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

机译:一种脑激发决策线性神经网络及其在自动驱动中的应用

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摘要

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.
机译:大脑的智能决策是当今世界的普遍趋势。然而,通过仿生学和计算机科学的启发,线性神经网络已成为实现人类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对驱动器驾驶行为进行分类的方法,建立脑启动决策线性神经网络。首先,通过驱动模拟器获得不同的驾驶员实验数据样本。然后,设计了一种客观模糊分类算法以在实验数据方面区分不同的驾驶行为。此外,建立了脑激发的线性神经网络,以实现人类的决策和控制。最后,通过培训和测试来验证所提出的方法的准确性。本研究通过驾驶模拟器测试提取驱动器的驱动特性,这为智能车辆的人类决策提供了驾驶行为参考。

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