JP7452333B2 - LIDAR correction parameter generation method, LIDAR evaluation method, and LIDAR correction device - Google Patents

Description

The present invention relates to a LIDAR correction parameter generation method, a LIDAR evaluation method, and a LIDAR correction device.

For example, Patent Document 1 listed below describes improving object detection accuracy using both a radar device and a camera device.

Japanese Patent Application Publication No. 2016-153775

By the way, when a vehicle is equipped with a plurality of sensors, deviations may occur in the detection results of objects by the plurality of sensors due to misalignment when the sensors are attached to the vehicle. Therefore, when a vehicle is equipped with a plurality of LIDAR devices, differences may occur in the results of object detection by the LIDAR devices.

Means for solving the above problems and their effects will be described.
1. The distance measuring point data by the first LIDAR device (LR1) that receives the reflected light of the laser beam irradiated around the vehicle is defined as the first distance measuring point data, and the distance measuring point data of the first LIDAR device (LR1) that receives the reflected light of the laser beam irradiated around the vehicle is defined as the first distance measuring point data. The distance measuring point data obtained by the 2LIDAR device (LR2) is defined as second ranging point data, one of the objects specified according to the first ranging point data is defined as a target object, and the distance measuring point data is based on the first ranging point data. A target coordinate calculation process (S16) that calculates the positional coordinates of the target object, and among the objects identified according to the second distance measurement point data, an object having a minimum distance to the target object is determined to be the same object. , a same coordinate calculation process (S44) of calculating the position coordinates of the same object based on the second distance measuring point data; and the position coordinates of the same object, and the relative rotation angle when the absolute value of the difference between the position coordinates becomes the minimum is corrected for the position of the object based on the ranging point data. This is a LIDAR correction parameter generation method that causes an execution device (42, 44) to execute a selection process (S34) for selecting a correction parameter.

In the above method, the absolute value of the difference between the position coordinates of the target object based on the first distance measurement point data and the position coordinates of the target object based on the second distance measurement point data when the position coordinates of the target object based on the first distance measurement point data are relatively rotated is the minimum. Select the relative rotation angle at which the rotation angle becomes the correction parameter. Therefore, it is possible to suitably compensate for the deviation between the position coordinates of the first LIDAR device and the second LIDAR device due to the axis deviation of the two LIDAR devices.

FIG. 1 is a system configuration diagram according to an embodiment. 5 is a flowchart showing the procedure of processing executed by the evaluation device according to the embodiment. FIG. 3 is a flowchart showing detailed steps of a part of the above processing. FIG. FIG. 3 is a diagram showing effects according to the embodiment. (a) and (b) are diagrams showing a modification of the above embodiment.

<First embodiment>
The first embodiment will be described below with reference to the drawings.
FIG. 1 shows the configuration of a LIDAR evaluation system according to this embodiment.

The test vehicle VC shown in FIG. 1 includes a first optical sensor 10 and a second optical sensor 12. Both the first optical sensor 10 and the second optical sensor 12 emit laser light such as near-infrared light. The first optical sensor 10 also detects a distance variable, which is a variable indicating the distance between the object that reflected the laser beam and the vehicle, and a variable indicating the irradiation direction of the laser beam, based on the reception of the reflected laser beam. First ranging point data Drpc1 is generated which is ranging point data indicating a certain direction variable and an intensity variable which is a variable indicating the reflection intensity of a reflected object. Based on the reception of the reflected laser beam, the second optical sensor 12 generates a distance variable that is a variable that indicates the distance between the object that reflected the laser beam and the vehicle, and a direction variable that is a variable that indicates the irradiation direction of the laser beam. Second distance measuring point data Drpc2 is generated which is distance measuring point data indicating a variable and an intensity variable which is a variable indicating the reflection intensity of the reflected object.

The first LIDAR ECU 20 executes recognition processing of the object that reflected the laser beam based on the first distance measurement point data Drpc1. Specifically, the first LIDAR ECU 20 repeats the process of scanning the irradiation direction of the laser beam in the horizontal direction and the vertical direction, and one period of these operations in the horizontal direction and the vertical direction is defined as one scanning period. Then, for each scanning period, objects are recognized by clustering processing or the like based on the first distance measurement point data Drpc1. The first LIDAR ECU 20 includes a CPU 22, a ROM 24, a storage device 26, and a peripheral circuit 28, which can communicate with each other via a local network 29. Here, the peripheral circuit 28 includes a circuit that generates a clock signal that defines internal operations, a power supply circuit, a reset circuit, and the like. The first LIDAR ECU 20 executes recognition processing by having the CPU 22 execute a program stored in the ROM 24. The storage device 26 stores first detection data Dd1, which is data regarding the object recognized by the recognition process. The first detection data Dd1 is data based on the first ranging point data Drpc1 of a plurality of scanning cycles, and includes trajectory information of the recognized object.

