Detecting device and detecting method

Abstract

This detecting device comprises: an acquiring unit for acquiring measurement results from each of a GNSS 11 for locating the position of a vehicle by means of satellite communication, a LiDAR 12 for calculating the distance between the vehicle and an object by means of laser light, and an IMU 13 for measuring the acceleration of the vehicle; and an estimating unit for obtaining a confidence interval as a reliable range for at least one of the speed of the vehicle and the position of the vehicle on the basis of the measurement results, and taking a value included in a range in which the respective confidence intervals overlap as an estimated value. This makes it possible to provide a detecting device and a detecting method that maintain the reliability, safety and accuracy of estimated results of position and speed based on sensor outputs, even when affected by the environment.

Description

Detection device and detection method

[0001] The present invention relates to a detection device and a detection method for recognizing the speed state and position of a vehicle.

[0002] Traditionally, the speed and position of railway vehicles have been detected using a tachograph generator and a ground coil for position correction. This involves reading location information as the vehicle passes the ground coil, and then periodically reading speed data from the tachograph generator. The product of this data and the reading period is then added to the previously read location information to calculate the vehicle's position. However, this method presents a problem with the speed value when the vehicle's wheels are spinning or skidding.

[0003] In response to this, new vehicle position and speed estimation methods using satellite positioning, such as the Global Positioning System (GPS), and inertial sensors have been researched in recent years. This method estimates the vehicle's position by receiving satellite positioning signals from GPS, GNSS (Global Navigation Satellite System), and other sources. In environments where satellite communication conditions deteriorate, the position is corrected by integrating the speed estimates obtained by the inertial sensor along the time axis. Meanwhile, methods using satellite communication and laser sensors are being researched as positioning methods to replace ground coils and tachometer generators.

[0004] Patent Document 1 describes a vehicle position detection device that includes a position detection unit that detects the position of the vehicle based on axle rotation speed information, a position estimation unit that inputs a satellite positioning system signal and speed-related information (speed, acceleration) of the vehicle every unit time to sequentially estimate the position of the vehicle, and a feasibility determination unit that determines whether position correction is possible, and is configured so that the position detected by the position detection unit is corrected based on the position estimated by the position estimation unit depending on the determination result of the feasibility determination unit.

[0005] Japanese Patent Application Laid-Open No. 2021-160468

[0006] Satellite positioning signals are often subject to poor communication conditions due to the surrounding environment (tunnels, tall buildings, etc.), making it difficult to achieve the desired accuracy. Furthermore, while position estimation is corrected using speed-related information when satellite communication conditions deteriorate, there is a problem in that the accuracy of position and speed estimation results may continue to deteriorate if the satellite communication receiver or sensors that output speed fail.

[0007] On the other hand, positioning methods that use satellite communications or laser sensors can sometimes have poor positioning results and accuracy due to factors such as the positioning of satellites, communication obstructions caused by surrounding buildings and tunnels, and external environmental influences such as weather and the presence or absence of surrounding features. Therefore, measures are needed to improve the reliability and safety of the vehicle's position and speed estimation function and accuracy.

[0008] An object of the present invention is to provide a detection device and a detection method that can maintain the reliability, safety, and accuracy of position and speed estimation results based on sensor output even when affected by the environment.

[0009] In order to solve the above problems, the present invention provides a detection device that includes an acquisition unit that acquires measurement results from a positioning sensor that measures the position of a vehicle via satellite communication, a distance sensor that calculates the distance between the vehicle and an object using laser light, and an acceleration sensor that measures the acceleration of the vehicle, and an estimation unit that calculates a confidence interval as a reliable range for at least one of the vehicle speed and the vehicle position based on the measurement results, and sets a value included in the overlapping range of the confidence intervals as an estimated value.In this case, it is possible to provide a detection device that can maintain the reliability, safety, and accuracy of position and speed estimation results based on sensor output even when affected by the environment.

