JP2022179642A - Distance measuring device - Google Patents

Abstract

PROBLEM TO BE SOLVED: To estimate the distance for which a mobile body moves, using an arbitrary feature.

SOLUTION: The distance measuring device acquires the distances from mobile bodies at a first time and a second time to two features and acquires the distance between the two features, and calculates the distance between the two features. The distance measuring device thereafter calculates the distance for which the mobile body moves from the first time to the second time based on the result of acquisition, and calculates the distance for which the mobile body moves, using an arbitrary feature measured from the mobile body.

SELECTED DRAWING: Figure 2

COPYRIGHT: (C)2023,JPO&INPIT

Description

The present invention relates to a technique for estimating the moving distance of a moving body.

For example, Patent Literature 1 describes a method of correcting a vehicle speed sensor mounted on a mobile body by estimating a moving distance of the mobile body in a predetermined period. In Patent Document 1, a correction device detects the number of output pulses of a vehicle speed sensor from recognition of feature A by image recognition means to recognition of feature B, and detects feature A and feature B from map information. Get the distance D from Then, based on the relationship between the number of output pulses and the distance D, the correction device corrects the arithmetic expression for obtaining the traveling distance or traveling speed of the vehicle from the number of output pulses.

JP-A-2008-8783

However, in the method of Patent Document 1, only one feature can be recognized at a time by the image recognition means, and only features on the road on which the vehicle is traveling, such as signs drawn on the road, can be used. Can not.

Examples of the problems to be solved by the present invention include the above. An object of the present invention is to estimate the moving distance of a moving object using an arbitrary feature.

The invention according to claim 1 is a distance estimating device, comprising: a first acquisition unit that acquires the distance to the feature by receiving reflected light of the emitted light from the feature; a second acquisition unit that acquires the distance between two features whose distances have been acquired by the acquisition unit; and a calculation unit that calculates a moving distance of the moving object from the time to the second time.

9 is a flowchart showing distance factor update processing according to an embodiment; 2 shows an example of the positional relationship between two features and a moving vehicle. 4 shows a method of calculating a moving distance of a vehicle. It is a figure explaining an average pulse width. 4 is a flowchart of processing for obtaining an average pulse width by sequential calculation; Fig. 4 shows how to project the 3D position of a feature onto the horizontal plane of the vehicle. Fig. 3 shows another method of projecting the 3D position of a feature onto the horizontal plane of the vehicle. 1 is a block diagram showing the configuration of a distance factor update device according to a first embodiment; FIG. 4 is a flowchart of distance factor update processing according to the first embodiment; FIG. 9 is a block diagram showing the configuration of a distance factor update device according to a second embodiment; FIG. 11 is a flowchart of distance factor update processing according to the second embodiment; FIG. The relationship between running speed and the number of pulses per unit time, and the relationship between running speed and pulse width are shown. An example of the positional relationship between three features and a moving vehicle is shown.

In a preferred embodiment of the present invention, the distance estimating device includes a first acquisition unit that acquires the distances from the moving body to the two features at a first time and a second time, respectively; a second acquisition unit that acquires a distance; and a calculation unit that calculates the distance traveled by the moving object from the first time to the second time based on the acquisition results of the first acquisition unit and the second acquisition unit. , provided.

The distance estimation device described above acquires the distances from the moving object to the two features at the first time and the second time, respectively, and also acquires the distance between the two features. Then, based on the acquired result, the moving distance of the moving object from the first time to the second time is calculated. This makes it possible to calculate the moving distance of the moving body using any feature that can be measured from the moving body.

In another preferred embodiment of the present invention, the distance estimating device includes a first acquisition unit that acquires distances from the moving object to at least three features at a first time and a second time, respectively; a second acquisition unit that acquires distances between features, and a moving distance of the moving object from the first time to the second time based on the acquisition results of the first acquisition unit and the second acquisition unit; and a calculating unit for calculating.

The distance estimating device described above acquires the distances from the mobile object to at least three features at the first time and the second time, and also acquires the distances between the at least three features. Then, based on the acquired result, the moving distance of the moving object from the first time to the second time is calculated. This makes it possible to calculate the moving distance of the moving body using any feature that can be measured from the moving body.

