JP2015204068A - Approach detector and approach detection method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an approach detector capable of preventing a moving object from taking an undesired avoidance action as well as preventing the safe of the moving object from being degraded even when the situation relevant to the moving object changes.SOLUTION: When a difference is detected between a prediction error distribution calculated by a prediction error distribution calculation section 14 and a prediction error distribution stored in the prediction error distribution adjustment information data base 15, the prediction error distribution stored in the prediction error distribution adjustment information data base 15 is adjusted by using the sensitivity adjustment parameters α, α, βand βwhich are set according to the degree of reliability of the prediction error distribution calculated by the prediction error distribution calculation section 14.

Description

  The present invention relates to an approach detection device and an approach detection method for detecting an approach between a plurality of moving bodies when controlling traffic of the moving body (for example, a ship, an airplane, etc.).

There is an approach detection device that detects a time when an interval between a plurality of moving bodies becomes a predetermined value or less using a predicted trajectory value indicating a predicted trajectory of the moving body.
However, since the predicted trajectory value includes an error, it is difficult to detect with high accuracy the time when the interval between the plurality of moving objects is equal to or less than the predetermined value only by using the predicted trajectory value.
Accordingly, an approach detection device has been developed that not only detects the time when the interval between moving bodies is less than or equal to a predetermined value, but also calculates an approach probability in consideration of the prediction error of the trajectory (see, for example, Non-Patent Document 1). ).

In the approach detection device disclosed in Non-Patent Document 1, the prediction error of the trajectory of each moving object is modeled by a normal distribution (see FIG. 5), and the probability distribution of the prediction errors of the trajectories of a plurality of moving objects is overlapped. By integrating, an approach probability between a plurality of moving objects is calculated.
Here, the average and standard deviation of the normal distribution that models the prediction error are analyzed by analyzing the occurrence status of the prediction error for a set of orbital observations that indicate the trajectory of the moving object obtained from the radar in the past. Set by.

Heinz Erzberger, "Conflict Detection and Resolution In the Presence of Prediction Error", 1st USA / Europe Air Traffic Management R & D Seminar, 1997

Since the conventional approach detection device is configured as described above, even after setting the prediction error distribution of the moving object, even if the situation related to the moving object changes (for example, the position or speed of the moving object, the weather, etc.) Changes in traffic volume, traffic volume, etc.), and the prediction error distribution of moving objects is not adjusted. For this reason, when the set prediction error distribution may be too large or too small, and when the prediction error distribution is too large, an unnecessary approach avoidance action of the moving body occurs, and the prediction When the error distribution is too small, there is a problem that the safety of the moving body is lowered.
Note that a method of using the prediction error covariance provided by the tracking device as a setting value of the prediction error distribution is conceivable, but this method may cause a sudden change or an unstable change of the prediction error distribution. There is sex.

  The present invention has been made to solve the above-mentioned problems, and prevents occurrence of unnecessary approach avoidance behavior of the moving body and deterioration of the safety of the moving body even if the situation relating to the moving body changes. It is an object of the present invention to provide an approach detection device and an approach detection method that can be used.

  The approach detection device according to the present invention includes a database that stores initial values of prediction error distributions of a plurality of moving objects, and an orbit observation value that indicates the trajectory of the moving object for each moving object that exists in the observation area. A prediction error distribution calculating means for calculating a prediction error distribution that is a difference from the predicted trajectory value indicating the predicted trajectory of the mobile body, and an adjustment value corresponding to the reliability of the prediction error distribution calculated by the prediction error distribution calculating means The difference between the adjustment value setting means for setting the prediction error distribution calculated by the prediction error distribution calculation means and the prediction error distribution stored in the database for each moving object existing in the observation area If there is a prediction error distribution adjusting means for adjusting the prediction error distribution stored in the database using the adjustment value set by the adjustment value setting means, the approach probability calculating means is provided with an observation area. The approach probability between multiple mobile objects is calculated using the predicted trajectory value indicating the predicted trajectory of the multiple mobile objects existing in the database and the prediction error distribution of the multiple mobile objects stored in the database. It is what I did.

  According to the present invention, the adjustment value setting means for setting the adjustment value corresponding to the reliability of the prediction error distribution calculated by the prediction error distribution calculation means, and the prediction error for each mobile unit existing in the observation area. If there is a difference between the prediction error distribution calculated by the distribution calculation means and the prediction error distribution stored in the database, the difference is stored in the database using the adjustment value set by the adjustment value setting means. A prediction error distribution adjusting means for adjusting the prediction error distribution, and the approach probability calculating means includes a predicted trajectory value indicating a predicted trajectory of a plurality of mobile objects existing in the observation area, and a plurality of stored in the database. Since the approach probability between multiple mobile objects is calculated using the prediction error distribution of the mobile object, even if the situation related to the mobile object changes, unnecessary approach avoidance action of the mobile object is generated. And, there is an effect capable of preventing the deterioration of the safety of the movable body.

It is a block diagram which shows the approach detection apparatus by Embodiment 1 of this invention. It is a flowchart which shows the processing content (approach detection method) of the approach detection apparatus by Embodiment 1 of this invention. 4 is a flowchart showing a method for setting an adjustment sensitivity parameter by a prediction error distribution adjustment unit 16. It is explanatory drawing which shows the example of a setting of an adjustment sensitivity parameter. It is explanatory drawing which shows the image which modeled the prediction error of the track | orbit of each moving body which exists in an observation area by normal distribution.

Embodiment 1 FIG.
1 is a block diagram showing an approach detection apparatus according to Embodiment 1 of the present invention.
In FIG. 1, a trajectory prediction apparatus 1 is a known trajectory prediction apparatus that predicts the position and speed of a moving body after a predetermined time by inputting, for example, an observation value indicating the position and speed of the moving body to a Kalman filter or the like.
In the example of FIG. 1, the trajectory prediction device 1 is provided outside the approach detection device 2, but may be provided inside the approach detection device 2.

The orbit observation information database 11 is composed of, for example, a storage device such as a RAM or a hard disk. An orbit observation value indicating the orbit of each moving body is given from a radar device or a communication device (not shown), and the orbit observation value is obtained. Is stored.
This orbit observation value includes, for example, information such as a moving body ID for identifying the moving body, an observation time, and a position / speed of the moving body.

