Techniques are discussed herein for generating and using graph neural networks (GNNs) including vectorized representations of map elements and entities within the environment of an autonomous vehicle. Various techniques may include vectorizing map data into representations of map elements, and object data representing entities in the environment of the autonomous vehicle. In some examples, the autonomous vehicle may generate and/or use a GNN representing the environment, including nodes stored as vectorized representations of map elements and entities, and edge features including the relative position and relative yaw between the objects. Machine-learning inference operations may be executed on the GNN, and the node and edge data may be extracted and decoded to predict future states of the entities in the environment.


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    Title :

    GRAPH NEURAL NETWORKS WITH VECTORIZED OBJECT REPRESENTATIONS IN AUTONOMOUS VEHICLE SYSTEMS



    Publication date :

    2022-09-01


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    IPC:    B60W CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION , Gemeinsame Steuerung oder Regelung von Fahrzeug-Unteraggregaten verschiedenen Typs oder verschiedener Funktion / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen




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