In one aspect of the above-described distance estimation device, the calculation unit calculates, based on the distance traveled from the first time to the second time and the average pulse width of the vehicle speed pulse signal, for one pulse of the vehicle speed pulse signal Calculate the moving distance of Accordingly, it is possible to calibrate the vehicle speed pulse signal based on the calculated moving distance.

In another aspect of the distance estimating device, the calculator calculates the movement distance when the angular velocity in the yaw direction of the moving body or the steering angle is less than a predetermined threshold. Thereby, the calculation accuracy of the moving distance can be improved.

In a preferred example of the above-described distance estimation device, the second obtaining unit obtains the distances to the two features obtained by the first obtaining unit, Obtain the distance between the two features based on the angle between the directions. In another preferred example, the second acquisition unit acquires the distance between the two features based on map information.

In another aspect of the distance estimating device, the calculator changes the time interval from the first time to the second time according to the traveling speed of the moving body. Thereby, the calculation accuracy of the moving distance can be improved. Preferably, the calculator shortens the time interval as the running speed of the moving object increases.

In another preferred embodiment of the present invention, the distance estimating method executed by the distance estimating device includes a first acquiring step of respectively acquiring distances from the moving body to the two features at a first time and a second time. and a second obtaining step of obtaining the distance between the two features, and the moving object from the first time to the second time based on the obtained results of the first obtaining unit and the second obtaining unit and a calculating step of calculating the movement distance of the. This makes it possible to calculate the moving distance of the moving body using any feature that can be measured from the moving body.

In another preferred embodiment of the present invention, the distance estimating method performed by the distance estimating device comprises a first acquisition of acquiring distances from the moving object to at least three features at a first time and a second time, respectively. a second acquiring step of respectively acquiring the distances between the at least three features; and a period from the first time to the second time based on the acquisition results of the first acquiring unit and the second acquiring unit. and a calculating step of calculating a moving distance of the moving body. This makes it possible to calculate the moving distance of the moving body using any feature that can be measured from the moving body.

In another preferred embodiment of the present invention, a program executed by a distance estimating device comprising a computer comprises: a first acquisition for acquiring distances from a moving object to two features at a first time and a second time, respectively; a second acquisition unit that acquires the distance between the two features; the moving object from the first time to the second time based on the acquisition results of the first acquisition unit and the second acquisition unit; The computer is caused to function as a calculation unit that calculates the movement distance. This makes it possible to calculate the moving distance of the moving body using any feature that can be measured from the moving body.

In another preferred embodiment of the present invention, a program executed by a distance estimating device comprising a computer comprises a first distance estimating apparatus that acquires distances from a moving object to at least three features at a first time and a second time, respectively. an obtaining unit, a second obtaining unit that obtains the distances between the at least three features, respectively, and based on the obtained results of the first obtaining unit and the second obtaining unit, the The computer is caused to function as a calculation unit that calculates the moving distance of the moving object. This makes it possible to calculate the moving distance of the moving body using any feature that can be measured from the moving body.
The above program can be used by being stored in a storage medium.

Preferred embodiments of the present invention will be described below with reference to the drawings. An embodiment in which the moving distance of the mobile object obtained by the distance estimation method of the present invention is used for calibration of the vehicle speed pulse will be described below.

[background]
Self-localization systems installed in current car navigation systems detect the vehicle speed using a vehicle speed sensor and detect the direction of travel using an angular velocity sensor or a steering angle sensor to measure the movement of the vehicle. The current position is estimated by integrating these with information measured by GPS and external sensors. Therefore, in order to improve the self-position estimation accuracy, it is required to detect the vehicle speed with high accuracy.

The vehicle speed sensor, for example, outputs a vehicle speed pulse signal at time intervals proportional to the rotation speed of the output shaft of the transmission or the wheels. Then, the vehicle speed v can be calculated by dividing the distance coefficient α _d by the pulse width t _p as shown in the following equation (1). This distance coefficient _αd is the moving distance per pulse of the vehicle speed pulse signal.

