Vehicle trajectory prediction model with semantic map and LSTM
US11127142B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 31, 2019 |
| Grant date | Sep 21, 2021 |
| Priority date | — |
| Expiry date | Mar 7, 2040 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04L67/12
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A system and method for predicting the near-term trajectory of a moving obstacle sensed by an autonomous driving vehicle (ADV) is disclosed. The method applies neural networks such as a LSTM model to learn dynamic features of the moving obstacle's motion based on its past trajectory up to its current position and a CNN model to learn the semantic map features of the driving environment in a portion of an image map. From the learned dynamic features of the moving obstacle and the learned semantic map features of the environment, the method applies a neural network to iteratively predict the moving obstacle's positions for successive time points of a prediction interval. To predict the moving obstacle's position at the next time point from the currently predicted position, the methods may update the learned dynamic features of the moving obstacle based on its past trajectory up to the currently predicted position.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.