Map-anchored object detection
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.
1. 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.
2. The computer-implemented method of claim 1 , wherein the travel way markers include lane markers.
3. The computer-implemented method of claim 2 , wherein the lane markers comprise centerline markers.
4. The computer-implemented method of claim 1 , wherein obtaining the object data comprises subsampling, based on the travel way markers, a detection map generated by the machine-learned object detection model.
5. The computer-implemented method of claim 1 , wherein one or more portions of the machine-learned object detection model are configured to sparsely use portions of an output layer based on locations in the sensor data corresponding to the projected locations.
6. The computer-implemented method of claim 1 , wherein the machine-learned object detection model is trained by:
obtaining ground truth travel way marker labels indicating a ground truth association between the object and one or more of the travel way markers; and
determining, based on comparing the object data and the ground truth travel way marker labels, a sparse loss for the machine-learned object detection model.
7. The computer-implemented method of claim 1 , wherein (d) comprises:
determining an offset of a centroid of a boundary of the spatial region; and
determining one or more dimensions of the boundary.
8. The computer-implemented method of claim 1 , wherein (d) comprises:
determining a first offset of a centroid of a first boundary of the spatial region in two dimensions; and
determining a second offset of a centroid of a second boundary of the spatial region in three dimensions.
9. The computer-implemented method of claim 1 , comprising:
based on determining that a velocity of the object is below a threshold, outputting a characteristic for the object indicating that the object is a static object; and
outputting the characteristic to a motion planning system of the autonomous vehicle.
10. The computer-implemented method of claim 1 , comprising:
based on determining that a velocity of the object is below a threshold and that the object is located adjacent to a travel way in the environment, outputting a characteristic for the object indicating that the object is a static object; and
outputting the characteristic to a motion planning system of the autonomous vehicle.
11. The computer-implemented method of claim 1 , wherein (b) comprises:
sampling discrete travel way markers from continuous travel way map data.
12. The computer-implemented method of claim 1 , wherein the spatial region of the environment is beyond an effective range of a LIDAR sensor of the autonomous vehicle.
13. The computer-implemented method of claim 1 , wherein:
the machine-learned object detection model was trained using training sensor data having a training field of view and training travel way markers having a training resolution;
the sensor data is associated with a runtime field of view; and
the travel way markers are obtained in (c) at a runtime resolution selected based on a comparison of the training field of view and the runtime field of view.
14. The computer-implemented method of claim 1 , comprising:
projecting, using a projection transform, the travel way markers into a reference frame of the sensor data;
determining one or more offsets of the spatial region with respect to the travel way markers;
based on the determined one or more offsets, determining a projection error for the projected travel way markers; and
recalibrating the projection transform based on the determined projection error.
15. The computer-implemented method of claim 1 , comprising:
identifying a lane in which the object is located based on the one or more travel way markers.
16. The computer-implemented method of claim 1 , wherein (d) comprises:
regressing, by the machine-learned object detection model, the offset.
17. 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.
18. The autonomous vehicle control system of claim 17 , wherein obtaining the object data comprises subsampling, based on the travel way markers, a detection map generated by the machine-learned object detection model.
19. The autonomous vehicle control system of claim 17 , wherein (d) comprises:
determining a first offset of a centroid of a first boundary of the spatial region in two dimensions; and
determining a second offset of a centroid of a second boundary of the spatial region in three dimensions.
20. The autonomous vehicle control system of claim 17 , wherein the machine-learned object detection model is trained by:
obtaining ground truth travel way marker labels indicating a ground truth association between the object and one or more of the travel way markers; and
determining, based on comparing the object data and the ground truth travel way marker labels, a sparse loss for the machine-learned object detection model.
21. 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.