Machine-learned model for detecting object relevance to vehicle operation planning
US12415549B1 · kind B1 · utility
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20Claims
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Key dates
| Filing date | Apr 7, 2023 |
| Grant date | Sep 16, 2025 |
| Priority date | — |
| Expiry date | Jan 25, 2044 |
Classification
- Technology area (CPC B)Performing Operations; Transporting
- CPC primaryB60W2554/4045
- WIPO fieldTransport
- WIPO sectorMechanical engineering
Abstract
A machine-learned architecture for determining whether an object is relevant to a vehicle's action planning may comprise a convolutional neural network, graph neural network, and/or multi-layer perceptron that may determine a relevance score associated with an object that indicates indicating whether an object is likely to impact operation(s) of a vehicle. In some examples, the machine-learned architecture may use scene information and/or an object track to determine the relevance score.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.