JP2019124698A - Distance measuring device - Google Patents

Abstract

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

Description

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

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

JP 2008-8783 A

  However, in the method of Patent Document 1, only one feature can be recognized at a time by the image recognition means, and it is possible to use only features on the road where the vehicle is traveling, like a sign drawn on the road. Can not.

  As problems to be solved by the present invention, the above-mentioned ones can be mentioned as an example. An object of the present invention is to estimate the moving distance of a mobile using any feature.

  The invention according to claim 1 is a distance estimation device, wherein the first acquisition unit acquires a distance to the feature by receiving reflected light of the emitted light by the feature of the light; The first acquisition unit acquires the distance between the two features for which the distance has been acquired by the acquisition unit, and the first acquisition unit and the second acquisition unit acquire the first distance based on the first acquisition unit. And a calculator configured to calculate a moving distance of the moving object from a time to a second time.

It is a flowchart which shows the distance coefficient update process which concerns on an Example. The example of the positional relationship of two feature and a mobile vehicle is shown. The calculation method of the movement distance of a vehicle is shown. It is a figure explaining average pulse width. It is a flowchart of the process which calculates | requires average pulse width by sequential calculation. The method to project the three-dimensional position of a feature on the horizontal surface of a vehicle is shown. 2 illustrates another method of projecting the three-dimensional position of the feature on the horizontal plane of the vehicle. It is a block diagram showing composition of a distance coefficient update device concerning a 1st example. It is a flowchart of the distance coefficient update process by 1st Example. It is a block diagram which shows the structure of the distance coefficient update apparatus based on 2nd Example. It is a flowchart of the distance coefficient update process by 2nd Example. The relation between the traveling speed and the number of pulses per unit time, and the relation between the traveling speed and the pulse width are shown. An example of a positional relationship between three features and a moving vehicle is shown.

  In a preferred embodiment of the present invention, the distance estimation device acquires a distance between the moving object and two features at each of the first time and the second time, and a distance between the two features. A second acquisition unit that acquires a distance, and a calculation unit that calculates a moving distance of the moving body from the first time to the second time based on the acquisition results of the first acquisition unit and the second acquisition unit And.

  The above-described distance estimation device acquires the distance from the moving object to the two features at each of the first time and the second time, and obtains the distance between the two features. Then, based on the acquisition result, the moving distance of the moving body from the first time to the second time is calculated. Thereby, the movement distance of the mobile can be calculated using any feature that can be measured from the mobile.

  In another preferred embodiment of the present invention, the distance estimation apparatus further comprises: a first acquisition unit for acquiring distances from the moving object to at least three features at the first time and the second time respectively; The moving distance of the moving object from the first time to the second time is calculated based on the acquisition results of the second acquiring unit for acquiring the distance between features and the first acquiring unit and the second acquiring unit. And a calculation unit to calculate.

  The above-described distance estimation device acquires the distances from the moving object to the at least three features at the first time and the second time respectively, and acquires the distances between the at least three features. Then, based on the acquisition result, the moving distance of the moving body from the first time to the second time is calculated. Thereby, the movement distance of the mobile can be calculated using any feature that can be measured from the mobile.

  In one aspect of the above distance estimation device, the calculation unit is based on the movement distance from the first time to the second time and the average pulse width of the vehicle speed pulse signal, per pulse of the vehicle speed pulse signal. Calculate the movement distance of Thus, calibration of the vehicle speed pulse signal can be performed based on the calculated movement distance.

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

  In a preferred example of the above-described distance estimation device, the second acquisition unit is a distance between two features acquired by the first acquisition unit, a traveling direction of the movable body, and the two features. The distance between the two features is obtained based on the angle made by the direction. In another preferable example, the second acquisition unit acquires a distance between the two features based on map information.

  In another aspect of the above distance estimation device, the calculation unit changes a 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 movement distance can be improved. Preferably, the calculation unit shortens the time interval as the traveling speed of the movable body is higher.

  In another preferred embodiment of the present invention, a distance estimation method executed by the distance estimation device acquires a distance from a mobile body to two features at a first time and a second time respectively. , The second acquisition step of acquiring the distance between the two features, and the movable body from the first time to the second time based on the acquisition results of the first acquisition unit and the second acquisition unit Calculating a movement distance of Thereby, the movement distance of the mobile can be calculated using any feature that can be measured from the mobile.

