WO2017109979A1

WO2017109979A1 - Distance estimation device, distance estimation method, and program

Info

Publication number: WO2017109979A1
Application number: PCT/JP2015/086350
Authority: WO
Inventors: 諒子新原; 加藤　正浩; 一嗣金子
Original assignee: パイオニア株式会社
Priority date: 2015-12-25
Filing date: 2015-12-25
Publication date: 2017-06-29

Abstract

This distance estimation device acquires the distance to a physical object from a moving object at a first time and a second time, and acquires the distance to the physical object from the path of the moving object. On the basis of the acquisition results, the device calculates the distance which the moving object moves from a first time until a second time.

Description

Distance estimation device, distance estimation method and program

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

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

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 only the feature on the road on which the vehicle is traveling, such as a sign drawn on the road, can be used. Can not.

The above are examples of problems to be solved by the present invention. An object of this invention is to estimate the moving distance of a moving body using arbitrary features.

Invention of Claim 1 is a distance estimation apparatus, Comprising: The 1st acquisition part which each acquires the distance from the moving body to the feature in each of 1st time and 2nd time, and the said path | route from the said moving body Based on the second acquisition unit that acquires the distance to the feature, and the acquisition results of the first acquisition unit and the second acquisition unit, the moving distance of the moving body from the first time to the second time is calculated. And a calculating unit.

Invention of Claim 7 is the distance estimation method performed by the distance estimation apparatus, Comprising: The 1st acquisition process which each acquires the distance from the moving body in each of 1st time and 2nd time, and the feature, Based on the second acquisition step of acquiring the distance from the path of the moving body to the feature, and the acquisition results of the first acquisition step and the second acquisition step, the first time to the second time. And a calculating step of calculating a moving distance of the moving body.

The invention according to claim 8 is a program executed by a distance estimation device including a computer, and a first acquisition unit that acquires a distance from a moving object to a feature at each of a first time and a second time, The movement from the first time to the second time based on the acquisition results of the second acquisition unit, the first acquisition unit, and the second acquisition unit that acquire the distance from the route of the moving body to the feature The computer is caused to function as a calculation unit that calculates a movement distance of the body.

It is a flowchart which shows the distance coefficient update process which concerns on an Example. An example of the positional relationship between one feature and a moving vehicle is shown. The other example of the positional relationship of one feature and a moving vehicle is shown. It is a figure explaining average pulse width. It is a flowchart of the process which calculates | requires an average pulse width by sequential calculation. A perpendicular line from the feature position to the road is shown. The relationship between the lane center of the road data and the actual travel position is shown. It is a block diagram which shows the structure of the distance coefficient update apparatus which concerns on an Example. It is a flowchart of the distance coefficient update process by an Example. The relationship between travel speed and the number of pulses per unit time, and the relationship between travel speed and pulse width are shown.

In a preferred embodiment of the present invention, the distance estimation apparatus includes a first acquisition unit that acquires the distance from the moving object to the feature at each of the first time and the second time, and the feature from the path of the moving object. A second acquisition unit that acquires the distance to the calculation unit, and a calculation that calculates the 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 A section.

The distance estimation apparatus acquires the distance from the moving object to the feature at each of the first time and the second time, and acquires the distance from the path of the moving object to the feature. And based on those acquisition results, the moving distance of the moving body from the first time to the second time is calculated. Thereby, the movement distance of a moving body is computable using the arbitrary features which can be measured from a moving body.

In one aspect of the distance estimation apparatus, the calculation unit includes a first distance that is a distance in a traveling direction of the moving body between the moving body and the feature at the first time, and the second time. The moving distance is calculated on the basis of a second distance that is a distance in the traveling direction of the moving body between the moving body and the feature.

In another aspect of the distance estimation apparatus, the calculation unit may calculate 1 of the vehicle speed pulse signal based on a moving distance from the first time to the second time and an average pulse width of the vehicle speed pulse signal. Calculate the travel distance per pulse. As a result, the vehicle speed pulse signal can be calibrated based on the calculated moving distance.