[0010] Here, when estimating the vehicle speed, the estimation unit can calculate a weighted average according to the confidence interval for the speed obtained from the measurement results of the positioning sensor, the distance sensor, and the acceleration sensor, and use this as an estimated value of the vehicle speed. In this case, the accuracy of the estimated value is improved. Furthermore, when estimating the vehicle position, the estimation unit can calculate a weighted average according to the confidence interval for the estimated values ​​of the positioning sensor, the distance sensor, and the vehicle speed, and use this as an estimated value of the vehicle position. In this case, the accuracy of the estimated value is further improved. Furthermore, the estimation unit can calculate weights according to the proportions of overlapping ranges of the confidence intervals within each confidence interval, and calculate the weighted average. In this case, the weights can be calculated with high accuracy. Furthermore, the estimation unit can calculate a confidence interval using the average and standard deviation of the estimated vehicle speed and vehicle position, which are calculated based on the measurement results. In this case, a statistically based confidence interval can be calculated. Furthermore, when there is no overlap of the confidence intervals, the estimation unit can use the largest value for which the difference between the calculated estimated value and the previous estimated value is equal to or less than a predetermined upper limit value as the estimated value. In this case, it is possible to eliminate invalid estimated values. Furthermore, when estimating the vehicle speed, the upper limit value can be set to a value obtained by adding the speed when the vehicle is accelerated at the maximum acceleration or subtracting the speed when the vehicle is decelerated at the maximum deceleration from the previous estimated value, and when estimating the vehicle position, the upper limit value can be set to a value obtained by adding the position when the vehicle is accelerated at the maximum acceleration or subtracting the position when the vehicle is decelerated at the maximum deceleration from the previous estimated value. In this case, a more appropriate value can be set as the upper limit value.

[0011] Furthermore, the present invention provides a detection method in which a processor executes a program stored in a memory to acquire measurement results from a positioning sensor that measures the position of a vehicle via satellite communication, a distance sensor that calculates the distance between the vehicle and an object using laser light, and an acceleration sensor that measures the acceleration of the vehicle, and based on the measurement results, calculates a confidence interval as a reliable range for at least one of the vehicle speed and vehicle position, and sets a value included in the overlapping range of the confidence intervals as an estimated value.In this case, a detection method can be provided that can maintain the reliability, safety, and accuracy of position and speed estimation results based on sensor outputs even when affected by the environment.

[0012] According to the present invention, it is possible to provide a detection device and a detection method that can maintain the reliability, safety, and accuracy of position and speed estimation results based on sensor output even when affected by the environment.

[0013] It is a block diagram showing the functional configuration of the self-position estimation device according to the present embodiment. It is a block diagram explaining the processing operation of the speed estimation unit of Figure 1. It is a block diagram explaining the processing operation of the position estimation unit of Figure 1. It is a flowchart explaining the processing content of the speed estimation unit. It is a flowchart explaining the processing content of the position estimation unit.

[0014] [First embodiment] Next, a vehicle self-position estimation device according to an embodiment of the present invention will be described with reference to the drawings. Here, the self-position estimation device of the first embodiment will be described first. In the first embodiment, a speed estimation result and a position estimation result are obtained using three types of sensors: GNSS, LiDAR, and IMU (acceleration sensor).

[0015] <Explanation of Overall Configuration of Self-Location Estimation Device 1> Fig. 1 is a block diagram showing the functional configuration of the self-location estimation device 1 according to this embodiment. The self-location estimation device 1 shown in the figure is an example of a detection device that is provided in a train car and detects at least one of the speed and position of the car. The self-location estimation device 1 includes sensors such as a GNSS (Global Navigation Satellite System) 11, a LiDAR (Light Detection And Ranging) 12, and an IMU (Inertial Management Unit) 13. The self-location estimation device 1 also includes a speed estimation unit 14 and a position estimation unit 15.

[0016] The GNSS 11 is a positioning sensor that measures the position of the train through satellite communication. The GNSS 11 provides speed information Vgi based on the Doppler frequency of satellite radio waves and position information Xgi of latitude and longitude through satellite positioning.

[0017] The LiDAR 12 is a distance sensor that calculates the distance between the train and an object using laser light. The LiDAR 12 is a sensor that calculates the relative distance between the train itself and an object based on the difference between the transmission time of the laser light emitted by the LiDAR 12 and the reception time of the light reflected by the object. The LiDAR 12 transmits point cloud data X1, which is a collection of intensity data of the laser light reflected by the object. In this case, the object is not particularly limited, and may be a structure or the like that is located around the train and placed on the ground. Examples of such objects include telegraph poles, buildings, equipment, rails, sleepers, and overhead wires.

