JP2000180537A - Method for identifying target to be controlled of scan- type radar - Google Patents

Abstract

PROBLEM TO BE SOLVED: To add other elements to detection distance and angle and reliably identify such object as a preceding vehicle to be controlled by obtaining a presence probability value and an own vehicle line probability value considering detection distance, position deviation, the number of detection beams, relative speed, and the state of the object and judging whether the detected object is a preceding vehicle to be controlled or not. SOLUTION: A reflection signal from a radar antenna 1 is subjected to FFT processing by a radar signal processing part 12, thus detecting a power spectrum. Then, distance to an object, relative speed, the number and angles of beams for detecting the object, and position deviation are calculated. A control target recognition part 13 receives the data, instructs a scanning angle to a scanning angle control part 11 based on the received distance to the object, relative speed, and vehicle information from a steering sensor 4 and the like being received from an ECU 7 for controlling the distance between vehicles, and at the same time identifies a target to be controlled, and transmits it to the ECU 7 for controlling the distance between vehicles. The scanning angle control part 11 controls a scanning angle or the like when a vehicle drives along a curved road in the case of a fixed-type radar and controls a scanning angle in the case of a scan-type radar.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a scanning radar apparatus, and more particularly to a method for identifying a preceding vehicle to be controlled.

[0002]

2. Description of the Related Art In inter-vehicle distance control, an on-vehicle radar device is used to irradiate a radar beam in front of a host vehicle and detect a target such as a preceding vehicle. In this inter-vehicle distance control, a vehicle running immediately before the own vehicle in the own vehicle lane among the preceding vehicles is detected and set as an object of the inter-vehicle distance control. Therefore, it is extremely important to identify whether the object detected by the radar is an actual object or a vehicle running immediately before the own vehicle lane.

As described above, in order to identify the type of an object detected by the radar, for example, the existence probability is calculated from the detection distance and the detection angle of the object, and whether or not the detected object actually exists is determined. Identification has been done.

[0004]

In order to reliably identify whether the object detected by the on-vehicle radar device is a vehicle running immediately before the own vehicle lane to be controlled by the following distance, It is necessary to be able to reliably identify the presence of the detected object, and to be able to reliably identify that the object is traveling in its own lane.

Accordingly, an object of the present invention is to add other factors to the detection distance and the detection angle, and to more reliably identify an object such as a preceding vehicle to be controlled.

[0006]

According to the present invention, an existence probability value and an own lane are considered in consideration of a detection distance, a position deviation, the number of detection beams, a relative speed, and an object state (whether stationary or moving). A probability value is obtained, and it is determined whether or not the detected object is a preceding vehicle to be controlled based on the obtained probability value.

More specifically, in a scanning radar apparatus for detecting an object ahead in the traveling direction of the vehicle, the distance to the object is detected, and the distance to the detected object and the number of beams detected by the object are detected. Gives the existence probability value according to.
Next, the position deviation of the detected object is detected, and an existence probability weighting coefficient is given according to the position deviation. Further, a relative speed with respect to the detected object is detected, and an existence probability weighting coefficient corresponding to the relative speed is given. Then, the control target is identified by the existence probability value obtained by multiplying the existence probability value by the existence probability weighting coefficient.

In a scanning radar apparatus for detecting an object ahead in the traveling direction of the own vehicle, the distance to the object is detected, and the self-propelled radar device determines the distance to the detected object and the number of beams detected by the object. Give the lane probability value. Next, the position deviation of the detected object is detected, and it is determined whether or not the position deviation is detected in the own lane according to the detected position deviation of the object. As a result, a positive existence probability weighting coefficient is given when it is determined that the vehicle is detected in the own lane, and a negative existence probability weighting coefficient is provided when it is determined that the vehicle is detected outside the own vehicle lane. Then, the control target is identified by the own lane probability value obtained by multiplying the own lane probability value by the existence probability weighting coefficient.

