# US 12,001,211 B2 · Risk-aware executor with action set recommendations

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- Number: US 12,001,211 B2
- Document ID: US12001211B2
- Status: Granted
- Kind: B2
- Application number: 18/166,921
- Filed: 2023-02-09
- Published: 2023-06-15
- Granted: 2024-06-04
- Earliest claimed: 2019-11-26
- Assignees: Nissan North America, Inc.; University of Massachusetts
- Applicants: Nissan North America, Inc.; The University of Massachusetts; Renault S.A.S.
- Inventors: Kyle Hollins Wray; Stefan Witwicki; Shlomo Zilberstein
- Web page: https://patentgrep.com/patent/US12001211B2

## CPC

- B60W 30/04: related to roll-over prevention
- G05D 1/0088: {characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours (using knowledge based models G06N5/00)}
- B60W 30/08: {Active safety systems} predicting or avoiding probable or impending collision {or attempting to minimise its consequences}
- B60W 30/14: {Adaptive} cruise control
- B60W 30/18159: {Traversing an intersection}
- B60W 60/00: Drive control systems specially adapted for autonomous road vehicles
- G01C 21/3492: {employing speed data or traffic data, e.g. real-time or historical (traffic control systems for road vehicles involving transmission of navigation instructions to the vehicle G08G1/0968)}
- G05D 1/0212: {with means for defining a desired trajectory (involving a plurality of land vehicles G05D1/0287)}
- G05D 1/646: Following a predefined trajectory, e.g. a line marked on the floor or a flight path
- G05D 1/81: Handing over between on-board automatic and on-board manual control
- G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks
- G06N 3/006: based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
- B60W: CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- G05D: SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G01C: MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY (measuring liquid level G01F; radio navigation, determining distance or velocity by use of propagation effects, e.g. Doppler effects, propagation time, of radio waves, analogous arrangements using other waves G01S)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

A first distinct vehicle operational scenario is identified for an autonomous vehicle (AV). A first set of candidate vehicle control actions are received from a model that provides a first solution to the first distinct vehicle operational scenario. An action is selected from the first set of candidate vehicle control actions. The AV is controlled based on the action. The first solution is obtained offline in a first idealized situation that is decoupled from a current context of the AV.

## Claims

### Claim 1

A method for use in traversing a vehicle transportation network by an autonomous vehicle (AV), the method comprising:
identifying a first distinct vehicle operational scenario for the AV;
receiving, from a model that provides a first solution to the first distinct vehicle operational scenario, a first set of candidate vehicle control actions,
wherein the first solution is obtained offline in a first idealized situation that is decoupled from a current context of the AV;
selecting an action from the first set of candidate vehicle control actions; and controlling the AV based on the action.

### Claim 2 (depends on 1)

The method of claim 1, further comprising:
receiving, from a second model that provides a second solution to a second distinct vehicle operational scenario, a second set of candidate vehicle control actions,
wherein the second solution is obtained offline in a second idealized situation that is decoupled from the current context of the AV; and
determining a third set of actions as an intersection between the first set of candidate vehicle control actions and the second set of candidate vehicle control actions.

### Claim 3 (depends on 2)

The method of claim 2, wherein the second model is selected in response to receiving sensor information corresponding to an external object.

### Claim 4 (depends on 2)

The method of claim 2, wherein determining the third set of actions as the intersection between the first set of candidate vehicle control actions and the second set of candidate vehicle control actions comprises:
on a condition that the intersection is empty, adding a default action to the third set of actions.

### Claim 5 (depends on 4)

The method of claim 4, wherein controlling the AV based on the action comprises:
on a condition that the action is to stop, controlling the AV to be stationary;
on a condition that the action is to advance, controlling the AV to traverse a defined cautionary distance in the vehicle transportation network at a defined cautionary rate; and
on a condition that the action is to proceed, controlling the AV to traverse the vehicle transportation network in accordance with a previously identified vehicle control action.

### Claim 6 (depends on 1)

The method of claim 1, wherein the model uses state information of the AV.

### Claim 7 (depends on 6)

The method of claim 6, wherein the state information comprises at least one of a first state of a sensor of the AV, a second state of an actuator of the AV, a third state of availability of a tele-operator, or a fourth state relating to an autonomy competence level of the AV.

