Information processing apparatus and program

Abstract

To obtain arithmetic results of a plurality of parameters having a distribution with higher accuracy compared to the prior art.SOLUTION: An information processing apparatus comprises an acquisition unit, a matrix generation unit, and an arithmetic unit. The acquisition unit acquires, as operation objects, a plurality of parameters whose values have variations and for which probabilities of respective values are given as a distribution. The matrix generation unit generates a plurality of parameter matrices respectively corresponding to the plurality of parameters, wherein each of the plurality of parameter matrices has the number of parameters as the number of dimensions, a width of the distribution of each parameter as a length of the dimension corresponding to each parameter, and each parameter matrix has each value obtained by equally dividing the distribution of the corresponding parameter as a value of each element of the parameter matrix. The arithmetic unit updates each element of the plurality of parameter matrices by performing an operation on each element of the plurality of matrices.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an information processing device and a program. [Background technology]

[0002] Conventionally, in a calculation using a plurality of parameters, calculations in which each parameter has a variation (distribution) are known. Patent Document 1 discloses a calculation of parameters having a distribution. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] International Publication No. 2022 / 054253 Summary of the Invention [Problem to be solved by the invention]

[0004] However, when performing an integral calculation along parameters, such as in a simulation calculation, there is a correlation between the parameters, and the calculation must take this correlation into consideration.

[0005] The present invention has been made in consideration of the above points, and has as its object to obtain the calculation results of a plurality of parameters having distributions with higher accuracy than conventional methods. [Means for solving the problem]

[0006] The present invention is an information processing device comprising an acquisition unit, a matrix generation unit, and a calculation unit, wherein the acquisition unit acquires, as calculation targets, a plurality of parameters whose values ​​vary and whose probabilities for each value are given as a distribution, the matrix generation unit generates a plurality of parameter matrices corresponding to each of the plurality of parameters, each of which has a number of dimensions that is the same as or less than the number of parameters, the width of the distribution of each parameter being the length of the dimension corresponding to each parameter, and each parameter matrix has values ​​into which the distribution of the corresponding parameter is equally divided as the value of each element of the parameter matrix, and the calculation unit updates each element of the plurality of parameter matrices by performing calculations on each element of the plurality of matrices.

[0007] Another aspect of the present invention is a program for causing a computer to function as an acquisition unit, a matrix generation unit, and a calculation unit, in which the acquisition unit acquires, as calculation targets, a plurality of parameters whose values ​​vary and whose probabilities for each value are given as a distribution, the matrix generation unit generates a plurality of parameter matrices corresponding to each of the plurality of parameters, each of the plurality of parameter matrices having a number of dimensions that is the same as or smaller than the number of parameters, the width of the distribution of each parameter being the length of the dimension corresponding to each parameter, and each parameter matrix having values ​​into which the distribution of the corresponding parameter is equally divided as the value of each element of the parameter matrix, and the calculation unit updates each element of the plurality of parameter matrices by performing calculations on each element of the plurality of matrices. [Effects of the Invention]

[0008] According to the information processing device and program of the present invention, the calculation results of a plurality of parameters having a distribution can be obtained with higher accuracy than conventionally. [Brief explanation of the drawings]

[0009] [Figure 1]1 is a block diagram showing a configuration of an information processing apparatus according to an embodiment of the present invention; [Figure 2] FIG. 10 is an explanatory diagram of a correlation. [Figure 3] FIG. 10 is an explanatory diagram of a calculation process of a position and a velocity. [Figure 4] 10 is a flowchart showing a calculation process. [Figure 5] FIG. 10 is a diagram illustrating the distribution of parameters. [Figure 6] FIG. 10 is a diagram illustrating a parameter matrix. [Figure 7] FIG. 10 is a diagram illustrating a probability matrix. [Figure 8] FIG. 2 is a diagram showing a functional configuration related to missile control. [Figure 9] FIG. 1 illustrates the trajectories of a target and a missile. [Figure 10] FIG. 1 is a diagram showing the probability of approach between a target and a missile. [Figure 11] FIG. 2 is a diagram showing a functional configuration related to vehicle driving control. [Figure 12] FIG. 10 is a diagram illustrating a first condition. [Figure 13] 10 is a graph showing the vehicle trajectory obtained for the first condition. [Figure 14] FIG. 10 is a diagram illustrating a second condition. [Figure 15] 10 is a graph showing the vehicle trajectory obtained for the second condition. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. FIG. 1 is a block diagram showing the configuration of an information processing device 100 according to this embodiment.

[0011] FIG. 1 is a block diagram showing the configuration of an information processing device 100 according to this embodiment. The information processing device 100 performs calculations on multiple parameters whose values ​​have a distribution. As shown in FIG. 2, the position and velocity of a moving object may each be given a distribution with varying values. In this case, a moving object with a fast velocity moves farther away over time. On the other hand, a moving object with a slow velocity remains nearby. This indicates the following: Even if the position and velocity are given independent distributions as initial values, a correlation between the position and velocity will emerge at the next moment, and this correlation will become stronger over time. Furthermore, when considering a feedback calculation that controls acceleration according to velocity and assuming a distribution of acceleration, it is necessary to consider the correlation between these parameters that changes over time. The information processing device 100 according to this embodiment performs distribution calculations that take into account the correlation that changes over time.

