SYSTEM AND METHOD FOR AUTONOMOUS VEHICLE FLEET ROUTING

• US 20170192437A1
• Filed: 01/04/2017
• Published: 07/06/2017
• Est. Priority Date: 01/04/2016
• Status: Active Grant

• Current Assignee: GM Global Technology Operations LLC (General Motors Company)
• Original Assignee: Cruise Automation, Inc. (General Motors Company)
• Inventors: Reed, Brooks, Rust, Ian, Wisniowski, Matthias, Bier, Solomon, Gross, Drew Allyn
• Granted Patent: US 10,379,533 B2
• Time in Patent Office: Days
• CPC Class Codes: G01C 21/28 with correlation of data from several navigational instruments
• 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 21/3461 Preferred or disfavoured areas, e.g. dangerous zones, toll or emission zones, intersections, manoeuvre types, segments such as motorways, toll roads, ferries
• 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 1/0016 characterised by the operator's input device input arrangements for computing systems in general G06F3/00
• 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 1/0038 by providing the operator with simple or augmented images from one or more cameras located onboard the vehicle, e.g. tele-operation images analyzed by a computer and used for automatic navigation G05D1/0246
• CPC Class Codes: G05D 1/0077 using redundant signals or controls
• CPC Class Codes: G05D 1/0212 with means for defining a desired trajectory involving a plurality of land vehicles G05D1/0287
• CPC Class Codes: G05D 1/0238 using obstacle or wall sensors G05D1/0246 and G05D1/0289 take precedence; lidar systems designed for anti-collision purposes G01S17/93
• CPC Class Codes: G05D 1/0257 using a radar radar systems designed for anti-collision purposes between land vehicles or between land vehicle and fixed obstacles G01S13/931
• CPC Class Codes: G05D 1/0278 using satellite positioning signals, e.g. GPS
• CPC Class Codes: G05D 1/0287 involving a plurality of land vehicles, e.g. fleet or convoy travelling traffic control systems for road vehicles G08G1/00, particularly anticollision systems G08G1/16
• CPC Class Codes: G07C 5/008 communicating information to a remotely located station transmission systems for measured values G08C

Abstract

A system and method for providing routing instructions to one or more autonomous vehicles includes identifying a destination of an autonomous vehicle; identifying a starting position or an initial location of the autonomous vehicle; receiving autonomous vehicle sensor data; receiving one or more routing goals for a routing plan for the autonomous vehicle; generating one or more route modification parameters; and generating a route plan for the autonomous vehicle based on (a) the destination, (b) the starting position or the initial location, (c) the one or more route modification parameters and (d) the one or more routing goals.

Claims

1. A system providing routing instructions to one or more autonomous vehicles, the system comprising:

a subject autonomous vehicle requiring a route plan, the subject autonomous vehicle providing subject sensor data;

a fleet of vehicles comprising a plurality of autonomous vehicles, wherein each of the plurality of autonomous vehicles provide sensor data;

a routing server that;

collects subject sensor data and sensor data from the plurality of autonomous vehicles;

identifies source and destination data of the subject autonomous vehicle;

generates the route plan for the autonomous vehicle based on (i) subject sensor data, (ii) sensor data from the plurality of autonomous vehicles, and (iii) source and destination data of the subject autonomous vehicle.

2. The system of claim 1, further comprising:

a remote assistance interface that is configured to remotely assist the one or more autonomous vehicles, wherein the remote assistance interface is different from the routing server, and

wherein the routing server collects remote assistance data from the remote assistance interface and generates the route plan for the autonomous vehicle further based on the collected remote assistance data.

3. The system of claim 1, further comprising:

a plurality of user interfaces, wherein the routing server collects vehicle demand data from the plurality of user interfaces or a ride sharing platform, and

wherein the routing server generates the route plan for the autonomous vehicle further based on the collected vehicle demand data.

21. The method of claim 1, further comprising:

generating a plurality of route plans for the plurality of autonomous vehicles;

generating autonomous vehicle control data based on the plurality of route plans, and

controlling, using the autonomous vehicle control data, the plurality of autonomous vehicles.

22. The system of claim 1, further comprising:

generating autonomous vehicle control data based on the generated route plan for the one or more autonomous vehicles; and

controlling, using the autonomous vehicle control data, the one or more autonomous vehicles.

