Graph neural networks with vectorized object representations in autonomous vehicle systems
US12233901B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 26, 2021 |
| Grant date | Feb 25, 2025 |
| Priority date | — |
| Expiry date | Sep 5, 2042 |
Classification
- Technology area (CPC B)Performing Operations; Transporting
- CPC primaryB60W2554/80
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Techniques are discussed herein for generating and using graph neural networks (GNNs) including vectorized representations of map elements and entities within the environment of an autonomous vehicle. Various techniques may include vectorizing map data into representations of map elements, and object data representing entities in the environment of the autonomous vehicle. In some examples, the autonomous vehicle may generate and/or use a GNN representing the environment, including nodes stored as vectorized representations of map elements and entities, and edge features including the relative position and relative yaw between the objects. Machine-learning inference operations may be executed on the GNN, and the node and edge data may be extracted and decoded to predict future states of the entities in the environment.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.