[0012] Figure 3 is an explanatory diagram of the calculation process for position and velocity. Here, time series calculations will be explained using an example in which a moving object at a certain position moves at a certain velocity and the position of the moving object after the movement is calculated. The position is assumed to be a value with a variance in its value, i.e., a distribution. Similarly, the velocity is assumed to be a value with a variance in its value, i.e., a distribution.

[0013] Consider a two-dimensional plane, with the horizontal axis (axis along the long side of the paper) representing position x and the vertical axis (axis along the short side of the paper) representing velocity v, as shown in the graph in the upper right of Figure 3. Both x and v have a distribution (variation) for the parameters shown on each axis. The graph in the lower right of Figure 3 is a graph showing the distribution of position x, with the horizontal axis (axis along the long side of the paper) representing x and the vertical axis (axis along the short side of the paper) representing probability p. The graph shows a curve 1201 for position distribution x0, a curve 1202 for position distribution 1, and a curve 1203 for position distribution x2 as the distribution of position x (position distribution x). The graph on the left of Figure 3 is a graph showing the distribution of velocity v (velocity distribution v), with the horizontal axis (axis along the short side of the paper) representing velocity v and the vertical axis (axis along the long side of the paper) representing probability p. The graph shows a curve 1211 of velocity distribution v0 and a curve 1212 of velocity v1 as the distribution of velocity v (velocity distribution v). Note that in the graph, the position x and velocity v are scaled for calculation purposes.

[0014] The minimum and maximum values ​​of the initial position x0 of the moving body are defined as x0min and x0max, respectively. Furthermore, the minimum and maximum values ​​of the initial velocity v0 of the moving body are defined as v0min and v0max, respectively. The initial existence range of the moving body to be calculated is determined as the initial range 1301, which is a rectangular range shown in FIG. 3, from the range of the initial position distribution x0 and the initial velocity distribution v0. This range 1301 of x and v is divided into a grid, and the position and velocity of each point of the grid become the parameter values ​​stored in the matrix (grid points) explained in FIG. 6. The probability values ​​of the matrix explained in FIG. 7 also correspond to each point of this grid, and the probability value of the product of the probability value of the distribution x0 at the x value and the probability value of the distribution x0 at the v value of each point is stored.

[0015] The existence range 1302 of the moving object one second later is set based on the calculation of the position and velocity at each grid point obtained by dividing the existence range 1301 of the initial position distribution x0 and the initial velocity distribution v0 described above. The position distribution one second later is the range (the value obtained by adding velocity × 1 second to the position) between the two intersections with the x-axis (axis where v = 0) of auxiliary lines (dash-dotted lines) drawn from the top right and bottom left vertices of the initial range 1301 at a slope of -1. Assume that the velocity changes from v0min to v1min, and from v0max to v0max, and so on, following the distribution. In this case, the position range when the velocity is v1min is expressed by (Equation 1), and the position range when the velocity is v1max is expressed by (Equation 2). (x0min+v0min*1sec)~(x0max+v0min*1sec)…(Formula 1) (x0min+v0max*1sec)~(x0max+v0max*1sec)…(Formula 2) Therefore, the range of existence of a moving body at position x and velocity v one second from now, when expressed in xv coordinates, is a parallelogram-shaped range 1302 bounded by the following four points. (x0min+v0min*1sec,v1min) (x0max+v0min*1sec,v1min) (x0min+v0max*1sec,v1max) (x0max+v0max*1sec,v1max)

[0016] The parallelogram-shaped range 1303 shown by the dashed line to the right of range 1302 in Figure 3 is the range in which the moving object will exist after two seconds. The range 1301 is divided into a grid, and the position and speed of each point of the grid are calculated and updated to become each point of the diagonal grid of range 1303 after two seconds, which constitutes that range. If the position distribution is x2min to x2max, these values ​​are expressed as (Equation 5) and (Equation 6), respectively. x2min=x0min+v0min*1sec+v1min*1sec…(Formula 3) x2max=x0max+v0max*1sec+v1max*1sec…(Formula 4)

[0017] The range of existence of a moving object with a position x and a velocity v after two seconds, when expressed in xv coordinates, is a parallelogram-shaped range 1303 bounded by the following four points. (x0min+v0min*1sec+v1min*1sec,v2min) (x0max+v0min*1sec+v1min*1sec,v2min) (x0min+v0max*1sec+v1max*1sec,v2max) (x0max+v0max*1sec+v1max*1sec,v2max)

[0018] The probability value of distribution x2(1203) can be found by dividing the interval between x2min and x2max into infinitesimal intervals by an arbitrary number of divisions and adding up the probability values ​​of the grid points in range 1303 that belong to each of these divisions. For example, grid point 13031 in range 1303 in Figure 3 is located two seconds after grid point 13011 in range 1301, and inherits the probability value of grid point 13011. Grid point 13032 in range 1303 is located two seconds after grid point 13012 in range 1301, and inherits the probability value of grid point 13012. The curve of the probability value of distribution x2(1203) is obtained by adding up the probability values ​​of the grid points included in each of the infinitesimal position ranges into which the interval between x2min and x2max is divided.