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

In another aspect of the distance estimation apparatus, the calculation unit changes a time interval from the first time to the second time according to a 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 moving body increases.

In another preferred embodiment of the present invention, the distance estimation method executed by the distance estimation device includes a first acquisition step of acquiring the distance from the moving object to the feature at each of the first time and the second time, Based on the second acquisition step of acquiring the distance from the path of the moving body to the feature, and the acquisition results of the first acquisition step and the second acquisition step, the first time to the second time. And a calculating step for calculating a moving distance of the moving body. Thereby, the movement distance of a moving body is computable using the arbitrary features which can be measured from a moving body.

In another preferred embodiment of the present invention, a program executed by a distance estimation device including a computer acquires a distance from a moving object to a feature at each of a first time and a second time, The movement from the first time to the second time based on the acquisition results of the second acquisition unit, the first acquisition unit, and the second acquisition unit that acquire the distance from the route of the moving body to the feature The computer is caused to function as a calculation unit that calculates a movement distance of the body. Thereby, the movement distance of a moving body is computable using the arbitrary features which can be measured from a moving body. The above program can be stored in a storage medium and used.

Hereinafter, preferred embodiments of the present invention will be described with reference to the drawings. Below, the Example which uses the moving distance of the moving body obtained by the distance estimation method of this invention for the calibration of the vehicle speed pulse of a vehicle is described.

[background]
In the self-position estimation system installed in current car navigation systems, the vehicle speed is detected using a vehicle speed sensor, and the traveling state is detected using an angular velocity sensor or a steering angle sensor, thereby measuring the movement state of the vehicle. Then, the current position is estimated by integrating these with information measured by the GPS or the 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 a vehicle speed pulse signal at a time interval proportional to the output shaft of the transmission or the rotational speed of the wheels, for example. Then, as shown in the following formula (1), the distance coefficient alpha _d can be calculated vehicle speed v by dividing a pulse width t _p. This distance coefficient α _d 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 moving distance per pulse also changes. Furthermore, the moving distance per pulse varies depending on the traveling speed. Usually, the running resistance causes a difference between the wheel speed obtained from the vehicle speed pulse and the actual vehicle speed. Since the running resistance is higher 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 traveling and low speed traveling. 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 a distance coefficient. For example, using a moving distance ΔD the vehicle speed pulse number n of the vehicle obtained by the GPS position obtained from GPS, by the following equation (2) calculates the moving distance d _p per pulse, constantly subjected to averaging processing There is a method in which correction is performed.

However, depending on the conditions, the GPS information itself, which is a reference, may include a large error. When calibration calculation is performed using GPS information including a large error as a reference, there is a problem that the distance coefficient is deviated from the true value. is there. To obtain GPS information as a reference more accurately, the conditions should be strict, but the more strict the conditions, the less the number of times reference information is acquired, and the conflicting problem that the progress of calibration becomes slow. Comes out.

[Distance coefficient update processing]
From the above viewpoint, the distance coefficient updating apparatus according to the present embodiment (hereinafter also simply referred to as “updating apparatus”) does not use GPS information as a reference, but based on the measurement of a feature by an external sensor, the moving distance of the vehicle Is used as a reference for calibration of the vehicle speed pulse signal. As the external sensor, a camera, LiDAR (Light Detection And Ranging), a millimeter wave radar, or the like can be used.

FIG. 1 is a flowchart illustrating distance coefficient update processing according to the embodiment. First, in step P1, the update device, at time T _1, to measure one feature using external sensors. Next, in step P2, updating device, at time T _2, which has passed ΔT seconds from the time T _1, to measure the same feature as that measured at time T _1.

