# US 11,993,287 B2 · Fleet-level AV simulation system and method

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- Number: US 11,993,287 B2
- Document ID: US11993287B2
- Status: Granted
- Kind: B2
- Application number: 17/244,519
- Filed: 2021-04-29
- Published: 2022-11-03
- Granted: 2024-05-28
- Earliest claimed: 2021-04-29
- Assignees: GM Cruise Holdings LLC
- Applicants: GM Cruise Holdings LLC
- Inventors: Kyle Vogt; Thomas Busser; Benjamin Dunkin
- Web page: https://patentgrep.com/patent/US11993287B2

## CPC

- B60W 60/0023: {in response to energy consumption}
- G06Q 10/04: Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" (market predictions or forecasting for commercial activities G06Q30/0202)
- G01C 21/3469: {Fuel consumption; Energy use; Emission aspects}
- G01C 21/3822: {Road feature data, e.g. slope data}
- G05D 1/0217: {in accordance with energy consumption, time reduction or distance reduction criteria}
- G05D 1/0291: {Fleet control (monitoring fleets in traffic control systems for road vehicles G08G1/127, G08G1/127)}
- G05D 1/644: Optimisation of travel parameters, e.g. of energy consumption, journey time or distance
- G05D 1/69: Coordinated control of the position or course of two or more vehicles
- G07C 5/02: Registering or indicating driving, working, idle, or waiting time only (apparatus forming part of taximeters G07B13/00)
- G08G 1/202: {Dispatching vehicles on the basis of a location, e.g. taxi dispatching}
- B60W 2552/15: Road slope, i.e. the inclination of a road segment in the longitudinal direction
- 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
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- 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)
- G05D: SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G07C: TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G08G: TRAFFIC CONTROL SYSTEMS (guiding railway traffic, ensuring the safety of railway traffic B61L; arrangement of road signs or traffic signals E01F9/00; radar or analogous systems, sonar systems, lidar systems specially adapted for traffic control G01S13/91, G01S15/88, G01S17/88; {radar or analogous systems, sonar systems, lidar systems specially adapted for anti-collision purposes G01S13/93, G01S15/93, G01S17/93})

## Abstract

The subject disclosure relates to techniques for optimizing autonomous vehicle activities at a fleet level. A process of the disclosed technology can include measuring a first set of cost metrics for a first plurality of autonomous vehicles (AVs) associated with a first vehicle routing version, wherein the first set of cost metrics comprises energy consumption values for two or more of the first plurality of AVs, measuring a second set of cost metrics for a second plurality of AVs associated with a second vehicle routing version, wherein the second set of cost metrics comprises energy consumption values for two or more of the second plurality of AVs, and comparing the first set of cost metrics with the second set of cost metrics to determine routing updates configured to reduce fleetwide energy consumption.

## Claims

### Claim 1

A method comprising:
measuring a first set of cost metrics for a first plurality of autonomous vehicles (AVs) associated with a first vehicle routing version, wherein the first set of cost metrics comprises energy consumption values and degradation of vehicles parts values for a first plurality of AVs as the first plurality of AVs traverse a first route of an environment, and the first vehicle routing version specifies a preference to travel along streets with less changes in inclines and declines than a second vehicle routing version;
measuring a second set of cost metrics for a second plurality of AVs associated with the second vehicle routing version, wherein the second set of cost metrics comprises energy consumption values and degradation of vehicles parts values for a second plurality of AVs as the second plurality of AVs traverse a second route of the environment, and the second vehicle routing version specifies a preference to adjust an average speed of the second plurality of AVs to arrive at a red light when the red light changes to a green light;
comparing the first set of cost metrics with the second set of cost metrics to determine routing updates configured to reduce fleetwide energy consumption, generating an optimized heatmap using a machine learning model based on the routing updates that identifies locations with higher power consumption for an AV navigating within the environment, wherein the routing updates are configured to make changes based on road grade; and
navigating the AV in accordance with the routing updates.

### Claim 2 (depends on 1)

The method of claim 1, wherein the routing updates are configured to make changes based on distance.

### Claim 3 (depends on 1)

The method of claim 1, wherein the first and second set of cost metrics include duty cycle metrics.

