Scenario-based training data weight tuning for autonomous driving
US12428036B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 30, 2022 |
| Grant date | Sep 30, 2025 |
| Priority date | — |
| Expiry date | Feb 9, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V10/84
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
According to various embodiments, the disclosure discloses systems, methods and media for formulating training datasets for learning-based components in an autonomous driving vehicle (ADV). In an embodiment, an exemplary method includes allocating training datasets for training a learning-based model in the ADV, each training dataset being allocated to one of multiple predefined driving scenarios; determining a weight of each training dataset out of the training datasets; and optimizing the weight of each training dataset in one or more iterations according to a predetermined algorithm until a performance of the learning-based model reaches a predetermined threshold. The predetermined algorithm is one of a random search algorithm, a grid search algorithm, or a Bayesian algorithm.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.