Autonomous Vehicle Fleet Management for Improved Computational Resource Usage

• US 20200133306A1
• Filed: 06/19/2019
• Published: 04/30/2020
• Est. Priority Date: 10/31/2018
• Status: Active Grant

• Current Assignee: Uber Technologies, Inc.
• Original Assignee: Uber Technologies, Inc.
• Inventors: Chadha, Valerie Nina, Yuan, Ye, Stegall, Neil, Goldman, Brent Justin, Sweeney, Kane, Chiang, Rei
• Granted Patent: US 11,507,111 B2
• Time in Patent Office: Days
• CPC Class Codes: G05D 1/0088 characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours using knowledge based models G06N5/00
• CPC Class Codes: G05D 1/0276 using signals provided by a source external to the vehicle involving a plurality of vehicles G05D1/0287; automatically controlling vehicle speed responsive to externally generated signals B60K31/0058
• CPC Class Codes: G05D 1/0291 Fleet control monitoring fleets in traffic control systems for road vehicles G08G1/127, G08G1/127
• CPC Class Codes: G06Q 10/047 Optimisation of routes or paths, e.g. travelling salesman problem
• CPC Class Codes: G06Q 10/0631 Resource planning, allocation, distributing or scheduling for enterprises or organisations
• CPC Class Codes: G06Q 50/40 Business processes related to the transportation industry shipping G06Q10/083
• CPC Class Codes: G08G 1/00 Traffic control systems for road vehicles arrangement of road signs or traffic signals E01F9/00 ; automatic vehicle control B62D
• CPC Class Codes: G08G 1/202 Dispatching vehicles on the basis of a location, e.g. taxi dispatching

Abstract

Systems and methods for evaluating and deploying fleets of autonomous in operational domains are described. A computing system may obtain data indicative of one or more capabilities of at least one autonomous vehicle, data indicative of vehicle service dynamics in an operational domain over a period of time, and determining a plurality of resource performance parameters respectively for a plurality of autonomous vehicle fleets associated with potential deployment in the operational domain. Each autonomous vehicle fleet can be associated with a different number of autonomous vehicles The resource performance parameter for each autonomous vehicle fleet can be based at least in part on the one or more capabilities of the at least one autonomous vehicle and the vehicle service dynamics in the operational domain. The computing system can initiate an action associated with the operational domain based at least in part on the plurality of resource performance parameters.

Claims

1. A computer-implemented method for determining autonomous vehicle fleets, comprising:

obtaining, by a computing system comprising one or more computing devices, data indicative of one or more capabilities of at least one autonomous vehicle;

obtaining, by the computing system, data indicative of vehicle service dynamics in an operational domain over a period of time;

determining, by the computing system, a plurality of resource performance parameters respectively for a plurality of autonomous vehicle fleets associated with potential deployment in the operational domain, each autonomous vehicle fleet associated with a different number of autonomous vehicles, wherein the resource performance parameter for each autonomous vehicle fleet is based at least in part on the one or more capabilities of the at least one autonomous vehicle and the vehicle service dynamics in the operational domain; and

initiating, by the computing system, an action associated with the operational domain based at least in part on the plurality of resource performance parameters.

2. The computer-implemented method of claim 1, wherein:

the plurality of resource performance parameters includes a plurality of measures of resource return respectively for the plurality of autonomous vehicle fleets.

3. The computer-implemented method of claim 2, wherein:

the measure of resource return for each of the plurality of autonomous vehicle fleets is a measure of fleet-level return associated with such autonomous vehicle fleet.

4. The computer-implemented method of claim 2, wherein:

the measure of resource return for each of the plurality of autonomous vehicle fleets is a measure of marginal resource return associated with individual autonomous vehicles of such autonomous vehicle fleet.

5. The computer-implemented method of claim 1, wherein:

the plurality of resource performance parameters includes a plurality of measures of efficiency respectively for the plurality of autonomous vehicle fleets.

6. The computer-implemented method of claim 5, wherein:

the measure of efficiency for each of the plurality of autonomous vehicle fleets includes a measure of time efficiency associated with such autonomous vehicle fleet.

7. The computer-implemented method of claim 5, wherein:

the measure of efficiency for each of the plurality of autonomous vehicle fleets includes a measure of predicted mileage efficiency associated with such autonomous vehicle fleet.

8. The computer-implemented method of claim 1, further comprising:

obtaining, by the computing system, data indicative of a resource outflow associated with the at least one autonomous vehicle;

wherein determining the resource performance parameter associated with deployment of each of the plurality of autonomous vehicle fleets is based at least in part on the resource outflow associated with such at least one autonomous vehicle.