In this embodiment, the first LIDAR device LR1 including the first optical sensor 10 and the first LIDAR ECU 20 is actually installed and used in a mass-produced vehicle.
The second LIDAR ECU 30 executes recognition processing of the object that reflected the laser beam based on the second distance measurement point data Drpc2. Specifically, the second LIDAR ECU 30 repeats the process of scanning the irradiation direction of the laser beam in the horizontal direction and the vertical direction, and defines one period of the operations in the horizontal direction and the vertical direction as one scanning period. Then, objects are recognized by clustering processing or the like based on the second distance measurement point data Drpc2 for each scanning period. The second LIDAR ECU 30 includes a CPU 32, a ROM 34, a storage device 36, and a peripheral circuit 38, which can communicate with each other via a local network 39. Here, the peripheral circuit 38 includes a circuit that generates a clock signal that defines internal operations, a power supply circuit, a reset circuit, and the like. The second LIDAR ECU 30 executes recognition processing by the CPU 32 executing a program stored in the ROM 34. The storage device 36 stores second detection data Dd2, which is data related to the object recognized by the recognition process. The second detection data Dd2 is data based on the second ranging point data Drpc2 of a plurality of scanning cycles, and includes trajectory information of the recognized object.

In this embodiment, the second LIDAR device LR2 including the second optical sensor 12 and the second LIDAR ECU 30 has not yet been installed in a mass-produced vehicle, and it is difficult to evaluate its performance before installing it in a mass-produced vehicle. It is desired.

Evaluation device 40 is placed outside vehicle VC. The evaluation device 40 uses the vehicle speed data Dspd, which is time series data of the vehicle speed SPD detected by the vehicle speed sensor 50 mounted on the vehicle VC, the first detection data Dd1, and the second detection data Dd2, to evaluate the second LIDAR device LR2. This is a device that evaluates the performance of In this evaluation, first, the vehicle VC is driven on an actual road for a predetermined period of time. Specifically, in this embodiment, the vehicle VC is driven on a motorway at a vehicle speed that is less than or equal to the speed of the preceding vehicle. Then, the first LIDAR ECU 20 accumulates the first detection data Dd1 in the storage device 26 over a predetermined period while the vehicle VC is traveling. Further, the second LIDAR CU 30 accumulates the second detection data Dd2 in the storage device 36 over a predetermined period when the vehicle VC is traveling. After a predetermined period of time has elapsed, the evaluation device 40 acquires the first detection data Dd1 and the second detection data Dd2 from the vehicle VC, and stores them in the storage device 46. The evaluation device 40 then evaluates the performance of the second LIDAR device LR2.

Note that in this embodiment, one scanning period by the first LID ARECU 20 is shorter than one scanning period by the second LID ARECU 30. Therefore, the evaluation device 40 uses the first ranging point data Drpc1 in each of the scanning cycles whose starting point is before the starting point of each scanning cycle constituting the second detection data Dd2 and whose starting point is closest to the starting point. 1 detection data Dd1 is configured. As a result, each of the first ranging point data Drpc1 forming the first detection data Dd1 has a starting point before the start point of the scanning cycle of the corresponding second ranging point data Drpc2 forming the second sensing data Dd2. The data corresponds to the scanning cycle. Note that in the following, when "scanning period" is used alone, it means the scanning period by the second LIDAR ECU 30.

The evaluation device 40 includes a CPU 42, a ROM 44, a storage device 46, and a peripheral circuit 48, which can communicate with each other via a local network 49.
FIG. 2 shows the procedure of processing executed by the evaluation device 40. The processing shown in FIG. 2 is realized by the CPU 42 executing a program stored in the ROM 44. Note that in the following, the step number of each process is expressed by a number prefixed with "S".

In the series of processes shown in FIG. 2, the CPU 42 first selects one of the objects around the vehicle VC indicated by the first detection data Dd1 as a target object at a predetermined timing (S10). Next, the CPU 42 determines whether the target object is a moving object (S12). This process is performed in consideration of the fact that in this embodiment, it is assumed that the performance of the second LIDAR device LR2 is evaluated based on whether or not the second LIDAR device LR2 recognizes the moving object recognized by the first LIDAR device LR1. This is a very simple process.

When determining that the object is a moving object (S12: YES), the CPU 42 selects one parameter βx, βy, γ for correcting coordinate components (x1, y1) to be described later (S14). That is, in this embodiment, a plurality of combinations in which at least one of the three parameters βx, βy, and γ has a different value are determined in advance, and the CPU 42 selects one of the plurality of combinations. do. In particular, in this embodiment, although the parameter γ can take any of a positive value, a negative value, or "0", the parameters βx and βy are set to positive values. The parameters γ, βx, and βy are parameters for adjusting the amount of deviation between the position of the object detected by the first LID A RECU 20 and the position of the object detected by the second LID A RECU 30.

