ROAD CHARACTERISTIC PREDICTION

• US 20150112548A1
• Filed: 10/17/2013
• Published: 04/23/2015
• Est. Priority Date: 10/17/2013
• Status: Active Grant

• Current Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Original Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Inventors: D'Amato, Anthony Mario, Szwabowski, Steven Joseph, Filev, Dimitar Petrov
• Granted Patent: US 9,145,143 B2
• Time in Patent Office: Days
• US Class Current: 701/36
• CPC Class Codes: B60W 2552/15 Road slope, i.e. the inclination of a road segment in the longitudinal direction
• CPC Class Codes: B60W 2552/30 Road curve radius
• CPC Class Codes: B60W 2552/35 Road bumpiness, e.g. potholes
• CPC Class Codes: B60W 2555/20 Ambient conditions, e.g. wind or rain
• CPC Class Codes: B60W 30/143 Speed control B60W30/16 takes precedence
• CPC Class Codes: B60W 40/06 Road conditions
• CPC Class Codes: B60W 40/072 Curvature of the road
• CPC Class Codes: B60W 40/076 Slope angle of the road

Abstract

A vehicle system includes at least one sensor and a processing device. The sensor is configured to output sensor signals representing infrastructure information collected in real time. The processing device is configured to predict a future road characteristic based on the infrastructure information, evaluate a prediction error, adjust a prediction horizon until the prediction error is within a predetermined accuracy range, and control at least one vehicle subsystem in accordance with the predicted future road characteristic. A method includes receiving infrastructure information collected in real time, predicting a future road characteristic based on the infrastructure information, evaluating a prediction error, adjusting a prediction horizon until the prediction error is within a predetermined accuracy range, and controlling at least one vehicle subsystem in accordance with the predicted future road characteristic.

Claims

1. A vehicle system comprising:

at least one sensor configured to output sensor signals representing infrastructure information collected in real time; and

a processing device configured to predict a future road characteristic based on the infrastructure information, evaluate a prediction error, adjust a prediction horizon until the prediction error is within a predetermined accuracy range, and control at least one vehicle subsystem in accordance with the predicted future road characteristic.

2. The vehicle system of claim 1, wherein adjusting the prediction horizon includes, iteratively, increasing the prediction horizon until the prediction error exceeds a maximum threshold value and reducing the prediction horizon until the prediction error is below a minimum threshold value.

3. The vehicle system of claim 2, wherein the predetermined accuracy range is defined by the maximum threshold value and the minimum threshold value.

4. The vehicle system of claim 1, wherein the prediction horizon includes predictions of road conditions at a number of locations.

5. The vehicle system of claim 4, wherein increasing the number of the locations increases the prediction horizon and reducing the number of the locations reduces the prediction horizon.

6. The vehicle system of claim 1, wherein the infrastructure information includes at least one of road roughness, road curvature, road grade, and road construction guidelines.

7. The vehicle system of claim 1, wherein the at least one sensor is configured to collect environmental information.

8. The vehicle system of claim 7, wherein the environmental information includes at least one of geological information, climate information, and weather information.

9. A method comprising:

receiving infrastructure information collected in real time;

predicting a future road characteristic based on the infrastructure information;

evaluating a prediction error;

adjusting, via a computing device, a prediction horizon until the prediction error is within a predetermined accuracy range; and

controlling at least one vehicle subsystem in accordance with the predicted future road characteristic.

10. The method of claim 9, wherein adjusting the prediction horizon includes, iteratively:

increasing the prediction horizon until the prediction error exceeds a maximum threshold value, and

reducing the prediction horizon until the prediction error is below a minimum threshold value.

11. The method of claim 10, wherein the predetermined accuracy range is defined by the maximum threshold value and the minimum threshold value.

12. The method of claim 9, wherein the prediction horizon includes predictions of road conditions at a number of locations.

13. The method of claim 12, wherein increasing the number of the locations increases the prediction horizon and reducing the number of the locations reduces the prediction horizon.

14. The method of claim 9, wherein the infrastructure information includes at least one of road roughness, road curvature, road grade, and road construction guidelines.

15. The method of claim 9, further comprising receiving environmental information.

16. The method of claim 15, wherein the environmental information includes at least one of geological information, climate information, and weather information.

17. A non-transitory computer-readable medium tangibly embodying computer-executable instructions that cause a processor to execute operations comprising:

receiving infrastructure information collected in real time;

predicting a future road characteristic based on the infrastructure information;

evaluating a prediction error;

adjusting a prediction horizon until the prediction error is within a predetermined accuracy range; and

controlling at least one vehicle subsystem in accordance with the predicted future road characteristic.

18. The non-transitory computer-readable medium of claim 17, wherein adjusting the prediction horizon includes, iteratively:

increasing the prediction horizon until the prediction error exceeds a maximum threshold value, and

reducing the prediction horizon until the prediction error is below a minimum threshold value.

19. The non-transitory computer-readable medium of claim 17, wherein the predetermined accuracy range is defined by the maximum threshold value and the minimum threshold value.

20. The non-transitory computer-readable medium of claim 17, wherein the prediction horizon includes predictions of road conditions at a number of locations, and wherein increasing the number of the locations increases the prediction horizon and reducing the number of the locations reduces the prediction horizon.