METHOD FOR OPERATING RADAR SENSORS

Abstract

A method for operating radar sensors in a vehicle. At the outset, the acquired targets are divided into stationary targets and moving targets. The moving targets are then divided into primary targets, whereof the distances from the vehicle are less than a pre-definable threshold value, and secondary targets, whereof the distances from the vehicle are greater than a threshold value. The primary targets are fed to a first tracking device, which ascertains the states of the primary targets. The secondary targets are fed to a second tracking device, which ascertains the states of the secondary targets. The second tracking device carries out a computationally less powerful ascertainment of the states than the first tracking device.

Claims

1. A method for operating radar sensors in a vehicle, comprising the following steps: dividing acquired targets into stationary targets and moving targets; dividing the moving targets into primary targets having distances from the vehicle that are less than a pre-definable threshold value, and secondary targets having distances from the vehicle that are greater than the threshold value; feeding the primary targets to a first tracking device, which ascertains states of the primary targets; and feeding the secondary targets to a second tracking device, which ascertains the states of the secondary targets; wherein the second tracking device carries out a less computing power-intensive ascertainment of the states than the first tracking device.

2. The method as recited in claim 1, further comprising: ascertaining boundaries of a road from the stationary targets; identifying those of the stationary targets which are located within the boundaries of the road as obstacle targets for the vehicle; and feeding the obstacle targets to the first tracking device, which ascertains the states of the obstacle targets.

3. The method as recited in claim 2, wherein those of the moving targets located outside the boundaries of the road are not fed to the first and second tracking devices.

4. The method as recited in claim 2, wherein the boundaries of the road are ascertained for each side in relation to the vehicle and the steps of the method which are based on the boundaries of the road are carried out separately for each side.

5. The method as recited in claim 1, wherein the first tracking device implements an extended object tracking algorithm and/or a random finite set algorithm.

6. The method as recited in claim 1, wherein the second tracking device implements Kalman filtering.

7. The method as recited in claim 1, wherein the threshold value is defined as a function of an intrinsic speed of the vehicle and braking time.

8. A non-transitory machine-readable storage medium on which is stored a computer program for operating radar sensors in a vehicle, the computer program, when executed by a computer, causing the computer to perform the following steps: dividing acquired targets into stationary targets and moving targets; dividing the moving targets into primary targets having distances from the vehicle that are less than a pre-definable threshold value, and secondary targets having distances from the vehicle that are greater than the threshold value; feeding the primary targets to a first tracking device, which ascertains states of the primary targets; and feeding the secondary targets to a second tracking device, which ascertains the states of the secondary targets; wherein the second tracking device carries out a less computing power-intensive ascertainment of the states than the first tracking device.

9. An electronic control unit configured to operating radar sensors in a vehicle, the electronic control unit configured to: divide acquired targets into stationary targets and moving targets; divide the moving targets into primary targets having distances from the vehicle that are less than a pre-definable threshold value, and secondary targets having distances from the vehicle that are greater than the threshold value; feed the primary targets to a first tracking device, which ascertains states of the primary targets; and feed the secondary targets to a second tracking device, which ascertains the states of the secondary targets; wherein the second tracking device carries out a less computing power-intensive ascertainment of the states than the first tracking device.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Exemplary embodiments of the present invention are depicted in the drawings and explained in more detail in the description below.

[0019] FIG. 1 shows a schematic view of a vehicle with a radar sensor in a coordinate system.

[0020] FIG. 2 shows a flow chart of an exemplary embodiment of the method according to the present invention.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

[0021] FIG. 1 shows a vehicle F with a radar sensor S. The vehicle F has, in addition, an electronic control unit ECU, which is connected to the radar sensor S. A two-dimensional Cartesian coordinate system with an X axis and a Y axis is moreover depicted. The vehicle F is aligned along the X axis and moves along this axis on a road R. The exemplary radar sensor S is aligned in the direction of travel and therefore likewise in the direction of the X axis. Further radar sensors may also be provided, or the radar sensor may be in a different position and/or have a different orientation. In addition, as an example, four targets T.sub.1-T.sub.4 are depicted, of which three targets T.sub.1 to T.sub.3 are moving and one target T.sub.4 is stationary. The target T.sub.1 is located in the vicinity of the vehicle F on the road R and should be rated as relevant, and the target T.sub.2 is likewise located on the road R, but far away from the vehicle F, and should be rated as less relevant. Since the target T.sub.4 is a stationary target which is located on the road R, it should generally be rated as relevant. Furthermore, variables which are used for the exemplary embodiment, described below, of the method according to the invention are depicted in FIG. 1.