[0018] The IMU 13 is an acceleration sensor that measures the acceleration of the train and outputs acceleration data Ai and angular velocity information.

[0019] The speed estimation unit 14 acquires speed information from the GNSS 11, point cloud data Xl indicating the relative position from the LiDAR 12, and acceleration data Ai from the IMU 13, performs processing on each sensor described below, estimates the speed, and outputs the speed estimation result.

[0020] The position estimation unit 15 acquires latitude and longitude position information from GNSS 11, point cloud data Xl indicating the relative position from LiDAR 12, and velocity estimation result Ves from the velocity estimation unit 14, processes each of the input data described below, estimates the position, and outputs the position estimation result.

[0021] <Overview of Processing by the Speed ​​Estimation Unit 14> Figure 2 is a block diagram illustrating the processing operation of the speed estimation unit 14 in Figure 1. The speed estimation unit 14 is an example of an estimation unit, and includes a GNSS speed acquisition unit 21, a LiDAR point cloud data acquisition unit 22, a feature matching unit 23, a speed calculation unit 24, an IMU acceleration acquisition unit 25, a speed calculation unit 26, and a speed estimation unit 27.

[0022] The GNSS velocity acquisition unit 21 acquires velocity information Vgi from the GNSS 11, performs processing such as filtering to remove noise, and outputs the information to the velocity estimation unit 27 as velocity information Vgo.

[0023] The LiDAR point cloud data import unit 22 acquires the point cloud data Xl from the LiDAR 12 and performs processing such as noise removal filtering. The feature matching unit 23 extracts patterns with specific shapes of reflection intensity from the point cloud data Xl received through the continuous sampling process and identifies any feature. The velocity calculation unit 24 calculates the velocity by dividing the amount of change ΔX in the relative position of any feature with a specific shape at each sampling time of the point cloud data Xl by the sampling period Δt, and outputs the velocity information Vl to the velocity estimation unit 27.

[0024] The IMU acceleration acquisition unit 25 acquires acceleration data Ai from the IMU 13 and performs filtering to remove noise, etc. The velocity calculation unit 26 calculates velocity by integration processing in sampling period units and outputs it to the velocity estimation unit 27 as velocity information Vi.

[0025] The velocity estimation unit 27 outputs a velocity estimation result Ves based on the velocity information Vgo output by the GNSS velocity acquisition unit 21, the velocity information Vl output by the velocity calculation unit 24, and the velocity information Vi output by the velocity calculation unit 26. Detailed operation of the velocity estimation unit 27 will be described later. The GNSS velocity acquisition unit 21, the LiDAR point cloud data acquisition unit 22, and the IMU acceleration acquisition unit 25 function as acquisition units that acquire the measurement results of the GNSS 11, the LiDAR 12, and the IMU 13, respectively.

[0026] <Overview of Processing of Position Estimation Unit 15> Figure 3 is a block diagram illustrating the processing operation of the position estimation unit 15 in Figure 1. The position estimation unit 15 is an example of an estimation unit, and includes a GNSS latitude and longitude acquisition unit 31, a LiDAR point cloud data acquisition unit 32, a feature matching unit 33, a vehicle relative position calculation unit 34, a speed acquisition unit 35, a cumulative distance calculation unit 36, and a position estimation unit 37.

[0027] The GNSS latitude and longitude acquisition unit 31 acquires latitude and longitude position information Xgi from the GNSS 11, performs filtering to remove noise, and outputs the information to the position estimation unit 37 as position information Xgo.

[0028] The LiDAR point cloud data import unit 32 acquires the point cloud data Xl from the LiDAR 12 and performs processing such as noise removal filtering. The feature matching unit 33 extracts patterns with specific shapes of reflection intensity from the point cloud data Xl received through the continuous sampling process and identifies any feature. The vehicle relative position calculation unit 34 calculates the relative position of the feature by multiplying the difference between the transmission time of the laser beam and the reception time of the reflected wave by the traveling speed of the laser beam (≒ speed of light). The vehicle relative position calculation unit 34 then outputs absolute position information Xls, which serves as the cumulative position, to the position estimation unit 37 by feeding back and adding the position estimation result Xes output by the position estimation unit 37 in the previous process.