Next, the CPU 42 calculates the x-axis coordinate component x1 and the y-axis coordinate component y1 of the target object, and the relative velocity Vr between the preceding vehicle, which is the target object, and the vehicle VC, based on the first detection data Dd1 ( S16). Here, the x-axis is parallel to the longitudinal direction of the vehicle VC and passes through the center of gravity of the vehicle VC. Further, the y-axis is in the lateral direction of the vehicle VC and passes through the center of gravity of the vehicle VC. Specifically, in this embodiment, the center coordinates of the rear end of the preceding vehicle, which is the target object, are the coordinate components (x1, y1) of the target object. Note that in FIG. 2, the relative speed Vr is expressed as the vehicle speed SPD of the vehicle VC minus the vehicle speed of the target object. That is, it is described as a value obtained by subtracting the square root of the sum of the squares of the x-axis speed component Vx and the y-axis speed component Vy of the target object from the vehicle speed SPD. However, when calculating the velocity components (Vx, Vy) of the target object, the CPU 42 actually uses the vehicle speed SPD.

Next, the CPU 42 corrects the rotation of the coordinates regarding the position of the target object by "γ" around the center of gravity of the vehicle VC, which is the origin (S18). Here, the CPU 42 assumes that the coordinates (x1, y1) calculated in the process of S16 have been corrected according to the relative velocity Vr, as the coordinates related to the position of the target object, which are the coordinates to be rotationally corrected. That is, the CPU 42 reduces the coordinate component x1 by multiplying the value αx obtained by dividing the absolute value of the relative velocity Vr by the parameter βx and the velocity component Vx of the target object. Here, the parameter βx is positive. As a result, the coordinate component x1 is corrected to decrease. This is because, in the present embodiment, the start point of each scanning period of the data forming the first sensing data Dd1 is later than the starting point of the scanning period of the data forming the corresponding second sensing data Dd2. This setting is made in consideration of the fact that the vehicle VC travels at a speed lower than the speed of the preceding vehicle. That is, in that case, if the relative velocity Vr is not zero, the position of the object detected by the first LIDAR device LR1 is farther from the vehicle VC than the position of the object detected by the second LIDAR ECU 30.

Further, the CPU 42 corrects the coordinate component y1 by multiplying the value αy obtained by dividing the absolute value of the relative velocity Vr by the parameter βy and the velocity component Vy of the target object.
Next, the CPU 42 executes a process of determining which object is the same as the target object among the objects indicated by the second detection data Dd2 (S20).

FIG. 3 shows details of the process in S20.
In the series of processes shown in FIG. 3, the CPU 42 first determines whether the rear end of the target object is within a predetermined range from the vehicle VC (S40). The predetermined range is a range where the distance from the vehicle VC is greater than or equal to a predetermined distance and less than or equal to a predetermined distance. This is in consideration of the fact that in the case of an object located at a position more than a predetermined distance from the vehicle VC, the difference in accuracy due to the difference between the resolution of the first LIDAR ECU 20 and the resolution of the second LIDAR ECU 30 becomes significant. This is also taken into consideration that when the distance to the vehicle VC is shorter than the specified distance, the difference in accuracy due to the difference between the field of view of the first LID A RECU 20 and the field of view of the second LID A RECU 30 becomes significant.

When determining that the object is within the predetermined range (S40: YES), the CPU 42 selects the object indicated by the second detection data Dd2 (S42). Then, the CPU 42 calculates the x-axis and y-axis coordinate components (x2, y2) of the selected object based on the second detection data Dd2 (S44). Specifically, the coordinate component (x2, y2) is the center coordinate of the rear end of the selected object. Next, the CPU 42 determines that the Euclidean distance between the coordinate components (x1c, y1c) of the target object corrected by the processing in S18 and the coordinate components calculated in the processing in S44 is determined by adding a predetermined coefficient K to the absolute value of the relative velocity Vr. It is determined whether the value is smaller than the multiplied value (S46). This process is a process for determining whether or not the selected object has a possibility of being a target object. Here, the reason why the threshold value for determining the presence or absence of possibility is made proportional to the magnitude of the relative velocity Vr is that the starting point of one scanning period of the first ranging point data Drpc1 forming the first detection data Dd1, This is to take into consideration the influence of an error caused by a deviation from the starting point of one scanning period of the second ranging point data Drpc2 constituting the corresponding second detection data Dd2.

When determining that the absolute value of the relative velocity Vr is greater than or equal to the value obtained by multiplying the predetermined coefficient K (S46: NO), the CPU 42 determines that the object is not the same as the target object (S48).
On the other hand, if the CPU 42 determines that the absolute value of the relative velocity Vr is smaller than the value obtained by multiplying the predetermined coefficient K (S46: YES) or if the process of S48 is completed, the second detection data It is determined whether all the objects indicated by Dd2 have been selected through the process of S42 (S50). When determining that there is an object that has not been selected yet (S50: NO), the CPU 42 returns to the process of S42.

