Systems and methods for controlling autonomous vehicles that provide a vehicle service to users

• US 10,528,059 B2
• Filed: 07/28/2017
• Issued: 01/07/2020
• Est. Priority Date: 05/24/2017
• Status: Active Grant

• Current Assignee: Uber Technologies, Inc.
• Original Assignee: Uatc, LLC (Uber Technologies, Inc.)
• Inventors: Donnelly, Richard Brian, Aitken, Michael
• Primary Examiner(s): Shaawat, Mussa A
• Application Number:
  US15/662,314
• Publication Number:
  US 20180341274A1
• Time in Patent Office: 893 Days
• CPC Class Codes: B60R 16/037 for occupant comfort , e.g. for automatic adjustment of appliances according to personal settings, e.g. seats, mirrors, steering wheel
• CPC Class Codes: B60R 25/24 using electronic identifiers containing a code not memorised by the user
• 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 21/3492 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: G01C 21/3676 Overview of the route on the road map
• 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: G08G 1/005 including pedestrian guidance indicator
• CPC Class Codes: G08G 1/0112 from the vehicle, e.g. floating car data [FCD]
• CPC Class Codes: G08G 1/0129 for creating historical data or processing based on historical data
• CPC Class Codes: G08G 1/0137 for specific applications
• CPC Class Codes: G08G 1/0145 for active traffic flow control
• CPC Class Codes: G08G 1/09 Arrangements for giving variable traffic instructions railroad crossing signals B61L; reflectors E01F, G08B; indicating arrangements for variable information by selection or combination of individual elements G09F9/00
• CPC Class Codes: G08G 1/09623 Systems involving the acquisition of information from passive traffic signs by means mounted on the vehicle G08G1/0967 takes precedence
• CPC Class Codes: G08G 1/09626 where the origin of the information is within the own vehicle, e.g. a local storage device, digital map
• CPC Class Codes: G08G 1/096725 where the received information generates an automatic action on the vehicle control
• CPC Class Codes: G08G 1/096775 where the origin of the information is a central station
• CPC Class Codes: G08G 1/096791 where the origin of the information is another vehicle
• CPC Class Codes: G08G 1/096827 where the route is computed onboard
• CPC Class Codes: G08G 1/123 indicating the position of vehicles, e.g. scheduled vehicles; Managing passenger vehicles circulating according to a fixed timetable, e.g. buses, trains, tramstransmission of navigation instructions to vehicles G08G1/0968
• CPC Class Codes: G08G 1/143 : inside the vehicles
• CPC Class Codes: G08G 1/147 : where the parking area is within an open public zone, e.g. city centre
• 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: H04W 4/40 : for vehicles, e.g. vehicle-to-pedestrians [V2P]
• CPC Class Codes: H04W 4/46 : for vehicle-to-vehicle communication [V2V]

Abstract

Systems and methods for controlling autonomous vehicles are provided. In one example embodiment, a computer implemented method includes obtaining data indicative of a location associated with a user to which an autonomous vehicle is to travel. The autonomous vehicle is to travel along a first vehicle route that leads to the location. The method includes obtaining traffic data associated with a geographic area that includes the location. The method includes determining an estimated traffic impact of the autonomous vehicle on the geographic area based at least in part on the traffic data. The method includes determining vehicle action(s) based at least in part on the estimated traffic impact and causing the autonomous vehicle to perform the vehicle action(s) that include at least one of stopping the autonomous vehicle at least partially in a travel way within a vicinity of the location or travelling along a second vehicle route.

