Application of BP neural network in obstacle avoidance of driverless car

SONG Jian-hui; FAN Si-meng; LIU Yan-ju; ZOU Bo

doi:10.3788/OPE.20172513.0274

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Application of BP neural network in obstacle avoidance of driverless car

更新时间：2020-08-13

- Application of BP neural network in obstacle avoidance of driverless car
- Optics and Precision Engineering Vol. 25, Issue 10s, Pages: 274-280(2017)
- 作者机构：
  
  沈阳理工大学自动化与电气工程学院, 辽宁沈阳 110159
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20172513.0274
  CLC： TP24;U463.67
- Received：30 June 2017，
  
  Revised：10 July 2017，
  
  Published：25 November 2017
- 稿件说明：
移动端阅览
宋建辉, 樊思萌, 刘砚菊等. BP神经网络在无人车避障中的应用[J]. 光学精密工程, 2017,25(10s): 274-280

SONG Jian-hui, FAN Si-meng, LIU Yan-ju etc. Application of BP neural network in obstacle avoidance of driverless car[J]. Editorial Office of Optics and Precision Engineering, 2017,25(10s): 274-280
宋建辉, 樊思萌, 刘砚菊等. BP神经网络在无人车避障中的应用[J]. 光学精密工程, 2017,25(10s): 274-280 DOI： 10.3788/OPE.20172513.0274.

SONG Jian-hui, FAN Si-meng, LIU Yan-ju etc. Application of BP neural network in obstacle avoidance of driverless car[J]. Editorial Office of Optics and Precision Engineering, 2017,25(10s): 274-280 DOI： 10.3788/OPE.20172513.0274.

摘要

为了实现无人车环境障碍信息与执行动作模式指令之间的合理匹配，提出了一种基于BP（Back Propagation）神经网络的无人车避障技术。建立了BP神经网络环境信息识别模型，将单线激光雷达所探测到无人车前方180°的平面区域分成8个子区域，每个子区域的扫描范围为22.5°，将8个子区域内的环境障碍物信息作为BP神经网络系统的输入特征向量，将标识控制无人车的动作指令作为BP神经网络系统的输出向量，通过BP神经网络对输入的环境编码信息与执行的动作指令之间进行匹配。实验结果表明，基于BP神经网络的无人车避障模型得出的结果与期望的目标值之间误差控制在0.001内，实现了环境编码信息与执行的动作指令之间的准确快速分类匹配，达到了无人车合理有效躲避障碍物的目的。

Abstract

In order to achieve the reasonable matching of the environmental obstacle information of driverless car and the performed action command mode

the BP neural network applied to the obstacle avoidance of driverless car in the article and technology of obstacle avoidance of driverless car based on BP neural network was researched. The environmental information identification model of BP neural network was established. The 180° planar domain detected by single line laser radar in front of the driverless car was divided into 8 sub-domains and the scan range of each sub-domain was 22.5°. The information of environmental obstacles of the 8 sub-domains was taken as the input feature vector of BP neural network system and the action command of the driverless car controlled by identification was taken as the output vector of BP neural network system. The input environmental coding information matched with the performed action command by using BP neural network. The experimental result shows that the error between the obtained result of the obstacle avoidance model of driverless car based on BP neural network and expected target value is controlled within the range of 0.001 and the accurate and quick classification matching between environmental coding information and the performed action command is achieved

and the aim of driverless car reasonably and effectively avoiding obstacles is achieved.

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references

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