[0019] The speed and position ranges converge diagonally over time, from range 1301, to range 1302, to range 1303. In other words, this simulates the situation where speed and position were initially independent data, but the correlation becomes stronger. In this way, a matrix that calculates the probability distribution after movement is realized. The structure of the matrix is ​​explained in Figures 6 and 7.

[0020] Figure 3 above describes the parameter ranges and distribution probability values ​​for velocity and position. Figures 6 and 7 explain this using a matrix for a three-dimensional case where acceleration is added in addition to position and velocity. The relationship between velocity and position in Figure 3 changes in the same way as the relationship between acceleration and velocity, and the parameters and probability values ​​for each grid point are given as matrix values.

[0021] As shown in FIG. 1, the information processing device 100 includes a control unit 110, a storage unit 120, a UI unit 130, and a communication unit 140. The control unit 110 includes a CPU, a ROM, a RAM, etc. (not shown), and controls each unit of the information processing device 100 by the CPU executing various programs recorded in the ROM or the like using the RAM or the like. The control unit 110 may be configured with a single chip or multiple chips. Furthermore, the control unit 110 may employ an ASIC instead of the CPU. Furthermore, the control unit 110 may operate in cooperation with other processing circuits such as an ASIC or a GPU.

[0022] The storage unit 120 is, for example, a hard disk, and stores various information and various programs. The communication unit 140 includes a communication interface circuit for communicating with other devices connected to the information processing device 100 via wired or wireless communication in accordance with various communication protocols. The UI unit 130 includes a display unit such as a touch panel display, and input devices such as various keys, switches, and a mouse.

[0023] The control unit 110 performs calculations on two or more input parameters. Specifically, the control unit 110 functions as an acquisition unit 111, a matrix generation unit 112, a calculation unit 113, and a display processing unit 114 by executing a calculation program stored in a ROM or the like. Hereinafter, the processes described as being performed by the acquisition unit 111, the matrix generation unit 112, the calculation unit 113, and the display processing unit 114 are processes performed by the control unit 110 by executing the calculation program. The processes of the acquisition unit 111, the matrix generation unit 112, the calculation unit 113, and the display processing unit 114 will be described in detail with reference to FIG. 4 and subsequent figures.

[0024] 4 is a flowchart showing the calculation process by the control unit 110. In this process, calculations are performed on a plurality of parameters having distributions. In this embodiment, a case will be described in which a feedback control calculation is performed to control the acceleration of the vehicle to be controlled in accordance with the distance from the preceding vehicle.

[0025] Correspondingly, acceleration a, velocity v, and position x are the parameters to be calculated. Each of the three parameters, acceleration a, velocity v, and position x, has a variation in value, i.e., a distribution. Feedback control is a time-series calculation, and if an infinitesimal time is dt, velocity v changes by dt×a in the infinitesimal time, and position x changes by dt×v in the infinitesimal time. Furthermore, acceleration a also changes due to feedback of velocity v. Such feedback calculation changes the distribution of each parameter, acceleration a, velocity v, and position x. In the calculation process, such changes in the distribution of each parameter after feedback control calculation are found.

[0026] In this process, first, the acquisition unit 111 acquires the distributions of these three parameters as the objects of calculation (step S100). Here, it is assumed that each parameter has a value variation (range), and the probability of each value is given as a distribution. In this embodiment, it is assumed that the acceleration a has a distribution as shown in FIG. 5(a), the velocity v has a distribution as shown in FIG. 5(b), and the position x has a distribution as shown in FIG. 5(c).

[0027] Next, the matrix generation unit 112 generates a parameter matrix corresponding to each parameter (step S102). In this embodiment, the matrix generation unit 112 generates three parameter matrices corresponding to acceleration a, velocity v, and position x. Fig. 6 is a diagram showing the parameter matrices. Fig. 6(a) is a diagram showing a parameter matrix 201 for acceleration a. Fig. 6(b) is a diagram showing a parameter matrix 202 for velocity v. Fig. 6(c) is a diagram showing a parameter matrix 203 for position x.

[0028] Each of the parameter matrices 201 to 203 has dimensions (axes) corresponding to the number of parameters to be calculated. That is, in this embodiment, each of the parameter matrices 201 to 203 has three dimensions: acceleration a, velocity v, and position x. In each of the parameter matrices 201 to 203, the length of the acceleration a dimension corresponds to the width of the acceleration a distribution, the length of the velocity v dimension corresponds to the width of the velocity v distribution, and the length of the position x dimension corresponds to the width of the position x distribution. Each of the dimensions is divided equally, and each dimension has a value for each element formed by the divided width. In the example of FIG. 6, the acceleration a is divided into eight equal parts, the velocity v is divided into three equal parts, and the position x is divided into seven equal parts. Correspondingly, each of the parameter matrices 201 to 203 is divided into 168 (8 × 3 × 7) elements. Note that in actual calculations, each of the parameter matrices 201 to 203 may be divided into more elements.

