The current paper implements a methodology for automatically detecting vehicle maneuvers from vehicle telemetry data under naturalistic driving settings. Previous approaches have treated vehicle maneuver detection as a classification problem, although both time series segmentation and classification are required since input telemetry data are continuous. Our objective is to develop an end-to-end pipeline for the frame-by-frame annotation of naturalistic driving studies videos into various driving events including stop and lane-keeping events, lane changes, left-right turning movements, and horizontal curve maneuvers. To address the time series segmentation problem, the study developed an energy-maximization algorithm (EMA) capable of extracting driving events of varying durations and frequencies from continuous signal data. To reduce overfitting and false alarm rates, heuristic algorithms were used to classify events with highly variable patterns such as stops and lane-keeping. To classify segmented driving events, four machine-learning models were implemented, and their accuracy and transferability were assessed over multiple data sources. The duration of events extracted by EMA was comparable to actual events, with accuracies ranging from 59.30% (left lane change) to 85.60% (lane-keeping). Additionally, the overall accuracy of the 1D-convolutional neural network model was 98.99%, followed by the long-short-term-memory model at 97.75%, then the random forest model at 97.71%, and the support vector machine model at 97.65%. These model accuracies were consistent across different data sources. The study concludes that implementing a segmentation-classification pipeline significantly improves both the accuracy of driver maneuver detection and the transferability of shallow and deep ML models across diverse datasets.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Driver Maneuver Detection and Analysis Using Time Series Segmentation and Classification


    Weitere Titelangaben:

    J. Transp. Eng., Part A: Systems


    Beteiligte:


    Erscheinungsdatum :

    2023-03-01




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Real-Time Driver Maneuver Prediction Using LSTM

    Khairdoost, Nima / Shirpour, Mohsen / Bauer, Michael A. et al. | IEEE | 2020


    Driver and Path Detection through Time-Series Classification

    Mario Luca Bernardi / Marta Cimitile / Fabio Martinelli et al. | DOAJ | 2018

    Freier Zugriff

    Cislunar Maneuver Detection and Classification

    Wetterer, Charles J. | British Library Conference Proceedings | 2022


    Interaction-aware driver maneuver inference in highways using realistic driver models

    Gonzalez, David Sierra / Romero-Cano, Victor / Dibangoye, Jilles Steeve et al. | IEEE | 2017