  In another preferred embodiment of the present invention, the distance estimation method executed by the distance estimation device acquires first each of distances from the moving object to at least three features at the first time and the second time, respectively. From the first time to the second time based on the process, the second acquisition process for acquiring the distances between the at least three features, and the acquisition results of the first acquisition unit and the second acquisition unit. And a calculation step of calculating the movement distance of the moving body. Thereby, the movement distance of the mobile can be calculated using any feature that can be measured from the mobile.

  In another preferred embodiment of the present invention, a program executed by a distance estimation device provided with a computer acquires first each of distances from a moving body to two features at first and second times. Unit, a second acquisition unit that acquires a distance between the two features, and 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 functions as a calculation unit that calculates the movement distance. Thereby, the movement distance of the mobile can be calculated using any feature that can be measured from the mobile.

In another preferred embodiment of the present invention, a program executed by a distance estimation device comprising a computer obtains distances from a mobile to at least three features at first and second times respectively. An acquisition unit, a second acquisition unit acquiring each of the distances between the at least three features, the first acquisition unit, and the second acquisition unit based on the acquisition results of the second acquisition unit; The computer functions as a calculation unit that calculates the movement distance of the moving object. Thereby, the movement distance of the mobile can be calculated using any feature that can be measured from the mobile.
The above program can be stored and used in a storage medium.

  Hereinafter, preferred embodiments of the present invention will be described with reference to the drawings. Hereinafter, an embodiment will be described in which the moving distance of the moving object obtained by the distance estimation method of the present invention is used for calibration of a vehicle speed pulse of a vehicle.

[background]
In the current position estimation system installed in a car navigation system etc., the vehicle speed is detected using a vehicle speed sensor, and the traveling direction is measured using an angular velocity sensor or a steering angle sensor to measure the movement situation of the vehicle. The current position is estimated by integrating these with information measured by GPS and an external sensor. 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 outputs, for example, a vehicle speed pulse signal at time intervals proportional to the rotation speed of the output shaft or the wheel of the transmission. Then, as shown in the following equation (1), the vehicle speed v can be calculated by dividing the distance coefficient α _d by the pulse width t _p . The distance coefficient α _d is a movement distance per pulse of the vehicle speed pulse signal.

  The movement distance per pulse differs depending on the vehicle type. In addition, when the outer diameter of the tire changes due to the change of the air pressure of the tire, the change of the tire, etc., the moving distance per pulse also changes. Furthermore, the movement distance per pulse also changes depending on the traveling speed. Usually, the traveling resistance causes a difference between the wheel speed obtained from the vehicle speed pulse and the actual vehicle speed. At high speed running, the running resistance is greater than at low speed running, so the speed difference between the wheel speed and the body speed is also greater at high speed running than at low speed running. Therefore, the travel distance per pulse is different between high speed traveling and low speed traveling. As mentioned above, in order to obtain | require a vehicle speed with high precision, it is necessary to calibrate and update a distance coefficient suitably.

Conventionally, when performing calibration of the distance factor, information obtained from GPS is used as a reference. For example, using the travel distance ΔD of the vehicle determined by the GPS position obtained from the GPS and the vehicle speed pulse number n, the travel distance d _p per pulse is calculated by the following equation (2), and an averaging process is applied constantly There is a way to make corrections by

However, depending on the conditions, the GPS information itself, which is a reference, may include a large error, and if calibration calculation is performed using GPS information including a large error as a reference, the distance coefficient deviates from the true value. is there. To get more accurate reference GPS information, the conditions should be tightened, but as the conditions are tightened, the number of times the reference information is obtained decreases, which contradicts the problem that the progress of calibration is delayed. Comes out.
Distance factor update processing

  From the above point of view, the distance coefficient update device according to the present embodiment (hereinafter, also simply referred to as "update device") does not use GPS information as a reference, and the moving distance of the vehicle Is calculated and used as a reference for calibration of the vehicle speed pulse signal. As the external sensor, a camera, LiDAR (Light Detection And Ranging), millimeter wave radar, or the like can be used.

