Determination device

Abstract

To detect that an actual position of a feature does not match a position in map data and corrects the map data according to the needs.SOLUTION: A determination device acquires first distance information indicating distance from a mobile body to a target, measured by a measuring unit, and second distance information indicating distance from the mobile body to the target, estimated by an estimation unit. The determination device also acquires self-position accuracy information of the mobile body. The determination device then determines whether there is a deviation equal to or more than a predetermined value between a position of the target in map information and a position of the target in a real environment based on a difference value between positions indicated by the first distance information and the second distance information and the self-position accuracy information.SELECTED DRAWING: Figure 7

Description

[Technical Field]

[0001] This invention relates to a technique for correcting the position of features in map data. [Background technology]

[0002] A method has been proposed for updating map data based on surrounding information detected by external sensors mounted on a vehicle. For example, Patent Document 1 describes a method for generating change point candidate data by comparing surrounding information based on the output of a sensor with a partial map database, and updating the map data while considering the accuracy of the sensor at the time of acquisition of the change point candidate data. [Prior art documents] [Patent Documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2016-156973 [Overview of the Initiative] [Problems that the invention aims to solve]

[0004] Map data stored on a server includes the position coordinates of features, but if the position coordinates of a feature change for any reason after their measurement, the position coordinates of the feature in the map data will no longer match the actual position coordinates of the feature. For example, if a sign is moved due to a vehicle collision or if a ground-based installation moves, the actual position of the feature will no longer match its position in the map data. In such a state, estimating the vehicle's position using the position coordinates of the feature in the map data will result in a large estimation error, which may impair driver assistance or autonomous driving.

[0005] The above are examples of problems that this invention aims to solve. The present invention aims to detect when the actual location of a feature does not match the location in map data and to correct the map data as necessary. [Means for solving the problem]

[0006] The invention described in the claim is a determination device comprising: a first acquisition unit that acquires first distance information indicating the distance from a moving body to an object, measured by a measuring unit, and second distance information indicating the distance from the position of the moving body to the position of the object in map information, estimated by an estimation unit; a second acquisition unit that acquires measurement accuracy information indicating the measurement accuracy of the position of the object by the measuring unit, and estimation accuracy information indicating the estimation accuracy of the position of the moving body by the estimation unit; and a determination unit that determines whether or not there is a deviation of a predetermined value or more between the position of the object in map information and the position of the object in the real environment, based on the difference value of the distance indicated by the first distance information and the second distance information, the measurement accuracy information, and the estimation accuracy information.

[0007] The invention described in the claim is a determination method performed by a determination device, comprising: a first acquisition step of acquiring first distance information indicating the distance from a moving body to an object, measured by a measuring unit, and second distance information indicating the distance from the position of the moving body to the position of the object in map information, estimated by an estimation unit; a second acquisition step of acquiring measurement accuracy information indicating the measurement accuracy of the position of the object by the measuring unit, and estimation accuracy information indicating the estimation accuracy of the position of the moving body by the estimation unit; and a determination step of determining whether there is a deviation of a predetermined value or more between the position of the object in map information and the position of the object in the real environment, based on the difference value of the distances indicated by the first distance information and the second distance information, the measurement accuracy information, and the estimation accuracy information.

[0008] The invention described in the claim is a program executed by a determination device equipped with a computer, characterized in that the computer functions as follows: a first acquisition unit that acquires first distance information indicating the distance from a moving body to an object, measured by a measurement unit, and second distance information indicating the distance from the position of the moving body to the position of the object in map information, estimated by an estimation unit; a second acquisition unit that acquires measurement accuracy information indicating the measurement accuracy of the position of the object by the measurement unit, and estimation accuracy information indicating the estimation accuracy of the position of the moving body by the estimation unit; and a determination unit that determines whether or not there is a deviation of a predetermined value or more between the position of the object in map information and the position of the object in the real environment, based on the difference value of the distance indicated by the first distance information and the second distance information, the measurement accuracy information, and the estimation accuracy information.

[0009] The invention described in the claim is a determination device comprising: a first acquisition unit that acquires first distance information indicating the distance from a moving body to an object measured by a measuring unit, and second distance information indicating the distance from the position of the moving body to the position of the object in map information estimated by an estimation unit; a second acquisition unit that acquires measurement accuracy information indicating the measurement accuracy of the position of the object by the measuring unit, and estimation accuracy information indicating the estimation accuracy of the position of the moving body by the estimation unit; and a determination unit that determines whether or not there is a deviation of a predetermined value or more between the position of the object in map information and the position of the object based on the first distance, based on the difference value of the distances indicated by the first distance information and the second distance information, the measurement accuracy information, and the estimation accuracy information. [Brief explanation of the drawing]

[0010] [Figure 1] This is a schematic diagram of the driver assistance system. [Figure 2] This is a block diagram showing the functional configuration of an in-vehicle device. [Figure 3] This is a block diagram showing the functional configuration of a server device. [Figure 4] This is a diagram showing the state variable vector represented in two-dimensional Cartesian coordinates. [Figure 5]This diagram shows the general relationship between the prediction step and the measurement update step. [Figure 6] This shows the positional relationship between the vehicle and the feature during position estimation. [Figure 7] This is a flowchart of the difference value evaluation process according to the first embodiment. [Figure 8] This is a flowchart of the map correction process according to the first embodiment. [Figure 9] This is a diagram showing the state variable vector represented in three-dimensional Cartesian coordinates. [Figure 10] This diagram shows the general relationship between the prediction step and the measurement update step. [Figure 11] This shows the positional relationship between the vehicle and the feature during position estimation. [Figure 12] This is a flowchart of the difference value evaluation process according to the second embodiment. [Figure 13] This is a flowchart of the map correction process according to the second embodiment. [Figure 14] This diagram illustrates the effects of the examples provided. [Figure 15] This shows an example of multiple road signs being installed side by side. [Figure 16] Examples of complex white lines and white lines with road studs are shown. [Modes for carrying out the invention]

