All mover priors

1. A computer-implemented method comprising:

obtaining, with a computing system comprising one or more processors, sensor data captured by at least one sensor of an autonomous vehicle at a geographic location;

determining, with the computing system, that at least a portion of the sensor data corresponds to a first object in an object class, the object class comprising moving objects at the geographic location;

determining, with the computing system, one or more prior probability distributions of one or more motion paths for the first object, the one or more prior probability distributions being specific to the geographic location, and the determining of the one or more prior probability distributions being based at least in part on the geographic location and at least in part on previously-observed motion of objects of the object class at the geographic location;

generating, with the computing system, a driving path including one or more trajectories for the autonomous vehicle on a roadway based on the one or more prior probability distributions; and

controlling travel of the autonomous vehicle on the driving path.

2. The computer-implemented method of claim 1 , wherein determining the one or more prior probability distributions further comprises:

detecting a plurality of objects in the geographic location based on the sensor data; and

identifying the first object in the object class from the plurality of objects based on the sensor data.

3. The computer-implemented method of claim 1 , wherein the one or more prior probability distributions are determined based on at least one prior probability associated with at least one condition parameter of the following plurality of condition parameters: one or more velocities associated with the one or more objects, one or more acceleration and/or deceleration rates associated with the first object, one or more orientations associated with the first object, a time of day associated with the sensor data, a date associated with the sensor data, a geographic region of a plurality of geographic regions including the geographic location, or any combination thereof.

4. The computer-implemented method of claim 1 , wherein the one or more prior probability distributions are associated with one or more probability values that correspond to one or more elements of a plurality of elements in a map of the geographic location, and wherein the one or more probability values include one or more probabilities of the first object at one or more positions in the geographic location associated with the one or more elements in the map moving over the one or more motion paths.

5. The computer-implemented method of claim 4 , wherein the one or more probability values further include at least one probability associated with at least one of the following: one or more velocities associated with the first object, one or more acceleration and/or deceleration rates associated with the first object, one or more orientations associated with the first object, a time of day associated with the one or more motion paths, a date associated with the one or more motion paths, a geographic region of a plurality of geographic regions including the geographic location, or any combination thereof.

6. The computer-implemented method of claim 4 , further comprising:

obtaining, with the computing system, map data associated with the map of the geographic location; and

generating, with the computing system, the driving path including the one or more trajectories for the autonomous vehicle on the roadway in the map based on the map data and the one or more prior probability distributions.

7. The computer-implemented method of claim 6 , further comprising:

obtaining, with the computing system, user input associated with at least one element of the plurality of elements of the map of the geographic location; and

generating, with the computing system, the driving path including the one or more trajectories for the autonomous vehicle on the roadway in the map based on the map data, the one or more prior probability distributions, and the user input.

8. The computer-implemented method of claim 7 , wherein the user input is associated with a first element of the plurality of elements of the map of the geographic location and a second element of the plurality of elements of the map of the geographic location different than the first element, and wherein the driving path is generated on the roadway in the map between the first element and the second element.

9. A computing system comprising:

one or more processors programmed and/or configured to:

obtain sensor data captured by at least one sensor of an autonomous vehicle at a geographic location;

determine that at least a portion of the sensor data corresponds to a first object in an object class, the object class comprising moving objects in the geographic location;

determine one or more prior probability distributions of one or more motion paths for the first object, the one or more prior probability distributions being specific to the geographic location, and the determining of the one or more prior probability distributions being based at least in part on the geographic location and at least in part on previously-observed motion of objects of the object class at the geographic location;

generate a driving path including one or more trajectories for the autonomous vehicle on a roadway based on the one or more prior probability distributions; and

controlling travel of the autonomous vehicle on the driving path.

10. The computing system of claim 9 , wherein the one or more processors are further programmed and/or configured to determine the one or more prior probability distributions by:

detecting a plurality of objects based on the sensor data; and

identifying the first object in the object class from the plurality of objects based on the sensor data.

11. The computing system of claim 9 , wherein the one or more prior probability distributions are determined based on at least one prior probability associated with at least one condition parameter of the following plurality of condition parameters: one or more velocities associated with the first object, one or more acceleration and/or deceleration rates associated with the first object, one or more orientations associated with the first object, a time of day associated with the sensor data, a date associated with the sensor data, a geographic region of a plurality of geographic regions including the geographic location, or any combination thereof.

12. An autonomous vehicle comprising:

one or more sensors for detecting objects in an environment surrounding the autonomous vehicle; and

a vehicle computing system comprising one or more processors, wherein the vehicle computing system is programmed and/or configured to:

obtain feature data associated with a current geographic location surrounding the autonomous vehicle, the feature data including characteristic features of the current geographic location;

determine one or more predicted probability distributions of one or more predicted motion paths for a first object in the current geographic location based on (i) the feature data associated with the current geographic location, (ii) one or more prior probability distributions of one or more prior motion paths for one or more other objects previously-observed at the current geographic location, and (iii) feature data associated with the one or more other geographic locations, the feature data associated with one or more other geographic locations including characteristic features corresponding to the characteristic features of the current geographic location, the one or more predicted probability distributions being specific to the geographic location;

determine one or more predictive probability scores based on the one or more predicted probability distributions, wherein the one or more predictive probability scores include one or more predictions of whether an object is moving over the one or more predicted motion paths in the current geographic location; and

control travel of the autonomous vehicle on a roadway in the current geographic location based on the one or more predictive probability scores.

13. The autonomous vehicle of claim 12 , wherein the feature data associated with the current geographic location includes sensor data obtained from the one or more sensors.

14. The autonomous vehicle of claim 12 , wherein the feature data associated with the current geographic location includes map data associated with a map of the current geographic location.

15. The autonomous vehicle of claim 12 , wherein determining the one or more predictive probability distributions includes classifying the characteristic features of the current geographic location and the characteristic features of the one or more other geographic locations.

16. The autonomous vehicle of claim 12 , wherein the characteristic features includes at least one of the following: a roadway marking, an intersection, a park, a roadway defect, a bridge, a bump, a depression, a landscape, an embankment, a barrier, a static object of a roadway, a sign, a curb, a building, or any combination thereof.

17. The autonomous vehicle of claim 12 , wherein the one or more predicted probability distributions of the one or more predicted motion paths includes a probability distribution for an object that is not yet located in the current geographic location.

18. The autonomous vehicle of claim 12 , wherein the one or more predictive probability scores include one or more predictions about where the object not yet located in the current geographic location may move when it enters the current geographic location.