Information processing device, map data generation device, method, and program

Abstract

To provide map data containing advance information for grasping objects to be used for self-position estimation in advance.SOLUTION: An in-vehicle device 1 estimates the self-position based on measurements of objects measured using a LiDAR 2 mounted on a vehicle and position information of the objects included in a map DB 10. The map DB 10 includes object recommendation information indicative of a recommendation value of each object for the position estimation or voxel recommendation information indicative of a recommendation value of each voxel for the position estimation.SELECTED DRAWING: Figure 6

Description

[Technical Field]

[0001] The present invention relates to a self-location estimation technology. [Background technology]

[0002] Conventionally, there have been known techniques for detecting features installed ahead of a vehicle using radar or a camera and calibrating the vehicle's position based on the detection results. For example, Patent Document 1 discloses a technique for estimating the vehicle's position by comparing the output of a measurement sensor with position information of features registered in advance on a map. Patent Document 2 discloses a vehicle's position estimation technique using a Kalman filter. Furthermore, Non-Patent Document 1 discloses specifications for a data format for collecting data detected by a vehicle's sensor on a cloud server. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2013-257742 [Patent Document 2] Japanese Patent Application Publication No. 2017-72422 [Non-patent literature]

[0004] [Non-Patent Document 1] Here Corporation website, Vehicle Sensor Data Cloud Ingestion Interface Specification (v2.0.2), [Retrieved March 2, 2018], Internet <URL:https: / / lts.cms.here.com / static-cloud-content / Company_Site / 2015_06 / Vehicle_Sensor_Data_Cloud_Ingestion_Interface_Specification.pdf> Summary of the Invention [Problem to be solved by the invention]

[0005] When estimating the vehicle's position by comparing the measurement results of objects around the vehicle's position taken by an external sensor such as a lidar with the position information of the objects on a map, if the target object cannot be detected due to occlusion by other vehicles or the effects of rain or snow, the accuracy of the vehicle's position estimation will deteriorate. Similarly, if the position or shape of the object itself changes, the matching result will be shifted due to a mismatch with the position of the object on the map, and the accuracy of the vehicle's position estimation will deteriorate.

[0006] The present invention has been made to solve the above-mentioned problems, and its main object is to provide map data including advance information for identifying objects to be used for self-location estimation in advance. [Means for solving the problem]

[0007] The invention described in the claims is an information processing device comprising: a detection unit that detects objects around a moving body; a first acquisition unit that acquires an estimated position of the moving body; a second acquisition unit that acquires map information including position information of objects around the estimated position and recommended value information that indicates a recommended value for estimating the position of the moving body; and a position estimation unit that estimates the position of the moving body based on the detection result of the object detected by the detection unit, the estimated position, and the position information of the object included in the map information, wherein the position estimation unit estimates the position of the moving body by a weighting calculation based on the recommended value information.

[0008] The invention described in the claims is also an information processing device having a memory unit that stores map data including position information of an object and information indicating a recommended value for estimating the position of the moving body using the positional relationship with the object measured by a measuring device mounted on the moving body, the recommended value information being used in a weighting calculation for the position estimation.

[0009] The invention described in the claims is a map data generation device, which has a generation unit that generates map data by associating recommended value information used in weighting calculations for position estimation, the recommended value information being generated based on identification information of an object and effectiveness information regarding the effectiveness of improving the accuracy of position estimation of the moving body using the object, with the position information of the object. The invention described in the claims is a method executed by a computer, comprising: a detection step of detecting objects around a moving body; a first acquisition step of acquiring an estimated position of the moving body; a second acquisition step of acquiring map information including position information of objects around the estimated position and recommended value information indicating recommended values ​​for estimating the position of the moving body; and a position estimation step of estimating the position of the moving body based on the detection result of the object detected by the detection step, the estimated position, and the position information of the object included in the map information, wherein the position estimation step estimates the position of the moving body by a weighting calculation based on the recommended value information. The invention described in the claims is also a program that causes a computer to function as a position estimation unit that estimates the position of the moving body based on the detection result of the object detected by the detection unit, the estimated position, and the position information of the object included in the map information, the program comprising: a detection unit that detects objects around a moving body; a first acquisition unit that acquires an estimated position of the moving body; a second acquisition unit that acquires map information including position information of objects around the estimated position and recommended value information that indicates a recommended value for estimating the position of the moving body; and the position estimation unit that estimates the position of the moving body based on the detection result of the object detected by the detection unit, the estimated position, and the position information of the object included in the map information, the program characterized in that the position estimation unit estimates the position of the moving body by a weighting calculation based on the recommended value information. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 is a schematic configuration diagram of a driving assistance system. [Figure 2] FIG. 2 is a block diagram showing the functional configuration of an in-vehicle device and a server device. [Figure 3] This is a diagram showing a state variable vector in two-dimensional orthogonal coordinates. [Figure 4]FIG. 10 is a diagram illustrating a schematic relationship between a prediction step and a measurement update step. [Figure 5] 3 shows functional blocks of a vehicle position estimation unit. [Figure 6] 10 is an example of a data structure of object recommendation information. [Figure 7] FIG. 2 is a diagram showing an outline of the data structure of upload information. [Figure 8] 10 shows the data structure of an "object recognition event" included in the event information. [Figure 9] FIG. [Figure 10] An example of setting the valid flag is shown below. [Figure 11] 10 is a graph showing the transition of diagonal elements of a covariance matrix during a white line detection period. [Figure 12] 10 is a graph showing the transition of diagonal elements of a covariance matrix during a marker detection period. [Figure 13] 10 is a flowchart illustrating an example of an outline of a process related to transmission and reception of upload information including a valid flag. [Figure 14] 2 shows functional blocks of a vehicle position estimation unit that performs position estimation based on voxel data. [Figure 15] 1 shows an example of a schematic data structure of voxel data. [Figure 16] 10 is an example of a data structure of voxel recommendation information. [Figure 17] FIG. [Figure 18] An example of valid values ​​is shown below. [Figure 19] 10 is a graph showing the transition of the individual evaluation function value for voxel ID "4." [Figure 20] 10 is a graph showing the transition of the individual evaluation function value for voxel ID "12." [Figure 21] 10 is a flowchart illustrating an example of an outline of a process related to sending and receiving upload information including a valid value. DETAILED DESCRIPTION OF THE INVENTION

[0011] According to a preferred embodiment of the present invention, there is provided a data structure of map data used for estimating the position of a mobile body, the data structure including position information of an object and recommended value information indicating a recommended value for estimating the position of the mobile body using a positional relationship with the object determined by a measuring device mounted on the mobile body. By having such a data structure, the map data can preferably include advance information for identifying in advance an object to be used for self-location estimation.

