Methods and systems for trajectory forecasting with recurrent neural networks using inertial behavioral rollout
US11131993B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 29, 2019 |
| Grant date | Sep 28, 2021 |
| Priority date | — |
| Expiry date | Feb 28, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/086
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method and a system for forecasting trajectories in an autonomous vehicle using recurrent neural networks. The method includes receiving a first set of data that comprises time series information corresponding to states of a plurality of objects, analyzing the first set of data to determine a plurality of object trajectory sequences corresponding to the plurality of objects, and using one or more of the plurality of object trajectory sequences as input to train a prediction model for predicting future trajectories of the plurality of objects. The predication model can be trained by defining a first prediction horizon, training the prediction model over the first prediction horizon to generate a semi-trained prediction model, defining a second prediction horizon that is longer than the first prediction horizon, and training the semi-trained prediction model to generate a trained prediction model.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.