The moving distance per pulse varies depending on the vehicle type. Further, when the outer diameter of the tire changes due to a change in tire air pressure or tire replacement, the movement distance per pulse also changes. Furthermore, the moving distance per pulse also changes depending on the running speed. Normally, due to running resistance, a difference occurs between the wheel speed obtained from the vehicle speed pulse and the actual vehicle speed. Since running resistance is greater during high-speed running than during low-speed running, the speed difference between the wheel speed and the vehicle body speed is also greater during high-speed running than during low-speed running. Therefore, the moving distance per pulse differs between high-speed running and low-speed running. As described above, in order to obtain the vehicle speed with high accuracy, the distance coefficient needs to be appropriately calibrated and updated.

Conventionally, information obtained from GPS has been used as a reference when calibrating the distance factor. For example, using the travel distance ΔD of the vehicle obtained from the GPS position obtained from the GPS and the vehicle speed pulse number n, the travel distance _dp per pulse is calculated by the following equation (2), and averaging processing is always performed. There is a method of performing correction by

However, depending on the conditions, the GPS information itself, which is the reference, may contain a large error, and if the calibration calculation is performed using the GPS information containing a large error as the reference, the distance coefficient will deviate from the true value. be. In order to obtain more accurate reference GPS information, the conditions should be stricter. comes out.
[Distance factor update process]

From the above point of view, the distance factor update device according to the present embodiment (hereinafter also simply referred to as "update device") can calculate the distance traveled by the vehicle based on the measurement of the feature by the external sensor without using GPS information as a reference. is calculated and used as a reference for calibration of the vehicle speed pulse signal. A camera, a LiDAR (Light Detection And Ranging), a millimeter wave radar, or the like can be used as the external sensor.

FIG. 1 is a flowchart showing distance factor update processing according to an embodiment. First, in process P1, the updating device measures two features using the external sensor at time _T1 . Next, in step P2, the update device measures the same two features as those measured at time _T1 at time _T2 , which is ΔT seconds after time _T1 . Next, in step P3, the updating device obtains the relative distance between the two features.

Next, in step P4, the update device uses the distance from the vehicle center position to each feature at each time and the relative distance between each feature acquired at time _T1 and time _T2 , and calculates from time _{T1 to} A moving distance ΔD of the vehicle up to time _T2 is calculated.

Next, in step P5, the updating device updates the average pulse width _tp of the vehicle speed pulse signal between time _T1 and time _T2 , the elapsed time ΔT from time _T1 to time _T2 , and the Using the obtained travel distance ΔD of the vehicle from time _T1 to time _T2 , the travel distance _dp per pulse is calculated. Then, in step P6, the updating device updates the distance coefficient α _d using the movement distance d _p per pulse obtained in steps P5 and P6.

Next, each step of the distance factor updating process will be described in detail.
(1) Acquisition of distances between features (processes P1 to P3)
FIG. 2 shows an example of the positional relationship between two features and a moving vehicle. Assume that the vehicle moves as shown in FIG. ₂ between time _T1 and time T2. First, the update device detects the feature 1 and the feature 2 at time _T1 , and determines the distance L1 from the vehicle to the feature ₁ , the angle φ1 between the traveling direction Hd of the vehicle and the feature ₁ , and the vehicle to the feature ₂ and the angle φ ₂ between the traveling direction Hd of the vehicle and the feature 2 are obtained (step P1). At this time, the relative distance L between the feature 1 and the feature 2 can be calculated as follows using L ₁ , L ₂ , φ ₁ and φ ₂ (process P3).

Next, the updating device detects the feature 1 and the feature ₂ at the time T2 as well as at the time _T1 , and detects the distance L' ₁ from the vehicle to the feature 1, the traveling direction Hd' of the vehicle, and the feature 1, the distance L' ₂ from the vehicle to the feature ₂ , and the angle φ'2 formed between the traveling direction Hd' of the vehicle and the feature ₂ are obtained (process P2). At this time, the relative distance between features can be calculated using L _{' 1} , L _{' 2} , φ _{' 1} , and φ _{' 2} in the same manner as at time T ₁ . The relative distance L' between features calculated at time _T2 is obtained by the following formula (step P3).

When calculating the movement distance ΔD of the vehicle in step P4, which will be described later, the updating device uses either the relative distance L or L' between the features. Alternatively, the update device may calculate and use the average value L _ave of the relative distances L and L' using the following formula.

なお、以下の説明では、地物間の相対距離を「Ｌ」として説明する。

In the following description, the relative distance between features is assumed to be "L".