[0012] In one aspect of the data structure, the recommendation value information includes a recommendation value for performing the position estimation for a direction relative to the moving object. In a preferred example, the recommendation value information further includes a recommendation value for performing the position estimation for an orientation of the moving object. According to this aspect, the recommendation level of position estimation for an object used in position estimation can be recorded in the map data for each state variable estimated in the position estimation process.

[0013] In another aspect of the above data structure, position information of the object and the recommended value information are provided for each unit area obtained by dividing a space, and the recommended value information indicates a recommended value for each unit area when estimating the position of the moving body by comparing, for each unit area, the position information of the object with a positional relationship with the object. According to this aspect, the map data preferably includes advance information for determining in advance which unit areas should be used for position estimation by comparing, for each unit area, the position information of the object with a positional relationship with the object.

[0014] According to another preferred embodiment of the present invention, an information processing device includes a storage unit that stores map data including position information of an object and recommended value information indicating a recommended value for estimating the position of the moving object using a positional relationship with the object determined by a measuring device mounted on the moving object. With this aspect, the information processing device can perform position estimation by preferably referring to advance information for identifying in advance an object to be used for self-location estimation, or can distribute the advance information to another device that performs position estimation.

[0015] According to another preferred embodiment of the present invention, the map data generation device includes a generation unit that generates map data by associating position information of the object with recommendation value information indicating a recommendation value for estimating the position of the moving body using the object, the recommendation value information being generated based on identification information of the object and effectiveness information regarding effectiveness of improving accuracy of estimating the position of the moving body using the object. With this aspect, the map data generation device can preferably include advance information for identifying objects to be used for self-location estimation in advance in the map data. [Example]

[0016] Hereinafter, first and second preferred embodiments of the present invention will be described with reference to the drawings. For the sake of convenience, in this specification, a character with "^" or "-" above any symbol will be referred to as "A ^ " or "A - " (where "A" is any letter).

[0017] <First Example> (1-1) Overview of the driving assistance system 1 shows a schematic configuration of a driving assistance system according to a first embodiment. The driving assistance system includes an onboard device 1 that travels with each vehicle, which is a moving object, and a server device 6 that communicates with each onboard device 1 via a network. The driving assistance system updates a delivery map DB 20, which is a map for delivery held by the server device 6, based on information transmitted from each onboard device 1. Hereinafter, the term "map" will include not only data referenced by conventional onboard devices for route guidance, but also data used for an Advanced Driver Assistance System (ADAS) or autonomous driving.

[0018] The vehicle-mounted device 1 is electrically connected to a lidar 2, a gyro sensor 3, a vehicle speed sensor 4, and a GPS receiver 5, and performs, based on the outputs of these devices, detection of predetermined objects and estimation of the position of the vehicle on which the vehicle-mounted device 1 is mounted (also referred to as "subject vehicle position"). Based on the result of estimating the subject vehicle position, the vehicle-mounted device 1 performs, for example, automatic driving control of the vehicle so that the vehicle travels along a route to a set destination. The vehicle-mounted device 1 stores a map database (DB) 10 in which road data and information on landmarks and objects such as road markings and lane lines installed near the road are registered. Examples of the landmarks include kilometer posts, 100-meter posts, delineators, traffic infrastructure (e.g., signs, direction signs, traffic lights), utility poles, streetlights, and other landmarks periodically arranged along the side of the road. The vehicle-mounted device 1 then estimates the subject vehicle position based on this map DB 10 by comparing it with the outputs of the lidar 2 and the like. Furthermore, the vehicle-mounted device 1 transmits upload information "Iu" including information relating to the detected object to the server device 6. The vehicle-mounted device 1 is an example of an information transmitting device.

[0019] The LIDAR 2 emits a pulsed laser beam over a predetermined range of angles in the horizontal and vertical directions to discretely measure the distance to an object in the external world and generate three-dimensional point cloud information indicating the position of the object. In this case, the LIDAR 2 includes an irradiation unit that irradiates a laser beam while changing the irradiation direction, a light receiving unit that receives the reflected (scattered) light of the irradiated laser beam from an object, and an output unit that outputs scan data based on the light receiving signal output by the light receiving unit. The scan data is point cloud data and is generated based on the irradiation direction corresponding to the laser beam received by the light receiving unit and the distance to the object in that irradiation direction, which is determined based on the above-mentioned light receiving signal. Hereinafter, objects such as lane markings and features that are the targets of measurement by the LIDAR 2 in the vehicle position estimation process are also referred to as "landmarks." The LIDAR 2, gyro sensor 3, vehicle speed sensor 4, and GPS receiver 5 each provide output data to the vehicle-mounted device 1. The LIDAR 2 is an example of a "measurement device."

[0020] The server device 6 receives and stores upload information Iu from each vehicle-mounted device 1. The server device 6, for example, updates the delivery map DB 20 based on the collected upload information Iu. The server device 6 also transmits download information Id including update information for the delivery map DB 20 to each vehicle-mounted device 1. The server device 6 is an example of an information processing device and a map data generating device.

[0021] 2(A) is a block diagram showing the functional configuration of the vehicle-mounted device 1. The vehicle-mounted device 1 mainly includes an interface 11, a storage unit 12, a communication unit 13, an input unit 14, a control unit 15, and an information output unit 16. These elements are interconnected via a bus line.

[0022] The interface 11 acquires output data from sensors such as the lidar 2, the gyro sensor 3, the vehicle speed sensor 4, and the GPS receiver 5, and supplies the data to the control unit 15.

[0023] The storage unit 12 stores programs executed by the control unit 15 and information required for the control unit 15 to execute predetermined processes. In this embodiment, the storage unit 12 stores a map DB 10 including attribute information such as the position, size, and shape of landmark objects, as well as object recommendation information, which will be described later. Here, the object recommendation information is information indicating, for each object, a recommendation value (also referred to as an "object recommendation value") for estimating the vehicle position using the target object. The data structure of the object recommendation information will be described later.

[0024] The communication unit 13 transmits upload information Iu and receives download information Id under the control of the control unit 15. The input unit 14 is a button, touch panel, remote controller, voice input device, etc. that the user operates. The information output unit 16 is, for example, a display, speaker, etc. that outputs information under the control of the control unit 15.

