Method and system for vision-centric deep-learning-based road situation analysis
US9760806B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | May 11, 2016 |
| Grant date | Sep 12, 2017 |
| Priority date | — |
| Expiry date | Jun 1, 2036 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/30261
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
In accordance with various embodiments of the disclosed subject matter, a method and a system for vision-centric deep-learning-based road situation analysis are provided. The method can include: receiving real-time traffic environment visual input from a camera; determining, using a ROLO engine, at least one initial region of interest from the real-time traffic environment visual input by using a CNN training method; verifying the at least one initial region of interest to determine if a detected object in the at least one initial region of interest is a candidate object to be tracked; using LSTMs to track the detected object based on the real-time traffic environment visual input, and predicting a future status of the detected object by using the CNN training method; and determining if a warning signal is to be presented to a driver of a vehicle based on the predicted future status of the detected object.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.