[0009]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS FIG. 1 is a diagram showing an outline of a configuration of an inter-vehicle distance control device using a scanning radar used in a method of identifying an object to be controlled according to the present invention. The radar sensor unit is, for example, a millimeter-wave radar, and includes a radar antenna 1, a scanning mechanism 2, and a signal processing circuit 3. Inter-vehicle distance control E
The CU 7 receives signals from the steering sensor 4, the yaw rate sensor 5, the vehicle speed sensor 6, and the signal processing circuit 3 of the radar sensor unit, and controls the alarm 8, the brake 9, the throttle 10, and the like. Further, the following distance control ECU 7
The signal is also sent to the signal processing circuit 3 of the radar sensor unit.

FIG. 2 shows the configuration of the signal processing circuit 3 of FIG. The signal processing circuit 3 includes a scanning angle control unit 1
1, a radar signal processing unit 12, and a control target recognition unit 13. The radar signal processing unit 12 performs FFT processing on the reflected signal from the radar antenna 1, detects a power spectrum, calculates the distance to the object, the relative speed, the number and angle of the beam that has detected the object, and the position deviation described below, Control target recognition unit 1
3 and the data is transmitted. The control target recognition unit 13 is based on the distance to the object, the relative speed, and the like received from the radar signal processing unit 12, and the vehicle information from the steering sensor 4, the yaw rate sensor 5, the vehicle speed sensor 6, and the like, received from the inter-vehicle distance control ECU 7. The scan angle is instructed to the scan angle control unit 11, and the target to be controlled is identified and transmitted to the following distance control ECU 7. The scanning angle control unit 11
In the case of the fixed type radar, the scanning angle and the like when traveling on a curve are controlled, and in the case of the scanning type radar, the scanning angle is controlled.

In the above configuration, the radar sensor unit is, for example, a millimeter wave radar, but may be a laser radar.
FIG. 3 is an example of a MAP created in advance for calculating a probability value in the present invention. In FIG. 3, (a) is a MAP that gives an existence probability value corresponding to the detection distance,
(B) MA for giving the own lane probability value corresponding to the detection distance
P. Further, (c) is a MAP that provides an existence probability weighting coefficient corresponding to the position deviation, and (d) is a MAP that provides an existence probability weighting coefficient corresponding to the relative speed. Note that the above MAP is one example, and for example, MAP
In (a) and (b), the intervals of the detection distances may be shorter or longer according to the road conditions in which the vehicle normally travels, for example, the width of the road, the degree of congestion, the average traveling speed, and the like. Then, the probability value corresponding to the detection distance can be arbitrarily set according to the road condition. Similarly MAP
In (c) and (d), the interval between the positional deviation or the relative distance may be smaller or larger. And
Coefficients corresponding to the position deviation and the relative speed can also be arbitrarily set.

FIG. 4 is a flow chart showing the procedure of a method for determining the existence probability value of a detected object according to the present invention for identifying an object to be controlled. When the operation is started, it is determined whether the distance of the detected object is far or short (S1). This is determined by the control target recognition unit in FIG.
Whether the distance is far or close is determined by setting a predetermined distance, for example, 5
If it is 0 m or more, it is determined that it is far, and if it is less than 50 m, it is determined that it is close. If the distance of the detected object is, for example, 60 m, it is determined that the distance of the detected object is long, and 15 is given as the existence probability value from MAP (a) shown in FIG. 3 (S4). Note that the MAP shown in FIG. 3 is stored in the control target recognition unit, and extracts the probability value according to the distance of the object as described above. In the following flow, the MA of FIG.
A probability value or coefficient is extracted based on P. In the case of a scanning radar, the beams are typically fired at 1 ° intervals to illuminate the object. When the distance to the detected object is long, it is often detected by one beam. Therefore, a relatively large value of 15 is given as the existence probability value as described above. The existence probability value is set to a maximum of 30 in MAP (a), but this value can be set arbitrarily.