FIG. 1 is a flowchart showing distance coefficient update processing according to the embodiment. First, in step P1, the updating device measures two features at time T ₁ using an external sensor. Next, in step P2, updating device, at time T _2, which has passed ΔT seconds from the time T _1, to measure the same two features as measured at time T _1. Next, in step P3, the updating device obtains the relative distance between the two features.

Next, in step P4, the update device was acquired at time T ₁ and time T _2, and the distance to each feature from the vehicle center position at each time, using the relative distances between each feature, from time T ₁ The movement distance ΔD of the vehicle up to time T ₂ is calculated.

Next, in step P5, the update device, an average pulse width _{t p} of the vehicle speed pulse signal from the time _{T 1} of the time _{T 2,} the elapsed time ΔT from the time _{T 1} to time _{T 2,} determined in step P4 from time T ₁ by using the the moving distance ΔD the vehicle up to the time T _2, and calculates the moving distance d _p per pulse. 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 process of the above-mentioned distance coefficient update process will be described in detail.
(1) Acquisition of distance between features (steps P1 to P3)
FIG. 2 shows an example of the positional relationship between two features and a moving vehicle. Vehicle during the period from the time T ₁ time T _2, is to have moved as shown in FIG. First, the updating device detects the feature 1 and the feature 2 at time T ₁ and determines the distance L ₁ from the vehicle to the feature 1 and the angle φ ₁ between the traveling direction Hd of the vehicle and the feature 1 and the vehicle obtaining the distance L _2, and the angle phi ₂ in the traveling direction Hd and feature 2 of the vehicle to feature 2 from (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 (step P3).

Next, the update device detects feature 1 and feature 2 at time T ₂ as well as time T _1, and the distance L ′ ₁ from the vehicle to feature ₁ and the traveling direction Hd ′ of the vehicle and the feature An angle φ ′ _{1 of 1} and a distance L ′ ₂ from the vehicle to the feature 2 and an angle φ ′ ₂ between the traveling direction Hd ′ of the vehicle and the feature ₂ are obtained (step P 2). 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 . Relative distance L'between features calculated by time T _2, is obtained by the following equation (step P3).

When calculating the movement distance ΔD of the vehicle in step P4 described later, the updating device uses either of the relative distances L and L ′ between features. Alternatively, the updating device may calculate and use the average value L _ave of the relative distances L and L ′ according to the following equation.

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 described as "L".

  In the above example, the relative distance L between features (hereinafter, also referred to as “feature distance L”) is obtained by calculation based on the feature measurement result by the external sensor in the process P3. If a map can be used, the feature distance L may be acquired from data of the high precision map. When calculating the feature distance L from the feature measurement result by the external sensor, the feature distance L changes depending on the feature measurement accuracy. That is, if the measurement accuracy is poor, the accuracy of the feature distance L calculated is also degraded, and the accuracy of the movement distance ΔD of the vehicle to be calculated later is also degraded. In this respect, when the high accuracy map is used, the feature distance L can be obtained with high accuracy, so that the accuracy of the movement distance ΔD of the vehicle can be improved.

(2) Calculation of movement distance ΔD (step P4)
Next, the update device includes a distance _L 1, _{L 2} obtained at time _{T 1,} the distance _L'1 obtained at time _{T 2,} and _L'2, by using the relative distance L between the features, time calculates a moving distance ΔD the vehicle from T ₁ to time _{T 2.} FIG. 3 shows a method of calculating the movement distance ΔD. In FIG. 3, the angle α is determined by the cosine theorem 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 cosine theorem 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 movement distance ΔD is obtained by the cosine theorem as follows.

  In the above example, the movement distance ΔD is calculated using the angles α and β on the feature 2 side in FIG. 3, but instead, the angles α ′ and β ′ on the feature 1 side are used. The movement distance ΔD may be calculated. Further, an average value of the movement distances ΔD calculated by the above method may be calculated.

(3) Calculation of moving distance d _p per pulse (step P5)
Next, the update device, by using the moving distance ΔD the vehicle in ΔT seconds from the time T ₁ to time T _2, the average pulse width t _p of the vehicle speed pulse signal, as described below, the movement per pulse The distance d _p is calculated.

FIG. 4 is a diagram for explaining the average pulse width t _p . Average pulse width t _p is the pulse width measured between the time T ₁ of the time T _2, leave buffers can be calculated by taking the average as the following equation (11).