The prediction error distribution initial value database 12 is composed of a storage device such as a RAM or a hard disk, for example, and is given initial values of prediction error distributions of a plurality of moving bodies, and initial values of prediction error distributions of the plurality of moving bodies. Is stored.
For example, when the prediction error distribution is modeled as a normal distribution, the standard deviation and the initial value regarding the average are stored for each main axis of the prediction error distribution.
For example, when the moving body is a ship, the main axis of the prediction error distribution is two-dimensional “lateral / traveling direction”, and when the moving body is an aircraft, the main axis is “horizontal / traveling direction / altitude direction”. It is possible to become.
In the first embodiment, for the sake of simplicity of explanation, a case where the prediction error distribution is modeled by a two-dimensional normal distribution will be described as an example.
Regarding the two-dimensional normal distribution, the horizontal axis is the x axis, the traveling direction is the y axis, the average of the prediction error distribution is μ _X , μ _Y , and the standard deviation of the prediction error distribution is σ _X , σ _Y.

As the initial value of the prediction error distribution of the moving object, a different value is set depending on the classification of the moving object, the moving phase, the moving environment, and the like. The classification is performed by the following items, for example.
[Type of mobile object]
Ship: Large ship / Small ship / High speed ship Aircraft: Large machine / Medium machine / Small machine [Movement phase]
Ship: Near land / offshore Aircraft: Climb / cruise / descent [mobile environment]
Traffic volume: High / Low Weather: Sunny / Cloudy / Rain / Fog Weather: Wind direction, wind speed Direction and speed of tide / current (in case of ship)

The prediction error distribution initial value database 12 also stores information on the moving environment of the moving body used when setting the initial value of the prediction error distribution.
As information related to the moving environment of the moving object, the following information can be considered.
Traffic volume: Number of mobile objects present in the observation area Weather: Sunny, cloudy, rain, fog Meteorology: Wind direction, wind speed, rainfall Tidal current / current (in case of ship): Direction of flow, speed For example, prediction error If the initial value of the distribution is set by analyzing a set of trajectory observations indicating the trajectories of multiple moving bodies acquired from radars in the past, each information on traffic volume, weather, weather, tidal current and ocean current Is considered to store the average of the values in each mobile unit.

The moving environment database 13 is composed of a storage device such as a RAM or a hard disk, for example, and is given information on the moving environment of the moving object when the orbit observation value indicating the orbit of the moving object is observed. Stores information about the environment.
As information related to the moving environment of the moving object, the following information can be considered.
Traffic volume: Number of moving objects in the observation area Weather: Sunny, cloudy, rain, fog Meteorology: Wind direction, wind speed, rainfall Tidal current / current (in the case of a ship): Direction of flow, speed Schedule confirmation status : "Destination or next between the traffic controller and the mobile operator
Information indicating whether or not the “arrival time at the waypoint” has been confirmed. Movement prohibited area: Accident occurrence area, thundercloud occurrence area (for aircraft)
(Information on prohibited areas suddenly occurs)

The prediction error distribution calculation unit 14 is composed of, for example, a semiconductor integrated circuit on which a CPU is mounted, a one-chip microcomputer, or the like, and an orbit observation value indicating the orbit of each moving body stored in the orbit observation information database 11. Is acquired, and the trajectory observation value is given to the trajectory prediction device 1 so that the trajectory prediction value indicating the predicted trajectory of each moving body is acquired from the trajectory prediction device 1.
Further, the prediction error distribution calculation unit 14 is the difference between the trajectory observation value indicating the trajectory of the mobile object and the predicted trajectory value indicating the predicted trajectory of the mobile object for each mobile object existing in the observation area. A process of calculating a prediction error distribution and calculating an average and a standard deviation of the prediction error distribution is performed. The prediction error distribution calculating unit 14 constitutes a prediction error distribution calculating unit.

The prediction error distribution adjustment information database 15 is composed of, for example, a storage device such as a RAM or a hard disk, and stores the following information regarding each moving object.
・ Classification of initial values of prediction error distribution (type of mobile object, mobile phase, mobile environment, etc.)
A history of the average (μ _{EP_X} , μ _{EP_Y} ) and standard deviation (σ _{EP_X} , σ _{EP_Y} ) of the prediction error distribution calculated by the prediction error distribution calculation unit 14, and calculation of the prediction error distribution by the prediction error distribution calculation unit 14 Number of orbital observation data used N _EP
The average (μ _{C — X} , μ _{C — Y} ) and standard deviation (σ _{C — X} , σ _{C — Y} ) of the prediction error distribution adjusted by the prediction error distribution adjustment unit 16
In the initial stage, the initial value of the prediction error distribution stored in the prediction error distribution initial value database 12 is stored.
As an adjustment sensitivity parameter calculated by the prediction error distribution adjustment unit 16, an average adjustment sensitivity parameter of the prediction error distribution (adjustment sensitivity parameter α _X for horizontal direction, adjustment sensitivity parameter α _Y for travel direction), and prediction error Adjustment sensitivity parameter for standard deviation of distribution (adjustment sensitivity parameter β _X for lateral direction, adjustment sensitivity parameter β _Y for traveling direction)

  The prediction error distribution adjustment unit 16 is composed of, for example, a semiconductor integrated circuit on which a CPU is mounted, a one-chip microcomputer, or the like, and an adjustment corresponding to the reliability of the prediction error distribution calculated by the prediction error distribution calculation unit 14. A process for setting a sensitivity parameter (adjustment value) is performed. That is, the prediction error distribution adjustment unit 16 has a higher reliability of the prediction error distribution calculated by the prediction error distribution calculation unit 14 than a preset threshold (the reliability of the prediction error distribution is high (hereinafter, “high reliability”). In the case of “degree”), an adjustment value for high reliability is set as the adjustment sensitivity parameter of the prediction error distribution, and the reliability of the prediction error distribution calculated by the prediction error distribution calculation unit 14 is lower than the threshold value. In this case (when the reliability of the prediction error distribution is low (hereinafter referred to as “low reliability”)), the adjustment sensitivity parameter of the prediction error distribution is adjusted for low reliability smaller than the adjustment value for high reliability. Perform the process to set the value.

