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

Claim 9:
10. A system for predicting lane line types utilizing a heterogeneous convolutional neural network (HCNN), the system comprising:
one or more optical sensors disposed on a host member, the one or more optical sensors capturing an input image; and
an HCNN having at least three distinct sub-networks, the three distinct sub-networks comprising:
a first sub-network predicting object locations in the input image;
a second sub-network predicting lane line locations in the input image; and
a third sub-network predicting lane line types for each predicted lane line in the input image, and, wherein the HCNN receives the input image and passes the input image through the three distinct sub-networks, wherein when the HCNN passes the input image through the three distinct sub-networks, the input image is directly received in a feature extraction layer (FEL) portion of the HCNN, the HCNN having multiple convolution, pooling and activation layers stacked together with each other;
the FEL portion conducts 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;
the first sub-network directly receives the data from the FEL portion and performs a first task of object detection, classification, and localization for classes of objects in the input image to create a detected object table;
the second sub-network directly receives the data from the FEL portion and performs a second task of lane line detection to create a lane line location table; and
the third sub-network directly receives the data from the FEL portion and performs a third task of lane line type detection to create a lane line type table.