[0029] The value of acceleration a is entered into each element of the parameter matrix 201 for acceleration a. As shown in Fig. 6(a), the same acceleration value (e.g., ai) is entered into 21 (3 x 7) elements at the same position in the acceleration dimension direction, and different acceleration values ​​are entered into elements at different positions in the dimension direction.

[0030] Similarly, the value of velocity v is entered into each element of velocity v parameter matrix 202. As shown in FIG. 6(b), the same velocity value (e.g., vi) is entered into 56 (8×7) elements at the same position in the dimension direction of velocity v, and different velocity v values ​​are entered into each element at different positions in the dimension direction. Similarly, the value of position x is entered into each element of position parameter matrix 203. As shown in FIG. 6(c), the same position value (e.g., xi) is entered into 24 (8×3) elements at the same position in the dimension direction of position x, and different position x values ​​are entered into each element at different positions in the dimension direction.

[0031] After the process of step S102 shown in FIG. 4, the matrix generation unit 112 generates a probability matrix (step S104). In this embodiment, the matrix generation unit 112 generates a probability matrix corresponding to acceleration a, velocity v, and position x. FIG. 7 is a diagram showing the probability matrix. The probability matrix is ​​a matrix with the same number of dimensions and the same size as the three parameter matrices corresponding to acceleration a, velocity v, and position x. That is, the probability matrix has three dimensions corresponding to acceleration a, velocity v, and position x. Similarly to the parameter matrix, each dimension in the probability matrix is ​​equally divided and includes multiple elements. The probability matrix shown in FIG. 7 corresponds to the parameter matrix shown in FIG. 6 and includes 168 elements. Each element contains the product of the probability values ​​in the corresponding dimension.

[0032] In this embodiment, the product of three probability values ​​(probability value of acceleration a, probability value of velocity v, and probability value of position v) becomes the value of one element. Therefore, the sum of the values ​​of all elements is 1. Also, the sum of the values ​​of elements (21 elements in the example of FIG. 7) corresponding to a predetermined acceleration ai becomes the probability value of the predetermined acceleration ai.

[0033] The probability matrix generation process may be performed before the process of generating a post-calculation parameter distribution (described later), and the order of the processes is not limited to that described in the embodiment. For example, the probability matrix generation process may be performed after the calculation process (step S106) (described later), or before the parameter matrix generation process (step S102).

[0034] Next, the calculation unit 113 performs a feedback control calculation. In this embodiment, the calculation unit 113 repeats the calculation using multiple parameters to be calculated a number of times specified in the time-series calculation (step S106). For example, in the case of a calculation for 100 seconds with an infinitesimal time dt of 0.01, the calculation is repeated 10,000 (100 / 0.01) times. In the feedback control calculation, the velocity changes depending on the acceleration and position, and the position changes depending on the acceleration and position. Furthermore, the acceleration changes depending on the velocity and position. This feedback control calculation updates the value of each element of the parameter matrix 202 for velocity v, and updates the value of each element of the parameter matrix 203 for position x. Furthermore, the value of each element of the parameter matrix 201 for acceleration a is updated. In this way, the calculation is performed, and the values ​​of the elements of the parameter matrices 201 to 203 are updated.

[0035] As a result, for example, the acceleration value a1 is updated to a2 through the calculation. In this way, the values ​​of each element of the parameter matrices 201 to 203 are updated. As a result, different values ​​are entered into each element of the parameter matrices 201 to 203. For example, the 21 elements constituting the plane shown in FIG. 6(a) all contained the acceleration ai, but these values ​​are updated to different accelerations. On the other hand, there are also cases where two different acceleration values ​​are updated to the same acceleration through the calculation. In this case, the elements that contained these two accelerations will contain the same acceleration value after the calculation.

[0036] The calculations for each element by the calculation unit 113 may be performed as parallel processing, which can speed up the processing. The parallel processing may be realized by, for example, a GPU.

[0037] Next, the calculation unit 113 generates a post-calculation distribution of each parameter (step S108). In the parameter matrix after the feedback control calculation, the width of each dimension and its minimum and maximum values ​​may have changed from before the calculation. Therefore, the calculation unit 113 divides the length from the minimum value to the maximum value after the calculation into the same number of equal parts as the number of dimensions before the calculation. For example, in the case of acceleration a, the length from the minimum value to the maximum value after the calculation is divided into eight equal parts.

