US 2026/0127963 A1 · TRAFFIC LIGHT CLASSIFICATION FOR AUTONOMOUS OR SEMI-AUTONOMOUS SYSTEMS AND APPLICATIONS

Abstract

In various examples, the systems and methods of the present disclosure may train and use machine learning models to determine attributes and, in some instances, classifications associated with traffic lights to determine traffic rules for operating a machine (e.g., an autonomous or semi-autonomous machine or vehicle) in an environment. For instance, an image depicting a traffic light device may be applied to a machine learning model that includes a plurality of component heads. Each one of component heads may be trained to detect different attributes and/or combinations of attributes associated with the traffic light device. Additionally, in some examples, the machine learning model may include a fusion head that is trained to classify the traffic light device. For instance, the fusion head may classify the traffic light device using the detected attributes and/or using a combined feature vector of multiple feature vectors applied to the plurality of component heads.

Claims20

Dependent claims

A method comprising:

applying, as input to one or more deep neural networks (DNNs), image data representing an image depicting one or more traffic light devices;
determining, using one or more component heads of the one or more DNNs, one or more attributes associated with the one or more traffic light devices;
determining, using a fusion head of the one or more DNNs and based at least on the one or more attributes, one or more classifications associated with the one or more traffic light devices;
and
causing a machine to perform one or more control operations based on at least one of the one or more attributes or the one or more classifications associated with the one or more traffic light devices.

2 more elements

A system comprising:

one or more processors to:
determine, based at least on one or more first layers of a machine learning model processing sensor data obtained using one or more sensors having fields of view or sensory fields including a traffic light device, first data corresponding to one or more first attributes associated with the traffic light device;
determine, based at least on one or more second layers of the machine learning model processing the first data, second data corresponding to one or more second attributes associated with the traffic light device;
and
perform one or more operations associated with a machine based on at least one of the one or more first attributes or the one or more second attributes.

2 more elements

One or more processors comprising:

processing circuitry to perform one or more operations corresponding to a machine based at least on one or more attributes associated with a first traffic light device, the one or more attributes determined using one or more machine learning models, the one or more machine learning models trained, at least, by:
obtaining an image depicting a second traffic light device having one or more first attributes;
updating one or more portions of the image to generate an updated image, the updated image depicting the second traffic light device having one or more second attributes;
and
updating one or more parameters associated with one or more component heads of the one or more machine learning models based at least on applying the updated image as a training input to the one or more machine learning models.

2 more elements

Dependencies are inferred from the claim text.

Traffic light classification for autonomous or semi-autonomous systems and applications

Life of this patent

Traffic light classification for autonomous or semi-autonomous systems and applications

Life of this patent

Abstract

Claims20