WO2018212292A1

WO2018212292A1 - Information processing device, control method, program and storage medium

Info

Publication number: WO2018212292A1
Application number: PCT/JP2018/019153
Authority: WO
Inventors: 岩井　智昭; 多史藤谷; 加藤　正浩
Original assignee: パイオニア株式会社
Priority date: 2017-05-19
Filing date: 2018-05-17
Publication date: 2018-11-22
Also published as: JP6806891B2; JPWO2018212292A1

Abstract

A landmark characteristic detection/location measurement unit 17 of an in-vehicle device 1 calculates a landmark measurement location on the basis of the output signal from an external sensor 12 such as a lidar 2 for measuring a landmark Ltag, which is a peripheral physical object. A landmark location estimation unit 16 calculates an estimated landmark location on the basis of the landmark information stored in a feature-enhanced map DB 10. In addition, a reliability determination unit 18 determines the reliability R, on the basis of the difference change amount dD(t) between the difference D(t-1) between the landmark measurement location and the landmark estimation location at a previous time t-1 and the difference D(t) between the landmark measurement location and the landmark estimation location at the current time t. The reliability determination unit 18 also outputs information about the determined reliability R to a vehicle location estimation unit 19.

Description

Information processing apparatus, control method, program, and storage medium

The present invention relates to a technique for measuring a position.

2. Description of the Related Art Conventionally, a technique for detecting features installed around a vehicle using a radar or a camera and calibrating the position of the vehicle based on the detection result is known. For example, Patent Document 1 discloses a technique for estimating a self-position by collating the output of a measurement sensor with the position information of a feature registered in advance on a map. Patent Document 2 discloses a vehicle position estimation technique using a Kalman filter.

JP 2013-257742 A JP 2017-72422 A

When measuring the position of a feature (landmark) that serves as a reference for position estimation from measurement data obtained by an external sensor such as a lidar, it is a landmark because it is occluded by other vehicles traveling around the vehicle or trees on the road edge. In some cases, only a part of the area can be measured, and an accurate landmark position cannot be measured. In this case, if position estimation is performed using an erroneous landmark measurement position as it is, an incorrect position estimation result is obtained.

The present invention has been made to solve the above-described problems, and provides an information processing apparatus capable of outputting information related to the measurement position of a landmark in consideration of the possibility of occlusion. The main purpose.

The invention according to claim 1 is an information processing apparatus, which is based on map information and a first acquisition unit that acquires a measurement position of the feature based on an output signal of a measurement unit that measures surrounding features. Based on the second acquisition unit that acquires the predicted position of the feature, the difference between the predicted position and the measured position at the first time, and the difference between the predicted position and the measured position at the second time And an output unit for outputting information related to the reliability of the measurement position.

The invention according to claim 5 is a control method executed by the information processing apparatus, and a first acquisition step of acquiring a measurement position of the feature based on an output signal of a measurement unit that measures the surrounding feature; The second acquisition step of acquiring the predicted position of the feature based on the map information, the difference between the predicted position and the measured position at the first time, and the predicted position and the measured position at the second time And an output step of outputting information on the reliability of the measurement position based on the difference.

The invention according to claim 6 is a program executed by a computer, and a first acquisition unit that acquires a measurement position of the feature based on an output signal of a measurement unit that measures surrounding features, and map information Based on the second acquisition unit for acquiring the predicted position of the feature, the difference between the predicted position at the first time and the measured position, and the difference between the predicted position at the second time and the measured position. Based on this, the computer is caused to function as an output unit that outputs information related to the reliability of the measurement position.

It is a schematic block diagram of a driving assistance system. It is the figure which showed the position of the vehicle on the two-dimensional coordinate. The functional block block diagram of a vehicle equipment is shown. Indicates the landmark measurement position according to the presence or absence of occlusion. It is the figure which showed roughly the landmark prediction position and landmark measurement position in a time series when no occlusion has occurred. It is the figure which showed roughly the landmark prediction position and landmark measurement position in a time series when occlusion generate | occur | produces. It is a flowchart of the own vehicle position estimation process.

