance message to the corresponding target object.

Abstract: Disclosed is a system performing a method for detecting intersection traffic light information including a traffic light detection module including an image sensor for generating first signal data based on traffic light image data in which a traffic light is included, a communication module that receives second signal data for communication with a surrounding object and an external device, an object information collection module that collects dynamic data of the surrounding object, and a signal information inference module that infers third signal data based on the dynamic data. The dynamic data of the surrounding object includes at least one information of whether the surrounding object moves, a moving direction of the surrounding object, and whether the surrounding object accelerates or decelerates. Each of the signal data includes pieces of information about a type of the traffic light and a signal direction of the traffic light.

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 apparatus and method for adaptive autonomous driving control. The apparatus includes memory in which at least one program is recorded and a processor for executing the program. The program may perform control of a target vehicle by converting a theoretical control value based on a vehicle control algorithm into a hardware-dependent control value, which is dependent on the platform or hardware of the target vehicle, and may modify at least one parameter or a conversion equation for conversion of the hardware-dependent control value such that an error is minimized based on the difference between a response value according to the control of the target vehicle and a control value.

Abstract: A display panel and a display device. The display panel includes: an array substrate; a light-emitting layer which is provided on the array substrate; and a encapsulation layer which covers the light-emitting layer. The encapsulation layer includes at least one inorganic film layer and at least one organic film layer which are stacked and alternately arranged, and the at least one inorganic film layer includes a first region and a second region arranged around the first region, the first region of the at least one inorganic film layer located between the light-emitting layer and the organic film layer has a surface energy greater than the second region, and the organic film layer is stacked over the first region.

Abstract: Disclosed herein is a method for deidentifying a driver image dataset. The method includes generating a combination dataset having a preset size based on a driver image dataset, extracting face shape information from each of pieces of driver image data forming the driver image dataset, and generating a deidentified dataset using the combination dataset and the face shape information.

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.