Distance to obstacle detection in autonomous machine applications
US11182916B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 27, 2019 |
| Grant date | Nov 23, 2021 |
| Priority date | — |
| Expiry date | Apr 22, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/30261
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
In various examples, a deep neural network (DNN) is trained to accurately predict, in deployment, distances to objects and obstacles using image data alone. The DNN may be trained with ground truth data that is generated and encoded using sensor data from any number of depth predicting sensors, such as, without limitation, RADAR sensors, LIDAR sensors, and/or SONAR sensors. Camera adaptation algorithms may be used in various embodiments to adapt the DNN for use with image data generated by cameras with varying parameters—such as varying fields of view. In some examples, a post-processing safety bounds operation may be executed on the predictions of the DNN to ensure that the predictions fall within a safety-permissible range.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.