Selecting vehicle type for providing transport

• US 9,547,309 B2
• Filed: 05/13/2015
• Issued: 01/17/2017
• Est. Priority Date: 05/13/2015
• Status: Active Grant

• Current Assignee: Uber Technologies, Inc.
• Original Assignee: Uber Technologies, Inc.
• Inventors: Ross, William, Bares, John, LaRose, David, Sweeney, Matthew
• Primary Examiner(s): Kan, Yuri
• Application Number:
  US14/711,602
• Publication Number:
  US 20160334797A1
• Time in Patent Office: 615 Days
• Field of Search: 701/5, 701/25, 701/29.1, 705/26.2, 382/104
• US Class Current: 1/1
• CPC Class Codes: G01C 21/3438 Rendez-vous, i.e. searching a destination where several users can meet, and the routes to this destination for these users; Ride sharing, i.e. searching a route such that at least two users can share a vehicle for at least part of the route
• CPC Class Codes: G01C 21/3484 Personalized, e.g. from learned user behaviour or user-defined profiles
• CPC Class Codes: 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
• CPC Class Codes: G05D 1/0027 involving a plurality of vehicles, e.g. fleet or convoy travelling fleet control of land vehicles from a control room G05D1/0297; traffic control systems for road vehicles G08G1/00; for marine craft G08G3/00; for aircraft G08G5/00
• 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 1/0217 in accordance with energy consumption, time reduction or distance reduction criteria
• CPC Class Codes: G05D 1/0234 using optical markers or beacons optical beacons per se G01S1/70
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/08 Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
• CPC Class Codes: G06Q 30/0283 Price estimation or determination
• CPC Class Codes: G06Q 50/40 Business processes related to the transportation industry shipping G06Q10/083
• CPC Class Codes: G08G 1/20 Monitoring the location of vehicles belonging to a group, e.g. fleet of vehicles, countable or determined number of vehicles
• CPC Class Codes: G08G 1/202 Dispatching vehicles on the basis of a location, e.g. taxi dispatching
• CPC Class Codes: H05K 999/99 dummy group

Abstract

A transport arrangement system operates to receive a transport request from a user, and to make a selection of a vehicle type for the user based at least in part on a set of criteria associated with the transport request or user information. For example, the determination of whether an autonomous vehicle is to be provided can be based at least in part on the destination specified with the transport request.

Claims

1. A system for arranging transport, the system comprising:

a memory that stores a set of instructions;

one or more processors which use the set of instructions to;

receive a transport request from a user; and

make a selection of a vehicle type for the user based at least in part on a destination specified with the transport request, the selection specifying whether a selected vehicle is an autonomous vehicle.

2. The system of claim 1, wherein the one or more processors make the selection based at least in part on a preference of the user for the vehicle type.

3. The system of claim 1, wherein the one or more processors make the selection based at least in part on an added requirement of the user.

4. The system of claim 1, wherein the one or more processors make the selection by (i) determining one or more routes for the autonomous vehicle to the destination, and (ii) comparing the one or more routes to an optimal route that can be provided by a human driven vehicle.

5. The system of claim 4, wherein the one or more processors make the selection by:

comparing lowest estimated time of arrival for the autonomous vehicle with a lowest estimated time of arrival for the human driven vehicle.

6. The system of claim 4, wherein the one or more processors make the selection by:

comparing a distance traveled to the destination for the autonomous vehicle with a distance traveled to the destination for the human driven vehicle.

7. The system of claim 4, wherein the one or more processors make the selection by:

comparing a road type for a selected route of the autonomous vehicle with a road type for a selected route of the human driven vehicle.

8. The system of claim 4, wherein the one or more processors make the selection by determining an availability parameter for a second vehicle to lead the autonomous vehicle through at least a portion of a selected route.

9. The system of claim 4, wherein the one or more processors:

determine one of an alternative pickup location or destination for the transport request in order to (i) determine a route that accommodates an ability of the autonomous vehicle, (ii) reduce an estimated time of arrival in using the autonomous vehicle for the transport request, and/or (iii) reduce a distance traveled in using the autonomous vehicle for the transport request.

10. The system of claim 1, wherein the one or more processors make the selection of the vehicle type by (i) determining a route using the destination, and (ii) predicting whether the autonomous vehicle will need to request assistance on the route.

11. The system of claim 10, wherein the one or more processor predict whether the autonomous vehicle will need to request assistance based on geographic specific information about a temporal event or condition affecting one or more road segments of the route.

12. The system of claim 1, wherein the one or more processors make the selection of the vehicle type by (i) determining a route using the destination, and (ii) predicting whether the autonomous vehicle will need to request a human driven vehicle to provide guide assistance.

13. The system of claim 12, wherein the one or more processors make the selection by calculating a cost for providing the autonomous vehicle as compared to a human driven vehicle.

14. The system of claim 1, wherein the one or more processors make the selection against a preference of the user, by providing a suggestion in favor of one of the autonomous vehicle or a human driven vehicle.

15. The system of claim 14, wherein the suggestion is based on a comparison of one or more of a cost or time of trip for the autonomous vehicle and the human driven vehicle.

16. A non-transitory computer-readable medium that stores instructions, which when executed by one or more processors, cause the one or more processors to perform operations comprising:

receiving a transport request from a user; and

making a selection of a vehicle type for the user based at least in part on a destination specified with the transport request, the selection specifying whether a selected vehicle is an autonomous vehicle.

17. The non-transitory computer-readable medium of claim 16, wherein making the selection is based at least in part on a preference of the user for the vehicle type.

18. The non-transitory computer-readable medium of 16, wherein making the selection is based at least in part on an added requirement of the user.

19. The non-transitory computer-readable medium of 16, wherein making the selection includes (i) determining one or more routes for the autonomous vehicle to the destination, and (ii) comparing the one or more routes to an optimal route that can be provided by a human driven vehicle.

20. A method for arranging transport, the method being implemented by one or more processors and comprising:

receiving a transport request from a user; and

making a selection of a vehicle type for the user based at least in part on a destination specified with the transport request, the selection specifying whether a selected vehicle is an autonomous vehicle.