The prediction error distribution adjustment unit 16 also calculates the average (μ _{EP_X} , μ _{EP_Y} ) and standard deviation (σ _{EP_X} ) of the prediction error distribution calculated by the prediction error distribution calculation unit 14 for each moving object existing in the observation area. , Σ _{EP_Y} ) and the average (μ _{C_X} , μ _{C_Y} ) and standard deviation (σ _{C_X} , σ _{C_Y} ) of the prediction error distribution stored in the prediction error distribution adjustment information database 15 are set. The adjusted sensitivity parameters α _X , α _Y , β _X , β _Y are used to _adjust the average (μ _{C_X} , μ _{C_Y} ) and standard deviation (σ _{C_X} , σ _{C_Y} ) of the prediction error distribution, and make prediction after adjustment The average (μ _{C — X} , μ _{C — Y} ) and standard deviation (σ _{C — X} , σ _{C — Y} ) of the error distribution are overwritten in the prediction error distribution adjustment information database 15. The prediction error distribution adjustment unit 16 constitutes an adjustment value setting unit and a prediction error distribution adjustment unit.

The approach detection unit 17 is composed of, for example, a semiconductor integrated circuit on which a CPU is mounted, a one-chip microcomputer, or the like, and orbit observation values indicating the trajectories of a plurality of moving bodies stored in the orbit observation information database 11 are obtained. The trajectory observation value is obtained and given to the trajectory prediction apparatus 1, thereby executing a process of acquiring trajectory prediction values indicating the predicted trajectories of a plurality of moving bodies from the trajectory prediction apparatus 1.
Further, the approach detection unit 17 calculates the trajectory prediction value indicating the predicted trajectory of the plurality of moving bodies and the average (μ _{C_X} , μ _{C_Y} ) of the prediction error distributions of the plurality of moving bodies stored in the prediction error distribution adjustment information database 15. _And the process which calculates the approach probability between several mobile bodies is implemented using standard deviation ((sigma) _{C_X} , (sigma) _{C_Y} ). Note that the approach detection unit 17 constitutes an approach probability calculation unit.

In the example of FIG. 1, the orbit observation information database 11, the prediction error distribution initial value database 12, the movement environment database 13, the prediction error distribution calculation unit 14, the prediction error distribution adjustment information database 15, and the prediction error that are components of the approach detection device. Although each of the distribution adjustment unit 16 and the approach detection unit 17 is assumed to be configured by dedicated hardware, the approach detection device may be configured by a computer.
When the approach detection device is configured by a computer, the orbit observation information database 11, the prediction error distribution initial value database 12, the movement environment database 13 and the prediction error distribution adjustment information database 15 are configured on the internal memory or external memory of the computer, A program in which the processing contents of the prediction error distribution calculation unit 14, the prediction error distribution adjustment unit 16, and the approach detection unit 17 are described is stored in the memory of a computer, and the CPU of the computer executes the program stored in the memory. You just have to do it.
FIG. 2 is a flowchart showing the processing contents (approach detection method) of the approach detection device according to Embodiment 1 of the present invention.

Next, the operation will be described.
The orbit observation information database 11 is provided with an orbit observation value D _R = {d _Ri = (x _Ri , y _Ri ) indicating the orbit of each moving object existing in the observation area from a radar device or a communication device (not shown). ; i = 1, 2, · · _·, each _{of N EP}} is given, and stores the orbit observations _{D R} that indicates the trajectory of each mobile.
The prediction error distribution initial value database 12 stores the initial values of the prediction error distributions of a plurality of mobile objects, given the initial values of the prediction error distributions of the plurality of mobile objects in advance.
In the first embodiment, since the prediction error distribution is modeled by a two-dimensional normal distribution, the average μ _X and μ _Y of the prediction error distribution and the standard deviation σ _X , Let σ _Y be given.

Prediction error distribution calculation section 14, a new trajectory observations D _R is each time stored in orbit observation information database 11, and acquires a new trajectory observations D _R from orbit observation information database 11.
Prediction error distribution calculation unit 14 refers to the mobile ID included in the new orbital observations, whether it is information related to the mobile for its trajectory observed value D _R is detected in the new in the observation area (Step ST1), if the information is about a newly detected moving body, the prediction error distribution (μ _{C_X} , μ _{C_Y} , σ _{C_X} ) of the newly detected moving body from the prediction error distribution initial value database 12 , Σ _{C_Y} ) is acquired, and initial values of the prediction error distributions (μ _{C_X} , μ _{C_Y} , σ _{C_X} , σ _{C_Y} ) are registered in the prediction error distribution adjustment information database 15, and the prediction error distribution The classification of the initial value is registered in the prediction error distribution adjustment information database 15 (step ST2).
The prediction error distribution calculation unit 14 notifies the prediction error distribution adjustment unit 16 that the adjustment process of the prediction error distribution is not necessary when the information is about a newly detected moving object (step ST3).

Prediction error distribution calculation unit 14 is not a mobile a new trajectory observations D _R is detected newly, if it is already information on previously detected moving body is included in the new orbital observations D _R comparing the data number _{N EP} and the reference data number _{N ref} (step ST4), when the data number _{N EP} contained in the new orbital observations _{D R} is less than the reference data number _{N ref,} the prediction error distribution adjusting process Is notified to the prediction error distribution adjusting unit 16 (step ST3).
Prediction error distribution calculation unit 14, when the data number N _EP contained in the new orbital observations D _R is greater than or equal to the reference data number N _ref, by giving its orbit observations D _R the track prediction unit 1, The trajectory prediction value D _P = {d _Pi = (x _Pi , y _Pi ); i = 1, 2,..., N _EP } indicating the predicted trajectory of the moving object is acquired from the trajectory prediction device 1 (step ST5). ).
Incidentally, the trajectory prediction value D _P, if the trajectory observed value D _R is the observed value of ΔT time period, a prediction result of the trajectory of the moving object to be predicted start time before ΔT time.

Prediction error distribution calculation unit 14 obtains the trajectory prediction value D _P from the orbit predictions apparatus 1 showing the predicted trajectory of the moving object, and trajectory observed value D _R that indicates the trajectory of the moving object, the predicted trajectory of the moving object prediction error distribution which is a difference between the trajectory prediction value _{D P} indicating the _{E P = {E Pi = (} x Ri -x Pi, y Ri -y Pi); i = 1,2, ···, N EP} a Calculate (step ST6).
Further, the prediction error distribution calculation unit 14, average _{mu EP_X} prediction error distribution _{E P,} μ _{EP_Y} and the standard deviation sigma _{EP_X,} calculates the sigma _{EP_Y,} average _{mu EP_X} prediction error distribution _{E P,} μ _{EP_Y} and the standard deviation σ _{EP_X} , σ _{EP_Y} and the number of data of orbital observation values N _EP are stored in the prediction error distribution adjustment information database 15.
The prediction error distribution calculation unit 14 notifies the prediction error distribution adjustment unit 16 that the adjustment process of the prediction error distribution is necessary (step ST7).