According to a preferred embodiment of the present invention, the information processing apparatus is based on map information and a first acquisition unit that acquires a measurement position of the feature based on an output signal of a measurement unit that measures surrounding features. Based on the second acquisition unit that acquires the predicted position of the feature, the difference between the predicted position and the measured position at the first time, and the difference between the predicted position and the measured position at the second time And an output unit for outputting information related to the reliability of the measurement position. According to this aspect, the information processing apparatus can appropriately output information on reliability with respect to the measurement position of the feature in consideration of the possibility of occurrence of occlusion.

In one aspect of the information processing apparatus, the output unit lowers the reliability as the change amount of the difference at the second time with respect to the difference at the first time is larger. In general, when occlusion occurs in a feature to be measured, the degree of occlusion changes with time, so the difference between the predicted position of the feature and the measured position of the feature changes. Therefore, according to this aspect, the information processing apparatus can accurately determine the reliability of the measurement position of the feature.

In another aspect of the information processing apparatus, the output unit is configured to perform the first based on the difference at the first time in the first direction and the difference at the second time in the first direction. Determining the reliability in the direction and based on the difference at the first time in the second direction intersecting the first direction and the difference at the second time in the second direction in the second direction. The reliability is determined. According to this aspect, the information processing apparatus can accurately determine and output the reliability regarding the measurement position of the feature in each of the first direction and the second direction.

In another aspect of the information processing apparatus, the information processing apparatus includes a prediction unit that predicts a self position, and a correction unit that corrects the self position based on a difference between the predicted position and the measurement position at a current time. The correction unit reduces the gain for correcting the self-position based on the difference as the reliability is lower. According to this aspect, the information processing apparatus suitably corrects the self-position according to the reliability of the measurement position of the feature serving as a reference for position estimation, and suitably suppresses a decrease in position estimation accuracy when the occlusion occurs. Can do.

According to another preferred embodiment of the present invention, there is provided a control method executed by the information processing apparatus, wherein the measurement position of the feature is acquired based on the output signal of the measurement unit that measures the surrounding feature. 1 acquisition step, a second acquisition step of acquiring a predicted position of the feature based on map information, a difference between the predicted position and the measured position at a first time, the predicted position at a second time, and the And an output step of outputting information on the reliability of the measurement position based on the difference from the measurement position. By executing this control method, the information processing apparatus can suitably output reliability information for the measurement position of the feature.

According to another preferred embodiment of the present invention, a first acquisition unit that is a program executed by a computer and acquires a measurement position of the feature based on an output signal of a measurement unit that measures a surrounding feature. A second acquisition unit that acquires a predicted position of the feature based on map information, a difference between the predicted position and the measured position at a first time, and the predicted position and the measured position at a second time The computer is caused to function as an output unit that outputs information related to the reliability of the measurement position based on the difference between the two. By executing this program, the computer can suitably output reliability information for the measurement position of the feature. Preferably, the program is stored in a storage medium.

Hereinafter, preferred embodiments of the present invention will be described with reference to the drawings. For convenience, a character with “^” or “-” attached on an arbitrary symbol is represented as “A ^{^} ” or “A ⁻ ” (“A” is an arbitrary character) in this specification.

[Schematic configuration]
FIG. 1 is a schematic configuration diagram of a driving support system according to the present embodiment. The driving support system shown in FIG. 1 is mounted on a vehicle and controls an on-vehicle device 1 that performs control related to driving support of the vehicle, and an external sensor such as a lidar (Lider: Light Detection and Ranging or Laser Illuminated Detection And Ranging) 2. And an internal sensor such as a gyro sensor 3, a vehicle speed sensor 4, and a satellite positioning sensor 5.

The in-vehicle device 1 is electrically connected to an external sensor such as a lidar 2 and an internal sensor such as a gyro sensor 3, a vehicle speed sensor 4, and a satellite positioning sensor 5, and the in-vehicle device 1 is mounted based on these outputs. The position of the vehicle (also referred to as “own vehicle position”) is estimated. And the vehicle equipment 1 performs automatic driving | operation control etc. of a vehicle based on the estimation result of the own vehicle position. In the present embodiment, the in-vehicle device 1 calculates the measurement position of the landmark Ltag based on the measurement data obtained by an external sensor such as the lidar 2 with respect to the feature (which is also referred to as “landmark Ltag”) used as a reference in position estimation. . In this case, the in-vehicle device 1 generates information on the reliability of the measurement position of the landmark Ltag (also referred to as “reliability R”). The landmark Ltag is, for example, a feature such as a kilometer post, a 100 m post, a delineator, a traffic infrastructure facility (for example, a sign, a direction signboard, a signal), a power pole, a streetlight, and the like periodically arranged along the road.