[0025] The control unit 15 includes a CPU that executes a program and controls the entire vehicle-mounted device 1. In this embodiment, the control unit 15 includes a vehicle position estimation unit 17 and an upload control unit .

[0026] The vehicle position estimation unit 17 corrects the vehicle position estimated from the output data of the gyro sensor 3, the vehicle speed sensor 4, and / or the GPS receiver 5 based on the distance and angle measurements from the LIDAR 2 relative to the landmarks and the position information of the landmarks extracted from the map DB 10. In this embodiment, for example, the vehicle position estimation unit 17 alternately executes a prediction step in which the vehicle position is predicted from the output data of the gyro sensor 3, the vehicle speed sensor 4, etc., based on a state estimation method based on Bayesian estimation, and a measurement update step in which the predicted value of the vehicle position calculated in the immediately preceding prediction step is corrected. Various filters developed for Bayesian estimation can be used as the state estimation filter used in these steps, such as an extended Kalman filter, an unscented Kalman filter, and a particle filter. In the first embodiment, the vehicle position estimation unit 17 estimates the vehicle position using an extended Kalman filter, for example. Vehicle position estimation using an extended Kalman filter will be described in the section "Position Estimation Based on an Extended Kalman Filter."

[0027] When the upload control unit 18 detects a predetermined object based on the output of an external sensor such as the lidar 2, the upload control unit 18 generates upload information Iu including information about the detected object and transmits the upload information Iu to the server device 6. Furthermore, when the host vehicle position estimation unit 17 estimates the host vehicle position using the detected object as a landmark, the upload control unit 18 determines the effectiveness of the host vehicle position estimation for improving the accuracy of the position estimation for each state variable, and transmits flag information (also referred to as a "validity flag") indicating the determination result to the server device 6. The upload control unit 18 is an example of a "generation unit," a "transmission unit," and a "computer" that executes a program. Furthermore, the validity flag is an example of "validity information."

[0028] 2(B) is a block diagram showing the functional configuration of the server device 6. The server device 6 mainly includes a communication unit 61, a storage unit 62, and a control unit 65. These elements are connected to each other via a bus line.

[0029] The communication unit 61 receives upload information Iu and transmits download information Id under the control of the control unit 65. The storage unit 62 stores programs executed by the control unit 65 and information necessary for the control unit 65 to execute predetermined processes. In this embodiment, the storage unit 62 stores a delivery map DB 20 and an upload information DB 27 that accumulates upload information Iu received from each in-vehicle device 1. The delivery map DB 20 includes object recommendation information generated by the control unit 65 with reference to the upload information DB 27.

[0030] The control unit 65 includes a CPU that executes programs and the like, and controls the entire server device 6. In this embodiment, the control unit 65 performs processes such as storing the upload information Iu received from each vehicle-mounted device 1 via the communication unit 61 in the upload information DB 27, generating object recommendation information based on the upload information DB 27, and transmitting the generated map update information such as the object recommendation information to each vehicle-mounted device 1 via the communication unit 61.

[0031] (1-2) Position Estimation Based on Extended Kalman Filter FIG. 3 is a diagram showing the vehicle position to be estimated in two-dimensional Cartesian coordinates. As shown in FIG. 3, the vehicle position on a plane defined on the two-dimensional Cartesian coordinate system of x and y is represented by coordinates "(x, y)" and the vehicle's orientation (yaw angle) "ψ." Here, the yaw angle ψ is defined as the angle between the vehicle's traveling direction and the x-axis. In this embodiment, the vehicle position is estimated using four variables (x, y, z, ψ) as state variables for the vehicle position, taking into account the coordinate of the z-axis perpendicular to the x-axis and y-axis in addition to the coordinates (x, y) and yaw angle ψ described above. Note that, since typical roads have gentle gradients, the pitch angle and roll angle of the vehicle are generally ignored in this embodiment.

[0032] FIG. 4 is a diagram showing a schematic relationship between the prediction step and the measurement update step. FIG. 5 shows an example of the vehicle position estimation unit 17, which is a functional block of the control unit 15. As shown in FIG. 4, the prediction step and the measurement update step are repeated to sequentially calculate and update the estimated value of the state variable vector "X" indicating the vehicle position. As shown in FIG. 5, the vehicle position estimation unit 17 has a position prediction unit 21 that executes the prediction step, and a position estimation unit 22 that executes the measurement update step. The position prediction unit 21 includes a dead reckoning block 23 and a position prediction block 24, and the position estimation unit 22 includes a landmark search / extraction block 25 and a position correction block 26. In FIG. 4, the state variable vector of the reference time (i.e., current time) "k" to be calculated is calculated as "X - (k)" or "X ^ (k)" where the provisional estimate (predicted value) estimated in the prediction step is indicated by " - " is added to the character representing the value, and the more accurate estimated value updated in the measurement update step is added with " ^ " is added.

[0033] In the prediction step, the dead reckoning block 23 of the control unit 15 calculates the vehicle's moving speed "v" and angular velocity "ω" (collectively referred to as the "control value u(k)=(v(k), ω(k))) T "). The position prediction block 24 of the control unit 15 calculates the movement distance and change in direction from the previous time by using the state variable vector X ^ The calculated travel distance and direction change are added to (k-1) to obtain the predicted value of the vehicle position at time k (also called the "predicted position") X - (k) is calculated. At the same time, the predicted position X - The covariance matrix P corresponding to the error distribution of (k) - (k)” is the covariance matrix “P ^ (k-1)".

[0034] In the measurement update step, the landmark search and extraction block 25 of the control unit 15 associates the position vector of the landmark registered in the map DB 10 with the scan data of the LIDAR 2. Then, when this association is established, the landmark search and extraction block 25 of the control unit 15 calculates the measurement value "Z(k)" of the associated landmark by the LIDAR 2 and the predicted position X - (k) and the landmark position vector registered in the map DB 10 are used to model the measurement process by the LIDAR 2, and the landmark measurement value (referred to as the "measurement predicted value") "Z - The measured value Z(k) is a vector value in the vehicle coordinate system (also called the "vehicle coordinate system"), which is obtained by converting the distance and scan angle of the landmark measured by the lidar 2 at time k into components whose axes are the traveling direction and lateral direction of the vehicle. Then, the position correction block 26 of the control unit 15 calculates the measured value Z(k) and the predicted measured value Z as shown in the following equation (1). - (k) is multiplied by the Kalman gain "K(k)" and used as the predicted position X - (k) to obtain the updated state variable vector (also called the "estimated position") X ^ Calculate (k).

