AUTONOMOUS VEHICLE OPERATED WITH GUIDE ASSISTANCE OF HUMAN DRIVEN VEHICLES

• US 20170060129A1
• Filed: 11/11/2016
• Published: 03/02/2017
• Est. Priority Date: 05/13/2015
• Status: Active Grant

• Current Assignee: Uatc, LLC (Uber Technologies, Inc.)
• Original Assignee: Uber Technologies, Inc.
• Inventors: Ross, William, Bares, John, LaRose, David, Sweeney, Matthew
• Granted Patent: US 10,126,742 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: B60W 2556/45 External transmission of data to or from the vehicle
• CPC Class Codes: B60W 30/00 Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
• CPC Class Codes: B60W 30/165 Automatically following the path of a preceding lead vehicle, e.g. "electronic tow-bar"
• CPC Class Codes: B60W 50/029 Adapting to failures or work around with other constraints, e.g. circumvention by avoiding use of failed parts
• CPC Class Codes: G01C 21/34 Route searching; Route guidance
• CPC Class Codes: G01S 13/867 Combination of radar systems with cameras
• CPC Class Codes: G01S 2013/9316 combined with communication equipment with other vehicles or with base stations
• CPC Class Codes: G01S 2013/9322 using additional data, e.g. driver condition, road state or weather data
• CPC Class Codes: G01S 2013/9323 Alternative operation using light waves
• CPC Class Codes: G01S 2013/9324 Alternative operation using ultrasonic waves
• CPC Class Codes: G05D 1/0022 characterised by the communication link data switching networks in general H04L12/00
• 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 1/0234 using optical markers or beacons optical beacons per se G01S1/70
• CPC Class Codes: G05D 1/0274 using mapping information stored in a memory device navigation using map-matching G01C21/30
• 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: G06V 20/56 exterior to a vehicle by using sensors mounted on the vehicle
• CPC Class Codes: G08B 25/00 Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems
• CPC Class Codes: G08G 1/0962 having an indicator mounted inside the vehicle, e.g. giving voice messages
• CPC Class Codes: G08G 1/161 Decentralised systems, e.g. inter-vehicle communication
• CPC Class Codes: G08G 1/165 : for passive traffic, e.g. including static obstacles, trees
• CPC Class Codes: G08G 1/166 : for active traffic, e.g. moving vehicles, pedestrians, bikes
• CPC Class Codes: G08G 1/22 : Platooning, i.e. convoy of communicating vehicles
• CPC Class Codes: H04L 67/12 : specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks

Abstract

Sensor information is collected from human driven vehicles which are driven in a given region. From the sensor information, a road condition is detected on a road segment, where the road condition has a sufficiently high likelihood of impairing autonomous vehicles in safely navigating through the one or more road segments. Information about the one or more road segments is communicated to the one or more autonomous vehicles.

Claims

1. A method for operating an autonomous vehicle, the method being implemented by one or more processors and comprising:

collecting sensor information from one or more sensors of a human-driven vehicle as the human-driven vehicle is driven in a given region;

determining, from the sensor information, one or more road segments which have a road condition with a sufficiently high likelihood of impairing autonomous vehicles in safely navigating through the one or more road segments; and

communicating information about the one or more road segments to one or more autonomous vehicles.

2. The method of claim 1, further comprising:

analyzing the sensor information to determine data for guiding autonomous vehicles through the one or more road segments; and

communicating instructions based on the data to the one or more autonomous vehicles.

3. The method of claim 2, wherein the data for guiding autonomous vehicles includes a plurality of driving operations performed by the human-driven vehicle through the one or more road segments.

4. The method of claim 2, wherein the instructions based on the data is authoritative over a default set of driving rules for the one or more autonomous vehicles.

5. The method of claim 1, further comprising:

generating a record that identifies the one or more road segments which have a road condition with a sufficiently high likelihood of impairing autonomous vehicles.

6. The method of claim 5, further comprising:

updating a map of the given region based on the record, and communicating at least an updated portion of the map to a network service.

7. The method of claim 1, further comprising:

determining one or more attributes of the road condition, including an estimated time or duration for when the road condition is present on the road segment.

8. The method of claim 1, wherein communicating information about the one or more road segments to the one or more autonomous vehicles is performed at a transport arrangement system.

9. The method of claim 1, wherein communicating information about the one or more road segments to the one or more autonomous vehicles is performed by the human-driven vehicle.

10. 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:

collecting sensor information from one or more sensors of a human-driven vehicle as the human-driven vehicle is driven in a given region;

determining, from the sensor information, one or more road segments which have a road condition with a sufficiently high likelihood of impairing autonomous vehicles in safely navigating through the one or more road segments; and

communicating information about the one or more road segments to one or more autonomous vehicles.

11. The non-transitory computer-readable medium of claim 10, storing further instructions to cause the one or more processors to perform operations comprising:

analyzing the sensor information to determine data for guiding autonomous vehicles through the one or more road segments; and

communicating instructions based on the data to the one or more autonomous vehicles.

12. The non-transitory computer-readable medium of claim 11, wherein the data for guiding autonomous vehicles includes a plurality of driving operations performed by the human-driven vehicle through the one or more road segments.

13. The non-transitory computer-readable medium of claim 11, wherein the instructions based on the data is authoritative over a default set of driving rules for the one or more autonomous vehicles.

14. The non-transitory computer-readable medium of claim 10, storing further instructions to cause the one or more processors to perform operations comprising:

generating a record that identifies the one or more road segments which have a road condition with a sufficiently high likelihood of impairing autonomous vehicles.

15. The non-transitory computer-readable medium of claim 14, storing further instructions to cause the one or more processors to perform operations comprising:

updating a map of the given region based on the record, and communicating at least an updated portion of the map to a network service.

16. The non-transitory computer-readable medium of claim 10, storing further instructions to cause the one or more processors to perform operations comprising:

determining one or more attributes of the road condition, including an estimated time or duration for when the road condition is present on the road segment.

17. The non-transitory computer-readable medium of claim 10, wherein communicating information about the one or more road segments to the one or more autonomous vehicles is performed at a transport arrangement system.

18. The non-transitory computer-readable medium of claim 10, wherein communicating information about the one or more road segments to the one or more autonomous vehicles is performed by the human-driven vehicle.

19. A computer system comprising:

a memory to store a set of instructions;

one or more processors to execute the set of instructions to;

collect sensor information from one or more sensors of a human-driven vehicle as the human-driven vehicle is driven in a given region;

determine, from the sensor information, one or more road segments which have a road condition with a sufficiently high likelihood of impairing autonomous vehicles in safely navigating through the one or more road segments; and

communicate information about the one or more road segments to one or more autonomous vehicles.

20. The computer system of claim 19, further comprising instructions to:

analyze the sensor information to determine data for guiding autonomous vehicles through the one or more road segments; and

communicate instructions based on the data to the one or more autonomous vehicles.