[0038] The calculation unit 113 then obtains the element values ​​(probability values) of the probability matrix for positions corresponding to all elements updated to the values ​​of each element obtained by the division. For example, to calculate the probability value for the third position from the right in the position distribution 1203 after two seconds in FIG. 3, the probability values ​​of all position parameter lattice points in the range 1303 included in the infinitesimal partition of that position parameter can be summed up. One of these lattice points is 13031, which corresponds to the position parameter lattice point 13011 in the initial range 1301. In terms of position matrix elements, the position distribution corresponds to the second element from the bottom, and the velocity distribution corresponds to the first element from the top. The probability values ​​of elements at the same position in the probability value matrix are used. Similarly, the probability values ​​included in the infinitesimal partition of the position parameter are searched for and summed up to calculate the probability value for the third position from the right in the position distribution 1203 after two seconds. In the same manner, the calculation unit 113 calculates the probability values ​​corresponding to each element (each acceleration value) from the minimum to maximum value of the position parameter after calculation.

[0039] Then, the calculation unit 113 multiplies the summed probability value by the length of the element of the parameter matrix at position x before the calculation (the length obtained by equally dividing the width of the distribution). Furthermore, the calculation unit 113 divides the value obtained by multiplying the length of the element of the parameter matrix by the probability value by the equally divided width of each dimension after the calculation. This makes it possible to correct any discrepancies in values ​​caused by differences in the width of dimensions due to the calculation.

[0040] As another example, the calculation unit 113 may correct the summed probability value by the ratio of the equal division widths of the parameters before and after the calculation.

[0041] Based on the probability values ​​corresponding to the respective equal division widths thus obtained, the calculation unit 113 generates a distribution of parameters for the calculated acceleration a. Similarly, the calculation unit 113 generates a distribution of parameters for the calculated velocity v, and further generates a distribution of parameters for the calculated position x.

[0042] Next, the display processing unit 114 displays the calculated distribution of each parameter on the display unit (step S112). As described above, the information processing device 100 of this embodiment generates, for each parameter, a parameter matrix having dimensions equal to the number of parameters having a distribution, and further generates a probability matrix corresponding to the probability of each parameter. The information processing device 100 then performs calculations on the parameter matrix and applies the probability matrix to determine the distribution of each parameter after the calculation. By calculating a parameter matrix having dimensions equal to the number of parameters in this way, it is possible to perform calculations that take into account all values ​​corresponding to the distribution of each parameter. Therefore, it is possible to determine the calculation results of multiple parameters having distributions with higher accuracy than conventional methods.

[0043] Next, an example of calculations for controlling the missile's trajectory will be described. The control unit 110 performs calculations to control the missile's trajectory and thereby shoot down the target. In this control, the missile follows a parabolic motion until the distance between the missile and the target falls below a threshold, at which point missile trajectory control begins. Then, the probability that the missile will approach the target is calculated. In this embodiment, it is determined that the missile is approaching the target when the distance between the missile and the target falls below a reference value (e.g., 20 m). As another example, the probability of contact between the missile and the target may be calculated.

[0044] 8 is a diagram showing the functional configuration related to missile control. By executing a calculation program, control unit 110 functions as time series distribution generator 301, velocity integral calculator 302, position integral calculator 303, subtractor 304, distance calculator 305, comparison calculator 306, and control vector calculator 307. Hereinafter, the processes described as being performed by time series distribution generator 301, velocity integral calculator 302, position integral calculator 303, subtractor 304, distance calculator 305, comparison calculator 306, and control vector calculator 307 are processes performed by control unit 110 by executing the calculation program.

[0045] First, the time series distribution generator 301 generates a parameter matrix for acceleration a0, a parameter matrix for velocity v0, and a parameter matrix for position x0 from the initial acceleration a0, initial velocity v0, and initial position x0, respectively. Furthermore, the time series distribution generator 301 generates a probability parameter matrix. These parameter matrices are input to the velocity integral calculator 302. The parameter matrix for acceleration a0 is updated based on the parameter distribution a0 as control data input from the control vector calculator 307 (described later), and the parameter matrix for acceleration a1 after the update is input to the velocity integral calculator 302. Similarly, a parameter matrix for acceleration after the update based on the parameter distribution input as control data is generated and input to the velocity integral calculator 302.

[0046] The velocity integral calculator 302 multiplies each element of the parameter matrix of acceleration a0 by an infinitesimal interval (assumed to be dt seconds). Then, the velocity integral calculator 302 adds this value to each element of the parameter matrix of velocity v0 to generate a parameter matrix of velocity v1 after dt seconds. The parameter matrix of velocity v1 is input to the position integral calculator 303 and the control vector calculator 307. Furthermore, the velocity integral calculator 302 generates a parameter matrix of velocity v2 based on the obtained parameter matrix of velocity v1 and the parameter matrix of acceleration a1 after dt seconds. Similarly, by using the parameter matrix of acceleration after dt seconds, parameter matrices of velocity after dt seconds are sequentially generated and input to the position integral calculator 303 and the control vector calculator 307.

[0047] Position integral calculator 303 multiplies each element of the parameter matrix of velocity v1 by the infinitesimal interval (dt). Then, position integral calculator 303 adds this value to the value of the parameter matrix of position x0 to generate a parameter matrix of position x1 dt seconds later. The parameter matrix of position x1 is input to subtractor 304. Furthermore, position integral calculator 303 generates a parameter matrix of position x2 based on the obtained parameter matrix of position x1 and the parameter matrix of position x2 dt seconds later. Similarly, by using the parameter matrix of velocity dt seconds later, parameter matrices of positions dt seconds later are sequentially generated and input to subtractor 304.