[0022] FIG. 2 shows a flow chart of an exemplary embodiment of the method according to the invention. The method is carried out by the electronic control unit ECU of the vehicle. At the outset, the targets T are received by the sensor S. The targets T are divided 1 into stationary targets T.sub.S and moving targets T.sub.B. For this purpose, the speed of each target is compared to the intrinsic speed of the vehicle and the difference is calculated. The intrinsic speed of the vehicle is obtained from the vehicle odometry FO. If the difference between the speeds is greater than a speed threshold value, then the target T is a moving target T.sub.B. If the difference between the speeds is less than, or equal to, the speed threshold value, then the target T is a stationary target T.sub.S.

[0023] The boundaries y.sub.R of the road R on which the vehicle F moves are then ascertained 2 from the stationary targets T.sub.S. In this case, the boundaries y.sub.R of the road R depend on the distance in the X direction, i.e. y.sub.R(x) applies. This is particularly relevant for curves. In the exemplary embodiment described here, the road boundaries y.sub.R(x) are calculated separately for each side of the road R, i.e. the left road boundary y.sub.l(x) and the right road boundary y.sub.r(x) are ascertained 2. For this purpose, the stationary targets T.sub.S are divided into right and left according to their position in relation to the vehicle. Stationary targets T.sub.S on the left have a Y value of greater than 0 in the coordinate system of FIG. 1, and stationary targets T.sub.S on the right have a Y value of less than or equal to 0. For each side (right and left), disturbance data C (also known as clutter) are removed for the stationary targets T.sub.S. Disturbance data represent first-order extreme values and/or satisfy the condition |y−y.sub.m|>k.sub.1 σ.sub.y, y.sub.m being the mean of the Y values on the respective side, σ.sub.y the standard deviation of the Y values on the respective side and k.sub.1 a proportionality factor, e.g. 2.5. In FIG. 1, clutter C, which is outside the standard deviation σ.sub.y, is shown by way of example for the left side. Finally, the road boundary y.sub.l(x), y.sub.r(x) is ascertained for each side. The boundary y.sub.R may be ascertained by curve fitting. By way of example, a second-order polynomial may be used for the curve fitting. This is particularly suitable in the case of relatively straight roads, such as, e.g., an expressway (freeway), and requires relatively little computational expenditure. In the case of roads which have many curves, as is normal, e.g., in cities, a base spline (B spline) is used in the curve fitting.

[0024] When ascertaining 2 the boundaries y.sub.R of the road R, it may be provided that, if the method is repeated in closed loops, previously ascertained road boundaries y.sub.R are used. The road boundaries y.sub.l and y.sub.r may also be ascertained separately for each side, left and right, here. Stationary targets T.sub.S close to the road boundaries y.sub.R are then selected and the curve fitting is implemented at these points. If a new curve differs greatly from the previously used curve (i.e. their coefficients differ), the new curve is rejected and the previously ascertained curve is used instead.

[0025] In other exemplary embodiments, when ascertaining 2 the boundaries y.sub.R of the road R, a clustering algorithm may be implemented. In this case, the stationary targets T.sub.S are not divided into right and left. Curve fitting is implemented for each cluster here and a curve is ultimately applied in each case as a road boundary y.sub.R for each side (right and left).

[0026] The position of the stationary targets T.sub.S* which are extricated from the disturbance data is now investigated with regard to the road boundaries y.sub.R. If the stationary target T.sub.S* is located between the road boundaries y.sub.R, i.e. |y|<|y.sub.R| applies, and if it is far enough away from each of the boundaries y.sub.R of the road R, i.e. |y−y.sub.R|>k.sub.2 RMSE applies, RMSE being the root of the mean squared error and k.sub.2 being a further proportionality factor, e.g. 1.8, then the stationary target T.sub.S* is identified 3 as an obstacle target HT on the road R. As described above, the boundaries y.sub.R of the road R depend on the distance in the X direction on the one hand and are split into the sides left and right on the other. Therefore, to identify 3 an obstacle target HT, the following conditions apply for the left side: y<y.sub.l(x) and |y−y.sub.l(x)|>k.sub.2 RMSE.sub.l, RMSE.sub.l being the root of the mean squared error of the Y values on the left side, and the following conditions apply for the right side: y>y.sub.r(x) and |y−y.sub.r(x)|>k.sub.2 RMSE.sub.r, RMSE.sub.r being the root of the mean squared error of the Y values on the right side. In FIG. 1, the target T.sub.4 has the coordinates (x.sub.4, y.sub.4). The following apply: y.sub.4<y.sub.l(x.sub.4) and y.sub.4>y.sub.r (x.sub.4) and therefore |y.sub.1|<|y.sub.R(x.sub.4)|, and |y.sub.4−y.sub.l(x.sub.4)|>k.sub.2 RMSE.sub.l and |y.sub.4−y.sub.r(x.sub.4)|>k.sub.2 RMSE.sub.r and therefore |y.sub.4−y.sub.R(x.sub.4)|>k.sub.2 RMSE. The target T.sub.4 therefore satisfies the above-mentioned conditions and is identified 3 as obstacle target HT.

