WO2021181687A1 - Dispositif et procédé de création de modèle de prédiction et programme - Google Patents

Dispositif et procédé de création de modèle de prédiction et programme Download PDF

Info

Publication number
WO2021181687A1
WO2021181687A1 PCT/JP2020/011219 JP2020011219W WO2021181687A1 WO 2021181687 A1 WO2021181687 A1 WO 2021181687A1 JP 2020011219 W JP2020011219 W JP 2020011219W WO 2021181687 A1 WO2021181687 A1 WO 2021181687A1
Authority
WO
WIPO (PCT)
Prior art keywords
prediction model
positioning error
failure rate
distribution
observations
Prior art date
Application number
PCT/JP2020/011219
Other languages
English (en)
Japanese (ja)
Inventor
山田　昌弘
剛志 是永
健司 ▲高▼尾
陽平 知識
Original Assignee
三菱重工機械システム株式会社
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 三菱重工機械システム株式会社 filed Critical 三菱重工機械システム株式会社
Priority to JP2022505712A priority Critical patent/JP7235931B2/ja
Priority to PCT/JP2020/011219 priority patent/WO2021181687A1/fr
Publication of WO2021181687A1 publication Critical patent/WO2021181687A1/fr

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/13—Receivers
    • G01S19/23—Testing, monitoring, correcting or calibrating of receiver elements