[0035]

number

[0036] In this way, the prediction step and the measurement update step are repeated to obtain the predicted position X - (k) and estimated position X ^By successively calculating (k), the most probable vehicle position is calculated.

[0037] Here, whether or not the accuracy of position estimation has improved can be determined by the values ​​of the diagonal elements of the covariance matrix P. Here, if the covariance matrix calculated based on the measurement values ​​for the landmark at time "k" is P(k), the covariance matrix P(k) is expressed by the following equation (2).

[0038]

number

[0039] If the state variables x, y, and z are in the global coordinate system used in the map, then the matrix "C ψ (k)" to convert it into the vehicle coordinate system (X, Y, Z).

[0040]

number

[0041] matrix C ψ (k) is expressed by the following equation (4).

[0042]

number

[0043] (1-3) Object Recommendation Data Structure Next, the object recommendation information that is commonly included in the map DB 10 and the delivery map DB 20 will be described.

[0044] FIG. 6 shows an example of a data structure of object recommendation information. As shown in FIG. 6, the object recommendation information is information in which an "object ID," a "position," and an "object recommendation value" are associated with each other. The "object ID" specifies an object ID, which is an identification number assigned to each landmark object. The object ID is an example of "object identification information." The "position" specifies the latitude, longitude, and altitude of the target landmark. The "object recommendation value" specifies the object recommendation value assigned to the target object for each state variable of the vehicle localization (here, the traveling direction (x), lateral direction (y), vertical direction (z), and direction (ψ)). The object recommendation value is set to a value between 0 and 1, and the closer to 1 it is, the more effective it is in improving the estimation accuracy of the vehicle localization for the target state variable.

[0045] As described above, in this embodiment, object recommendation information indicating the effectiveness (suitability) of position estimation for each object is included in the map DB 10. This allows the vehicle-mounted device 1 to refer to the object recommendation information and identify objects that will improve the accuracy of vehicle position estimation. Note that the vehicle-mounted device 1 may receive download information Id including object recommendation information related to objects around the vehicle position from the server device 6 by transmitting a predetermined request signal including current position information to the server device 6. In this case, the vehicle-mounted device 1 controls the vehicle to improve the accuracy of vehicle position estimation based on the object recommendation information included in the received download information Id.

[0046] Here, a specific example of vehicle control based on object recommendation information will be described.

[0047] For example, the vehicle-mounted device 1 determines that the lateral accuracy of the current vehicle position estimation result is poor (i.e., σ y 2 When the target object recommendation value (k) is high), the vehicle may search for an object present within a predetermined distance from the current position from the object recommendation information, and determine the vehicle's movement route based on the position of an object with a high object recommendation value of the target state variable (here, the lateral direction) among the searched objects. In this case, for example, the vehicle-mounted device 1 moves the vehicle to a lane where the target object is easier to detect (for example, the lane closest to the target object). Furthermore, in addition to or instead of the above-described vehicle control, when estimating the vehicle position using multiple objects, the vehicle-mounted device 1 may perform calculations such as a Kalman filter by weighting the measurement value of an object with a high object recommendation value of the target state variable. Furthermore, when there is no object with a high object recommendation value of the target state variable in the vicinity of the current position, the vehicle-mounted device 1 may switch the position estimation method (for example, switch to position estimation by dead reckoning or a position estimation method of a second embodiment described later).

[0048] (1-4) Upload information data structure Next, a specific example of the data structure of the upload information Iu will be described.

[0049] Fig. 7 is a diagram showing an outline of the data structure of the upload information Iu transmitted by the vehicle-mounted device 1. As shown in Fig. 7, the upload information Iu includes header information, driving route information, event information, and media information.

[0050] The header information includes the following items: "version," "sender," and "vehicle metadata." The vehicle-mounted device 1 specifies information on the version of the data structure of the upload information Iu to be used in "version," and specifies information on the name of the company sending the upload information Iu (vehicle OEM name or system vendor name) in "sender." The vehicle-mounted device 1 also specifies vehicle attribute information (e.g., vehicle type, vehicle ID, vehicle width, vehicle height, etc.) in "vehicle metadata." The driving route information includes a "position estimation" item. In this "position estimation," the vehicle-mounted device 1 specifies timestamp information indicating the time of position estimation, as well as latitude, longitude, and altitude information indicating the estimated vehicle position, and information on the accuracy of these estimates.

[0051] The event information includes an item for "object recognition event." When the vehicle-mounted device 1 detects an object recognition event, it designates the information resulting from the detection as the "object recognition event." The media information is a data type used when transmitting raw data, which is output data (detection information) from an external sensor such as the lidar 2.

[0052] Figure 8 shows the data structure of an "object recognition event" included in the event information. For each element (sub-item) included in the "object recognition event," Figure 8 shows information on whether the specification of information corresponding to each element is mandatory or optional.

[0053] As shown in FIG. 8, the "object recognition event" includes the following elements: "timestamp," "object ID," "offset position," "object type," "object size," "object size precision," "media ID," and "valid flag."

[0054] When an object is detected based on the output of an external sensor such as the lidar 2, the control unit 15 of the vehicle-mounted device 1 generates event information of an "object recognition event" having the data structure shown in Fig. 8. Here, the vehicle-mounted device 1 specifies the time when the object was detected in the "timestamp" and the object ID of the detected object in the "object ID".

[0055] In addition, in the "offset position," the vehicle-mounted device 1 specifies information about the relative position of the detected object from the vehicle (for example, latitude and longitude differences, etc.). In the "object type," the vehicle-mounted device 1 specifies information indicating the type of the detected object. Furthermore, if the vehicle-mounted device 1 is able to generate size information and size accuracy information for the detected object, it specifies this information in the "object size" and "object size accuracy" elements. Furthermore, when it is necessary to transmit raw data such as images, videos, and point cloud data output by the sensor unit 7, the vehicle-mounted device 1 specifies identification information assigned to the raw data in the "media ID." Further, detailed information, etc., of the media (raw data) specified in the "media ID" element is stored separately in the "media information" item.

