Aurora Operations, Inc.

Yan Li

Davis Edward King

Systems and methods for determining a current state, of at least one traffic light, for use in controlling an autonomous vehicle are described herein. Implementations determine, based on a pose instance of the autonomous vehicle and a stored mapping of an environment of the autonomous vehicle, a region of the environment including the traffic light and a configuration that is assigned to the traffic light. Further, those implementations process an image capturing the region, using a machine learning classifier, to generate predicted output associated with multiple candidate states of the traffic light, and determine a current state of the traffic light based on the predicted output. Processing the image using the machine learning classifier can be based on the configuration of the traffic light.

Traffic light identification and/or classification for use in controlling an autonomous vehicle

A method includes obtaining a first track associated with an object. A first set of parameters is generated based on the first track. Measurement data are obtained from one or more sensors. A first set of features are extracted from the measurement data. Based on the first set of parameters and the first set of features, a second set of parameters are generated by a machine learning model. The second set of parameters represent an adjustment to the first set of parameters. Based on the second set of parameters, the first track is adjusted to generate a second track associated with the object. The second track is provided to an autonomous vehicle control system for autonomous control of a vehicle.

System and method for adjusting track using sensor data

Systems and methods for determining a current state of at least one traffic light ('TL') for use in controlling an autonomous vehicle are described herein. Implementations can generate a corresponding numerical measure, for each of multiple candidate states of the TL, based on processing an image capturing a region that is predicted to include the TL using machine learning classifier(s). Implementations further generate a corresponding final measure, for each of the candidate states, based on processing each of the corresponding numerical measures using TL model(s), select one of the candidate states as a current state of the TL based on the final measures, and control the autonomous vehicle based on the current state of the TL. Notably, each of the corresponding final measures can be generated as a function of the corresponding numerical measures of the candidate states, and the corresponding numerical measures of the other candidate states.

Determining current state of traffic light(s) for use in controlling an autonomous vehicle

Systems and methods for determining a current state, of at least one traffic light, for use in controlling an autonomous vehicle are described herein. Implementations determine, based on a pose instance of the autonomous vehicle and a stored mapping of an environment of the autonomous vehicle, a region of the environment includes the traffic light and a configuration that is assigned to the traffic light. Further, those implementations process an image capturing the region, using a machine learning classifier, to generate predicted output associated with multiple candidate states of the traffic light, and determine a current state of the traffic light based on the predicted output. Processing the image using the machine learning classifier can be based on the configuration of the traffic light.

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San Francisco, CA, United States of America

Yan Li