Definitions

  • the present invention relates to a predictive model creation device for predicting a positioning error distribution, a predictive model creation method, and a program.
  • Patent Document 1 describes a technique for calculating a positioning error based on an error between a pseudo distance from a plurality of satellites and a distance from a positioned position to the satellite.
  • a vehicle traveling on the road A shown in FIG. 8 positions the traveling position of its own vehicle by a satellite positioning system, it may be traveling on a nearby road B depending on the degree of error in the positioned position information. Such results may be obtained.
  • misrecognition of the road on which the vehicle travels becomes a problem.
  • a technique for calculating the probability of making a mistake on the road on which the vehicle is traveling hereinafter referred to as a failure rate
  • Both L1 and L2 in FIG. 8 show the distribution of positioning error.
  • the positioning error is normally distributed, and the standard deviation of the normal distribution is defined as the "positioning error".
  • the distribution L1 shows a probability distribution when the positioning error is smaller than the distribution L2. See FIG.
  • the vertical axis of the graph of FIG. 9 shows the failure rate, and the horizontal axis shows the magnitude of the positioning error.
  • the difference D1 between the positioning error R1 and the positioning error R2 and the difference D2 between the positioning error R2 and the positioning error R3 are equal.
  • the difference D3 between the failure rate when the positioning error R1 and the failure rate when the positioning error R2 is R2 is significantly different from the difference D4 between the failure rate when the positioning error R2 and the failure rate when the positioning error R3 is set. That is, the magnitude of the positioning error is not proportional to the failure rate. Therefore, when dealing with the failure rate, the difference D1 between the positioning error R1 and the positioning error R2 and the difference D2 between the positioning error R2 and the positioning error R3 should not be treated as equivalent.
  • a prediction model when creating a prediction model, learning is performed so as to minimize or maximize a predetermined evaluation function. For example, when a prediction model is created by using the least squares error as an evaluation function and reducing the error between the predicted value and the measured value of the positioning error, the positioning error is predicted to be R3 when the true magnitude of the positioning error is R2.
  • R1 when the true magnitude of the positioning error is R2, and both of them have the value of the evaluation function. It is possible that they will be equivalent.
  • this prediction model may become a model in which the failure rate of erroneously recognizing road A and road B is significantly different from the actual model. With such a prediction model, it is not possible to grasp the accurate failure rate.
  • the present disclosure provides a predictive model creation device, a predictive model creation method, and a program capable of solving the above-mentioned problems.
  • the prediction model creation device is a prediction model creation device that creates a prediction model that predicts a positioning error indicating an error in the position positioned by the satellite positioning system, and is a feature of the terrain at a certain point.
  • a unit a prediction model creation unit that creates a prediction model that outputs the probability distribution of the positioning error when the topographical feature data is input, a probability distribution of the positioning error output by the prediction model, a predetermined function, and the like.
  • the evaluation value of the failure rate is calculated based on the number of observations for each positioning error and the function. It includes an evaluation unit that calculates based on the failure rate, and an update unit that updates the prediction model so that the evaluation value becomes an optimum value.
  • the prediction model creation method is a prediction model creation method for creating a prediction model that predicts a positioning error indicating an error in the position positioned by the satellite positioning system, and is a feature of the terrain at a certain point.
  • the topographical feature data is input, there is a step of creating a prediction model that outputs the probability distribution of the positioning error, and in the step of creating the prediction model, the probability of the positioning error output by the prediction model.
  • the evaluation value of the failure rate is the number of observations for each positioning error and the above. It is calculated based on the observation failure rate calculated based on the function, and the prediction model is updated so that the evaluation value becomes the optimum value.
  • the program is a prediction model creation method for creating a prediction model for predicting a positioning error indicating an error in the position positioned by a satellite positioning system on a computer, and is a feature of the terrain at a certain point.
  • the topographical feature data is input, there is a step of creating a prediction model that outputs the probability distribution of the positioning error, and in the step of creating the prediction model, the probability of the positioning error output by the prediction model.
  • the evaluation value of the failure rate is the number of observations for each positioning error and the above. The process of updating the prediction model so that the evaluation value becomes the optimum value, which is calculated based on the observation failure rate calculated based on the function, is executed.
  • prediction model creation device prediction model creation method and program, it is possible to create a prediction model that estimates the distribution of the error of the positioned position in the positioning using satellites.
  • FIG. 1 is a diagram illustrating a positioning error and a failure rate.
  • FIG. 2 is a diagram illustrating a positioning error and a failure rate. It is a figure which shows an example of the hardware configuration of the prediction model creation apparatus in each embodiment of this invention.
  • FIG. 1 is a diagram showing an example of a prediction model creation device according to the first embodiment of the present invention.
  • the prediction model creation device 10 includes a data acquisition unit 11, a prediction model creation unit 12, and a storage unit 13.
  • the data acquisition unit 11 acquires the training data necessary for creating the prediction model M that predicts the probability distribution of the positioning error of the satellite positioning system at an arbitrary point.
  • the training data includes, for example, topographical feature data indicating the topographical features of each of a plurality of points, speed data when the vehicle travels at that point, and positioning error when the point is traveled multiple times. It contains information such as the number of observations and the total number of runs (total number of observations).