  Instead, the average pulse width can also be determined by sequential calculation using equation (11). When obtaining the average pulse width by sequential calculation, it is not necessary to buffer the measured pulse width, so the memory usage in the device can be reduced.

FIG. 5 is a flowchart of processing for obtaining an average pulse width by sequential calculation. At time T = _{T 1,} the update unit resets the coefficient k indicating the number of detected pulses to "0" (step S51), and acquires the current time T (step S52). Next, the update unit determines whether the present time T reaches time T ₂ (step S53).

If the current time T is not in time _{T 2} (step S53: NO), the update unit detects the vehicle speed pulse signal, and obtains the pulse width _{t k} (step S54). Next, the updating device increments the coefficient k by 1 (step S55), and determines whether the coefficient k = 1 or not (step S56).

If the coefficient k = 1 (step S56: YES), and assigns the pulse width _{t k} to the average pulse width tp (step S58), the flow returns to step S52. On the other hand, if not the coefficient k = 1 (step S56: NO), the update device, by the equation (11), obtained by dividing the difference between the average pulse width _{t p} and the current pulse width _{t k} at the time by a factor k value _{(t k -t p) / k} , that is, to update the current pulse width _{t k} average pulse _{t p} the variation of adding the average pulse width _{t p} of the time average pulse width _{t p} by, step S52 Return to Then, in step S53, if the current time T reaches time _{T 2} (step S53: YES), the process ends.

(4) Update of distance coefficient α _d (process P6)
Next, the updating device updates the distance coefficient α _d using the moving distance d _p obtained in the process P5. Specifically, the obtained movement distance d _p is set as a new distance coefficient α _d . The updated distance coefficient α _d obtained in this way is used for calculation of the vehicle speed v according to equation (1).

(5) A method of projecting the three-dimensional position of the feature on the horizontal plane of the vehicle In the above description, assuming that the road surface is flat, the vehicle moves in the plane and the feature is in the same plane as the vehicle It is something that holds true by thinking. However, many features in the real environment, such as road signs and traffic lights, exist with height in space. Therefore, by projecting the three-dimensional coordinates of the feature on the horizontal plane of the vehicle, the movement distance of the vehicle can be calculated by the same method as described above. This method is described below.

  Here, as shown to FIG. 6 (B), a vehicle coordinate system (XYZ coordinate system) shall be defined. 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 in the horizontal plane of the vehicle, and the Z axis indicates the height direction of the vehicle.

(I) When the 3D position of the feature can be obtained When the 3D coordinates of the feature can be obtained using an external sensor capable of measuring the 3D position of the feature, or in the map data, the 3D feature of the feature When coordinate data is included, it is assumed that three-dimensional coordinates P of a feature in the vehicle coordinate system can be acquired as shown in FIG. 6 (C).

At this time, assuming that the foot of the perpendicular drawn from point P to the XY plane is point P ′, the length L _xy of line segment OP ′ and the angle φ _xy between line segment OP ′ and the X axis are It can be calculated as

Therefore, in the processes in the processes P1 to P4, the horizontal distance L _xy and the angle φ _xy may be determined using Equation (12) and used. Specifically, in step P1, the horizontal distance _{L 1xy, L'2xy} and angle phi _1Xy, seek phi _2xy. Similarly, in the process P2, horizontal distances L' _1xy , L' _2xy and angles _{? '1xy,?' 2xy} are determined. Then, based on these, the feature distances L and L 'are obtained in a process P3, and the movement distance ΔD is obtained in a process P4.

(Ii) When the distance and angle to the feature can be acquired As shown in FIG. 7 using an external sensor capable of measuring the distance and angle to the feature, the distance L from the vehicle to the feature, the progress of the vehicle It is assumed that the azimuth angle φ _xy of the feature with respect to the direction (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 (XY plane of the vehicle coordinate system) can be acquired.

At this time, when the foot of the perpendicular drawn from the point P to the XY plane is set as a point P ′, the length L _xy 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 the processes P1 to P4, as in the case of (i), the horizontal distance L _xy and the angle φ _xy obtained by the equation (13) may be used.

