Information processing device, railway vehicle, and information processing method

Abstract

An information processing device that estimates the speed and the position of a traveling vehicle comprises: an inertial data acquisition unit 7 and a GNSS data acquisition unit 8 that are data acquisition units for performing processing of disabling the use of unique information for each piece of information about the acceleration, the speed, and the position of the traveling vehicle; a speed estimation unit 3 that estimates the speed of the traveling vehicle by using acceleration information 71 and speed information 81 which correspond to information about the acceleration and the speed after the processing on the unique information; and a position estimation unit 4 that estimates the position of the traveling vehicle by using a speed estimation result 51 regarding the estimated speed, and position information 82 regarding information about the position after the processing. Accordingly, even when a sensor has detected a value that has deviated from a steady-state value, the speed and the position of the traveling vehicle can be accurately detected.

Description

Information Processing Apparatus, Railway Vehicle, and Information Processing Method

[0001] The present invention relates to an information processing apparatus, a railway vehicle, and an information processing method. In particular, the present invention relates to an information processing apparatus or the like that can calculate the speed and position of a traveling vehicle.

[0002] Conventionally, for detecting the position and speed of a moving traveling vehicle, methods of calculating from satellite positioning information using a GPS (Global Positioning System) receiver or the like, or methods of calculating from detection results of an acceleration sensor, an inertial sensor, etc. are known. However, a GPS receiver cannot always output positioning information depending on the environmental conditions around the traveling vehicle, and moreover, since the sensors contain not a few errors, there is a problem that the calculation results of the speed and position based on the cumulative results have large errors. Therefore, for example, there are conventional techniques for correcting the errors in the detection results of these sensors.

[0003] Patent Document 1 describes that a vehicle behavior detection apparatus calibrates detection information obtained from a self-standing sensor for detecting a change in the behavior of a vehicle based on position / orientation / vehicle speed information obtained from a GPS receiver. The detection information is calibrated by performing Kalman filter processing on the detection information obtained from, for example, a vehicle speed sensor and an angular velocity sensor.

[0004] JP-A-2004-286724

[0005] However, even when correction is performed, it is impossible to completely remove the errors in the detection results of various sensors, and as a result, there is a problem of estimating incorrect positions and speeds. As one of the factors that increase the error in the estimation result, when a value that greatly deviates from a steady value is detected by a sensor, it exceeds the controllable range in the subsequent speed estimation calculation and position estimation calculation, and the correction does not function sufficiently. An object of the present invention is to provide an information processing apparatus, a railway vehicle, and an information processing apparatus that can accurately detect the speed and position of a traveling vehicle even when a sensor detects a value deviating from a steady value.

[0006] To solve the above problems, the present invention provides an information processing device for estimating the speed and position of a moving vehicle, comprising: a data acquisition unit that processes the acceleration, speed, and position information of the moving vehicle to exclude unique information; a speed estimation unit that estimates the speed of the moving vehicle using the acceleration and speed information after processing the unique information; and a position estimation unit that estimates the position of the moving vehicle using the estimated speed and the position information after processing. In this case, an information processing device can be provided that can accurately detect the speed and position of a vehicle even when the sensor detects a value that deviates from a steady value.

[0007] Here, for example, the data acquisition unit performs a process to exclude unique information based on the results of statistical calculations using the previously acquired information on acceleration, velocity, and position. In this case, unique information can be extracted with high accuracy through statistical calculations. Alternatively, for example, the data acquisition unit performs a process to exclude unique information based on the results of statistical calculations on angular velocity in addition to acceleration, velocity, and position, and the velocity estimation unit estimates the speed of the moving vehicle by adding the angular velocity information after processing the unique information. In this case, the accuracy of the velocity estimation result is improved. Furthermore, for example, the statistical calculation is an operation to find the variance value of past information, and the data acquisition unit finds unique information based on the variance value. In this case, information that deviates from steady values ​​can be extracted as unique information. Moreover, for example, the statistical calculation is an operation to find at least one of the maximum value of past information and the difference between the newly acquired information and the previous value which was the previously acquired value, and the data acquisition unit finds unique information by comparing the newly acquired information with at least one of the maximum value and the difference. In this case, information that does not actually occur can be extracted as unique information. For example, when the data acquisition unit finds the maximum value, it identifies newly acquired information as unique if it is not within the range obtained by adding a predetermined value to the maximum value. In this case, a margin of error can be added to the measurement data from the sensor. Also, for example, statistical calculations are performed by an external device. In this case, the processing load on statistical calculations can be reduced.

