Deep neural network for segmentation of road scenes and animate object instances for autonomous driving applications
US12051206B2 · kind B2 · utility
Inventors
Key dates
| Filing date | Jul 24, 2020 |
| Grant date | Jul 30, 2024 |
| Priority date | — |
| Expiry date | May 8, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V10/454
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A deep neural network(s) (DNN) may be used to perform panoptic segmentation by performing pixel-level class and instance segmentation of a scene using a single pass of the DNN. Generally, one or more images and/or other sensor data may be stitched together, stacked, and/or combined, and fed into a DNN that includes a common trunk and several heads that predict different outputs. The DNN may include a class confidence head that predicts a confidence map representing pixels that belong to particular classes, an instance regression head that predicts object instance data for detected objects, an instance clustering head that predicts a confidence map of pixels that belong to particular instances, and/or a depth head that predicts range values. These outputs may be decoded to identify bounding shapes, class labels, instance labels, and/or range values for detected objects, and used to enable safe path planning and control of an autonomous vehicle.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.