Claims

1. A computer-implemented method of controlling autonomous vehicles, comprising:

obtaining, by a vehicle computing system that comprises one or more computing devices, data indicative of a location associated with a user to which an autonomous vehicle is to travel, wherein the autonomous vehicle is to travel along a first vehicle route that leads to the location associated with the user;

obtaining, by the computing system via one or more sensors of the autonomous vehicle, sensor data associated with a surrounding environment of the autonomous vehicle that is within a vicinity of the location associated with the user;

determining, by the computing system, a level of traffic associated with a geographic area that includes the location associated with the user based at least in part on the sensor data;

determining, by the vehicle computing system, an estimated traffic impact of the autonomous vehicle on the geographic area based at least in part on the level of traffic that is based at least in part on the sensor data, wherein the estimated traffic impact is indicative of an estimated impact of the autonomous vehicle on one or more objects within the surrounding environment of the autonomous vehicle in the event that the autonomous vehicle were to stop at least partially in a travel way within the vicinity of the location associated with the user;

determining, by the vehicle computing system, one or more vehicle actions based at least in part on the estimated traffic impact; and

causing, by the vehicle computing system, the autonomous vehicle to perform the one or more vehicle actions, wherein the one or more vehicle actions comprise at least one of stopping the autonomous vehicle at least partially in the travel way within the vicinity of the location associated with the user or travelling along a second vehicle route.

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

obtaining, by the vehicle computing system, location data associated with a user device associated with the user;

determining, by the vehicle computing system, an estimated time of user arrival based at least in part on the location data associated with the user device; and

determining, by the vehicle computing system, the one or more vehicle actions also based at least in part on the estimated time of user arrival.

3. The computer-implemented method of claim 1, wherein the vicinity of the location associated with the user is defined at least in part by a distance from the location associated with the user, and wherein the distance from the location associated with the user is based at least in part on an acceptable walking distance from the location associated with the user.

4. The computer-implemented method of claim 1, wherein determining, by the vehicle computing system, the estimated traffic impact comprises:

comparing, by the vehicle computing system, the level of traffic to a traffic constraint.

5. The computer-implemented method of claim 4, wherein the traffic constraint comprises a traffic threshold indicative of a threshold level of traffic.

6. The computer-implemented method of claim 4, wherein the traffic constraint is determined at least in part on a machine-learned model.

7. The computer-implemented method of claim 1, wherein the second vehicle route is at least partially different from the first vehicle route and wherein the second vehicle route includes a route that leads to the location associated with the user.

8. The computer-implemented method of claim 1, wherein the autonomous vehicle stops at least partially in the travel way within the vicinity of the location associated with the user, the method further comprising:

providing, by the vehicle computing system to a user device associated with the user, a communication indicating that the autonomous vehicle is stopped, and

wherein in response to receiving the communication the user device displays a map user interface that indicates a vehicle location of the autonomous vehicle and a user route to the vehicle location of the autonomous vehicle.

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

determining, by the vehicle computing system, that a parking location that is out of the travel way is unavailable for the autonomous vehicle.

10. A vehicle computing system onboard an autonomous vehicle, comprising:

one or more processors; and

one or more memory devices, the one or more memory devices storing instructions that when executed by the one or more processors cause the computing system to perform operations, the operations comprising;

obtaining data indicative of a location associated with a user, wherein the user is associated with a request for a vehicle service provided by the autonomous vehicle, and wherein the autonomous vehicle is to travel along a first vehicle route to arrive within a vicinity of the location associated with the user;

obtaining, via one or more sensors of the autonomous vehicle, sensor data associated with a surrounding environment of the autonomous vehicle that is within the vicinity of the location associated with the user;

determining a level of traffic associated with a geographic area that includes the location associated with the user based at least in part on the sensor data;

obtaining location data associated with a user device associated with the user;

determining an estimated traffic impact of the autonomous vehicle on the geographic area based at least in part on the level of traffic that is based at least in part on the sensor data and an estimated time of user arrival based at least in part on the location data associated with the user device,

wherein the estimated traffic impact is indicative of an estimated impact of the autonomous vehicle on one or more objects within the surrounding environment of the autonomous vehicle in the event that the autonomous vehicle were to stop at least partially in a travel way within the vicinity of the location associated with the user;

determining one or more vehicle actions based at least in part on at least one of the estimated traffic impact or the estimated time of user arrival; and

causing the autonomous vehicle to perform the one or more vehicle actions, wherein the one or more vehicle actions comprise at least one of stopping the autonomous vehicle at least partially in the travel way within the vicinity of the location associated with the user or travelling along a second vehicle route.