  • the prediction model creation unit 12 creates a prediction model M that predicts the probability distribution of the positioning error of the satellite positioning system so that an accurate failure rate can be calculated.
  • the prediction model creation unit 12 includes a prediction value calculation unit 121, an evaluation unit 122, and an update unit 123.
  • the predicted value calculation unit 121 estimates the true distribution of the positioning error.
  • the predicted value calculation unit 121 calculates the predicted value of the failure rate based on the estimated positioning error distribution. As will be described later, the predicted value of the failure rate is calculated as a distribution.
  • the evaluation unit 122 evaluates by comparing the failure rate calculated based on the positioning error predicted by the prediction model M with the failure rate calculated from the distribution of the observed positioning error. In this embodiment, the failure rate is used as the evaluation function (loss function) instead of the positioning error.
  • the update unit 123 updates the parameters of the prediction model M based on the evaluation of the evaluation unit 122 with respect to the failure rate based on the prediction model M.
  • the storage unit 13 stores the prediction model M and various data.
  • the positioning error indicates the magnitude of the error of the position information positioned by the satellite positioning system, and indicates that an error can occur in the range of a circle centered on the position information and having the positioning error as a radius, for example.
  • the variation in positioning error is represented by a probability distribution. For example, while traveling a certain point Q 100 times by a vehicle, the position information and the positioning error at the point Q are received from the satellite positioning system.
  • the prediction model M of the present embodiment outputs the probability distribution of the positioning error observed at the point Q when the topographical feature data of the point Q, the speed of the vehicle, and the like are input.
  • the failure rate f is calculated by the following equation (1).
  • f f 2 ⁇ r 2 + f 3 ⁇ r 3 + ... + f 11 ⁇ r 11 ...
  • f k failure rate when the positioning error is k in [m] r k is the true probability that the positioning error becomes k [m].
  • the true probability, number of positioning error of n k is k [m] is observed, the total number of observations is N, the value of n k ⁇ N which is assumed when the N ⁇ ⁇ .
  • the failure rate Since the posterior probability p follows the Dirichlet distribution, the failure rate also has a distribution, and the distribution can be calculated by the following equation (3).
  • f ⁇ (f 2 ⁇ r 2 + f 3 ⁇ r 3 + ⁇ + f 11 ⁇ r 11) ⁇ p (r 2, r 3, ⁇ , r 11
  • the predicted value calculation unit 121 calculates the estimated value of the true positioning error distribution using the formula (2), and calculates the observed value of the failure rate by the formula (3).
  • the probability distribution of the positioning error predicted by the prediction model M and the failure rate f pred calculated from the equation (1) are obtained.
  • the distribution of positioning error was estimated by the Dirichlet distribution, but it may be calculated by the product of the binomial distribution instead of the Dirichlet distribution.
  • evaluation function Assuming that the probability distribution of the positioning error predicted by the prediction model M and the failure rate calculated from the equation (1) are f- pred , the evaluation value L of the f- pred is the equation (4) from the observation result as shown in the following equation (4). It is the cumulative probability of the posterior probability of the failure rate estimated by 3) near the predicted value.
  • b in the equation (4) is a margin of error of the failure rate.
  • the update unit 123 adjusts the parameters of the prediction model M so that the evaluation value L of the equation (4) is maximized.
  • a recurrent neural network RNN
  • Topographical feature data, velocity data, etc. are used as input variables for the prediction model M.
  • the update unit 123 adjusts the parameters based on the distribution of the failure rate.
  • FIGS. 2 and 3 are a first diagram and a second diagram for explaining a method of creating the prediction model of the present disclosure, respectively.
  • FIG. 2 is a diagram when the total number of observations is small
  • FIG. 3 is a diagram when the total number of observations is large.
  • FIG. 2A is a graph showing the positioning error observation frequency space.
  • n 2 and n 3 indicate the number of times the positioning error 2 [m] was observed and the number of times the positioning error 3 [m] was observed, respectively.
  • n 2 and n 3 of n 2 to n 11 are taken out and shown in the figure. This also applies to the following.
  • FIG. 2B is a graph showing the positioning error distribution space.
  • R 2 and r 3 indicate the probability that the positioning error 2 [m] is observed and the probability that the positioning error 3 [m] is observed, respectively.
  • Point X 12 in FIG. 2 (a) shows that the number of times that the positioning error 2 [m] has been observed in n 2, and the number of times that the positioning error 3 [m] is observed is n 3 ..
  • the distribution Y 12 in FIG. 2 (b) shows the distribution of the true posterior probabilities of the positioning error because the number of observations is a combination of the values of the points X 12. That is, the distribution Y 12 shows the Dirichlet distribution according to the equation (2) in the case of the point X 12. Since the total number of observations in FIG. 2 is small, the spread of the distribution Y 12 is large as compared with the case of FIG. 3 described below.
  • FIG. 2C shows a graph showing the distribution W of the failure rate (observation failure rate) calculated from the posterior distribution of the positioning error, and the failure rate f pred based on the predicted value Z 12 by the prediction model M.
  • the vertical axis of FIG. 2C shows the probability, and the horizontal axis shows the failure rate.
  • the predicted value calculation unit 121 Based on the true posterior distribution Y 12 , the predicted value calculation unit 121 has a large number of random numbers (r 1 , r 2 , ..., R 11 ) that follow the Dirichlet distribution by a method such as MCMC, that is, positioning according to the Dirichlet distribution. Generate an error distribution.
  • the predicted value calculation unit 121 calculates the graph W showing the distribution of the observation failure rate from the generated positioning error distribution and the equation (3).
  • the evaluation unit 122 calculates the failure rate f pred from the predicted value Z 12 in FIG. 2 (b) and the equation (1).
  • the width b in FIG. 2C corresponds to the number of observations or a predetermined allowable width, and corresponds to b in the formula (4).
  • the size of the shaded area Lw indicates an evaluation value for the correctness of the prediction, and corresponds to L in the equation (4).
