Systems and methods for training probabilistic object motion prediction models using non-differentiable prior knowledge
US12205004B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 26, 2023 |
| Grant date | Jan 21, 2025 |
| Priority date | — |
| Expiry date | Oct 26, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/20
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The present disclosure provides systems and methods for training probabilistic object motion prediction models using non-differentiable representations of prior knowledge. As one example, object motion prediction models can be used by autonomous vehicles to probabilistically predict the future location(s) of observed objects (e.g., other vehicles, bicyclists, pedestrians, etc.). For example, such models can output a probability distribution that provides a distribution of probabilities for the future location(s) of each object at one or more future times. Aspects of the present disclosure enable these models to be trained using non-differentiable prior knowledge about motion of objects within the autonomous vehicle's environment such as, for example, prior knowledge about lane or road geometry or topology and/or traffic information such as current traffic control states (e.g., traffic light status).
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.