In this article, we address the problem of using non-interpretable Machine Learning (ML) algorithms in safety critical applications, especially automated driving functions. We focus on the lane change prediction of vehicles on a highway. In order to understand wrong decisions, which may lead to accidents, we want to interpret the reasons for a ML algorithm's decision making. To this end, we use motif discovery—a data mining method—to obtain sub-sequences representing typical driving behavior. With the help of these meaningful sub-sequences (motifs), we can study typical driving maneuvers on a highway. On top of this, we propose to replace non-interpretable ML algorithms with an interpretable alternative: a Mixture of Experts (MoE) classifier. We present an MoE classifier consisting of different $k$-Nearest Neighbors ($k$-NN) classifiers trained only on motifs, which represent a few samples from the dataset. These $k$-NN-based experts are fully interpretable, making the lane change prediction fully interpretable, too. Using our proposed MoE classifier, we are able to solve the lane change prediction problem in an interpretable manner. These MoE classifiers show a classification performance comparable to common non-interpretable ML methods from the literature.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Interpretable Classifiers Based on Time-Series Motifs for Lane Change Prediction


    Beteiligte:

    Erschienen in:

    Erscheinungsdatum :

    2023-07-01


    Format / Umfang :

    1096026 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Vehicle trajectory and lane change prediction using ANN and SVM classifiers

    Izquierdo, R. / Parra, I. / Munoz-Bulnes, J. et al. | IEEE | 2017


    An Interpretable Lane Change Detector Algorithm based on Deep Autoencoder Anomaly Detection

    De Candido, Oliver / Binder, Maximilian / Utschick, Wolfgang | IEEE | 2021


    Lane Change Prediction Using Gaussian Classification, Support Vector Classification and Neural Network Classifiers

    Rákos, Olivér / Aradi, Szilárd / Bécsi, Tamás | BASE | 2020

    Freier Zugriff

    LANE CHANGE PREDICTION SYSTEM AND LANE CHANGE PREDICTION METHOD

    UMEDA TAKU / MORIMOTO TAKUJI / UNO TOMOKI et al. | Europäisches Patentamt | 2024

    Freier Zugriff

    LANE CHANGE PREDICTION APPARATUS AND LANE CHANGE PREDICTION METHOD

    YOON JI HYUN | Europäisches Patentamt | 2016

    Freier Zugriff