[0008] Furthermore, the present invention relates to a railway vehicle equipped with the above-mentioned information processing device.

[0009] Furthermore, the present invention relates to an information processing method for estimating the speed and position of a moving vehicle, wherein a processor executes a program stored in memory to process the acceleration, speed, and position information of the moving vehicle, removing any unique information, estimating the speed of the moving vehicle using the acceleration and speed information after processing, and estimating the position of the moving vehicle using the estimated speed and the processed position information. In this case, an information processing method is provided that can accurately detect the speed and position of a vehicle even when a sensor detects a value that deviates from a steady state.

[0010] Here, for example, with respect to acceleration, velocity, and position, a process is performed to exclude unique information based on the results of statistical calculations using the previously acquired information. In this case, unique information can be extracted with high accuracy through statistical calculations. Furthermore, for example, in addition to acceleration, velocity, and position, a process is performed to exclude unique information based on the results of statistical calculations on angular velocity, and the speed of the moving vehicle is estimated by adding the angular velocity information after processing the unique information. In this case, unique information can be extracted for information that is important for determining the speed and position of the moving vehicle. Moreover, for example, the statistical calculation is an operation to calculate the variance value of past information, and unique information is obtained based on the variance value. In this case, information that deviates from steady values ​​can be extracted as unique information. Furthermore, for example, the statistical calculation is an operation to calculate at least one of the maximum value of past information and the difference between the newly acquired information and the previous value which was the previously acquired value, and unique information is obtained by comparing the newly acquired information with at least one of the calculated maximum value and difference. In this case, information that does not actually occur can be extracted as unique information. For example, when the maximum value is determined, if the newly acquired information is not within the range obtained by adding a predetermined value to the maximum value, it is considered unique information. In this case, a margin of error can be added to the measurement data from the sensor. Also, for example, statistical calculations are performed by an external device. In this case, the processing load on statistical calculations can be reduced.

[0011] According to the present invention, it is possible to provide an information processing device, a railway vehicle, and an information processing method that can accurately detect the speed and position of a moving vehicle even when the sensor detects a value that deviates from a steady state.

[0012] Figures (a) and (b) are block diagrams showing the overall configuration of the train control system in this embodiment. This is a block diagram showing the functional configuration of the information processing means in Embodiment 1. This is a diagram showing an example of statistical information calculated by the statistical processing unit and stored in the statistical information storage unit. This is a flowchart explaining the processing performed by the inertia data acquisition unit. This is a flowchart explaining the processing performed by the GNSS data acquisition unit. This is a block diagram showing the functional configuration of the information processing means in Embodiment 2.

[0013] The embodiments of the present invention will be described in detail below with reference to the attached drawings. The present invention will be described below with reference to Examples 1 and 2.

[0014] <Description of the overall configuration of the train control system 100> Figures 1(a) and 1(b) are block diagrams showing the overall configuration of the train control system 100 in this embodiment. Figure 1(a) shows a case where multiple vehicles (running vehicles, railway vehicles) 120 are coupled together and run as a train. These vehicles 120 move along the rails R in the left direction in the figure. A bogie 121 is mounted on the vehicle 120, and wheels 122 are provided on the bogie 121. A rotating electric motor 123 is connected to each axle of the wheel 122 via gears. The rotating electric motor 123 is, for example, an AC motor that operates using AC power. Furthermore, a drive unit 124 and a control unit 125 are mounted on the vehicle 120. The drive unit 124 performs voltage control to drive the rotating electric motor 123. The control unit 125 performs speed calculations to safely run and stop the vehicle 120. Furthermore, the vehicle 120 is equipped with a higher-level control device 126 for the drive unit 124 and control device 125. The higher-level control device 126 is, for example, a driver's cab monitor device and a transmission device. In addition, the vehicle 120 is equipped with sensors, including an inertial sensor 1 for detecting the acceleration and angular velocity of the vehicle 120, and a GNSS receiver 2 for detecting speed and position using satellite information received from a GNSS antenna 127 installed on top of the vehicle 120.

