Systems and methods for predicting the trajectory of an object with the aid of a location-specific latent map

1. A system for predicting a trajectory of an object, the system comprising:

one or more sensors;

one or more processors; and

a memory communicably coupled to the one or more processors and storing:

a prediction module including instructions that when executed by the one or more processors cause the one or more processors to:

receive sensor data from the one or more sensors that includes a location of the object in an environment of the object;

access a location-specific latent map, the location-specific latent map having been learned together with a neural-network-based trajectory predictor during a training phase, wherein the neural-network-based trajectory predictor is deployed in a robot; and

input, to the neural-network-based trajectory predictor, the location of the object and the location-specific latent map, the location-specific latent map providing, to the neural-network-based trajectory predictor, a set of location-specific biases regarding the environment of the object; and

an output module including instructions that when executed by the one or more processors cause the one or more processors to output, from the neural-network-based trajectory predictor, a predicted trajectory of the object.

2. The system of claim 1 , wherein the robot is an ego vehicle and the object is a road agent external to the ego vehicle.

3. The system of claim 2 , wherein the ego vehicle is an autonomous vehicle.

4. The system of claim 2 , wherein the predicted trajectory of the object includes information regarding roadway geometries inferred by the neural-network-based trajectory predictor.

5. The system of claim 1 , wherein the robot and the object are one and the same thing and the robot is an ego vehicle that is at least partially controlled by a human driver.

6. The system of claim 5 , wherein the predicted trajectory of the object includes information regarding roadway geometries inferred by the neural-network-based trajectory predictor.

7. The system of claim 1 , wherein the set of location-specific biases regarding the environment of the object pertains to both visual and non-visual features of the environment of the object.

8. The system of claim 1 , further comprising an encoding module including instructions that when executed by the one or more processors cause the one or more processors to encode the location-specific latent map using a convolutional neural network (CNN).

9. The system of claim 1 , wherein the location-specific latent map corresponds to at least a portion of the environment of the object.

10. The system of claim 1 , wherein the neural-network-based trajectory predictor includes one of a social generative adversarial network (S-GAN) and a social long short-term memory (Social-LSTM) network.

11. A non-transitory computer-readable medium for predicting a trajectory of an object and storing instructions that when executed by one or more processors cause the one or more processors to:

receive sensor data that includes a location of the object in an environment of the object;

access a location-specific latent map, the location-specific latent map having been learned together with a neural-network-based trajectory predictor during a training phase, wherein the neural-network-based trajectory predictor is deployed in a robot;

input, to the neural-network-based trajectory predictor, the location of the object and the location-specific latent map, the location-specific latent map providing, to the neural-network-based trajectory predictor, a set of location-specific biases regarding the environment of the object; and

output, from the neural-network-based trajectory predictor, a predicted trajectory of the object.

12. The non-transitory computer-readable medium of claim 11 , wherein the set of location-specific biases regarding the environment of the object pertains to both visual and non-visual features of the environment of the object.

13. A method of predicting a trajectory of an object, the method comprising:

receiving sensor data that includes a location of the object in an environment of the object;

accessing a location-specific latent map, the location-specific latent map having been learned together with a neural-network-based trajectory predictor during a training phase, wherein the neural-network-based trajectory predictor is deployed in a robot;

inputting, to the neural-network-based trajectory predictor, the location of the object and the location-specific latent map, the location-specific latent map providing, to the neural-network-based trajectory predictor, a set of location-specific biases regarding the environment of the object; and

outputting, from the neural-network-based trajectory predictor, a predicted trajectory of the object.

14. The method of claim 13 , wherein the robot is an ego vehicle and the object is a road agent external to the ego vehicle.

15. The method of claim 14 , wherein the ego vehicle is an autonomous vehicle.

16. The method of claim 14 , wherein the predicted trajectory of the object includes information regarding roadway geometries inferred by the neural-network-based trajectory predictor.

17. The method of claim 13 , wherein the robot and the object are one and the same thing and the robot is an ego vehicle that is at least partially controlled by a human driver.

18. The method of claim 17 , wherein the predicted trajectory of the object includes information regarding roadway geometries inferred by the neural-network-based trajectory predictor.

19. The method of claim 13 , wherein the robot is deployed inside a building.

20. The method of claim 13 , further comprising encoding the location-specific latent map using a convolutional neural network.