Assigning obstacles to lanes using neural networks for autonomous machine applications
US12026955B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 29, 2021 |
| Grant date | Jul 2, 2024 |
| Priority date | — |
| Expiry date | May 6, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V20/64
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
In various examples, live perception from sensors of an ego-machine may be leveraged to detect objects and assign the objects to bounded regions (e.g., lanes or a roadway) in an environment of the ego-machine in real-time or near real-time. For example, a deep neural network (DNN) may be trained to compute outputs—such as output segmentation masks—that may correspond to a combination of object classification and lane identifiers. The output masks may be post-processed to determine object to lane assignments that assign detected objects to lanes in order to aid an autonomous or semi-autonomous machine in a surrounding environment.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.