[0011] In one preferred embodiment of the present invention, the determination device includes: a first acquisition unit that acquires first distance information indicating the distance from a moving body to an object, measured by a measurement unit, and second distance information indicating the distance from the moving body to the object, estimated by an estimation unit; a second acquisition unit that acquires self-position accuracy information of the moving body; and a determination unit that determines whether there is a deviation of a predetermined value or more between the position of the object in map information and the position of the object in the real environment, based on the difference value of the distances indicated by the first distance information and the second distance information, and the self-position accuracy information.

[0012] The determination device described above acquires first distance information, which indicates the distance from the moving object to the target object, measured by the measurement unit, and second distance information, which indicates the distance from the moving object to the target object, estimated by the estimation unit. The determination device also acquires the self-position accuracy information of the moving object. Based on the difference in position values ​​indicated by the first distance information and the second distance information, and the self-position accuracy information, the device determines whether there is a discrepancy of a predetermined value or more between the position of the target object in the map information and the position of the target object in the real environment. According to the determination device described above, it is determined whether there is a discrepancy between the position of the target object in the map information and the actual position of the target object measured by the measurement unit.

[0013] In one embodiment of the determination device described above, the second acquisition unit further acquires measurement accuracy information from the measurement unit, and the determination unit determines whether or not there is a deviation greater than the predetermined value based on the self-position accuracy information and the measurement accuracy information. In this embodiment, the deviation of the object's position is determined by also considering the measurement accuracy of the measurement unit.

[0014] Another embodiment of the above-described determination device includes a third acquisition unit that acquires error information, including the difference value, the self-position accuracy information, and the measurement accuracy information, for an object that has been determined to have a deviation of a predetermined value or more, and a correction unit that, when the third acquisition unit acquires a predetermined number or more of error information for the same object, corrects the position of the object in the map information by statistical processing of the difference values ​​included in the acquired error information. In this embodiment, the position of the object in the map information can be corrected by statistically processing the difference values ​​included in the error information.

[0015] In another embodiment of the determination device described above, the correction unit converts the difference value from the moving object coordinate system to the map coordinate system and corrects the position in the map information.

[0016] In another embodiment of the above-described determination device, the correction unit reduces the level of positional accuracy information assigned to the object in the map information if the third acquisition unit acquires error information from a predetermined number of vehicles for the same object, and the variation in the difference values ​​included in the acquired error information is greater than or equal to a predetermined value. In this embodiment, information indicating low positional accuracy is assigned to the object in the map information.

[0017] In another embodiment of the above-described determination device, the determination device is mounted on a vehicle and includes a transmission unit that, when the determination unit determines that there is a deviation greater than or equal to a predetermined value, transmits error information, including the difference value, the self-position accuracy information, and the measurement accuracy information, to an external server. In this embodiment, if it is determined that there is a deviation greater than or equal to a predetermined value in the position of the object, the error information is transmitted to the server.

[0018] In another embodiment of the above-described determination device, when the transmitting unit acquires multiple pieces of error information for the same object, if the variance value based on the multiple pieces of error information is less than or equal to a predetermined value, it transmits the multiple pieces of error information or the information obtained using the multiple pieces of error information to the external server. In this embodiment, the transmission frequency can be reduced because error information is transmitted only when there is a high probability that the position of the feature is shifted.

[0019] In another embodiment of the determination device described above, the error information includes date and time information and identification information of the object. In this embodiment, it is possible to determine when the displacement of the object's position occurred based on the date and time information.

[0020] In another embodiment of the determination device described above, the measurement unit and the estimation unit are mounted on a vehicle, the determination device is installed on a server, and the first acquisition unit acquires the first distance information and the second distance information from the vehicle. In this embodiment, the determination device installed on the server can determine the deviation of the object's position based on the information acquired from the vehicle.

[0021] In another preferred embodiment of the present invention, the determination method performed by the determination device includes: a first acquisition step of acquiring first distance information indicating the distance from a moving body to an object, measured by a measuring unit, and second distance information indicating the distance from the moving body to the object, estimated by an estimation unit; a second acquisition step of acquiring self-position accuracy information of the moving body; and a determination step of determining whether there is a discrepancy of a predetermined value or more between the position of the object in map information and the position of the object in the real environment, based on the difference value of the distances indicated by the first distance information and the second distance information, and the self-position accuracy information. According to this method, it is determined whether there is a discrepancy between the position of the object in map information and the actual position of the object measured by the measuring unit.

[0022] In another preferred embodiment of the present invention, a program executed by a determination device equipped with a computer causes the computer to function as follows: a first acquisition unit that acquires first distance information indicating the distance from a moving body to an object, measured by a measurement unit, and second distance information indicating the distance from the moving body to the object, estimated by an estimation unit; a second acquisition unit that acquires self-position accuracy information of the moving body; and a determination unit that determines whether there is a deviation of a predetermined value or more between the position of the object in map information and the position of the object in the real environment, based on the difference value of the distances indicated by the first and second distance information and the self-position accuracy information. The above determination device can be realized by executing this program on the computer. This program can be stored and handled on a storage medium.