[0015] The control device 125 calculates the speed and position of the vehicle 120 and controls the movement of the vehicle 120 based on the calculated speed and position. Figure 1(b) is a block diagram showing the functional configuration of the control device 125. The control device 125 comprises an information processing means 131 and a control means 132. The information processing means 131 is an example of an information processing device and estimates the speed and position of the vehicle 120. As will be described in detail later, the information processing means 131 acquires the detection results of the inertial sensor 1 and the GNSS receiver 2 and estimates the speed and position of the vehicle 120. The control means 132 controls the movement of the train based on the estimated speed and position. The control means 132 controls the movement of the vehicle 120 by, for example, operating a vehicle control application that operates using the calculated speed and position. An example of such an application is an automatic train operation (ATO) system that automatically controls acceleration and deceleration so that the vehicle 120 travels between predetermined points (e.g., between stations) according to a scheduled time (timetable). In addition to ATO, another example of an application that uses the estimated speed of vehicle 120 is a signaling system (ATP: Automatic Train Protection) that controls the brakes to ensure a safe distance between vehicles.

[0016] The control device 125 can be implemented by a computer device that operates through the cooperation of software and hardware resources. Specifically, a processor such as a CPU (Central Processing Unit) (not shown) located inside the computer provided in the control device 125 executes programs that realize each function, thereby realizing these functions. These programs are stored in storage devices such as HDDs (Hard Disk Drives) and SSDs (Solid State Drives) or auxiliary storage devices such as ROMs (Read Only Memory), and are loaded from these auxiliary storage devices into main memory (RAM: Random Access Memory) and executed. These functions can also be implemented by hardware such as dedicated circuits.

[0017] <Description of Information Processing Means 131> Figure 2 is a block diagram showing the functional configuration of the information processing means 131 in Embodiment 1. The information processing means 131 consists of an inertial data acquisition unit 7 that acquires the output of the inertial sensor 1, a GNSS data acquisition unit 8 that acquires the output of the GNSS receiver 2, a speed estimation unit 3 that calculates the speed of the vehicle 120 based on the data output from the inertial data acquisition unit 7 and the GNSS data acquisition unit 8, a position estimation unit 4 that calculates the position of the vehicle 120 based on the speed estimation result output from the speed estimation unit 3 and the data output from the GNSS data acquisition unit 8, a statistical processing unit 5 that calculates statistical information for the outputs of the inertial sensor 1 and the GNSS receiver 2, and a statistical information storage unit 6 that stores the output results of the statistical processing unit 5. In addition to the information processing means 131, the inertial sensor 1 and the GNSS receiver 2 are also shown in Figure 2.

[0018] The inertial sensor 1 outputs acceleration information 11, which is information about the acceleration of the vehicle 120, and angular velocity information 12, which is information about the angular velocity of the vehicle 120. The GNSS receiver 2 outputs velocity information 21, which is information about the speed of the vehicle 120, and position information 22, which is information about its position. GNSS stands for "Global Navigation Satellite System". The GNSS receiver 2 observes the positioning signals transmitted from the positioning satellites using an antenna and determines the position information of latitude and longitude, and the velocity information based on the Doppler frequency of the satellite radio waves.

[0019] The statistical processing unit 5 acquires acceleration information 11 and angular velocity information 12 from the inertial sensor 1, and velocity information 21 and position information 22 from the GNSS receiver, performs statistical calculations on each piece of information, and outputs various statistical information 51. The statistical information 51 is stored in the statistical information storage unit 6. For example, non-volatile memory can be used as the implementation form of the statistical information storage unit 6. Alternatively, a hard disk can be used as another implementation form. The statistical processing unit 5 also outputs statistical information 61 to the inertial data acquisition unit 7 and statistical information 62 to the GNSS data acquisition unit 8. As will be described in more detail later, statistical information 61 is the result of statistical calculations performed on the acceleration information 11 and angular velocity information 12, and is statistical information related to acceleration and angular velocity. Statistical information 62 is the result of statistical calculations performed on the velocity information 21 and position information 22, and is statistical information related to velocity and position.

