Learned validation metric for evaluating autonomous vehicle motion planning performance
US12151707B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 11, 2024 |
| Grant date | Nov 26, 2024 |
| Priority date | — |
| Expiry date | Apr 11, 2044 |
Classification
- Technology area (CPC B)Performing Operations; Transporting
- CPC primaryB60W2050/0088
- WIPO fieldTransport
- WIPO sectorMechanical engineering
Abstract
The present disclosure provides an example method for validating a trajectory generated by an autonomous vehicle control system (AV trajectory) in a driving scenario. The example method includes (a) obtaining the AV trajectory and a reference trajectory, wherein the reference trajectory describes a desired motion of a vehicle in the driving scenario; (b) determining a plurality of component divergence values for a plurality of divergence metrics, wherein a respective divergence value characterizes a respective difference between the AV trajectory and the reference trajectory; (c) providing the plurality of component divergence values to a machine-learned model to generate a score that indicates an aggregate divergence between the AV trajectory and the reference trajectory, wherein the machine-learned model comprises a plurality of learned parameters defining an influence of the plurality of component divergence values on the score; and (d) validating the AV trajectory based on the score.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.