In the above example, in step P3, the relative distance L between the features (hereinafter also referred to as "inter-feature distance L") is obtained by calculation based on the feature measurement result by the external sensor. If a map can be used, the inter-feature distance L may be obtained from high-precision map data. When the inter-feature distance L is calculated from the feature measurement result by the external sensor, the inter-feature distance L changes depending on the measurement accuracy of the feature. In other words, if the measurement accuracy is poor, the accuracy of the calculated distance L between features also deteriorates, and the accuracy of the moving distance ΔD of the vehicle to be calculated later also deteriorates. In this regard, the use of a high-precision map makes it possible to obtain the feature-to-feature distance L with high precision, thereby improving the precision of the movement distance ΔD of the vehicle.

(2) Calculation of moving distance ΔD (process P4)
Next, the updating device uses the distances L ₁ and L 2 obtained at time T ₁ , the distances L _{′ 1 and} L′ ₂ obtained at time T ₂ , and the relative distance L between the features to calculate the time A moving distance ΔD of the vehicle from _T1 to time _T2 is calculated. FIG. 3 shows a method of calculating the moving distance ΔD. In FIG. 3, the angle α is determined by the law of cosines as follows.

同様に、角度βは余弦定理により以下のように求められる。

Similarly, the angle β is determined by the law of cosines as follows.

従って、移動距離ΔＤは、余弦定理により以下のように求められる。

Therefore, the moving distance ΔD can be obtained by the law of cosines as follows.

In the above example, the movement distance ΔD is calculated using the angles α and β on the side of the feature 2 in FIG. A moving distance ΔD may be calculated. Also, an average value of the moving distances ΔD calculated by the above method may be calculated.

(3) Calculation of movement distance _dp per pulse (process P5)
Next, the update device uses the travel distance ΔD of the vehicle in ΔT seconds from time _T1 to time _T2 and the average pulse width _tp of the vehicle speed pulse signal to calculate the movement per pulse as follows: Calculate the distance _dp .

FIG. 4 is a diagram for explaining the average pulse width _tp . The average pulse width _tp can be calculated by buffering the pulse widths measured between time _T1 and time _T2 and averaging them according to the following equation (11).

Alternatively, the average pulse width can also be determined by iterative calculation using equation (11). When the average pulse width is obtained by sequential calculation, it is not necessary to buffer the measured pulse widths, so it is possible to reduce the amount of memory used in the device.

FIG. 5 is a flow chart of processing for obtaining an average pulse width by sequential calculation. First, at time T= _T1 , the updating device resets the coefficient k indicating the number of detected pulses to "0" (step S51), and acquires the current time T (step S52). Next, the updating device determines whether or not the current time T has reached time _T2 (step S53).

If the current time T has not reached time _T2 (step S53: NO), the updating device detects the vehicle speed pulse signal and acquires its pulse width _tk (step S54). Next, the updating device increases the coefficient k by 1 (step S55), and determines whether the coefficient k=1 (step S56).

If the coefficient k=1 (step S56: YES), the pulse width _tk is substituted for the average pulse width tp (step S58), and the process returns to step S52. On the other hand, if the coefficient k is not equal to 1 (step S56: NO), the updating device divides the difference between the average pulse width t _p at that time and the current pulse width t _k by the coefficient k using equation (11). (t _k - t _p )/k, that is, the variation of the average pulse t _p due to the current pulse width t _k is added to the average pulse width t _p at that time to update the average pulse width t _p , step S52. back to Then, in step S53, when the current time T reaches time _T2 (step S53: YES), the process ends.

(4) Updating the distance coefficient α _d (process P6)
Next, the updating device updates the distance factor α _d using the movement distance d _p obtained in step P5. Specifically, the obtained moving distance d _p is used as a new distance coefficient α _d . Note that the updated distance coefficient α _d obtained in this way is used for calculating the vehicle speed v using the equation (1).

(5) A method of projecting the 3D position of a feature onto the horizontal plane of the vehicle. It is established by considering it. However, like road signs and traffic lights, many of the features in the real environment have heights in space. Therefore, by projecting the three-dimensional coordinates of the feature onto the horizontal plane of the vehicle, the distance traveled by the vehicle can be calculated in the same manner as described above. This technique is described below.

