We are presenting a novel approach for the task of driving behavior classification based on stacked LSTM Recurrent Neural Networks. Given a nine different sensor data captured using a smart phone internal sensors during a naturalistic driving sessions, we formulated the driving behavior classification problem as time-series classification task. Whereas, given a window sequence of fused feature vectors of sensor data at any time step of a driving trip, we can accurately classify the driving behavior during that window sequence from a three distinctive driving behavior classes, namely normal, aggressive or drowsy driving. We evaluated our proposed Stacked-LSTM model on one of the recent naturalistic driving behavior analysis and classification dataset, UAH-DriveSet. Our proposed Stacked-LSTM model has achieved state-of-the-art results on the UAH-DriveSet with much higher true positive rate as well as lower false positive rate in comparison to the baseline approach. We have also compared the performance of our proposed Stacked-LSTM model against a number of the common classification algorithms used in the driving behavior classification and analysis studies and we achieved F1-measure score of 91% with an improvement of more than 10% over the closest compared approaches.


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    Titel :

    Driving behavior classification based on sensor data fusion using LSTM recurrent neural networks


    Beteiligte:
    Saleh, Khaled (Autor:in) / Hossny, Mohammed (Autor:in) / Nahavandi, Saeid (Autor:in)


    Erscheinungsdatum :

    2017-10-01


    Format / Umfang :

    471583 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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