[0056] For the "effectiveness flag", the vehicle-mounted device 1 specifies a validity flag indicating the effectiveness of improving the accuracy of position estimation when the vehicle position is estimated using the target object, for each state variable to be estimated in the vehicle position estimation. For example, the vehicle-mounted device 1 specifies the diagonal elements (also called "pre-estimation diagonal elements") σ of the covariance matrix P before the vehicle position is estimated using the target object. x 2 , σ y 2 , σ z 2 , σ ψ 2 and the diagonal elements of the covariance matrix P after estimating the vehicle position using the target object (also called "estimated diagonal elements") σ x 2 , σ y 2 , σ z 2 , σψ 2 Then, the vehicle-mounted device 1 sets the validity flag of a state variable whose estimated post-diagonal element is smaller than the estimated pre-diagonal element (i.e., accuracy has improved) to "1," and sets the validity flag of a state variable whose estimated post-diagonal element is larger than the estimated pre-diagonal element (i.e., accuracy has decreased) to "0."

[0057] Here, specific examples of setting the validity flag will be described with reference to FIGS.

[0058] FIG. 9 is a bird's-eye view showing the periphery of a vehicle equipped with the on-board device 1. FIG. 10 shows an example of the setting values ​​of the valid flags specified by the on-board device 1 in the upload information Iu indicating the detection results of each object with object IDs "1," "2," and "3." Furthermore, FIG. 11 shows the diagonal elements σ y 2 12 is a graph showing the transition of the diagonal element σ for a predetermined period including the execution period "Tw5" of the vehicle position estimation based on the sign 52 with the object ID "2". x 2 10 is a graph showing the change over time.

[0059] First, the vehicle-mounted device 1 refers to the position information, size information, etc. of the objects registered in the map DB 10, and sets prediction windows "Wp1" to "Wp3" that determine the range in which each object is to be detected, as shown in Figure 9, for areas where the white line 51 with object ID "1", the sign 52 with object ID "2", and the sign 53 with object ID "3" that serve as landmarks are estimated to exist.

[0060] Then, the vehicle-mounted device 1 detects the white line 51 with the object ID "1" within the prediction window Wp1 and performs vehicle position estimation based on the white line 51. In this case, as shown in Fig. 11, at the end of each of the execution periods Tw1 to Tw4 of vehicle position estimation based on the white line 51, the diagonal element σ y 2has decreased. That is, after the vehicle position is estimated based on the white line 51, the accuracy of the position estimation in the lateral direction (y) has improved. Therefore, in this case, the vehicle-mounted device 1 sets the validity flag in the lateral direction (y) to "1" as shown in FIG. 10. On the other hand, the vehicle-mounted device 1 sets the validity flag in the lateral direction (y) to "1" as shown in FIG. 10. x 2 , σ z 2 , σ ψ 2 Regarding the above, if there is no decrease before and after the execution periods Tw1 to Tw4, the vehicle-mounted device 1 sets the validity flags for the traveling direction, height direction, and orientation to "0." Note that the vehicle-mounted device 1 may set the validity flag for the target state variable to "1" if the decrease in the estimated rear diagonal element from the estimated front diagonal element for the target state variable is equal to or greater than a predetermined threshold, and may set the validity flag for the target state variable to "0" if the decrease is less than the predetermined threshold. Then, the vehicle-mounted device 1 generates upload information Iu including the object ID of the white line 51 and the set validity flag, and transmits the upload information Iu to the server device 6.

[0061] Furthermore, the vehicle-mounted device 1 detects the sign 52 with the object ID "2" within the prediction window Wp2 and performs vehicle position estimation based on the sign 52. In this case, as shown in FIG. 12 , at the end of the execution period Tw5 of vehicle position estimation based on the sign 52, the diagonal element σ x 2 decreases. That is, after the vehicle position is estimated based on the sign 52, the position estimation accuracy in the traveling direction (x) is improved. Therefore, in this case, the vehicle-mounted device 1 sets the validity flag for the traveling direction (x) to "1" as shown in FIG. 10. Similarly, the vehicle-mounted device 1 sets the validity flag for the diagonal element σ z 2 and diagonal elements σ ψ 2 Regarding the diagonal element σ, if it decreases before and after the execution period Tw5, the valid flags for the height direction and the azimuth direction are set to "1". y 2Regarding the sign 52, if there is no decrease before and after the execution period Tw5, the validity flag for the horizontal direction is set to "0." Then, the vehicle-mounted device 1 generates upload information Iu including the object ID of the sign 52 and the validity flag that has been set, and transmits the upload information Iu to the server device 6.

[0062] On the other hand, the vehicle-mounted device 1 cannot detect the sign 53 with object ID "3" within the prediction window Wp3 due to occlusion caused by obstacles 54 and 55. In this case, the vehicle-mounted device 1 sets the validity flags of the state variables of the object ID "3" to "0." The vehicle-mounted device 1 then generates upload information Iu including the object ID of the sign 53 and the set validity flags, and transmits the upload information Iu to the server device 6.

[0063] (1-5) Calculating recommended object values Next, the object recommendation for each object Recommended The server device 6 calculates the object estimation value for each object and for each state variable based on the valid flag included in the upload information Iu received from each vehicle-mounted device 1. Recommended Calculate the value.

[0064] For example, if the server device 6 defines the aggregate number (number of samples) of valid flag information for each state variable of each object ID as "S1" and the sum of the valid flag values ​​(0 or 1) for each state variable of each object ID as "S2," the server device 6 sets the recommended object value for each state variable of each object ID to "S2 / S1." For example, if 100 pieces of upload information Iu including valid flags for the traveling direction of each object ID "1" are received from each vehicle-mounted device 1 and the sum of these valid flags is 70, the vehicle-mounted device 1 sets the recommended object value for the traveling direction of object ID "1" to "0.7" (=70 / 100). The recommended object values ​​for the horizontal, vertical, and azimuth directions can also be calculated in a similar manner.

[0065] By doing so, the server device 6 can suitably set the recommended object value for each state variable of each object ID to be in the range from 0 to 1. In this case, the recommended object value of an object that was not detected due to occlusion or the influence of rain or snow will be small, and the recommended object value of an object that was detected but was determined to be effective for position estimation on a low rate will also be small. On the other hand, the recommended object value of an object that was determined to be effective for position estimation on a high rate will be high.

[0066] Preferably, the server device 6 calculates the object recommendation value using the validity flags of the upload information Iu received within a predetermined time period (e.g., 10 minutes) in the past. This allows the server device 6 to accurately set or update the object recommendation value based on the latest validity flags collected.