[0023] In another preferred embodiment of the present invention, the determination device includes a first acquisition unit that acquires first distance information indicating the distance from a moving body to an object, measured by a measurement unit, and second distance information indicating the distance from the moving body to the object, estimated by an estimation unit; a second acquisition unit that acquires self-position accuracy information of the moving body; and a determination unit that determines whether there is a deviation of a predetermined value or more between the position of the object in map information and the position of the object based on the first distance, based on the difference value of the distances indicated by the first distance information and the second distance information, and the self-position accuracy information.

[0024] The determination device described above acquires first distance information, which indicates the distance from the moving object to the target object, measured by the measurement unit, and second distance information, which indicates the distance from the moving object to the target object, estimated by the estimation unit. The determination device also acquires the self-position accuracy information of the moving object. Based on the difference value of the positions indicated by the first distance information and the second distance information, and the self-position accuracy information, the device determines whether there is a discrepancy of a predetermined value or more between the position of the target object in the map information and the position of the target object based on the first distance information. According to the determination device described above, it is determined whether there is a discrepancy between the position of the target object in the map information and the actual position of the target object measured by the measurement unit. [Examples]

[0025] Hereinafter, preferred embodiments of the present invention will be described with reference to the drawings. [Schematic configuration]

[0026] Figure 1 is a schematic diagram of the driver assistance system according to this embodiment. The driver assistance system is broadly composed of an on-board unit 10 mounted on a vehicle 1 and a server device 20. The vehicle 1 is equipped with an on-board unit 10 that performs control related to driver assistance for the vehicle, a Lidar (Light Detection and Ranging, or Laser Illuminated Detection and Ranging) 2, a gyro sensor 3, a vehicle speed sensor 4, and a GPS receiver 5. The server device 20 is equipped with a map database (hereinafter referred to as "DB") 23 that stores map data. The on-board unit 10 and the server device 20 transmit and receive data wirelessly. Although only one vehicle 1 and on-board unit 10 are shown in Figure 1 for convenience, in reality, the server device 20 communicates with on-board units 10 in multiple vehicles 1.

[0027] The in-vehicle unit 10 is electrically connected to the lidar 2, gyro sensor 3, vehicle speed sensor 4, and GPS receiver 5, and estimates the position of the vehicle on which the in-vehicle unit 10 is mounted (also called "vehicle position") based on their outputs. Then, based on the estimated vehicle position, the in-vehicle unit 10 performs automatic driving control of the vehicle so that it travels along a set route to a destination. The in-vehicle unit 10 stores a map DB 13 that stores road data and feature information, which is information about landmark features located near the road. The aforementioned landmark features include, for example, kilometer posts, 100m posts, delineators, traffic infrastructure equipment (e.g., signs, directional signs, traffic lights), utility poles, and streetlights that are periodically lined up along the side of the road, and the feature information is information that associates at least the feature ID, which is the identification information of each feature, the location information of the feature, and the orientation information of the feature. Then, the in-vehicle unit 10 estimates its own position by comparing it with the output of the lidar 2, etc., based on this geographic information.

[0028] The lidar 2 discretely measures the distance to an object in the external environment by emitting a pulsed laser within a predetermined angular range in the horizontal and vertical directions, and generates three-dimensional point cloud information indicating the position of the object. In this case, the lidar 2 has an irradiation unit that irradiates laser light while changing the irradiation direction, a light receiving unit that receives reflected light (scattered light) of the irradiated laser light, and an output unit that outputs scan data based on the received signal output by the light receiving unit. The scan data is generated based on the irradiation direction corresponding to the laser light received by the light receiving unit and the response delay time of the laser light specified based on the received signal described above. In this embodiment, the lidar 2 is assumed to be installed facing the direction of travel of the vehicle so as to scan at least the area in front of the vehicle. The lidar 2, gyro sensor 3, vehicle speed sensor 4, and GPS receiver 5 each supply output data to the in-vehicle unit 10.

[0029] Figure 2 is a block diagram showing the functional configuration of the in-vehicle unit 10. The in-vehicle unit 10 mainly consists of an interface 11, a storage unit 12, an input unit 14, a control unit 15, and an information output unit 16. Each of these elements is interconnected via a bus line.

[0030] Interface 11 acquires output data from sensors such as the rider 2, gyro sensor 3, vehicle speed sensor 4, and GPS receiver 5, and supplies it to the control unit 15. Interface 11 also supplies signals related to vehicle driving control generated by the control unit 15 to the vehicle's electronic control unit (ECU).

[0031] The storage unit 12 stores programs executed by the control unit 15 and information necessary for the control unit 15 to perform predetermined processes. In this embodiment, the storage unit 12 also stores a map database 13 containing feature information. Feature information is information associated with each feature, and in this case, it includes a feature ID, which is the identification information of the feature, location information, and shape information. Location information indicates the absolute position of the feature, expressed by latitude and longitude (and elevation), etc. Shape information is information about the shape of the feature, and includes normal information indicating the orientation of the feature (i.e., the normal direction relative to the front) and size information indicating the size of the feature. The map database 13 may be updated periodically. In this case, for example, the control unit 15 receives partial map information relating to the area to which the vehicle's position belongs from a server device 20 that manages map information via a communication unit (not shown), and reflects it in the map database 13.

