Compressive environmental feature representation for vehicle behavior prediction
US11055857B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Nov 30, 2018 |
| Grant date | Jul 6, 2021 |
| Priority date | — |
| Expiry date | Apr 12, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V2201/07
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
In an embodiment, a method for representing a surrounding environment of an ego autonomous driving vehicle (ADV) is described. The method represents the surrounding environment using a first set of features from a definition (HD) map and a second set of features from a target object in the surrounding environment. The first set of features are extracted from the high definition map using a convolutional neural network (CNN), and the second set of features are handcrafted features from the target object during a predetermined number of past driving cycles of the ego ADV. The first set of features and the second set of features are concatenated and provided to a number of fully connected layers of the CNN to predict behaviors of the target object. In one embodiment, the operations in the method can be repeated for each driving cycle of the ego ADV.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.