[0020] The inertial data acquisition unit 7 uses the statistical information 61 output from the statistical information storage unit 6 to select acceleration information 11 and angular velocity information 12 input from the inertial sensor 1 for each sampling period of each piece of information. The inertial data acquisition unit 7 then outputs acceleration information 71 and angular velocity information 72 as the selected acceleration information 11 and angular velocity information 12, respectively.

[0021] The GNSS data acquisition unit 8 uses the statistical information 62 output from the statistical information storage unit 6 to select the speed information 21 and position information 22 input from the GNSS receiver 2 for each sampling period of the information. The GNSS data acquisition unit 8 then outputs the speed information 81 and position information 82 as the selected speed information 21 and position information 22, respectively.

[0022] The inertial data acquisition unit 7 and the GNSS data acquisition unit 8 function as data acquisition units that process the acceleration, velocity, and position information of the vehicle 120, respectively, by excluding any unique information. This allows for the selection of acceleration information 11, angular velocity information 12, velocity information 21, and position information 22.

[0023] The speed estimation unit 3 calculates the speed using acceleration information 71 and angular velocity information 72 input from the inertial sensor 1 via the inertial data acquisition unit 7. The speed estimation unit 3 can utilize calculations of inertial navigation, such as the strapdown method. The speed estimation unit 3 then estimates the speed of the vehicle 120 using the speed calculated using the acceleration information 71 and angular velocity information 72, and speed information 81 input from the GNSS receiver 2 via the GNSS data acquisition unit 8, and outputs the speed estimation result 31. As a method for estimating the speed by the speed estimation unit 3, for example, it may calculate the standard deviation of the speed and speed information 81 calculated based on the acceleration information 71 and angular velocity information 72, perform weighted addition of each speed according to the magnitude of the standard deviation, and output the speed estimation result 31.

[0024] In this case, the speed estimation unit 3 uses the acceleration and velocity information (in this case, acceleration information 71 and velocity information 81) after processing the unique information in the inertial data acquisition unit 7 to estimate the speed of the vehicle 120 and outputs it as the speed estimation result 31. Alternatively, the speed estimation unit 3 can also estimate the speed of the vehicle 120 by adding the angular velocity information (in this case, angular velocity information 72) after processing the unique information and output it as the speed estimation result 31. This improves the accuracy of the speed estimation result.

[0025] The position estimation unit 4 estimates the position of the vehicle 120 using the speed estimated by the speed estimation unit 3 (in this case, the speed estimation result 31) and the position information processed by the GNSS data acquisition unit 8 (in this case, the position information 82), and outputs it as the position estimation result 41. Specifically, first, at each sampling, the position estimation unit 4 calculates the variation in movement between sampling times by multiplying the speed estimation result 31, which is the speed estimated by the speed estimation unit 3, by the sampling period, and calculates the position information by adding this to the position estimation result 41 that the position estimation unit 4 output in the previous processing. In particular, in applications where the distance traveled in the direction of travel is used as position information, such as in railway vehicles, the position information may be calculated as the cumulative distance from a pre-set reference point. Then, the position estimation unit 4 estimates the position of the vehicle 120 using the calculated position information and the position information 82 input from the GNSS receiver 2 via the GNSS data acquisition unit 8, and outputs the position estimation result 41.

[0026] Figure 3 shows an example of statistical information calculated by the statistical processing unit 5 and stored in the statistical information storage unit 6. Here, it is shown that the statistical information stored in the statistical information storage unit 6 includes the acceleration variance value 601, which is the variance value of acceleration acquired in the past; the acceleration maximum value 602, which is the maximum value of acceleration acquired in the past; and the previous acceleration value 603, which is the value of the previous acceleration. Also, it is shown that the statistical information stored in the statistical information storage unit 6 includes the angular velocity variance value 604, which is the variance value of angular velocity acquired in the past; the angular velocity maximum value 605, which is the maximum value of angular velocity acquired in the past; and the previous angular velocity value 606, which is the value of the previous angular velocity. The acceleration variance value 601, acceleration maximum value 602, acceleration previous value 603, angular velocity variance value 604, angular velocity maximum value 605, and angular velocity previous value 606 correspond to statistical information 61.