[0032] The input unit 14 includes buttons, a touch panel, a remote controller, a voice input device, etc., for user operation, and accepts inputs such as specifying a destination for route searching and specifying whether to turn autonomous driving on or off. The information output unit 16 includes, for example, a display or speaker that outputs based on the control of the control unit 15.

[0033] The control unit 15 includes a CPU for executing programs and controls the entire in-vehicle unit 10. In this embodiment, the control unit 15 includes a vehicle position estimation unit 17 and an automatic driving control unit 18. The vehicle position estimation unit 17 corrects the vehicle position estimated from the output data of the gyro sensor 3, vehicle speed sensor 4, and / or GPS receiver 5 based on distance and angle measurements taken by the lidar 2 to a feature and location information of the feature extracted from the map DB 13. In this embodiment, as an example, the vehicle position estimation unit 17 alternately performs a prediction step of estimating the vehicle position from output data of the gyro sensor 3, vehicle speed sensor 4, etc., based on a state estimation method based on Bayesian estimation, and a measurement update step of correcting the estimated value of the vehicle position calculated in the previous prediction step.

[0034] The automatic driving control unit 18 refers to the map DB 13 and transmits signals necessary for automatic driving control to the vehicle based on the set route and the vehicle's position estimated by the vehicle position estimation unit 17. Based on the set route, the automatic driving control unit 18 sets a target trajectory and controls the vehicle's position by transmitting guide signals to the vehicle so that the vehicle's position estimated by the vehicle position estimation unit 17 is within a predetermined width of the target trajectory.

[0035] In the above configuration, the rider 2 is an example of the measurement unit of the present invention, and the control unit 15 is an example of the estimation unit, first acquisition unit, second acquisition unit, and determination unit of the present invention.

[0036] Figure 3 is a block diagram showing the functional configuration of the in-vehicle server device 20. The server device 20 mainly comprises a communication unit 21, a storage unit 22, and a control unit 25. These elements are interconnected via a bus line. The communication unit 21 communicates with the in-vehicle device 10 via wireless communication or the like. The storage unit 22 stores programs executed by the control unit 25 and information necessary for the control unit 25 to perform predetermined processes. The storage unit 22 also stores a map DB 23 containing feature information. The control unit 25 includes a CPU that executes programs and controls the entire server device 20. Specifically, the control unit 25 performs the map correction process described later.

[0037] [First Embodiment] (Vehicle position estimation process) Next, a first embodiment of the vehicle position estimation process by the vehicle position estimation unit 17 will be described. The vehicle position estimation unit 17 estimates the vehicle position by sequentially repeating the prediction step and the measurement update step. Various filters developed for Bayesian estimation can be used as state estimation filters in these steps, such as extended Kalman filters, unscented Kalman filters, and particle filters. Thus, various methods have been proposed for position estimation based on Bayesian estimation. Below, a brief explanation of vehicle position estimation using an extended Kalman filter will be given as an example.

[0038] Figure 4 shows the state variable vector X represented in two-dimensional Cartesian coordinates. In the first embodiment, the z-coordinate is projected onto the two-dimensional xy Cartesian coordinate system. As shown in Figure 4, the position of the vehicle on a plane defined on the two-dimensional xy Cartesian coordinate system is represented by the coordinates "(x,y)" and the vehicle's bearing "Ψ". Here, the bearing Ψ is defined as the angle between the vehicle's direction of travel and the x-axis. The coordinates (x,y) represent an absolute position, for example, corresponding to a combination of latitude and longitude.

[0039] Figure 5 shows a schematic relationship between the prediction step and the measurement update step. As shown in Figure 5, the calculation and updating of the estimated value of the state variable vector X are performed sequentially by repeating the prediction step and the measurement update step. Here, the state variable vector for the reference time (i.e., the current time) "k" to be calculated is denoted as "Xe(k)" or "Xp(k)". Note that the provisional predicted value predicted in the prediction step is given the subscript "e", and the more accurate estimated value updated in the measurement update step is given the subscript "p". (State variable vector Xe(k) = (x e (k), y e (k), Ψ e (k)) T "State variable vector Xp(k)=(x p (k), y p (k), Ψ p (k)) TIt is written as "."

[0040] In the prediction step, the vehicle position estimation unit 17 applies the vehicle's movement speed "v" and the yaw angular velocity "ω" around the z axis (collectively referred to as the "control value u(k)") to the state variable vector Xp(k-1) calculated in the previous measurement update step at time k-1, thereby calculating the predicted value of the vehicle's position at time k (also called the "predicted vehicle position") Xe(k). Simultaneously, the vehicle position estimation unit 17 calculates a covariance matrix Pe(k), which corresponds to the error distribution of the predicted vehicle position Xe(k), from the covariance matrix Pp(k-1) at time k-1 calculated in the previous measurement update step.