If the distance of the detected object is short, for example, if the distance of the object is 20 m, it is determined whether the object is detected by the adjacent beam (S2). FIG. 5 is a diagram for explaining a case. FIG. 5A is a diagram illustrating a case in which an adjacent beam is also detected. FIG.
In (a), the beams a and b emitted by the vehicle A both detect the preceding vehicle B. On the other hand, FIG. 5B is a diagram showing a case in which a beam is not detected in an adjacent beam. FIG.
In (b), the preceding vehicle C is, for example, a motorcycle and has a small width. In such a case, the beams a and b emitted by the vehicle A
Among them, only the beam a detects the preceding vehicle C. S2
In the case of No, ie, as shown in FIG. 5B, since an object at a short distance is detected by one beam, the maximum value of 30 is given as the existence probability value (S4). In the case of Yes, that is, in the case as shown in FIG. 5A, since a plurality of beams have been detected, an existence probability value is given to each beam in which an object is detected (S3). For example, if the detection distance is 3
In the case where 0 m has been detected by three beams, MA in FIG.
According to P (b), the existence probability value is small and becomes 10. However, 10 × 3 = 30 is given as the total existence probability value. Further, for example, when the detection distance is 20 m and detection is performed with four beams, the existence probability value is small and 6 according to MAP (b) in FIG. 3, but the total existence probability value is 6 × 4 = 2.
4 is given. Note that the above calculation is performed by the control target recognition unit in FIG. The same applies to the following flow.

Next, a positional deviation d between the position where the object is detected and the traveling direction is calculated (S5). FIG. 6 is a diagram for explaining how the positional deviation between the position where the object is detected and the traveling direction changes according to the situation. Here, the positional deviation refers to a lateral displacement amount d between the traveling direction of the vehicle and the position where the object is detected. If the object detected for the own vehicle is a preceding vehicle running on the same lane as the own vehicle,
The position deviation becomes smaller. FIG. 6A shows a case where the positional deviation is small, and both the angle θ and the positional deviation d are small. When the object detected with respect to the own vehicle is a preceding vehicle running on the lane next to the own vehicle, the positional deviation increases. FIG. 6B shows a case where the positional deviation is large, and both the angle θ and the positional deviation d are large.

When the position deviation is obtained in S5, the existence probability is weighted by the position deviation (S6). The weighting coefficient is obtained by MAP (c) in FIG. If the position deviation d is small, the weighting coefficient increases, and if the position deviation d is large, the weighting coefficient decreases. If the position deviation is, for example, 0.58 m in S5, the existence probability weighting coefficient is 0.9. And S
Assuming that the existence probability value obtained in 3 or S4 is 30, 30 × 0.9 = 27 is obtained as the existence probability value.

Next, it is determined whether the detected object is a moving object or a stationary object (S7).
This is determined based on whether or not the relative speed between the detected object and the vehicle changes. Therefore, MAP (d) in FIG.
Is used. If the relative speed is always the same as the speed of the vehicle, the detected object is considered to be, for example, a sign provided at the end of the road, and the detected object is not a moving body but a stationary body (No). Therefore, 0 is given as a presence probability weighting coefficient in MAP (d) (S8). Assuming that the existence probability value in S6 is 27 as described above, 0 is given as the existence probability weighting coefficient in S8, so that the existence probability value becomes 0 and is not a target of the inter-vehicle distance control.

On the other hand, the relative speed is not the same as the own vehicle speed,
If it is determined in S7 that the object is a moving object (Yes), an existence probability weighting coefficient corresponding to the relative speed is given. M in FIG.
In AP (d), the presence probability weighting coefficient decreases as the relative speed increases in a positive direction. This is because, as the relative speed increases, the preceding vehicle moves away from the own vehicle, and it is not necessary to quickly identify whether the vehicle is a control target vehicle. Conversely, when the relative speed is negative, it is necessary to quickly identify whether or not the vehicle is a control target vehicle, so the existence probability weighting coefficient is large. For example, if the relative speed is 10 km / h, 0.9 is given as the existence probability weighting coefficient, and if the existence probability value in S6 is 27 as described above, the existence probability value in this flow is Is 27 × 0.9 = 24.3.