[0027] The ascertained boundaries y.sub.R of the road R are, in addition, used to remove disturbance data from the moving targets T.sub.B. If the moving target T.sub.B is located outside the road boundaries y.sub.R, i.e. |y|>|y.sub.R| applies, and if it is far enough away from each of the boundaries y.sub.R of the road R, i.e. |y−y.sub.R|>k.sub.3 RMSE applies, RMSE being the root of the mean squared error and k.sub.3 being a further proportionality factor, e.g. 1, then the moving target T.sub.B is excluded 4. In this case, the boundaries y.sub.R of the road R also depend on the distance in the X direction on the one hand and are split into the sides left and right on the other. Therefore, to exclude 4 the moving targets T.sub.B, the following conditions apply for the left side: y>y.sub.l(x) and |y−y.sub.l(x)|>k.sub.3 RMSE.sub.l, RMSE.sub.l being the root of the mean squared error of the Y values on the left side, and the following conditions apply for the right side: y<y.sub.r(x) and |y−y.sub.r(x)|>k.sub.3 RMSE.sub.r, RMSE.sub.r being the root of the mean squared error of the Y values on the right side. In FIG. 1, the moving target T.sub.3 has the coordinates (x.sub.3, y.sub.3) at the time of measurement. The following apply: y.sub.3>y.sub.l(x.sub.3) and therefore |y.sub.3|>|y.sub.R(x.sub.3)|, and |y.sub.3−y.sub.l(x.sub.3)|>k.sub.3 RMSE.sub.l. The target T.sub.4 therefore satisfies the above-mentioned conditions and is excluded 4.

[0028] The moving targets T.sub.B* which are extricated from the disturbance data are divided 5 into primary targets PT and secondary targets ST based on their distance from the vehicle F. For the division, a threshold value X.sub.S is defined for the distance between the vehicle F and the moving target T.sub.B*. Since the distance is ascertained by the radar sensor S of the vehicle F, the distance between the vehicle F and the moving target T.sub.B* may also be interpreted as the distance between the radar sensor S of the vehicle and the moving target T.sub.B*. The threshold value X.sub.S is calculated as the product of the intrinsic speed of the vehicle and the braking time. The intrinsic speed of the vehicle is in turn obtained from the vehicle odometry FO. The braking time may be ascertained using conventional methods and it may be present in the electronic control unit ECU of the vehicle F. If a moving target T.sub.B* is located at a distance from the vehicle F which is less than the threshold value X.sub.S, then this moving target T.sub.B* is classified as a primary target PT. In FIG. 1, this is the case for the target T.sub.1. If a moving target T.sub.B* is located at a distance from the vehicle F which is greater than the threshold value X.sub.S, then this moving target T.sub.B* is classified as a secondary target ST. In FIG. 1, this is the case for the target T.sub.2.

[0029] In further exemplary embodiments, the moving targets T.sub.B* are combined into clusters during the division 5. The following applies analogously to the division 5 for individual targets:

[0030] The moving targets T.sub.B*, which form a single cluster, are primary targets PT if each moving target T.sub.B* of the cluster is located at a distance from the vehicle F which is less than the threshold value X.sub.S. On the other hand, the moving targets T.sub.B*, which form a single cluster, are secondary targets ST if each moving target T.sub.B* of the cluster is located at a distance from the vehicle F which is greater than the threshold value X.sub.S.

[0031] The primary targets PT and the obstacle targets HT are fed to a first tracking device (tracker) 6. The first tracking device 6 implements an extended object tracking algorithm (ETO) and/or a random finite set algorithm (RFS), and thus ascertains the states of the primary targets PT and the obstacle targets HT with high accuracy. The secondary targets ST are fed to a second tracking device 7. The second tracking device 7 implements a simple Kalman filtering and thus ascertains the states of the secondary targets ST with less accuracy, but also with less computing expenditure. Finally, the results from the tracking devices 6 and 7 are merged 8 to acquire objects.