Lane Change (LC) detection is the foundation of LC studies using real-world data. Most current studies use rule-based methods for LC detection from experimental data. In this study, we propose a learning-based method to detect LC using large-scale naturalistic driving data. The dataset is analyzed using big data analytics method, and the potential LC maneuvers are extracted. The LC detection is reformulated as a one-class classification problem, and an autoencoder-based anomaly detection method is developed to solve it. The proposed method is robust to data noises and can achieve better detection performance than the one-class Support Vector Machine (SVM). This work lays the groundwork for future LC studies, such as driving behaviour modeling and traffic safety solutions.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Lane Change Detection Using Naturalistic Driving Data


    Contributors:


    Publication date :

    2021-06-16


    Size :

    5449475 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Driving Style Recognition Based on Lane Change Behavior Analysis Using Naturalistic Driving Data

    Gao, Zhen / Liang, Yongchao / Zheng, Jiangyu et al. | ASCE | 2020



    Deep Learning Approach for Detecting Lane Change Maneuvers Using SHRP2 Naturalistic Driving Data

    Das, Anik / Khan, Md Nasim / Ahmed, Mohamed M. | Transportation Research Record | 2022


    Analysis of Driving Control Model of Normal Lane Change based on Naturalistic Driving Data

    Zhang, Jiarui / Ma, Zhixiong / Zhu, Xichan et al. | IEEE | 2019


    Drive quality analysis of lane change maneuvers for naturalistic driving studies

    Satzoda, Ravi Kumar / Gunaratne, Pujitha / Trivedi, Mohan M. | IEEE | 2015