The above flow is performed once in one scan, and the same flow is repeated again. Although the existence probability value of 24.3 was obtained in the flow in the above example, such a flow is repeated many times. Then, the existence probability values of the detected objects are added, and when the sum exceeds, for example, 120, this is identified as the control target object. FIG. 7 is a flowchart illustrating a procedure of a method for determining the own lane probability of a detected object according to the present invention in order to identify an object to be controlled. The own lane probability is a probability of whether or not the detected object is in the own lane. In the following flow, FIG.
In the same manner as in the flow, the control target recognition unit performs processing based on data from the radar signal processing unit in FIG.

When the operation is started, it is determined whether the distance of the detected object is far or short (S1). As described above, a predetermined distance is set to determine whether the distance is long or short. For example, if the distance is 50 m or more, it is determined that the distance is long, and if it is less than 50 m, it is determined that the distance is short. The distance of the detected object is, for example, 60 m
If so, it is determined that the distance of the detected object is long, and 15 is given as the own lane probability value from MAP (b) shown in FIG. 3 (S4).

If the distance of the detected object is short, for example, if the distance of the object is 15 m, it is determined whether the object is detected by the adjacent beam (S2). The case where the object is detected even by the adjacent beam is as described in the description of FIG. In the case of No in S2, that is, FIG.
In the case shown in (b), since an object at a short distance is detected by one beam, 30 which is the maximum value of the own lane probability value is given (S4). Note that the own lane probability value is MAP
In (b), the maximum is 30, but this value can be set arbitrarily. In the case of Yes in S2, that is, in a case where the adjacent beam is detected as shown in FIG. 5A, it is determined whether or not the detection is performed in all the beams (S2).
3). In the case of FIG. 5, only a part of the beams is drawn and not all the beams. However, if all the beams are detected (Yes), the vehicle traveling ahead of the subject vehicle to be controlled is used. , The maximum own lane probability value 30 is given (S11). In the case of No in S3, since not all beams are detected by a plurality of beams,
An own lane probability value is given to each beam that has detected an object (S5). In this case, since there are a plurality of beams, the own lane probability value given to each beam is reduced. For example, when the detection distance is 20 m and four beams are detected, according to MAP (b) in FIG. 3, the own lane probability value is small and is 6. However, the total existence probability value is 6 × 4 = 24. Given. Further, for example, when the detection distance is 15 m and detection is performed by five beams, the own lane probability value is small according to MAP (b) in FIG. 3 and is 4.3, but the total own lane probability value is 4. 3 × 5 = 21.5 is given.

Next, a positional deviation d between the position where the object is detected and the traveling direction is calculated (S6). As described above in the description of FIG. 6, the position deviation refers to the amount of lateral deviation d between the traveling direction of the vehicle and the position where the object is detected. When the object detected with respect to the own vehicle is a preceding vehicle running in the same lane as the own vehicle, the positional deviation becomes small. Further, when the object detected with respect to the own vehicle is a preceding vehicle running in the lane next to the own vehicle, the positional deviation increases.