Next, in the process P3, the update device acquires the length L of the perpendicular line from the feature position to the road on which the vehicle is traveling (hereinafter referred to as “traveling road”) using the map data. Then, in step P4, the update device was acquired at time T ₁ and time T _2, the distance from the vehicle center position to feature at each time, and the length of a perpendicular to the vehicle travel road from the feature position Using this, the travel distance ΔD of the vehicle from time T ₁ 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 The moving distance d _p per pulse is calculated using the moving distance ΔD of the vehicle from time T ₁ to time T ₂ . In step P6, the updating device updates the distance coefficient α _d using the movement distance d _p per pulse obtained in step P5.

Next, each step of the distance coefficient update process will be described in detail.

(1) Feature measurement (process P1-P2)
FIG. 2 shows an example of the positional relationship between one feature 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 update device detects the feature at time T _1, to obtain the distance L ₁ from the vehicle to the feature (step P1).

Next, the update device detects the feature at time T ₂ as well as time T ₁ and acquires the distance L ₂ from the vehicle to the feature (step P2).

(2) obtaining the length of the normal to the traveling road Next, the update device, by using the map data, and acquires the length L _m of a perpendicular to the vehicle travel road from the feature position. This will be described in detail later.

(3) Calculation of movement distance ΔD (process P4)
Next, the update device uses the distance L ₁ acquired at time T ₁ , the distance L ₂ acquired at time T ₂ , and the length L _m of the perpendicular from the feature position to the traveling road to calculates a moving distance ΔD the vehicle from ₁ to time T _2. Specifically, in FIG. 2, the distance between the vehicle position and the feature position at the time T ₁ in the traveling direction of the vehicle (that is, the distance along the traveling road) is D _1, and the vehicle position and the feature at the time T ₂ When the distance in the traveling direction of the vehicle and the position and D _2, the moving distance ΔD the vehicle is obtained as follows.

FIG. 3 shows a calculation method of the moving distance ΔD of the vehicle when the three-dimensional position of the feature can be acquired using an external sensor capable of three-dimensional measurement. In this case as well, the moving distance ΔD of the vehicle can be calculated by the equations (3) to (5).

(4) Calculation of moving distance d _p per pulse (process 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.

Figure 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 (7).

Instead, the average pulse width can also be obtained by sequential calculation using Equation (8). When the average pulse width is obtained by sequential calculation, it is not necessary to buffer the measured pulse width, so that the amount of memory used in the apparatus can be reduced.

FIG. 5 is a flowchart of processing for obtaining the 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 update device increments the coefficient k by 1 (step S55), and determines whether or not the coefficient k = 1 (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 (7), 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.

(5) Updating the distance coefficient α _d (process P6)
Next, the update device updates the distance coefficient α _d using the movement distance d _p obtained in step P5. Specifically, the obtained moving 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 by the equation (1).

(6) from the calculation method feature position of length L _m of a perpendicular line from the feature position to the traveling road to determine the length L _m of a perpendicular to the traveling road, travels which road (A) vehicle Or (B) where the vehicle is traveling in the lane (particularly the position in the width direction of the lane). Among these, (A) can be acquired by an existing self-position estimation technique as implemented in a car navigation device. On the other hand, the accuracy of (B) affects the accuracy of the calculated movement distance ΔD. Even if the (B) is not known exactly, but the vehicle can calculate the length L _m of a perpendicular line by assuming that the vehicle is running the central lane, error could be large travel distance ΔD is is there. In this regard, it described later method for correcting the length L _m.

FIG. 6 shows the positional relationship between the feature position and road data indicating the road on which the vehicle is traveling. Now, let the feature position on the map be a point P (x ₁ , y ₁ ). It is assumed that road data is defined as a straight line Rd: ax + by + c = 0 on the map. The straight line Rd can be calculated from the position coordinates of the nodes before and after the road data.

In FIG. 6, the intersection of the perpendicular line drawn from the point P indicating the feature position to the straight line Rd and the straight line Rd is defined as a point H (x ₂ , y ₂ ). At this time, the length of the perpendicular PH can be calculated as follows.

