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Research on Adaptive Front-lighting Systems with the influence of multiple factors

Published: 28 January 2020 Publication History

Abstract

Aiming at the complexity of Adaptive Front-lighting Systems(AFS) modeling, a method of modeling AFS system by training neural network model is proposed. Through the analysis of prior knowledge and actual situation, Two typical working conditions are modeled in horizontal and vertical directions. RBF neural network is created on MATLAB to train and verify the network, and the fuzzy PID control strategy is added to AFS to optimize the performance of system. Simulation results show that the system model established by this method has a good precision, the fuzzy PID controller can greatly reduce the excessive deflection angle of headlamp, so the service life and the working accuracy of the headlamp can be improved.

References

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Chen Xu; Lin Guoyu. A semi-physical simulation platform for Adaptive Front Lighting System (AFS)[J]. Information Technology Journal, 2011, 10 (11): 2052--2059.
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Zhou Huaguo, F Pirinccioglu, P Hsu. A new roadway lighting measurement system Transportation[J]. Transportation Research Part C Emerging Technologies, 2009, 17 (3): 274--284.
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Kim K.H. and Yum D.H. Improving driver's visual field using estimation of curvature[J]. International Conference on Control Automation and Systems, 2010, 728--731.
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Tian Jihua. The application and research of new energy vehicle in China[J]. Technology Innovation and Application, 2013(1): 38.
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Zuo Cui. Research on the simulation of Adaptive Front-lighting Systems(AFS) and control strategy[D]. Changsha University of Science & Technology, 2012: 5.
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Wang Hongpei. Research and development of Adaptive Front Lighting System (AFS)[D]. Shandong University of Technology, 2011: 4--15.
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Li Lifu, Yang Mingjun, Zhang Jinyong. Research on the control method of bending mode of AFS system based on preview control[C]. Applied Mechanics and Materials, 2014, 844--849.
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Wang Shuzhang. Research on Adaptive Front Lighting System (AFS)[D]. Harbin Institute of Technology, 2013: 6.
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Cited By

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  • (2022)Design and Experimental Investigation of DMD Lamp to Improve Driver's Visual Perception2022 International Conference on Service Robotics (ICoSR)10.1109/ICoSR57188.2022.00011(1-9)Online publication date: Jun-2022

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  1. Research on Adaptive Front-lighting Systems with the influence of multiple factors

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    cover image ACM Other conferences
    ICNCC '19: Proceedings of the 2019 8th International Conference on Networks, Communication and Computing
    December 2019
    263 pages
    ISBN:9781450377027
    DOI:10.1145/3375998
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 28 January 2020

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    Author Tags

    1. A variety of road conditions
    2. Control optimization
    3. Intelligent headlamp system
    4. RBF neural network

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    • Research-article
    • Research
    • Refereed limited

    Funding Sources

    • the Natural Science Foundation of China
    • Science and Technology Innovation Outstanding Talents Program of Henan Province

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    ICNCC 2019

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    • (2022)Design and Experimental Investigation of DMD Lamp to Improve Driver's Visual Perception2022 International Conference on Service Robotics (ICoSR)10.1109/ICoSR57188.2022.00011(1-9)Online publication date: Jun-2022

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