[0067] Furthermore, the greater the traffic volume on a road on which the target object can be detected, the more upload information Iu containing validity flags can be collected in a short period of time. Therefore, the server device 6 may set the predetermined time period according to the traffic volume of the road. For example, the server device 6 may set the predetermined time period to 10 minutes for an object detectable on a road with heavy traffic, and to 1 hour for an object detectable on a road with light traffic. In another example, the server device 6 may calculate the recommended object value of the target object based on the validity flags included in a predetermined number of pieces of upload information Iu recently acquired for the target object. In this case, the value S1 is the predetermined number, and the in-vehicle device 1 can appropriately calculate the recommended object value using a certain number of samples.

[0068] (1-6) Processing Flow FIG. 13 is an example of a flowchart showing an outline of processing related to transmission and reception of upload information Iu including a valid flag and download information Id including object recommendation information.

[0069] First, the vehicle-mounted device 1 refers to the map DB 10, and if a landmark object is present, sets a prediction window for detecting the object (step S101). Then, the vehicle-mounted device 1 determines whether the target object has been detected (step S102). If the target object has been detected (step S102; Yes), the vehicle-mounted device 1 determines whether the accuracy of the position estimation based on the object has improved for each state variable before and after the vehicle position estimation based on the object has been performed, and sets a valid flag for each state variable (step S103). On the other hand, if the vehicle-mounted device 1 cannot detect the target object (step S102; No), it sets the valid flag for each state variable for the object to 0 (step S104). Then, the vehicle-mounted device 1 transmits upload information Iu including the object ID and valid flag of the target object to the server device 6 (step S105).

[0070] The server device 6 receives the upload information Iu transmitted in step S105 and stores the upload information Iu in the upload information DB 27 (step S201). The server device 6 then determines whether it is time to update the delivery map DB 20 (step S202). The update timing may be determined based on the length of time since the delivery map DB 20 was last updated, or may be determined based on the cumulative number of pieces of upload information Iu received since the delivery map DB 20 was last updated.

[0071] If it is time to update the delivery map DB 20 (step S202; Yes), the server device 6 references the upload information DB 27 to generate object recommendation information, etc., and updates the delivery map DB 20 using the generated object recommendation information, etc. (step S203). The server device 6 then transmits download information Id including the object recommendation information, etc., generated in step S203 to each in-vehicle device 1 (step S204). Note that the server device 6 may transmit the download information Id only to in-vehicle devices 1 that have requested the transmission of the download information Id. On the other hand, if it is not time to update the delivery map DB 20 (step S202; No), the server device 6 continues to execute step S201.

[0072] If the vehicle-mounted device 1 receives the download information Id (step S106; Yes), it updates the map DB 10 using the download information Id (step S107). As a result, the latest object recommendation information is recorded in the map DB 10. On the other hand, if the vehicle-mounted device 1 has not received the download information Id from the server device 6 (step S106; No), it returns the process to step S101.

[0073] <Second Example> In the driving assistance system according to the second embodiment, voxel data, which records position information and the like of stationary structures for each unit area (also referred to as a "voxel") obtained by dividing three-dimensional space into a plurality of areas, is recorded in the map DB 10 and the delivery map DB 20, and the vehicle-mounted device 1 estimates the vehicle position using the voxel data. In this case, the map DB 10 and the delivery map DB 20 contain information indicating, for each voxel, a recommended value for use in position estimation (also referred to as "voxel recommendation information"), instead of object recommendation information. Hereinafter, components similar to those in the driving assistance system according to the first embodiment will be appropriately designated by the same reference numerals, and their description will be omitted.

[0074] (2-1) Position estimation based on voxel data The voxel data includes data in which measured point cloud data of static structures in each voxel is expressed using a normal distribution, and is used for scan matching using NDT (Normal Distributions Transform).

[0075] Fig. 14 shows an example of the vehicle position estimation unit 17 for position estimation based on voxel data. The difference from the vehicle position estimation unit 17 for position estimation based on the extended Kalman filter shown in Fig. 5 is that instead of the landmark search / extraction unit 25, a point cloud data association block 27 is provided for processing to associate the point cloud data obtained from the LIDAR 2 with the voxels acquired from the map DB.

[0076] FIG. 15 shows an example of a schematic data structure of voxel data. The voxel data includes parameter information for expressing a point cloud within a voxel using a normal distribution. In this embodiment, as shown in FIG. 15, the voxel data includes a voxel ID, voxel coordinates, a mean vector, and a covariance matrix. Here, "voxel coordinates" indicate absolute three-dimensional coordinates of a reference position, such as the center position of each voxel. Note that each voxel is a cube obtained by dividing space into a grid, and since its shape and size are predetermined, it is possible to identify the space of each voxel using its voxel coordinates. The voxel coordinates may be used as a voxel ID. The voxel ID is an example of "object identification information."

[0077] The "mean vector" and "covariance matrix" are the parameters when expressing the point cloud in the target voxel as a normal distribution. The coordinates of any point "i" in any voxel "n" are expressed as X n (i)=[x n (i), y n (i), z n (i)] T and the number of points in voxel n is defined as "N n ", then the mean vector at voxel n is "μ n ” and the covariance matrix “V n " are expressed by the following equations (5) and (6), respectively.

[0078]

number

[0079]

number

[0080] In addition, the point cloud data obtained by LIDAR 2 is associated with the voxels to be matched, and the coordinates of any point in the corresponding voxel n are calculated as X L (i)=[x n (i), y n (i), z n (i)] T Then, X at voxel n L The average value of (i) "L' n " is expressed by the following equation (7).

[0081]

number

[0082]

number

[0083]

number

[0084]

number

[0085]

number

[0086] (2-2) Data Structure Next, the voxel recommendation information that is commonly included in the map DB 10 and the delivery map DB 20 will be described.

[0087] FIG. 16 shows an example of the data structure of voxel recommendation information. As shown in FIG. 16, voxel recommendation information is information in which "voxel ID," "position," and "recommended voxel value" are associated with each other. The "voxel ID" specifies the voxel ID assigned to each voxel. The "position" specifies the voxel coordinates (latitude, longitude, and altitude, or xyz coordinates from a reference point) of the target voxel. The "recommended voxel value" specifies the recommended voxel value for using the target voxel for position estimation. Here, the recommended voxel value is set to a value between 0 and 1, and the closer to 1 the value, the more effective it is in improving the accuracy of position estimation.