[0027] Furthermore, this indicates that the following statistical information is stored in the statistical information storage unit 6: the velocity variance value 607, which is the variance value of velocity acquired in the past; the velocity maximum value 608, which is the maximum value of velocity acquired in the past; and the previous velocity value 609, which is the value of the previous velocity. Furthermore, this indicates that the following statistical information is stored in the statistical information storage unit 6: the position variation variance value 610, which is the variance value of position variation acquired in the past; the position variation maximum value 611, which is the maximum value of position variation acquired in the past; and the previous position value 612, which is the value of the previous position. The velocity variance value 607, velocity maximum value 608, speed previous value 609, position variation variance value 610, position variation maximum value 611, and position previous value 612 correspond to statistical information 62.

[0028] <Explanation of the processes performed by the inertial data acquisition unit 7 and the GNSS data acquisition unit 8> Figure 4 is a flowchart explaining the processes performed by the inertial data acquisition unit 7. In S701, if the acceleration information 11 acquired this time is greater than the maximum acceleration value 602 included in the statistical information 61 (NO in S701), the data is not used (S708). In addition, in order to allow for a margin in the measurement data, a value obtained by adding a predetermined value a to the maximum acceleration value 602 can be adopted as the judgment value, and this case is illustrated here. On the other hand, if the acceleration information 11 is within the judgment value (YES in S701), the process proceeds to the next S702.

[0029] In S702, if the angular velocity information 12 acquired this time is greater than the maximum angular velocity value 605 included in the statistical information 61 (NO in S702), the data is not used (S708). In addition, to allow for a margin in the measurement data, a value obtained by adding a predetermined value b to the maximum angular velocity value 605 can be adopted as the judgment value, and this case is illustrated here. On the other hand, if the angular velocity information 12 is within the judgment value (YES in S702), the process proceeds to the next S703.

[0030] In S703, the validity of the acceleration information 11 acquired through sampling is verified using the acceleration variance value 601 included in the statistical information 61. One method for verifying validity is to calculate the range in which the data is not rejected by a chi-squared test based on the acceleration variance value 601, and if the acceleration information 11 acquired this time falls within the rejection range, the data is not used. If the acceleration information 11 is within the rejection range (NO in S703), the data is not used (S708). On the other hand, if the acceleration information 11 is outside the rejection range (YES in S703), the process proceeds to the next step, S704.

[0031] In S704, the validity of the angular velocity information 12, which was sampled in this instance, is verified using the angular velocity variance value 604 included in the statistical information 61. The method of verifying validity is the same as in S701, which involves discarding the data based on a chi-squared test using the angular velocity variance value 604. If the angular velocity information 12 falls within the rejection range (NO in S704), the data is discarded (S708). On the other hand, if the angular velocity information 12 falls outside the rejection range (YES in S704), the process proceeds to S705.

[0032] In S705, if the difference between the previously acquired acceleration information 11 and the previous acceleration value 603 included in the statistical information 61 is greater than a predetermined value c (NO in S705), the data is not used. This is effective when it can be assumed that the fluctuation in acceleration during the sampling period is small during the vehicle 120's operation. If the acceleration information 11 is within the judgment value, the process proceeds to the next S706. If the acceleration information 11 is within the rejection range (NO in S705), the data is not used (S708). On the other hand, if the acceleration information 11 is outside the rejection range (YES in S705), the process proceeds to the next S706.

[0033] In S706, if the difference between the previously acquired angular velocity value 606 included in the statistical information 61 and the acceleration information 11 acquired this time is greater than a predetermined value d (NO in S706), the data is not used (S708). Similar to S705, this is effective when it can be assumed that there is little fluctuation in angular velocity during the sampling period while the vehicle 120 is in motion. On the other hand, if the angular velocity information 12 is within the judgment value (YES in S706), the data is output to the speed estimation unit 3 (S707).

[0034] The inertial data acquisition unit 7 outputs acceleration information 71 and angular velocity information 72 to the velocity estimation unit 3 when it determines that the acceleration information 11 and angular velocity information 12 are valid in all of steps S701 to S706. The processes S701 to S706 may be selected and used depending on the system being applied. The execution order of the processes S701 to S706 may also be changed depending on the system being applied.