[0041] In the measurement update step, the vehicle position estimation unit 17 associates the position vectors of features registered in the map DB 13 with the scan data of the lidar 2. If this association is successful, the vehicle position estimation unit 17 obtains the measured value of the associated feature by the lidar 2 (referred to as the "feature measurement value") "Z(i)" and the predicted value of the feature (referred to as the "feature prediction value") "Ze(i)" obtained by modeling the measurement process by the lidar 2 using the predicted vehicle position Xe(k) and the position vectors of the features registered in the map DB 13. The feature measurement value Z(i) is a two-dimensional vector obtained by converting the distance and scan angle of the feature measured by the lidar 2 at time i into components with the vehicle's direction of travel and lateral direction as axes. The vehicle position estimation unit 17 then calculates the difference between the feature measurement value Z(i) and the feature prediction value Ze(i) as shown in equation (1) below.

number

[0042] Furthermore, the vehicle position estimation unit 17 calculates an updated state variable vector (also called the "measured updated vehicle position") Xp(k) by multiplying the difference between the measured feature value Z(i) and the predicted feature value Ze(i) by the Kalman gain "K(k)" as shown in equation (2) below, and adding this to the predicted vehicle position Xe(k).

number

[0043] Furthermore, in the measurement update step, the vehicle position estimation unit 17, similar to the prediction step, obtains a covariance matrix Pp(k) corresponding to the error distribution of the measured updated vehicle position Xp(k) from the prior covariance matrix Pe(k). Parameters such as the Kalman gain K(k) can be calculated in the same way as known self-positioning techniques using, for example, an extended Kalman filter.

[0044] Furthermore, if the vehicle position estimation unit 17 can associate the position vectors of multiple features registered in the map DB 13 with the scan data of the lidar 2, it may perform a measurement update step based on any one selected feature measurement value, or it may perform the measurement update step multiple times based on all the feature measurement values ​​that can be associated. When using multiple feature measurement values, the vehicle position estimation unit 17 takes into account that the accuracy of the lidar measurement deteriorates the further the feature is from the lidar 2, and reduces the weighting of the feature as the distance between the lidar 2 and the feature increases. In addition, generally, the major axis, minor axis, and angle of the error ellipse, which indicates the expected range of error (for example, the range based on a 99% confidence interval), can be calculated from the eigenvalues ​​and eigenvectors of the covariance matrix corresponding to the error distribution described above.

[0045] In this way, the prediction step and the measurement update step are repeatedly performed, and the predicted vehicle position Xe(k) and the measured updated vehicle position Xp(k) are calculated sequentially, thereby calculating the most likely vehicle position. The vehicle position calculated in this way is called the "estimated vehicle position".

[0046] In the above description, the measured feature value is an example of the first distance information of the present invention, the predicted feature value is an example of the second distance information of the present invention, the position estimation accuracy is an example of the self-position accuracy information of the present invention, and the lidar measurement accuracy is an example of the measurement accuracy information of the present invention.

[0047] (How to correct feature position) Next, we will explain how to correct the position of features. In EKF (Extended Karman Filter) position estimation using road signs and other features with a LiDAR, the vehicle's position is calculated using the difference between the measured feature value and the predicted feature value, as shown in equation (2) above. As can be seen from equation (2), when the difference value shown in equation (1) becomes large, the amount of correction to the predicted vehicle position Xe(k) becomes large. Here, the difference value becomes large due to one of the following reasons (A) to (C).

[0048] (A) There is an error in the measurement of geological features. This includes, for example, cases where the RIDA's measurement accuracy is low, or cases where the RIDA's measurement accuracy is not low, but the measurement accuracy is reduced because there is a moving object such as another vehicle between the object being measured and the vehicle at the time of measurement (so-called occlusion). (B) The estimated vehicle position used to determine the predicted values ​​for geographic features is incorrect. This occurs when there is a large error in the predicted vehicle position Xe(k) or the measured updated vehicle position Xp(k). (C) The position coordinates of the features in the map data used to determine the feature prediction values ​​are incorrect. This refers to a situation where, for example, after the map data was created, the location of a feature (such as a signpost) has changed due to a vehicle collision or other incident, or a structure (such as a feature) has been removed or moved, resulting in a discrepancy between the location coordinates of the feature in the map data and its actual location. In other words, there is a discrepancy between the location of the feature in the map data and its location in the real environment. Therefore, in this case, it is necessary to correct the location coordinates of the feature in the map data so that they match the location coordinates of the actual feature. In this embodiment, the location of such a feature is corrected using the following method.

[0049] Figure 6 shows the positional relationship between the estimated position of the vehicle and the ground objects. The coordinate system shown in Figure 6 is a body coordinate system based on the vehicle. The x-axis indicates the traveling direction of the vehicle, and the y-axis indicates the direction perpendicular thereto (the left-right direction of the vehicle). In Figure 6, the estimated own vehicle position has an error range indicated by the error ellipse 40. The error ellipse 40 is defined by the position estimation accuracy σ P (x) in the x direction and the position estimation accuracy σ P (y) in the y direction. Here, the position estimation accuracy σ P is obtained by converting the covariance matrix P(k) to the body coordinate system using the Jacobian matrix H(i) according to the following equation (3).

Equation

[0050] In Figure 6, the ground object predicted value 41 is obtained by modeling the measurement process by the lidar 2 using the predicted own vehicle position Xe(k) and the position vector of the ground object registered in the map DB13. The ground object measured value 42 is the measured value by the lidar 2 of the ground object when the position vector of the ground object registered in the map DB13 can be associated with the scan data of the lidar 2. The ground object measured value 42 has an error range indicated by the error ellipse 43. Generally, since the measurement error by the lidar increases in proportion to the square of the distance between the lidar and the ground object to be measured, the lidar measurement accuracy σ L (σ L (x), σ L (y)) is obtained by calculating the error ellipse 43 based on the distance from the estimated own vehicle position to the ground object measured value. Also, the difference value between the ground object predicted value 41 and the ground object measured value 42 is dx in the x direction and dy in the y direction as shown in Equation (1).