The lidar 2 emits a pulse laser in a predetermined angle range in the horizontal direction and the vertical direction, thereby discretely measuring the distance to an object existing in the outside world, and a three-dimensional point indicating the position of the object Generate group information. In this case, the lidar 2 includes an irradiation unit that emits laser light while changing the irradiation direction, a light receiving unit that receives reflected light (scattered light) of the irradiated laser light, and scan data based on a light reception signal output by the light receiving unit. Output unit. The scan data is generated based on the irradiation direction corresponding to the laser beam received by the light receiving unit and the response delay time of the laser beam specified based on the above-described received light signal. The lidar 2 is an example of the “measurement unit” in the present invention.

The server device 7 stores an advanced map DB (Data Base) 10 for distribution to the vehicle-mounted device 1 or the like. In the advanced map DB 10, landmark information that is information related to the feature serving as the landmark Ltag is stored in addition to the road data. The landmark information is used for the vehicle position estimation process and the reliability R determination process performed by the in-vehicle device 1.

The configuration of the driving support system shown in FIG. 1 is an example, and the configuration of the driving support system to which the present invention can be applied is not limited to the configuration shown in FIG. For example, instead of having the in-vehicle device 1 in the driving support system, the electronic control device of the vehicle may execute the processing of the in-vehicle device 1. In another example, the in-vehicle device 1 may store information corresponding to the advanced map DB 10 by itself instead of acquiring landmark information from the server device 7.

[Outline of the vehicle position estimation process]
First, the outline | summary of the own vehicle position estimation process in a present Example is demonstrated. The vehicle position estimation reflecting the reliability R will be described later.

FIG. 2 is a diagram showing the position of the vehicle-mounted device 1 mounted on the vehicle on a two-dimensional coordinate. As shown in the figure, there is a vehicle equipped with the vehicle-mounted device 1 in the map coordinate system (X _m , Y _m ), and the vehicle coordinate system (X _v , Y _v ) is defined based on the position of the vehicle. Specifically, the traveling direction of the vehicle is defined as “X _v axis” in the vehicle coordinate system, and the direction perpendicular thereto is defined as “Y _v axis” in the vehicle coordinate system.

In FIG. 2, there is a flat feature that becomes the landmark Ltag on the left side of the vehicle. The position of the landmark Ltag in the map coordinate system (also referred to as “landmark map position”) is included as part of the landmark information registered in the advanced map DB 10. In FIG. 2, the landmark map position is (mx _m , my _m ), and the provisional vehicle position (“predicted vehicle position”) in the map coordinate system predicted based on the output of the internal sensor such as the gyro sensor 3 is used. Is also given by (x ⁻ _m , y ⁻ _m ), and the provisional vehicle azimuth (also called “predicted vehicle azimuth”) in the map coordinate system is given by “Ψ ⁻ _m ”. .

Here, the in-vehicle device 1 uses the coordinates (L ⁻ x _v , L ⁻ y _v ) of the vehicle coordinate system of the provisional predicted position of the landmark (also referred to as “landmark predicted position”) as the landmark map position. Using (mx _m , my _m ), the predicted host vehicle position (x ⁻ _m , y ⁻ _m ), and the predicted host vehicle azimuth angle Ψ ⁻ _m , the following calculation (1) is performed.