[0035] Figure 5 is a flowchart illustrating the processing performed by the GNSS data acquisition unit 8. In S801, in order to evaluate the position information using relative distance, the position variation between the previous position value 612 included in the statistical information 62 and the position information 22 acquired in the current sampling period is calculated.

[0036] In S802, if the position variation calculated in S801 is greater than the maximum position variation value 611 included in the statistical information 62 (NO in S802), the data is not used (S808). In addition, to allow for a margin in the measurement data, a value obtained by adding a predetermined value e to the maximum position variation value 611 can be adopted as the judgment value, and this case is illustrated here. On the other hand, if the position variation is within the judgment value (YES in S802), the process proceeds to S803.

[0037] In S803, the validity of the positional variation deviation calculated in S802 is verified using the positional variation variance value 610 included in the statistical information 62. As a method of verifying validity, for example, the standard deviation σ is calculated from the positional variation variance value 610, and if the positional variation deviation calculated in S802 is outside the distribution range of ±3σ (NO in S803), the data is not used (S808). On the other hand, if the positional variation is within the distribution range of ±3σ (YES in S803), the process proceeds to the next step, S804.

[0038] In S804, if the speed information 21 acquired this time is greater than the maximum speed 608 included in the statistical information 62 (NO in S804), the data is not used (S808). In addition, to allow for a margin in the measurement data, a value obtained by adding a predetermined value f to the maximum speed 608 can be adopted as the judgment value, and this case is illustrated here. On the other hand, if the speed information 21 is within the judgment value (YES in S804), the process proceeds to the next S805.

[0039] In S805, the validity of the velocity information 21 acquired through sampling is verified using the velocity variance value 607 included in the statistical information 62. As a method of verifying validity, for example, the standard deviation σ is calculated from the velocity variance value 607, and if the velocity information 21 falls outside the distribution range of ±3σ (NO in S805), the data is not used (S808). On the other hand, if the velocity information 21 is within the distribution range of ±3σ (YES in S805), the process proceeds to the next step, S806.

[0040] In S806, if the difference between the previously acquired speed information 21 and the previous speed value 609 included in the statistical information 62 is greater than a predetermined value g (NO in S806), the data is not used (S808). This is effective when it can be assumed that there is little fluctuation in speed during the sampling period in the vehicle's operation. On the other hand, if the speed information 21 is within the judgment value (YES in S806), the data is output to the speed estimation unit 3 and the position estimation unit 4 (S807).

[0041] The GNSS data acquisition unit 8 outputs speed information 81 and position information 82 to the speed estimation unit 3 and position estimation unit 4 when it determines that the speed information 21 and position information 22 are valid in all of steps S803 to S806. The processes in S803 to S806 may be selected and used depending on the system being applied. The execution order of the processes in S803 to S806 may also be changed depending on the system being applied.

[0042] In the processes shown in Figures 4 and 5, the inertial data acquisition unit 7 and the GNSS data acquisition unit 8 perform statistical calculations using previously acquired information on acceleration, velocity, and position (in this case, statistical information 61 and 62) to exclude unique information. Such statistical calculations allow for the accurate extraction of unique information. In steps S702, S704, and S706 of Figure 7, the inertial data acquisition unit 7 performs statistical calculations on angular velocity to exclude unique information. In steps S703 and S704 of Figure 7, and S803 and S805 of Figure 8, the statistical calculations determine the variance of past information, and the inertial data acquisition unit 7 and the GNSS data acquisition unit 8 determine unique information based on the variance. This allows for the extraction of information that deviates from steady-state values ​​as unique information. In steps S701, S702, S705, and S706 in Figure 7, and S802, S804, and S806 in Figure 8, the statistical calculation is performed to find at least one of the maximum value of past information and the difference between the newly acquired information and the previous value (the previously acquired value). The inertial data acquisition unit 7 and the GNSS data acquisition unit 8 find unique information by comparing the newly acquired information with at least one of the calculated maximum value and difference. This makes it possible to extract information that does not actually occur as unique information. Here, in steps S701, S702 in Figure 7, and S802, and S804 in Figure 8, the inertial data acquisition unit 7 and the GNSS data acquisition unit 8 consider information unique if, when the maximum value is found, the newly acquired information is not within the value obtained by adding a predetermined value to the maximum value (in this case, a, b, e, f). This allows for a margin of error in the measurement data from the sensor.

