Generating trajectories from implicit neural models
US12287213B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 20, 2024 |
| Grant date | Apr 29, 2025 |
| Priority date | — |
| Expiry date | Oct 20, 2044 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N7/01
- WIPO fieldMeasurement
- WIPO sectorInstruments
Abstract
Generating trajectories from an implicit neural representation (INR) model to predict human mobility in uncertain traffic conditions includes receiving geocoordinate data representing vehicle motion observations of a traffic pattern; receiving a road network based on the geocoordinate data; training the INR model to learn continuous, latent fields of stochastic traffic properties over space and time based on the geocoordinate data; utilizing the INR model to extract spatio-temporal speed distributions from the geocoordinate data; applying a near-shortest-path, heuristic algorithm, weighted by predictions of the INR model, to produce real-world routing choices for traversing the road network; generating trajectories for transportation between an origin and destination in the road network using the algorithm and the predictions of the INR model, wherein the trajectories reflect non-deterministic and diverse route choices in the road network; and outputting generated trajectories to improve routing choices for a GPS and to provide the route choices for selection.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.