4. A method providing routing instructions to one or more autonomous vehicles, the method comprising:

(i) identifying a destination of an autonomous vehicle;

(ii) identifying a starting position or an initial location of the autonomous vehicle;

(iii) receiving autonomous vehicle sensor data;

(iv) receiving one or more routing goals for a routing plan for the autonomous vehicle;

(iii) generating one or more route modification parameters; and

(iv) generating a route plan for the autonomous vehicle based on (a) the destination, (b) the starting position or the initial location, (c) the one or more route modification parameters and (d) the one or more routing goals.

5. The method of claim 4, further comprising:

receiving vehicle demand data, the vehicle demand data indicating one or more desires or estimated desire for one or more autonomous vehicles to be present in a specific region or a specific location.

6. The method of claim 4, wherein the routing goals establish a set of routing parameters that identify metrics from an analysis of the sensor data that are considered in determining routes or route modifications for the routing plan of the autonomous vehicles.

7. The method of claim 6, wherein each of the one or more route modification parameters links predetermined cost/benefit values to one or more route segments of the generated route plan, and

wherein generating the route plan for the autonomous vehicle is further based on identifying a route having an aggregated low cost/benefit to the autonomous vehicle.

8. The method of claim 4, further comprising:

receiving blacklist data from a remote assistance interface, wherein blacklist data identifies one or more conditions or events rendering one or more lanes or one or more road segments not available to be used in generating the route plan for the autonomous vehicle.

9. The method of claim 4, further comprising:

receiving avoidance scenario data from one or more autonomous vehicles in a fleet of autonomous vehicles,

generating blacklist data from the avoidance scenario data, wherein blacklist data identifies one or more conditions or events rendering one or more lanes or one or more road segments not available to be used in generating the route plan for the autonomous vehicle

wherein generating the route plan for the autonomous vehicle is further based on the identified blacklist data.

10. The method of claim 4, wherein receiving autonomous vehicle sensor data includes:

receiving sensor data from the autonomous vehicle,

receiving sensor data from a fleet of autonomous vehicles, wherein the fleet includes a plurality of autonomous vehicles other than the autonomous vehicle for which the route plan is generated, and

wherein generating the route plan for the autonomous vehicle is further based on both sensor data from the autonomous vehicle and the fleet of autonomous vehicles.

11. The method of claim 4, further comprising:

generating autonomous vehicle control data based on the generated route plan for the autonomous vehicle; and

controlling, using the autonomous vehicle control data, the autonomous vehicle.

12. The method of claim 4, wherein the destination of the autonomous vehicle and the starting position or the initial location of the autonomous vehicle for the route plan are identified by disparate entities,

generating autonomous vehicle control data based on the generated route plan for the autonomous vehicle, and

controlling autonomous vehicles even when the destination and the initial starting position or initial location of the autonomous vehicle for the route plan are identified by disparate entities.

13. The method of claim 4, wherein generating the route plan for the autonomous vehicle includes:

applying a probabilistic algorithm to one or more route segments of one or more proposed route plans, and

identifying one or more route segments for the route plan of the autonomous vehicle based on the application of the probabilistic algorithm.

14. The method of claim 13, wherein applying the probabilistic algorithm includes:

(i) identifying proposed maneuvers in the one or more route segments;

(ii) assigning to each proposed maneuver a probability of success and a probability of failure of accomplishing the proposed maneuver by the autonomous vehicle;

(iii) associating a path of failure with the probability of failure and path of success with the probability of success; and

(iv) identifying a cost of the path of failure and a cost of the path of success.

15. The method of claim 14, wherein generating the route plan for the autonomous vehicle is further based on one of the cost of the path of failure and the cost of the path of success for one of the one or more route segments.

16. The method of claim 14, wherein applying the probabilistic algorithm further includes identifying a probabilistic maneuver cost based on a sum of the cost of the path of failure and the cost of the path of success, and

wherein generating the route plan for the autonomous vehicle is further based on the probabilistic maneuver cost.

17. The method of claim 4, wherein the autonomous vehicle sensor data comprises data relating to a state of the autonomous vehicle and a state of circumstances around the autonomous vehicle.

18. The method of claim 4, further comprising:

receiving construction data and traffic data,

wherein generating the route plan for the autonomous vehicle is further based on the construction data and the traffic data.

23. The method of claim 4, further comprising:

using the received autonomous vehicle sensor data from the autonomous vehicle to determine a state or performance of another autonomous vehicle.

19. A method providing a route plan to an autonomous vehicle, the method comprising:

receiving vehicle demand data;

receiving routing goals for the autonomous vehicle;

generating route modification parameters for the route plan; and

generating a route plan based on the route modification parameters.

20. The method of claim 19, further comprising:

generating control data for controlling the autonomous vehicle based on the generated route plan.