Method, device, apparatus and medium for extracting obstacle perception error data

A technology of error data and extraction method, applied in the field of obstacle perception error data extraction, can solve the problems of missed identification of low obstacles, wrong identification of non-obstacles, hidden dangers of unmanned vehicle driving safety, etc., to reduce safety hidden dangers, improve accuracy and reliability, and optimize the effect of obstacle perception models

Active Publication Date: 2019-01-22
APOLLO INTELLIGENT DRIVING (BEIJING) TECHNOLOGY CO LTD
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  • Summary
  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

[0003] However, due to the wide variety of obstacles, non-obstacles such as green plants, branches, and water on the ground are misidentified, and / or low obstacles such as traffic cones, tripods, or children and pedestrians are sometimes missed. Occurs, bringing hidden dangers to the driving safety of unmanned vehicles

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  • Method, device, apparatus and medium for extracting obstacle perception error data
  • Method, device, apparatus and medium for extracting obstacle perception error data
  • Method, device, apparatus and medium for extracting obstacle perception error data

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Embodiment 1

[0023] figure 1 It is a flowchart of a method for extracting obstacle perception error data in Embodiment 1 of the present invention. The embodiment of the present invention is applicable to the situation where the training samples of the obstacle perception model are collected through the open-loop mode of manual driving in the iterative development stage of the algorithm of the unmanned driving perception system. The device is implemented by software and / or hardware, and is specifically configured in an unmanned vehicle.

[0024] Such as figure 1 The method for extracting the obstacle perception error data shown includes:

[0025] S110. Acquire a drive test data set of the vehicle; the drive test data set includes sensor data for sensing obstacles, obstacle perception result data, and manual driving behavior data.

[0026] Wherein, the sensor data includes the image data output by the camera in the vehicle, the point cloud data output by the laser radar in the vehicle, an...

Embodiment 2

[0043] figure 2 It is a flowchart of a method for extracting obstacle perception error data in Embodiment 2 of the present invention. The embodiment of the present invention performs additional optimization on the basis of the technical solutions of the foregoing embodiments.

[0044] Further, after the operation "obtaining the instance data of missing obstacle recognition", add "combine road network data and positioning data, and filter the instance data of missing obstacle recognition" to filter the instance data of missing recognition Eliminate non-erroneous data to improve the purity of obstacle perception error data.

[0045] Such as figure 2 The method for extracting the obstacle perception error data shown includes:

[0046] S210. Acquire a drive test data set of the vehicle; the drive test data set includes sensor data for sensing obstacles, obstacle perception result data, and manual driving behavior data.

[0047] S220. For the preset manual driving behavior in...

Embodiment 3

[0059] image 3 It is a schematic structural diagram of a device for extracting obstacle perception error data in Embodiment 3 of the present invention. The embodiment of the present invention is applicable to the situation where the training samples of the obstacle perception model are collected through the open-loop mode of manual driving in the iterative development stage of the algorithm of the unmanned driving perception system. The device is implemented by software and / or hardware, and specifically deployed in unmanned vehicles. image 3 The device for extracting obstacle perception error data includes: a drive test data set acquisition module 310 and an instance data acquisition module 320 .

[0060] The drive test data set acquisition module 310 is used to acquire the drive test data set of the vehicle; the drive test data set includes sensor data for perceiving obstacles, obstacle perception result data and manual driving behavior data;

[0061] The instance data ob...

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Abstract

The embodiment of the invention discloses a method, a device, an apparatus and a medium for extracting obstacle perception error data. The method comprises the following steps of: acquiring a road test data set of a vehicle, wherein the road test data set comprises sensor data for sensing obstacles, obstacle perception result data and artificial driving behavior data, obtaining example data of obstacle false identification and / or obstacle leakage identification by comparing and analyzing the obstacle perception result data and the artificial driving behavior data; wherein the example data includes sensor data. The technical scheme extracts a large amount of corresponding instance data such as obstacle leakage identification and / or error identification in the driving process of a vehicle during road measurement, provides data support for the retraining of obstacle perception model, which can optimize the obstacle perception model, improve the accuracy and reliability of obstacle recognition, and reduce the potential safety hazards of unmanned vehicles.

Description

technical field [0001] The embodiments of the present invention relate to the field of unmanned driving technology, and in particular to a method, device, device and medium for extracting obstacle perception error data. Background technique [0002] In the unmanned driving perception system, the perception and recognition of obstacles mainly rely on the image data output by the camera, the point cloud data output by the lidar, and the data output by the radio radar. In the prior art, in the iterative development stage of the algorithm, a large amount of data collection and standards are usually carried out through the open-loop mode of manual driving, which is used for obstacle perception model training, so as to identify and classify obstacles through the obstacle perception model. [0003] However, due to the wide variety of obstacles, non-obstacles such as green plants, branches, and water on the ground are misidentified, and / or low obstacles such as traffic cones, tripod...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00
CPCG06V20/56G06V20/58
Inventor 费雯凯
Owner APOLLO INTELLIGENT DRIVING (BEIJING) TECHNOLOGY CO LTD
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