  • Evaluation unit 122 in the vicinity of the failure rate f pred calculated from the predicted value Z 12, to evaluate the correctness of the predicted value Z 12 depending distribution W of the observed failure rate how much of gather.
  • the prediction model M is such that the region Lw included in the width of b centered on the failure rate f pred based on the prediction value Z 12 becomes large.
  • Parameter coefficient of RNN network
  • FIG. 3A shows a positioning error observation frequency space when the number of observations is large.
  • n 2 and n 3 are the number of times the positioning errors 2 [m] and 3 [m] were observed, respectively, but the total number of observations is larger than in the case of FIG.
  • the spread of the positioning error distribution Y 12 becomes small. This indicates that the positioning error distribution Y 12 has converged closer to the true distribution due to the increase in the number of observations.
  • Point Z 12 in FIG. 3B is a predicted value by the prediction model M.
  • the predicted value calculation unit 121 uses the random numbers generated based on the positioning error distribution Y 12 of FIG. 3 (b) and the equation (3) to be used in FIG.
  • the distribution W of the observation failure rate shown in c) is calculated.
  • the evaluation unit 122 calculates the failure rate f pred shown in FIG. 3 (c) by using the predicted value Z 12 in FIG. 3 (b) and the equation (1).
  • the width Wh of the observation failure rate distribution W becomes narrower as the spread of the positioning error distribution Y 12 decreases.
  • the expected value Wc of the distribution W and the failure rate f pred based on the predicted value are separated from each other, and the failure rate f pred is outside the width Wh of the distribution W.
  • the size is smaller than that in FIG. 2 (c).
  • the prediction model creating unit 12 creates a prediction model M for calculating such a predicted value. For example, if a prediction model M is created using the positioning errors observed at points Q1 and Q2 with different topographical features as training data, and the number of observations at point Q1 is small and the number of observations at point Q2 is large, prediction is made.
  • the model creation unit 12 makes sure that the expected value Wc of the distribution W having a spread as illustrated in FIG. 2C and the failure rate fpred are separated by a certain degree. Is allowed, and when the topographical feature data of the point Q2 is input, it is predicted to output a predicted value such that the expected value Wc of the narrow distribution W as illustrated in FIG. 3C and the failure rate f pred approach each other. Create a model.
  • FIG. 4 is a flowchart showing an example of the prediction model creation process of the present disclosure.
  • the data acquisition unit 11 acquires the learning data (step S11).
  • the data acquisition unit 11 has, for each of the plurality of points, the topographical feature data of that point, the speed of the vehicle, the magnitude of the positioning error observed when passing the target point a plurality of times, the number of observations thereof, and the total number of observations.
  • the topographical feature data is, for example, the number and height of buildings existing in each direction around the target point, the route (road shape) on which the vehicle traveled, and the like.
  • Both the terrain feature data and the vehicle speed are factors that affect the reception of signals from the satellite positioning system at the target point.
  • Topographic feature data can be obtained by analyzing three-dimensional map data.
  • the vehicle speed and positioning error the vehicle is actually driven multiple times in the same direction at the same speed at the same target point, and the speed at that time, the position information acquired from the satellite positioning system, and the positioning error information are recorded. Can be obtained by doing.
  • the terrain feature data, the vehicle speed, the number of observations for each positioning error, and the total number of observations are set as one set of learning data.
  • the data acquisition unit 11 acquires learning data for a plurality of points having various topographical features and records them in the storage unit 13.
  • the prediction model creation unit 12 selects a plurality of sets from the learning data recorded in the storage unit 13 (step S12).
  • the prediction model creation unit 12 acquires the terrain feature data and the vehicle speed for each of the selected learning data and inputs them to the prediction model M being trained.
  • the prediction model M predicts the positioning error distribution corresponding to the input terrain feature data and the vehicle speed (step S13).
  • the prediction model M is "a building with a height of 20 m in the north direction, which includes topographical feature data and vehicle speed, and runs on a road passing from west to east on the south side of the building, from west to east at a speed of west to east.
  • the prediction model creation unit 12 evaluates the predicted value (probability distribution of positioning error) (step S14).
  • the prediction value is evaluated using the failure rate, instead of evaluating the difference between the probability distribution of the positioning error output by the prediction model M and the probability distribution of the actually observed positioning error.
  • the predicted value calculation unit 121 uses the number of observations and the total number of observations of each positioning error of the training data, and the equation (2) to distribute the Dirichlet distribution (FIGS. 2 (b) and 3 (b)). Y 12 ) is calculated.
  • the predicted value calculation unit 121 generates a random number based on the Dirichlet distribution, and calculates the distribution of the observation failure rate (W in FIGS.
  • the evaluation unit 122 uses the predicted values (Z 12 in FIGS. 2 (b) and 3 (b)) predicted by the prediction model M during learning in step S13 and the equation (1) to determine the failure rate based on the predicted values.
  • f pred (Fig 2 (b), f pred in FIG. 3 (b)) is calculated.
  • the evaluation unit 122 calculates the evaluation value L (Lw in FIGS. 2 (c) and 3 (c)) by the formula (4).
  • the update unit 123 updates the parameters of the prediction model M so as to maximize the evaluation value L (step S15).
  • the prediction model creation unit 12 determines whether or not the end condition is satisfied (step S16). For example, the prediction model creation unit 12 determines that the end condition is satisfied when the value of the evaluation value L reaches a predetermined target value. Alternatively, the prediction model creation unit 12 determines that the end condition is satisfied when the number of times of learning (the number of times of execution of steps S12 to S15) reaches a predetermined threshold value. If the end condition is not satisfied (step S16; No), the prediction model creation unit 12 repeats the process from step S12. When the end condition is satisfied (step S16; Yes), the prediction model creating unit 12 ends the process of creating the prediction model M. The prediction model creation unit 12 records the learned prediction model M in the storage unit 13.
