Vehicle Automatic Iterative Learning Control based on Drivers’ Starting Behavior

Gao, Zhenhai; Sun, Tianjun; Zhou, Shuhui; Wang, Xiaohan; Mei, Xingtai

doi:10.1109/cac48633.2019.8996633

Cited by 1 publication

(2 citation statements)

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“…According to the fuzzy theory [ 18 , 19 ], driving behavioral characteristics are not an absolute concept, or “black or white” in the traditional sense, but they have a mutual connection with each other. In order to achieve a more accurate classification through the experimental data, we established two key indicators corresponding to characteristic parameters: safety indicator and efficiency indicator.…”

Section: Design Of Fuzzy Classification Algorithmmentioning

confidence: 99%

“…The second-order partial differential for the square of training error, with respect to the network weight during the k cycle, is calculated in Equation (19). The second-order partial differential for the square of training error, with respect to the threshold value during the k cycle, is calculated in Equation ( 20): The shining point of the linear neural network is its LMS learning Algorithm, also called "∆ rules".…”

Section: Linear Neural Network For Brain-inspired Decision-makingmentioning

confidence: 99%

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

Sun

Gao

et al. 2021

Sensors

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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.

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Section: Design Of Fuzzy Classification Algorithmmentioning

confidence: 99%

Section: Linear Neural Network For Brain-inspired Decision-makingmentioning

confidence: 99%