Sensor fusion for autonomous machine applications using machine learning
US11688181B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 21, 2021 |
| Grant date | Jun 27, 2023 |
| Priority date | — |
| Expiry date | Dec 18, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V10/16
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
In various examples, a multi-sensor fusion machine learning model—such as a deep neural network (DNN)—may be deployed to fuse data from a plurality of individual machine learning models. As such, the multi-sensor fusion network may use outputs from a plurality of machine learning models as input to generate a fused output that represents data from fields of view or sensory fields of each of the sensors supplying the machine learning models, while accounting for learned associations between boundary or overlap regions of the various fields of view of the source sensors. In this way, the fused output may be less likely to include duplicate, inaccurate, or noisy data with respect to objects or features in the environment, as the fusion network may be trained to account for multiple instances of a same object appearing in different input representations.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.