Abstract: Disclosed are a method, a device, and a vehicle for collecting vehicle data for autonomous driving. The method of collecting vehicle data includes: acquiring sensor data around a vehicle; outputting a recognition redundancy value based on training data that is based on the acquired sensor data and previously accumulated sensor data; receiving a unique situation message from a peripheral device; determining whether to collect driving data including the acquired sensor data based on the recognition redundancy value and the unique situation message; and collecting and managing the driving data in response to the collection of the driving 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 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.