METHOD FOR OPERATING AN ASSISTANCE SYSTEM FOR DETERMINING A LENGTH OF AN OBJECT, COMPUTER PROGRAM PRODUCT, COMPUTER-READABLE STORAGE MEDIUM AND ASSISTANCE SYSTEM

Abstract

The invention relates to a method for operating an assistance system (2), in which an object (9) is detected and the object (9) is classified for further evaluation by means of an electronic computing device (5), wherein the classification is taken as a basis for predefining a first length (L) of the object (9), and wherein additionally the object (9) is captured by means of a camera (4), evaluated and classified and a second length (L) of the object (9) is determined and the classification and the second length (L) are transmitted to the electronic computing device (5), wherein the predefined first length (L) is adapted on the basis of the second length (L) to produce a current length (L) and a limited Kalman filter (7) is used to update the current length (L), the limitation of the Kalman filter (7) being predefined by the classification determined by means of the camera (4). The invention also relates to a computer program product, a computer-readable storage medium and an assistance system (2).

Claims

1. A method for operating an assistance system of a motor vehicle, the method comprising: detecting an object in the surroundings of the motor vehicle by a detection device of the assistance system; classifying the object by an electronic computing device of the assistance system for further evaluation by the electronic computing device, wherein a first length of the object is specified for the further evaluation by the electronic computing device as a function of the classification, and wherein the object is detected and evaluated by a camera of the assistance system and is classified by the camera; determining a second length of the object, wherein the classification and the second length are transferred to the electronic computing device for further evaluation, wherein the specified first length is adapted as a function of the second length determined by the camera to form a current length by the electronic computing device; and updating the current length by a constrained Kalman filter of the electronic computing device, wherein the constraint of the Kalman filter is specified by the classification determined by the camera.

2. The method as claimed in claim 1, wherein the object is classified in the camera by a Bayesian filter of the camera.

3. The method as claimed in claim 1, wherein a passenger vehicle and a truck and a pedestrian and a bicycle and a motorcycle are specified for classification as object classes of the camera and/or the electronic computing device.

4. The method as claimed in claim 2, wherein each of the object classes of the camera is assigned an equal probability in the Bayesian filter at the beginning of a classification.

5. The method as claimed in claim 4, wherein the probabilities in the Bayesian filter result in a value of 1 when added up.

6. The method as claimed in claim 4, wherein an object class is determined by means of a further electronic computing device of the camera and this is transferred to the Bayesian filter and a respective probability of an object class in the Bayesian filter is increased after a respective object class determination by the further electronic computing device of the camera.

7. The method as claimed in claim 6, wherein if a probability threshold value for one of the object classes is reached by the Bayesian filter, the classification of the object is carried out by the camera and this is transferred to the electronic computing device.

8. The method as claimed in claim 7, wherein at a value of 0.6 as the probability threshold value, a classification of the object is carried out by the Bayesian filter.

9. The method as claimed in claim 1, wherein the constraint of the Kalman filter is specified as a linear constraint.

10. The method as claimed in claim 1, wherein if the second length of the object determined of the camera is greater than the specified first length, the current length is adapted by the constrained Kalman filter to the second length determined by the camera.

11. The method as claimed in claim 1, wherein if the second length of the object determined by the camera is less than the specified first length, the current length is adapted by the constrained Kalman filter to the specified first length.

12. The method as claimed in claim 1, wherein the object is detected in the surroundings by an ultrasonic sensor device and/or by means of a radar sensor device and/or by means of a lidar sensor device as the detection device.

13. A computer program product having program code stored in a computer-readable medium in order to carry out the method as claimed in claim 1 when the computer program product is run on a processor of an electronic computing device.

14. A computer-readable storage medium having a computer program product as claimed in claim 13.

15. An assistance system for a motor vehicle comprising: at least one detection device; a camera; and an electronic computing device, which has at least one constrained Kalman filter, wherein the assistance system is configured to carry out a method as claimed in claim 1.

Description

[0034] In the figures:

[0035] FIG. 1 shows a schematic top view of a motor vehicle having one embodiment of an assistance system; and

[0036] FIG. 2 shows a schematic flow chart according to one embodiment of the method.

[0037] In the figures, identical or functionally identical elements are provided with the same reference numerals.

[0038] FIG. 1 shows a schematic top view of a motor vehicle 1 having one embodiment of an assistance system 2. The assistance system 2 has at least one detection device 3 and a camera 4. Furthermore, the assistance system 2 has an electronic computing device 5. The camera 4 furthermore in particular has a further electronic computing device 6. The electronic computing device 5 furthermore in particular has a constrained Kalman filter 7. The detection device 3 can be designed in particular as an ultrasonic sensor device and/or as a radar sensor device and/or as a lidar sensor device.

[0039] Furthermore, FIG. 1 shows that an object 9 can be detected in surroundings 8 of the motor vehicle 1. The object 9 can be, for example, a passenger vehicle, a truck, a pedestrian, a bicycle, or a motorcycle. In the present case, the object 9 is shown in particular as a truck.