[First embodiment]
Next, a first embodiment of the above update apparatus 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 calculates the 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 the gyro sensor 10, the vehicle speed sensor 11, the external sensor 12, the traveling direction acquisition unit 13, the vehicle speed pulse measurement unit 14, the feature measurement unit 15, and the distance between features A calculation unit 16, a distance factor calibration unit 17, and a movement distance calculation unit 18 are provided. The traveling direction acquisition unit 13, the vehicle speed pulse measurement unit 14, the feature measurement unit 15, the feature distance calculation unit 16, the distance coefficient calibration unit 17, and the movement distance calculation unit 18 are prepared in advance by a computer such as a CPU. It can be realized by executing the specified program.

The traveling direction acquisition unit 13 acquires the traveling direction Hd of the vehicle based on the output of the gyro sensor 10 and supplies the traveling direction Hd to the feature measurement unit 15 and the distance coefficient calibration unit 17. Vehicle speed pulse measuring unit 14, a vehicle speed pulse outputted from the vehicle speed sensor 11 measures and supplies the distance coefficient calibration unit 17 calculates the like mean pulse width t _p of the vehicle speed pulse signal.

The external sensor 12 is, for example, a camera, a LiDAR, a millimeter wave radar or the like, and the feature measuring unit 15 measures the distance to the feature based on the output of the external sensor 12. Specifically, the feature measurement unit 15 measures the distances L ₁ and L ₂ from the vehicle to the two features at time T ₁ , and the traveling direction Hd of the vehicle supplied from the traveling direction acquisition unit 13 The angles φ ₁ and φ ₂ formed by the directions of the two features are calculated, and are supplied to the feature distance calculating unit 16 and the movement distance calculating unit 18. Further, feature measurement unit 15, at time T _2, the distance L'1 to two features from the _vehicle, as well as measuring the _L'2, the traveling direction of the vehicle supplied from the traveling direction acquisition unit 13 HD ' When the angle the? _'1 between the direction of the two _features, calculates the?' _2, and supplies the feature distance calculation unit 16 and the moving distance calculating unit 18.

The inter-feature distance calculating unit 16 calculates the inter-features according to the above-mentioned equation (3) 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 measuring unit 15. The distance L is calculated and supplied to the movement distance calculation unit 18.

Moving distance calculation unit 18, the distance _{L 1,} L 2 supplied from the feature measurement unit 15, _{L'1, L'2,} and, based on the feature distance L of the feature distance calculation unit 16 has calculated Then, the movement distance ΔD of the vehicle is calculated by the aforementioned equations (6) to (8) and supplied to the distance factor calibration unit 17.

Distance coefficient calibration unit 17, and the average pulse width t _p supplied from the vehicle speed pulse measuring unit 14, based on a moving distance ΔD supplied from the moving distance calculating unit 18, the moving distance d _p per pulse _(i.e. , Distance coefficients α _d ) are calculated. The vehicle speed may be calculated from the movement distance per pulse thus determined.

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

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

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

Next, the update device 1 determines whether flag = 0 (step S14). Note that “flag” is reset to “0” at the start of the process. If flag = 0 (step S14: YES), the updating apparatus 1 sets "1" to flag (step S15), starts calculating the average pulse width t _p (step S16), and returns to step S11. .

On the other hand, when flag = 0 is not satisfied (step S14: NO), the update device 1 calculates the movement distance ΔD as described above (step S17), and calculates the movement distance d _p per pulse using the movement distance ΔD. (Step S18), and the distance coefficient α _d is updated (step S19). Then, the process ends.

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

  In the updating device 1x of the second embodiment, the feature distance acquiring unit 16 obtains the feature distance L between two features using the high-accuracy map data stored in the map DB 19. .

  FIG. 11 is a flowchart of distance coefficient update processing according to the second embodiment. Compared with the distance coefficient update process of the first embodiment shown in FIG. 9, in the distance coefficient update process according to the second embodiment, the distance between features from the map DB at step S26 instead of step S13 of the first embodiment. Although the point of acquiring L is different, the other points are basically the same as the distance coefficient update processing according to the first embodiment. Specifically, steps S21-22, S23-25, and S27-29 in the distance coefficient update process of the second embodiment are steps S11-12, S14-16, and S17 in the distance coefficient update process of the first embodiment, respectively. To S19.

