Automatic driving track prediction method and device, equipment and storage medium

A technology for automatic driving and trajectory prediction, applied in the field of automatic driving trajectory prediction methods, devices, equipment and storage media, can solve the problems of long time-consuming prediction and difficulty in meeting the real-time performance of prediction, so as to reduce the trajectory prediction time and improve the trajectory prediction efficiency. , the effect of reducing the number of map features

Pending Publication Date: 2022-07-22
CHINA FIRST AUTOMOBILE
View PDF0 Cites 4 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] At present, autonomous vehicles can predict the trajectory based on the perceived obstacle trajectory and map environment information, but due to the complex situation of roads and obstacles, the prediction takes a long time, and it is difficult to meet the real-time prediction requirements

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
  • Automatic driving track prediction method and device, equipment and storage medium
  • Automatic driving track prediction method and device, equipment and storage medium
  • Automatic driving track prediction method and device, equipment and storage medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0033] figure 1 This is a flowchart of an automatic driving trajectory prediction method provided in Embodiment 1 of the present invention. This embodiment can be applied to the situation of predicting the motion trajectory of surrounding obstacles when an automatic driving vehicle is driving. The method can be performed by an automatic driving trajectory prediction device, which can be implemented in the form of hardware and / or software, and the device can be configured in an electronic device of a vehicle. like figure 1 As shown, the method specifically includes the following steps:

[0034] S110. Acquire first historical trajectory data of the automatic driving vehicle, second historical trajectory data of surrounding obstacles, and current map data.

[0035] Wherein, the first historical trajectory data may be historical trajectory data of the autonomous driving vehicle before the current moment when it is driving; the second historical trajectory data may be historical ...

Embodiment 2

[0066] image 3 This is a flowchart of another automatic driving trajectory prediction method provided in the second embodiment of the present invention. The embodiment adds a training process of a preset trajectory prediction model on the basis of the above-mentioned embodiment. The explanations of terms that are the same as or corresponding to the above embodiments are not repeated here. like image 3 As shown, the training process of the preset trajectory prediction model provided by this embodiment specifically includes the following steps:

[0067] S210. Acquire training data corresponding to the self-driving vehicle, where the training data includes: first historical trajectory sample data and corresponding first actual trajectory of the self-driving vehicle, second historical trajectory sample data of surrounding obstacles and corresponding second trajectory sample data Actual trajectories and map sample data around the autonomous vehicle.

[0068] Wherein, the first...

Embodiment 3

[0105] Image 6 This is a schematic structural diagram of an automatic driving trajectory prediction device provided in Embodiment 3 of the present invention. like Image 6 As shown, the apparatus specifically includes: a data acquisition module 610 , a data processing module 620 , a downsampling module 630 and a trajectory prediction module 640 . in:

[0106] a data acquisition module 610, configured to acquire first historical trajectory data of the autonomous vehicle, second historical trajectory data of surrounding obstacles, and current map data;

[0107] The data processing module 620 is used to preprocess the first historical trajectory data, the second historical trajectory data and the current map data, and determine the first historical trajectory feature information, the second historical trajectory feature information and the current map characteristic information;

[0108] A downsampling module 630, configured to downsample the lane nodes in the current map fe...

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 embodiment of the invention discloses an automatic driving track prediction method and device, equipment and a storage medium. The method comprises the following steps: acquiring first historical trajectory data of the autonomous vehicle, second historical trajectory data of surrounding obstacles and current map data; preprocessing the first historical trajectory data, the second historical trajectory data and the current map data, and determining first historical trajectory feature information, second historical trajectory feature information and current map feature information; on the basis of the lane type, performing down-sampling on lane nodes in the current map feature information to obtain down-sampled target map feature information; and inputting the first historical track feature information, the second historical track feature information and the target map feature information into a preset track prediction model, and based on the output of the preset track prediction model, obtaining a target prediction track of the obstacles around the autonomous vehicle, thereby improving the track prediction efficiency of the obstacles around the autonomous vehicle.

Description

technical field [0001] Embodiments of the present invention relate to automatic driving technology, and in particular, to a method, apparatus, device and storage medium for predicting an automatic driving trajectory. Background technique [0002] In order to ensure the safe and effective movement of autonomous vehicles on the road, autonomous vehicles not only need to perceive the current state of surrounding obstacles, but also actively predict their future motion trajectories in order to make reasonable and safe behavioral decisions. [0003] At present, autonomous vehicles can predict the trajectory based on the perceived obstacle trajectory and map environment information. However, due to the complex situation of roads and obstacles, the prediction takes a long time, and it is difficult to meet the requirements of real-time prediction. SUMMARY OF THE INVENTION [0004] The embodiments of the present invention provide an automatic driving trajectory prediction method, d...

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 Applications(China)
IPC IPC(8): B60W60/00B60W40/00
CPCB60W60/001B60W40/00B60W2556/10B60W2552/05
Inventor 卢丽婧李荣华陈红丽王宁
Owner CHINA FIRST AUTOMOBILE
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