9. The computer-implemented method of claim 8, wherein the resource outflow is based on at least one of operation or deployment of the at least one autonomous vehicle.

10. The computer-implemented method of claim 1, wherein:

the data indicative of vehicle service dynamics includes data indicative of usage associated with one or more vehicle service types.

11. The computer-implemented method of claim 10, wherein:

the usage includes a supply and a demand associated with the one or more vehicle service types.

12. The computer-implemented method of claim 1, further comprising:

determining, by the computing system, a measure of breakeven utilization for the plurality of autonomous vehicle fleets, the measure of breakeven utilization is based at least in part on a number of autonomous vehicles having a total resource outflow per unit that is equal to a total resource outflow per unit for a fleet of non-autonomous vehicles.

13. The computer-implemented method of claim 1, further comprising:

obtaining, by the computing system, data associated with a plurality of potential travel routes within the operational domain of the at least one autonomous vehicle; and

determining, by the computing system, a level of addressability of the operational domain based at least in part on the one or more capabilities of the at least one autonomous vehicle and the plurality of potential travel routes within the operational domain of the at least one autonomous vehicle;

wherein the plurality of resource performance parameters is based at least in part on the level of addressability of the operational domain.

14. The computer-implemented method of claim 13, further comprising:

determining data indicative of vehicle service requests that are serviceable by the at least one autonomous vehicle in the operational domain based on the plurality of potential travel routes and the vehicle service dynamics;

wherein the plurality of resource parameters is based at least in part on the vehicle service requests that are serviceable by the at least one autonomous vehicle.

15. The computer-implemented method of claim 14, wherein the one or more capabilities of the autonomous vehicle are indicative of at least one of an ability of the autonomous vehicle to operate in a weather condition, a vehicle operating condition, or a vehicle maneuverability.

16. The computer-implemented method of claim 1, wherein initiating, by the computing system, the action associated with the operational domain based at least in part on the plurality of resource performance parameters associated with the plurality of autonomous vehicle fleets comprises:

generating, by the computing system, data indicative of the plurality of resource performance parameters; and

outputting, by the computing system, the data indicative of the plurality of resource performance parameters.

17. The computer-implemented method of claim 1, wherein:

the plurality of resource performance parameters includes a first plurality of resource performance parameters based on a first profile, the first profile including at least one of a first set of capabilities of the at least one autonomous vehicle or a first set of vehicle service dynamics; and

the plurality of resource performance parameters includes a second plurality of resource performance parameters based on a second profile, the second profile including at least one of a second set of capabilities of the at least one autonomous vehicle or a second set of vehicle service dynamics.

18. A computing system comprising:

one or more processors; and

one or more tangible, non-transitory, computer readable media that collectively store instructions that when executed by the one or more processors cause the computing system to perform operations comprising;

obtaining data indicative of one or more capabilities of at least one autonomous vehicle;

obtaining data indicative of vehicle service dynamics in an operational domain over a period of time;

determining a plurality of resource performance parameters respectively for a plurality of autonomous vehicle fleets associated with potential deployment in the operational domain, each autonomous vehicle fleet associated with a different number of autonomous vehicles, wherein the resource performance parameter for each autonomous vehicle fleet is based at least in part on the one or more capabilities of the at least one autonomous vehicle and the vehicle service dynamics in the operational domain; and

initiating, an action associated with the operational domain based at least in part on the plurality of resource performance parameters.

19. One or more tangible, non-transitory, computer-readable media that collectively store instructions that, when executed by one or more processors, cause the one or more processors to perform operations, the operations comprising:

obtaining data indicative of one or more capabilities of at least one autonomous vehicle;

obtaining data indicative of a resource outflow associated with operation of a first fleet of autonomous vehicles and a resource outflow associated with a second fleet of non-autonomous vehicles in the operational domain;

determining a measure of utilization based at least in part on the one or more capabilities of the least one autonomous vehicle, a measure of the resource outflow associated with the first fleet of autonomous vehicles, and a measure of the resource outflow associated with the second fleet non-autonomous vehicles;

determining a fleet size of the first fleet of autonomous vehicles for deployment in the operational domain based at least in part on the measure of utilization; and

initiating an action associated with the first fleet of autonomous vehicles in the operational domain based at least in part on the fleet size of the first fleet of autonomous vehicles determined for deployment in the operational domain.

20. The one or more tangible, non-transitory, computer-readable media of claim 19, wherein:

the operations further comprise determining a convergence associated with the measure of the resource outflow associated with the first fleet of autonomous vehicles and the measure of the resource outflow associated with the second fleet of autonomous vehicles; and

determining the measure of utilization comprises determining the measure of utilization based at least in part on the convergence.