[Feature measurement cycle]
Moving distance d _p per pulse obtained at a distance coefficient update process described above, the average value of the moving distance per one pulse during the time interval ΔT from time T ₁ to time T _2. Therefore, if the variation of the pulse width during the time interval ΔT is large, the accuracy of the calculated movement distance d _p deteriorates. Therefore, it is desirable that the number of pulses during the time interval ΔT be as small as possible.

  The number of pulses per unit time varies depending on the traveling speed of the vehicle. For example, as shown in FIG. 12A, the number of pulses per second is considered. The number of pulses per second is 3 pulses at 10 km / hr, 17 pulses at 50 km / hr and 35 pulses at 100 km / hr, and there is a large difference depending on the traveling speed.

Therefore, if the time interval ΔT is changed according to the traveling speed in view of the measurement cycle of the external sensor and the type of vehicle, it is possible to suppress the deterioration of the accuracy of the movement distance d _p due to the fluctuation of the pulse width. FIG. 12 (B) shows the relationship between the traveling speed and the pulse width. For example, in the case of a vehicle type in which the measurement cycle of the external sensor is 50 ms (20 Hz) and two pulses are output per rotation, ΔT = 300 ms when the traveling speed is less than 20 km / hr and ΔT = 200 ms when the traveling speed is 20 km / hr or more and less than 30 km / hr. Assuming that ΔT = 100 ms for 30 km or more and less than 60 km / h, and ΔT = 50 ms for 60 km or more, the number of pulses that can be measured during time interval ΔT becomes about 1 pulse or 2 pulses, and travel distance with high accuracy d _p can be calculated.

[Modification]
(Modification 1)
In the above distance factor update processing, two features are measured, but when three or more features can be measured simultaneously, the movement distance ΔD is calculated in a plurality of ways, and the value obtained by averaging them is the distance factor Can be used for updating.

For example, when three features can be measured, it is possible to select a combination of features 1 and 2, features 2 and 3, features 3 and 1 as shown in FIG. 13. In each combination, calculating the distance traveled between the time T _2, the time T ₁ by way of the foregoing steps P1 to P3. The movement distance obtained by the combination of the feature 1 and the feature 2 is ΔD ₁₂ , the movement distance obtained by the combination of the feature 2 and the feature 3 is ΔD _23, and the combination of the feature 3 and the feature 1 Assuming that the obtained movement distance is ΔD ₃₁ , a value obtained by averaging these as follows can be used as the movement distance ΔD.

  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 feature distance L is calculated from the measurement results of the two features, and in the second embodiment, the feature distance L is obtained using map data. It may be used in combination. For example, in the area where high precision map data exists, the distance L between features is obtained using high precision map data, and in the area where high precision map data does not exist, the distance between features is obtained from measurement results of features It is also good. Also, according to the condition of the vehicle, it is possible to use the feature distance L obtained by one of high accuracy.

(Modification 3)
As shown in step S11 of FIG. 9 and step S21 of FIG. 11, in the distance coefficient update process of the embodiment, the distance coefficient is basically updated while the vehicle is traveling straight. However, in reality, although the vehicle appears to travel straight, it does not travel straight and there is a slight fluctuation. Therefore, the movement distance ΔD obtained in the process P4 is not an actual movement 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 the process P4 becomes large. From this viewpoint, it is desirable to minimize the time interval ΔT from time T ₁ to time T _2.

(Modification 4)
If the external sensor is mounted at a low position of the vehicle, it is considered that the surrounding vehicles increase the occlusion, and the frequency of detecting a suitable feature for updating the distance coefficient decreases. Therefore, it is preferable to install the external sensor so that it can measure above the height of the surrounding vehicles. As a result, the detection frequency of the feature increases, and the number of updates of the distance coefficient increases, so that the accuracy of the distance coefficient can be improved.

Reference Signs List 10 gyro sensor 11 vehicle speed sensor 12 environment sensor 13 traveling direction acquisition unit 14 vehicle speed pulse measurement unit 15 feature measurement unit 16 inter-feature distance calculation unit 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 feature of the emitted light;
A second acquisition unit that acquires a distance between the two features for which the distance has been acquired by the first acquisition unit;
A calculation unit that calculates the 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;
A distance estimation apparatus comprising:

Images