[0051] Figure 7 is a flowchart of the difference value evaluation process performed by the on-board unit 10 of vehicle 1. This process is actually performed by the control unit 15 of the on-board unit 10 executing a pre-prepared program. First, the on-board unit 10 obtains the difference value (dy, dy) between the measured feature value 42 and the predicted feature value 41 using equation (1) above (step S11). Next, the on-board unit 10 obtains the position estimation accuracy σ from the covariance matrix P(k) using equation (3). P (Step S12) The vehicle-mounted device 10 calculates the rider measurement accuracy σ based on the distance to the feature. L We find (step S13).

[0052] Thus, the difference value (dx, dy), the position estimation accuracy σ P (σ P (x), σ P (y)), Rider measurement accuracy σ L (σ L (x), σ L Once (y) is obtained, the in-vehicle unit 10 evaluates the difference value using the position estimation accuracy and the lidar measurement accuracy. That is, it looks at the ratio of the difference value to the error range 40 of the estimated vehicle position and the lidar error range 43 to determine the validity of the difference between the feature measurement value 42 and the feature prediction value 41. Specifically, the in-vehicle unit 10 calculates the evaluation values ​​Ex and Ey using the following evaluation formula (4) (step S14).

number

[0053] Next, the in-vehicle device 10 compares the evaluation values ​​Ex and Ey with predetermined values ​​(step S15). If at least one of the evaluation values ​​Ex and Ey is greater than the predetermined value (step S15: Yes), the in-vehicle device 10 determines that the cause of the large difference is either (A) or (C) above, i.e., that (C) is likely. Therefore, the in-vehicle device 10 collects the following error information: the date and time information at that time, the estimated vehicle position at that time, the feature ID of the target feature, the difference value (dx, dy), and the position estimation accuracy σ. P (σ P (x), σ P(y)), Rider measurement accuracy σ L (σ L (x), σ L (y)) is sent to the server device 20 (step S16). Then the process ends. The reason why date and time information is included in the error information sent to the server device 20 is that if the actual position coordinates of a feature change due to environmental changes of the feature, the date and time information allows us to know when that change occurred.

[0054] On the other hand, if both evaluation values ​​Ex and Ey are smaller than a predetermined value (step S15: No), the in-vehicle device 10 terminates processing. In the case of (B) above, the denominator of evaluation formula (4) is large, so even if the numerators |dx| and |dy| are large, the evaluation values ​​Ex and Ey will not be large. In this way, the difference value evaluation processing in the in-vehicle device 10 detects that the cause of the large difference value may be (C) above, and the error information at that time is sent to the server device 20.

[0055] Next, the map correction process performed by the server device 20 will be described. Figure 8 is a flowchart of the map correction process. This process is actually performed by the control unit 25 of the server device 20 executing a pre-prepared program. First, the server device 20 acquires error information from multiple vehicles (step S21). Next, the server device 20 determines whether or not it has acquired a predetermined number of error information points for the same feature (step S22).

[0056] If a predetermined number of error information points have not been obtained for the same feature (Step S22: No), the server device 20 determines that the reason the difference value increased on the vehicle side is (A), that is, the difference value increased by chance due to occlusion or the like, and terminates the process.

[0057] On the other hand, if a predetermined number of error information points are obtained for the same feature (Step S22: Yes), the server device 20 determines that the reason for the large difference value on the vehicle side is (C), that is, the position coordinates of the feature in the map data do not match the position coordinates of the actual feature. Since the difference value is the amount of correction in the direction of travel and left-right direction of the vehicle in the body coordinate system shown in Figure 6, the server device 20 converts the difference values ​​(dx, dy) included in the error information received from multiple in-vehicle devices 10 into the amount of deviation in the map coordinate system. Then, the server device 20 determines a correction difference value that indicates the amount of correction for the feature position in the map data by weighted statistical processing (Step S23). Specifically, the server device 20 determines the position estimation accuracy σ of each vehicle included in the error information received from the in-vehicle devices 10. P and rider measurement accuracy σ L The difference values ​​are statistically processed using weighting based on the following: for example, the position estimation accuracy σ P and rider measurement accuracy σ L Error information with a small sum is judged to be highly accurate, and its weight is increased. In this way, the server device 20 determines the difference value for correction.

[0058] Furthermore, if the server device 20 has not acquired a predetermined number of error information for the same feature (step S22: No), it determines that the reason the difference value increased on the vehicle side is (A), and if it has acquired a predetermined number of error information for the same feature (step S22: Yes), it determines that the reason the difference value increased on the vehicle side is (C). However, it is not necessary to determine whether the reason the difference value increased is (A) or (C). In other words, if the server device 20 has not acquired a predetermined number of error information for the same feature (step S22: No), it terminates processing, and if it has acquired a predetermined number of error information for the same feature (step S22: Yes), the server device 20 may determine a correction difference value indicating the amount of correction for the feature position in the map data by weighted statistical processing (step S23).

[0059] Next, the server device 20 determines whether the variation in each difference value during the calculation process of the correction difference value is less than or equal to a predetermined value (step S24). If the variation in the correction difference value is less than or equal to a predetermined value (step S24: Yes), the server device 20 uses the correction difference value to correct the position coordinates of the feature in the map data (step S25). Since the correction difference value is the amount of correction in the direction of travel and left-right direction of the vehicle in the body coordinate system shown in Figure 6, the server device 20 converts these into the amount of deviation in the map coordinate system before correcting the position coordinates of the feature in the map data. In this way, the position coordinates of the feature in the map data are corrected based on error information obtained from multiple vehicles. The correction difference value used here is obtained by weighting the difference values ​​included in the error information obtained from multiple in-vehicle devices 10 using position estimation accuracy and lidar measurement accuracy, so the position coordinates in the map data can be corrected without being affected by the position estimation accuracy or lidar measurement accuracy of each in-vehicle device 10.

