IP Library Granted Patent US 11,307,594
Granted Patent B2
US 11,307,594 · App. 17/483,539 · Granted Apr 19, 2022

Method and system for deterministic trajectory selection based on uncertainty estimation for an autonomous agent

Inventors: Gautam Narang (Palo Alto, CA); Apeksha Kumavat (Palo Alto, CA); Arjun Narang (Palo Alto, CA); Kinh Tieu (Palo Alto, CA); Michael Smart (Palo Alto, CA); Marko Ilievski (Palo Alto, CA)
Assignee: Gatik AI Inc.
G05D1/0221G05D1/0088G05D1/0214G06K9/6259G06K9/6262G06N20/00
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Quick Facts
Patent No.
US 11,307,594
App. No.
17/483,539
Granted
Apr 19, 2022
Kind
B2
Abstract

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.

Claims (35)

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.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 23, 2021
From: NARANG, GAUTAM; KUMAVAT, APEKSHA; NARANG, ARJUN; TIEU, KINH; SMART, MICHAEL; ILIEVSKI, MARKO
To: GATIK AI INC.
Reel/Frame 057583/0139 →
Continuity (4)
Continuation 17127599 · Dec 18, 2020
Provisional Application 63055794 · Jul 23, 2020
Provisional Application 63035401 · Jun 5, 2020
Related Publication 20220011776A1 · Jan 13, 2022