Configuration of vehicle compartments

• US 10,118,577 B1
• Filed: 08/10/2017
• Issued: 11/06/2018
• Est. Priority Date: 08/10/2017
• Status: Active Grant

• Current Assignee: Uatc, LLC (Uber Technologies, Inc.)
• Original Assignee: Uber Technologies, Inc.
• Inventors: Sweeney, Matthew
• Primary Examiner(s): Nguyen, Tan Q
• Application Number:
  US15/674,377
• Time in Patent Office: 453 Days
• Field of Search: 701 49
• CPC Class Codes: B60N 2/002 Seats provided with an occupancy detection means mounted therein or thereon detection means responsive to presence or absence of children B60N2/266
• 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 5/00 Compartments within vehicle body primarily intended or sufficiently spacious for trunks, suit-cases, or the like primarily intended for stowing loads in load-transporting vehicles B60P; arrangements for stowing spare wheels B62D43/00
• CPC Class Codes: B60R 7/02 in separate luggage compartment B60R7/005 takes precedence
• CPC Class Codes: G01C 21/3697 Output of additional, non-guidance related information, e.g. low fuel level G01C21/3679 takes precedence
• 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

Abstract

Systems, methods, tangible, non-transitory computer-readable media, and devices for configuration of a vehicle compartment are provided. For example, a method can include receiving, by a computing system, occupancy data based in part on one or more states of one or more objects. Based in part on the occupancy data and compartment data, a compartment configuration can be determined for one or more compartments of an autonomous vehicle. The compartment data can be based in part on a state of the one or more compartments. The compartment configuration can specify one or more spatial relations of one or more compartment components associated with the one or more compartments. One or more configuration signals can be generated based in part on the compartment configuration to control the one or more compartments of the autonomous vehicle.

Claims

1. A computer-implemented method of operating an autonomous vehicle, the computer-implemented method comprising:

receiving, by a computing system comprising one or more computing devices, occupancy data based in part on one or more states of one or more objects;

determining, by the computing system, based in part on the occupancy data and compartment data, a compartment configuration for one or more compartments of an autonomous vehicle, the compartment data based in part on a state of the one or more compartments, wherein the compartment configuration specifies one or more spatial relations of one or more compartment components associated with the one or more compartments; and

generating, by the computing system, one or more configuration signals based in part on the compartment configuration to control the one or more compartments of the autonomous vehicle.

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

activating, by the computing system, and based in part on the one or more configuration signals, one or more vehicle control systems to modify the one or more spatial relations of the one or more compartment components.

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

generating, by the computing system, compartment modification data based in part on the one or more configuration signals, wherein the compartment modification data comprises one or more instructions for performing the one or more modifications to the one or more spatial relations of the one or more compartment components; and

sending, by the computing system, the compartment modification data to a remote computing device.

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

determining, by the computing system, a plurality of spatial configurations of the one or more objects with respect to the one or more compartments; and

determining, by the computing system, a set of available configurations based in part on the plurality of spatial configurations that satisfy one or more compartment criteria associated with a location or position of the one or more objects within the one or more compartments, wherein the compartment data comprises the set of available configurations.

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

determining, by the computing system, and based in part on the occupancy data, one or more object types for the one or more objects, the one or more object types comprising passengers, perishable cargo, non-perishable cargo, or durable cargo; and

determining, by the computing system, a set of available compartments comprising the one or more compartments that satisfy one or more compartment criteria associated with the one or more object types, wherein the compartment data comprises the set of available compartments.

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

receiving, by the computing system, feedback data associated with the one or more compartments, wherein the feedback data comprises an indication of an adverse effect on the operation of the one or more compartments or the autonomous vehicle, wherein the compartment data comprises the feedback data.

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

determining, by the computing system, a time of day or date that the autonomous vehicle is scheduled to arrive at a destination location; and

determining, by the computing system, and based on navigation data, an orientation or position of the autonomous vehicle at the destination location, the navigation data including traffic flow patterns associated with an area including the destination location, wherein the compartment data comprises the orientation or position of the autonomous vehicle at the destination location.

