Vision-aided system and method for guiding a vehicle

• US 20060149417A1
• Filed: 04/15/2005
• Published: 07/06/2006
• Est. Priority Date: 01/04/2005
• Status: Active Grant

• Current Assignee: Deere & Company
• Original Assignee: Deere & Company
• Inventors: Reid, John Franklin, Han, Shufeng, Rovira-Mas, Francisco
• Granted Patent: US 7,792,607 B2
• Time in Patent Office: Days
• US Class Current: 700/245
• CPC Class Codes: A01B 69/001 Steering by means of optical assistance, e.g. television cameras steering devices for road marking vehicles E01C23/163
• CPC Class Codes: A01B 69/008 automatic
• CPC Class Codes: G01C 21/20 Instruments for performing navigational calculations G01C21/24, G01C21/26 take precedence
• CPC Class Codes: G01S 17/42 Simultaneous measurement of distance and other co-ordinates indirect measurement G01S17/46
• CPC Class Codes: G01S 17/86 Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
• CPC Class Codes: G01S 17/87 Combinations of systems using electromagnetic waves other than radio waves
• CPC Class Codes: G01S 19/41 Differential correction, e.g. DGPS [differential GPS]
• CPC Class Codes: G01S 7/497 Means for monitoring or calibrating
• CPC Class Codes: G05D 1/0246 using a video camera in combination with image processing means
• CPC Class Codes: G05D 1/0278 using satellite positioning signals, e.g. GPS

Abstract

A method and system for guiding a vehicle comprises a location determining receiver for collecting location data for the vehicle. A vision module collects vision data for the vehicle. A location quality estimator estimates the location quality data for the location data during an evaluation time window. A vision module estimates vision quality data for the vision data during the evaluation time window. A supervisor module selects a mixing ratio for the vision data and location data (or error signals associated therewith) based on the quality data.

Claims

1. A method for guiding a vehicle, the method comprising:

collecting location data for the vehicle based on a location-determining receiver associated with the vehicle;

collecting vision data for the vehicle based on a vision module associated with the vehicle;

estimating location quality data for the location data during an evaluation time window;

estimating vision quality data for the vision data during the evaluation time window; and

selecting a mixing ratio based on the quality data for the following equation;

y=α

×

y_vision+(1−

α

)×

y_gps, where y is the aggregate error control signal, α

is the mixing ratio, y_vision is the vision error signal and y_gps is the location data error signal.

2. The method according to claim 1 further comprising:

determining whether a radius of curvature of path of the vehicle is lesser than a first reference radius of curvature or greater than a second reference radius of curvature; and

applying a first set of rules if the radius of curvature is less than a first reference radius of curvature.

3. The method according to claim 1 further comprising:

determining whether a radius of curvature of path of the vehicle is lesser than a first reference radius of curvature or greater than a second reference radius of curvature; and

and applying a second set of rules if the radius of curvature is greater than the second reference radius of curvature.

4. The method according to claim 1 wherein the estimating of the location quality data comprises determining whether the location quality is in a good state, a fair state, or in a poor state for a time interval.

5. The method according to claim 4 wherein estimating the vision quality data comprises determining whether the vision quality is in a good state, a fair state, or a poor state for a time interval.

6. The method according to claim 1 wherein the y, y_vision, y_gps, and α

are multidimensional vectors in accordance with the following expressions;

$y=\left[\begin{array}{c}{E}_{\mathrm{off}}\\ E_{\mathrm{head}}\\ \mathrm{\rho <br><br>}\end{array}\right],$

E_off is the aggregate off-track error from the aggregation of off-track error data from location module and the vision module, E_head is the aggregate heading error from the aggregation of the heading error data from the location module, the vision module and ρ

is the curvature error data;

$\mathrm{\alpha <br><br>}=\left[\begin{array}{c}{\mathrm{\alpha <br><br>}}_{\mathrm{off}}\\ {\mathrm{\alpha <br><br>}}_{\mathrm{head}}\\ {\mathrm{\alpha <br><br>}}_{\mathrm{curv}}\end{array}\right],$

where α

is the aggregate mixing ratio or mixing ratio matrix, α

_off is the mixing ratio for off-track error data, α

_head is the mixing ratio for heading error data, and α

_curv is the mixing ratio for curvature error data;

$y_{\mathrm{gps}}=\left[\begin{array}{c}{E}_{\mathrm{off\_gps}}\\ E_{\mathrm{head\_gps}}\\ {\mathrm{\rho <br><br>}}_{\mathrm{gsp}}\end{array}\right],$

where E<sub>off—

gps</sub> is the off-track error estimated by the location module 26, E<sub>head—

gps</sub> is the heading error estimated by the location module, and ρ

_gps is the curvature estimate error associated with the location module.

$y_{\mathrm{vision}}=\left[\begin{array}{c}{E}_{\mathrm{off\_vision}}\\ E_{\mathrm{head\_vision}}\\ 0\end{array}\right],$

where E<sub>off—

vision</sub> is the off track error estimated by the vision module and E<sub>head—

vision</sub> is the heading error estimated by the vision module.

