In order to improve the accuracy of driver intention recognition and recognize the driver's lane changing intention earlier, a driver intention recognition system based on computer vision is established in this paper. The system judges the driver's head posture based on the fine-grained structure aggregation network (FSA-Net), determines the driver's observation behavior of the rear-view mirrors on both sides during driving by comparing with the non lane changing stage, divides the identification section in combination with the vehicle parameters, and uses the conditional random field (CRF) identify and predict the driver's lane change intention, provide the driver's behavior data for controlling lane change and other behaviors in the driver assisted driving system, and improve driving safety. Through experimental comparison, the system can detect the driver's lane change intention 4.3 seconds before lane change; the overall recognition rate is more than 98%; the recognition time is 0.013026s, which meets the requirements of online recognition.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Driver Intention Recognition Based on Computer Vision


    Additional title:

    Sae Technical Papers


    Contributors:
    Hua, Hang (author) / Chen, Huan'ming (author) / Li, Xuehan (author) / Wang, Yalun (author)

    Conference:

    2022 World General Artificial Intelligence Congress ; 2022



    Publication date :

    2022-06-28




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




    Driver Intention Recognition Based on Computer Vision

    Li, Xuehan / Chen, Huan'ming / Hua, Hang et al. | British Library Conference Proceedings | 2022


    Driver intention recognition method based on probability correction

    TANG XIAOLIN / YANG XIN / PU HUAYAN et al. | European Patent Office | 2020

    Free access

    Multi-parameter driver intention recognition based on neural network

    Feng, Zhao / Bo, Xie / Yantao, Tian | IEEE | 2020


    Driver overtaking intention recognition method based on GM-HMM

    CAI JINKANG / ZHAO RUI / DENG WEIWEN et al. | European Patent Office | 2021

    Free access

    Continuous Driver Intention Recognition with Hidden Markov Models

    Berndt, Holger / Emmert, Jorg / Dietmayer, Klaus | IEEE | 2008