In various examples, robust trajectory predictions against adversarial attacks in autonomous machines and applications are described herein. Systems and methods are disclosed that perform adversarial training for trajectory predictions determined using a neural network(s). In order to improve the training, the systems and methods may devise a deterministic attach that creates a deterministic gradient path within a probabilistic model to generate adversarial samples for training. Additionally, the systems and methods may introduce a hybrid objective that interleaves the adversarial training and learning from clean data to anchor the output from the neural network(s) on stable, clean data distribution. Furthermore, the systems and methods may use a domain-specific data augmentation technique that generates diverse, realistic, and dynamically-feasible samples for additional training of the neural network(s).


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    Title :

    ROBUST TRAJECTORY PREDICTIONS AGAINST ADVERSARIAL ATTACKS IN AUTONOMOUS MACHINES AND APPLICATIONS


    Contributors:
    XIAO CHAOWEI (author) / CAO YOLONG (author) / XU DANFEI (author) / ANANDKUMAR ANIMASHREE (author) / PAVONE MARCO (author) / WENG XINSHUO (author)

    Publication date :

    2024-01-25


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    IPC:    G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung / 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



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