11. The computing system of claim 10, wherein determining one or more vehicle actions based at least in part on at least one of the estimated traffic impact or the estimated time of user arrival comprises:

applying a first weighting factor to the estimated traffic impact and a second weighting factor to the estimated time of user arrival.

12. The computing system of claim 10, wherein the autonomous vehicle stops at least partially in the travel way within the vicinity of the location associated with the user, and wherein the operations further comprise:

determining at least one of an updated estimated traffic impact or an updated estimated time of user arrival; and

causing the autonomous vehicle to travel along the second vehicle route based at least in part on at least one of the updated estimated traffic impact or the updated estimated time of user arrival.

13. The computing system of claim 10, wherein the autonomous vehicle travels along the second vehicle route, and wherein the operations further comprise:

providing a communication to the user device associated with the user, wherein the communication indicates that the autonomous vehicle is travelling to return to the location associated with the user.

14. The computing system of claim 10, wherein the user does not arrive at the autonomous vehicle, and wherein the operations further comprise:

providing, to one or more computing devices that are remote from the computing system, data indicating that the request for the vehicle service provided by the autonomous vehicle is cancelled.

15. An autonomous vehicle comprising:

one or more sensors;

a communication system;

one or more processors; and

one or more memory devices, the one or more memory devices storing instructions that when executed by the one or more processors cause the autonomous vehicle to perform operations, the operations comprising;

obtaining data indicative of a location associated with a user, wherein the user is associated with a request for a vehicle service provided by the autonomous vehicle;

controlling the autonomous vehicle to travel along a first vehicle route to arrive within a vicinity of the location associated with the user;

obtaining, via the one or more sensors, sensor data associated with a surrounding environment of the autonomous vehicle that is within the vicinity of the location associated with the user;

determining a level of traffic associated with a geographic area that includes the location associated with the user based at least in part on the sensor data;

obtaining, via the communication system, location data associated with a user device associated with the user, wherein the location data associated with the user device is indicative of one or more locations of the user device associated with the user at one or more times;

determining an estimated traffic impact of the autonomous vehicle on the geographic area based at least in part on the traffic data, wherein the estimated traffic impact is indicative of an estimated impact of the autonomous vehicle on one or more objects within the surrounding environment of the autonomous vehicle in the event that the autonomous vehicle were to stop at least partially in a travel way within the vicinity of the location associated with the user;

determining an estimated time of user arrival based at least in part on the location data associated with the user device;

determining one or more vehicle actions based at least in part on at least one of the estimated traffic impact or the estimated time of user arrival; and

causing the autonomous vehicle to perform the one or more vehicle actions, wherein the one or more vehicle actions comprise at least one of stopping the autonomous vehicle within the vicinity of the location associated with the user or travelling along a second vehicle route.

16. The autonomous vehicle of claim 15, wherein causing the autonomous vehicle to stop within the vicinity of the location associated with the user comprises:

providing one or more control signals to a control system of the autonomous vehicle to cause the autonomous vehicle to decelerate to a stopped position that is at least partially in a travel way within the vicinity of the location associated with the user.

17. The autonomous vehicle of claim 15, wherein causing the autonomous vehicle to travel along the second vehicle route comprises:

providing one or more control signals to cause the autonomous vehicle to implement a motion plan that causes the autonomous vehicle to travel in accordance with the second vehicle route.

18. The autonomous vehicle of claim 15, wherein obtaining location data associated with the user device comprises:

obtaining the location data associated with user device using multiple input, multiple output communication between the autonomous vehicle and the user device associated with the user.