[0029] The speed acquisition unit 35 acquires the speed estimation result Ves from the speed estimation unit 14. The cumulative distance calculation unit 36 ​​calculates the relative movement distance between sampling times by multiplying the speed information calculated at each sampling time by the sampling period. The speed acquisition unit 35 then feeds back and adds the position estimation result Xes output by the position estimation unit 37 in the previous process, and outputs absolute position information Xs, which is the cumulative distance, to the position estimation unit 37.

[0030] The position estimation unit 37 outputs a position estimation result Xes based on the position information Xgo output by the GNSS latitude / longitude acquisition unit 31, the absolute position information Xls output by the vehicle relative position calculation unit 34, and the absolute position information Xs output by the cumulative distance calculation unit 36. The detailed operation of the position estimation unit 37 will be described later.

[0031] 4 is a flowchart illustrating the processing performed by the speed estimation unit 27. First, the speed estimation unit 27 receives the speed information Vgo, Vl, and Vi calculated from the sensor data from the GNSS 11, the LiDAR 12, and the IMU 13 (S101). Next, the speed estimation unit 27 calculates the standard deviation of each of the speed information Vgo, Vl, and Vi (S102).

[0032] Thereafter, the speed estimation unit 27 calculates the confidence interval of each piece of speed information Vgo, Vl, and Vi based on the standard deviation. Furthermore, the speed estimation unit 27 specifies the range in which the confidence intervals of each piece of speed information Vgo, Vl, and Vi overlap (S103). For example, if the average value and deviation of each piece of speed information Vgo, Vl, and Vi are calculated to calculate the range in which the confidence intervals overlap, the result is as follows. In this case, the confidence interval of each piece of speed information Vgo, Vl, and Vi is set to a range of ±1σ around the average value.

[0033] Vgo: Mean value 61 km / h Standard deviation 1.5 km / h Confidence interval 59.5 km / h to 62.5 km / h Vl: Mean value 60 km / h Standard deviation 2.5 km / h Confidence interval 57.5 km / h to 62.5 km / h Vi: Mean value 60.5 km / h Standard deviation 0.8 km / h Confidence interval 59.7 km / h to 61.3 km / h Overlap of confidence intervals 59.7 km / h to 61.3 km / h

[0034] Next, the speed estimation unit 27 calculates the proportion (Pvg, Pvl, Pvi) of the overlapping range of the confidence intervals of the speed information Vgo, Vl, Vi calculated from each sensor (S104). In accordance with the above example, the proportion of the overlapping range of the confidence intervals of the speed information Vgo, Vl, Vi can be calculated as follows.

[0035] Vgo: Confidence interval 59.5km / h to 62.5km / h Ratio of overlapping sections (61.3-59.7) / (62.5-59.5) = 0.53 Vl: Confidence interval 57.5km / h to 62.5km / h Ratio of overlapping sections (61.3-59.7) / (62.5-57.5) = 0.32 Vi: Confidence interval 59.7km / h to 61.3km / h Ratio of overlapping sections (61.3-59.7) / (61.5-59.7) = 1.00

[0036] Thereafter, the speed estimation unit 27 calculates weighting factors Kvg, Kvl, and Kvi for the speed information Vgo, Vl, and Vi (S105). In the above example, these are calculated as follows.

[0037] Kvg:0.53 / (0.53+0.32+1.00)=0.29 Kvl:0.32 / (0.53+0.32+1.00)=0.17 Kvi:1.00 / (0.53+0.32+1.00)=0.54

[0038] Based on the weighting coefficients, the speed estimation unit 27 obtains the speed estimation result Ves as follows (S106).

[0039] Ves=Kvg・Vgo+Kvl・Vl+Kvi・Vi=60.6km / h

[0040] 5 is a flowchart illustrating the processing performed by the position estimation unit 37. First, the position estimation unit 37 acquires position information Xgo and Xls from sensor data from the GNSS 11 and the LiDAR 12, and also acquires position information Xs obtained by integrating velocity information output from the velocity estimation unit 14 (S201). Next, the position estimation unit 37 calculates the standard deviation of each piece of position information Xgo, Xls, and Xs (S202).

