System and method for managing submaps for controlling autonomous vehicles

• US 10,739,786 B2
• Filed: 06/30/2017
• Issued: 08/11/2020
• Est. Priority Date: 07/01/2016
• Status: Active Grant

• Current Assignee: Uatc, LLC (Uber Technologies, Inc.)
• Original Assignee: Uatc, LLC (Uber Technologies, Inc.)
• Inventors: Milstein, Adam, Browning, Brett
• Primary Examiner(s): Edwards, Jerrah
• Application Number:
  US15/640,296
• Publication Number:
  US 20180004226A1
• Time in Patent Office: 1,138 Days
• CPC Class Codes: B60W 30/095 Predicting travel path or likelihood of collision
• CPC Class Codes: G01C 21/28 with correlation of data from several navigational instruments
• CPC Class Codes: G01C 21/3602 Input other than that of destination using image analysis, e.g. detection of road signs, lanes, buildings, real preceding vehicles using a camera
• CPC Class Codes: G01C 21/3811 Point data, e.g. Point of Interest [POI]
• CPC Class Codes: G01C 21/3815 Road data
• CPC Class Codes: G01C 21/3819 Road shape data, e.g. outline of a route
• CPC Class Codes: G01C 21/3822 Road feature data, e.g. slope data
• CPC Class Codes: G01C 21/3841 Data obtained from two or more sources, e.g. probe vehicles
• CPC Class Codes: G01C 21/3848 Data obtained from both position sensors and additional sensors
• CPC Class Codes: G01C 21/3867 Geometry of map features, e.g. shape points, polygons or for simplified maps
• CPC Class Codes: G01C 21/3878 Hierarchical structures, e.g. layering
• 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 1/0212 with means for defining a desired trajectory involving a plurality of land vehicles G05D1/0287
• CPC Class Codes: G05D 1/0231 using optical position detecting means position-fixing by using electromagnetic waves other than radio waves, e.g. optical position detecting means G01S5/16
• CPC Class Codes: G05D 1/024 in combination with a laser lasers per se H01S
• CPC Class Codes: G05D 1/0246 using a video camera in combination with image processing means
• CPC Class Codes: G05D 1/0251 extracting 3D information from a plurality of images taken from different locations, e.g. stereo vision stereoscopic image analysis H04N13/00; depth recovery from images G06T7/593
• CPC Class Codes: G05D 1/0274 using mapping information stored in a memory device navigation using map-matching G01C21/30
• CPC Class Codes: G05D 1/0276 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: G06T 2207/10012 Stereo images
• CPC Class Codes: G06T 2207/30252 : Vehicle exterior; Vicinity of vehicle
• CPC Class Codes: G06T 7/33 : using feature-based methods
• CPC Class Codes: G06T 7/70 : Determining position or orientation of objects or cameras camera calibration G06T7/80
• CPC Class Codes: G06V 20/56 : exterior to a vehicle by using sensors mounted on the vehicle
• CPC Class Codes: G06V 20/588 : Recognition of the road, e.g. of lane markings; Recognition of the vehicle driving pattern in relation to the road
• 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
• CPC Class Codes: H04L 67/52 : specially adapted for the location of the user terminal

Abstract

A system to use submaps to control operation of a vehicle is disclosed. A storage system may be provided with a vehicle to store a collection of submaps that represent a geographic area where the vehicle may be driven. A programmatic interface may be provided to receive submaps and submap updates independently of other submaps.

Claims

1. A method for maintaining a map system comprising:

storing a collection of submaps, each submap of the collection presenting an area of a road network for an autonomous vehicle, the road network associated with a geographic region;

wherein each submap of the collection is provided (i) an identifier from the collection, (ii) multiple data layers, each data layer representing a feature set of the area of the road network of that submap, and (iii) a connector data set to link the submap with another submap that represents an adjacent area to the area of the road network of that submap,

wherein a compatibility between the submap and the other submap is determinable from the connector data set, the connector data set indicating a particular data layer of the multiple data layers of the submap that was updated within the submap; and

updating individual submaps independently of other submaps of the collection, the autonomous vehicle being provided with a series of compatible submaps for use when traversing the road network for the geographic area, wherein the updating comprises changing at least one of;

information conveyed in one or more data layers, a structure of one or more data layers, organizational data of one or more data layers, or a type and availability of one or more data layers.

2. The method of claim 1, wherein the method is performed on a network service, and wherein the method further comprises:

transmitting an updated submap to the autonomous vehicle that uses the submap in a trip to travel to a destination location.

3. The method of claim 1, further comprising:

obtaining sensor input from one or more vehicles that traverse the road network of the geographic region;

analyzing the sensor input to detect one or more conditions of the road network; and

identifying one or more submaps that are to include data that identifies the one or more conditions; and

wherein updating individual submaps includes updating the identified one or more submaps independently of other submaps of the collection.

4. The method of claim 3, wherein the sensor input includes at least one of image input or Lidar data from the one or more vehicles.

5. The method of claim 4, wherein analyzing the sensor input includes:

aggregating the sensor input over a duration of time for a road segment, and

determining a vehicle usage pattern for the road segment.

6. The method of claim 5, wherein the vehicle usage pattern is for a defined interval of time.

7. The method of claim 5, wherein determining the vehicle usage pattern for the road segment includes determining a duration of time during which vehicles traversing the road segment can be expected to change a shape of a lane or form an additional lane.

