Trajectory classification

1. A system comprising:

one or more processors; and

one or more non-transitory computer-readable storage media storing instructions executable by the one or more processors, wherein the instructions, when executed, cause the system to perform operations comprising:

receiving sensor data associated with an autonomous vehicle in an environment;

determining data based at least in part on the sensor data, the data comprising a top down representation of the environment and an object in the environment;

inputting the data into a machine learned model;

receiving, from the machine learned model, an output comprising a discretized representation of a portion of the environment, wherein a cell of the discretized representation is associated with a classification probability of a location of the object at a future time;

determining, based at least in part on the discretized representation and the classification probability, a predicted trajectory associated with the object and a weight associated with the predicted trajectory; and

causing an operation of the autonomous vehicle to be controlled based at least in part on the predicted trajectory associated with the object and the weight associated with the predicted trajectory.

2. The system of claim 1 , wherein the classification probability associated with the cell is indicative of a probability of the object being at the location at the future time.

3. The system of claim 1 , wherein:

the location is a first location;

the cell is a first cell;

the classification probability is a first classification probability;

the predicted trajectory is a first predicted trajectory;

the weight is a first weight;

the discretized representation comprises a second cell associated with a second classification probability of a second location of the object at the future time; and

the operations further comprise:

determining, based at least in part on map data, that the first location is associated with a first destination;

determining, based at least in part on the map data, that the second location is associated with a second destination;

determining, based at least in part on the second classification probability and the second location, a second predicted trajectory associated with the object at the future time; and

causing the operation of the autonomous vehicle to be controlled is further based at least in part on the second predicted trajectory and a second weight associated with the second predicted trajectory.

4. The system of claim 1 , the operations further comprising:

determining the weight based at least in part on the classification probability and another classification probability.

5. The system of claim 1 , wherein:

the location represents an offset based at least in part on an object location of the object at a previous time before the future time.

6. One or more non-transitory computer-readable storage media storing instructions that, when executed, cause one or more processors to perform operations comprising:

inputting data into a model, the data comprising a top down representation of an environment at a first time;

receiving, from the model, an output comprising a discretized representation of a portion of the environment, wherein a cell of the discretized representation is associated with a probability associated with an object at a second time after the first time;

determining, based at least in part on the discretized representation and the probability, a trajectory associated with the object and a weight associated with the trajectory; and

causing an operation of a vehicle to be controlled based at least in part on the trajectory and the weight.

7. The one or more non-transitory computer-readable storage media of claim 6 , wherein:

the data comprises at least one of sensor data, map data, or data based on the sensor data representing one or more channel images to form the top down representation, and

the probability associated with the cell is indicative of a probability of the object being at a location at the second time.

8. The one or more non-transitory computer-readable storage media of claim 7 , wherein:

the location is a first location;

the cell is a first cell;

the probability is a first probability;

the trajectory is a first trajectory;

the weight is a first weight;

the discretized representation comprises a second cell associated with a second probability of a second location of the object at the second time; and

the operations further comprise:

determining, based at least in part on map data, that the first location is associated with a first destination;

determining, based at least in part on the map data, that the second location is associated with a second destination;

determining, based at least in part on the second probability and the second location, a second trajectory associated with the object at the second time; and

causing the operation of the vehicle to be controlled is further based at least in part on the second trajectory and a second weight associated with the second trajectory.

9. The one or more non-transitory computer-readable storage media of claim 6 , the operations further comprising:

sending data comprising the trajectory and the weight to a planning component of the vehicle; and

causing the planning component to determine a candidate trajectory for the vehicle to follow in the environment based at least in part on the data.

10. The one or more non-transitory computer-readable storage media of claim 6 , the operations further comprising:

receiving map data associated with the environment;

determining, based at least in part on the map data and a location associated with the cell, that the location is associated with a semantic destination; and

determining the weight based at least in part on the probability and the location being associated with the semantic destination at the second time.

11. The one or more non-transitory computer-readable storage media of claim 6 , the operations further comprising:

determining the weight based at least in part on the probability and another probability.

12. The one or more non-transitory computer-readable storage media of claim 6 , wherein the model is a machine learned model trained based at least in part on a comparison between data associated with a previous output of the model and ground truth data.

13. The one or more non-transitory computer-readable storage media of claim 6 , the operations further comprising:

interpolating a position of the object at the first time and a location associated with the probability at the second time, and

wherein the trajectory is based at least in part on the interpolating.

14. The one or more non-transitory computer-readable storage media of claim 6 , the operations further comprising:

receiving map data associated with the environment;

determining, based at least in part on the map data and a location associated with the cell, that the location is associated with a semantic destination; and

determining, based at least in part on the semantic destination and the probability, an intent associated with the object; and

wherein causing the operation of the vehicle to be controlled is further based at least in part on the intent.

15. A method comprising:

inputting image data into a model, the image data comprising a top down representation of an environment at a first time;

receiving, from the model, an output comprising a discretized representation of a portion of the environment, wherein a cell of the discretized representation is associated with a probability associated with an object at a second time after the first time;

determining, based at least in part on the discretized representation and the probability, a trajectory associated with the object and a weight associated with the trajectory; and

causing an operation of a vehicle to be controlled based at least in part on the trajectory and the weight.

16. The method of claim 15 , wherein:

the probability associated with the cell is indicative of a probability of the object being at a location at the second time.

17. The method of claim 16 , wherein:

the location represents an offset based at least in part on an object location of the object at a previous time before the second time.

18. The method of claim 15 , wherein the top down representation of the environment represents one or more of: an object position, an object velocity, an object acceleration, an object yaw, an attribute of the object, crosswalk permission, or traffic light permissibility.

19. The method of claim 15 , further comprising:

receiving sensor data of the environment associated with a sensor of the vehicle;

determining, based at least in part on the sensor data, a first object type and a second object type associated with the object in the environment, the second object type different from the first object type;

determining a first probability of the object being the first object type; and

determining a second probability of the object being the second object type, wherein inputting the image data into the model comprises inputting an indication of the first probability associated with the first object type and the second probability associated with the second object type.

20. The method of claim 15 , wherein the vehicle is an autonomous vehicle, and further comprising:

sending data comprising the trajectory and the weight to a planning component of the autonomous vehicle; and

causing the planning component to determine a candidate trajectory for the autonomous vehicle to follow in the environment based at least in part on the data.