Determining pickup and destination locations for autonomous vehicles

• US 10,156,449 B2
• Filed: 07/10/2017
• Issued: 12/18/2018
• Est. Priority Date: 06/22/2015
• Status: Active Grant

• Current Assignee: Waymo LLC (Alphabet Inc.)
• Original Assignee: Waymo LLC (Alphabet Inc.)
• Inventors: Colijn, Peter, Herbach, Joshua Seth, McNaughton, Matthew Paul
• Primary Examiner(s): Marc-Coleman, Marthe Y
• Application Number:
  US15/645,297
• Publication Number:
  US 20170370734A1
• Time in Patent Office: 526 Days
• Field of Search: 701461, 701538, 701400, 701409, 701 23, 705 14, 705 58
• CPC Class Codes: G01C 21/34 Route searching; Route guidance
• CPC Class Codes: G01C 21/343 Calculating itineraries, i.e. routes leading from a starting point to a series of categorical destinations using a global route restraint, round trips, touristic trips travelling salesman problem G06Q10/04; optimisation of routes G06Q10/047
• 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: G08G 1/202 Dispatching vehicles on the basis of a location, e.g. taxi dispatching
• CPC Class Codes: G08G 1/205 Indicating the location of the monitored vehicles as destination, e.g. accidents, stolen, rental
• CPC Class Codes: G08G 1/22 Platooning, i.e. convoy of communicating vehicles

Abstract

Aspects of the disclosure provide systems and methods for providing suggested locations for pick up and destination locations. Pick up locations may include locations where an autonomous vehicle can pick up a passenger, while destination locations may include locations where the vehicle can wait for an additional passenger, stop and wait for a passenger to perform some task and return to the vehicle, or for the vehicle to drop off a passenger. As such, a request for a vehicle may be received from a client computing device. The request may identify a first location. A set of one or more suggested locations may be selected by comparing the predetermined locations to the first location. The set may be provided to the client computing device.

Claims

1. A system comprising:

memory storing detailed map information identifying a plurality of predetermined locations where a vehicle is able to pick up or drop off passengers; and

one or more server computers each having one or more processors, the one or more server computers being configured to;

receive a request from a client computing device, the request identifying a first location;

select a set of one or more suggested locations by;

comparing the plurality of predetermined locations to the first location;

identifying predetermined locations of the plurality of predetermined locations that are within a threshold distance of the first location; and

scoring by assigning a value to each of the identified predetermined locations based on logistical constraints a person or autonomous vehicle encounters in reaching the predetermined locations from the first location; and

provide the set of one or more suggested locations to the client computing device.

2. The system of claim 1, wherein the one or more server computers are configured to conduct the scoring of the identified predetermined locations based on a first plurality of factors related to the logistical constraints the person encounters in reaching the identified predetermined locations from the first location.

3. The system of claim 2, wherein the one or more server computers are configured to conduct the scoring of the identified predetermined locations based on a second plurality of factors related to the logistical constraints the autonomous vehicle encounters in reaching and stopping at the predetermined locations from the first location.

4. The system of claim 3, wherein the one or more server computers are further configured to determine at least one of the second plurality of factors based on a current location of the autonomous vehicle.

5. The system of claim 3, wherein the one or more server computers are further configured to determine at least one of the second plurality of factors based on whether the autonomous vehicle would have to first pass the identified predetermined locations and turn around.

6. The system of claim 2, wherein the one or more server computers are further configured to determine at least one of the first plurality of factors based on a distance from the first location to the identified predetermined locations.

7. The system of claim 2, wherein the one or more server computers are further configured to determine at least one of the first plurality of factors based on walking conditions from the first location to the identified predetermined locations.

8. A computer-implemented method comprising:

accessing, by one or more processors of one or more server computing devices, detailed map information identifying a plurality of predetermined locations where a vehicle is able to pick up or drop off passengers;

receiving, by the one or more processors, a request from a client computing device, the request identifying a first location;

selecting, by the one or more processors, a set of one or more suggested locations by;

comparing the plurality of predetermined locations to the first location;

identifying predetermined locations of the plurality of predetermined locations that are within a threshold distance of the first location; and

scoring by assigning a value to each of the identified predetermined locations based on logistical constraints a person or autonomous vehicle encounters in reaching the predetermined locations from the first location; and

providing, by the one or more processors, the set of one or more suggested locations to the client computing device.

9. The method of claim 8, wherein the scoring of the identified predetermined locations is based on a first plurality of factors related to the logistical constraints the person encounters in reaching the identified predetermined locations from the first location.

10. The method of claim 9, wherein the scoring of the identified predetermined locations is based on a second plurality of factors related to the logistical constraints the autonomous vehicle encounters in reaching and stopping at the predetermined locations from the first location.

11. The method of claim 10, further comprising determining at least one of the second plurality of factors is based on a current location of the autonomous vehicle.

12. The method of claim 10, further comprising determining at least one of the second plurality of factors is based on whether the autonomous vehicle would have to first pass the identified predetermined locations and turn around.

13. The method of claim 9, further comprising determining at least one of the first plurality of factors based on a distance from the first location to the identified predetermined locations.

14. The method of claim 9, further comprising determining at least one of the first plurality of factors based on walking conditions from the first location to the identified predetermined locations.

15. A non-transitory, tangible, computer readable medium on which instructions are stored, the instructions, when executed by one or more processors, cause the one or more processors to perform a method, the method comprising:

accessing detailed map information identifying a plurality of predetermined locations where a vehicle is able to pick up or drop off passengers;

receiving a request from a client computing device, the request identifying a first location;

selecting a set of one or more suggested locations by;

comparing the plurality of predetermined locations to the first location;

identifying predetermined locations of the plurality of predetermined locations that are within a threshold distance of the first location; and

scoring by assigning a value to each of the identified predetermined locations based on logistical constraints a person or autonomous vehicle encounters in reaching the predetermined locations from the first location; and

providing the set of one or more suggested locations to the client computing device.

16. The medium of claim 15, wherein the scoring of the identified predetermined locations is based on a first plurality of factors related to the logistical constraints the person encounters in reaching the identified predetermined locations from the first location.

17. The medium of claim 16, further comprising determining at least one of the first plurality of factors based on a distance from the first location to the identified predetermined locations.

18. The medium of claim 15, wherein the scoring of the identified predetermined locations is based on a second plurality of factors related to the logistical constraints the autonomous vehicle encounters in reaching and stopping at the predetermined locations from the first location.

19. The medium of claim 18, further comprising determining at least one of the second plurality of factors is based on a current location of the autonomous vehicle.

20. The medium of claim 18, further comprising determining at least one of the second plurality of factors is based on whether the autonomous vehicle would have to first pass the identified predetermined locations and turn around.