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一种基于大脑启发的决策线性神经网络及其在自动驾驶中的应用。

A Brain-Inspired Decision-Making Linear Neural Network and Its Application in Automatic Drive.

机构信息

State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130012, China.

College of Automotive Engineering, Jilin University, Changchun 130012, China.

出版信息

Sensors (Basel). 2021 Jan 25;21(3):794. doi: 10.3390/s21030794.

DOI:10.3390/s21030794
PMID:33504010
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7865542/
Abstract

Brain-like intelligent decision-making is a prevailing trend in today's world. However, inspired by bionics and computer science, the linear neural network has become one of the main means to realize human-like decision-making and control. This paper proposes a method for classifying drivers' driving behaviors based on the fuzzy algorithm and establish a brain-inspired decision-making linear neural network. Firstly, different driver experimental data samples were obtained through the driving simulator. Then, an objective fuzzy classification algorithm was designed to distinguish different driving behaviors in terms of experimental data. In addition, a brain-inspired linear neural network was established to realize human-like decision-making and control. Finally, the accuracy of the proposed method was verified by training and testing. This study extracts the driving characteristics of drivers through driving simulator tests, which provides a driving behavior reference for the human-like decision-making of an intelligent vehicle.

摘要

类脑智能决策是当今世界的一个流行趋势。然而，受仿生学和计算机科学的启发，线性神经网络已成为实现类人决策和控制的主要手段之一。本文提出了一种基于模糊算法的驾驶员驾驶行为分类方法，并建立了一种类脑启发式决策线性神经网络。首先，通过驾驶模拟器获得不同驾驶员的实验数据样本。然后，设计了一种客观的模糊分类算法，根据实验数据来区分不同的驾驶行为。此外，建立了一个类脑启发式线性神经网络来实现类人决策和控制。最后，通过训练和测试验证了所提出方法的准确性。本研究通过驾驶模拟器测试提取驾驶员的驾驶特征，为智能车的类人决策提供了驾驶行为参考。

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