On the other hand, when determining that all objects have been selected (S50: YES), the CPU 42 determines whether the acceleration of the object with the minimum error as a Euclidean distance from the target object is less than or equal to the threshold value. (S52). This process is a process for determining whether or not the object with the minimum error can be made the same object as the target object. Here, the threshold value is set to a value larger than the maximum value assumed as the acceleration of the vehicle. Note that the CPU 42 uses the amount of change in the velocity of the object calculated in each of the adjacent scanning periods as the acceleration.

When determining that the difference is larger than the threshold (S52: NO), the CPU 42 determines that the objects with the smallest error are not the same object (S54). That is, in the case of an object with an excessively large change in speed between adjacent scanning periods, it is unlikely that this is a displacement of the preceding vehicle, so in that case, the determination that the object is the same object is refrained from being made.

On the other hand, when the CPU 42 determines that it is less than or equal to the threshold value (S52: YES), the CPU 42 determines that the objects with the smallest error are the same object (S56). Note that when the CPU 42 completes the processing of S54 and S56 or when a negative determination is made in the processing of S40, the CPU 42 completes the processing of S20 in FIG.

Then, the CPU 42 determines whether the addition condition for the error associated with the set of parameters γ, βx, and βy is satisfied (S22). In this embodiment, the addition condition is a condition that the objects determined to be the same object in the process of S20 continue to be determined to be the same object for a predetermined length of time. When determining that the addition condition is not satisfied (S22: NO), the CPU 42 returns to the process of S16. As a result, the coordinate components (x1, y1) and the coordinate components (x2, y2) are calculated based on the recognition result of the scanning cycle following the scanning cycle when the target object was selected in the process of S10. . In addition, when the process of FIG. 3 is performed over a plurality of scanning periods, the object for which the average value of the Euclidean distance is the minimum in the process of S52 of FIG. 3 may be used as a candidate for the same object.

On the other hand, when determining that the addition condition is satisfied (S22: YES), the CPU 42 calculates the average value of the Euclidean distances to the target object calculated during the period until the addition condition is satisfied as the error err (S24). Then, the CPU 42 adds the error err calculated in the process of S24 to the accumulated error value Inerr (γ, βx, βy) regarding the set of parameters γ, βx, βy selected in the process of S14 (S28).

Next, in the process of S14, the CPU 42 determines whether all sets of parameters γ, βx, and βy have been selected (S30). If the CPU 42 determines that there is a group that has not yet been selected (S30: NO), the process returns to S14. On the other hand, when determining that all the groups have been selected (S30: YES), in the process of S10, the CPU 42 determines whether all the objects indicated by the first detection data Dd1 have been selected. (S32). If the CPU 42 determines that there is an object that has not been selected yet (S32: NO) or if it makes a negative determination in the process of S12, it returns to the process of S10.

On the other hand, when determining that all objects have been selected (S32: YES), the CPU 42 selects the set that minimizes the integrated value Inerr as the correction parameter Dc from among the sets of parameters γ, βx, and βy. and stores it in the storage device 46 (S34). The correction parameter Dc is a parameter for reducing the deviation between the position of the object indicated by the first detection data Dd1 and the position of the object indicated by the second detection data Dd2.

Then, the CPU 42 corrects the position of the object indicated by the first detection data Dd1 using the correction parameter Dc, and then evaluates the performance of the second LIDAR device LR2 (S36). That is, the CPU 42 evaluates the performance of the second LIDAR device LR2 based on whether the second LIDAR device LR2 accurately detects the same object as the object detected by the first LIDAR device LR1. Here, the CPU 42 determines that accurate detection has been made on the condition that the lane in which the object detected by the first LIDAR device LR1 is located matches the lane in which the same object detected by the second LIDAR device LR2 is located. judge.

Note that when the CPU 42 completes the process of S36, it ends the series of processes shown in FIG.
Here, the functions and effects of this embodiment will be explained.

The CPU 42 compensates for the positional deviation between the object indicated by the first detection data Dd1 and the object indicated by the second detection data Dd2, based on the first detection data Dd1 and the second detection data Dd2 generated while the vehicle VC is running. Determine the correction parameter Dc for. Then, the CPU 42 corrects the position of the object indicated by the first detection data Dd1 based on the correction parameter Dc, and based on whether the second LIDAR device LR2 accurately detects the object corresponding to the corrected position, Evaluate the performance of the second LIDAR device LR2.

That is, when assembling the first LIDAR device LR1 and the second LIDAR device LR2 to the vehicle VC, axis misalignment may occur between the first LIDAR device LR1 and the second LIDAR device LR2. This causes an error in which the position of the object indicated by the first detection data Dd1 is a relative rotation of the position of the object indicated by the second detection data Dd2. Further, if a timing difference occurs between the first detection data Dd1 and the second detection data Dd2 at the start point of the scanning period, and if the detected relative speed Vr between the preceding vehicle and the vehicle VC is not zero, the first LIDAR device LR1 The second LIDAR device LR2 causes a difference in the distance between the vehicle VC and the preceding vehicle.

When the above-described shift occurs, an error due to the shift occurs in the positions of the object detected by the first LIDAR device LR1 and the object detected by the second LIDAR device LR2. This becomes a factor that prevents accurate evaluation of the performance of the second LIDAR device LR2.

In contrast, in this embodiment, by using the value corrected by the correction parameter Dc, it is possible to eliminate the problem caused by the timing deviation or axis deviation of the scanning period of the data corresponding to each other between the first detection data Dd1 and the second detection data Dd2. The performance of the second LIDAR device LR2 can be evaluated while suppressing deviations between the detected detection results.

FIG. 4 shows the effect of correction using the correction parameter Dc.
In FIG. 4, "self", "right 1", "right 2", "left 1", and "left 2" each indicate a lane. Furthermore, vehicle VC(1) indicates a vehicle equipped with a first LIDAR device LR1 and a second LIDARLR2. Furthermore, the preceding vehicle VC(2) detected by the first LIDAR device LR1 is indicated by a two-dot chain line L2, and the same preceding vehicle VC(2) detected by the second LIDAR device LR2 is indicated by a solid line L. In this case, the second LIDAR LR2 detects that the preceding vehicle VC(2) is located in the lane to the right of the lane in which the vehicle VC(1) is traveling, while the first LIDAR device LR1 detects that the preceding vehicle VC(2) This means that it is detected that the vehicle is located in the second lane on the right side of the lane in which the vehicle VC(1) is traveling. On the other hand, as shown by the dashed line L1 in FIG. 4, the detection result based on the first detection data Dd1 corrected by the correction parameter Dc indicates that the preceding vehicle VC(2) is ) indicates that the vehicle is located in the lane to the right of the lane in which the vehicle is traveling. Therefore, it can be evaluated that the second LIDAR device LR2 also accurately detects the preceding vehicle VC(2) detected by the first LIDAR device LR1.

On the other hand, if the correction parameter Dc is not used, even though the second LIDAR device LR2 can accurately detect the preceding vehicle VC(2) detected by the first LIDAR device LR1, the axis deviation during assembly may occur. etc., there is a risk that it will be judged that the detection is not accurate.

According to the present embodiment described above, the following effects and effects can be obtained.
(1) A condition that the acceleration of the object is less than or equal to a threshold value was added to the conditions for determining that the object is the same as the target object. Thereby, it is possible to prevent erroneous determination that the objects are the same object due to the influence of noise or the like.

(2) Each of the plurality of objects was used as a target object, and the parameters γ, βx, and βy that minimized the integrated value Inerr obtained by integrating the errors err for these objects were set as the correction parameters Dc. As a result, the influence of noise can be suppressed, and the correction parameter Dc can be calculated with higher accuracy compared to the case where the correction parameters Dc are the parameters γ, βx, and βy that minimize the error err regarding a single object. .

(3) The error addition conditions include a condition that the object is determined to be the same over a predetermined length of time. Thereby, the influence of noise can be suppressed, so that the correction parameter Dc can be calculated with higher accuracy.

<Other embodiments>
Note that this embodiment can be implemented with the following modifications. This embodiment and the following modified examples can be implemented in combination with each other within a technically consistent range.

"About coordinates"
- In the above embodiment, the coordinates are determined by the x-axis parallel to the longitudinal direction of the vehicle VC and the y-axis parallel to the lateral direction, but the coordinates are not limited thereto. The coordinates may be determined by any pair of directions that are linearly independent from each other within a plane defined by the longitudinal and lateral directions of the vehicle VC.

"About the same coordinate calculation process"
- In the process of FIG. 3, in the process of S46, the coordinate components (x1c, x1c) corrected by the temporarily determined parameters γ, βx, βy are used, but the present invention is not limited to this. For example, coordinate components (x1, x1) before being corrected by temporarily determined parameters γ, βx, and βy may be used.

- As a threshold value in the process of determining whether the object indicated by the first detection data Dd1 and the object indicated by the second detection data Dd2 are the same object, as exemplified in the process of S46, the relative The value is not limited to a value that is variable depending on the speed, but may be a predetermined fixed value, for example.

- In the above embodiment, the object used to evaluate the temporarily determined parameters γ, βx, and βy is an object that satisfies the condition that the acceleration is less than the threshold value, but the object is not limited to this.
- The process of S40 may be omitted.

"About relative velocity acquisition processing"
- In the above embodiment, the relative velocity is calculated based on the first ranging point data Drpc1, but the invention is not limited to this. For example, it may be calculated based on the second distance measuring point data Drpc2. That is, for example, the vehicle speed components (Vx, Vy) of the objects determined to be the same object in the process of S20 may be calculated based on the second ranging point data Drpc2.

"About error calculation process"
- In the process of S24 in FIG. 2, the average value of the Euclidean distance between the coordinate components (x1c, y1c) and the coordinate components (x2, y2) is used as the error err, but the error is not limited to this. For example, the integrated value of all the sampling numbers of the same Euclidean distance may be set as the error err.

- In the process of S28 in FIG. 2, the integrated value Inerr of the errors err is used as the error used to determine the correction parameter Dc, but the invention is not limited to this. For example, it may be the average value of the errors err.

"About rotation processing"
- In the above embodiment, the origin of the coordinates is the center of gravity of the vehicle VC, and the rotation angle is defined by the parameter γ around the center of gravity, but the invention is not limited to this. For example, the origin of the coordinates may be the position where the first optical sensor 10 is located, the position where the second optical sensor 12 is located, or the midpoint thereof.

・In the process of FIG. 2, the component whose coordinate component (x1, y1) has been corrected is rotated using the parameter γ as the rotation angle, but the invention is not limited to this. For example, the coordinate component (x2, y2) is ) may rotate the corrected component. Here, when the correction due to relative velocity is ignored, the angle between the coordinate component (x1, y1) after rotating the coordinate component (x1, y1) by the parameter γ and the origin of the coordinate component (x2, y2) is , y2) by "-γ" and the angle formed by the origin of the coordinate component (x2, y2). Figure 5(a) shows the case where the coordinate component (x2, y2) is rotated by the parameter γ, and Figure 5(b) shows the case where the coordinate component (x1, y1) is rotated by "-γ". Indicate the case. In either case, the angle between the pair of coordinates after rotation and the origin is the same angle θ0. Note that instead of rotating the corrected coordinate component (x2, y2) using the parameter γ as the rotation angle, the coordinate component (x2, y2) itself may be rotated using the parameter γ as the rotation angle. In that case, the error err may be calculated according to the Euclidean distance between the coordinate component (x1, y1) corrected according to the relative velocity Vr and the coordinate component (x2, y2) itself rotated.

・In the process of FIG. 2, the component whose coordinate component (x1, y1) has been corrected is rotated using the parameter γ as the rotation angle, but the invention is not limited to this. For example, the coordinate component (x1, y1) is ) itself may be rotated. In that case, the error err may be calculated according to the Euclidean distance between the coordinate component (x2, y2) corrected according to the relative velocity Vr and the coordinate component (x1, y1) itself rotated.

- The object to be rotated is not limited to either one of the coordinate components (x1, y1) and the coordinate components (x2, y2). The point is that either one of the coordinate components (x1, y1) and the coordinate components (x2, y2) needs to be rotated relative to the other, so for example, if the relative rotation angle is the parameter γ, the coordinate The component (x1, y1) may be rotated by a rotation angle θ1, and the coordinate component (x2, y2) may be rotated by a rotation angle θ2. However, it is assumed that "|θ1-θ2|=|γ|".

"About correction processing"
- In the above embodiment, it is known in advance that the sampling timing of the first ranging point data Drpc1 is later than the sampling timing of the second ranging point data Drpc2, and that the traveling speed of the preceding vehicle is Although the coordinate component x1 was corrected to decrease on the premise that the traveling speed is greater than or equal to the traveling speed of the vehicle, the present invention is not limited to this. For example, if it cannot be determined which comes first between the sampling timing of the first distance measurement point data Drpc1 and the sampling timing of the second distance measurement point data Drpc2, positive values and negative values can be used as candidates for the correction parameter Dc. Both values may be prepared. As a result, it is determined whether the coordinate component x1 is to be corrected to decrease or increase, depending on the sign of the temporarily selected parameter βx.

- In the process of S18, the correction amount of the x-axis coordinate component x1 is made proportional to the x-axis velocity component Vx of the target object, but the invention is not limited to this. For example, the correction amount of the x-axis coordinate component Good too. Furthermore, if it cannot be determined which comes first between the sampling timing of the first ranging point data Drpc1 and the sampling timing of the second ranging point data Drpc2, the absolute value of the relative velocity Vr may be changed to a positive value or a negative value. The parameter αx may be a value divided by a parameter that can have any of the values. In addition, for example, if the sign of the relative velocity Vr can be positive or negative, the parameter αx may be a value obtained by dividing the relative velocity Vr by a parameter that can be either a positive value or a negative value. Further, for example, the correction amount of the x-axis coordinate component x1 may be a value obtained by multiplying the x-axis component of the relative velocity Vr by a coefficient that can be positive or negative.

- In the process of S18, the correction amount of the y-axis coordinate component y1 is made proportional to the y-axis velocity component Vy of the target object, but the invention is not limited to this. For example, the correction amount of the y-axis coordinate component y1 can be set as the parameter αy. Good too. Further, for example, the correction amount of the y-axis coordinate component y1 may be a value obtained by multiplying the y-axis component of the relative velocity Vr by a coefficient that can be positive or negative.

- Although the x-axis coordinate component x1 is corrected according to the relative velocity, the y-axis coordinate component y1 may not be corrected.
- As described in the column "About rotation processing", instead of correcting the coordinate component (x1, y1), the coordinate component (x2, y2) may be corrected. However, in an embodiment in which the sign of the correction amount for the coordinate component (x1, y1) is limited, the sign of the correction amount is reversed when changing the correction target to the coordinate component (x2, y2).

"About selection processing"
For example, if some processing is performed to reduce the difference in sampling timing between the first distance measurement point data Drpc1 and the second distance measurement point data Drpc2, only the parameter γ may be used as the correction parameter.

- The method of specifying the value of the parameter γ is not limited to the method of calculating the error for each of the various values of the parameter γ and specifying the value with the minimum error. For example, the process may search for the value of the parameter γ in which the value obtained by differentiating the equation for calculating the error err in the process of S24 with the parameter γ is set to zero.

- The process is not limited to the process in which the parameters γ, βx, and βy when the integrated value Inerr becomes the minimum are used as the correction parameters Dc. For example, the correction parameters Dc may be the parameters γ, βx, and βy that minimize the error err between one target object and the same object.

"About the 1st AF point data and 2nd AF point data"
- In the above embodiment, the distance measuring point data by the LIDAR device to be evaluated is the second distance measuring point data, but the present invention is not limited to this, and the ranging point data by the LIDAR device to be evaluated is the first focusing point data. May be used as data. That is, for example, a target object may be selected based on the second detection data Dd2 in the process of S10, and the same object may be selected based on the first detection data Dd1 in the process of S20. Note that it is not essential that one of the data be distance measuring point data by a LIDAR device to be evaluated; for example, both may be ranging point data by a LIDAR device to be evaluated. Such a situation may occur, for example, when comparing the performance of a pair of different LIDAR devices.

"About the use of correction parameters"
- In the above embodiment, the correction parameter Dc is used to evaluate the second LIDAR device 30 to be evaluated, but the present invention is not limited to this. For example, when a plurality of LIDAR devices are mounted on a mass-produced vehicle, the correction parameter Dc may be used to reduce the deviation between the positions detected by each of the plurality of LIDAR devices. This can be realized by executing the processes of S10 to S34 in FIG. 2 by the execution device installed in each mass-produced vehicle after shipment.

“About the 1st LIDAR device”
- The division of roles between the first LIDAR CU 20 and the first optical sensor 10 in the first LIDAR device LR1 is not limited to that illustrated in the above embodiment. For example, the first LID ARECU 20 may receive a signal received by the first optical sensor 10, and based on this, the first LID ARECU 20 may generate distance measurement point data.

- In the above embodiment, the first optical sensor 10 and the first LIDAR ECU 20 are separate devices that can communicate with each other, but the invention is not limited to this, and they may be integrated.
“About the second LIDAR device”
- The division of roles between the second LIDAR CU 30 and the second optical sensor 12 in the second LIDAR device LR2 is not limited to that illustrated in the above embodiment. For example, the second LID ARECU 30 may receive a signal received by the second optical sensor 12, and based on this, the second LID ARECU 30 may generate distance measurement point data.

- In the above embodiment, the second optical sensor 12 and the second LIDAR ECU 30 are separate devices that can communicate with each other, but the invention is not limited to this, and they may be integrated.
“About the evaluation device”
- In FIG. 2, the same evaluation device 40 executes the processes S10 to S34 and the process S36, but the present invention is not limited to this. The device may be a separate device.

"About the correction device"
- The correction device is not limited to the evaluation device 40. For example, as described in the column "Uses of correction parameters," when a plurality of LIDAR devices are mounted on a mass-produced vehicle, the LIDAR devices may be mounted on the vehicle. Note that in this case, it is not essential that the correction device and the first LIDAR device LR1 and the second LIDAR device LR2 be separate members. For example, the first LID A RECU 20 and the second LID A RECU 30 may be integrated with a correction device that executes the process of calculating the correction parameter Dc.

"About the execution device"
- The execution device that executes the processes of S10 to S36 and the processes in each of their modification examples is not limited to one that includes a CPU and a ROM and executes software processing. For example, a dedicated hardware circuit (for example, ASIC, etc.) may be provided to perform hardware processing on at least a portion of what was processed by software in the above embodiments. That is, the execution device may have any of the following configurations (a) to (c). (a) It includes a processing device that executes all of the above processing according to a program, and a program storage device such as a ROM that stores the program. (b) It includes a processing device and a program storage device that execute part of the above processing according to a program, and a dedicated hardware circuit that executes the remaining processing. (c) A dedicated hardware circuit is provided to execute all of the above processing. Here, there may be a plurality of software execution devices including a processing device and a program storage device, and a plurality of dedicated hardware circuits.

