Car-following trajectory generation and anomaly detection are critical functions in the sensing module of an automated vehicle. However, developing models that capture realistic trajectory data distribution and detect anomalous driving behaviors could be challenging. This paper proposes ‘TrajGAN’, an unsupervised learning approach based on the Generative Adversarial Network (GAN) to exploit vehicle car following trajectory data for generation and anomaly detection. The proposed TrajGAN consists of two modules, an encoder-decoder Long Short-Term Memory (LSTM)-based generator and an LSTM-multilayer perceptron (MLP) based discriminator, whose former component is used to generate vehicular car following trajectories and the latter one is for trajectory anomaly detection. By letting these two modules game with each other in training, we can simultaneously achieve robust trajectory generators and anomaly detectors. Trained with the Next Generation Simulation (NGSIM) dataset, TrajGAN can generate realistic trajectories with a similar distribution of training data and identify a manifold of anomalous trajectories based on an anomaly scoring scheme. Simulation results indicate that the proposed approach is efficient in reproducing artificial trajectories and identifying anomalous driving behaviors.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Generative adversarial network for car following trajectory generation and anomaly detection


    Additional title:

    H. Shi et al.
    Journal of Intelligent Transportation Systems


    Contributors:
    Shi, Haotian (author) / Dong, Shuoxuan (author) / Wu, Yuankai (author) / Nie, Qinghui (author) / Zhou, Yang (author) / Ran, Bin (author)


    Publication date :

    2025-01-02


    Size :

    14 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Fishing boat trajectory anomaly analysis method based on generative adversarial network

    YAN ZHIWEI / WU ZIYAN / LI ZHIYONG | European Patent Office | 2023

    Free access


    Anomaly Detection Using Convolutional Neural Network and Generative Adversarial Network

    Mohanan, Amritha / Gangadharan Santha, Sarika / Padmanabha Rajeswari, Priyanka Pillai et al. | British Library Conference Proceedings | 2023


    Anomaly Detection Using Convolutional Neural Network and Generative Adversarial Network

    Mohanan, Amritha / Padathil Veerendrakumar, Praveen / Padmanabha Rajeswari, Priyanka Pillai et al. | SAE Technical Papers | 2023


    Anomaly Monitoring Framework in Lane Detection With a Generative Adversarial Network

    Kim, Hayoung / Park, Jongwon / Min, Kyushik et al. | IEEE | 2021