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

determining, by the computing system, and based in part on the occupancy data, one or more access regulations for the one or more compartments, the one or more access regulations comprising contact limitations or minimum proximity distances associated with the one or more objects, wherein the compartment data comprises the one or more access regulations.

9. The computer-implemented method of claim 1, wherein the one or more spatial relations of the one or more compartment components are based in part on spatial characteristics comprising a size, a shape, a position, a location, or an orientation of the one or more compartment components.

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

receiving, by the computing system, sensor data from one or more sensors or state data from a remote computing system, the sensor data or the state data comprising a mass, a weight, a volume, a size, a shape, a temperature, an odor, or a texture of the one or more objects, wherein the occupancy data is based in part on the sensor data or the state data.

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

generating, by the computing system, and based in part on the occupancy data or the compartment data, one or more placement cues to indicate a location to place the one or more objects within the one or more compartments, wherein the one or more placement cues comprise visual cues, auditory cues, or haptic cues.

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

modifying, by the computing system, operation of the autonomous vehicle based in part on the one or more configuration signals, wherein the modifying operation of the autonomous vehicle comprises modifying a velocity of the autonomous vehicle, an acceleration of the autonomous vehicle, a deceleration of the autonomous vehicle, or a path of the autonomous vehicle.

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

receiving occupancy data based in part on one or more states of one or more objects;

determining, based in part on the occupancy data and compartment data, a compartment configuration for one or more compartments of an autonomous vehicle, the compartment data based in part on a state of the one or more compartments, wherein the compartment configuration specifies one or more spatial relations of one or more compartment components associated with the one or more compartments; and

generating one or more configuration signals based in part on the compartment configuration.

14. The one or more tangible, non-transitory computer-readable media of claim 13, further comprising:

activating, based in part on the one or more configuration signals, one or more vehicle control systems to modify the one or more spatial relations of the one or more compartment components.

15. The one or more tangible, non-transitory computer-readable media of claim 13, further comprising:

determining, based in part on the occupancy data, one or more object types for the one or more objects, the one or more object types comprising passengers, perishable cargo, non-perishable cargo, or durable cargo; and

determining, a set of available compartments comprising the one or more compartments that satisfy one or more compartment criteria associated with the one or more object types, wherein the compartment data is based in part on the set of available compartments.

16. The one or more tangible, non-transitory computer-readable media of claim 13, further comprising:

determining a time of day or date that the autonomous vehicle is scheduled to arrive at a destination location; and

determining, based on navigation data, an orientation or position of the autonomous vehicle at the destination location, the navigation data including traffic flow patterns associated with an area including the destination location, wherein the compartment data is based in part on the orientation or position of the autonomous vehicle at the destination location.

17. A computing system comprising:

one or more processors;

a memory comprising one or more computer-readable media, the memory storing computer-readable instructions that when executed by the one or more processors cause the one or more processors to perform operations comprising;

receiving occupancy data based in part on one or more states of one or more objects;

determining, based in part on the occupancy data and compartment data, a compartment configuration for one or more compartments of an autonomous vehicle, the compartment data based in part on a state of the one or more compartments, wherein the compartment configuration specifies one or more spatial relations of one or more compartment components associated with the one or more compartments; and

generating one or more configuration signals based in part on the compartment configuration.

18. The computing system of claim 17, further comprising:

activating, based in part on the one or more configuration signals, one or more vehicle control systems to modify the one or more spatial relations of the one or more compartment components.

19. The computing system of claim 17, further comprising:

determining, based in part on the occupancy data, one or more object types for the one or more objects, the one or more object types comprising passengers, perishable cargo, non-perishable cargo, or durable cargo; and

determining, a set of available compartments comprising the one or more compartments that satisfy one or more compartment criteria associated with the one or more object types, wherein the compartment data is based in part on the set of available compartments.

20. The computing system of claim 17, further comprising:

determining a time of day or date that the autonomous vehicle is scheduled to arrive at a destination location; and

determining, based on navigation data, an orientation or position of the autonomous vehicle at the destination location, the navigation data including traffic flow patterns associated with an area including the destination location, wherein the compartment data comprises the orientation or position of the autonomous vehicle at the destination location.