Method and device for predicting the course of motor vehicles

• US 8,126,640 B2
• Filed: 12/07/2002
• Issued: 02/28/2012
• Est. Priority Date: 04/27/2002
• Status: Active Grant

• Current Assignee: Robert Bosch GmbH
• Original Assignee: Robert Bosch GmbH
• Inventors: Winner, Hermann, Urban, Werner, Lueder, Jens, Maass, Alexander
• Primary Examiner(s): Tran, Khoi
• Assistant Examiner(s): Jen, Ian
• Application Number:
  US10/512,183
• Publication Number:
  US 20050228580A1
• Time in Patent Office: 3,370 Days
• Field of Search: 701/200, 701/300, 340/436, 340/903, 340/904, 342/70
• US Class Current: 701/200
• CPC Class Codes: B60W 2554/00 Input parameters relating to objects
• CPC Class Codes: B60W 2554/20 Static objects
• CPC Class Codes: B60W 2554/80 Spatial relation or speed relative to objects
• CPC Class Codes: G01S 13/931 of land vehicles
• CPC Class Codes: G01S 2013/932 using own vehicle data, e.g. ground speed, steering wheel direction
• CPC Class Codes: G01S 2013/9325 for inter-vehicle distance regulation, e.g. navigating in platoons
• CPC Class Codes: G01S 2013/9329 cooperating with reflectors or transponders
• CPC Class Codes: G06T 7/20 Analysis of motion motion estimation for coding, decoding, compressing or decompressing digital video signals H04N19/43, H04N19/51
• CPC Class Codes: G06V 20/58 Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads

Abstract

A method for travel course prediction in a motor vehicle having a position finding system for objects situated ahead of the vehicle is provided. In accordance with the method, a function describing the shape of the roadside is calculated on the basis of measured distance data and angle data for stationary roadside targets, wherein multiple stationary targets are identified and tracked. The path of the road is estimated for various subsets of the set of tracked stationary targets, under the assumption that these stationary targets are situated along the roadside, and roadside targets are differentiated from interfering objects on the basis of the plausibility of the resulting possible shapes of the roadside, the most probable shape of the roadside being determined on the basis of the roadside targets.

Claims

1. A method for travel course prediction in a motor vehicle, the motor vehicle having a position finding system for objects situated ahead of the motor vehicle traveling on a road, a function describing the shape of a roadside being computable on the basis of measured distance data and angle data for stationary targets, the method comprising:

identifying and tracking stationary targets with the position finding system;

estimating, with a processor, a possible shape of the roadside for each of a plurality of subsets of the stationary targets, using an assumption that the stationary targets of that particular subset are located on the roadside;

differentiating, with the processor, stationary targets into roadside targets and interfering objects on the basis of at least one plausibility criterion applied to the estimated possible shape of the roadside for a particular subset; and

determining, with the processor, the most probable shape of the roadside on the basis of the roadside targets.

2. The method as recited in claim 1, wherein measured distance data and angle data of preceding vehicles are used as the at least one plausibility criterion.

3. The method as recited in claim 1, wherein the function describing the shape of the roadside is a polynomial function.

4. The method as recited in claim 3, wherein the polynomial function is a polynomial function of the second degree.

5. The method as recited in claim 3, wherein the coordinates of at least one stationary target are used as data points for calculation of the coefficients of the polynomial function, and an estimate of the distance between the motor vehicle and the roadside is used for a constant term of the polynomial function.

6. The method as recited in claim 1, wherein the at least one plausibility criterion is that a curvature of the roadside is less than a first limiting value.

7. The method as recited in claim 6, wherein the first limiting value depends on the motor vehicle speed.

8. The method as recited in claim 1, wherein the at least one plausibility criterion is that the rate of change of the curvature of the roadside per unit of length is smaller than a second limiting value.

9. The method as recited in claim 8, wherein the second limiting value depends on the motor vehicle speed.

10. The method as recited in claim 1, further comprising:

differentiating stationary targets into stationary targets on the left side of the roadway and stationary targets on the right side of the roadway, based on at least one further plausibility criterion.

11. The method as recited in claim 10, wherein the at least one further plausibility criterion for two stationary targets being on the same side of the roadway is that a hypothetical road path having a predetermined road width between the two stationary targets has a maximum curvature greater than a third limiting value.

12. The method as recited in claim 11, wherein the third limiting value depends on the motor vehicle speed.

13. The method as recited in claim 10, wherein measured distance data and angle data of preceding vehicles are used as the at least one further plausibility criterion.

14. The method as recited in claim 1, wherein the at least one plausibility criterion for a stationary target being an interfering object is that the distance between the stationary target and a shape of the roadside which approximates at least one previously recognized roadside target is greater than a fourth limiting value.

15. The method as recited in claim 1, further comprising:

performing a statistical analysis of the shape of the roadside determined from the plurality of subsets of the stationary targets, wherein the at least one plausibility criterion is that at least one parameter of the function describing the shape of the roadside has a significant frequency maximum.

16. The method as recited in claim 15, wherein each of the subsets of the stationary targets includes at least two stationary targets.

17. The method as recited in claim 15, wherein the function describing the shape of the roadside is a polynomial function.

18. The method as recited in claim 17, wherein the polynomial function is a polynomial function of the second degree.

19. The method as recited in claim 18, wherein the at least one parameter is the coefficient of the quadratic term of the polynomial function.

20. The method as recited in claim 15, wherein the statistical analysis of the shape of the roadside is performed accounting for an error range of the measured distance data and angle data.

21. The method as recited in claim 1, wherein the stationary targets include objects that have left a detection range of the position finding system and whose positions are calculated on the basis of the motor vehicle movement.

23. The method as recited in claim 1, wherein the at least one plausibility criterion is that a curvature of the roadside is less than a first limiting value, and wherein the first limiting value depends on the motor vehicle speed.

24. The method as recited in claim 1, wherein the at least one plausibility criterion is that the rate of change of the curvature of the roadside per unit of length is smaller than a second limiting value, and wherein the second limiting value depends on the motor vehicle speed.

25. The method as recited in claim 1, wherein the at least one further plausibility criterion for two stationary targets being on the same side of the roadway is that a hypothetical road path having a predetermined road width between the two stationary targets has a maximum curvature greater than a third limiting value, and wherein the third limiting value depends on the motor vehicle speed.

22. A device for travel course prediction in a motor vehicle, the motor vehicle having a position finding system for objects situated ahead of the motor vehicle traveling on a road, a function describing the shape of a roadside being computable on the basis of measured distance data and angle data for stationary targets, the device comprising:

an identifying arrangement to identify and tracking stationary targets;

an estimating arrangement to estimate a possible shape of the roadside for each of a plurality of subsets of the stationary targets, using an assumption that the stationary targets of that particular subset are located on the roadside;

a differentiating arrangement to differentiate stationary target into roadside targets and interfering objects on the basis of at least one plausibility criterion applied to the estimated possible shape of the roadside; and

a determining arrangement to determine a most probable shape of the roadside on the basis of the roadside targets.