Further, the in-vehicle device 1 determines a range where the landmark is predicted to exist (also referred to as “landmark predicted range WL”) based on the landmark predicted position. The landmark prediction range WL is set to a range within a predetermined distance from the landmark prediction position, for example. In addition, when the error information of the predicted vehicle position can be acquired, the in-vehicle device 1 may change the size of the landmark prediction range WL according to the size of the error indicated by the error information. In this case, the in-vehicle device 1 can prevent a detection failure of the landmark Ltag by increasing the landmark prediction range WL as the error in the predicted vehicle position increases, and the landmark as the error in the predicted vehicle position decreases. By reducing the prediction range WL, it is possible to reduce the influence of noise and reduce the processing amount. Similarly, the in-vehicle device 1 may change the size of the landmark prediction range WL according to the measurement accuracy of the external sensor (for example, the angular resolution of the lidar 2).

Next, the vehicle-mounted device 1 extracts the scan data measured as the feature points within the set landmark prediction range WL as data obtained by measuring the landmarks (also referred to as “landmark measurement data WLD”). For example, when the vehicle-mounted device 1 uses the lidar 2 as a landmark Ltag to measure a sign or signboard having a higher retroreflectivity than other features as a landmark Ltag, the reflection intensity is more than a predetermined degree from the point cloud data of the measured measurement points. Data of measurement points to be extracted as landmark measurement data WLD. The vehicle unit 1 calculates the center of gravity (average) in the vehicle coordinate system of a coordinate indicated by each measurement point landmarks measurement data WLD, as landmarks measurement position (Lx v, _Ly _v). In the example of FIG. 2, there is a difference between the predicted landmark position and the landmark measurement position due to an error in the predicted vehicle position.

The in-vehicle device 1 calculates a vehicle position (also referred to as “estimated vehicle position”) obtained by correcting the predicted vehicle position based on the difference between the landmark predicted position and the landmark measurement position. For example, when the in-vehicle device 1 performs vehicle position estimation using the extended Kalman filter, if the reference time (that is, the current time) is “t” and the Kalman gain is “K (t)”, the following equation (2) ) To calculate the state variable vector “X ^{^} (t) = (x ^{^} _m (t), y ^{^} _m (t), Ψ ^{^} _m (t)) ^T " indicating the estimated host vehicle position.

^{^{_{Here, X - (t) = (}}} x - m (t), y - m (t), Ψ - m (t)) T, Z (t) = (Lx v, Ly v) T, Z - ( t) = (L ⁻ x _v , L ⁻ y _v ) ^T. Then, the in-vehicle device 1 calculates a predicted own vehicle position, and a measurement update step that calculates the estimated own vehicle position by correcting the predicted own vehicle position calculated in the immediately preceding prediction step based on the equation (2). Execute alternately. The state estimation filter used in these steps can use various filters developed to perform Bayesian estimation, and is not limited to the extended Kalman filter, and may be, for example, an unscented Kalman filter, a particle filter, or the like. .

Here, when occlusion occurs due to vehicles traveling around the vehicle or trees on the road edge, and only a part of the landmark Ltag can be measured, the landmark measurement position cannot be measured accurately. And if position estimation based on Formula (2) etc. is performed using an incorrect landmark measurement position, the accuracy of the estimated own vehicle position will be reduced. Considering the above, the in-vehicle device 1 determines the reliability R according to the degree of occlusion with respect to the landmark Ltag, and reflects the determined reliability R in the vehicle position estimation process, thereby reducing the position estimation accuracy. Suppress. The method for determining the reliability R will be described in the section [Determining the reliability].

[Block configuration]
FIG. 3 shows a functional block configuration diagram of the in-vehicle device 1. The in-vehicle device 1 mainly includes a host vehicle position prediction unit 13, a communication unit 14, a landmark information acquisition unit 15, a landmark position prediction unit 16, a landmark feature detection / position measurement unit 17, and a reliability. It has the determination part 18 and the own vehicle position estimation part 19. The vehicle position prediction unit 13, the landmark information acquisition unit 15, the landmark position prediction unit 16, the landmark feature detection / position measurement unit 17, the reliability determination unit 18, and the vehicle position estimation unit 19 are actually Is realized by a computer such as a CPU executing a program prepared in advance.

The own vehicle position prediction unit 13 is based on the output of the internal sensor 11 including the gyro sensor 3, the vehicle speed sensor 4, and the satellite positioning sensor 5, and uses the GNSS (Global Navigation Satellite System) / IMU (Inertia Measurement Unit) combined navigation. And the predicted vehicle position is supplied to the landmark position prediction unit 16. The own vehicle position prediction unit 13 is an example of the “prediction unit” in the present invention.