First, since the straight line Rd and the perpendicular line PH are orthogonal,

It becomes. here,

After all,

It becomes.

Since point H is a point on the straight line Rd,

Is obtained. Substituting equations (11) and (12) into equation (13),

Is obtained. Solving for k,

Is obtained. The length L _m of the perpendicular line PH is from the three square theorem,

It becomes. Substituting equation (15) into equation (16),

Is obtained. Therefore, the length L _m of the perpendicular PH is

It becomes. Thus, it is possible to use feature position included in the map with the _(x 1, _{y 1)} Road information (a, b, c) and calculates the length _{L m} of a perpendicular line.

Further, as described with reference to FIG. 3, when the three-dimensional position of the feature can be acquired, if the feature position is given by P (x ₁ , y ₁ , z ₁ ), the length of the perpendicular PH L _m is as follows.

Next, with reference to FIG. 7, a description will be given of a correction of the length L _m of a perpendicular line. FIG. 7 shows the positional relationship between the actual traveling position of the vehicle and the road data on the map. It is assumed that the road data in the map includes position information of the center line of the lane. The vehicle does not always travel in the center of the lane, and in many cases, the vehicle actually travels at a position deviated from the center of the lane. If the travel distance of the vehicle is calculated using the road data in the map and the measurement results from the external sensors, the difference between the road data in the map and the actual vehicle travel position will affect the error, and the accuracy will deteriorate. Conceivable. Therefore, L _m ′ (L _m ′ = L _m −) obtained by subtracting the deviation amount ΔL _m between the lane center and the actual travel position as shown in FIG. 7 from L _m calculated as described above using the map data. By using ΔL _m ) instead of the length L _m in the calculation of the movement distance ΔD in the process P4, it is considered that deterioration in accuracy can be suppressed. Incidentally, the deviation amount [Delta] L _m, for example using a camera or LiDAR detects white lines on both sides of the vehicle, the vehicle can be determined by recognizing whether running anywhere in the lane by using the detection result .

[Example]
Next, an embodiment of the above update device will be described. FIG. 8 is a block diagram illustrating the configuration of the update device 1 according to the embodiment. In this embodiment, the updating device 1 obtains the movement distance ΔD based on the measurement result of one feature by the external sensor and the length L _{m of the} perpendicular drawn from the feature position to the traveling road.

As shown in the figure, the updating device 1 includes a gyro sensor 10, a vehicle speed sensor 11, a GPS receiver 12, an external sensor 13, a map database (hereinafter referred to as "map DB") 14, and an average pulse width acquisition. Unit 15, distance coefficient calibration unit 16, own vehicle position acquisition unit 17, road information acquisition unit 18, feature-road distance calculation unit 19, movement distance calculation unit 20, feature information acquisition unit 21, And a feature measurement unit 22. The average pulse width acquisition unit 15, the distance coefficient calibration unit 16, the own vehicle position acquisition unit 17, the road information acquisition unit 18, the feature-road distance calculation unit 19, the movement distance calculation unit 20, the feature information acquisition unit 21, And the feature measurement part 22 is realizable when computers, such as CPU, run the program prepared beforehand.

Average pulse width obtaining unit 15 obtains the average pulse width t _p on the basis of the vehicle speed pulse signal a vehicle speed sensor 11 outputs, and outputs to the distance factor correcting unit 16.

The vehicle position acquisition unit 17 acquires the vehicle position of the vehicle based on the outputs of the gyro sensor 10, the vehicle speed sensor 11, and the GPS receiver 12, and outputs the vehicle position to the road information acquisition unit 18 and the feature information acquisition unit 21. The road information acquisition unit 18 refers to the map DB 14 based on the own vehicle position input from the own vehicle position acquisition unit 17 and acquires road information of the traveling road. Specifically, the road information acquisition unit 18 acquires the position of the end point of the link corresponding to the traveling road from the map DB 14, obtains a straight line Rd: ax + by + c = 0 indicating the road, and calculates a feature-road distance calculation unit. 19 is supplied.