[0088] As described above, in this embodiment, voxel recommendation information indicating the effectiveness (suitability) of position estimation for each voxel is included in the map DB 10. As a result, the vehicle-mounted device 1 can control the vehicle to improve the accuracy of vehicle position estimation by referring to the voxel recommendation information, similar to the case of using the object recommendation information in the first embodiment. For example, if the accuracy of the current vehicle position estimation is poor (i.e., the overall evaluation function value E(k) is low), the vehicle-mounted device 1 may move the vehicle to a lane where voxels with high voxel recommendation values ​​are more likely to be detected, or may increase the weighting of voxels with high voxel recommendation values ​​in NDT matching. Furthermore, the vehicle-mounted device 1 may switch the position estimation method if there are no voxels with high voxel recommendation values ​​in the vicinity.

[0089] The vehicle-mounted device 1 may receive download information Id including voxel recommendation information related to voxels around the vehicle position from the server device 6 by transmitting a predetermined request signal including current position information to the server device 6. In this case, the vehicle-mounted device 1 controls the vehicle based on the received download information Id to improve the accuracy of vehicle position estimation.

[0090] In the second embodiment, the vehicle-mounted device 1 determines a value (also called an "effectiveness value") indicating the effectiveness of the target voxel in improving the accuracy of estimating the vehicle's own position, instead of the validity flag. Hereinafter, the vehicle-mounted device 1 determines the individual evaluation function value E nis defined as the valid value. Then, the vehicle-mounted device 1 transmits upload information Iu including at least information associating a valid value with each voxel ID to the server device 6. The valid value is an example of "validity information."

[0091] Here, the individual evaluation function value E n is close to 1 when the matching degree is high, and close to 0 when the matching degree is low. n The larger the voxel, the more it contributes to increasing the overall evaluation function value E(k), and therefore the more effective it can be said to be. n is suitable as the effective value for each voxel.

[0092] Here, specific examples of setting the effective values ​​will be described with reference to FIGS.

[0093] FIG. 17 is a bird's-eye view showing the periphery of a vehicle equipped with the on-board device 1. Here, voxels with voxel IDs "1" to "35" are present within a predetermined distance from the vehicle. The voxels with voxel IDs "1" to "35" are located on the surfaces of objects 56 to 58. FIG. 18 shows an example of setting valid values ​​specified by the on-board device 1 in the upload information Iu indicating the detection results of voxel IDs "1" to "35." Furthermore, FIG. 19 is a graph showing the transition of the individual evaluation function value E4 for voxel ID "4" during a predetermined period including the detection period "Tw6" for voxel ID "4," and FIG. 20 is a graph showing the transition of the individual evaluation function value E4 for voxel ID "12" during a predetermined period including the detection period "Tw7" for voxel ID "12." 12 10 is a graph showing the change over time.

[0094] First, the vehicle-mounted device 1 acquires voxel data of voxel IDs "1" to "35" that exist within a predetermined distance from the vehicle from the map DB 10, and performs measurement using the LIDAR 2. As a result, the vehicle-mounted device 1 detects voxels with voxel IDs "4" to "11" that are located on the surface of the object 56 and voxels with voxel IDs "12" to "19" that are located on the surface of the object 57 at the same or different times, and performs vehicle position estimation by NDT matching. Then, the vehicle-mounted device 1 calculates the individual evaluation function value E of each voxel calculated in the vehicle position estimation by NDT matching. n Set as valid values ​​for the corresponding voxel ID.

[0095] At this time, preferably, the vehicle-mounted device 1 calculates the effective value corresponding to each voxel ID as the individual evaluation function value E calculated during the period in which the voxel of each voxel ID was detected. n For example, in the case of voxel ID "4", as shown in Fig. 19, the vehicle-mounted device 1 sets the average value of the individual evaluation function values ​​E4 calculated in the detection period Tw6 as the valid value for voxel ID "4". Similarly, in the case of voxel ID "12", as shown in Fig. 20, the vehicle-mounted device 1 sets the average value of the individual evaluation function values ​​E4 calculated in the detection period Tw7 as the valid value for voxel ID "4". 12 The average value of these is set as the valid value for voxel ID "12." Note that the detection period may be set to all the times when the target voxel is detected, or may be set to only the times when the target voxel is detected within a predetermined distance range.

[0096] On the other hand, the vehicle-mounted device 1 is unable to detect voxels with voxel IDs "22" to "31" located on the surface of object 58, which corresponds to a building covered with protective sheeting for exterior wall repair, because the presence of the protective sheeting causes deviations in the measurement values. Therefore, in this case, the vehicle-mounted device 1 sets the valid values ​​corresponding to voxel IDs "22" to "31" to 0. Then, the vehicle-mounted device 1 generates upload information Iu indicating combinations of voxel IDs and valid values, for example, as shown in FIG. 18, and transmits the information to the server device 6.

[0097] (2-3) Calculating recommended voxel values Next, the voxel estimate for each voxel is calculated. Recommended The server device 6 calculates the voxel estimation value for each voxel ID based on the valid value for each voxel ID included in the upload information Iu received from each vehicle-mounted device 1. Recommended Calculate the value.

[0098] For example, if the aggregate number (number of samples) of valid value information for each voxel ID is "S3" and the sum of the valid values ​​for each voxel ID is "S4," the server device 6 sets the recommended voxel value for each voxel ID to "S4 / S3." For example, if upload information Iu including valid values ​​for each voxel ID "1" is received 100 times from each vehicle-mounted device 1 and the sum of these valid values ​​is 70.0, the vehicle-mounted device 1 sets the recommended voxel value for voxel ID "1" to "0.7" (=70.0 / 100).

[0099] By doing so, the server device 6 can suitably set the recommended voxel value of each voxel ID to be in the range from 0 to 1. In this case, the recommended voxel value of a voxel that was not detected due to occlusion or the influence of rain or snow becomes small, and even if a voxel is detected, the recommended voxel value E n The voxel recommendation value for voxels where the individual evaluation function value E n The voxel recommendation value is higher for voxels where the value is statistically high (in other words, effective for position estimation).

[0100] Preferably, the server device 6 calculates the recommended voxel value using valid values ​​of the upload information Iu received within a predetermined time period in the past (e.g., 10 minutes). More preferably, the server device 6 sets the predetermined time period shorter as the traffic volume on the road on which the target voxel can be detected increases. In another example, the server device 6 may calculate the recommended voxel value of the target voxel based on valid values ​​included in a predetermined number of pieces of upload information Iu most recently acquired for the target voxel. In this case, the value S3 is the predetermined number, and the vehicle-mounted device 1 can preferably calculate the recommended voxel value using a certain number of samples.

