US 12,737,438 B2 · Robust trajectory predictions against adversarial attacks in autonomous machines and applications

Abstract

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 attack 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).

Claims20

Dependent claims

A method comprising:

receiving input data representing one or more points associated with a past trajectory of an object through an environment;
receiving ground truth data associated with the input data, the ground truth data representing a future trajectory of the object through the environment;
generating, using a deterministic attack that creates a deterministic gradient path within a probabilistic generative model, training data representing an adversarial trajectory of the object, the deterministic gradient path guiding an obfuscation of at least a point of the one or more points associated with the past trajectory;
and
training, based at least on the input data, the training data, and the ground truth data, one or more neural networks comprising the probabilistic generative model for determining future trajectories.

2 more elements

At least one processor comprising:

one or more processing units to:
receive input data representing at least a past trajectory of an object through an environment;
receive ground truth data associated with the input data, the ground truth data representing a future trajectory of the object through the environment;
generate, using a deterministic attack that creates a deterministic gradient path within a probabilistic generative model, training data representing an adversarial past trajectory, the deterministic gradient path guiding an obfuscation of at least a portion of the past trajectory;
and
train, based at least on the training data and the ground truth data, one or more neural networks comprising the probabilistic generative model for determining trajectories.

3 more elements

Dependencies are inferred from the claim text.

Robust trajectory predictions against adversarial attacks in autonomous machines and applications

Life of this patent

Robust trajectory predictions against adversarial attacks in autonomous machines and applications

Life of this patent

Abstract

Claims20