[0048] The target position is input to subtractor 304. Subtractor 304 generates a relative position vector based on the target position and the position parameter matrix input from position integral calculator 303. Here, the relative position vector is a vector directed from the missile to the target. The relative position vector is input to distance calculator 305 and control vector calculator 307.

[0049] Distance calculator 305 calculates the distance from the missile to the target and inputs this to comparison calculator 306. When the distance from the missile to the target falls below a preset threshold, comparison calculator 306 outputs a calculation start trigger to control vector calculator 307. This simulates an autonomous sensor detecting the target's position as the missile approaches the target and controlling the missile's direction of travel toward it. When the calculation start trigger is input, control vector calculator 307 generates a distribution of acceleration a as control data based on the velocity parameter matrix and the relative position vector, and inputs this to time series distribution generator 301. Time series distribution generator 301 updates the acceleration parameter matrix based on this distribution of acceleration a.

[0050] Figures 9(a) and (b) show the target trajectory 401 and missile trajectory 402 when there is no missile control. Figures 9(c) and (d) show the target trajectory 411 and missile trajectory 412 when there is missile control. The three axes of each graph correspond to the x, y, and z directions, with the missile's starting point as the origin.

[0051] In Figures 9(a) and (c), the target flies from left to right on the paper, and the missile flies from the back to the front right on the paper. Figures 9(b) and (d) are graphs of Figures 9(a) and (c) at different angles, and in Figures 9(b) and (d), the target flies from the front right to the back on the paper, and the missile flies from the left to the back right on the paper.

[0052] Also, Figure 10(a) shows the approach probability when there is no missile control. Figure 10(b) shows the contact probability when there is missile control. The horizontal axis of the graphs shown in Figures 11 and 12 shows the approach distance, and the vertical axis shows the probability. Approach is determined when the target distance is less than the threshold (20 m).

[0053] As shown in Figure 10(a), without missile control, the probability of the missile approaching the target is 1%, whereas as shown in Figure 10(b), with missile control, the probability of approaching the target is 70%. Note that the probability of approaching the target is the total probability of an approach distance of less than 20m (the area to the left of the 20m line).

[0054] The probability of approach is calculated as follows: The control unit 110 calculates the probability of approach based on the parameter matrices obtained during or after the missile control calculations. The control unit 110 then calculates the total value of these probabilities as the total value of the probability of approach.

[0055] Next, an example of calculations for controlling vehicle travel will be described. When a stopped vehicle is present ahead, the control unit 110 performs control calculations for steering the vehicle to avoid the collision. It is assumed that the speed of the vehicle to be controlled is constant. FIG. 11 is a diagram showing the functional configuration related to vehicle travel control. By executing a calculation program, the control unit 110 functions as an initial distribution generator 501, a time series distribution generator 502, a dr distribution and a db distribution generator 503, a b integral calculator 504, an r integral calculator 505, an la integral calculator 506, a K distribution and a tgts distribution calculator 507, a dx distribution and a dy distribution generator 508, a dx and dy calculator 509, an x ​​and y integral calculator 510, and a distribution converter 511.

[0056] The processes described below as being performed by the initial distribution generator 501, the time series distribution generator 502, the dr distribution and db distribution generator 503, the b integral calculator 504, the r integral calculator 505, the la integral calculator 506, the K distribution and tgts distribution calculator 507, the dx distribution and dy distribution generator 508, the dx and dy calculator 509, the x and y integral calculator 510, and the distribution converter 511 are processes performed by the control unit 110 executing a calculation program.

[0057] Before explaining the processing, various parameters will be explained. The vehicle's forward direction is defined as x, and its lateral direction as y. The vehicle's motion is expressed by the yaw rate (r), the vehicle's slip angle (b), the tire steering angle (s), and the direction the vehicle is facing (la). Here, the slip angle is the angle between the vehicle's forward direction and the direction the vehicle is facing. The equations of motion are shown in (Equation 5) to (Equation 9). r n+1 =r n +dr*dt…(Formula 5) b n+1 =b n +db*dt…(Formula 6) la n+1 =la n +r*dt…(Formula 7) y n+1 =y n +v*sin(la+b)*dt ... (Equation 8) x n+1 =x n +v*cos(la+b)*dt ... (Equation 9)

[0058] As shown in FIG. 11, an initial distribution generator 501 generates an initial distribution of initial cornering power K and detection distance d. The initial distribution is input to a time series distribution generator 502. The time series distribution generator 502 generates a parameter matrix of cornering power K (hereinafter, referred to as K distribution where Kf = Kr = K) and a parameter matrix of detection distance d. Furthermore, the time series distribution generator 502 generates a probability parameter matrix. These parameter matrices are input to a dr distribution / db distribution generator 503. The dr distribution / db distribution generator 503 obtains the dr distribution and db distribution from the parameter matrix of cornering power K and the parameter matrix of detection distance d using the equation of motion shown in (Equation 10). TIFF2025169723000002.tif41163 Here, Kf is the front cornering power, and Kr is the rear cornering power. lf is the distance from the center of gravity to the position of the front tire, and lr is the distance from the center of gravity to the position of the rear tire. m is the mass of the vehicle body. lz is the moment of inertia. Here, the cornering power K and the detection distance d are the two distributions with independent variations, and the distributions of all other values ​​are dependent variations obtained by calculating the distribution of the two distributions. Therefore, the matrix is ​​calculated as two-dimensional.