Here, a vehicle coordinate system (XYZ coordinate system) is defined as shown in FIG. 6(B). Here, the X-axis indicates the traveling direction of the vehicle, the Y-axis indicates the direction perpendicular to the traveling direction of the vehicle within the horizontal plane of the vehicle, and the Z-axis indicates the height direction of the vehicle.

(i) When the 3D position of a feature can be obtained Assume that, when coordinate data is included, the three-dimensional coordinates P of the feature in the vehicle coordinate system can be obtained as shown in FIG. 6(C).

At this time, if the foot of the perpendicular drawn from the point P to the XY plane is a point P', the length L _xy of the line segment OP' and the angle φ _xy between the line segment OP' and the X axis are as follows. can be calculated as

Therefore, in the processing in steps P1 to P4, the horizontal distance L _xy and the angle φ _xy can be obtained using equation (12) and used. Specifically, in step P1, horizontal distances L _1xy and L′ _2xy and angles φ _1xy and φ _2xy are obtained. Similarly, in step P2, horizontal distances L' _1xy , L' _2xy and angles _φ'1xy , _φ'2xy are obtained. Then, based on these, the distances L and L' between features are obtained in step P3, and the moving distance ΔD is obtained in step P4.

(ii) When the distance and angle to the feature can be acquired Using an external sensor capable of measuring the distance and angle to the feature, as shown in FIG. Assume that the azimuth angle φ _xy of the feature with respect to the direction (the X axis of the vehicle coordinate system) and the elevation angle φ _xyz of the feature with respect to the horizontal plane of the vehicle (the XY plane of the vehicle coordinate system) have been obtained.

At this time, if the foot of the perpendicular drawn from the point P to the XY plane is the point P', the length _Lxy of the line segment OP' can be calculated as follows.

よって、工程Ｐ１～Ｐ４における処理では、（ｉ）と同様に、式（１３）によって求められる水平距離Ｌ_ｘｙ及び角度φ_ｘｙを使用すればよい。

Therefore, in the processes in steps P1 to P4, the horizontal distance L _xy and the angle φ _xy obtained by equation (13) can be used as in (i).

[First embodiment]
Next, a first embodiment of the update device will be described. FIG. 8 is a block diagram showing the configuration of the update device 1 according to the first embodiment. In the first embodiment, the updating device 1 obtains the inter-feature distance L by calculation based on the measurement results of the two features by the external sensor.

As illustrated, the update device 1 includes a gyro sensor 10, a vehicle speed sensor 11, an external sensor 12, a traveling direction acquisition unit 13, a vehicle speed pulse measurement unit 14, a feature measurement unit 15, an inter-feature distance A calculator 16 , a distance factor corrector 17 , and a movement distance calculator 18 are provided. Note that the traveling direction acquiring unit 13, the vehicle speed pulse measuring unit 14, the feature measuring unit 15, the inter-feature distance calculating unit 16, the distance coefficient correcting unit 17, and the movement distance calculating unit 18 are prepared in advance by a computer such as a CPU. can be realized by executing the program.

The traveling direction obtaining unit 13 obtains the traveling direction Hd of the vehicle based on the output of the gyro sensor 10 , and supplies it to the feature measuring unit 15 and the distance factor calibrating unit 17 . The vehicle speed pulse measurement unit 14 measures the vehicle speed pulse output from the vehicle speed sensor 11 , calculates the average pulse width _{tp of the vehicle speed pulse signal, and supplies the calculated average pulse width tp} to the distance factor calibration unit 17 .

The external sensor 12 is, for example, a camera, LiDAR, millimeter wave radar, or the like. Specifically, at time T ₁ , the feature measurement unit 15 measures the distances L ₁ and L ₂ from the vehicle to the two features, and calculates the travel direction Hd of the vehicle supplied from the travel direction acquisition unit 13 . and the directions of the two features are _calculated , and supplied to the inter-feature distance calculation unit ₁₆ and the moving distance calculation unit 18 . At time T ₂ , the feature measurement unit 15 measures the distances L _{′ 1} and L′ ₂ from the vehicle to the two features, and calculates the travel direction Hd′ of the vehicle supplied from the travel direction acquisition unit 13 . and the directions of the two features are calculated, and supplied to the inter-feature distance calculation unit ₁₆ and the moving distance _calculation unit 18 .