  • the prediction model M that predicts the probability distribution of the positioning error when the terrain feature data and the vehicle speed are input, but the prediction model creation unit 12 creates the positioning error when the terrain feature data is input. It may be configured to create a prediction model M that predicts the probability distribution of. This also applies to the second embodiment.
  • FIG. 5 is a diagram showing an example of a prediction model creation device according to the second embodiment of the present disclosure.
  • the prediction model creation device 10A includes a data acquisition unit 11, a prediction model creation unit 12, a storage unit 13, and a reliability evaluation unit 14. The description of the same configuration as that of the first embodiment will be omitted.
  • the reliability evaluation unit 14 evaluates the reliability of the probability distribution of the positioning error predicted by the prediction model M.
  • the reliability evaluation unit 14 executes either or both of (1) reliability evaluation based on the uncertainty of the model and (2) reliability evaluation based on the total number of observations during learning.
  • the reliability evaluation unit 14 applies a method called dropout in which some of the elements of the network are intentionally omitted. ..
  • the reliability evaluation unit 14 drops out different networks each time a predetermined number of times, and records the predicted value output by the predicted model M to which the dropout has been applied for the same input.
  • the reliability evaluation unit 14 evaluates the variation of the predicted value output by the prediction model M. When the variation is large, the reliability of the probability distribution of the positioning error predicted by the prediction model M is low, and when the variation is small, the reliability is evaluated as high.
  • the reliability evaluation unit 14 when the spread of the Diricle distribution is large, the reliability of the probability distribution of the positioning error predicted by the prediction model M is low (the total number of observations is small), and when the spread is small (the total number of observations is large). ), The reliability is evaluated as high.
  • FIG. 6 is a diagram illustrating an evaluation method of a prediction model according to the second embodiment of the present disclosure.
  • 6 (a) to 6 (c) are diagrams for explaining the evaluation process when the total number of observations is large.
  • FIG. 6A shows a feature space.
  • the feature quantity is a parameter to be input to the prediction model.
  • Point H 1 is feature value g 1 (e.g., feature values of the terrain) indicated by the value of G 1, characteristic quantity g 2 (e.g., vehicle speed) of the input parameter value of is in G2.
  • FIG. 6B shows a positioning error distribution space.
  • the point I 1 indicates the output when the input parameter of the point H 1 is input to the trained prediction model M, that is, the probability distribution of the positioning error.
  • Distribution J 1 indicates the variability that occurred when the dropout was applied, the variability according to the total number of observations, or both.
  • Reliability evaluation unit 14 applies a drop out trained predictive model M, a predictive value when inputting the input parameters as at point H 1, is recorded in the storage unit 13.
  • the reliability evaluation unit 14 records the calculated posterior probability distribution in the storage unit 13.
  • the distribution J 1 is either or both of a plot of the predicted values recorded when the dropout is applied and a calculated posterior probability distribution.
  • the distribution W 1 of the observation failure rate is calculated based on the distribution J 1 , the distribution as shown in FIG. 6 (c) is obtained.
  • FIG. 6 (d) to 6 (e) are diagrams for explaining the evaluation process when the total number of observations is small.
  • FIG. 6 (d) shows the feature space
  • FIG. 6 (e) shows the positioning error distribution space.
  • Point I 2 is a predicted value when the input parameter of point H 2 is input to the trained prediction model M.
  • Distribution J 2 for the predicted value I 2 is the variation occurred in dropout application, or variations corresponding to all the observation times, or show both.
  • Reliability evaluation unit 14 performs the same process as described in FIG. 6 (b), calculates a distribution J 2 in FIG. 6 (e). When the distribution W 2 of the observation failure rate is calculated based on the distribution J 2 , the distribution as shown in FIG. 6 (f) is obtained.
  • the reliability evaluation unit 14 evaluates the reliability of the predicted value based on the magnitude of the spread of the distribution J 1 and the distribution J 2. For example, the reliability evaluation unit 14 compares the area of the distribution J 1 and the distribution J 2 with a predetermined threshold, and the predicted value predicted by the prediction model M based on the fact that the area of the distribution J 1 is less than the threshold. I 1 is evaluated as highly reliable. For example, the reliability evaluation unit 14 evaluates that the predicted value I 2 predicted by the prediction model M is highly reliable based on the area of the distribution J 2 being equal to or larger than the threshold value.
  • FIG. 7 is a flowchart showing an example of the evaluation process of the prediction model according to the second embodiment of the present disclosure.
  • the reliability evaluation unit 14 evaluates the reliability based on the uncertainty of the model (step S21). With respect to a certain predicted value, the reliability evaluation unit 14 inputs the input parameter n value when the predicted value is obtained into the prediction model M to which the dropout is applied, and acquires the predicted value. The reliability evaluation unit 14 performs the same process a plurality of times by changing the network to drop out, and calculates the variation of the predicted value. Next, the reliability evaluation unit 14 evaluates the reliability based on the total number of observations during learning (step S22).
  • the reliability of the predicted value predicted by the trained prediction model M can be evaluated. For example, when the reliability of the predicted value is low, as shown in FIG. 6 (f), the distribution W 2 of the failure rate calculated based on the predicted value has a certain spread or more. In such a case, a look at the distribution W 2 spread, "this point, the failure rate is because there is a possibility of erroneous recognition of the road uncertain, it is better not to perform processing related to road pricing" a decision such as It can be carried out.
  • FIG. 10 is a diagram showing an example of the hardware configuration of the prediction model creation device according to each embodiment of the present disclosure.
  • the computer 900 is, for example, a PC (Personal Computer) including a CPU 901, a main storage device 902, an auxiliary storage device 903, an input / output interface 904, and a communication interface 905, a server terminal device, and the like.
  • the above-mentioned prediction model creation devices 10 and 10A are mounted on the computer 900.