Prediction error distribution adjusting section 16, the prediction of the adjustment process of the prediction error distribution from the error distribution calculation unit 14 receives a notification is required, the reliability of the prediction error distribution E _P calculated by the prediction error distribution calculation section 14 Is highly reliable, an adjustment value for high reliability is set as the adjustment sensitivity parameter α _X , α _Y , β _X , β _Y , and the prediction error distribution has low reliability with low reliability. In some cases, an adjustment value for low reliability smaller than the adjustment value for high reliability is set as the adjustment sensitivity parameters α _X , α _Y , β _X , β _Y (step ST8).
Hereinafter, a method of setting the adjustment sensitivity parameters α _X , α _Y , β _X , β _Y by the prediction error distribution adjustment unit 16 will be described.
FIG. 3 is a flowchart showing a method of setting the adjustment sensitivity parameters α _X , α _Y , β _X , β _Y by the prediction error distribution adjusting unit 16.

When the prediction error distribution calculating unit 14 calculates the prediction error distribution E _P of the mobile object, the prediction error distribution adjusting unit 16 has a high reliability with which the reliability of the prediction error distribution E _P is higher than a preset threshold value. determining located in or, whether the the reliability of the prediction error distribution E _P is lower than the threshold low confidence.
In other words, the prediction error distribution adjusting section 16 compares the reference data number N _ref of the data number N _EP orbital observations used in the calculation of the prediction error distribution E _P of the moving body is set in advance (Fig. 3 step ST21) of, determining if its orbit observations data number _{N EP} reference data number _{N ref} or more _{(N EP} ≧ _{N ref),} the reliability of the prediction error distribution _{E P} to be reliable (step ST22), it determines that the less than the data number _{N EP} reference data number _{N ref} of its orbit observations _(N EP _{<N ref),} the reliability of the prediction error distribution _{E P} is a low confidence (Step ST23).

Here, an example is determined by comparing the number of data N _EP and the reference data number N _ref of orbital observations, by comparing the variance of the reference that is previously set and variance of the prediction error distribution E _P You may make it determine.
In other words, the prediction error distribution adjusting section 16, as a dispersion of the prediction error distribution _{E P,} for example, prediction error distribution control information database 15 and the prediction error distribution of a predetermined number of times in the past which is stored in the _{(μ EP_X, μ EP_Y, σ} EP_X , Σ _{EP_Y} ), the history of standard deviation σ _{EP_X} of the prediction error distribution for the past predetermined number of times is acquired, and the variance of the standard deviation σ _{EP_X} of the prediction error distribution for the past predetermined number of times is calculated.
Also, if the latest standard deviation σ _{EP_X} is smaller than the minimum value of the standard deviation σ _{EP_X} of the prediction error distribution for the past predetermined number of times, the minimum value is updated with the latest standard deviation σ _{EP_X} and the prediction for the past predetermined number of times is performed. If the latest standard deviation _{σEP_X} is larger than the maximum value of the standard deviation _{σEP_X} of the error distribution, the minimum value is updated with the latest standard deviation _{σEP_X} .

The prediction error distribution adjustment unit 16 sets the set of “prediction error distribution standard deviation σ _{EP_X} and latest standard deviation σ _{EP_X for} the past predetermined number of times” rather than “dispersion of standard deviation σ _{EP_X for} the past predetermined number of prediction error distributions”. when the dispersion "by a small, or if" distributed by a set of standard deviation sigma _{EP_X} the latest standard deviation sigma _{EP_X} prediction error distribution of a predetermined number of times in the past "is smaller than a predetermined value, reliability of the prediction error distribution E _P It is determined that the degree is highly reliable.
Otherwise, it determines the reliability of the prediction error distribution E _P is the low reliability.

Prediction error distribution adjusting section 16, the reliability of the prediction error distribution E _P calculated by the prediction error distribution calculation section 14 is determined to be reliable, the adjustment sensitivity parameters _{α X, α Y, β X} , β Y Then, an adjustment value for high reliability is set (step ST24).
On the other hand, if it is determined that the reliability of the prediction error distribution E _P is low, the adjustment sensitivity parameters α _X , α _Y , β _X , β _Y are for low reliability smaller than the adjustment value for high reliability. Is set (step ST25).
Thus, when the reliability of the prediction error distribution E _P is high, large adjustment sensitivity parameters α _X , α _Y , β _X , β _Y are set, and when the reliability of the prediction error distribution E _P is low, the small adjustment sensitivity is set. Since the parameters α _X , α _Y , β _X , and β _Y are set, the prediction error distribution only needs to be adjusted to a minimum when the reliability of the prediction error distribution E _P is low. It is possible to avoid sudden changes and unstable changes. Meanwhile, in a situation having high reliability of the prediction error distribution E _P, when the situation relating to the mobile body is changed, to quickly adjust the prediction error distribution, generation of unnecessary approach avoidance behavior of the moving body, the moving body It is possible to prevent a decrease in safety.

Prediction error distribution adjusting section 16, adjusts the sensitivity parameter _{α X, α Y, β X} , setting the beta _Y, the average of the prediction error distribution _{E P} of the moving body calculated by the prediction error distribution calculation section 14 (μ _{EP_X} , Μ _{EP_Y} ) and standard deviation (σ _{EP_X} , σ _{EP_Y} ), and the average (μ _{C_X} , μ _{C_Y} ) and standard deviation (σ _{C_X} ) of the prediction error distribution of the moving object stored in the prediction error distribution adjustment information database 15. , Σ _{C — Y} ), Δμ _X , Δμ _Y , Δσ _X , Δσ _Y are calculated (step ST9 in FIG. 2).
Δμ _X = μ _{C_X} −μ _{EP_X}
Δμ _Y = μ _{C_Y} −μ _{EP_Y}
Δσ _X = σ _{C_X} −σ _{EP_X}
Δσ _Y = σ _{C_Y} −σ _{EP_Y}

The prediction error distribution adjustment unit 16 does not adjust the average μ _{C_X} of the prediction error distribution when Δμ _X = 0, but if Δμ _X > 0 or Δμ _X <0 (step ST10), the following is shown. Then, the average μ _{C — X} of the prediction error distribution is adjusted (step ST11).
Δμ _X > 0 → μ _{C_X} = μ _{C_X} −α _X
Δμ _X <0 → μ _{C_X} = μ _{C_X} + α _X
α _X is an adjustment sensitivity parameter. As described above, α _X in the case where the reliability of the prediction error distribution E _P is high reliability is equal to that in the case where the reliability of the prediction error distribution E _P is low reliability. it is a value greater than α _X.