Based on the distances L ₁ and L ₂ and the angles φ ₁ and φ ₂ of the two features measured by the feature measurement unit 15 , the feature distance calculation unit 16 calculates the distance between the features using the above equation (3). The distance L is calculated and supplied to the movement distance calculation unit 18 .

Based on the distances L ₁ , L ₂ , L _{′ 1} , and L _{′ 2} supplied from the feature measurement unit 15 and the inter-feature distance L calculated by the inter-feature distance calculation unit 16 , the movement distance calculation unit 18 Then, the moving distance ΔD of the vehicle is calculated by the above-described formulas (6) to (8) and supplied to the distance coefficient calibration unit 17 .

Based on the average pulse width t _p supplied from the vehicle speed pulse measurement unit 14 and the movement distance ΔD supplied from the movement distance calculation unit 18, the distance coefficient calibration unit 17 calculates the movement distance d _p per pulse (that is, , the distance factor α _d ) is calculated. The vehicle body speed may be calculated from the obtained moving distance per pulse.

Next, the distance factor updating process according to the first embodiment will be described. FIG. 9 is a flowchart of distance factor update processing according to the first embodiment.

First, the update device 1 determines whether or not the vehicle is traveling straight based on the traveling direction of the vehicle output by the traveling direction acquisition unit 13 (step S11). This is because the accuracy of the movement distance ΔD output by the movement distance calculation unit 18 is lowered when the vehicle is not traveling straight ahead. Specifically, when the gyro sensor 10 can detect the angular velocity ω of the vehicle in the yaw direction, it may be determined that the vehicle is running straight when |ω|<Δω (Δω: a predetermined threshold value). . Further, when the steering angle δ of the vehicle can be detected, it may be determined that the vehicle is running straight when |δ|<Δδ (Δδ: a predetermined threshold value).

If the vehicle is not traveling straight ahead (step S11: NO), the process ends. On the other hand, if the vehicle is traveling straight ahead (step S11: YES), the updating device 1 measures the two features 1 and 2 (step S12) and calculates the relative distance L therebetween (step S13).

Next, the update device 1 determines whether or not flag=0 (step S14). Note that "flag" is reset to "0" at the start of processing. If flag=0 (step S14: YES), the updating device 1 sets the flag to "1" (step S15), starts calculating the average pulse width _tp (step S16), and returns to step S11. .

On the other hand, if flag is not 0 (step S14: NO), the updating device 1 calculates the moving distance ΔD as described above (step S17), and uses the moving distance ΔD to calculate the moving distance _dp per pulse. (step S18), and the distance coefficient _αd is updated (step S19). Then, the process ends.

[Second embodiment]
Next, a second embodiment of the update device will be described. FIG. 10 is a block diagram showing the configuration of the update device 1x according to the second embodiment. The updating device 1x differs from the updating device 1 of the first embodiment in that it includes a map database (DB) 19 storing high-precision map data, but the other components are the same as those of the updating device 1 of the first embodiment. Therefore, the explanation is omitted.

In the updating device 1x of the second embodiment, the feature-to-feature distance acquisition unit 16 uses the high-precision map data stored in the map DB 19 to acquire the feature-to-feature distance L between two features. .

FIG. 11 is a flowchart of distance factor update processing according to the second embodiment. Compared with the distance factor updating process of the first embodiment shown in FIG. 9, in the distance factor updating process according to the second embodiment, instead of step S13 of the first embodiment, in step S26, the distance between features is calculated from the map DB. The difference is that L is acquired, but other points are basically the same as the distance factor updating process according to the first embodiment. Specifically, steps S21 to 22, S23 to 25, and S27 to 29 in the distance factor update process of the second embodiment correspond to steps S11 to 12, S14 to 16, and S17 in the distance factor update process of the first embodiment, respectively. to S19.

[Feature measurement cycle]
The moving distance _dp per pulse obtained in the above distance coefficient updating process is the average value of the moving distance per pulse during the time interval ΔT from time _T1 to time _T2 . Therefore, if the pulse width fluctuates significantly during the time interval ΔT, the accuracy of the calculated moving distance _dp deteriorates. Therefore, it is desirable to have as few pulses as possible during the time interval ΔT.

