Patent ID: 11881028
Assignee: GM GLOBAL TECHNOLOGY OPERATIONS LLC
Field: Computer technology (Electrical engineering)
Classification: CPC G  B | IPC B  G

Claim 9:
10. A method comprising:
obtaining, using a lidar system, an initial point cloud;
implementing a first neural network, using the lidar system, to obtain a dual density point cloud based on the-first neural network and based on the initial point cloud, wherein the dual density point cloud results from reducing point density of the initial point cloud outside a region of interest (ROI), wherein implementing the first neural network results in defining the ROI within the FOV that results in the dual density point cloud, the ROI being a region of fixed area with a center that is selected from a set of potential centers based on an output of the first neural network, wherein the first neural network is a Deep Q-Network (DQN) and implementing the DQN includes obtaining point-wise feature vectors from an encoder and decoder stage of a second neural network;
implementing the second neural network to output the detection result based on the dual density point cloud, wherein the implementing the second neural network includes implementing the encoder and decoder stage to provide point-wise feature vectors such that each feature vector of the point-wise feature vectors is associated respectively with each point of the dual density point cloud;
training the DQN based on comparing the detection result obtained with the dual density point cloud with a ground truth detection result to produce a number of true positives and comparing a second detection result obtained by reducing the point density of the initial point cloud throughout the FOV with the ground truth detection result to produce a second number of true positives; and
processing the dual density point cloud to obtain a detection result that indicates any objects in a field of view (FOV) of the lidar system.