[0059] The b integral calculator 504 generates a parameter matrix of b by adding each element of the parameter matrix of db*dt to the previous value of b. The parameter matrix of b is input to the K distribution, tgts distribution calculator 507 and the dx distribution, dy distribution generator 508. Furthermore, the b integral calculator 504 updates the parameter matrix of b based on the obtained parameter matrix of b and db. In this way, the parameter matrix of b is updated in chronological order, and the updated parameter matrix is ​​input to the K distribution, tgts distribution calculator 507 and the dx distribution, dy distribution generator 508.

[0060] The r integral calculator 505 generates a parameter matrix of r by adding each element of the parameter matrix of dr*dt to the previous value of r. The parameter matrix of r is input to the la integral calculator 506 and the K distribution and tgts distribution calculator 507. Furthermore, the r integral calculator 505 updates the parameter matrix of r based on the obtained parameter matrix of r and dr. In this way, the parameter matrix of r is updated in chronological order, and the updated parameter matrix is ​​input to the la integral calculator 506 and the K distribution and tgts distribution calculator 507.

[0061] The la integral calculator 506 generates a parameter matrix of la by adding each element of the parameter matrix of r*dt to the previous value of la. The parameter matrix of la is input to the K distribution, tgts distribution calculator 507 and the dx distribution, dy distribution generator 508. Furthermore, the la integral calculator 506 updates the parameter matrix of la based on the obtained parameter matrix of la and the parameter matrix of r. In this way, the parameter matrix of la is updated in chronological order, and the updated parameter matrix is ​​input to the K distribution, tgts distribution calculator 507 and the dx distribution, dy distribution generator 508.

[0062] The K distribution and tgts distribution calculator 507 calculates the K distribution and the tgts distribution based on the parameter matrix of r, the parameter matrix of b, and the parameter matrix of la. Here, the tgts distribution is the distribution of the target steering angle of the tires of the vehicle to be controlled. The K distribution and the tgts distribution are input to the time series distribution generator 502. The time series distribution generator 502 updates the parameter matrix of K and the parameter matrix of d based on these distributions.

[0063] Furthermore, a dx distribution, dy distribution generator 508 generates a dx distribution and a dy distribution from the parameter matrix of la and the parameter matrix of b, and a dx, dy calculator 509 calculates dx and dy, which are input to an x, y integral calculator 510. Furthermore, the dx, dy calculator 509 updates the parameter matrix of dx, dy based on the obtained parameter matrix of dx, dy.

[0064] The x,y integral calculator 510 performs an integral calculation based on the dx, dy parameter matrix to determine the x, y parameter matrix. The x, y parameter matrix is ​​input to the distribution converter 511. Furthermore, the x, y integral calculator 510 updates the x, y parameter matrix based on the obtained x, y parameter matrix. The distribution converter 511 converts the x, y parameter matrix into an x, y distribution.

[0065] 12(a) and 12(b) are diagrams illustrating the first condition. As shown in FIG. 12(a), under the first condition, the detection distance is distributed between 60 m and 80 m. The cornering power in this case is distributed as shown in FIG. 12(b). FIG. 13 is a graph showing the vehicle trajectory obtained under the first condition. The solid line shows the trajectory at both front ends of the vehicle, and the dashed line shows the trajectory at both rear ends of the vehicle. 500 indicates the leading vehicle. From the graph in FIG. 13, it can be seen that the leading vehicle 600 is successfully avoided.

[0066] 14(a) and 14(b) are diagrams illustrating the second condition. As shown in FIG. 14(a), under the second condition, the detection process assumes a distribution between 40 m and 60 m. FIG. 15 is a graph showing the vehicle trajectory obtained under the second condition. From the graph in FIG. 15, it can be seen that although the vehicle is successful in avoiding the preceding vehicle 600, there is a probability that the vehicle will collide with the adjacent wall 610. The collision probability in this case can be precisely calculated.

[0067] As described above, the information processing device 100 of this embodiment can obtain the calculation results of a plurality of parameters having distributions with higher accuracy than conventional methods.

[0068] The above embodiment is one example for implementing the present invention, and various other embodiments are possible. For example, various modifications and changes are possible within the scope of the gist of the present invention as described in the claims, such as applying one modified example to another modified example. For example, at least some of the components constituting the information processing device 100 may exist as separate devices or systems. Furthermore, some components of the above embodiment may be omitted, and the order of processing may be changed or omitted.

[0069] In such a first modified example, the information processing device 100 does not need to perform calculations of distributions using probability matrices. By performing calculations between multiple parameter matrices, calculations involving distributions can be performed without omission.