The number of pulses per unit time varies depending on the running speed of the vehicle. For example, consider the number of pulses per second as shown in FIG. In a vehicle type that outputs 2 pulses per tire rotation, the number of pulses per second is 3 pulses at 10 km/h, 17 pulses at 50 km/h, and 35 pulses at 100 km/h, and there is a large difference depending on the running speed.

Therefore, by changing the time interval ΔT according to the running speed in consideration of the measurement period of the external sensor and the vehicle type, it is possible to suppress deterioration in the accuracy of the movement distance _dp due to fluctuations in the pulse width. FIG. 12B shows the relationship between running speed and pulse width. For example, in the case of a vehicle type that outputs 2 pulses per rotation at a measurement cycle of the external sensor of 50 ms (20 Hz), ΔT = 300 ms when the traveling speed is less than 20 km/h, and ΔT = 200 ms when the speed is 20 km/h or more and less than 30 km/h. , ΔT = 100 ms when the speed is 30 km/h or more and less than 60 km/h, and ΔT = 50 ms when the speed is 60 km/h or more. _dp can be calculated.

[Modification]
(Modification 1)
In the above distance factor updating process, two features are measured, but if three or more features can be measured at the same time, the moving distance ΔD is calculated multiple times, and the average value of them is the distance factor can be used to update

For example, when three features can be measured, a combination of feature 1 and feature 2, feature 2 and feature 3, and feature 3 and feature 1 can be selected as shown in FIG. For each combination, the movement distance between time _T1 and time _T2 is calculated by the method of steps P1 to P3 described above. Let ΔD ₁₂ be the movement distance obtained by the combination of feature 1 and feature 2, ΔD 23 be the movement distance obtained by the combination of feature 2 and feature 3, and ΔD ₂₃ be the movement distance obtained by the combination of feature 3 and feature 1. Assuming that the resulting traveled distance is ΔD ₃₁ , the average value of these can be used as the traveled distance ΔD as follows.

As a result, the accuracy of the movement distance ΔD can be statistically improved, and the accuracy of the movement distance per pulse is also improved.

(Modification 2)
In the first embodiment described above, the inter-feature distance L is calculated from the measurement results of the two features, and in the second embodiment, the inter-feature distance L is obtained using the map data. May be used in combination. For example, in an area where high-precision map data exists, the distance L between features is obtained using the high-precision map data, and in an area where high-precision map data does not exist, the distance between features is obtained from the measurement results of the features. good too. Further, depending on the situation of the vehicle, the inter-feature distance L obtained with the higher accuracy may be used.

(Modification 3)
As shown in step S11 in FIG. 9 and step S21 in FIG. 11, in the distance factor update process of the embodiment, the distance factor is basically updated when the vehicle is traveling straight. However, even if the vehicle appears to be traveling straight ahead in reality, it is not strictly traveling straight and there is slight fluctuation. Therefore, the moving distance ΔD obtained in step P4 is not an actual moving distance but an approximate value. Therefore, if the time interval ΔT is too large, the difference between the actual movement distance and the movement distance calculated in step P4 will become large. From this point of view, it is desirable to make the time interval ΔT from time _T1 to time _T2 as short as possible.

(Modification 4)
If the external sensor is installed at a low position on the vehicle, occlusion will increase due to surrounding vehicles, and it is thought that the frequency of detecting suitable features for updating the distance factor will decrease. Therefore, it is preferable to install the external sensor so that it can measure above the height of the surrounding vehicles. This increases the frequency of feature detection and increases the number of times the distance coefficient is updated, thereby improving the accuracy of the distance coefficient.

REFERENCE SIGNS LIST 10 gyro sensor 11 vehicle speed sensor 12 external sensor 13 traveling direction acquisition unit 14 vehicle speed pulse measurement unit 15 feature measurement unit 16 distance calculation unit between features 17 distance coefficient configuration unit 18 movement distance calculation unit 19 map database

Claims (1)

a first acquisition unit that acquires a distance to the feature by receiving reflected light from the emitted light from the feature;
a second acquisition unit that acquires a distance between two features whose distances have been acquired by the first acquisition unit;
a calculation unit that calculates a moving distance of the moving object from a first time to a second time based on the acquisition results of the first acquisition unit and the second acquisition unit;
A distance estimation device comprising:

Images