System for artificial intelligence learning based on entropy relating to semantic segmentation of point cloud and method implementing the same

A method performed by an apparatus of a vehicle, the method comprising:

• training, using a first dataset, artificial intelligence (AI) model that recognizes an object based on data input from a sensor of the vehicle, wherein the AI model is trained to perform a recognition function for objects, wherein the objects are classified into multiple classes from an arbitrary point cloud based on deep learning and semantic segmentation of a sensor point cloud;
• performing the recognition function on a point cloud input from the sensor;
• based on the point cloud input, object classifications, and a prediction rate of the AI model, determining entropy of the AI model;
• determining, based on the determined entropy, an edge case and an edge class associated with the recognition function;
• generating a segmentation ground truth (GT) as a pseudo label for a second dataset, wherein the second data set comprises point cloud data associated with at least one object belonging to the edge class;
• based on the second data set and the segmentation ground truth, retraining the AI model to perform the recognition function;
• outputting, based on the retrained AI model, a signal indicating recognition of at least one object;
• and
• controlling, based on the signal, autonomous driving of the vehicle.

A vehicle comprising:

• a sensor;
• a driving control circuit configured to control autonomous driving of the vehicle;
• a processor;
• and
• a memory storing at least one instruction that, when executed by the processor communicating with the memory, is configured to cause the vehicle to:
• obtain, from the sensor, a point cloud representing a surrounding environment of the vehicle,
• perform, using a trained artificial intelligence (AI) model, a semantic segmentation on the point cloud to recognize at least one object and assign the at least one object to a class among a plurality of classes,
• based on the point cloud, the class, and a prediction rate of the AI model, determine a prediction entropy value of the AI model,
• identify a frame having a highest prediction entropy among a plurality of frames, wherein the plurality of frames comprises point clouds, and wherein the frame is defined as an edge case,
• identify, among the plurality of classes, a class of an object contributing most to a highest entropy in the edge case, the class being defined as an edge class,
• generate, based on the edge class, a second dataset, wherein the second dataset comprises a plurality of point clouds corresponding to the edge class,
• generate, for the second dataset, segmentation ground truth data using pseudo-labeling or manual labeling,
• based on the second dataset and the segmentation ground truth data, retrain the AI model,
• output, based on the retrained AI model, a signal indicating recognition of the object, and
• control, via the driving control circuit and based on the signal, autonomous driving of the vehicle.