The communication unit 14 is a communication unit for wirelessly communicating with the server device 7. The landmark information acquisition unit 15 receives landmark information related to the landmark Ltag existing around the vehicle from the server device 7 via the communication unit 14. Here, as shown in FIG. 3, the landmark information includes at least position information indicating the landmark map position for each feature that becomes the landmark Ltag.

Based on the landmark map position indicated by the position information of the landmark information and the predicted vehicle position acquired from the vehicle position prediction unit 13, the landmark position prediction unit 16 calculates the landmark position predicted by the above-described equation (1). Is supplied to the reliability determination unit 18. Further, the landmark position prediction unit 16 determines a landmark prediction range WL based on the landmark prediction position and supplies the landmark prediction range WL to the landmark feature detection / position measurement unit 17. The landmark position prediction unit 16 is an example of the “second acquisition unit” in the present invention.

The landmark feature detection / position measurement unit 17 acquires measurement data from the external sensor 12. Here, the external sensor 12 is a sensor that detects an object around the vehicle, and includes the lidar 2 and the stereo camera 6. Then, the landmark feature detection / position measurement unit 17 performs landmark measurement from the measurement data based on the landmark prediction range WL supplied from the landmark position prediction unit 16 and the measurement data acquired from the external sensor 12. Data WLD is extracted. The landmark feature detection / position measurement unit 17 calculates the landmark measurement position based on the landmark measurement data WLD. Then, the landmark feature detection / position measurement unit 17 supplies the calculated landmark measurement position information to the reliability determination unit 18. The landmark feature detection / position measurement unit 17 is an example of the “first acquisition unit” in the present invention.

The reliability determination unit 18 determines the difference between the landmark prediction position supplied from the landmark position prediction unit 16 and the landmark measurement position supplied from the landmark feature detection / position measurement unit 17 at the current time t (“ The difference D (t) "is also calculated for each of the _Xv axis and _Yv axis of the vehicle coordinate system. Incidentally, the reliability determination unit 18, X _v-axis of the vehicle coordinate system, in addition to Y _v axes, may calculate the difference D (t) also Z _v coordinates in the height direction. Then, the reliability determination unit 18 changes the above-described difference D (t) with respect to the difference D (t−1) calculated at the previous time t−1 (also referred to as “difference change amount dD (t)”). Calculate for each coordinate axis of the vehicle coordinate system. Then, the reliability determination unit 18 determines the reliability R based on the difference change amount dD (t). The determination of the reliability R based on the difference change amount dD (t) will be described later. The reliability determination unit 18 is an example of the “output unit” in the present invention.

The own vehicle position estimation unit 19 calculates an estimated own vehicle position based on the predicted landmark position, the landmark measurement position, the reliability R, and the like. Note that a method of calculating the estimated vehicle position using the reliability R will be described later. The own vehicle position estimation unit 19 is an example of the “correction unit” in the present invention.

[Judgment of reliability]
Schematically, the in-vehicle device 1 considers that the accuracy of the landmark measurement position is lower as the difference change amount dD (t) is larger, and sets the reliability R according to the difference change amount dD (t).

First, changes in landmark measurement positions based on the presence or absence of occlusion will be described with reference to FIG. FIG. 4A shows the landmark measurement position (Lx _v , Ly _v ) when no occlusion has occurred in the landmark Ltag, and FIG. 4B shows that the occlusion of the landmark Ltag is caused by another vehicle. The landmark measurement position (Lx _v , Ly _v ) is shown.

In the example of FIG. 4A, since no occlusion has occurred, the entire front face of the landmark Ltag is to be measured, and the landmark measurement position (Lx _v , Ly _v ) is the landmark map position indicated by the landmark information. Consistent with. Therefore, the in-vehicle device 1 can suitably improve the position estimation accuracy by performing the vehicle position estimation using the landmark measurement position calculated in the case of FIG.