### Claim 4 (depends on 1)

The method of claim 1, wherein measuring the first set of cost metrics for the first plurality of AVs and the second set of cost metrics for the second plurality of AVs includes simulating the first plurality of AVs and the second plurality of AVs.

### Claim 5 (depends on 1)

The method of claim 1, wherein the first set of cost metrics includes a first set of locations for facilities and the second set of cost metrics includes a second set of locations for facilities.

### Claim 6

A system comprising:
a storage configured to store instructions;
a processor configured to execute the instructions and cause the processor to:
measure a first set of cost metrics for a first plurality of autonomous vehicles (AVs) associated with a first vehicle route version, wherein the first set of cost metrics comprises energy consumption values and degradation of vehicles parts values for a first plurality of AVs as the first plurality of AVs traverse a first route of an environment, and the first vehicle routing version specifies a preference to travel along streets with less changes in inclines and declines than a second vehicle routing version;
measure a second set of cost metrics for a second plurality of AVs associated with the second vehicle route version, wherein the second set of cost metrics comprises energy consumption values and degradation of vehicles parts values for a second plurality of AVs as the second plurality of AVs traverse a second route of the environment, and the second vehicle routing version specifies a preference to adjust an average speed of the second plurality of AVs to arrive at a red light when the red light changes to a green light;
compare the first set of cost metrics with the second set of cost metrics to determine routing updates configured to reduce fleetwide energy consumption;
generate an optimized heatmap using a machine learning model based on the routing updates that identifies locations with higher power consumption for an AV navigating within the environment, wherein the routing updates are configured to make changes based on road grade; and
navigate the AV in accordance with the routing updates.

### Claim 7 (depends on 6)

The system of claim 6, wherein the routing updates are configured to make changes based on distance.

### Claim 8 (depends on 6)

The system of claim 6, wherein the first and second set of cost metrics include duty cycle metrics.

### Claim 9 (depends on 6)

The system of claim 6, wherein measuring the first set of cost metrics for the first plurality of AVs and the second set of cost metrics for the second plurality of AVs includes simulating the first plurality of AVs and the second plurality of AVs.

### Claim 10 (depends on 6)

The system of claim 6, wherein the first set of cost metrics includes a first set of locations for facilities and the second set of cost metrics includes a second set of locations for facilities.

### Claim 11

A non-transitory computer readable medium comprising instructions, the instructions, when executed by a computing system, cause the computing system to:
measure a first set of cost metrics for a first plurality of autonomous vehicles (AVs) associated with a first vehicle route version, wherein the first set of cost metrics comprises energy consumption values and degradation of vehicles parts values for a first plurality of AVs as the first plurality of AVs traverse a first route of an environment, and the first vehicle routing version specifies a preference to travel along flatter streets than a second vehicle routing version;
measure a second set of cost metrics for a second plurality of AVs associated with the second vehicle route version, wherein the second set of cost metrics comprises energy consumption values and degradation of vehicles parts values for a second plurality of AVs as the second plurality of AVs traverse a second route of the environment, and the second vehicle routing version specifies a preference to adjust an average speed of the second plurality of AVs to arrive at a red light when the red light changes to a green light;
compare the first set of cost metrics with the second set of cost metrics to determine routing updates configured to reduce fleetwide energy consumption, generate an optimized heatmap using a machine learning model based on the routing updates that identifies locations with higher power consumption for an AV navigating within the environment, wherein the routing updates are configured to make changes based on road grade; and
navigate the AV in accordance with the routing updates.

### Claim 12 (depends on 11)

The computer readable medium of claim 11, the routing updates are configured to make changes based on distance.

### Claim 13 (depends on 11)

The computer readable medium of claim 11, the first and second set of cost metrics include duty cycle metrics.

### Claim 14 (depends on 11)

The computer readable medium of claim 11, measuring the first set of cost metrics for the first plurality of AVs and the second set of cost metrics for the second plurality of AVs includes simulating the first plurality of AVs and the second plurality of AVs.

### Claim 15 (depends on 11)

The computer readable medium of claim 11, the first set of cost metrics includes a first set of locations for facilities and the second set of cost metrics includes a second set of locations for facilities.