The external sensor 13 detects the feature and sends the detection result to the feature measuring unit 22. The feature measurement unit 22 measures the distance L from the vehicle to the feature and outputs the distance L to the movement distance calculation unit 20 and the feature information acquisition unit 21. The feature information acquisition unit 21 refers to the map DB 14 and based on the own vehicle position input from the own vehicle position acquisition unit 17 and the distance L to the feature input from the feature measurement unit 22. The object position (x ₁ , y ₁ ) is acquired and output to the feature-road distance calculation unit 19.

Feature - the road distance calculating unit 19, based on the straight line Rd indicating the road that is input from the road information acquisition unit 18, the feature position input from the feature information acquiring unit 21 and the (x _{1, y} ₁₎ Then, the length L _m of the perpendicular line drawn from the feature position to the traveling road is calculated and output to the movement distance calculation unit 20.

The movement distance calculation unit 20 includes a distance L (L ₁ , L ₂ ) to the feature input from the feature measurement unit 22 and a perpendicular length L _m input from the feature-road distance calculation unit 19. Based on the above, the movement distance ΔD is calculated by the equations (3) to (5) and output to the distance coefficient calibration unit 16.

Distance coefficient calibration unit 16, and the average pulse width t _p input from the average pulse width obtaining unit 15, based on the moving distance ΔD input from the travel distance calculating section 20, the moving distance per pulse d _p ( That is, the distance coefficient α _d ) is calculated. The vehicle body speed may be calculated from the obtained movement distance per pulse.

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

First, the updating device 1 measures a feature using the external sensor 13 and acquires a distance L from the vehicle to the feature (step S11). Next, the update device 1 determines whether or not the vehicle is traveling straight on the basis of the vehicle position output by the vehicle position acquisition unit 17 (step S11). This is because the accuracy of the movement distance ΔD output by the movement distance calculation unit 20 decreases when the vehicle is not traveling straight ahead. 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: a predetermined threshold). . When the steering angle δ of the vehicle can be detected, it may be determined that the vehicle is traveling straight when | δ | <Δδ (Δδ: a predetermined threshold).

If the vehicle is not traveling straight (step S12: NO), the process ends. On the other hand, when the vehicle is traveling straight (step S12: YES), the update device 1 determines whether flag = 0 (step S13). Note that “flag” is reset to “0” at the start of processing. If a flag = 0 (step S13: YES), updating apparatus 1 is set to "1" to the flag (step S14), and starts the calculation of the average pulse width _{t p} (step S15), and returns to step S11 .

On the other hand, when flag = 0 is not satisfied (step S13: NO), the updating device 1 acquires the road information that is being traveled by the road information acquisition unit 18 (step S16), and acquires the feature information by the feature information acquisition unit 21. (Step S17). Next, the updating device 1 calculates the length Lm of the perpendicular from the feature to the traveling road by the feature-road distance calculation unit 19 (step S18), and calculates the movement distance ΔD by the movement distance calculation unit 20 (Step S19). The updating apparatus 1 uses the moving distance ΔD calculates the moving distance _{d p} per pulse (step S20), and updates the distance coefficient alpha _d (step S21). Then, the process ends.

[Feature measurement cycle]
The movement distance d _p per pulse obtained in the above-described distance coefficient update process is an average value of the movement distance per pulse during the time interval ΔT from time T ₁ to time T ₂ . Therefore, the large variation of the pulse width of the time interval [Delta] T, the accuracy of the moving distance d _p which is calculated is deteriorated. Therefore, it is desirable that the number of pulses during the time interval ΔT is as small as possible.