On the other hand, in the example of FIG. 4B, occlusion by another vehicle has occurred, and only a part of the landmark Ltag is a measurement target. As a result, the landmark measurement position (Lx _v , Ly _v ) is biased to the right of the landmark Ltag where no occlusion has occurred, and is a position that matches the landmark map position (ie, the land in FIG. 4A). It will deviate from the mark measurement position. Therefore, when the vehicle-mounted device 1 performs its own vehicle position estimation using the landmark measurement position calculated in the case of FIG. 4B, the position estimation accuracy decreases.

Thus, even when the same landmark Ltag is a measurement target, the landmark measurement position changes depending on the presence or absence of occlusion. On the other hand, when occlusion has occurred, the relative positional relationship between the vehicle of the vehicle-mounted device 1, the landmark Ltag, and the obstacle that generates occlusion changes with time. Therefore, in this case, the degree of occlusion, the presence / absence of occlusion, etc. change with the passage of time, and the landmark measurement position changes with the passage of time.

Next, the relationship between the change in the landmark measurement position over time and the difference change amount dD (t) will be described with reference to FIGS.

FIG. 5 is a diagram schematically showing a predicted landmark position and a landmark measurement position when no occlusion occurs at both time t-1 and time t. FIG. 6 shows an occlusion at time t. It is the figure which showed schematically the landmark prediction position and landmark measurement position when this occurred. In subsequent, the difference D (t) of at _{X v} axis "Dx (t)", _{Y v} in axis difference D (t) the "Dy (t)", _{X v} difference variation in shaft dD (t ) to "DDX (t)", the difference amount of change in _{Y v} axis dD (t) is referred to as "ddY (t)". These values are calculated by the following equation (3) based on the respective definitions.

In the example of FIG. 5, since no occlusion occurs at both time t-1 and time t, the in-vehicle device 1 shows a point cloud of similar measurement points at both time t-1 and time t. A landmark measurement position is calculated based on the landmark measurement data WLD. As a result, the landmark measurement position (Lx _v (t−1), Ly _v (t−1)) and the predicted landmark position (L ⁻ x _v (t−1), L ⁻ y _{v at time} t−1 are obtained. (T-1)) difference (Dx (t-1), Dy (t-1)), landmark measurement position (Lx _v (t), Ly _v (t)) and landmark prediction at time t The difference (Dx (t), Dy (t)) between the positions (L ⁻ x _v (t), L ⁻ y _v (t)) is almost equal. From the above, in the example of FIG. 5, the difference change amounts dDx (t) and dDy (t) are values close to 0, respectively.

On the other hand, in the example of FIG. 6, occlusion occurs when the landmark Ltag is measured at time t, and the landmark measurement position (Lx _v (t), Ly _v (t) at time t is attributed to the occlusion. )) Is shifted from the landmark measurement position (Lx _v (t−1), Ly _v (t−1)) at time t−1 by a predetermined distance in the Y _v axis direction. In this case, the difference Dx in _{X v-axis} (t), Dx (t- 1) is substantially equal, but the differential variation DDX (t) becomes substantially zero, the difference in _{Y v} axis Dy (t) , Dy (t−1) are different, the difference change amount dDy (t) is a value for the predetermined distance described above.

Thus, when the accuracy of the landmark measurement position decreases due to occlusion, the difference change amount dD (t) corresponding to any axis of the vehicle coordinate system increases. Considering the above, the in-vehicle device 1 considers that the landmark measurement position cannot be measured correctly as the difference change amount dD (t) is larger, and sets the reliability R according to the difference change amount dD (t).

In the example of FIGS. 5 and 6, the vehicle-mounted device 1 calculates the difference change amounts dDx (t) and dDy (t) for each axis of the vehicle coordinate system. Based on 4), one difference change amount dD (t) may be calculated based on the straight line distance.

Further, in equations (3) and (4), only the two-dimensional coordinate system in the horizontal direction is considered, but the coordinates in the height direction are further taken into consideration, and for each axis of the vehicle coordinate system, as in equation (3), Alternatively, the difference change amount dD (t) may be calculated using the straight line distance as a reference, as in the equation (4).

Next, an example of setting the reliability R according to the difference change amount dD (t) will be described. Hereinafter, the reliability R is a value from 0 to 1.

