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Automatic driving data identification method and device, computer equipment and storage medium

A technology for automatic driving and data recognition, applied in the field of computer equipment and storage media, devices, and data recognition methods for automatic driving, can solve the problems of low efficiency and high cost of long-tail data, improve efficiency, ensure accuracy, and reduce costs. Effect

Pending Publication Date: 2021-11-23
GUANGZHOU WERIDE TECH LTD CO
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] The embodiment of the present invention proposes a data recognition method, device, computer equipment and storage medium for automatic driving to solve the problem of low efficiency and high cost of manual mining of long-tail data for automatic driving

Method used

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  • Automatic driving data identification method and device, computer equipment and storage medium
  • Automatic driving data identification method and device, computer equipment and storage medium
  • Automatic driving data identification method and device, computer equipment and storage medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0032] figure 1 It is a flow chart of an automatic driving data recognition method provided by Embodiment 1 of the present invention. This embodiment can be applied to the situation of using the teacher network and the student network to mine the long tail data of automatic driving. This method can be obtained from the automatic driving data The automatic driving data identification device can be implemented by software and / or hardware, and can be configured in a computer device, such as a server, a personal computer, etc., and specifically includes the following steps:

[0033] Step 101. Obtain raw data collected during automatic driving.

[0034] The vehicle in this embodiment can support automatic driving. The so-called automatic driving can refer to the ability of the vehicle itself to perceive the environment, plan the path and realize the vehicle control autonomously, that is, to use electronic technology to control the vehicle for humanoid driving.

[0035] According t...

Embodiment 2

[0117] image 3 It is a flowchart of a data recognition method for automatic driving provided by Embodiment 2 of the present invention. This embodiment is based on the foregoing embodiments, and further increases the operation of using long-tail data to train the student network. The method specifically includes the following steps:

[0118] Step 301. Obtain raw data collected during automatic driving.

[0119] Step 302, respectively determine the student network and the teacher network suitable for automatic driving.

[0120] Step 303 , calculating the difference between the student network and the teacher network in predicting the original data as the distribution difference.

[0121] Step 304: Identify the original data belonging to the long-tail data for automatic driving according to the distribution difference as the target data.

[0122] Step 305, labeling the target data.

[0123] In this embodiment, for the target data belonging to the long-tail data, it can be lab...

Embodiment 3

[0137] Figure 4 The structural block diagram of an automatic driving data recognition device provided in Embodiment 3 of the present invention may specifically include the following modules:

[0138] Raw data acquisition module 401, configured to acquire raw data collected during automatic driving;

[0139] A network determination module 402, configured to respectively determine a student network and a teacher network suitable for the automatic driving;

[0140] A distribution difference calculation module 403, configured to calculate the difference between the student network and the teacher network for predicting the original data as a distribution difference;

[0141] The original data identification module 404 is configured to identify the original data belonging to the long-tail data for the automatic driving as target data according to the distribution difference.

[0142] In one embodiment of the present invention, the network determination module 402 includes:

[0...

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PUM

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Abstract

The embodiment of the invention provides an automatic driving data identification method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining original data collected during automatic driving, determining a student network and a teacher network which are suitable for automatic driving, calculating a difference of predicting original data between a student network and a teacher network as a distribution difference, identifying the original data belonging to long-tail data for automatic driving according to the distribution difference as target data, and automatically mining the long-tail data according to a difference of generalization ability of the student network and the teacher network for the long-tail data. According to the method, the accuracy of long-tail data mining can be guaranteed, manual long-tail data mining is avoided, the efficiency of long-tail data mining is greatly improved, and the cost is greatly reduced.

Description

technical field [0001] The embodiments of the present invention relate to the technical field of automatic driving, and in particular to a data identification method, device, computer equipment and storage medium for automatic driving. Background technique [0002] In order to ensure the accurate decision-making of the vehicle in autonomous driving, the vehicle will perceive its own state and surrounding environment, and obtain a large amount of data, such as the speed of the vehicle, the attitude of the vehicle, traffic flow information, road conditions, traffic signs, and so on. [0003] For decision-making, these data usually have a basic assumption that the data is approximately uniformly distributed. However, in the real road test environment, the environmental data often presents an unbalanced phenomenon, and there are long-tail data, that is, Environmental data with low probability and difficult to collect. [0004] For example, for traffic lights, traffic lights may...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/24
Inventor 夏润刘传秀韩旭
Owner GUANGZHOU WERIDE TECH LTD CO