[0070] In a second modified example, the number of parameters to be calculated is not limited to that of the embodiment. The number of parameters to be calculated may be two, or may be four or more. In either case, the control unit 110 generates matrices with dimensions corresponding to the number of parameters, and performs calculations using the parameters.

[0071] Furthermore, although the matrix generation unit 112 generates a parameter matrix with the same number of dimensions as the number of parameters, it may also generate a parameter matrix with the number of dimensions being fewer than the number of parameters to be calculated.

[0072] A third modified example will be described. In this embodiment, a case has been described in which mutually independent parameters are used as the dimensions of the parameter matrix. However, dependent parameters may also be added to the dimensions of the parameter matrix. For example, in a calculation for controlling vehicle travel, a three-dimensional parameter matrix may be generated by adding a yaw rate r, which is a dependent parameter of the cornering power K, to the cornering power K and the detection distance d.

[0073] Furthermore, the present invention can also be applied as a program or method. The above-described systems, programs, and methods may be realized as standalone devices or may be realized using shared components, and include various other aspects. For example, it is possible to provide a method or program realized by the above-described system. Furthermore, it is possible to modify the invention as appropriate, for example, by making some parts software and some parts hardware. Furthermore, the invention can also be realized as a recording medium for a program that controls the device. Of course, the recording medium for the software may be a magnetic recording medium or a semiconductor memory, and any recording medium developed in the future can be considered in the same way. [Explanation of symbols]

[0074] 100 Information processing device 110 control section 111 Acquisition Department 112 Matrix generation unit 113 Arithmetic section 114 Display processing unit 120 Storage section 130 UI section 140 Communications Department

Claims

1. An information processing device including an acquisition unit, a matrix generation unit, and a calculation unit, the acquisition unit acquires, as calculation targets, a plurality of parameters whose values ​​vary and whose probabilities for each value are given as a distribution; the matrix generation unit generates a plurality of parameter matrices corresponding to the plurality of parameters, Each of the plurality of parameter matrices has a number of dimensions equal to or less than the number of the parameters, and the width of the distribution of each parameter is set to the length of the dimension corresponding to the parameter; Each parameter matrix has values ​​into which the distribution of the corresponding parameter is equally divided as values ​​of each element of the parameter matrix, The information processing device wherein the calculation unit updates each element of the plurality of parameter matrices by performing calculations on each element of the plurality of matrices.

2. The matrix generation unit a probability matrix corresponding to the plurality of parameters, further generating the probability matrix in which the product of probability values ​​for each combination of the plurality of parameters is the value of each element; The calculation unit 2. The information processing device according to claim 1, wherein the parameter matrix after calculation is classified according to the value of each element, and the probability of each element of the probability matrix associated with each element of the parameter matrix before calculation according to the classification is summed to determine a probability value for each element of the parameter matrix after calculation, and the distribution of the plurality of parameters after calculation is determined based on the probability value.

3. The calculation unit multiplying the length of the elements of the parameter matrix before the operation by a probability value determined by the probability matrix; The information processing apparatus according to claim 2 , wherein the distribution of the plurality of parameters after the calculation is determined by dividing the multiplied value by a minute width obtained by equally dividing the width of the distribution of the parameters after the calculation.

4. The calculation unit 3. The information processing device according to claim 2, wherein the distribution of the plurality of parameters after the calculation is determined by correcting the probability value determined by the probability matrix by the ratio of the length obtained by equally dividing the distribution of the parameters before the calculation to the length obtained by equally dividing the distribution of the parameters after the calculation.

5. The calculation unit The information processing device according to claim 1 , wherein when one of the parameters has a plurality of dimensions, the plurality of parameters are aggregated into a parameter of one dimension.

6. The information processing apparatus according to claim 1 , wherein the number less than the number of parameters is the number of parameters that are independent of each other.

7. The calculation determines the probability that the controlled object will approach or come into contact with an obstacle, the parameter is a movement trajectory of the control object; The calculation unit 2. The information processing device according to claim 1, wherein, based on the parameter matrix obtained during and after the calculation, the controlled object is judged to be approaching or in contact with the obstacle when it is located within a range determined according to the position of the obstacle, and the sum of the probability values ​​of the judgment of approach or contact during the movement of the controlled object is calculated as the probability of approach or contact.

8. Computer, A program for causing the acquisition unit, the matrix generation unit, and the calculation unit to function, the acquisition unit acquires, as calculation targets, a plurality of parameters whose values ​​vary and whose probabilities for each value are given as a distribution; the matrix generation unit generates a plurality of parameter matrices corresponding to the plurality of parameters, Each of the plurality of parameter matrices has a number of dimensions equal to or less than the number of the parameters, and the width of the distribution of each parameter is set to the length of the dimension corresponding to the parameter; Each parameter matrix has values ​​into which the distribution of the corresponding parameter is equally divided as values ​​of each element of the parameter matrix, The calculation unit updates each element of the plurality of parameter matrices by performing a calculation on each element of the plurality of matrices.

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