The prediction error distribution adjustment unit 16 does not adjust the average μ _{C_Y} of the prediction error distribution when Δμ _Y = 0, as in the case of the average μ _{C_X} of the prediction error distribution, but Δμ _Y > 0 or Δμ _Y < If 0 (step ST10), as shown below, the average μ _{C_Y} of the prediction error distribution is adjusted (step ST11).
Δμ _Y > 0 → μ _{C_Y} = μ _{C_Y} −α _Y
Δμ _Y <0 → μ _{C_Y} = μ _{C_Y} + α _Y
α _Y is an adjustment sensitivity parameter, and as described above, α _Y when the reliability of the prediction error distribution E _P is high reliability is equal to α _Y when the reliability of the prediction error distribution E _P is low reliability. is a value greater than α _Y.

As in the case of the average μ _{C_X} of the prediction error distribution, the prediction error distribution adjustment unit 16 does not adjust the standard deviation σ _{C_X} of the prediction error distribution when Δσ _X = 0, but Δσ _X > 0 or Δσ _X If <0 (step ST10), as shown below, the standard deviation σ _{C_X} of the prediction error distribution is adjusted (step ST11).
Δσ _X > 0 → σ _{C_X} = σ _{C_X} −β _X
Δσ _X <0 → σ _{C_X} = σ _{C_X} + β _X
β _X is an adjustment sensitivity parameter, and, as described above, β _X when the reliability of the prediction error distribution E _P is high reliability is equal to β _X when the reliability of the prediction error distribution E _P is low reliability. , which is a larger value than the β _X.

As in the case of the standard deviation σ _{C_X} of the prediction error distribution, the prediction error distribution adjustment unit 16 does not adjust the standard deviation σ _{C_Y} of the prediction error distribution when Δσ _Y = 0, but Δσ _Y > 0 or Δσ _{If Y} <0 (step ST10), as shown below, the standard deviation σ _{C_Y} of the prediction error distribution is adjusted (step ST11).
Δσ _Y > 0 → σ _{C_Y} = σ _{C_Y} −β _Y
Δσ _Y <0 → σ _{C_Y} = σ _{C_Y} + β _Y
β _Y is an adjustment sensitivity parameter, and as described above, β _Y when the reliability of the prediction error distribution E _P is high reliability is equal to β _Y when the reliability of the prediction error distribution E _P is low reliability. , which is a larger value than the β _Y.

When the prediction error distribution adjustment unit 16 adjusts the average (μ _{C — X} , μ _{C — Y} ) and standard deviation (σ _{C — X} , σ _{C — Y} ) of the prediction error distribution, the average (μ _{C — X} , μ _{C — Y} ) of the adjusted distribution prediction error distribution is _adjusted. ) And standard deviation (σ _{C — X} , σ _{C — Y} ) are overwritten in the prediction error distribution adjustment information database 15.

The approach detection unit 17 obtains orbit observation values indicating the trajectories of a plurality of moving bodies stored in the orbit observation information database 11 and gives the orbit observation values to the orbit prediction apparatus 1, so that the orbit prediction apparatus 1. To obtain predicted trajectories indicating predicted trajectories of a plurality of moving bodies.
Next, the approach detection unit 17 calculates a trajectory prediction value indicating a predicted trajectory of the plurality of moving _objects and an average of the prediction error distributions of the plurality of moving _objects stored in the prediction error distribution adjustment information database 15 (μ _{C — X} , μ _{C_Y} ) and standard deviation (σ _{C_X} , σ _{C_Y} ) are used to calculate an approach probability between a plurality of moving _objects (step ST12).
The approach probability calculating process by the approach detecting unit 17 is a known technique, and thus detailed description thereof is omitted. For example, the approach probability is calculated by integrating the overlap of the prediction error distribution of the trajectory between a plurality of moving objects. (For example, refer nonpatent literature 1).

As can be seen from the above description, according to the first embodiment, each moving body is present in the observation area, the average of the prediction error distribution E _P calculated by the prediction error distribution calculation section 14 (μ _{EP_X,} μ _{EP_Y} ) and standard deviation (σ _{EP_X} , σ _{EP_Y} ), average (μ _{C_X} , μ _{C_Y} ) and standard deviation (σ _{C_X} , σ _{C_Y} ) of the prediction error distribution stored in the prediction error distribution adjustment information database 15 If there is a difference between the two, there is a prediction using the adjustment sensitivity parameters α _X , α _Y , β _X , β _Y set according to the reliability of the prediction error distribution E _P calculated by the prediction error distribution calculation unit 14. average prediction error distribution stored in the error distribution control information database 15 (μ _{c_X,} μ _{c_Y)} and standard deviation (σ _{c_X,} σ _{c_Y)} prediction error distribution adjusting section 16 to adjust the provided The approach detection unit 17 calculates a trajectory prediction value indicating a predicted trajectory of a plurality of mobile objects existing in the observation area, and an average of the prediction error distributions of the plurality of mobile objects stored in the prediction error distribution adjustment information database 15. Since (μ _{C_X} , μ _{C_Y} ) and standard deviation (σ _{C_X} , σ _{C_Y} ) are used to calculate the approach probability between a plurality of moving _objects , even if the situation related to the moving object changes, There is an effect that it is possible to prevent an unnecessary approach avoidance action of the moving body and a decrease in the safety of the moving body.

Embodiment 2. FIG.
In the first embodiment, the prediction error distribution adjusting section 16, the reliability of the prediction error distribution E _P calculated by the prediction error distribution calculation section 14 is determined to be reliable, the adjustment sensitivity parameter alpha _X, alpha _When adjustment values for high reliability are set as _{Y 1} , β _X , β _Y and it is determined that the reliability of the prediction error distribution E _P is low reliability, adjustment sensitivity parameters α _X , α _Y , β _X , Although β _Y indicates an adjustment value for low reliability that is smaller than an adjustment value for high reliability, an adjustment value for high reliability is set according to the moving environment of the moving body. It may be.
Specifically, it is as follows. FIG. 4 is an explanatory diagram showing a setting example of the adjustment sensitivity parameter.

