Map-Anchored Object Detection
AssigneeAurora Operations, Inc.
InventorLouis Foucard
Life of this patent
Estimating the term…
Abstract
An example method includes (a) obtaining sensor data descriptive of an environment of an autonomous vehicle; (b) obtaining a plurality of travel way markers from map data descriptive of the environment; (c) determining, using a machine-learned object detection model and based on the sensor data, an association between one or more travel way markers of the plurality of travel way markers and an object in the environment; and (d) generating, using the machine-learned object detection model, an offset with respect to the one or more travel way markers of a spatial region of the environment associated with the object.
Claims23
A computer-implemented method, comprising:
- (a) obtaining sensor data descriptive of an environment of an autonomous vehicle;
- (b) obtaining a plurality of travel way markers from map data descriptive of the environment;
- (c) determining, using a machine-learned object detection model and based on the sensor data, an association between one or more travel way markers of the plurality of travel way markers and an object in the environment;
- and
- (d) generating, using the machine-learned object detection model, an offset with respect to the one or more travel way markers of a spatial region of the environment associated with the object;
- wherein (c) comprises:
- inputting the travel way markers and the sensor data to the machine-learned object detection model;
- and
- obtaining object data from the machine-learned object detection model at projected locations of the travel way markers in a reference frame of the sensor data, wherein the object data indicates that the object is likely to be present at a projected location of the one or more travel way markers.
(canceled)
An autonomous vehicle control system for controlling an autonomous vehicle, the autonomous vehicle control system comprising:
- one or more processors;
- and
- one or more non-transitory computer-readable media storing instructions that are executable by the one or more processors to cause the autonomous vehicle control system to perform operations, the operations comprising:
- (a) obtaining sensor data descriptive of an environment of the autonomous vehicle;
- (b) obtaining a plurality of travel way markers from map data descriptive of the environment;
- (c) determining, using a machine-learned object detection model and based on the sensor data, an association between one or more travel way markers of the plurality of travel way markers and an object in the environment;
- and
- (d) generating, using the machine-learned object detection model, an offset with respect to the one or more travel way markers of a spatial region of the environment associated with the object;
- wherein (c) comprises:
- inputting the travel way markers and the sensor data to the machine-learned object detection model;
- and
- obtaining object data from the machine-learned object detection model at projected locations of the travel way markers in a reference frame of the sensor data, wherein the object data indicates that the object is likely to be present at a projected location of the one or more travel way markers.
(canceled)
One or more non-transitory computer-readable media storing instructions that are executable by one or more processors to cause an autonomous vehicle control system to perform operations, the operations comprising:
- (a) obtaining sensor data descriptive of an environment of an autonomous vehicle;
- (b) obtaining a plurality of travel way markers from map data descriptive of the environment;
- (c) determining, using a machine-learned object detection model and based on the sensor data, an association between one or more travel way markers of the plurality travel way markers and an object in the environment;
- and
- (d) generating, using the machine-learned object detection model, an offset with respect to the one or more travel way markers of a spatial region of the environment associated with the object;
- wherein (c) comprises:
- inputting the travel way markers and the sensor data to the machine-learned object detection model;
- and
- obtaining object data from the machine-learned object detection model at projected locations of the travel way markers in a reference frame of the sensor data, wherein the object data indicates that the object is likely to be present at a projected location of the one or more travel way markers.
Dependencies are inferred from the claim text.