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 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 are an apparatus and method for transforming augmented reality information of a head-up display (HUD) for a vehicle.

Abstract: Provided are an apparatus and method for transforming augmented reality information of a head-up display (HUD) for a vehicle.