Driving Intention Identification Based on Long Short-Term Memory Neural Network

被引：5
|
作者：
Liu, Yonggang [1 ,2 ]
Zhao, Pan [1 ,2 ]
Qin, Datong [1 ,2 ]
Yang, Yang [1 ,2 ]
Chen, Zheng [3 ]
机构：
[1] Chongqing Univ, State Key Lab Mech Transmiss, Chongqing, Peoples R China
[2] Chongqing Univ, Sch Automot Engn, Chongqing, Peoples R China
[3] Kunming Univ Sci & Technol, Fac Transportat Engn, Kunming, Yunnan, Peoples R China
来源：
2019 IEEE VEHICLE POWER AND PROPULSION CONFERENCE (VPPC) | 2019年
基金：
欧盟地平线“2020”; 美国国家科学基金会;
关键词：
LSTM; driving intentions; identification;
D O I：
10.1109/vppc46532.2019.8952563
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In order to avoid frequent or accidental shift problems during the driving process, it is necessary to implement identification of driving intention based on vehicle driving data. In this study, the Long Short-Term Memory (LSTM) Neural Network is proposed to identify driving intentions in real time. First, according to the vehicle road test data, each driving intention to be identified is defined. Then, the intentions when driving on a straight and flat road are divided into acceleration, rapid acceleration, cruise, deceleration and rapid deceleration. Subsequently, a LSTM classification model is established to identify the driving intention with inputs of opening degree of the accelerator pedal, vehicle speed and brake pedal force. Identification results reveal that the highest accuracy of the proposed algorithm attains 95.36%, which is around 20% higher than that of the traditional back propagation neural network.
引用
收藏
页数：6
相关论文
共 50 条
  • [1] Driving Intention Identification Model Based on Long and Short-Term Memory Network
    Yuan, Tian
    Chai, Hua
    Ma, Ke-Xin
    CICTP 2020: TRANSPORTATION EVOLUTION IMPACTING FUTURE MOBILITY, 2020, : 2392 - 2402
  • [2] Modeling of Scroll Expander Based on Long Short-Term Memory Neural Network
    Zheng, Tianyou
    Li, Ke
    Ma, Xin
    Qu, Chao
    Zhang, Chenghui
    2019 CHINESE AUTOMATION CONGRESS (CAC2019), 2019, : 732 - 736
  • [3] Short-term wind speed forecasting based on long short-term memory and improved BP neural network
    Chen, Gonggui
    Tang, Bangrui
    Zeng, Xianjun
    Zhou, Ping
    Kang, Peng
    Long, Hongyu
    INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2022, 134
  • [4] Microgrid Equivalent Modeling Based on Long Short-Term Memory Neural Network
    Cai, Changchun
    Liu, Haolin
    Tao, Yuan
    Deng, Zhixiang
    Dai, Weil
    Chen, Jie
    IEEE ACCESS, 2020, 8 : 23120 - 23133
  • [5] Short-term runoff forecasting in an alpine catchment with a long short-term memory neural network
    Frank, Corinna
    Russwurm, Marc
    Fluixa-Sanmartin, Javier
    Tuia, Devis
    FRONTIERS IN WATER, 2023, 5
  • [6] Lossless Image Compression Algorithm Based on Long Short-term Memory Neural Network
    Zhu, Caixin
    Zhang, Huaiyao
    Tang, Yun
    2020 5TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND APPLICATIONS (ICCIA 2020), 2020, : 82 - 88
  • [7] Automated Cloud Based Long Short-Term Memory Neural Network Based SWE Prediction
    Meyal, Alireza Yekta
    Versteeg, Roelof
    Alper, Erek
    Johnson, Doug
    Rodzianko, Anastasia
    Franklin, Maya
    Wainwright, Haruko
    FRONTIERS IN WATER, 2020, 2
  • [8] Research on Predictive Maintenance of Aircraft Based on Long Short-Term Memory Neural Network
    Lee, Chin-Hsiung
    Lee, Chih-Yu
    2022 ASIA CONFERENCE ON ADVANCED ROBOTICS, AUTOMATION, AND CONTROL ENGINEERING (ARACE 2022), 2022, : 150 - 154
  • [9] Prediction of air pollutant concentrations based on the long short-term memory neural network
    Wu, Zechuan
    Tian, Yuping
    Li, Mingze
    Wang, Bin
    Quan, Ying
    Liu, Jianyang
    JOURNAL OF HAZARDOUS MATERIALS, 2024, 465
  • [10] Jaya-Based Long Short-Term Memory Neural Network for Structural Damage Identification with Consideration of Measurement Uncertainties
    Ding, Zhenghao
    Hou, Rongrong
    Xia, Yong
    INTERNATIONAL JOURNAL OF STRUCTURAL STABILITY AND DYNAMICS, 2022, 22 (14)
12345