[0101] (2-4) Processing Flow FIG. 21 is an example of a flowchart showing an outline of processing related to transmission and reception of upload information Iu including valid values ​​and download information Id including voxel recommendation information.

[0102] First, the vehicle-mounted device 1 refers to the map DB 10 and acquires voxel data of voxels that exist at positions that can be detected by the LIDAR 2 (step S111). Then, the vehicle-mounted device 1 determines whether or not each voxel for which voxel data has been acquired in step S111 has been detected by the LIDAR 2 and the individual evaluation function value E n In this case, the vehicle-mounted device 1 sets the validity value for each voxel ID based on the above (step S112). In this case, the vehicle-mounted device 1 sets the validity value for the voxel ID of a voxel for which point cloud data cannot be acquired by the LIDAR 2 to 0, and sets the validity value for the voxel ID of a voxel for which point cloud data can be acquired by the LIDAR 2 to the individual evaluation function value E of the voxel. n Then, the vehicle-mounted device 1 transmits the upload information Iu in which the valid value is associated with each voxel ID to the server device 6 (step S113).

[0103] The server device 6 receives the upload information Iu transmitted in step S113 and stores the upload information Iu in the upload information DB 27 (step S211). If it is time to update the delivery map DB 20 (step S212; Yes), the server device 6 references the upload information DB 27 to generate voxel recommendation information and the like, and updates the delivery map DB 20 using the generated voxel recommendation information and the like (step S213). The server device 6 then transmits download information Id including the voxel recommendation information and the like generated in step S213 to each in-vehicle device 1 (step S214). Note that the server device 6 may transmit the download information Id only to the in-vehicle device 1 that has requested the transmission of the download information Id. On the other hand, if it is not time to update the delivery map DB 20 (step S212; No), the server device 6 continues to execute step S211.

[0104] When the vehicle-mounted device 1 receives the download information Id (step S114; Yes), it updates the map DB 10 using the download information Id (step S115). As a result, the latest voxel recommendation information is recorded in the map DB 10. On the other hand, when the vehicle-mounted device 1 does not receive the download information Id from the server device 6 (step S114; No), it returns the process to step S111.

[0105] <Modification> The following describes preferred modifications of the first and second embodiments. The following modifications may be applied to these embodiments in combination.

[0106] (Variation 1) Instead of the vehicle-mounted device 1 determining the route (target trajectory) of the vehicle by referring to the map DB 10 including the object recommendation information or the voxel recommendation information, the server device 6 that receives a route search request from the vehicle-mounted device 1 may perform a process of determining the route (target trajectory) of the vehicle of the requesting vehicle-mounted device 1 by referring to the delivery map DB 20 including the object recommendation information or the voxel recommendation information. In this way, the object recommendation information and the voxel recommendation information are also preferably used by the server device 6.

[0107] (Variation 2) The configuration of the driving assistance system shown in Fig. 1 is one example, and the configuration of a driving assistance system to which the present invention can be applied is not limited to the configuration shown in Fig. 1. For example, instead of having an onboard device 1, the driving assistance system may have an electronic control device of the vehicle that executes the processes of the vehicle position estimation unit 17 and the upload control unit 18 of the onboard device 1. In this case, the map DB 10 may be stored in, for example, a storage unit in the vehicle, and the electronic control device of the vehicle may exchange upload information Iu and download information Id with the server device 6 via the onboard device 1 or a communication unit (not shown). [Explanation of symbols]

[0108] 1 On-vehicle device 2 Rider 3 Gyro sensor 4 Vehicle speed sensor 5 GPS receiver 6 Server equipment 10 Map DB 20 Distribution map database

Claims

1. a detection unit that detects objects around the moving object; a first acquisition unit that acquires an estimated position of the moving object; a second acquisition unit that acquires map information including position information of objects around the estimated position and recommended value information indicating recommended values ​​for performing position estimation of the moving body; a position estimation unit that estimates a position of the moving body based on a detection result of the object detected by the detection unit, the estimated position, and position information of the object included in the map information; Equipped with The information processing device, wherein the position estimation unit estimates the position of the moving object by performing a weighting calculation based on the recommendation value information.

2. the recommended value information includes a recommended value for each direction in estimating the position of the moving object, The information processing apparatus according to claim 1 , wherein the position estimation unit estimates the position of the moving object by performing a weighting calculation for each direction with the moving object as a reference, based on the recommendation value information for each direction.

3. the recommended value information further includes a recommended value of a direction in position estimation of the moving body; The information processing device according to claim 2 , wherein the position estimation unit estimates the position of the mobile unit by performing a weighting operation on the orientation based on the recommended orientation value information, with the mobile unit as a reference.

4. the map information includes, for each unit area, position information of an object and the recommended value information; The information processing apparatus according to claim 1 , wherein the position estimation unit estimates the position of the moving object by performing a weighting calculation based on the recommendation value information for each unit area.

5. An information processing device having a memory unit that stores map data including position information of an object and information indicating a recommended value for estimating the position of a moving body using the positional relationship with the object measured by a measuring device mounted on the moving body, the recommended value information being used in a weighting calculation for the position estimation.

6. A map data generation device having a generation unit that generates map data by associating recommended value information used in weighting calculations for position estimation, the recommended value information being generated based on identification information of the object and effectiveness information regarding the effectiveness of improving the accuracy of position estimation of the moving body using the object, with the position information of the object.

7. 1. A computer-implemented method comprising: a detection step of detecting an object around the moving body; a first acquisition step of acquiring an estimated position of the moving object; a second acquisition step of acquiring map information including position information of objects around the estimated position and recommended value information indicating recommended values ​​for performing position estimation of the moving body; a position estimation step of estimating a position of the moving body based on a detection result of the object detected in the detection step, the estimated position, and position information of the object included in the map information; and The method is characterized in that the position estimation step estimates the position of the mobile object by a weighting calculation based on the recommendation value information.

8. a detection unit that detects objects around the moving object; a first acquisition unit that acquires an estimated position of the moving object; a second acquisition unit that acquires map information including position information of objects around the estimated position and recommended value information indicating recommended values ​​for performing position estimation of the moving body; a position estimation unit that estimates the position of the moving body based on the detection result of the object detected by the detection unit, the estimated position, and position information of the object included in the map information; Make the computer function as The position estimation unit estimates the position of the moving object by performing a weighting calculation based on the recommendation value information.

Images