[0040] FIG. 2 shows a schematic view of a flow chart of the method. In the method for operating the assistance system 2 of the motor vehicle 1, the object 9 in the surroundings 8 of the motor vehicle 1 is detected by means of the detection device 3 of the assistance system 2 and the object 9 is classified by means of the electronic computing device 5 of the assistance system 2 for further evaluation by means of the electronic computing device 5, wherein a first length L of the object 9 is specified for the further evaluation by means of the electronic computing device 5 as a function of the classification, and wherein in addition the object 9 is detected and evaluated by means of the camera 4 of the assistance system 2 and is classified by means of the camera 4 and a second length L of the object 9 is determined and the classification and the second length L are transferred to the electronic computing device 5 for further evaluation.

[0041] It is provided that the specified first length L is adapted as a function of the second length L determined by means of the camera 4 to form a current length L by means of the electronic computing device 5 and the length L is updated by means of the constrained Kalman filter 7 of the electronic computing device 5, wherein the constraint of the Kalman filter 7 is specified by the classification determined by means of the camera 4.

[0042] In particular, it can be provided that the object 9 is classified in the camera 4 by means of a Bayesian filter 10 of the camera 4. A passenger vehicle and a truck and a pedestrian and a bicycle and a motorcycle can be specified for classification as object classes of the camera 4 and/or the electronic computing device 5, for example.

[0043] In particular, in a first step S1 of the method, an equal probability in the Bayesian filter 10 is assigned to each of the object classes of the camera 4 at the beginning of a classification. The probabilities in the Bayesian filter 10 result in particular in the value of 1 when added up.

[0044] In a second step S2 of the method, it is provided in particular that an object class is determined by means of the further electronic computing device 6 of the camera 4 and this is transferred to the Bayesian filter 10 and a respective probability of an object class in the Bayesian filter 10 is increased after a respective object class determination by the further electronic computing device 6 of the camera 4. In other words, it can be provided in particular that when the classification has been carried out by the camera 4, this is transferred to the Bayesian filter 10, wherein the probabilities are defined, for example, in such a way that a probability indicates that the camera 4 specifies, for example, that the object 9 is a motor vehicle, wherein the object 9 is also a motor vehicle. A true positive rate for the motor vehicle or the passenger vehicle can thus be specified. Furthermore, the Bayesian filter also requires the probabilities for the case that the camera 4 reflects that it is not a passenger vehicle, although it is a passenger vehicle. In sum, all probabilities are 1. In particular, the classification of the object 9 is carried out by means of the camera 4 and this is transferred to the electronic computing device 5 first when a probability threshold value for one of the object classes is reached by the Bayesian filter 10, wherein the probability threshold value can be 0.6, for example.

[0045] In a third step S3, the length L is then determined by means of the constrained Kalman filter 7, wherein the constraint is in particular a linear constraint For example, a class having the highest probability can be selected by the Bayesian filter 10. In dependence on this class, for example, a minimum length, for example 2.5 m for passenger vehicles or 5 m for trucks, or a maximum length, for example 5 m for passenger vehicles, can be specified, wherein this specification is then specified in turn as a constraint to the Kalman filter 7 for determining the length L, so that the determined length L is in these minimum and maximum value ranges. Furthermore, in the third step S3, it can be provided in particular that when the length L of the object 9 determined by means of the camera 4 is greater than the specified first length L, the current length L is then adapted by means of the constrained Kalman filter 7 to the second length L determined by means of the camera 4. Alternatively, if the length L of the object 9 determined by means of the camera 4 is less than the specified first length L, the current length L is then adapted by means of the constrained Kalman filter 7 to the specified first length L.

[0046] In particular, the constraint of the Kalman filtering can be carried out, for example, using a Kalman filter estimation after the first detection update x.sub.n and a further estimation x, wherein this then meets the linear constraint

D*x=d

[0047] In particular, the filtering by means of the constrained Kalman filter can be carried out by means of the following formula under the condition D*x=d.

λ=(DP.sub.nD.sup.T).sup.−1D(−{tilde over (x)})=(DP.sub.nD.sup.T).sup.−1(D−d)

[0048] The constraint results as an estimation in this way:

P.sub.n.sup.−1({tilde over (x)}−)+D.sup.Tλ=0⇔{tilde over (x)}−+P.sub.nD.sup.Tλ=0⇔{tilde over (x)}=−P.sub.nD.sup.Tλ=−P.sub.nD.sup.T(DP.sub.nD.sup.T).sup.−1(D−d)

, wherein λ corresponds to the Lagrange multiplier and is typically used to find the solution of a least square problem with a secondary condition. {tilde over (x)} describes the new estimation in consideration of the constraint. corresponds to the expectation of the estimation without consideration of the constraint, thus the result of the Kalman filter. P.sub.n is the covariance matrix of the estimation without consideration of the constraint, thus the result of the Kalman filter. D is the matrix, which specifies the linear constraint on the state, for example if only a specific value of the state is to be bound to a fixed value, D=[0,0,0,1], if this is the fourth value of the state. D can also be used to specify a constraint on a linear combination of the state parameters, for example D=[1,0,0,0,0.5] will specify a restriction on the first component of the state plus half the last component. Multiple linear constraints can be specified simultaneously, D then has multiple lines, for example D=[0,0,0,1; 1,0,0,0,0] specifies two constraints, one on the first value, one on the last value of the state. d is the value of the desired constraint(s). The number of the lines of d is equal to that of D.

[0049] Overall, the figure shows a determination of the length by means of a camera 4 based on a filtered class.