In this paper, we address the problem of vehicle behavior assessment for road safety. We propose a solution that leverages the driver's smartphone, utilizing data from the phone's GPS to provide real-time assessments and the vehicle's On-Board Diagnostics (OBD) system to predict the vehicle's state. The solution's decision-making process uses a Recurrent Neural Network (RNN) for extracting behavior features of the vehicle and a hybrid RNN- Transformer based approach to predict the vehicle state. We introduce a ‘Danger-Level’ metric to assess the potential road risk posed by the vehicle. Our experimental results show that the agent was able to compute the ‘Danger-Level’ of the vehicle with an accuracy of 80 %. Additionally, the agent computes the future state of the vehicle with a minimal error of 0.207.


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    Titel :

    Assessing Vehicle Behavior for Road Safety


    Beteiligte:


    Erscheinungsdatum :

    24.09.2024


    Format / Umfang :

    1272056 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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