Abstract: Techniques are discussed for predicting locations of an object based on attributes of the object and/or attributes of other object(s) proximate to the object. The techniques can predict locations of a pedestrian proximate to a crosswalk as they traverse or prepare to traverse through the crosswalk. The techniques can predict locations of objects as the object traverses an environment. Attributes can comprise information about an object, such as a position, velocity, acceleration, classification, heading, relative distances to regions or other objects, bounding box, etc. Attributes can be determined for an object over time such that, when a series of attributes are input into a prediction component (e.g., a machine learned model), the prediction component can output, for example, predicted locations of the object at times in the future. A vehicle, such as an autonomous vehicle, can be controlled to traverse an environment based on the predicted locations.

Abstract: Techniques are discussed for predicting locations of an object based on attributes of the object and/or attributes of other object(s) proximate to the object. The techniques can predict locations of a pedestrian proximate to a crosswalk as they traverse or prepare to traverse through the crosswalk. The techniques can predict locations of objects as the object traverses an environment. Attributes can comprise information about an object, such as a position, velocity, acceleration, classification, heading, relative distances to regions or other objects, bounding box, etc. Attributes can be determined for an object over time such that, when a series of attributes are input into a prediction component (e.g., a machine learned model), the prediction component can output, for example, predicted locations of the object at times in the future. A vehicle, such as an autonomous vehicle, can be controlled to traverse an environment based on the predicted locations.

Abstract: Techniques are discussed for predicting locations of an object based on attributes of the object and/or attributes of other object(s) proximate to the object. The techniques can predict locations of a pedestrian proximate to a crosswalk as they traverse or prepare to traverse through the crosswalk. The techniques can predict locations of objects as the object traverses an environment. Attributes can comprise information about an object, such as a position, velocity, acceleration, classification, heading, relative distances to regions or other objects, bounding box, etc. Attributes can be determined for an object over time such that, when a series of attributes are input into a prediction component (e.g., a machine learned model), the prediction component can output, for example, predicted locations of the object at times in the future. A vehicle, such as an autonomous vehicle, can be controlled to traverse an environment based on the predicted locations.

Abstract: Techniques are discussed for predicting locations of an object based on attributes of the object and/or attributes of other object(s) proximate to the object. The techniques can predict locations of a pedestrian proximate to a crosswalk as they traverse or prepare to traverse through the crosswalk. The techniques can predict locations of objects as the object traverses an environment. Attributes can comprise information about an object, such as a position, velocity, acceleration, classification, heading, relative distances to regions or other objects, bounding box, etc. Attributes can be determined for an object over time such that, when a series of attributes are input into a prediction component (e.g., a machine learned model), the prediction component can output, for example, predicted locations of the object at times in the future. A vehicle, such as an autonomous vehicle, can be controlled to traverse an environment based on the predicted locations.