Driverless car obstacle detection method based on TegraX1 radar data

A technology for obstacle detection and radar data, which is applied in radio wave measurement systems, measurement devices, and electromagnetic wave re-radiation. real-time effects

Active Publication Date: 2017-05-31
BEIJING UNION UNIVERSITY
View PDF4 Cites 14 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, because the radar data is too large, the processing algorithm has a huge amount of calculation, so the real-time perf...

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
  • Driverless car obstacle detection method based on TegraX1 radar data
  • Driverless car obstacle detection method based on TegraX1 radar data
  • Driverless car obstacle detection method based on TegraX1 radar data

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0024] The invention provides an unmanned vehicle obstacle detection method based on Tegra X1 radar data, adopts velodyne lidar as a sensor to collect environmental information, and is equipped with an NVIDIA Tegra X1 mobile processor to realize unmanned vehicle obstacle detection. In order to further illustrate the technical content of the present invention, the innovative points have good effects, and the following describes in detail in conjunction with the embodiments and accompanying drawings.

[0025] Such as figure 1 As shown, the inventive method is divided into two steps: 1, three-dimensional radar data conversion 2, obstacle detection

[0026] Since there are more complex logic processing and transaction management in the process of 3D radar data conversion, this process is not suitable for calculation in GPU. The present invention therefore handles this process by using the NVIDIA Tegra X1 arm processor.

[0027] Due to the large amount of radar point cloud data (...

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 discloses a driverless car obstacle detection method based on TegraX1 radar data, and the method comprises the steps: 1, employing a velodyne laser radar as a sensor for collecting the environment information, and carrying out the three-dimensional radar data conversion through an NVIDIA Tegra X1 mobile processor; 2, carrying out the obstacle detection based on grids, and employing a GPU to process grid data, wherein the step comprises three substeps: enabling three-dimensional data points to be projected to a grid map, enabling all grids with the relative heights being greater than one threshold value to be set as obstacle points, and filtering out all grids which are determined as the obstacles because there are suspension points in the grids. According to the technical scheme of the invention, the method employs the velodyne laser radar as the sensor for collecting the environment information, and carries out the optimization based on the NVIDIA Tegra X1 mobile processor through the GPU, and achieves the speeding up of the obstacle detection for a driverless car.

Description

technical field [0001] The invention belongs to the field of unmanned driving, and in particular relates to an unmanned vehicle obstacle detection method based on Tegra X1 radar data. Background technique [0002] In recent years, with the continuous development of sensor technology, control system, and artificial intelligence, ground mobile robots have made great progress. In a real dynamic environment, autonomous robots can stably and accurately detect obstacles and identify obstacle types in environmental perception, which can be of great help in establishing motion models for path planning, so as to make intelligent decision-making behaviors. Usually there are two main categories of dynamic objects in the environment of autonomous robots: vehicles and pedestrians. For the vehicle, it is the main interactive object in the traffic, the speed is relatively fast, and a certain safety distance needs to be maintained. When the traffic rules allow, it is necessary to choose wh...

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
IPC IPC(8): G01S17/93
CPCG01S17/931
Inventor 梁军许武鲍泓王晶李强
Owner BEIJING UNION UNIVERSITY
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