Prediction error distribution adjusting section 16, in the same manner as in the first embodiment, the reliability of the prediction error distribution E _P calculated by the prediction error distribution calculation section 14 is determined to be low reliability, adjusting the sensitivity parameter α Adjustment values for low reliability are set as _{X 1} , α _Y , β _X , and β _Y.
Taking the adjustment sensitivity parameter beta _X as an example, for example, set as follows.
β _X = | Δσ _X | × C ₁
However, _{C 1} is an arbitrary constant in the range 0 _<C 1 _<1 a.

Prediction error distribution adjusting section 16, in the same manner as in the first embodiment, the reliability of the prediction error distribution E _P calculated by the prediction error distribution calculation section 14 is determined to be reliable, the mobile environment database 13 Information on the moving environment of the moving body when the initial value of the prediction error distribution of the moving body is set (hereinafter referred to as “initial moving environment”), and at the prediction error distribution calculating unit 14 prediction error distribution E _P the mobile body movement environment when the trajectory observed value D _R, which is used to calculate was observed (hereinafter, referred to as "observation time mobile environment") to get information about.
Then, the prediction error distribution adjusting unit 16 compares the information on the initial moving environment with the information on the observation moving environment, and determines the change direction of the moving environment of the moving object.

The changing direction of the moving environment is determined as follows, for example.
Compare the rainfall information included in the information on the initial moving environment with the information on the rainfall included in the information on the moving environment at the time of observation. who at the time of observation of _R it is, determines whether or not the rainfall is increasing.
In addition, by comparing the wind speed information included in the information related to the initial moving environment and the information related to the wind speed included in the information related to the moving environment during observation, the orbital observation can be performed since the initial value of the prediction error distribution was set. who upon measurement value D _R determines whether the wind speed is increased.
If the rainfall increases or the wind speed increases, it is determined that the changing direction of the moving environment is “the direction in which the weather is getting worse”.
On the other hand, if the rainfall is reduced and the wind speed is reduced, it is determined that the direction of change of the moving environment is “the direction in which the weather is improving”.

Next, the prediction error distribution adjustment unit 16 compares the prediction error distribution calculated by the prediction error distribution calculation unit 14 with the prediction error distribution stored in the prediction error distribution adjustment information database 15 to calculate the prediction error distribution. The adjustment direction is determined, and it is determined whether or not the adjustment direction of the prediction error distribution matches the change direction of the moving environment.
For example, change the direction of the mobile environment is "direction weather has deteriorated", and found the adjustment direction of the prediction error distribution when observing the "initial value setting trajectory observed value D _R than when the prediction error distribution If the direction is “larger”, it is determined that the change direction of the moving environment and the adjustment direction of the prediction error distribution match.

Or, whether the traffic volume of the moving body has decreased by comparing the traffic volume information included in the information on the initial mobile environment with the traffic volume information included in the information on the mobile environment during observation Determine whether or not.
And the direction of change in the moving environment is “the direction in which the weather is getting worse” & “the direction in which the traffic volume of the moving body is decreasing”, and the adjustment direction of the prediction error distribution is “orbit from the initial value setting” who during observations observations D _R is determined that if the direction "the prediction error distribution is smaller, the adjustment direction of the prediction error distribution and change direction of movement environment match.

Alternatively, the schedule of the mobile object is determined by referring to the information on the fixed status of the schedule included in the information on the mobile environment during observation (for example, the arrival time at the destination or the next waypoint is determined) It is determined whether or not. When the arrival time is fixed, since the moving distance to the destination is short, the prediction error distribution of the moving body is expected to be small.
Then, the schedule of the mobile has deterministic, and adjust the direction of the prediction error distribution is "towards the observation time of the initial value setting trajectory observed value D _R than at is, the direction in which the prediction error distribution is smaller." If so, it is determined that the change direction of the mobile environment and the adjustment direction of the prediction error distribution match.

If the prediction error distribution adjustment unit 16 determines that the change direction of the movement environment and the adjustment direction of the prediction error distribution do not match, the prediction error distribution adjustment unit 16 uses the adjustment sensitivity parameters α _X , α _Y , β _X , β _Y for low reliability. A first high-reliability adjustment value that is larger than the adjustment value is set.
Taking the adjustment sensitivity parameter beta _X as an example, for example, set as follows.
β _X = min (| Δσ _X |, C ₂ × β _X )
However, C ₂ is an arbitrary constant in the range of 1 <C _2, the right side of the beta _X is an adjustment value for low confidence.

If the prediction error distribution adjustment unit 16 determines that the change direction of the moving environment and the adjustment direction of the prediction error distribution match, the prediction error distribution adjustment unit 16 uses the first high reliability as the adjustment sensitivity parameters α _X , α _Y , β _X , β _Y. The second adjustment value for high reliability that is larger than the adjustment value for degree is set.
Taking the adjustment sensitivity parameter beta _X as an example, for example, set as follows.
β _X = min (| Δσ _X |, C ₃ × β _X )
However, C ₃ is an arbitrary constant in the range of C 2 _{<C 3,} the right side of the beta _X is an adjustment value for low confidence.

Here, of the adjustment sensitivity parameters α _X , α _Y , β _X , and β _Y , the method of setting the adjustment sensitivity parameter β _X of the standard deviation σ _{C_X} of the prediction error distribution has been described as an example, but the adjustment of the standard deviation σ _{C_Y} is described. setting the sensitivity parameter beta _Y is the same as the method of setting the adjustment sensitivity parameter beta _Y.
Regarding the method of setting the adjustment sensitivity parameters α _X and α _Y of the average μ _{C — X} and μ _{C — Y} of the prediction error distribution, the “determination condition as to whether or not the change direction of the mobile environment matches the adjustment direction of the prediction error distribution” is except that different is the same as the method of setting the adjustment sensitivity parameter beta _X.
In the setting of the adjustment sensitivity parameters α _X and α _Y of the average μ _{C — X} and μ _{C — Y} of the prediction error distribution, it is determined whether or not the change direction of the moving environment and the adjustment direction of the prediction error distribution match as follows. To do.