  • the operation of each processing unit described above is stored in the auxiliary storage device 903 in the form of a program.
  • the CPU 901 reads the program from the auxiliary storage device 903, expands it to the main storage device 902, and executes the above processing according to the program.
  • the CPU 901 secures a storage area corresponding to the storage unit 13 in the main storage device 902 according to the program.
  • the CPU 901 secures a storage area for storing the data being processed in the auxiliary storage device 903 according to the program.
  • the auxiliary storage device 903 is an example of a non-temporary tangible medium.
  • Other examples of non-temporary tangible media include magnetic disks, magneto-optical disks, CD-ROMs, DVD-ROMs, semiconductor memories, etc. connected via the input / output interface 904.
  • the distributed computer 900 may expand the program to the main storage device 902 and execute the above processing.
  • the program may be for realizing a part of the above-mentioned functions.
  • the program may be a so-called difference file (difference program) that realizes the above-mentioned function in combination with another program already stored in the auxiliary storage device 903.
  • the prediction model creation devices 10 and 10A are prediction model creation devices for creating a prediction model for predicting a positioning error indicating an error in the position positioned by the satellite positioning system, and are at a certain point. Acquires topographical feature data indicating topographical features, the positioning error with respect to the position of the moving body observed by the satellite positioning system when the moving body passes through the point a plurality of times, and the number of observations for each positioning error. Data acquisition unit 11 to create a prediction model M that creates a prediction model M that outputs the probability distribution of the positioning error when the topographical feature data is input, and a prediction model creation unit 12 that outputs the probability distribution of the positioning error that the prediction model M outputs.
  • the evaluation value of the failure rate is set for each positioning error.
  • An evaluation unit 122 that calculates based on the number of observations and the observation failure rate calculated based on the function (Equation (3)), and an update unit that updates the prediction model M so that the evaluation value becomes an optimum value. 123 and. This makes it possible to create a prediction model M that predicts a positioning error for which an accurate failure rate can be calculated.
  • the predetermined function indicates a function for calculating the failure rate, and the equations (1) and (3) correspond to the predetermined functions.
  • the prediction model creation devices 10, 10A are the prediction model creation devices 10, 10A of (1), and the number of observations for each positioning error is acquired by the data acquisition unit 11. Based on the distribution of posterior probabilities of the positioning error to be the number of observations and the function (Equation (3)), the distribution W of the observation failure rate is calculated, and the distribution W is used as the failure rate.
  • the predicted value calculation unit 121 which outputs the predicted value of the above, is further provided. This makes it possible to calculate the true distribution of failure rates.
  • the prediction model creation devices 10, 10A are the prediction model creation devices 10, 10A of (1) to (2), and the evaluation unit 122 is based on the prediction model M.
  • the area Lw where the calculated region of the predetermined width b centered on the failure rate and the region occupied by the distribution W indicated by the predicted value of the failure rate overlap is calculated as the evaluation value L, and the update unit 123 ,
  • the parameters of the prediction model M are updated so that the evaluation value L becomes the maximum. This makes it possible to create a prediction model M capable of calculating an accurate failure rate.
  • the prediction model creation device 10A is the prediction model creation device 10A of (1) to (3), and the reliability of the prediction value of the probability distribution of the positioning error output by the prediction model.
  • a reliability evaluation unit 14 for evaluating sex is further provided.
  • the prediction model creation device 10A is the prediction model creation device 10A of (4), and when the prediction model M is constructed by a neural network, the reliability evaluation unit 14 , The reliability is evaluated based on the variation at the time of applying the dropout to the prediction model when the predetermined topographical feature data and the speed of the moving object are input to the prediction model.
  • the prediction model creation device 10A according to the sixth aspect is the prediction model creation device 10A of (4) to (5), and the reliability evaluation unit 14 is the magnitude of the total value of the number of observations.
  • the spread of the posterior probability distribution of the predicted value based on the above is calculated, and the reliability is evaluated based on the spread of the distribution.
  • the prediction model creation method is a prediction model creation method for creating a prediction model for predicting a positioning error indicating an error in the position positioned by the satellite positioning system, and is a feature of the terrain at a certain point.
  • the topographical feature data is input, there is a step of creating a prediction model that outputs the probability distribution of the positioning error, and in the step of creating the prediction model, the probability of the positioning error output by the prediction model.
  • the evaluation value of the failure rate is the number of observations for each positioning error and the above. It is calculated based on the observation failure rate calculated based on the function, and the prediction model is updated so that the evaluation value becomes the optimum value.
  • the program according to the eighth aspect is a prediction model creation method for creating a prediction model for predicting a positioning error indicating an error of a position positioned by a satellite positioning system on a computer, and is a feature of the terrain at a certain point.
  • the topographical feature data is input, there is a step of creating a prediction model that outputs the probability distribution of the positioning error, and in the step of creating the prediction model, the probability of the positioning error output by the prediction model.
  • the evaluation value of the failure rate is the number of observations for each positioning error and the above. The process of updating the prediction model so that the evaluation value becomes the optimum value, which is calculated based on the observation failure rate calculated based on the function, is executed.
  • prediction model creation device prediction model creation method and program, it is possible to create a prediction model that estimates the distribution of the error of the positioned position in the positioning using satellites.