[0041] Thereafter, the position estimation unit 37 calculates the confidence intervals of the position information Xgo, Xls, and Xs based on the standard deviation. Furthermore, the position estimation unit 37 identifies the range where the confidence intervals of the position information Xgo, Xls, and Xs overlap (S203). For example, if the average value and deviation of the position information Xgo, Xls, and Xs are calculated and the overlapping range with the confidence intervals is determined, the result is as follows: Note that the latitude and longitude are assumed to be converted into cumulative distances in the direction of the tracks. In this case, the confidence intervals of the position information Xgo, Xls, and Xs are within ±1σ of the average value.

[0042] Xgo: Mean value 30 km, standard deviation 0.04 km, confidence interval 29.96 km to 30.04 km Xls: Mean value 29.95 km, standard deviation 0.05 km, confidence interval 29.90 km to 30.00 km Xs: Mean value 30.05 km, standard deviation 0.07 km, confidence interval 29.98 km to 30.12 km, overlap of confidence intervals 29.98 km to 30.00 km

[0043] Next, the position estimation unit 37 calculates the proportion (Pxg, Pxl, Pxi) of the overlapping range of the confidence intervals of each piece of position information Xgo, Xls, Xs calculated from each sensor (S204). Explaining based on the above example, the proportion of the overlapping range of the confidence intervals of each piece of position information Xgo, Xls, Xs can be calculated as follows:

[0044] Xgo: Confidence interval Confidence interval 29.96 km to 30.04 km Percentage of overlapping sections (30.00 - 29.98) / (30.04 - 29.96) = 0.25 Xls: Confidence interval 29.90 km to 30.00 km Percentage of overlapping sections (30.00 - 29.98) / (30.00 - 29.90) = 0.2 Xs: Confidence interval 29.98 km to 30.12 km Percentage of overlapping sections (30.00 - 29.98) / (30.12 - 29.98) = 0.14

[0045] Thereafter, the position estimation unit 37 calculates the weighting factors Kxg, Kxl, and Kxi of the position information Xgo, Xls, and Xs (S205). In the above example, these are calculated as follows.

[0046] Kxg:0.25 / (0.25+0.2+0.14)=0.42 Kxl:0.2 / (0.25+0.2+0.14)=0.34 Kxi:0.14 / (0.25+0.2+0.14)=0.24

[0047] Based on the above weighting coefficients, the position estimation result Xes is calculated as follows (S206).

[0048] Xes=Kxg・Xgo+Kxl・Xls+Kxi・Xs=29.995km

[0049] In the first embodiment, a confidence interval is calculated as a reliable range for at least one of the vehicle speed and vehicle position based on the measurement results of the GNSS 11, LiDAR 12, and IMU 13, and a value included in the overlapping range of the respective confidence intervals is used as an estimated value. In this case, when estimating the vehicle speed, the speed estimation unit 14 calculates a weighted average according to the confidence interval for the speed calculated from the measurement results of the GNSS 11, LiDAR 12, and IMU 13, and uses this as an estimated value of the vehicle speed. At this time, the speed estimation unit 14 calculates the confidence interval using the average value and standard deviation of the vehicle speed calculated based on the measurement results. On the other hand, when estimating the vehicle position, the position estimation unit 15 calculates a weighted average according to the confidence interval for the estimated values ​​of the GNSS 11, LiDAR 12, and vehicle speed, and uses this as an estimated value. At this time, the position estimation unit 15 calculates the confidence interval using the average value and standard deviation of the vehicle position calculated based on the measurement results. Furthermore, the speed estimating unit 14 and the position estimating unit 15 determine weights according to the proportions of the overlapping ranges of the confidence intervals in each confidence interval, and calculate a weighted average.

[0050] Second Embodiment Next, a vehicle self-position estimation device 1 according to a second embodiment of the present invention will be described. In the processing flow of the speed estimation unit 14 in FIG. 4 , a case is also conceivable in which the overlap of the confidence intervals of the speed information calculated from the three types of sensors is zero. This can occur when the GNSS 11 satellite constellation is poor, the surrounding area is surrounded by trees, and communication conditions with the satellites are poor, making it impossible to accurately recognize the vehicle's position and speed. At the same time, there are only trees around the LiDAR 12, making it impossible to recognize features. Furthermore, the accuracy of the IMU 13 deteriorates due to fluctuations in the ambient temperature. In the second embodiment, processing for such a case will be described.

