Granted Patent B2

US 12,344,280 · App. 17/828,815 · Granted Jul 1, 2025

Inventors: Connor Basich (Northampton, MA); Kyle Hollins Wray (Mountain View, CA); Stefan Witwicki (San Carlos, CA); Shlomo Zilberstein (Amherst, MA)

Assignees: Nissan North America, Inc.; The University of Massachusetts

B60W60/0053B60W50/00G06N7/01B60W2050/0028

Abstract

Claims (67)

1. A method of autonomous driving by an autonomous vehicle (AV), comprising:

detecting, based on sensor data, an environment state;

selecting an action based on the environment state;

identifying a current set of indiscriminate states;

identifying a discriminator from the current set of indiscriminate states;

training a feedback model for the discriminator;

determining an autonomy level associated with the environment state and the action, wherein the autonomy level is selected based at least on an autonomy model and the feedback model; and

performing the action according to the autonomy level.

2. The method of claim 1 , wherein the autonomy level is selected from a set comprising a first autonomy level indicating “no autonomy”, a second autonomy level indicating “verified autonomy”, a third autonomy level indicating “supervised autonomy”, and a fourth autonomy level indicating “unsupervised autonomy”.

3. The method of claim 2 , wherein the autonomy level is the second autonomy level indicating “verified autonomy”, and wherein performing the action according to the autonomy level comprising:

receiving, for the action, an approval feedback signal or a disapproval feedback signal.

4. The method of claim 3 , wherein performing the action according to the autonomy level comprising:

querying for the approval feedback signal prior to receiving the approval feedback signal.

5. The method of claim 2 ,

wherein the autonomy level is the third autonomy level indicating “supervised autonomy”, and

wherein performing the action according to the autonomy level comprising:

determining that the AV is being monitored by a human before performing the action.

6. The method of claim 3 ,

wherein the approval feedback signal is received, and

wherein performing the action according to the autonomy level comprising:

determining that the AV is being monitored in response to determining that the AV is being monitored, performing the action;

receiving an override signal; and

in response to receiving the override signal, stopping the action and switching to a manual operation mode of the AV.

7. The method of claim 1 , wherein performing the action according to the autonomy level comprising:

determining, based on the autonomy level, whether to request approval from a human for the action before performing the action.

8. The method of claim 1 ,

wherein the autonomy level is an “no autonomy” such that the AV is not allowed to perform autonomous actions, and

wherein performing the action according to the autonomy level comprising:

enabling the AV to be manually controlled by a human.

9. The method of claim 1 ,

wherein the autonomy model comprises a utility model and an autonomy profile,

wherein utility model describes a utility of performing a first action in a first autonomy level with respect to a first environment state given that the AV transitioned from a second autonomy level, and

wherein the autonomy profile maps respective environment states to respective actions and prescribing constraints on allowed levels of autonomy for particular environment states.

10. The method of claim 1 , further comprising:

updating at least one of an autonomy profile, a feedback profile, or a human transition function in response to the performing the action.

11. A system for autonomous comprising:

a memory; and

a processor, the processor configured to execute instructions stored in the memory to:

calculate, by solving an extended Stochastic Shortest Path (SSP) problem, a policy for solving a task,

identify a discriminator from a current set of indiscriminate states; and

train a feedback model for the discriminator;

wherein the policy maps environment states and autonomy levels to actions and autonomy levels, and

wherein to calculate the policy comprises to:

generate plans that operate across multiple levels of autonomy.

12. The system of claim 11 , wherein to generate plans that operate across the multiple levels of autonomy comprises to:

generate plans subject to constraints on allowed levels of autonomy in respective states.

13. The system of claim 12 , wherein a constraint maps a state and an action to a subset of levels of autonomy.

14. The system of claim 11 , wherein the instructions further comprise instructions to:

update the feedback model representing a first probability that the system receives a first signal when performing a first action at a second autonomy level given that the system is in a first state and the system transitioned from a first autonomy level.

15. The system of claim 11 , wherein the instructions further comprise instructions to:

update a human state transition function representing a second probability of a human transitioning to a second state of an environment model given that the system selected to perform a second action in a first state and the human took manual control.

16. The system of claim 11 , wherein the instructions further comprise instructions to:

update an autonomy profile, wherein the autonomy profile defines a set of acceptable autonomy levels given a current state and an action to be performed next.

17. A method for autonomous driving, comprising:

calculating, by solving an extended Stochastic Shortest Path (SSP) problem, a policy for solving a task,

identifying a discriminator from a current set of indiscriminate states; and

training a feedback model for the discriminator;

wherein the policy maps environment states and autonomy levels to actions and autonomy levels, and

wherein calculating the policy comprising:

generating plans that operate across multiple levels of autonomy.

18. The method of claim 17 , wherein generating plans that operate across the multiple levels of autonomy comprising:

generating plans subject to constraints on allowed levels of autonomy in respective states.

19. The method of claim 18 , wherein a constraint maps a state and an action to a subset of levels of autonomy.

20. The method of claim 17 , further comprising:

updating the feedback model representing a first probability of receiving a first signal when performing a first action at a second autonomy level given that an agent is in a first state and the agent transitioned from a first autonomy level;

updating a human state transition function representing a second probability of a human transitioning to a second state of an environment model given that the agent selected to perform a second action in a third state and the human took manual control; and

updating an autonomy profile, wherein the autonomy profile defines a set of acceptable autonomy levels given a current state and an action to be performed next.

Assignments (3)

Continuity (1)

References Cited (23)