Bayesian Global optimization-based parameter tuning for vehicle motion controllers
US11673584B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 15, 2020 |
| Grant date | Jun 13, 2023 |
| Priority date | — |
| Expiry date | Aug 10, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/10
- WIPO fieldTransport
- WIPO sectorMechanical engineering
Abstract
In one embodiment, a computer-implemented method for optimizing a controller of an autonomous driving vehicle (ADV) includes obtaining several samples, each sample having a set of parameters, iteratively performing, until a predetermined condition is satisfied: determining, for each sample, a score according to a configuration of the controller based on the set of parameters of the sample, applying a machine learning model to the samples and corresponding scores to determine a mean function and a variance function, producing a new sample as a minimum of a function of the mean function and the variance function with respect to an input space of the set of parameters, adding the new sample to the several samples, and outputting the new sample as an optimal sample, where parameters of the optimal sample are utilized to configure the controller to autonomously drive the ADV.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.