8. The method of claim 5, wherein determining the vehicle usage pattern for the road segment includes determining a duration of time during which traffic on the road segment can be expected to change as a result in a turn restriction at or near the road segment.

9. The method of claim 5, wherein determining the vehicle usage pattern for the road segment includes determining an availability of parking or stopping spaces alongside the road segment.

10. The method of claim 3, wherein analyzing the sensor input includes determining a transient or sudden event at a particular road segment, and then updating the submap of the road segment to identify an occurrence of the transient or sudden event.

11. The method of claim 10, further comprising:

transmitting the updated submap to a set of multiple vehicles that are near or are expected to traverse the corresponding road segment.

12. The method of claim 11, wherein transmitting the updated submap includes transmitting the updated submap based on a priority order in which one or more vehicles that are closest to, or which are more likely to traverse the corresponding road segment receive the updated submap before one or more other vehicles.

13. The method of claim 3, wherein updating individual submaps includes updating the submap corresponding to the road segment to identify a trajectory or lane of travel for the autonomous vehicle.

14. The method of claim 3, wherein updating individual submaps includes updating the submap corresponding to the road segment to include logic for enabling the autonomous vehicle to pre-select a trajectory or lane of travel as the autonomous vehicle approaches the road segment.

15. The method of claim 1, wherein the submap comprises an algorithm or model for processing data for one of the multiple data layers and wherein the algorithm or model are updated.

16. The method of claim 1, wherein updating individual submaps includes:

receiving an updated submap on the autonomous vehicle; and

storing the updated submap independently of other submaps stored on the autonomous vehicle.

17. The method of claim 16, further comprising:

obtaining sensor data on the autonomous vehicle for a road segment of the road network, and transmitting the sensor data to a submap network service for analyzing and updating the submap.

18. A method for distributing mapping information to vehicles for use in autonomous driving, the method comprising:

maintaining a series of submaps from a collection of submaps, each submap of the collection representing an area of a road network for an autonomous vehicle, the road network associated with a geographic region;

wherein each submap of the collection is provided (i) an identifier from the collection, (ii) multiple data layers, each data layer representing a feature set of the area of the road network of that submap, and (iii) a connector data set to link the submap with another submap that represents an adjacent area to the area of the road network of that submap,

wherein a compatibility between the submap and the other submap is determinable from the connector data set, the connector data set indicating a particular data layer of the multiple data layers of the submap that was updated within the submap; and

updating individual submaps independently of other submaps of the collection, the autonomous vehicle being provided with a series of compatible submaps for use when traversing the road network for the geographic area, wherein the updating comprises changing at least one of;

information conveyed in one or more data layers, a structure of one or more data layers, organizational data of one or more data layers, or a type and availability of one or more data layers.

19. The method of claim 18, further comprising distributing the series of submaps to each of multiple vehicles of the geographic region.

20. The method of claim 19, further comprising determining a version of a submap based on updates to individual data layers of the multiple data layers, and wherein maintaining the series of submaps includes maintaining multiple submaps for a given area of the geographic region.

21. The method of claim 19, wherein distributing the series of submaps is based on providing individual vehicles of the multiple vehicles with submaps that are consistent with individual submaps in use on the autonomous vehicle.

22. The method of claim 19, wherein updating individual submaps includes receiving data from individual vehicles of the multiple vehicles in the geographic region.

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

mapping information to vehicles for use in autonomous driving, including;

maintaining a series of submaps from a collection of submaps, each submap of the collection representing an area of a road network for an autonomous vehicle, the road network associated with a geographic region;

wherein each submap of the collection is provided (i) an identifier from the collection, (ii) multiple data layers, each data layer representing a feature set of the area of the road network of that submap, and (iii) a connector data set to link the submap with another submap that represents an adjacent area to the area of the road network of that submap,

wherein a compatibility between the submap and the other submap is determinable from the connector data set, the connector data set indicating a particular data layer of the multiple data layers of the submap that was updated within the submap; and

updating individual submaps independently of other submaps of the collection, the autonomous vehicle being provided with a series of compatible submaps for use when traversing the road network for the geographic area, wherein the updating comprises changing at least one of;

information conveyed in one or more data layers, a structure of one or more data layers, organizational data of one or more data layers, or a type and availability of one or more data layers.

24. A computer system comprising:

a memory that stores a set of instructions;

one or more processors that access the instructions stored in the memory to;

map information to vehicles for use in autonomous driving, by;

maintaining a series of submaps from a collection of submaps, each submap of the collection representing an area of a road network for an autonomous vehicle, the road network associated with a geographic region;

wherein each submap of the collection is provided (i) an identifier from the collection, (ii) multiple data layers, each data layer representing a feature set of the area of the road network of that submap, and (iii) a connector data set to link the submap with another submap that represents an adjacent area to the area of the road network of that submap,

wherein a compatibility between the submap and the other submap is determinable from the connector data set, the connector data set indicating a particular data layer of the multiple data layers component of the submap that was updated within the submap; and

updating individual submaps independently of other submaps of the collection, the autonomous vehicle being provided with a series of compatible submaps for use when traversing the road network for the geographic area, wherein the updating comprises changing at least one of;

information conveyed in one or more data layers, a structure of one or more data layers, organizational data of one or more data layers, or a type and availability of one or more data layers.