Connected autonomous vehicles (CAVs) offer significant enhancements in coordinated traffic and safety through real-time vehicle-to-vehicle or vehicle-to-infrastructure communications, establishing them as a potent tool for augmenting driving tasks. However, the extensive information-sharing framework inherent in CAVs amplifies the risk associated with sensor anomalies, posing challenges to the reliability and security of the system. Responding to this timely research challenge, this study proposes a novel anomaly detection method, namely Dual-channel Self-attention-based Convolutional Neural Network (DSA-CNN) for multivariate time series data. Through the introduction of the Dual-channel Self-attention Mechanism, DSA-CNN can progressively and autonomously extract spatiotemporal features from multivariate time series data. The proposed method was tested under a variety of common threatening sensor anomaly patterns of CAVs summarised in the literature, and evaluated under multiple different performance metrics. The results demonstrate its advantages in detecting minor anomalies and enhancing sensitivity, outperforming previously reported methods in the literature. Across all experimental scenarios, an average sensitivity improvement of 2.53% was observed, complemented by an average F1 score increase of 1.47%. In CAV settings, maintaining high sensitivity to ensure fewer undetected anomalies, alongside the ability to detect small anomalies, can be more important for the robustness and safety measures of CAV systems.


    Access

    Download


    Export, share and cite



    Title :

    Time Series Anomaly Detection in Vehicle Sensors Using Self-Attention Mechanisms


    Contributors:

    Published in:

    Publication date :

    2024-11-01


    Size :

    2929018 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Real-Time Sensor Anomaly Detection and Recovery in Connected Automated Vehicle Sensors

    Wang, Yiyang / Masoud, Neda / Khojandi, Anahita | IEEE | 2021


    Unsupervised Video Anomaly Detection with Self-Attention Based Feature Aggregating

    Ye, Zhenhao / Li, Yanlong / Cui, Zhichao et al. | IEEE | 2023


    Anomaly Scoring for Prediction-Based Anomaly Detection in Time Series

    Li, Tianyu / Comer, Mary L. / Delp, Edward J. et al. | IEEE | 2020


    Exploiting Consistency Among Heterogeneous Sensors for Vehicle Anomaly Detection

    Ganesan, Arun / Rao, Jayanthi / Shin, Kang | SAE Technical Papers | 2017


    Spacecraft Time-Series Online Anomaly Detection Using Deep Learning

    Baireddy, Sriram / Desai, Sundip R. / Foster, Richard H. et al. | IEEE | 2023