A roadside perspective over-the-horizon global fusion perception system based on deep learning

A technology of deep learning and perception system, which is applied in the field of roadside viewing angle over-the-horizon global fusion perception system, which can solve problems such as road congestion, achieve high accuracy, improve perceived sight distance, and improve reliability.

Active Publication Date: 2022-07-22
YANSHAN UNIV
View PDF7 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Multi-sensor data fusion has expanded the perception distance and optimized the perception viewing angle to a certain extent, but the limitations of the perception method based on vehicle sensors in terms of field of view, viewing angle, price, etc. cannot be completely overcome due to the location of the sensor, and road congestion conditions Occurs often

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • A roadside perspective over-the-horizon global fusion perception system based on deep learning
  • A roadside perspective over-the-horizon global fusion perception system based on deep learning
  • A roadside perspective over-the-horizon global fusion perception system based on deep learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0091] see Figure 1-7 shown:

[0092] The present invention provides a roadside perspective over-the-horizon global fusion perception system based on deep learning, including a camera module, a laser radar module, a millimeter-wave radar module, an image splicing module, a millimeter-wave radar splicing module, a data layer fusion module, and an image splicing module. Algorithm module, lidar algorithm module, millimeter wave radar forwarding module, decision layer fusion module and result output module; specifically include the following steps:

[0093] Step 1. Arrange sensor modules 1 on the roadside gantry, namely camera modules, lidar modules and millimeter-wave radar modules, including at least four cameras 2, one lidar 3 and at least two millimeter-wave radars 4; cameras 2. Lidar 3 and millimeter-wave radar 4 are arranged at intersections or areas with blind areas of vision. When camera 2 is arranged, the acquisition range of camera 2 should cover the entire road to be ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention relates to a roadside viewing angle over-the-horizon global fusion perception system based on deep learning, which collects and fuses heterogeneous data of cameras, laser radars and millimeter-wave radars arranged on the roadside, and uses deep learning to process the fusion respectively. The final image and lidar point cloud data are finally used to achieve fusion perception through decision layer fusion. By arranging the perception system on the roadside, and combining the respective advantages of image, lidar point cloud, and millimeter wave radar point cloud data, the perception distance can be improved, and the scene understanding of the monitoring area can be carried out at multiple levels, and finally the network is automatically connected. Driving a vehicle provides sufficient and reliable sensory information.

Description

technical field [0001] The invention relates to an intelligent traffic infrastructure system, in particular to a roadside viewing angle over-the-horizon global fusion perception system based on deep learning. Background technique [0002] The development of intelligent transportation infrastructure is the key to realizing the function of connected autopilot. As the source of roadside sensing information in the vehicle-road collaborative sensing system, the stability and reliability of the roadside perception system will directly affect the performance of the connected autopilot function. implementation and security. Today, with the rapid development of intelligent transportation technology and autonomous driving technology, the stability and reliability of the roadside perception system are still the key points that need to be solved urgently. [0003] An autonomous driving system usually includes three modules: perception, decision-making, and control. Perception is the in...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Patents(China)
IPC IPC(8): G06V20/40G06K9/62G06N20/00G06T7/10G06V10/80
CPCG06T7/10G06N20/00G06T2207/10016G06V20/41G06F18/25
Inventor 金立生贺阳谢宪毅潘景剑郭柏苍韩广德张哲金秋坤魏永利
Owner YANSHAN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap