Vehicle position determining device, vehicle position determining method, light distribution control device, and vehicle lighting system

Abstract

To improve the accuracy in tracking using a time series of positions of a preceding vehicle.SOLUTION: In a vehicle position specification device comprising a controller, the controller obtains a duration period of a state in which a mutual change amount between successive positional data in time series is equal to a prescribed reference value or less, for each data group, with regard to a plurality of data groups respectively containing positional data in time series related to a preceding vehicle. Further, when a plurality of new positional data related to the preceding vehicle are generated, the controller obtains the strength of correlation between the latest positional data in time series of each data group and the plurality of new positional data, in order to add the plurality of new positional data respectively to any of the respective data groups, on the basis of the correlation strength, in order starting with data groups with relatively long duration periods.SELECTED DRAWING: Figure 6

Description

[Technical field]

[0001] The present disclosure relates to a vehicle position identifying device, a vehicle position identifying method, a light distribution control device, and a vehicle lighting system. [Background technology]

[0002] There is known a light distribution control technology that sets a dimming range (or a light blocking range) within the range in which the high beam can be irradiated according to the position of a vehicle ahead of the vehicle (a preceding vehicle, an oncoming vehicle, etc.) (see, for example, Patent Document 1). The position of the vehicle ahead can be obtained by detecting light points (positions of headlights and taillights) by performing image recognition processing on an image of the space ahead of the vehicle captured by, for example, a camera.

[0003] However, when tracking the position of a vehicle ahead over time, noise data can sometimes cause data to be mixed up, making it appear as if a vehicle ahead that was actually present has disappeared and been replaced by a new vehicle ahead that was not actually present. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2015-58802 A Summary of the Invention [Problem to be solved by the invention]

[0005] One of the objectives of a specific embodiment of the present disclosure is to improve the accuracy of tracking the position of a forward vehicle over time. [Means for solving the problem]

[0006] [1] A vehicle position identification device according to one aspect of the present disclosure includes: 1. A vehicle location device comprising a controller, The controller: For a plurality of data groups each including time-series position data relating to a forward vehicle, a state in which the amount of change between adjacent pieces of position data in the time series is equal to or less than a predetermined reference value for each of the data groups is continued. time Seeking A plurality of new position data for the forward vehicle The first position data is When this occurs, each of the data groups In The most recent location data in chronological order Each of the second position data is and the multiple No. 1 Find the strength of correlation with location data, Of each of the data groups Relatively speaking, time is long The From the data set In order of priority, The above No. 1 Location Data The first position data having the strongest correlation with the second position data of the data group having the long duration is obtained by making the time series of the second position data clear for the data group having the long duration. Add, A vehicle position determination device. [2] A vehicle position determination method according to one aspect of the present disclosure includes: 1. A vehicle location method executed by a controller, comprising: The controller: For a plurality of data groups each including time-series position data relating to a forward vehicle, a state in which the amount of change between adjacent pieces of position data in the time series is equal to or less than a predetermined reference value for each of the data groups is continued. time To ask for A plurality of new position data for the forward vehicle The first position data is When this occurs, each of the data groups In The most recent location data in chronological order Each of the second position data is and the multiple No. 1 Finding the strength of correlation with location data; Of each of the data groups Relatively speaking, time is long The From the data set In order of priority , each The above No. 1 Location Data The first position data having the strongest correlation with the second position data of the data group having the long duration is obtained by making the time series of the second position data clear for the data group having the long duration. To add, The vehicle position determination method includes: [3] A light distribution control device according to one aspect of the present disclosure includes: The light distribution control device controls a light distribution pattern of a vehicle lamp using each of the data groups obtained by the vehicle position identification device described in [1] above. [4] A vehicle lighting system according to one embodiment of the present disclosure, A vehicle lighting system including the light distribution control device of [3] above and a vehicle lighting device connected to the light distribution control device.

[0007] According to the above configuration, it is possible to improve the accuracy in tracking the position of the forward vehicle over time. [Brief description of the drawings]

[0008] [Figure 1] Fig. 1(A) is a diagram for explaining a situation in which a forward vehicle is erroneously detected in this embodiment, and Fig. 1(B) is a diagram showing the transition of the positions of each object in the situation assumed in Fig. 1(A). [Diagram 2] FIG. 2 is a diagram for explaining the Euclidean distance. [Diagram 3] FIG. 3 is a diagram showing a configuration of a vehicle lighting system according to an embodiment. [Figure 4] FIG. 4 is a diagram illustrating an example of the configuration of a computer that realizes the controller. [Diagram 5] FIG. 5 is a diagram for explaining the survival time of the location data. [Figure 6] Fig. 6(A) is a diagram showing an example of transition of position data, Fig. 6(B) is a diagram for explaining the tracking process of this embodiment, and Fig. 6(C) is a diagram for explaining the tracking process of a comparative example. [Figure 7] FIG. 7 is a flowchart showing an operation procedure of the controller of the vehicle lighting system. [Figure 8] FIG. 8 is a diagram illustrating an example of light distribution control using position data corresponding to each object. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] FIG. 1(A) is a diagram for explaining a situation of erroneous detection of a forward vehicle assumed in this embodiment. For example, at time (t-3), one forward vehicle exists, and its position (right angle, left angle) is detected. The right angle and left angle are, for example, the positions of tail lights arranged on the left and right of the forward vehicle. In this embodiment, it is assumed that there are a plurality of forward vehicles to be detected, and a data group including time-series position data corresponding to each object is managed by labeling each object as, for example, object A and object B. In the illustrated example, object A is not detected, and the position of object B is detected. The position data of the object is specified by a relative angle based on the host vehicle. In the illustrated example, the position data of object B is -1.2° left angle and +0.8° right angle.

[0010] If a false positive occurs at the next time (t-2) even though there is only one vehicle ahead, the positions of two vehicles ahead are detected as being present. At this time, the position data of each object A and B is acquired in association with each object. Since this is a false positive for a vehicle ahead that is actually one, the position data of object A and the position data of object B are very close to each other. In the illustrated example, the position data of object A is specified as a left angle of -0.9° and a right angle of +1.0°, and the position data of object B is specified as a left angle of -1.2° and a right angle of +0.8°. It is assumed that a similar false positive occurs at the next time (t-1). In the illustrated example, the position of object A is specified as a left angle of -0.9° and a right angle of +0.9°, and the position of object B is specified as a left angle of -1.2° and a right angle of +0.8°.

[0011] At the next time (t), the false detection is resolved, object A is not detected, and the position of object B is detected. In the illustrated example, the position data of object B is specified as a right angle of -1.2° and a left angle of +0.8°.

[0012] FIG. 1(B) is a diagram showing the transition of the position of each object in the situation assumed in FIG. 1(A). Here, the transition of the right angle is shown for ease of explanation, but the same is true for the left angle. The data group for the right angle of object B continues to be included in the normal detection range and transitions gradually. In contrast, the data group for the right angle of object A, which should not actually exist, was outside the normal detection range (not detected), but suddenly enters the normal detection range at time (t-2) and remains included in the normal detection range until the next time (t-1).

[0013] Here, even if position data corresponding to object A suddenly appears as shown in the figure, the position data of objects A and B will not be swapped as long as the data group of position data for each object is managed while maintaining continuity. However, for example, if the continuity (strength of correlation) of the position data of each object between one time and the next time is determined based only on Euclidean distance, the position data associated with each object A and B may be swapped. If such a swap occurs, it will cause inconvenience in a system that controls a light distribution pattern according to a change in the position of an object, for example.

[0014] In this embodiment, the Euclidean distance is an index that indicates the strength of correlation between position data, and is represented by the distance D between plotted points when position data is plotted by associating right and left angles at a certain time and the next time on two axes, as shown in Fig. 2. In the example shown in Fig. 1(B), if the Euclidean distance is calculated between two position data at time (t-2) and two position data at the next time (t-1), and the position data with the smallest Euclidean distance are determined to be consecutive, the position data may be swapped as described above in some cases.

[0015] Hereinafter, an embodiment will be described that makes it possible to prevent replacement of position data that may occur when position data is added to the data group of each object by determining the strength of correlation only based on the Euclidean distance between position data.

[0016] 3 is a diagram showing the configuration of a vehicle lighting system according to an embodiment. The vehicle lighting system shown in the figure is for irradiating light to the space around (here, in front of) the vehicle, and selectively irradiates light by setting a dimming range according to the position of the vehicle ahead within a so-called high beam irradiable range and setting the remaining range as the light irradiation range. Such light distribution control is also called ADB (Adaptive Driving Beam) control, and contributes to improving the visibility around the vehicle.

[0017] The illustrated vehicle lighting system includes an imaging device 10, a controller (controller) 20, and a pair of headlamp units (vehicle lighting devices) 30L, 30R. The imaging device 10 and the controller 20 are connected by a predetermined communication means. The controller 20 and each of the headlamp units 30L, 30R are connected via a predetermined wiring. In this embodiment, the imaging device 10 and the controller 20 correspond to a "vehicle position identification device" and also correspond to a "light distribution control device."

[0018] The imaging device 10 includes a camera 11 and an image processor 12, and detects the position of a vehicle ahead of the vehicle by performing predetermined image processing in the image processor 12 based on an image captured by the camera 11. In this embodiment, the imaging device 10 detects the left and right positions of each vehicle ahead as the position of each vehicle ahead. The left and right positions here correspond to the positions of the taillights when the vehicle ahead is a leading vehicle, and correspond to the positions of the headlights when the vehicle ahead is an oncoming vehicle. The position data of the vehicle ahead is expressed as a relative angle with respect to the vehicle ahead. In addition, when there are multiple vehicles ahead, labeling data is added to each vehicle ahead, and the position data (or signal) is output to the controller 20.

[0019] The controller 20 specifies the position of the vehicle ahead based on the position data of each vehicle ahead output from the imaging device 10, sets a light distribution pattern including a dimming range and a light irradiation range corresponding to each vehicle ahead according to the specified position, and controls the headlamp units 30L and 30R so that light is irradiated according to the light distribution pattern. The controller 20 is realized by using a computer having, for example, a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM (Random Access Memory), etc., and executing a predetermined operation program in the computer. In order to make it easier to understand the functions realized by the controller 20, the following description will be given using functional blocks. The controller 20 has a vehicle position acquisition unit 21, a survival time calculation unit 22, a tracking processing unit 23, a light distribution pattern setting unit 24, and a control signal generation unit 25 as functional blocks. The survival time calculation unit 22 corresponds to the "duration calculation unit", and the tracking processing unit 23 corresponds to the "correlation calculation unit" and the "data addition unit".

[0020] The vehicle position acquisition unit 21 acquires the position data (or signals) of the preceding vehicle output from the imaging device 10. The position data of each preceding vehicle is obtained in time series at predetermined time intervals (e.g., every 50 milliseconds). In addition, data on the type of preceding vehicle (preceding vehicle, oncoming vehicle, etc.) is added to the position data.

[0021] The survival time calculation unit 22 calculates a survival time, which is the time during which a change in the amount of change between adjacent position data in a time series is equal to or less than a predetermined reference value, for a plurality of data groups including position data relating to a forward vehicle, each of which is obtained in a time series. The method of calculating the survival time will be described later in detail.

[0022] When multiple new position data regarding a vehicle ahead are obtained, the tracking processing unit 23 calculates the strength of correlation between each data group and the most recent position data in the time series of each data group, and performs a process (tracking process) of adding each of the new position data to one of the data groups based on the strength of correlation, starting with the data group with the relatively longest survival time.

[0023] The light distribution pattern setting unit 24 sets a light distribution pattern in which, in the high beam irradiable range, a predetermined range including the position of each forward vehicle is set as a dimming range and the other range is set as a light irradiation range in accordance with the position of the forward vehicle.

[0024] The control signal generating section 25 generates a control signal according to the light distribution pattern set by the light distribution pattern control section 24, and supplies this to each of the headlamp units 30L, 30R.

[0025] Each of the headlamp units 30L, 30R operates in response to a control signal supplied from the controller 20 to irradiate light ahead of the host vehicle, and each of the headlamp units 30L, 30R includes an ADB unit 31 and a low beam unit 32.

[0026] The ADB unit 31 is a lamp unit capable of selectively irradiating light in the high beam irradiation range according to the position of the vehicle ahead. As such an ADB unit 31, various known units can be used, such as one that forms irradiation light by combining a light source and a liquid crystal element, one that forms irradiation light by individually turning on and off a plurality of light emitting elements (LEDs), one that forms irradiation light by scanning light emitted from a laser element with a MEMS device or the like, and one that forms irradiation light by partially blocking the light of the light source using a shielding plate (shade).

[0027] The low beam unit 32 is a lamp unit capable of irradiating light to an area relatively closer to the vehicle than the high beam. Various known units can be used as the low beam unit 32. The low beam may be formed by a single unit having both the function of the low beam unit 32 and the function of the ADB unit 31.

[0028] 4 is a diagram showing an example of the configuration of a computer that realizes a controller. The computer shown in the figure includes a CPU 201, a ROM 202, a RAM 203, a storage device 204, and an external interface (I / F) 205 that are communicably connected to each other. The CPU 201 operates based on a basic control program read from the ROM 202, and realizes the above-mentioned functions of the controller 20 by reading and executing a program (application program) 206 stored in the storage device 204. The RAM 203 temporarily stores data to be used during the operation of the CPU 201. The storage device 204 is a non-volatile storage device such as a hard disk or a solid state drive, and stores various data such as the program 206. The external interface 205 is an interface that connects the CPU 201 to an external device, and is used to connect, for example, the imaging device 10 to the CPU 201.

[0029] FIG. 5 is a diagram for explaining the survival time of position data. As shown in FIG. 1(B) above, the position data of the data group corresponding to object A due to erroneous detection suddenly appears and then immediately becomes a value outside the detection range (for example, a certain upper limit value), so the detection of the position data continues for a short time. In contrast, the position data of the data group corresponding to object B is continuously detected, so the detection of the position data continues for a long time. Taking such characteristics into consideration, in this embodiment, the data group of position data corresponding to each object is managed in chronological order, and the survival time, which is the time the detection continues, is obtained. The upper part of the figure shows the transition of the right angle, but the transition of the left angle is similar. The lower part of the figure shows the transition of the survival time.

[0030] As shown in the example, if the position data (here, the right angle) of the data group corresponding to the object does not fluctuate significantly over time and remains within a certain threshold range (below the reference value), the survival time is gradually increased. A long survival time means that the position data of the data group has high continuity and high reliability (accuracy). This survival time is long for a data group that includes position data that has been detected correctly and has not been detected erroneously. On the other hand, if the position data fluctuates significantly outside the threshold range, such as points a and b in the figure, the survival time is reset and recounted from the initial value (for example, 0). A short survival time means that the data group including that position data has low continuity and low reliability (accuracy). When a false detection occurs, the position data changes suddenly, so the survival time is short.

[0031] A method of counting the survival time will be specifically described. In each data group, the right angle at a certain time is θa, and the right angle at the time immediately before is θb, and the case is classified according to whether or not the absolute value of the difference (amount of change) between them, |θa-θb|, is equal to or less than a predetermined threshold value N. Here, the threshold value N may be a single value regardless of the type of the forward vehicle, and it is also preferable to set it individually according to the type. In this embodiment, it is set individually according to the type of the forward vehicle. As an example, the threshold value N1 is set to 0.7° when the type is a preceding vehicle, the threshold value N2 is set to 1.5° when the type is an oncoming vehicle, and the threshold value N3 is set to 1.0° when the type is other than the preceding vehicle / oncoming vehicle. Similarly, the case is classified according to whether or not the left angle is equal to or less than the threshold value. Then, when the absolute value of the difference between both the right angle and the left angle is equal to or less than the threshold value, the survival time is increased by a certain value. For example, the survival time is increased by +1. On the other hand, when the absolute value of the difference between either the right angle or the left angle exceeds the threshold value, the change amount is too large and the continuity is low, so the survival time is reset to the initial value. As a result, the survival time of the data group of object B described above becomes longer, and the survival time of the data group of object A becomes shorter. By using the survival time calculated in this way in combination with the Euclidean distance between the position data described above, it is possible to prevent the replacement of position data, as will be described below.

[0032] FIG. 6(A) is a diagram showing an example of the transition of position data. This diagram corresponds to the example of the transition of the position data shown in FIG. 1(B) described above. Here, for the sake of easy understanding, a right angle is shown as the position data, but the same applies to the left angle. Also, the Euclidean distance is obtained by associating the right angle and the left angle with two axes as described above. As shown in the figure, in a situation where the position data corresponding to the data group of object B (indicated by black circles in the figure) has been continuously detected since before time (t-2), consider the case where the position data corresponding to the data group of object A (indicated by black triangles in the figure) occurs due to false detection at time (t-2) and time (t-1). At this time, for the position data (black circles) at time (t), the Euclidean distances from each position data at the previous time (t-1) are x1 and x2 respectively, and it is assumed that these relationships are x2 < x1. Also, assume that the data group corresponding to object B has a longer survival time than the data group of object A.

[0033] At this time, in the present embodiment, first, the association of the position data based on the Euclidean distance (that is, the addition to the data group) is performed in order from the data group with the longer survival time between the position data at the next time. Specifically, as shown in FIG. 6(B), the distance between the position data (black circles) of the data group of object B at the most recent time (t-1) and the position data at the next time (t) is obtained. It is assumed that there is other position data outside the drawing range of the figure (see FIG. 1(B)). When comparing the obtained Euclidean distances x1 and x3, x1 is shorter, so the position data with this Euclidean distance of x1 is associated as the tracking destination of the data group of object B.

[0034] Next, the distance between the data group of object A (black triangle), which is a data group with a short survival time, and the position data at the next time (t) is calculated. In this case, when comparing the Euclidean distances x2 and x4, x2 is shorter, but since the position data whose Euclidean distance corresponds to x2 has already been associated as the tracking destination of the data group of object B, other position data outside the drawing range of the figure is associated as the tracking destination of the data group of object A.

[0035] In this way, by first associating (adding to the data group) location data with the longest survival time based on Euclidean distance, the continuity of the location data in the data group of object B is maintained and the mismatch of location data is prevented. In contrast, as shown in the comparative example in Fig. 6(C), if the concept of survival time is not used and the association is simply based on the Euclidean distance between each piece of location data at time (t-1) and time (t), the tracking destination will be mismatched. Specifically, object B disappears (is not detected) after time (t-1), and only object A is present (detected) at time (t).

[0036] 7 is a flowchart showing the operation procedure of the controller of the vehicle lighting system. The operation procedure shown here is repeated at regular intervals (for example, every 50 ms). Note that the order of the illustrated processing blocks may be changed and other processing blocks not shown may be added as long as no contradiction or inconsistency occurs in the results of information processing, and such embodiments are not excluded.

[0037] The vehicle position acquisition unit 21 of the controller 20 acquires position data (right angle, left angle) of the forward vehicle from the imaging device 10 (step S11). When there are multiple forward vehicles, label data for distinguishing each forward vehicle may be added to the position data output from the imaging device 10, but in this embodiment, the controller 20 manages the forward vehicles (objects) corresponding to each position data regardless of the label data. This is because the label data added by the imaging device 10 does not necessarily correspond uniquely to each forward vehicle, and the correspondence may change at each time.

[0038] The survival time calculation unit 22 of the controller 20 calculates the survival time of each data group, and stores the survival times in memory in association with the data group of each object (step S12). Note that, in the initial processing, previous position data does not exist, and in that case, for example, a predetermined initial value can be used to calculate the survival time, or the survival time can be uniformly set to 0.

[0039] Next, the tracking processing unit 23 of the controller 20 performs tracking processing of the new position data acquired in step S11 in order of the longest survival time based on the survival time of the data group of each object obtained in the previous processing opportunity (step S13). For example, if the survival time of the data group of object B is relatively long, the tracking processing is performed on the previous position data (most recent position data) corresponding to this object B based on the Euclidean distance first, i.e., the tracking processing is performed on the previous position data corresponding to another object A based on the Euclidean distance.

[0040] When the tracking process is completed, the tracking processing unit 23 stores the data group to which the new position data for each object has been added in the memory (step S14). The position data is stored in the memory in such a way that the time series of the previous and previous data can be understood, and the data is linked to the survival time.

[0041] When the position data corresponding to each object is determined, the light distribution pattern setting unit 24 sets a light distribution pattern using the position data corresponding to each object (step S15), and a control signal corresponding to this set light distribution pattern is generated by the control signal generating unit 25 and output to each of the vehicle lamps 30L and 30R. Then, the process returns to step S11.

[0042] FIG. 8 is a diagram showing a schematic example of light distribution control using position data corresponding to each object. In the illustrated example, a vehicle ahead 100 exists, and light is irradiated according to a light distribution pattern in which a certain range including the vehicle ahead 100 within the so-called high beam irradiation range is set as a dimming range (or non-irradiation range) 110a, and the rest is set as a light irradiation range 110b. At this time, the vehicle ahead 100 is changing lanes to the right in the figure, and control is performed to relatively reduce the brightness of a part 100c (a part close to the vehicle ahead 100) of the light irradiation range 110b corresponding to the moving direction in advance. This control is performed for the purpose of preventing glare from being given to the vehicle ahead 100 even when a delay occurs in the control to switch from the light irradiation range 110b to the dimming range 110a due to a sudden movement of the vehicle ahead 100, for example. When performing such control, a transition of the position data corresponding to the vehicle ahead 100 is required. By preventing the replacement of the object corresponding to the vehicle ahead 100 and obtaining continuous position data, such light distribution control can be performed with high accuracy.

[0043] According to the embodiment described above, it is possible to improve the accuracy in tracking the position of a forward vehicle over time.

[0044] The present disclosure is not limited to the contents of the above-mentioned embodiment, and various modifications can be made within the scope of the gist of the present disclosure. For example, in the above-mentioned embodiment, the controller 20 generates two pieces of position data of the vehicle ahead, but three or more pieces of position data may be generated. Even in this case, each piece of position data is individually associated with an object to obtain their survival time, and the tracking destination of each piece of position data at the next time is determined based on their survival time and Euclidean distance.

[0045] In addition, in the above embodiment, an example of light distribution control using position data corresponding to each object is given as a control in which the movement direction of a vehicle ahead is predicted and the brightness of a part of the light irradiation range is reduced in accordance with that movement direction, but the content of the light distribution control is not limited to this. [Explanation of symbols]

[0046] 10: imaging device, 11: camera, 12: image processing processor, 20: controller, 21: vehicle position acquisition unit, 22: survival time calculation unit, 23: tracking processing unit, 24: light distribution pattern setting unit, 25: control signal generation unit, 30L, 30R: vehicle lamp, 31: ADB unit, 32: low beam unit

Claims

1. 1. A vehicle location device comprising a controller, The controller: determining, for each of a plurality of data groups each including time-series position data relating to a forward vehicle, a duration during which an amount of change between adjacent time-series position data is equal to or less than a predetermined reference value; When a plurality of first position data, which are new position data relating to the forward vehicle, are generated, a strength of correlation between each of the second position data, which is the most recent position data in time series in each of the data groups, and the plurality of first position data is calculated; Adding the first position data having the strongest correlation with the second position data of the data group having the longest duration among the first position data to the data group having the longest duration in a manner that the time series with the second position data is clear, in order of priority from the data group having the relatively longest duration among the data groups; Vehicle location device.

2. 1. A vehicle location device comprising a controller, The controller: a duration calculation unit that calculates, for each of a plurality of data groups each including time-series position data relating to a forward vehicle, a duration of a state in which an amount of change between adjacent time-series position data of the forward vehicle is equal to or less than a predetermined reference value; a correlation calculation unit that, when a plurality of first position data are obtained, calculates a strength of correlation between each of the plurality of first position data and each of the second position data, which is the most recent position data in time series in each of the data groups; and a data adding unit that adds, in order of priority from the data group having the relatively longest duration among the data groups, the first position data that has the strongest correlation with the second position data of the data group having the longest duration among the first position data based on the strength of the correlation, to the data group having the longest duration in such a way that a time series with the second position data can be known; A vehicle location determination device comprising:

3. It is determined that the correlation is stronger as the Euclidean distance between each of the plurality of new position data and the nearest position data in each of the data groups is smaller.

3. A vehicle position determining device according to claim 1 or 2.

4. the duration increases as the amount of change between the position data successive in time series in each of the data groups continues to be equal to or less than the reference value, and is reset to an initial value when the amount of change exceeds the reference value; The vehicle position specifying device according to any one of claims 1 to 3.

5. The reference value is set to a different value depending on the type of the forward vehicle. The vehicle position specifying device according to any one of claims 1 to 4.

6. 1. A vehicle location method executed by a controller, comprising: The controller: determining, for each of a plurality of data groups each including time-series position data relating to a forward vehicle, a duration during which an amount of change between adjacent time-series position data of the data group is equal to or less than a predetermined reference value; When a plurality of first position data, which are new position data relating to the forward vehicle, are generated, a strength of correlation between each of the second position data, which are the most recent position data in time series in each of the data groups, and the plurality of first position data is obtained; adding, in order of priority from the data group having the relatively longest duration among the data groups, the first position data having the strongest correlation with the second position data of the data group having the longest duration among the first position data, to the data group having the longest duration in such a manner that a time series with the second position data can be understood; A vehicle location method comprising:

7. A light distribution pattern of a vehicle lamp is controlled by using each of the data groups obtained by the vehicle position identification device according to any one of claims 1 to 5. Light distribution control device.

8. A vehicle lamp including the light distribution control device according to claim 7 and a vehicle lamp connected to the light distribution control device. Vehicle lighting system.

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