10...First optical sensor 12...Second optical sensor 20...First LIDARECU
30...Second LIDAR CU
40...Evaluation device

Claims (6)

The distance measuring point data by the first LIDAR device (LR1) that receives the reflected light of the laser beam irradiated around the vehicle is defined as the first distance measuring point data, and the distance measuring point data of the first LIDAR device (LR1) that receives the reflected light of the laser beam irradiated around the vehicle is defined as the first distance measuring point data. The distance measurement point data by the 2LIDAR device (LR2) is set as the second distance measurement point data,
A target coordinate calculation process (S16) that takes one of the objects specified according to the first distance measurement point data as a target object and calculates the position coordinates of the target object based on the first distance measurement point data;
Among the objects identified according to the second distance measuring point data, an object having a minimum distance from the target object is determined to be the same object, and the position coordinates of the same object are calculated based on the second distance measuring point data. same coordinate calculation process (S44),
The absolute value of the difference between the position coordinates of the target object and the position coordinates of the same object after relative rotation within a plane extending in the longitudinal direction and the lateral direction of the vehicle is minimum. causing the execution device (42, 44) to perform a selection process (S34) of selecting the relative rotation angle when the following occurs as a correction parameter for correcting the position of the object based on the distance measurement point data;
The target coordinate calculation process includes, with respect to the position of the target object, a first axis component and a first axis component, which are components in a coordinate axis each having a pair of linearly independent directions within a plane extending in the longitudinal direction and the lateral direction of the vehicle. A process of calculating two-axis components based on the first distance measurement point data,
The same coordinate calculation process is a process of calculating the first axis component and the second axis component regarding the position of the same object based on the second distance measurement point data,
At least one of two sets: the first axis component and the second axis component calculated by the target coordinate calculation process, and the first axis component and the second axis component calculated by the same coordinate calculation process. a rotation process (S18) in which one set is input and the input first axis component and second axis component are rotated according to each of the plurality of relative rotation angles;
causing the execution device to perform an error calculation process (S24) of calculating a difference between the position coordinates of the target object and the position coordinates of the same object after being relatively rotated by the rotation process; ,
Relative velocity acquisition processing (S16) that acquires the relative velocity between the target object and the vehicle;
causing the execution device to perform a correction process (S18) in which each of the first axis component and the second axis component, which are input to the rotation process, is corrected by a first correction amount and a second correction amount, respectively; ,
The correction process includes a process of increasing the absolute value of the first correction amount and the second correction amount when the relative speed is large compared to when the relative speed is small. In the selection process, a parameter that defines a relationship between the relative velocity and the first correction amount when the absolute value of the difference between the position coordinates after the relative rotation is a minimum is selected as the correction parameter. 2. The LIDAR correction parameter generation method according to claim 1 , further comprising a process of selecting a LIDAR correction parameter. The distance measuring point data by the first LIDAR device (LR1) that receives the reflected light of the laser beam irradiated around the vehicle is defined as the first distance measuring point data, and the distance measuring point data of the first LIDAR device (LR1) that receives the reflected light of the laser beam irradiated around the vehicle is defined as the first distance measuring point data. The distance measurement point data by the 2LIDAR device (LR2) is set as the second distance measurement point data,
A target coordinate calculation process (S16) that takes one of the objects specified according to the first distance measurement point data as a target object and calculates the position coordinates of the target object based on the first distance measurement point data;
Among the objects identified according to the second distance measuring point data, an object having a minimum distance from the target object is determined to be the same object, and the position coordinates of the same object are calculated based on the second distance measuring point data. same coordinate calculation process (S44),
The absolute value of the difference between the position coordinates of the target object and the position coordinates of the same object after relative rotation within a plane extending in the longitudinal direction and the lateral direction of the vehicle is minimum. causing the execution device (42, 44) to perform a selection process (S34) of selecting the relative rotation angle when the following occurs as a correction parameter for correcting the position of the object based on the distance measurement point data ;
The same coordinate calculation process is a LIDAR correction parameter generation method including a process of specifying the object as the same object on the condition that the absolute value of the acceleration of the object having the minimum distance from the target object is equal to or less than a predetermined value. . The target coordinate calculation process includes a process of setting each of a plurality of objects recognized based on the first ranging point data as the target object,
causing the execution device to execute the same coordinate calculation process, the rotation process, and the error calculation process for each of the plurality of objects;
The selection process is a process of selecting the relative rotation angle that minimizes the sum of the absolute values of the differences for each of the plurality of objects as a correction parameter for correcting the position of the object based on the distance measurement point data. The method for generating correction parameters for LIDAR according to claim 1 or 2 . After relative rotation of the object recognition result by the first LIDAR device and the object recognition result by the second LIDAR device using the correction parameter in the correction parameter generation method according to any one of claims 1 to 4 , . A LIDAR evaluation method for evaluating the performance of at least one of the first LIDAR device and the second LIDAR device by comparing the pair of recognition results after the relative rotation. A LIDAR correction device comprising the execution device in the correction parameter generation method according to any one of claims 1 to 4 .