Multi-task deep convolutional neural networks for efficient and robust traffic lane detection
US9286524B1 · kind B1 · utility
Assignees
Inventors
Key dates
| Filing date | Apr 15, 2015 |
| Grant date | Mar 15, 2016 |
| Priority date | — |
| Expiry date | Apr 15, 2035 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/30256
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Disclosed herein are devices, systems, and methods for detecting the presence and orientation of traffic lane markings. Deep convolutional neural networks are used with convolutional layers and max-pooling layers to generate fully connected nodes. After the convolutional and max-pooling layers, two sublayers are applied, one to determine presence and one to determine geometry. The presence of a lane marking segment as detected by the first sublayer can serve as a gate for the second sublayer by regulating the credit assignment for training the network. Only when the first sublayer predicts actual presence will the geometric layout of the lane marking segment contribute to the training of the overall network. This achieves advantages with respect to accuracy and efficiency and contributes to efficient robust model selection.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.