Patent · US Active

Robust multimodal sensor fusion for autonomous driving vehicles

US11983625B2 · kind B2 · utility

9Cited by

0References

21Claims

0Family size

Assignee

Intel Corporation · US

Inventors

Nilesh Ahuja · Cupertino, US
Ignacio Alvarez · Portland, US
Ranganath Krishnan · Hillsboro, US
Ibrahima J. Ndiour · Portland, US
Mahesh Subedar · Chandler, US
Omesh Tickoo · Portland, US

Key dates

Filing date	Jun 24, 2020
Grant date	May 14, 2024
Priority date	—
Expiry date	Sep 14, 2042

Classification

Technology area (CPC G)Physics
CPC primaryG06N20/00
WIPO fieldComputer technology
WIPO sectorElectrical engineering

Abstract

Techniques are disclosed for using neural network architectures to estimate predictive uncertainty measures, which quantify how much trust should be placed in the deep neural network (DNN) results. The techniques include measuring reliable uncertainty scores for a neural network, which are widely used in perception and decision-making tasks in automated driving. The uncertainty measurements are made with respect to both model uncertainty and data uncertainty, and may implement Bayesian neural networks or other types of neural networks.

Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.