### Claim 8

An autonomous vehicle, comprising:
a processor configured to execute instructions stored on a non-transitory computer readable medium to:
identify a first distinct vehicle operational scenario for the autonomous vehicle;
receive, from a model that provides a first solution to the first distinct vehicle operational scenario, a first set of candidate vehicle control actions,
wherein the first solution is obtained offline in a first idealized situation that is decoupled from a current context of the autonomous vehicle;
select an action from the first set of candidate vehicle control actions; and
control the autonomous vehicle based on the action.

### Claim 9 (depends on 8)

The autonomous vehicle of claim 8, wherein the processor is further configured to execute instructions stored in the non-transitory computer readable medium to:
receive, from a second model that provides a second solution to a second distinct vehicle operational scenario, a second set of candidate vehicle control actions,
wherein the second solution is obtained offline in a second idealized situation that is decoupled from the current context of the autonomous vehicle; and
determine a third set of actions as an intersection between the first set of candidate vehicle control actions and the second set of candidate vehicle control actions.

### Claim 10 (depends on 9)

The autonomous vehicle of claim 9, wherein the second model is selected in response to receiving sensor information corresponding to an external object.

### Claim 11 (depends on 9)

The autonomous vehicle of claim 9, wherein to determine the third set of actions as the intersection between the first set of candidate vehicle control actions and the second set of candidate vehicle control actions comprises to:
on a condition that the intersection is empty, add a default action to the third set of actions.

### Claim 12 (depends on 11)

The autonomous vehicle of claim 11, wherein controlling the autonomous vehicle based on the action comprises:
on a condition that the action is to stop, controlling the autonomous vehicle to be stationary;
on a condition that the action is to advance, controlling the autonomous vehicle to traverse a defined cautionary distance at a defined cautionary rate; and
on a condition that the action is to proceed, controlling the autonomous vehicle to proceed in accordance with a previously identified vehicle control action.

### Claim 13 (depends on 8)

The autonomous vehicle of claim 8, wherein the model uses state information of the autonomous vehicle.

### Claim 14 (depends on 13)

The autonomous vehicle of claim 13, wherein the state information comprises at least one of a first state of a sensor of the autonomous vehicle, a second state of an actuator of the autonomous vehicle, a third state of availability of a tele-operator, or a fourth state relating to an autonomy competence level of the autonomous vehicle.

### Claim 15

A non-transitory computer readable medium storing instructions operable to cause one or more processors to perform operations, comprising:
identifying a first distinct vehicle operational scenario for an autonomous vehicle (AV);
receiving, from a model that provides a first solution to the first distinct vehicle operational scenario, a first set of candidate vehicle control actions,
wherein the first solution is obtained offline in a first idealized situation that is decoupled from a current context of the AV;
selecting an action from the first set of candidate vehicle control actions; and
controlling the AV based on the action.

### Claim 16 (depends on 15)

The non-transitory computer readable medium of claim 15, further comprising:
receiving, from a second model that provides a second solution to a second distinct vehicle operational scenario, a second set of candidate vehicle control actions,
wherein the second solution is obtained offline in a second idealized situation that is decoupled from the current context of the AV; and
determining a third set of actions as an intersection between the first set of candidate vehicle control actions and the second set of candidate vehicle control actions.

### Claim 17 (depends on 16)

The non-transitory computer readable medium of claim 16, wherein the second model is selected in response to receiving sensor information corresponding to an external object.

### Claim 18 (depends on 16)

The non-transitory computer readable medium of claim 16, wherein determining the third set of actions as the intersection between the first set of candidate vehicle control actions and the second set of candidate vehicle control actions comprises:
on a condition that the intersection is empty, adding a default action to the third set of actions.

### Claim 19 (depends on 18)

The non-transitory computer readable medium of claim 18, wherein controlling the AV based on the action comprises:
on a condition that the action is to stop, controlling the AV to be stationary;
on a condition that the action is to advance, controlling the AV to traverse a defined cautionary distance at a defined cautionary rate; and
on a condition that the action is to proceed, controlling the AV to proceed in accordance with a previously identified vehicle control action.

### Claim 20 (depends on 15)

The non-transitory computer readable medium of claim 15, wherein the model uses state information of the AV, wherein the state information comprises at least one of a first state of a sensor of the AV, a second state of an actuator of the AV, a third state of availability of a tele-operator, or a fourth state relating to an autonomy competence level of the AV.