Abstract

La présente invention concerne un dispositif de création de modèle de prédiction destiné à créer un modèle de prédiction pour prédire des erreurs de positionnement indiquant des erreurs dans des positions mesurées par un système de positionnement par satellite. Le dispositif de création de modèle de prédiction effectue un calcul sur la base d'une distribution de probabilité d'erreurs de positionnement, calcule une valeur évaluée sur la base d'un taux de défaillance qui indique une possibilité d'apparition d'une erreur de reconnaissance par rapport à chaque position mesurée par le système de positionnement par satellite et qui est basé sur une valeur prédite par le modèle de prédiction et sur la base d'un taux de défaillance d'observation calculé sur la base d'une fonction et du nombre de fois d'observation pour chaque erreur de positionnement et effectue un apprentissage pour obtenir un modèle de prédiction de telle sorte que la valeur évaluée devienne une valeur optimale.
PCT/JP2020/011219 2020-03-13 2020-03-13 Dispositif et procédé de création de modèle de prédiction et programme WO2021181687A1 (fr)

Priority Applications (2)

Application Number Priority Date Filing Date Title
JP2022505712A JP7235931B2 (ja) 2020-03-13 2020-03-13 予測モデル作成装置、予測モデル作成方法及びプログラム
PCT/JP2020/011219 WO2021181687A1 (fr) 2020-03-13 2020-03-13 Dispositif et procédé de création de modèle de prédiction et programme

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/JP2020/011219 WO2021181687A1 (fr) 2020-03-13 2020-03-13 Dispositif et procédé de création de modèle de prédiction et programme

Publications (1)

Publication Number Publication Date
WO2021181687A1 true WO2021181687A1 (fr) 2021-09-16

Family

ID=77672172

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/JP2020/011219 WO2021181687A1 (fr) 2020-03-13 2020-03-13 Dispositif et procédé de création de modèle de prédiction et programme

Country Status (2)

