Multi-task machine-learned models for object intention determination in autonomous driving
US11794785B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 20, 2022 |
| Grant date | Oct 24, 2023 |
| Priority date | — |
| Expiry date | May 20, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/044
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Generally, the disclosed systems and methods utilize multi-task machine-learned models for object intention determination in autonomous driving applications. For example, a computing system can receive sensor data obtained relative to an autonomous vehicle and map data associated with a surrounding geographic environment of the autonomous vehicle. The sensor data and map data can be provided as input to a machine-learned intent model. The computing system can receive a jointly determined prediction from the machine-learned intent model for multiple outputs including at least one detection output indicative of one or more objects detected within the surrounding environment of the autonomous vehicle, a first corresponding forecasting output descriptive of a trajectory indicative of an expected path of the one or more objects towards a goal location, and/or a second corresponding forecasting output descriptive of a discrete behavior intention determined from a predefined group of possible behavior intentions.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.