Autonomous vehicle controlled based upon a lidar data segmentation system
AssigneeGM Global Technology Operations LLC
InventorsAndrea Allais, Micah Christopher Chambers, William Gongshu Xie, Adam Samuel Cadien, Elliot Branson
Life of this patent
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Abstract
An autonomous vehicle is described herein. The autonomous vehicle includes a lidar sensor system. The autonomous vehicle additionally includes a computing system that executes a lidar segmentation system, wherein the lidar segmentation system is configured to identify objects that are in proximity to the autonomous vehicle based upon output of the lidar sensor system. The computing system further includes a deep neural network (DNN), where the lidar segmentation system identifies the objects in proximity to the autonomous vehicle based upon output of the DNN.
Claims20
An autonomous vehicle (AV) comprising
- a lidar sensor;
- and
- a computing system that is in communication with the lidar sensor, wherein the computing system comprises:
- a processor;
- and
- memory that stores instructions that, when executed by the processor, cause the processor to perform acts comprising:
- receiving lidar data, the lidar data based upon output of the lidar sensor, the lidar data comprising a plurality of points representative of positions of objects in a driving environment of the AV;
- providing a first input feature that pertains to a first point in the lidar data to a deep neural network (DNN), wherein the DNN generates first output that pertains to the first point responsive to receiving the first input feature;
- providing a second input feature that pertains to a second point in the lidar data to the DNN, wherein the DNN generates second output that pertains to the second point responsive to receiving the second input feature;
- and
- assigning respective labels to the first point and the second point based upon the first output and the second output of the DNN, wherein the labels indicate that the first point and the second point are representative of a same object in the driving environment.
A method comprising:
- generating lidar data by way of a lidar sensor mounted on an autonomous vehicle (AV), the lidar data comprising a plurality of points representative of positions of objects in a driving environment of the AV;
- providing a first input feature that pertains to a first point in the lidar data to a deep neural network (DNN), wherein the DNN generates first output that pertains to the first point responsive to receiving the first input feature;
- providing a second input feature that pertains to a second point in the lidar data to the DNN, wherein the DNN generates second output that pertains to the second point responsive to receiving the second input feature;
- and
- assigning respective labels to the first point and the second point based upon the first output and the second output of the DNN, wherein the labels indicate that the first point and the second point are representative of a same object in the driving environment.
A computer-readable storage medium comprising instructions that, when executed by a processor, cause the processor to perform acts comprising:
- receiving a lidar point cloud comprising a plurality of points representative of positions of objects in a driving environment of an AV, the lidar point cloud based upon output of a lidar sensor mounted on the AV;
- providing a first input feature that pertains to a first point in the lidar data to a deep neural network (DNN), wherein the DNN outputs first data that pertains to the first point responsive to receiving the first input feature;
- providing a second input feature that pertains to a second point in the lidar data to the DNN, wherein the DNN generates outputs second data that pertains to the second point responsive to receiving the second input feature;
- and
- assigning respective labels to the first point and the second point based upon the first data and the second data output by the DNN, wherein the labels indicate that the first point and the second point are representative of different objects in the driving environment.
Dependencies are inferred from the claim text.