[0051] Before this situation occurred, it is believed that the speed estimation unit 14 had been obtaining highly reliable positioning results from two or more of the three types of sensors, with overlapping confidence intervals. The disappearance of the overlap between the confidence intervals of the three types of data means that the positioning results from each sensor were becoming increasingly inaccurate.

[0052] In other words, when observing the time series data of the speed calculation results, the results shown are significantly different from the previous calculation results.

[0053] On the other hand, vehicles such as trains and automobiles are made of moving metal casings with large masses, and there are physical limitations to their operating characteristics, making it impossible for them to move rapidly in a short period of time. In other words, there is an upper limit to the amount of movement per unit time.

[0054] Furthermore, there is a fail-safe concept in the speed estimation of trains and automobiles. In other words, safety in speed control is ensured by setting the perceived speed higher than the actual value.

[0055] Therefore, in such a situation, the estimated vehicle speed is determined by setting the upper limit of the difference between the speed information calculated from the three sensors and the previously calculated value, and then adding or subtracting the maximum acceleration or deceleration of the vehicle to or from the previously calculated speed, thereby ensuring the reliability and safety of the speed estimation.

[0056] Third Embodiment Next, a vehicle self-position estimation device 1 according to a third embodiment of the present invention will be described.

[0057] Similarly, the same concept applies to the case where the overlap of the confidence intervals of the three types of position information is 0 in the processing flow of the position estimation unit in Fig. 5. As described in the second embodiment, this can occur in a situation where the communication conditions with the GNSS 11 satellites deteriorate, the LiDAR 12 cannot recognize features, and the influence of temperature fluctuations in the IMU 13 is compounded. In the third embodiment, processing for such a case will be described.

[0058] Before this situation occurred, the position estimation unit 15 obtained highly reliable positioning results for two or more of the three types of position information calculation results, and it is thought that there was overlap in the confidence intervals.The disappearance of the overlap in the confidence intervals of the three types of position information from that point on means that each piece of position information has deviated significantly.

[0059] In other words, when the time series data of the position calculation results is observed, the results shown are significantly different from the previous calculation results.

[0060] Therefore, in such a situation, the maximum value that the vehicle can move at at a speed that is the difference between the previous calculated value and the maximum acceleration or deceleration of the vehicle is set as the upper limit value of the three position information calculation results, and the largest value is used as the estimated value of the vehicle's position. This makes it possible to maintain the reliability and safety of the position estimation.

[0061] In the second and third embodiments, when there is no overlap of the confidence intervals, the speed estimation unit 14 and the position estimation unit 15 use the largest value that is equal to or smaller than a predetermined upper limit value as the estimated value, where the difference between the obtained estimated value and the previous estimated value is equal to or smaller than the predetermined upper limit value. When estimating the speed of the vehicle, the upper limit value is preferably set to a value obtained by adding the speed when the vehicle is accelerated at the maximum acceleration or subtracting the speed when the vehicle is decelerated at the maximum deceleration from the previous estimated value, and when estimating the position of the vehicle, the upper limit value is preferably set to a value obtained by adding the position when the vehicle is accelerated at the maximum acceleration or subtracting the position when the vehicle is decelerated at the maximum deceleration from the previous estimated value.

[0062] In the embodiment described above, the confidence intervals of the velocity information Vgo, Vl, and Vi are within a range of ±1σ from the average value, and the confidence intervals of the position information Xgo, Xls, and Xs are within a range of ±1σ from the average value, but are not limited to this. For example, the confidence intervals may be within a range of σ±2σ or ±3σ.

[0063] According to the embodiment described above, the GNSS 11, LiDAR 12, and IMU 13 do not affect the measurement results even if the vehicle's wheels spin or slide. Therefore, errors in the estimated speed and position do not occur due to wheel spin or slide. Furthermore, according to the embodiment described above, it is possible to provide a vehicle self-position estimation device that maintains the reliability, safety, and accuracy of position and speed estimation results based on sensor output even when affected by the environment. Furthermore, compared to conventional methods using a tachometer generator and a position-correcting ground coil, the embodiment described above has the advantage of eliminating the construction and outfitting costs for installing the ground coil and the speed sensor.