The prediction error distribution adjustment unit 16 compares the information on the wind direction included in the information on the initial moving environment with the information on the wind direction included in the information on the moving environment at the time of observation. determining the wind direction setting, whether the wind direction at the time of observation of the trajectory observed value D _R match.
[If the wind direction does not match]
If the following condition (1) or (2) is satisfied, it is determined that the change direction of the mobile environment and the adjustment direction of the prediction error distribution match.
(1) wind direction of observation time trajectory observations D _R is the traveling direction of the moving object, and adjust the direction of the prediction error distribution is the "direction to increase the value of mu _{c_y".}
(2) wind direction of observation time trajectory observations D _R is traveling the opposite direction of the moving object, and adjust the direction of the prediction error distribution is the "direction of reducing the value of mu _{c_y".}

[When the wind direction matches]
By comparing the wind speed information included in the information on the initial moving environment and the information on the wind speed included in the information on the moving environment at the time of observation, the orbital observation value D from the time of setting the initial value of the prediction error distribution is compared. It is determined whether or not the wind speed is higher during the _R observation.
When the wind speed is high, if the following condition (1) or (2) is satisfied, it is determined that the change direction of the moving environment and the adjustment direction of the prediction error distribution match.
(1) wind direction of observation time trajectory observations D _R is the traveling direction of the moving object, and adjust the direction of the prediction error distribution is the "direction to increase the value of mu _{c_y".}
(2) wind direction of observation time trajectory observations D _R is traveling the opposite direction of the moving object, and adjust the direction of the prediction error distribution is the "direction of reducing the value of mu _{c_y".}

Or the direction which avoids the new movement prohibition area is determined with reference to the information of the movement prohibition area contained in the information regarding the movement environment at the time of observation.
If the following condition (1) or (2) is satisfied, it is determined that the change direction of the mobile environment and the adjustment direction of the prediction error distribution match.
(1) The direction of avoiding the movement prohibited area is the traveling direction of the moving body, and the adjustment direction of the prediction error distribution is the “direction in which the value of μ _{C_Y} is increased”.
(2) The direction avoiding the movement prohibited area is opposite to the traveling direction of the moving body, and the adjustment direction of the prediction error distribution is “the direction in which the value of μ _{C_Y} is decreased”.

As is apparent from the above, according to the second embodiment, the prediction error distribution adjusting unit 16 sets adjustment values for high reliability as the adjustment sensitivity parameters α _X , α _Y , β _X , β _Y. At this time, the information on the initial moving environment and the information on the moving environment at the time of observation are compared to determine the direction of change of the moving environment of the mobile body, and the prediction error distribution calculated by the prediction error distribution calculating unit 14 The prediction error distribution stored in the prediction error distribution adjustment information database 15 is compared to determine the adjustment direction of the prediction error distribution. If the change direction of the mobile environment does not match the adjustment direction of the prediction error distribution, If the first high-reliability adjustment value that is larger than the low-reliability adjustment value is set and the change direction of the mobile environment matches the adjustment direction of the prediction error distribution, the first high-reliability adjustment value Second high confidence greater than the adjustment value Since it is configured to set the adjustment values for degrees, in addition to the same effects as in the first embodiment, in a mobile environment should not labile changes, adjusting the sensitivity parameter α _X, α _Y, β _X , Β _Y can be adjusted quickly.

  In the present invention, within the scope of the invention, any combination of the embodiments, or any modification of any component in each embodiment, or omission of any component in each embodiment is possible. .

  DESCRIPTION OF SYMBOLS 1 Trajectory prediction apparatus, 2 Approach detection apparatus, 11 Trajectory observation information database, 12 Prediction error distribution initial value database, 13 Mobile environment database, 14 Prediction error distribution calculation part (prediction error distribution calculation means), 15 Prediction error distribution adjustment information database , 16 Prediction error distribution adjustment unit (adjustment value setting unit, prediction error distribution adjustment unit), 17 Approach detection unit (approach probability calculation unit).

Claims (12)