Next, it is determined whether an object is detected in the own lane (S7). For example, as shown in FIG. 8, the width of one lane on a highway is usually 3.5 m. When the own vehicle A detects the own vehicle B ahead and the positional deviation is 1.17 m, it can be determined that the detected vehicle is in the own lane.
When the vehicle A detects the vehicle C ahead of the vehicle and the positional deviation is 2.33 m, it can be determined that the detected vehicle is in the adjacent lane. In the MAP (c) of the existence probability weighting coefficient corresponding to the position deviation in FIG. 3, the right half of the own lane is divided into three equal parts as shown in FIG. 8, and the own vehicle is traveling in the middle of the lane. 0,58m, 1.17m,
A position deviation is set to 1.75 m, and a positive existence probability weighting coefficient is given in accordance with the position deviation. Although not shown, the left half of the own lane is also divided into three equal parts, and assuming that the own vehicle is running in the middle of the lane, similarly, −0, 58 m, −
Position deviations are set at 1.17 m and -1.75 m, and a positive existence probability weighting coefficient is given corresponding to the position deviation. In addition, in order to detect a vehicle traveling in the next lane, the left half of the next lane is divided into three equal parts, as shown in FIG. 8, to 2.33 m, 2.91 m, and 3.50 m. The position deviation is set, and a negative existence probability weighting coefficient is given according to the position deviation. Although not shown, it is also assumed that there is a lane on the left side, the right half of the lane on the left is divided into three equal parts, and it is assumed that the own vehicle is running in the middle of the lane. , 33m, 2.91m, and 3.50m, and a negative existence probability weighting coefficient is given in accordance with the position deviation. Note that, in MAP (c) in FIG. 3, the positional deviation of the right half of the lane adjacent to the right in FIG. 8 is omitted. Similarly, the positional deviation of the left half of the lane adjacent to the left is omitted. The existence probability weighting coefficients corresponding to the positional deviations of these parts are, for example, minus 1
It can be.

In S6, the detected position deviation is 2.
If the distance is 33 m, the position at which the object is detected is the next lane as shown in FIG.
And the existence probability weighting coefficient is negative. That is,
-0.5 is given as the existence probability weighting coefficient corresponding to the position deviation of 2.33 m (S8). On the other hand, when the detected position deviation is 1.17 m, since the position where the object is detected is the own lane as shown in FIG. 8, the determination in S7 is Yes, and the existence probability weighting coefficient is positive. Become. That is, 0.7 is given as the existence probability weighting coefficient corresponding to the position deviation of 1.17 m (S9).

Next, the own lane probability value is calculated using the own lane probability value obtained in S4 or S5 and the existence probability weighting coefficient obtained in S8 or S9 (S10). For example, S
5, the existence probability weighting coefficient is 0.7 in S9, and the own lane probability value is 24 ×
0.7 = 16.8. The above flow is 1 for 1 scan
And the same flow is repeated again. Although the own lane probability value of 16.8 is obtained in the flow of the above example, such a flow is repeated, and when the total of the own lane probability values of the detected object exceeds, for example, 120, this is controlled. Identify as a target object.

According to the present invention, the existence probability value or the own lane probability value is obtained by the above-mentioned flow, and either one of the existence probability value and the maximum probability value is determined.
In the above case, if the number exceeds 120, the detected object is identified as a control target. However, by combining the above flows, the object detected when both the existence probability value and the own lane probability value exceed the maximum probability value of the control target can be identified as the control target object.

[0026]

As described above, according to the present invention, an object existence probability value or an own lane probability value is obtained by using the detected distance to the object, the position deviation, the number of detected beams, and the relative speed, and the detection is performed based on this value. Since it is identified whether the detected object is a preceding vehicle to be controlled, it is possible to accurately identify whether the object is a controlled vehicle. In addition, by changing the values of the detection distance and the position deviation and the like and the probability values and the coefficients set in the MAP in order to derive the probability values and the coefficients according to the width and the degree of congestion of the road on which the vehicle normally travels, the object can be more accurately detected. Can be identified.

[Brief description of the drawings]

FIG. 1 is a diagram showing an outline of a configuration of an inter-vehicle distance control device using a scanning radar used in an object existence probability calculation method of the present invention.

FIG. 2 is a diagram illustrating a signal processing circuit of FIG. 1;

FIG. 3 is an example of a MAP for calculating a probability value according to the present invention.

FIG. 4 is a flowchart showing a procedure of a method for obtaining a detection probability of a detected object according to the present invention.

FIG. 5 is a diagram for describing a case where an object is detected even with an adjacent beam.

FIG. 6 is a diagram for explaining how the position deviation changes according to the situation.