[0064] <Description of Detection Method> The processing performed by the self-location estimation device 1 described above is realized by the cooperation of software and hardware resources. That is, a processor within a computer provided in the self-location estimation device 1 loads software that realizes each of the above-mentioned functions into memory and executes it to realize each of these functions. Therefore, the processing performed by the self-location estimation device 1 can be considered a detection method in which the processor executes a program recorded in memory to acquire measurement results from a positioning sensor that measures the vehicle's position via satellite communication, a distance sensor that calculates the distance between the vehicle and an object using laser light, and an acceleration sensor that measures the vehicle's acceleration, and based on the measurement results, calculates a confidence interval as a reliable range for at least one of the vehicle's speed and vehicle position, and sets a value included in the overlapping range of the respective confidence intervals as an estimated value.

[0065] Although the present embodiment has been described above, the technical scope of the present invention is not limited to the scope of the above embodiment. It is clear from the claims that various modifications and improvements to the above embodiment are also included in the technical scope of the present invention.

[0066] 1... Self-position estimation device, 11... GNSS, 12... LiDAR, 13... IMU, 14... Speed ​​estimation unit, 15... Position estimation unit, 21... GNSS speed acquisition unit, 22... LiDAR point cloud data acquisition unit, 23... Feature matching unit, 24... Speed ​​calculation unit, 25I... MU acceleration acquisition unit, 26... Speed ​​calculation unit, 27... Speed ​​estimation unit, 31... GNSS latitude and longitude acquisition unit, 32... LiDAR point cloud data acquisition unit, 33... Feature matching unit, 34... Vehicle relative position calculation unit, 35... Speed ​​acquisition unit, 36... Cumulative distance calculation unit, 37... Position estimation unit

Claims

1. A detection device comprising: an acquisition unit that acquires the measurement results of a positioning sensor that measures the position of a vehicle through satellite communication, a distance sensor that calculates the distance between the vehicle and an object using laser light, and an acceleration sensor that measures the acceleration of the vehicle; and an estimation unit that, based on the measurement results, calculates a confidence interval as a reliable range for at least one of the vehicle speed and the vehicle position, and estimates a value included in the overlapping range of the respective confidence intervals.

2. A detection device as described in claim 1, wherein the estimation unit, when estimating the vehicle speed, calculates a weighted average according to the confidence interval for the speed obtained from the measurement results of the positioning sensor, the distance sensor and the acceleration sensor, and uses this as an estimate of the vehicle speed.

3. A detection device as described in claim 2, wherein the estimation unit, when estimating the vehicle position, calculates a weighted average according to the confidence interval for the estimated values ​​of the positioning sensor, the distance sensor and the vehicle speed, and uses this as an estimate of the vehicle position.

4. A detection device according to claim 2 or 3, wherein the estimation unit determines weights according to the proportions of the overlapping ranges of the confidence intervals within each of the confidence intervals, and calculates a weighted average.

5. A detection device as described in claim 2 or 3, wherein the estimation unit obtains the confidence interval by calculating an average value and a standard deviation for the estimated vehicle speed and vehicle position based on the measurement results.

6. A detection device as described in claim 1, wherein when there is no overlap of the confidence intervals, the estimation unit determines as the estimated value the largest value whose difference between the obtained estimated value and the previous estimated value is less than a predetermined upper limit value.

7. A detection device as described in claim 6, wherein, when estimating the vehicle speed, the upper limit value is obtained by adding to the previously estimated value the speed when the vehicle accelerates at its maximum acceleration or subtracting from the previously estimated value the speed when the vehicle decelerates at its maximum deceleration; and, when estimating the vehicle position, the upper limit value is obtained by adding to the previously estimated value the position when the vehicle accelerates at its maximum acceleration or subtracting from the previously estimated value the position when the vehicle decelerates at its maximum deceleration.

8. A detection method in which a processor executes a program recorded in memory to obtain measurement results from a positioning sensor that determines the position of the vehicle via satellite communication, a distance sensor that calculates the distance between the vehicle and an object using laser light, and an acceleration sensor that measures the acceleration of the vehicle, and based on the measurement results, calculates a confidence interval as a reliable range for at least one of the vehicle speed and the vehicle position, and sets a value included in the overlapping range of each of the confidence intervals as an estimated value.