Machine-learning models are described detecting the signaling state of a traffic signaling unit. A system can obtain an image of the traffic signaling unit, and select a model of the traffic signaling unit that identifies a position of each traffic lighting element on the unit. First and second neural network inputs are processed with a neural network to generate an estimated signaling state of the traffic signaling unit. The first neural network input can represent the image of the traffic signaling unit, and the second neural network input can represent the model of the traffic signaling unit. Using the estimated signaling state of the traffic signaling unit, the system can inform a driving decision of a vehicle.


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    Title :

    DETECTING TRAFFIC SIGNALING STATES WITH NEURAL NETWORKS


    Additional title:

    ERKENNUNG VON VERKEHRSSIGNALISIERUNGSZUSTÄNDEN MIT NEURONALEN NETZWERKEN
    DÉTECTION D'ÉTATS DE SIGNALISATION DE TRAFIC AVEC DES RÉSEAUX NEURONAUX


    Contributors:
    HSIAO EDWARD (author) / OUYANG YU (author) / YAO MAOQING (author)

    Publication date :

    2023-05-24


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    English



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