Method and system for deterministic trajectory selection based on uncertainty estimation for an autonomous agent
A system for deterministic trajectory selection based on uncertainty estimation includes a set of one or more computing systems. A method for deterministic trajectory selection includes receiving a set of inputs; determining a set of outputs; determining uncertainty parameters associated with any or all of the set of inputs and/or any or all of the set of outputs; and evaluating the uncertainty parameters and optionally triggering a process and/or action in response.
1. A method for trajectory determination of an autonomous agent, the method comprising:
collecting a set of inputs;
determining a first trajectory of a set of candidate trajectories based on the set of inputs, the first trajectory associated with a first uncertainty;
determining a second trajectory of the set of candidate trajectories;
determining a second uncertainty associated with the set of inputs;
comparing the first uncertainty with a first threshold, wherein in an event that the first uncertainty exceeds the first threshold, eliminating the first trajectory from the set of candidate trajectories;
comparing the second uncertainty with a second threshold, wherein in an event that the second uncertainty exceeds the second threshold, eliminating the first trajectory from the set of candidate trajectories;
determining a final trajectory for the autonomous agent based on the comparison with the set of constraints; and
implementing the final trajectory at the autonomous agent.
2. The method of claim 1 , wherein the first trajectory is a learned trajectory determined with a set of learned models.
3. The method of claim 2 , wherein the first uncertainty is determined with the set of learned models.
4. The method of claim 2 , wherein the second trajectory is a programmed trajectory.
5. The method of claim 4 , wherein the second trajectory is determined with a classical motion planner.
6. The method of claim 1 , wherein the second uncertainty is determined with an out-of-distribution detector.
7. The method of claim 6 , wherein the out-of-distribution detector comprises a generative adversarial network.
8. The method of claim 1 , wherein the first uncertainty is determined based on at least one of an aleatoric and an epistemic uncertainty.
9. The method of claim 8 , wherein the first uncertainty is determined based on the aleatoric uncertainty and the epistemic uncertainty.
10. A method for trajectory determination of an autonomous agent, the method comprising:
receiving, at a gatekeeper module:
a set of candidate trajectories, the set of candidate trajectories comprising a first trajectory and a second trajectory;
a set of uncertainties associated with the set of candidate trajectories, wherein the set of uncertainties comprises:
a first uncertainty associated with the first trajectory; and
a second uncertainty associated with a set of inputs, wherein the set of inputs is used to determine at least one of the first and second trajectories;
comparing the set of uncertainties with a set of thresholds; and
determining a final trajectory based on the comparison; and
implementing the final trajectory at the autonomous agent.
11. The method of claim 10 , further comprising determining the set of candidate trajectories based on a set of inputs.
12. The method of claim 11 , wherein a first portion of the set of candidate trajectories is determined with a set of learned models.
13. The method of claim 12 , wherein a second portion of the set of candidate trajectories is determined with a classical motion planner.
14. The method of claim 10 , further comprising comparing the set of candidate trajectories with a set of constraints.
15. The method of claim 14 , wherein the final trajectory is further determined based on the comparison with the set of constraints.
16. The method of claim 15 , wherein the set of constraints comprises a collision-avoidance constraint.
17. The method of claim 16 , wherein the set of constraints is organized into a hierarchy of constraints, wherein the collision-avoiding constraint has a highest priority within the hierarchy.
18. The method of claim 10 , wherein the first trajectory is determined based on the set of inputs.
19. The method of claim 18 , wherein the second trajectory is determined based on the set of inputs.