VEHICLE TRACTION MAP FOR AUTONOMOUS VEHICLES

• US 20170167881A1
• Filed: 12/12/2016
• Published: 06/15/2017
• Est. Priority Date: 12/10/2015
• Status: Active Grant

• Current Assignee: Uatc, LLC (Uber Technologies, Inc.)
• Original Assignee: Uber Technologies, Inc.
• Inventors: Rander, Peter, Wood, Matthew, Bradley, David McAllister
• Granted Patent: US 10,712,160 B2
• Time in Patent Office: Days
• CPC Class Codes: B60W 2420/403 Image sensing, e.g. optical camera
• CPC Class Codes: B60W 2420/408 Radar; Laser, e.g. lidar
• CPC Class Codes: B60W 2552/15 Road slope, i.e. the inclination of a road segment in the longitudinal direction
• CPC Class Codes: B60W 2552/40 Coefficient of friction
• CPC Class Codes: B60W 2555/20 Ambient conditions, e.g. wind or rain
• CPC Class Codes: B60W 30/18172 Preventing, or responsive to skidding of wheels
• CPC Class Codes: B60W 40/068 Road friction coefficient
• CPC Class Codes: B60W 60/0011 involving control alternatives for a single driving scenario, e.g. planning several paths to avoid obstacles
• CPC Class Codes: B60W 60/0016 of the vehicle or its occupants
• CPC Class Codes: G01C 21/3822 Road feature data, e.g. slope data
• CPC Class Codes: G01C 21/3837 Data obtained from a single source
• CPC Class Codes: G01C 21/3848 Data obtained from both position sensors and additional sensors
• CPC Class Codes: G01C 7/04 involving a vehicle which moves along the profile to be traced
• CPC Class Codes: G01N 21/4738 Diffuse reflection precedence is given to G01N21/55 - G01N21/57 if specular component is taken into consideration, e.g. also for testing fluids, fibrous materials
• CPC Class Codes: G05D 1/0214 in accordance with safety or protection criteria, e.g. avoiding hazardous areas monitoring the location of vehicles within a certain area, e.g. forbidden or allowed areas, in traffic control systems for road vehicles G08G1/13
• CPC Class Codes: G05D 1/0274 using mapping information stored in a memory device navigation using map-matching G01C21/30

Abstract

A vehicle determines a traction value for a surface of a road segment, and associates the traction value with a location of the surface. The vehicle stores the traction value and location as part of a traction map.

Claims

1. A method of operating a vehicle, the method comprising:

determining a traction value for a surface of a road segment, the traction value being correlative to a coefficient of friction;

associating the traction value with a location of the surface; and

storing the traction value with the location of the surface.

2. The method of claim 1, wherein determining the traction value includes classifying a traction condition of the surface of the road segment.

3. The method of claim 2, wherein the traction condition corresponds to an accumulated precipitation condition.

4. The method of claim 2, wherein the traction condition corresponds to accumulated snow, rain or ice.

5. The method of claim 1, wherein determining the traction value includes remotely determining the traction value.

6. The method of claim 5, wherein remotely determining the traction value includes using image data from a light-sensitive device.

7. The method of claim 6, wherein using image data includes using a variation in a reflectance of a directed light.

8. The method of claim 6, wherein using image data includes using an image of the surface of the road segment to determine a type of accumulated precipitation.

9. The method of claim 1, wherein determining the traction value includes measuring a sensor characteristic that is correlative to a coefficient of friction, as the vehicle travels over the location of the surface.

10. The method of claim 1, further comprising transmitting the traction value and the location of the surface to a network service.

11. The method of claim 1, wherein determining the traction value for the surface of the road segment includes receiving, on a network computer, a set of sensor data transmitted from at least the vehicle, the set of sensor data being correlative to the traction value.

12. The method of claim 11, wherein associating the traction value to the location includes receiving the set of sensor data along with location data to identify the location where the set of sensor data was determined.

13. The method of claim 1, wherein determining the traction value for the surface of the road segment includes receiving, on a network computer, a set of sensor data transmitted from each of multiple vehicles, wherein each set of sensor data is (i) correlative to a corresponding traction value, and (ii) associated with a location of the geographic region where the set of sensor data was generated.

14. The method of claim 1, further comprising:

aggregating the determined traction values; and

storing the aggregated traction values in a geographic data structure that pairs a location of the geographic region with at least one traction value determined at least in part from the sensor data transmitted from one or more of the multiple vehicles.

15. The method of claim 14, further comprising determining the traction value of one or more locations of the geographic region by spatially extrapolating a traction value of a first location using the traction value determined from a sensor set of one of the multiple vehicles traveling over a second location that is deemed to be sufficiently near to the first location.

16. The method of claim 15, wherein determining the traction value of one or more locations of the geographic region includes applying a timing function to the traction value at one or more subsequent times based on an environmental condition that is deemed present.

17. The method of claim 1, further comprising:

communicating with multiple vehicles that operate over a geographic region to receive a set of sensor data and a set of location data, the set of sensor data being generated from one or more sensor components of each of the multiple vehicles at a corresponding location identified by the location data;

determining traction values from the set of sensor data received from each of the multiple vehicles; and

associating the traction value determined from each set of sensor data with the corresponding location where the sensor data was generated.

18. The method of claim 1, further comprising:

on the vehicle, receiving a set of traction values from a network computer, each traction value being associated with a location of a road surface within a geographic region;

when traveling over a particular location of the road surface, obtaining a sensor value from one or more sensors of the vehicle;

determining a traction value for the particular location based on the obtained sensor value; and

comparing the determined traction value to the traction value in the set of traction values that is associated with the location of the road surface.

19. An autonomous vehicle comprising:

memory to store instructions;

one or more processors to execute the instructions to;

determine a traction value for a surface of a road segment, the traction value being correlative to a coefficient of friction;

associate the traction value with a location of the surface; and

store the traction value with the location of the surface.

20. A non-transitory computer readable medium that stores instructions, which when executed by one or more processors of a vehicle, cause the vehicle to perform operations that include:

determine a traction value for a surface of a road segment, the traction value being correlative to a coefficient of friction;

associate the traction value with a location of the surface; and

store the traction value with the location of the surface.