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 two 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, in view of the measurement cycle of the external sensor and the vehicle type, if the time interval ΔT is changed according to the traveling speed, it is possible to suppress the deterioration of the accuracy of the moving distance d _p due to the fluctuation of the pulse width. FIG. 9B 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 revolution, 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. When 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, the number of pulses that can be measured during the time interval ΔT is about 1 pulse or 2 pulses, and the moving distance is high. d _p can be calculated.

[Modification]
(Modification 1)
In the above step P3, based on the equation and the position of the feature of the straight traveling road, but to calculate the length L _m of a perpendicular to the traveling road from the feature, the shortest distance to travel the road from the feature If is included in the map data, the value may be used.

(Modification 2)
As shown in step S11 of FIG. 9, in the distance coefficient updating process of the embodiment, the distance coefficient is basically updated when the vehicle is traveling straight ahead. However, in reality, even if the vehicle appears to be traveling straight ahead, it is not strictly going 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. For this reason, if the time interval ΔT is too large, the difference between the actual moving distance and the moving distance calculated in the process P4 becomes large. From this point of view, it is desirable to make the time interval ΔT from time T ₁ to time T ₂ as small as possible.

(Modification 3)
If the external sensor is attached to a low position of the vehicle, it is considered that the occlusion increases by surrounding vehicles, and the frequency with which a suitable feature for updating the distance coefficient can be detected decreases. Therefore, it is preferable to install the external sensor so that the upper side can be measured above the height of the surrounding vehicle. Thereby, since the detection frequency of the feature increases and the number of updates of the distance coefficient increases, the accuracy of the distance coefficient can be improved.

The present invention can be used for an apparatus mounted on a moving body.

DESCRIPTION OF SYMBOLS 10 Gyro sensor 11 Vehicle speed sensor 12 External sensor 13 Travel direction acquisition part 14 Vehicle speed pulse measurement part 15 Feature measurement part 17 Distance coefficient structure part 18 Travel distance calculation part 19 Map database

Claims

A first acquisition unit for acquiring the distance from the moving object to the feature at each of the first time and the second time;
A second acquisition unit that acquires a distance from the path of the moving object to the feature;
Based on the acquisition results of the first acquisition unit and the second acquisition unit, a calculation unit that calculates the moving distance of the moving body from the first time to the second time;
A distance estimation apparatus comprising:
The calculation unit includes a first distance that is a distance in a traveling direction of the moving body between the moving body and the feature at the first time, and the movement between the moving body and the feature at the second time. The distance estimation apparatus according to claim 1, wherein the moving distance is calculated based on a second distance that is a distance in a traveling direction of the body.
The calculation unit calculates a movement distance per pulse of the vehicle speed pulse signal based on a movement distance from the first time to the second time and an average pulse width of the vehicle speed pulse signal. The distance estimation apparatus according to claim 1 or 2.
The distance according to any one of claims 1 to 3, wherein the calculation unit calculates the movement distance when an angular velocity or a steering angle in a yaw direction of the moving body is less than a predetermined threshold. Estimating device.
The distance according to claim 1, wherein the calculation unit changes a time interval from the first time to the second time according to a traveling speed of the moving body. Estimating device.
6. The distance estimating apparatus according to claim 5, wherein the calculating unit shortens the time interval as the traveling speed of the moving body increases.
A distance estimation method executed by a distance estimation device,
A first acquisition step of acquiring the distance from the moving object to the feature at each of the first time and the second time;
A second acquisition step of acquiring a distance from the path of the moving body to the feature;
A calculation step of calculating a moving distance of the moving body from the first time to the second time based on the acquisition results of the first acquisition step and the second acquisition step;
A distance estimation method comprising:
A program executed by a distance estimation device including a computer,
A first acquisition unit for acquiring the distance from the moving object to the feature at each of the first time and the second time;
A second acquisition unit for acquiring a distance from the path of the moving body to the feature;
A calculation unit for calculating 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;
A program for causing the computer to function as:
A storage medium storing the program according to claim 8.