Autonomous vehicle systems provide improved safety in comparison to human driven vehicles by reducing crashes. However, in future scenarios, the autonomous vehicles have to share the road with human-driven vehicles. But human drivers make decision errors during maneuvers which lead to crashes. Autonomous vehicles therefore have to predict human-initiated safe and unsafe maneuvers to react appropriately for the safety and comfort of its passengers. The present disclosure addresses unresolved problems of the conventional autonomous vehicle systems by providing a stacked autoencoder based singular deep neural network that uses sensor data to predict if any vehicle within its field of view would perform safe or unsafe lane maneuvers into its headway or into any other vehicle's headway. The method of the present disclosure also helps surrounding vehicles to know intention of a target vehicle with the help of vehicle-to-vehicle communication systems in a road driving environment.


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    Title :

    SYSTEMS AND METHODS FOR VEHICLE MANEUVER PREDICTION USING DEEP LEARNING


    Additional title:

    SYSTEME UND VERFAHREN ZUR FAHRZEUGMANÖVERVORHERSAGE UNTER VERWENDUNG VON TIEFENLERNEN
    SYSTÈMES ET PROCÉDÉS DE PRÉDICTION DE MAN UVRE DE VÉHICULE À L'AIDE D'UN APPRENTISSAGE PROFOND



    Publication date :

    2025-06-04


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    IPC:    G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen



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