Patent ID: 11887381
Assignee: NEW EAGLE, LLC
Field: Computer technology (Electrical engineering)
Classification: CPC G  B | IPC B  G

Claim 0:
1. A method of predicting lane line types utilizing a heterogeneous convolutional neural network (HCNN), the method comprising:
capturing an input image with one or more optical sensors disposed on a host member;
passing the input image through the HCNN, the HCNN having at least three distinct sub-networks, the three distinct sub-networks:
predicting object locations in the input image with a first sub-network;
predicting lane line locations in the input image with a second sub-network; and
predicting lane line types for each predicted lane line in the input image with a third sub-network, wherein passing the input image through the HCNN further comprises:

directly receiving the input image in a feature extraction layer (FEL) portion of the HCNN, the HCNN having multiple convolution, pooling and activation layers stacked together with each other;
conducting within the FEL portion a learning operation to learn to represent at least a first stage of data of the input image in a form including horizontal and vertical lines and simple blobs of colors, and outputting the first stage of data to at least: the first sub-network, the second sub-network, and the third sub-network;
directly receiving by the first sub-network the first stage of data from the FEL portion and performing a first task of object detection, classification, and localization for classes of objects in the input image to create a detected object table;
directly receiving by the second sub-network the first stage of data from the FEL portion and performing a second task of lane line detection to create a lane line location table; and
directly receiving by the third sub-network the first stage of data from the FEL portion and performing a third task of lane line type detection to create a lane line type table.