In the first setting example of the reliability R, the in-vehicle device 1 determines that the reliability R is “1” when the difference change amount dD (t) is equal to or less than a predetermined threshold, and the difference change amount dD (t ) Is larger than a predetermined threshold, it is determined that the reliability R is “0”. The above threshold value is set in advance based on an experiment or the like in consideration of the degree of deterioration in position estimation accuracy, for example. In addition, when the vehicle-mounted device 1 calculates the difference change amount dD (t) for each axis of the vehicle coordinate system, when any of the difference change amounts dD (t) for each axis of the vehicle coordinate system is larger than a predetermined threshold value. The reliability R may be set to “0”. According to the first setting example, the in-vehicle device 1 can preferably set the reliability R with respect to the landmark measurement value when the occlusion occurs to 0, and suppress a decrease in position estimation accuracy.

In addition, the vehicle equipment 1 may set the reliability R for every axis | shaft of a vehicle coordinate system, when the difference variation | change_quantity dD (t) is calculated for every axis | shaft of a vehicle coordinate system. For example, when the vehicle coordinate system and the two-dimensional coordinates, the vehicle-mounted device 1 calculates the reliability R of X _v-axis direction based on a difference variation DDX (t), based on the difference variation ddY (t) Y A reliability R in the _v- axis direction is calculated. In this case, the vehicle-mounted unit 1, it is possible to calculate the respective reliability R in the X _v-axis and Y _v axes, such as in vehicle position estimation processing, the direction of the correction amount reliability R is high, By relatively increasing the correction amount in the direction in which the reliability R is low, the position estimation accuracy can be suitably increased. Similarly, in the following second and third setting examples, the reliability R may be set for each axis of the vehicle coordinate system.

In the second setting example, the in-vehicle device 1 is set so that the reliability R approaches 0 as the difference change amount dD (t) increases. In this case, the in-vehicle device 1 sets, for example, a value obtained by normalizing the difference change amount dD (t) to a value from 0 to 1 as the reliability R. In the third setting example, the in-vehicle device 1 determines the reliability R based on the combination of the first example and the second example. For example, the in-vehicle device 1 sets the reliability R to “0” when the difference change amount dD (t) is larger than a predetermined threshold, and the second change when the difference change amount dD (t) is less than or equal to the predetermined threshold. Based on the setting example, the reliability R is set according to the difference change amount dD (t).

The reliability R obtained in this way is output in association with the calculation result of the landmark measurement position, and is used for the vehicle position estimation. For example, in the vehicle position estimation, weighting is performed based on the reliability R when the vehicle position is estimated based on the landmark measurement position. Thereby, the landmark measurement position with low reliability is not used for the vehicle position estimation or is used with low weighting. Thus, it is possible to prevent the vehicle position estimation with low accuracy from being performed based on the landmark measurement position with low reliability.

[Processing flow]
FIG. 7 shows a flowchart executed by the vehicle-mounted device 1 in this embodiment.

First, the in-vehicle device 1 acquires a predicted host vehicle position based on the outputs of the internal sensors 11 such as the gyro sensor 3 and the vehicle speed sensor 4 (step S101). And the vehicle equipment 1 acquires the landmark information in which the positional information in the periphery of the prediction own vehicle position is included from the advanced map DB 10 (step S102).

And the vehicle equipment 1 determines the landmark prediction range WL based on the landmark map position specified from the position information of the acquired landmark information and the predicted host vehicle position acquired in step S101 (step S103). . And the vehicle equipment 1 acquires measurement data from the external sensors 12 such as the lidar 2 (step S104). And the vehicle equipment 1 extracts the landmark measurement data WLD satisfying a predetermined condition from the measurement data within the landmark prediction range WL, and calculates the landmark measurement position from the landmark measurement data WLD (step S105).

Next, the vehicle-mounted device 1 calculates a difference D (t) between the landmark measurement position calculated in step S105 and the landmark predicted position calculated based on the equation (1) (step S106). Then, the in-vehicle device 1 calculates the difference change amount dD (t) based on the difference D (t−1) and the difference D (t) calculated at the previous time t−1 (Step S107). In this case, the vehicle-mounted device 1 may calculate the difference change amount dD (t) for each axis of the vehicle coordinate system.