A database that stores initial values of prediction error distributions of a plurality of moving objects;
A prediction error distribution that calculates a prediction error distribution that is a difference between the trajectory observation value indicating the trajectory of the mobile object and the predicted trajectory value indicating the predicted trajectory of the mobile object for each mobile object existing in the observation area. A calculation means;
Adjustment value setting means for setting an adjustment value corresponding to the reliability of the prediction error distribution calculated by the prediction error distribution calculation means;
If there is a difference between the prediction error distribution calculated by the prediction error distribution calculation means and the prediction error distribution stored in the database for each moving object existing in the observation area, the adjustment Prediction error distribution adjusting means for adjusting the prediction error distribution stored in the database using the adjustment value set by the value setting means;
Using a predicted trajectory value indicating a predicted trajectory of a plurality of moving objects existing in the observation area, and a prediction error distribution of the plurality of moving objects stored in the database, between the plurality of moving objects. An approach detection device comprising: an approach probability calculating means for calculating an approach probability.   The adjustment value setting means is an adjustment value used by the prediction error distribution adjustment means when the reliability of the prediction error distribution calculated by the prediction error distribution calculation means is higher than a preset threshold value. As an adjustment value used by the prediction error distribution adjusting means when the reliability of the prediction error distribution is lower than the threshold value, an adjustment value for the high reliability is set. The approach detection device according to claim 1, wherein an adjustment value for low reliability smaller than the adjustment value is set.   The adjustment value setting means, if the number of data of the orbit observation values used for the calculation of the prediction error distribution by the prediction error distribution calculation means is greater than or equal to a preset reference data number, It is determined that the reliability is high reliability, and if the number of data of the orbital observation value is less than the reference data number, it is determined that the reliability of the prediction error distribution is low reliability. The approach detection apparatus according to claim 2.   The adjustment value setting means calculates a variance of the prediction error distribution from a history of the prediction error distribution calculated by the prediction error distribution calculation means, and if the variance of the prediction error distribution is smaller than a preset reference variance The reliability of the prediction error distribution is determined to be high reliability, and if the variance of the prediction error distribution is greater than or equal to the variance of the reference, the reliability of the prediction error distribution is determined to be low reliability. The approach detection device according to claim 2.   When the adjustment value setting unit sets an adjustment value for high reliability as an adjustment value used by the prediction error distribution adjustment unit, the orbit observation used for calculating the prediction error distribution by the prediction error distribution calculation unit The moving environment of the mobile object when the value is observed is compared with the mobile environment of the mobile object when the initial value of the prediction error distribution of the mobile object is set. The prediction error distribution calculating unit compares the prediction error distribution of the moving object calculated by the prediction error distribution calculating unit with the prediction error distribution of the moving object stored in the database, and the prediction error distribution adjusting unit The adjustment direction of the prediction error distribution of the mobile body is determined, and if the change direction of the moving environment and the adjustment direction of the prediction error distribution do not match, the first high reliability greater than the adjustment value for the low reliability For If an adjustment value is set and the change direction of the moving environment and the adjustment direction of the prediction error distribution match, the adjustment value for the second high reliability that is larger than the adjustment value for the first high reliability The approach detection device according to any one of claims 2 to 4, wherein:   The adjustment value setting means includes a moving environment of the moving body when the orbit observation value used for the calculation of the prediction error distribution is observed by the prediction error distribution calculating means, and an initial prediction error distribution of the moving body. Compared with the moving environment of the mobile object when the value is set, it is determined whether or not the weather when the orbital observation value is observed has deteriorated since the initial value was set. If the weather is worse and the prediction error distribution of the mobile body calculated by the prediction error distribution calculation means is larger than the prediction error distribution of the mobile body stored in the database, the mobile environment The approach detection device according to claim 5, wherein it is determined that the direction of change of the prediction and the adjustment direction of the prediction error distribution match.   The adjustment value setting means includes a moving environment of the moving body when the orbit observation value used for the calculation of the prediction error distribution is observed by the prediction error distribution calculating means, and an initial prediction error distribution of the moving body. Compared with the moving environment of the moving object when the value is set, it is determined whether or not the weather when the orbital observation value is observed has improved since the initial value was set. In addition, it is determined whether or not the traffic volume is decreasing, the weather is improved, the traffic volume is decreased, and the prediction error distribution of the mobile body calculated by the prediction error distribution calculating means is the database. The approach detection according to claim 5, wherein if it is smaller than the prediction error distribution of the moving object stored in, the change direction of the moving environment and the adjustment direction of the prediction error distribution coincide with each other. apparatus.   The adjustment value setting means refers to the moving environment of the moving object when the orbit observation value used for the calculation of the prediction error distribution is observed by the prediction error distribution calculating means, and the schedule of the moving object is It is determined whether or not the mobile object has a fixed schedule, and the mobile object prediction error distribution calculated by the prediction error distribution calculating means is stored in the database. The approach detection device according to claim 5, wherein if it is smaller than a body prediction error distribution, it is determined that a change direction of the movement environment and an adjustment direction of the prediction error distribution coincide with each other. The adjustment value setting means includes a moving environment of the moving body when the orbit observation value used for the calculation of the prediction error distribution is observed by the prediction error distribution calculating means, and an initial prediction error distribution of the moving body. Compared with the moving environment of the moving object when the value is set, whether the wind direction when the initial value is set and the wind direction when the orbital observation value is observed coincide with each other. Judgment,
If the wind direction is inconsistent,
The wind direction when the orbital observation value is observed is the direction of the main axis related to the prediction error distribution of the moving object, and the prediction error distribution of the moving object calculated by the prediction error distribution calculating means is If it is moved in the direction of the main axis related to the prediction error distribution, it is determined that the change direction of the moving environment and the adjustment direction of the prediction error distribution match,
Alternatively, the wind direction when the orbital observation value is observed is opposite to the direction of the main axis related to the prediction error distribution of the moving object, and the prediction error distribution of the moving object calculated by the prediction error distribution calculating unit Is moved in the direction opposite to the direction of the main axis related to the prediction error distribution of the mobile body, it is determined that the change direction of the moving environment and the adjustment direction of the prediction error distribution coincide with each other. Item 6. The approach detection device according to Item 5. The adjustment value setting means includes a moving environment of the moving body when the orbit observation value used for the calculation of the prediction error distribution is observed by the prediction error distribution calculating means, and an initial prediction error distribution of the moving body. Compared with the moving environment of the moving object when the value is set, whether the wind direction when the initial value is set and the wind direction when the orbital observation value is observed coincide with each other. And determining whether the wind speed when the orbital observation value is observed is greater than the wind speed when the initial value is set,
When the wind direction matches and the wind speed is increased,
The wind direction when the orbital observation value is observed is the direction of the main axis related to the prediction error distribution of the moving object, and the prediction error distribution of the moving object calculated by the prediction error distribution calculating means is If it is moved in the direction of the main axis related to the prediction error distribution, it is determined that the change direction of the moving environment and the adjustment direction of the prediction error distribution match,
Alternatively, the wind direction when the orbital observation value is observed is opposite to the direction of the main axis related to the prediction error distribution of the moving object, and the prediction error distribution of the moving object calculated by the prediction error distribution calculating unit is The movement direction of the moving environment and the adjustment direction of the prediction error distribution coincide with each other if the movement direction is opposite to the direction of the main axis related to the prediction error distribution of the moving body. 5. The approach detection device according to 5. The adjustment value setting means is newly generated with reference to the moving environment of the moving body when the orbit observation value used for calculating the prediction error distribution is observed by the prediction error distribution calculating means. Determine the direction to avoid the prohibited area,
The direction avoiding the movement prohibited area is the direction of the main axis related to the prediction error distribution of the mobile object, and the prediction error distribution of the mobile object calculated by the prediction error distribution calculating means relates to the prediction error distribution of the mobile object. If it is moved in the direction of the main axis, it is determined that the change direction of the moving environment and the adjustment direction of the prediction error distribution match,
Alternatively, the direction avoiding the movement prohibited area is opposite to the direction of the main axis related to the prediction error distribution of the mobile object, and the prediction error distribution of the mobile object calculated by the prediction error distribution calculating unit is the mobile object. 6. The method according to claim 5, wherein the direction of change of the moving environment and the adjustment direction of the prediction error distribution coincide with each other if the direction of the main axis related to the prediction error distribution is moved in the opposite direction. Approach detection device. For each moving object existing in the observation area, the prediction error distribution calculating means calculates a prediction error that is a difference between the observed trajectory value indicating the trajectory of the mobile object and the predicted trajectory value indicating the predicted trajectory of the mobile object. A prediction error distribution calculation processing step for calculating a distribution;
An adjustment value setting means for setting an adjustment value corresponding to the reliability of the prediction error distribution calculated in the prediction error distribution calculation processing step;
A prediction error distribution adjusting means, for each moving object existing in the observation area, a prediction error distribution calculated in the prediction error distribution calculation processing step, and an initial value of the prediction error distribution stored in the database, If there is a difference between the two, the prediction error distribution adjustment processing step of adjusting the prediction error distribution stored in the database using the adjustment value set in the adjustment value setting processing step;
The approach probability calculation means uses the predicted trajectory value indicating the predicted trajectory of a plurality of moving objects existing in the observation area, and the prediction error distribution of the plurality of moving objects stored in the database, An approach detection method comprising: an approach probability calculation processing step for calculating an approach probability between a plurality of moving objects.

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