[0060] On the other hand, if the variation in the correction difference values ​​is not below a predetermined value (step S24: No), it is difficult to accurately determine the amount of correction needed for the position coordinates of the feature in the map data. Therefore, the server device 20 reduces the position accuracy information of that feature in the map data (step S26). That is, the server device 20 adds information to the feature in the map data indicating that its position accuracy is low. Then the processing ends. As a result, the in-vehicle unit 10 can see that the position accuracy of the feature in the map data is low when using that information, and can take measures such as reducing the weight used in the vehicle position estimation process or not using it at all. In practice, such features are often measured again by a dedicated measurement vehicle, but as an interim measure, adding information indicating that the position accuracy is low prevents the in-vehicle unit 10 on the vehicle 1 from using low-accuracy position information to perform driving assistance or autonomous driving.

[0061] [Second Example] (Vehicle position estimation process) Next, a second embodiment of the vehicle position estimation process by the vehicle position estimation unit 17 will be described. The vehicle position estimation unit 17 estimates the vehicle position by sequentially repeating the prediction step and the measurement update step. Various filters developed for Bayesian estimation can be used as state estimation filters in these steps, such as extended Kalman filters, unscented Kalman filters, and particle filters. Thus, various methods have been proposed for position estimation based on Bayesian estimation. Below, a brief explanation of vehicle position estimation using an extended Kalman filter will be given as an example.

[0062] Figure 9 shows the state variable vector X represented in three-dimensional Cartesian coordinates. In the second embodiment, the z-coordinate is considered. As shown in Figure 9, the position of the vehicle on a plane defined on the three-dimensional Cartesian coordinate system (x,y,z) is represented by the coordinates "(x,y,z)" and the vehicle's orientation "Ψ". Here, the orientation Ψ is defined as the angle between the vehicle's direction of travel and the x-axis. The coordinates (x,y,z) represent an absolute position, which corresponds, for example, to a combination of latitude, longitude, and height. Furthermore, since the road has gradients in the front, back, left, and right directions, the attitude also includes a roll angle φ and a pitch angle θ. Therefore, the vehicle's position and attitude can be defined by a total of six variables.

[0063] Figure 10 shows a schematic relationship between the prediction step and the measurement update step. As shown in Figure 10, the calculation and updating of the estimated value of the state variable vector X are performed sequentially by repeating the prediction step and the measurement update step. Here, the state variable vector for the reference time (i.e., the current time) "k" to be calculated is denoted as "Xe(k)" or "Xp(k)". Note that the provisional predicted value predicted in the prediction step is given the subscript "e", and the more accurate estimated value updated in the measurement update step is given the subscript "p". (State variable vector Xe(k) = (x e (k), y e (k), z e (k), φ e (k), θ e (k), Ψ e (k)) T"State variable vector Xp(k)=(x p (k), y p (k), z p (k), φ p (k), θ p (k), Ψ p (k)) T It is written as "."

[0064] In the prediction step, the vehicle position estimation unit 17 uses the vehicle's movement speed "v" and roll angular velocity "ω" to determine the state variable vector Xp(k-1) at time k-1 calculated in the previous measurement update step. x " and pitch angular velocity "ω y " and yaw angular velocity "ω z By applying the control value (which is collectively referred to as the control value u(k)), the system calculates the predicted value of the vehicle's position at time k (also called the "predicted vehicle position") Xe(k). Simultaneously, the vehicle position estimation unit 17 calculates the covariance matrix Pe(k), which corresponds to the error distribution of the predicted vehicle position Xe(k), from the covariance matrix Pp(k-1) at time k-1, which was calculated in the previous measurement update step.

[0065] In the measurement update step, the vehicle position estimation unit 17 associates the position vectors of features registered in the map DB 13 with the scan data of the lidar 2. If this association is successful, the vehicle position estimation unit 17 obtains the measured value of the associated feature by the lidar 2 (referred to as the "feature measurement value") "Z(i)" and the predicted value of the feature (referred to as the "feature prediction value") "Ze(i)" obtained by modeling the measurement process by the lidar 2 using the predicted vehicle position Xe(k) and the position vectors of the features registered in the map DB 13. The feature measurement value Z(i) is a three-dimensional vector obtained by converting the distance and scan angle of the feature measured by the lidar 2 at time i into components with the vehicle's direction of travel, lateral direction, and vertical direction as axes. The vehicle position estimation unit 17 then calculates the difference between the feature measurement value Z(i) and the feature prediction value Ze(i) as shown in equation (5) below.

number

[0066] Furthermore, the vehicle position estimation unit 17 calculates an updated state variable vector (also called the "measured updated vehicle position") Xp(k) by multiplying the difference between the measured feature value Z(i) and the predicted feature value Ze(i) by the Kalman gain "K(k)" as shown in equation (6) below, and adding this to the predicted vehicle position Xe(k).

number

[0067] Furthermore, in the measurement update step, the vehicle position estimation unit 17, similar to the prediction step, obtains a covariance matrix Pp(k) corresponding to the error distribution of the measured updated vehicle position Xp(k) from the prior covariance matrix Pe(k). Parameters such as the Kalman gain K(k) can be calculated in the same way as known self-positioning techniques using, for example, an extended Kalman filter.

