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 detecting an obstacle adaptively to vehicle speed. The apparatus includes a control unit, a speed-adaptive camera sensor, a speed-adaptive laser scanner sensor, and a detection data integration unit. The control unit receives information about the speed of a vehicle and a Pulse Per Second (PPS) signal from a Global Positioning System (GPS) module. The speed-adaptive camera sensor adjusts the range of a detection region based on the information about the speed of the vehicle, and generates first detection data on an object. The speed-adaptive laser scanner sensor adjusts the range of the angle of the field of view of a laser scanner based on the information about the speed of the vehicle, and generates second detection data on the object. The detection data integration unit outputs obstacle detection data.