Mixture distribution estimation for future prediction
US12014270B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 29, 2020 |
| Grant date | Jun 18, 2024 |
| Priority date | — |
| Expiry date | Jul 21, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V20/58
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A computer-implemented method for mixture distribution estimation of multi-modal future predictions comprising a training phase of a convolutional neural network comprising the steps of: (1) inputting a set of images of a driving environment, each containing at least one object of interest, and a set of future ground truths corresponding to the objects of interest; (2) sampling the solution space of the multi-modal future of the object of interest with an evolving winner-takes-all loss strategy by generating a predetermined number of hypotheses, penalizing all hypotheses equally, gradually releasing one part of the hypotheses by penalizing only the other part of the hypotheses being closer to the corresponding ground truth, so-called winning hypotheses, until only the best hypothesis being the closest one is penalized, and outputting final hypotheses; (3) sequentially fitting a multi-modal mixture distribution of future predictions to the final hypotheses.
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