[0043] Here, focusing on the behavior of the vehicle 120, it is unlikely that the vehicle 120 moves randomly in the front-back, left-right directions, and in the running of the vehicle 120, the direction of position change, speed, acceleration, angular velocity, etc. rarely change abruptly. That is, it is considered that the change remains within a specific distribution range. In particular, when the vehicle 120 is a railway vehicle, since it runs on a specific rail R, it can take a more limited distribution of position change and speed.

[0044] In the above-described configuration, the special data of the acceleration information 11, angular velocity information 12, speed information 21, and position information 22 output from the inertial sensor 1 and the GNSS receiver 2 are made unused by utilizing the statistical characteristics of the traveling route. In the present embodiment, the data determined to be special data by statistical processing is made unused. Thereby, it is possible to improve the estimation accuracy of the subsequent speed estimation unit 3 and position estimation unit 4. When traveling on the target route for the first time, since the statistical information has not been aggregated, the information output from the inertial sensor 1 and the GNSS receiver 2 is used without being made unused. Also, for example, in an application where the same route is repeatedly traveled, such as a railway vehicle, the accumulation of statistical information increases, and it can be expected that the estimation speed of the speed and position will be further improved.

[0045] Next, Example 2 will be described. FIG. 6 is a block diagram showing the functional configuration of the information processing means 131 in Example 2. Comparing with the information processing means 131 of Example 1 shown in FIG. 2, a recording medium 9 is added to the information processing means 131 of Example 2 shown in FIG. 6, and an external statistical processing unit 10 is provided as an external device of the information processing means 131 instead of the statistical processing unit 5. Here, the speed estimation result 31 and the position estimation result 41 are output to the recording medium 9, and the information 91 of the recording medium 9 is read offline by the externally installed external statistical processing unit 10 to calculate the statistical information 101. That is, in Example 2, the statistical calculation is performed by an external device (in this case, the external statistical processing unit). Also, by writing the statistics to the statistical information storage unit 6 from the external statistical processing unit 10 before vehicle running, the data determination processing in the inertial data capture unit 7 and the GNSS data capture unit 8 can be realized in the same manner as the information processing means 131 of FIG. 2.

[0046] As shown in the configuration of Figure 6, by not having a processing unit for statistical calculations within the device itself, it is expected that the processing load on statistical calculations will be reduced. Furthermore, by performing statistical calculations offline using the external statistical processing unit 10, it is expected that updating the statistical analysis method will be made easier, and it will be possible to calculate more accurate judgment criteria.

[0047] <Explanation of Information Processing Method> The processing performed by the information processing means 131 is realized through the cooperation of software and hardware resources. That is, a processor such as a CPU provided in the information processing means 131 loads a program that realizes each function of the information processing means 131 into the main memory and executes it to realize each of these functions. Therefore, the processing performed by the information processing means 131 described above is an information processing method for estimating the speed and position of the vehicle 120. The processor executes a program recorded in memory to process the acceleration, speed, and position information of the vehicle 120, removing any unusual information. Using the acceleration and speed information after processing the unusual information, the speed of the vehicle 120 is estimated. Using the estimated speed and position information after processing, the position of the vehicle 120 is estimated. This provides an information processing method that can accurately detect the speed and position of a moving vehicle even when the sensor detects a value that deviates from a steady state.