Country Link
JP (1) JP7235931B2 (fr)
WO (1) WO2021181687A1 (fr)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113834792A (zh) * 2021-09-22 2021-12-24 中国科学院合肥物质科学研究院 一种基于max-doas的50米分辨率痕量气体廓线反演方法

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH0792388B2 (ja) * 1989-04-17 1995-10-09 住友電気工業株式会社 位置検出装置
JPH0944244A (ja) * 1995-07-27 1997-02-14 Shinko Electric Co Ltd 無人車の走行制御装置
JP2003215228A (ja) * 2002-01-23 2003-07-30 Hitachi Ltd 位置表示機能付き移動端末装置及び位置表示方法
JP2007203860A (ja) * 2006-02-01 2007-08-16 Xanavi Informatics Corp 車両速度制御装置および同装置における目標速度設定方法ならびにプログラム
US20120139775A1 (en) * 2010-12-02 2012-06-07 Honda Motor Co., Ltd. Method for Testing GNSS-Based Positioning Systems in Obstructed Environments

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH0792388B2 (ja) * 1989-04-17 1995-10-09 住友電気工業株式会社 位置検出装置
JPH0944244A (ja) * 1995-07-27 1997-02-14 Shinko Electric Co Ltd 無人車の走行制御装置
JP2003215228A (ja) * 2002-01-23 2003-07-30 Hitachi Ltd 位置表示機能付き移動端末装置及び位置表示方法
JP2007203860A (ja) * 2006-02-01 2007-08-16 Xanavi Informatics Corp 車両速度制御装置および同装置における目標速度設定方法ならびにプログラム
US20120139775A1 (en) * 2010-12-02 2012-06-07 Honda Motor Co., Ltd. Method for Testing GNSS-Based Positioning Systems in Obstructed Environments

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113834792A (zh) * 2021-09-22 2021-12-24 中国科学院合肥物质科学研究院 一种基于max-doas的50米分辨率痕量气体廓线反演方法

Also Published As

Publication number Publication date
JPWO2021181687A1 (fr) 2021-09-16
JP7235931B2 (ja) 2023-03-08

Similar Documents

Publication Publication Date Title
US20170139423A1 (en) Control system and method for multi-vehicle systems
Scholte et al. A nonlinear set‐membership filter for on‐line applications
JP5089281B2 (ja) 状態推定装置及び状態推定方法
US20110302116A1 (en) Data processing device, data processing method, and program
Ren et al. A fuzzy logic map matching for wheelchair navigation
CN112800616B (zh) 基于比例加速退化建模的设备剩余寿命自适应预测方法
EP2981136A1 (fr) Dispositif terminal, procédé d'estimation de position, système d'estimation de position, et programme
US11615293B2 (en) Reinforcement learning with a stochastic action set
Mohamed et al. Population MCMC methods for history matching and uncertainty quantification
WO2021181687A1 (fr) Dispositif et procédé de création de modèle de prédiction et programme
Hakobyan et al. Distributionally robust risk map for learning-based motion planning and control: A semidefinite programming approach
Ghosh et al. A new simulation metric to determine safe environments and controllers for systems with unknown dynamics
Ngufor et al. Unsupervised labeling of data for supervised learning and its application to medical claims prediction
CN111256710A (zh) 一种地图匹配方法及系统
Louati Cloud-assisted collaborative estimation for next-generation automobile sensing
WO2021090413A1 (fr) Dispositif de commande, système de commande, procédé de commande et programme
CN110749325A (zh) 航迹规划方法和装置
EP4266004A1 (fr) Procédé de positionnement de trajectoire d'utilisateur, dispositif électronique et support de stockage informatique
US11475255B2 (en) Method for adaptive context length control for on-line edge learning
Král et al. Policy search for active fault diagnosis with partially observable state
KR102105032B1 (ko) 시스템의 다중 성능 최적화를 위한 효율적인 파레토 집합 선택 방법
CN113075713A (zh) 一种车辆相对位姿测量方法、系统、设备及存储介质
CN112710306A (zh) 列车用bds与ins组合导航的自定位方法
CN114693052A (zh) 风险预测模型的训练方法、装置、计算设备和介质
Mayer et al. Stochastic Optimal Control Using Gaussian Process Regression over Probability Distributions

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 20923897

Country of ref document: EP

Kind code of ref document: A1

ENP Entry into the national phase

Ref document number: 2022505712

Country of ref document: JP

Kind code of ref document: A

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 20923897

Country of ref document: EP

Kind code of ref document: A1