FIG. 7 is a flowchart showing a procedure of a method for calculating the own lane probability of a detected object according to the present invention.

FIG. 8 is a diagram for explaining how to determine whether an object is detected in the own lane.

[Explanation of symbols]

 DESCRIPTION OF SYMBOLS 1 ... Radar antenna 2 ... Scanning mechanism 3 ... Signal processing circuit 4 ... Steering sensor 5 ... Yaw rate sensor 6 ... Vehicle speed sensor 7 ... Vehicle distance control ECU 8 ... Warning device 9 ... Brake 10 ... Throttle 11 ... Scanning control unit 12 ... Radar Signal processing unit 13: Control target recognition unit

──────────────────────────────────────────────────続 き Continued on the front page (51) Int.Cl. ⁷ Identification code FI Theme coat ゛ (Reference) G08G 1/16 B60R 21/00 624B 624D F term (Reference) 5H180 AA01 CC03 CC12 CC14 EE02 LL01 LL04 5J070 AB24 AC02 AC06 AC11 AE01 AE09 AF03 AG03 AG07 AH04 AH19 AJ14 AK22 BD10 BF10 BF11 BF12 BF16 BF18 BF22 5J084 AA02 AA05 AA07 AA10 AB01 AC02 BA03 BA11 EA04 EA22

Claims (9)

[Claims] 1. A scanning radar apparatus for detecting an object ahead of a host vehicle in a traveling direction. The scanning radar apparatus detects a distance to an object, and detects a distance according to the distance to the detected object and the number of beams detected by the object. Giving a probability value, detecting the position deviation of the detected object, giving an existence probability weighting coefficient according to the position deviation, detecting the relative speed with the detected object, and detecting the existence probability according to the relative speed. A method of providing a weighting coefficient, and identifying a control object based on the existence probability value obtained by multiplying the existence probability value by the existence probability weighting coefficient. 2. The method according to claim 1, wherein a relative speed with respect to the detected object is detected, and whether or not the detected object is a moving object is determined. If the detected object is not a moving object, the existence probability weighting coefficient is set to 0. A method for identifying the described control target. 3. The relative speed with respect to the detected object is detected, and it is determined whether the detected object is a moving object. 2. The method for identifying a control target according to claim 1, wherein the control object is set to be smaller and the relative speed is set to be larger as the relative speed is larger. 4. An existence probability value according to the distance to the detected object and the number of beams that have detected the object, an existence probability weighting coefficient according to the position deviation, and an existence probability weight according to the relative speed. The method according to claim 1, wherein the assignment coefficient is obtained based on a MAP created in advance. 5. A scanning radar apparatus for detecting an object in front of the own vehicle in a traveling direction, detecting a distance to the object, and detecting an object according to the distance to the detected object and the number of beams detecting the object. The lane probability value is given, the position deviation of the detected object is detected, it is determined whether or not the detected object is detected in the own lane according to the detected position deviation of the object. The existence probability weighting coefficient is given, and when it is determined that the detection is performed outside the own lane, a negative existence probability weighting coefficient is given, and the own lane probability value obtained by multiplying the own lane probability value and the existence probability weighting coefficient is obtained. A method of identifying an object to be controlled. 6. The method according to claim 5, wherein when the number of beams that have detected the object is all beams, a maximum own lane probability value is given. 7. The method according to claim 5, wherein when the detected position deviation is within a predetermined range, it is determined that the position deviation has been detected in the own lane, and when it exceeds a predetermined range, it is determined that the position deviation has been detected outside the own lane. How to identify the control object of the. 8. An existence probability value according to the distance to the detected object and the number of beams that have detected the object, and an existence probability weighting coefficient according to whether the object is detected in the own lane, The method for identifying a control object according to claim 5, wherein the method is performed based on the created MAP. 9. The existence probability value or the own lane probability value obtained by the multiplication is calculated for each scan, and when the sum exceeds a predetermined value, the detected object is identified as a control target object. The method for identifying a control object according to claim 1, wherein