Network based storage of vehicle and infrastructure data for optimizing vehicle routing

• US 10,690,507 B2
• Filed: 07/09/2018
• Issued: 06/23/2020
• Est. Priority Date: 05/06/2016
• Status: Active Grant

• Current Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Original Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Inventors: Stephens, Brett, Mullen, Jonathan Thomas, Elwart, Shane
• Primary Examiner(s): Marc-Coleman, Marthe Y
• Application Number:
  US16/030,624
• Publication Number:
  US 20180313655A1
• Time in Patent Office: 715 Days
• Field of Search: 701 25
• CPC Class Codes: G01C 21/3453 Special cost functions, i.e. other than distance or default speed limit of road segments
• CPC Class Codes: G01C 21/3807 characterised by the type of data
• CPC Class Codes: G01C 21/3841 Data obtained from two or more sources, e.g. probe vehicles
• 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, and devices for network-based storage of vehicle and infrastructure data for vehicle optimization are disclosed. A method includes receiving sensor data from a vehicle sensor, uploading the sensor data to a network, and storing the sensor data in a vehicle map database. The method includes downloading aggregated drive data from the network and storing the aggregated drive data in the vehicle map database. The method includes optimizing a vehicle route based on route costs for a plurality of potential driving routes based on a reliability of an automated driving feature or driver assistance feature.

Claims

1. A method comprising:

downloading aggregated drive data comprising driving conditions for a route, wherein the driving conditions are captured by a plurality of other vehicles that have previously traversed at least a portion of the route and uploaded sensor data to a network;

storing the aggregated drive data in a vehicle map database of a vehicle; and

optimizing performance of a driver assistance feature of the vehicle when traversing the route based on the aggregated drive data and one or more of a drive history of the vehicle, an electronic map, or real-time sensor data.

2. The method of claim 1, further comprising selecting the route by:

calculating a reliability of the driver assistance feature for each route of a plurality of potential driving routes based on the aggregated drive data stored in the vehicle map database; and

determining the vehicle route having a lowest route cost based on having a highest reliability of the automated driving feature.

3. The method of claim 2, further comprising determining the route costs for the plurality of potential driving routes, wherein determining the route costs comprises categorizing a plurality of roadway sections as in a reliable category or an unreliable category for the automated driving feature.

4. The method of claim 1, wherein the driving conditions for the route comprises information on one or more of road bumpiness, presence of lane markings, lane marking quality, vehicle traffic levels, presence of pedestrians, road curvature, or an accident history.

5. The method of claim 1, further comprising assessing, by a vehicle controller local to the vehicle, the aggregated drive data to optimize performance of an autonomous driving feature of the vehicle.

6. The method of claim 1, further comprising calculating reliability of the driver assistance feature for at least a section of roadway in the route based on the aggregated drive data.

7. The method of claim 1, further comprising determining route costs for a plurality of potential driving routes based on the aggregated drive data, the drive history of the vehicle, and the electronic map.

8. The method of claim 1, further comprising receiving sensor data from a sensor of the vehicle and uploading the sensor data to the network to be included in the aggregated drive data.

9. The method of claim 1, further comprising generating merged data for the route by combining the aggregated drive data, the drive history of the vehicle, the electronic map, and the real-time sensor data, and wherein optimizing the performance of the driver assistance feature comprises optimizing based on the merged data.

10. The method of claim 1, wherein the driving conditions for the route are determined based on data captured by one or more vehicle sensors of the plurality of other vehicles.

11. Non-transitory computer readable storage media storing instructions that, when executed by one or more processors, cause the one or more processors to:

download aggregated drive data comprising driving conditions for a route, wherein the driving conditions are captured by a plurality of other vehicles that have previously traversed at least a portion of the route and uploaded sensor data to a network;

store the aggregated drive data in a vehicle map database of a vehicle; and

optimize performance of a driver assistance feature of the vehicle when traversing the route based on the aggregated drive data and one or more of a drive history of the vehicle, an electronic map, or real-time sensor data.

12. The non-transitory computer readable storage media of claim 11, wherein the instructions further cause the one or more processors to select the route by:

calculating a reliability of the driver assistance feature for each route of a plurality of potential driving routes based on the aggregated drive data stored in the vehicle map database; and

determining the vehicle route having a lowest route cost based on having a highest reliability of the driver assistance feature.

13. The non-transitory computer readable storage media of claim 12, wherein the instructions further cause the one or more processors to determine the route costs for the plurality of potential driving routes, wherein determining the route costs comprises categorizing a plurality of roadway sections as in a reliable category or an unreliable category for the driver assistance feature.

14. The non-transitory computer readable storage media of claim 11, wherein the driving conditions for the route comprises information on one or more of road bumpiness, presence of lane markings, lane marking quality, vehicle traffic levels, presence of pedestrians, road curvature, or an accident history.

15. The non-transitory computer readable storage media of claim 11, wherein the instructions further cause the one or more processors to assess the aggregated drive data to optimize performance of an autonomous driving feature of the vehicle.

16. The non-transitory computer readable storage media of claim 11, wherein the instructions further cause the one or more processors to calculate reliability of the driver assistance feature for at least a section of roadway in the route based on the aggregated drive data.

17. The non-transitory computer readable storage media of claim 11, wherein the instructions further cause the one or more processors to calculate route costs for a plurality of potential driving routes based on the aggregated drive data, the drive history of the vehicle, and the electronic map.

18. A system comprising:

a vehicle comprising one or more vehicle sensors; and

a vehicle controller comprising one or more processors configurable to execute instructions stored in non-transitory computer readable storage media, the instructions comprising;

downloading aggregated drive data comprising driving conditions for a route, wherein the driving conditions are captured by a plurality of other vehicles that have previously traversed at least a portion of the route and uploaded sensor data to a network;

storing the aggregating drive data in a vehicle map database of a vehicle that is local to the vehicle; and

optimizing performance of a driver assistance feature of the vehicle when traversing the route based on the aggregated drive data and one or more of a drive history of the vehicle, an electronic map, or real-time sensor data captured by the one or more vehicle sensors.

19. The system of claim 18, wherein the instructions further cause the one or more processors of the vehicle controller to select the route by:

calculating a reliability of driver assistance feature for each route of a plurality of potential driving routes based on the aggregated drive data stored in the vehicle map database; and

determining the vehicle route having a lowest route cost based on having a highest reliability of the driver assistance feature.

20. The system of claim 18, wherein the driving conditions for the route comprises information on one or more of road bumpiness, presence of lane markings, lane marking quality, vehicle traffic levels, presence of pedestrians, road curvature, or accident history.