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 is an apparatus and method for providing location and heading information of an autonomous driving vehicle on a road within a housing complex. The apparatus includes an image sensor installed on an autonomous driving vehicle and configured to detect images of surroundings depending on motion of the autonomous driving vehicle. A wireless communication unit is installed on the autonomous driving vehicle and is configured to receive a Geographic Information System (GIS) map of inside of a housing complex transmitted from an in-housing complex management device in a wireless manner. A location/heading recognition unit is installed on the autonomous driving vehicle, and is configured to recognize location and heading of the autonomous driving vehicle based on the image information received from the image sensor and the GIS map of the inside of the housing complex received via the wireless communication unit.
Abstract:
Disclosed herein is a method for integrated anomaly detection. The method includes detecting a thing object and a human object in input video using a first neural network, and tracking the human object, and detecting an anomalous situation based on an object detection result and a human object tracking result.
Abstract:
Disclosed herein are an apparatus and method for automatically parking a vehicle. The apparatus for automatically parking a vehicle includes a location/heading information provision unit and a parking algorithm computation unit. The location/heading information provision unit calculates the corrected distances of movement of first and second wheels of the vehicle from the time at which parking is started using a plurality of correction factors that are calculated during a movement in any one of forward and rearward headings and during determination of a parking space, and calculates the changes in a heading and location of the vehicle using the corrected distances of movement. The parking algorithm computation unit generates a vehicle control signal intended to automatically park the vehicle in the parking space based on the changes in the heading and location of the vehicle.
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:
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:
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:
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 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.