[0048] Note that the present invention is not limited to the above-described embodiments, and includes various modifications. For example, the above-described embodiments have been described in detail for easy understanding of the present invention, and are not necessarily limited to those having all the configurations described. Also, a part of the configuration of one embodiment can be replaced with the configuration of another embodiment, and the configuration of another embodiment can be added to the configuration of one embodiment. Further, for a part of the configuration of each embodiment, addition, deletion, or replacement with other configurations is possible. Also, each of the above configurations, functions, processing units, processing means, etc. may be realized in hardware by designing a part or all of them, for example, by an integrated circuit. Also, each of the above configurations, functions, etc. may be realized in software by a processor interpreting and executing a program for realizing each function. Information such as a program, table, file, etc. for realizing each function can be placed in a memory, a recording device such as a hard disk, SSD (Solid State Drive), or a recording medium such as an IC card, SD card, DVD. Also, control lines and information lines are shown as those considered necessary for explanation, and not all control lines and information lines are necessarily shown on the product. In practice, it may be considered that almost all configurations are interconnected.

[0049] 1... Inertial sensor, 2... GNSS receiver, 3... Speed estimation unit, 4... Position estimation unit, 5... Statistical processing unit, 6... Statistical information storage unit, 7... Inertial data capture unit, 8... GNSS data capture unit, 9... Recording medium, 10... External statistical processing unit, 11, 71... Acceleration information, 12, 72... Angular velocity information, 21, 81... Speed information, 22, 82... Position information, 61, 62, 101... Statistical information, 100... Train control system, 120... Vehicle, 126... Upper control device, 131... Information processing means, 132... Control means

Claims

1. An information processing device for estimating the speed and position of a moving vehicle, comprising: a data acquisition unit that processes the acceleration, speed, and position information of the moving vehicle to exclude unique information; a speed estimation unit that estimates the speed of the moving vehicle using the acceleration and speed information after processing the unique information; and a position estimation unit that estimates the position of the moving vehicle using the estimated speed and the position information after processing.

2. The information processing apparatus according to claim 1, wherein the data acquisition unit performs a process to exclude unique information based on the results of statistical calculations using the previously acquired information regarding acceleration, velocity, and position.

3. The data acquisition unit performs a process to exclude unique information based on the results of statistical calculations on acceleration, velocity, and position, as well as angular velocity, and the velocity estimation unit estimates the speed of the moving vehicle by adding the angular velocity information after processing the unique information, as described in claim 2.

4. The information processing apparatus according to claim 2 or 3, wherein the statistical calculation is an calculation to determine the variance value of past information, and the data acquisition unit determines unique information based on the variance value.

5. The statistical calculation is an operation to find at least one of the past maximum value of the information and the difference between the newly acquired information and the previous value which is the previously acquired value, and the data acquisition unit finds unique information by comparing the newly acquired information with at least one of the maximum value and the difference, according to any one of claims 2 to 4.

6. The information processing apparatus according to claim 5, wherein, when the data acquisition unit determines the maximum value, the newly acquired information is considered unique if it is not within the value obtained by adding a predetermined value to the maximum value.

7. The information processing apparatus according to claim 2, wherein the statistical calculation is performed by an external device.

8. A railway vehicle equipped with an information processing device according to any one of claims 1 to 7.

9. An information processing method for estimating the speed and position of a moving vehicle, wherein a processor executes a program stored in memory to process the acceleration, speed, and position information of the moving vehicle by removing unique information, estimates the speed of the moving vehicle using the acceleration and speed information after processing the unique information, and estimates the position of the moving vehicle using the estimated speed and the position information after processing.

10. The information processing method according to claim 9, which performs a process to exclude unique information based on the results of statistical calculations using the previously acquired information regarding acceleration, velocity, and position.

11. The information processing method according to claim 10, wherein, in addition to acceleration, velocity, and position, the results of statistical calculations on angular velocity are used to process and discard outlier information, and the speed of the moving vehicle is estimated by adding the angular velocity information after processing the outlier information.

12. The statistical calculation is an calculation to determine the variance value of past information, and the information processing method according to claim 10 or 11, which determines unique information based on the variance value.

13. The statistical calculation is an operation to find at least one of the past maximum value of the information and the difference between the newly acquired information and the previous value which is the value acquired last time, and the information processing method according to any one of claims 10 to 12, wherein unique information is found by comparing the newly acquired information with at least one of the maximum value and the difference.

14. The information processing method according to claim 13, wherein, when the maximum value is determined, the newly acquired information is considered unique if it is not within the value obtained by adding a predetermined value to the maximum value.

15. The information processing method according to claim 10, wherein the statistical calculation is performed by an external device.

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