The in-vehicle device 1 determines the reliability R based on the difference change amount dD (t) (step S108). In this case, the in-vehicle device 1 determines the reliability R based on the determination method of the reliability R described in the section “Determining the reliability”. At this time, the in-vehicle device 1 may determine the reliability R for each coordinate axis of the vehicle coordinate system.

And the vehicle equipment 1 performs the own vehicle position estimation based on the landmark measurement position, the landmark prediction position, the reliability R determined in step S108, and the like (step S109).

For example, when performing vehicle position estimation using an extended Kalman filter based on Expression (2), the in-vehicle device 1 sets the reliability R of the X _v axis to “R _X ” and the reliability R of the Y _v axis to “R”. _If " _Y ", the Kalman gain K (t) is set as shown in the following equation (5).

Incidentally, when calculating the reliability R without particular distinction between the _{X v-axis} and _{Y v} axes, sets the Kalman gain K (t) based on equation (5) as _"R X _{= R} Y = R" Good. In this way, by adjusting the Kalman gain K (t) based on the reliability R (R _X , R _Y ), the in-vehicle device 1 uses the landmark measurement position with the low reliability R and uses the low-accuracy vehicle. It is possible to suitably prevent the position estimation.

As described above, the landmark feature detection / position measurement unit 17 of the vehicle-mounted device 1 according to the present embodiment is based on the output signal of the external sensor 12 such as the lidar 2 that measures the landmark Ltag that is a surrounding feature. The landmark measurement position is calculated. Further, the landmark position prediction unit 16 calculates a predicted landmark position based on the landmark information stored in the advanced map DB 10. Then, the reliability determination unit 18 calculates the difference D (t−1) between the landmark predicted position and the landmark measured position at the previous time t−1, and the landmark predicted position and the landmark measured position at the current time t. Based on the difference change amount dD (t) from the difference D (t), the reliability R is determined, and information on the determined reliability R is output to the vehicle position estimation unit 19. By this aspect, the vehicle equipment 1 can improve the position estimation accuracy suitably.

DESCRIPTION OF SYMBOLS 1 In-vehicle apparatus 2 Lidar 3 Gyro sensor 4 Vehicle speed sensor 5 Satellite positioning sensor 7 Server apparatus 10 Sophisticated map DB

Claims

A first acquisition unit that acquires a measurement position of the feature based on an output signal of a measurement unit that measures surrounding features;
A second acquisition unit for acquiring a predicted position of the feature based on map information;
An output unit that outputs information on the reliability of the measurement position based on the difference between the predicted position at the first time and the measurement position and the difference between the predicted position at the second time and the measurement position;
An information processing apparatus comprising:
The information processing apparatus according to claim 1, wherein the output unit lowers the reliability as the change amount of the difference at the second time with respect to the difference at the first time is larger.
The output unit determines the reliability in the first direction based on the difference at the first time in the first direction and the difference at the second time in the first direction;
The reliability in the second direction is determined based on the difference at the first time in the second direction intersecting the first direction and the difference at the second time in the second direction. Or the information processing apparatus according to 2;
A prediction unit for predicting self-position;
A correction unit that corrects the self-position based on the difference between the predicted position at the current time and the measurement position;
The information processing apparatus according to any one of claims 1 to 3, wherein the correction unit reduces the gain for correcting the self-position based on the difference as the reliability is lower.
A control method executed by the information processing apparatus,
A first acquisition step of acquiring a measurement position of the feature based on an output signal of a measurement unit that measures surrounding features;
A second acquisition step of acquiring a predicted position of the feature based on map information;
An output step of outputting information on the reliability of the measurement position based on the difference between the predicted position and the measurement position at a first time and the difference between the prediction position and the measurement position at a second time;
A control method.
A program executed by a computer,
A first acquisition unit that acquires a measurement position of the feature based on an output signal of a measurement unit that measures surrounding features;
A second acquisition unit for acquiring a predicted position of the feature based on map information;
Based on the difference between the predicted position and the measured position at the first time and the difference between the predicted position and the measured position at the second time, the output unit outputs information related to the reliability of the measured position. A program that causes a computer to function.
A storage medium storing the program according to claim 6.