# US 2021/0241005 A1 · Lane mask generation for autonomous machine applications > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0241005 A1 - Document ID: US20210241005A1 - Status: Published application - Kind: A1 - Application number: 17/234,487 - Filed: 2021-04-19 - Published: 2021-08-05 - Earliest claimed: 2019-08-08 - Assignees: NVIDIA Corporation - Applicants: NVIDIA Corporation - Inventors: Josh Abbott; Miguel Sainz Serra; Zhaoting Ye; David Nister - Web page: https://patentgrep.com/patent/US20210241005A1 ## CPC - G06T 7/73: using feature-based methods - G06V 20/588: {Recognition of the road, e.g. of lane markings; Recognition of the vehicle driving pattern in relation to the road} - G06T 7/12: Edge-based segmentation - G06T 7/70: Determining position or orientation of objects or cameras (camera calibration G06T7/80) - G06T 11/00: Two-dimensional [2D] image generation - G06T 11/20: Drawing from basic elements - G06V 20/58: Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads - G06T 2207/20081: Training; Learning - G06T 2207/20084: Artificial neural networks [ANN] - G06T 2207/20132: Image cropping - G06T 2207/30256: Lane; Road marking - G06T 2207/30261: Obstacle - G06T 2210/12: Bounding box - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract In various examples, object fence corresponding to objects detected by an ego-vehicle may be used to determine overlap of the object fences with lanes on a driving surface. A lane mask may be generated corresponding to the lanes on the driving surface, and the object fences may be compared to the lanes of the lane mask to determine the overlap. Where an object fence is located in more than one lane, a boundary scoring approach may be used to determine a ratio of overlap of the boundary fence, and thus the object, with each of the lanes. The overlap with one or more lanes for each object may be used to determine lane assignments for the objects, and the lane assignments may be used by the ego-vehicle to determine a path or trajectory along the driving surface. ## Claims ### Claim 1 A processor comprising: processing circuitry to perform one or more operations of an ego machine in an environment using a mask, the mask generated for each lane, at least in part, by: connecting, in a crisscross pattern, one or more first points from a first polyline to one or more second points of a second polyline corresponding to the lane to generate a plurality of triangles; and generating a polygon corresponding to the lane using the plurality of triangles. ### Claim 2 (depends on 1) The processor of claim 1, wherein the lane mask is generated using a triangulation algorithm. ### Claim 3 (depends on 2) The processor of claim 2, wherein the triangulation algorithm includes monotone polygon triangulation. ### Claim 4 (depends on 1) The processor of claim 1, wherein at least one of the one or more points of the first polyline or the one or more points of the second polyline is determined using one or more outputs of one or more deep neural networks (DNNs) computed based at least in part on sensor data generated using one or more sensors of the ego machine. ### Claim 5 (depends on 1) The processor of claim 1, wherein the one or more operations include at least one of: assigning an object to a lane using the lane mask, determining a path or trajectory through at least a portion of the environment, performing obstacle avoidance, or updating a world model. ### Claim 6 (depends on 1) The processor of claim 1, the performing the one or more operations is further executed using an object fence corresponding to an object detected in the environment. ### Claim 7 (depends on 1) The processor of claim 1, wherein the one or more operations include assigning one or more objects to one or more lanes represented by the mask. ### Claim 8 (depends on 7) The processor of claim 7, wherein the assigning includes comparing first pixels associated with the one or more objects to second pixels corresponding to the one or more lanes. ### Claim 9 A system comprising: one or more processing units; and one or more memory units storing instructions thereon that, when executed using the one or more processing units, cause the one or more processing units to execute operations comprising: determining locations of a first polyline corresponding to a lane and a second polyline corresponding to the lane; connecting, in a crisscross pattern, first points of the first polyline to second points of the second polyline to generate triangles; generating at least a portion of a mask corresponding to the lane using the triangles; and performing one or more operations by an ego machine using the mask. ### Claim 10 (depends on 9) The system of claim 9, wherein at least another portion of the mask is generated using a lane extension algorithm to extend one or more lanes of the mask beyond a detected portion of the one or more lanes. ### Claim 11 (depends on 10) The system of claim 10, wherein the lane extension algorithm includes a curve fitting algorithm or a lane extrapolation algorithm. ### Claim 12 (depends on 9) The system of claim 9, wherein at least a portion of another lane of the mask is generated as a virtual lane. ### Claim 13 (depends on 9) The system of claim 9, wherein the one or more operations include lane assignment, and an object representation corresponding to an object is compared to the mask to determine an assigned lane for the object. ### Claim 14 (depends on 13) The system of claim 13, wherein the object representation includes an object fence, the object fence generated using a bounding shape corresponding to the object. ### Claim 15 (depends on 9) The system of claim 9, further comprising one or more sensors, wherein the determining the locations corresponding to the first polyline and the second polyline includes computing, using one or more deep neural networks (DNNs) and based at least in part on sensor data generated using the one or more sensors, one or more outputs indicative of the locations. ### Claim 16 (depends on 9) The system of claim 9, wherein the system is comprised in at least one of: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing simulation operations; a system for performing deep learning operations; a system implemented using an edge device; a system implemented using a robot; a system incorporating one or more virtual machines (VMs); a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources. ### Claim 17 A method comprising: computing, based at least in part on sensor data generated using one or more sensors of an ego machine, first locations corresponding to first points of a first polyline and second locations corresponding to second points of a second polyline; connecting the first points to the second points to generate one or more triangles; generating a polygon corresponding to a lane of a mask using the one or more triangles; and performing one or more operations by the ego machine using the mask. ### Claim 18 (depends on 17) The method of claim 17, wherein the mask is generated using a triangulation algorithm. ### Claim 19 (depends on 17) The method of claim 17, wherein the computing the first locations and the second locations is executed using one or more deep neural networks (DNNs). ### Claim 20 (depends on 17) The method of claim 17, wherein the one or more operations include assigning one or more objects to one or more lanes represented by the mask.