Researchers in the automotive industry aim to enhance the performance, safety and energy management of intelligent vehicles with driver assistance systems. The performance of such systems can be improved with a better understanding of driving behaviors. In this paper, a driving behavior recognition algorithm is developed with a Long Short Term Memory (LSTM) Network using driver models of IPG's TruckMaker. Six driver models are designed based on longitudinal and lateral acceleration limits. The proposed algorithm is trained with driving signals of these drivers controlling a realistic truck model with five different trailer loads on an artificial training road. This training road is designed to cover possible road curves that can be seen in freeways and rural highways. Finally, the algorithm is tested with driving signals that are collected with the same method on a realistic road. Results show that the LSTM structure has a substantial capability to recognize dynamic relations between driving signals even in small time periods.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Driving Behavior Classification Using Long Short Term Memory Networks


    Contributors:


    Publication date :

    2019-07-01


    Size :

    1501173 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Driver driving behavior automatic analysis method based on long-term and short-term memory network

    LIU JIAJIA / XUN YIJIE | European Patent Office | 2020

    Free access

    Modular Multitarget Tracking Using Long Short-Term Memory Networks

    Verma, Rishabh / Rajesh, R. / Easwaran, M. S. | AIAA | 2021


    Aircraft Trajectory Prediction Using Deep Long Short-Term Memory Networks

    Zhao, Ziyu / Zeng, Weili / Quan, Zhibin et al. | ASCE | 2019


    Driving behavior prediction method and system based on bidirectional long and short term memory network

    WANG JINXIANG / FANG ZHENWU / XIAO SUYANG et al. | European Patent Office | 2022

    Free access

    Long short-term memory networks for vehicle sensor fusion

    Gandy, Jonah T. / Ball, John E. | British Library Conference Proceedings | 2022