7. A method for guiding a vehicle, the method comprising:

collecting location data for the vehicle based on a location-determining receiver associated with the vehicle;

collecting vision data for the vehicle based on a vision module associated with the vehicle;

estimating location quality data for the location data during an evaluation time window;

estimating vision quality data for the vision data during the evaluation time window; and

selecting a mixing ratio based on the quality data for the following equation;

y=α

×

y_vision+(1−

α

)×

y_gps, where y is the aggregate error control signal, α

is the mixing ratio, y_vision is the vision error signal and y_gps is the location data error signal, wherein the values of α

are based on a radius of curvature of a path of the vehicle being less than a reference radius of curvature.

8. The method according to claim 7 wherein if the location quality data is in a good state and if the vision quality is in a good state for the time interval, then the following values are applied to α

;

α

_off is large, ahead is medium, and α

_curve is approximately zero.

9. The method according to claim 7 wherein if the location quality data is in a good state and if the vision quality is in a fair state for the time interval, then the following values are applied to α

;

α

_off is medium, α

_head is medium, and α

_curve is approximately zero.

10. The method according to claim 7 wherein if the location quality data is in a good state and if the vision quality is in a poor state for the time interval, then the following values are applied to α

;

α

_off is small, α

_head is small, and α

_curve is approximately zero.

11. The method according to claim 7 wherein if the location quality data is in a fair state and if the vision quality is in a good state for the time interval, then the following values are applied to α

;

α

_off is large, α

_head is large, and α

_curve is approximately zero.

12. The method according to claim 7 wherein if the location quality data is in a fair state and if the vision quality is in a fair state for the time interval, then the following values are applied to α

;

α

_off is large, α

_head is medium, and α

_curve is approximately zero.

13. The method according to claim 7 wherein if the location quality data is in a fair state and if the vision quality is in a poor state for the time interval, then the following values are applied to α

;

α

_off is medium, α

_head is small, and α

_curve is approximately zero.

14. The method according to claim 7 wherein if the location quality data is in a poor state and if the vision quality is in a good state for the time interval, then the following values are applied to α

;

α

_off is approximately equal to 1, α

_head is large, and α

_curve is large.

15. The method according to claim 7 wherein if the location quality data is in a poor state and if the vision quality is in a fair state for the time interval, then the following values are applied to α

;

α

_off is large, α

_head is medium, and α

_curve is large.

16. The method according to claim 7 wherein if the location quality data is in a poor state and if the vision quality is in a poor state for the time interval, then the following values are applied to α

;

α

_off is medium, α

_head is medium, and α

_curve is large.

17. A method for guiding a vehicle, the method comprising:

collecting location data for the vehicle based on a location-determining receiver associated with the vehicle;

collecting vision data for the vehicle based on a vision module associated with the vehicle;

estimating location quality data for the location data during an evaluation time window;

estimating vision quality data for the vision data during the evaluation time window; and

selecting a mixing ratio based on the quality data for the following equation;

y=α

×

y_vision+(1−

α

)×

y_gps, where y is the aggregate error control signal, α

is the mixing ratio, y_vision is the vision error signal and y_gps is the location data error signal, wherein the values of α

are based on a radius of curvature of a path of the vehicle being greater than a reference radius of curvature.

18. The method according to claim 17 wherein if the location quality data is in a good state and if the vision quality is in a good state for the time interval, then the following values are applied to α

;

α

_off is medium, α

_head is medium, and α

_curve is approximately zero.

19. The method according to claim 17 wherein if the location quality data is in a good state and if the vision quality is in a fair state for the time interval, then the following values are applied to α

;

α

_off is small, α

_head is small, and α

_curve is approximately zero.

20. The method according to claim 17 wherein if the location quality data is in a good state and if the vision quality is in a poor state for the time interval, then the following values are applied to α

;

α

_off is approximately zero, α

_head is approximately zero, and α

_curve is approximately zero.

21. The method according to claim 17 wherein if the location quality data is in a fair state and if the vision quality is in a good state for the time interval, then the following values are applied to α

;

α

_off is large, α

_head is medium, and α

_curve is approximately zero.

22. The method according to claim 17 wherein if the location quality data is in a fair state and if the vision quality is in a fair state for the time interval, then the following values are applied to α

;

α

_off is medium, α

_head is small, and α

_curve is approximately zero.

23. The method according to claim 17 wherein if the location quality data is in a fair state and if the vision quality is in a poor state for the time interval, then the following values are applied to α

;

α

_off is small, α

_head is small, and α

_curve is approximately zero.

24. The method according to claim 17 wherein if the location quality data is in a poor state and if the vision quality is in a good state for the time interval, then the following values are applied to α

;

α

_off is large, α

_head is medium, and α

_curve is approximately zero.

25. The method according to claim 17 wherein if the location quality data is in a poor state and if the vision quality is in a fair state for the time interval, then the following values are applied to α

;

α

_off is medium, α

_head is medium, and α

_curve is approximately zero.

26. The method according to claim 17 wherein if the location quality data is in a poor state and if the vision quality is in a poor state for the time interval, then the following values are applied to α

;

α

_off is medium, α

_head is medium, and α

_curve is approximately zero.