[0068] Furthermore, if the vehicle position estimation unit 17 can associate the position vectors of multiple features registered in the map DB 13 with the scan data of the lidar 2, it may perform a measurement update step based on any one selected feature measurement value, or it may perform the measurement update step multiple times based on all the feature measurement values ​​that can be associated. When using multiple feature measurement values, the vehicle position estimation unit 17 takes into account that the accuracy of the lidar measurement deteriorates the further the feature is from the lidar 2, and reduces the weighting of the feature as the distance between the lidar 2 and the feature increases. In addition, generally, the major axis, minor axis, and angle of the error ellipse, which indicates the expected range of error (for example, the range based on a 99% confidence interval), can be calculated from the eigenvalues ​​and eigenvectors of the covariance matrix corresponding to the error distribution described above.

[0069] In this way, the prediction step and the measurement update step are repeatedly performed, and the predicted vehicle position Xe(k) and the measured updated vehicle position Xp(k) are calculated sequentially, thereby calculating the most likely vehicle position. The vehicle position calculated in this way is called the "estimated vehicle position".

[0070] In the above description, the measured feature value is an example of the first distance information of the present invention, the predicted feature value is an example of the second distance information of the present invention, the position estimation accuracy is an example of the self-position accuracy information of the present invention, and the lidar measurement accuracy is an example of the measurement accuracy information of the present invention.

[0071] (How to correct feature position) Next, we will explain how to correct the position of features. In EKF (Extended Karman Filter) position estimation using road signs and other features with a LiDAR, the vehicle's position is calculated using the difference between the measured feature value and the predicted feature value, as shown in equation (6) above. As can be seen from equation (6), when the difference value shown in equation (5) becomes large, the amount of correction to the predicted vehicle position Xe(k) becomes large. Here, the difference value becomes large due to one of the following reasons (A) to (C).

[0072] (A) There is an error in the measurement of geological features. This includes, for example, cases where the RIDA's measurement accuracy is low, or cases where the RIDA's measurement accuracy is not low, but the measurement accuracy is reduced because there is a moving object such as another vehicle between the object being measured and the vehicle at the time of measurement (so-called occlusion). (B) The estimated vehicle position used to determine the predicted values ​​for geographic features is incorrect. This occurs when there is a large error in the predicted vehicle position Xe(k) or the measured updated vehicle position Xp(k). (C) The position coordinates of the features in the map data used to determine the feature prediction values ​​are incorrect. This refers to a situation where, for example, after the map data was created, the location of a feature (such as a signpost) has changed due to a vehicle collision or other incident, or a structure (such as a feature) has been removed or moved, resulting in a discrepancy between the location coordinates of the feature in the map data and its actual location. In other words, there is a discrepancy between the location of the feature in the map data and its location in the real environment. Therefore, in this case, it is necessary to correct the location coordinates of the feature in the map data so that they match the location coordinates of the actual feature. In this embodiment, the location of such a feature is corrected using the following method.

[0073] Figure 11 shows the positional relationship between the estimated vehicle's position and the surrounding objects on the xy-plane. The coordinate system shown in Figure 11 is a body coordinate system based on the vehicle, where the x-axis represents the direction of vehicle movement, and the y-axis represents the direction perpendicular to it (the vehicle's left-right direction). The front of the paper is the z-axis direction, which represents the vehicle's vertical direction. In Figure 11, the estimated vehicle position has an error range shown by the error ellipse 40. The error ellipse 40 represents the position estimation accuracy σ in the x-direction. P Position estimation accuracy in the (x) and y directions σ P Position estimation accuracy σ in the (y) and z directions P (z) is defined by the position estimation accuracy σ. P This is obtained by transforming the covariance matrix P(k) into the body coordinate system using the Jacobian matrix H(i) according to equation (7) below.

number

[0074] In Figure 11, the predicted feature value 41 is obtained by modeling the measurement process by LIDA 2 using the predicted vehicle position Xe(k) and the location vector of the feature registered in map DB13. The measured feature value 42 is the measured value of the feature by LIDA 2 when the location vector of the feature registered in map DB13 is matched with the scan data of LIDA 2. The measured feature value 42 has an error range indicated by the error ellipse 43. Generally, the measurement error by LIDA increases proportionally to the square of the distance between LIDA and the feature being measured. Therefore, by calculating the error ellipse 43 based on the distance from the estimated vehicle position to the measured feature value, the LIDA measurement accuracy σ can be calculated. L (σ L (x), σ L (y), σ L (z)) is obtained. Also, the difference between the predicted feature value 41 and the measured feature value 42 is dx in the x direction, dy in the y direction, and dz in the z direction, as shown in equation (5).

[0075] Figure 12 is a flowchart of the difference value evaluation process performed by the on-board unit 10 of vehicle 1. This process is actually performed by the control unit 15 of the on-board unit 10 executing a pre-prepared program. First, the on-board unit 10 obtains the difference value (dy, dy, dz) between the measured feature value 42 and the predicted feature value 41 using equation (5) above (step S31). Next, the on-board unit 10 obtains the position estimation accuracy σ from the covariance matrix P(k) using equation (7). P (Step S32) The vehicle-mounted device 10 calculates the rider measurement accuracy σ based on the distance to the feature. L We find this (step S33).

[0076] Thus, the difference values ​​(dx, dy, dz) and the position estimation accuracy σ are obtained. P (σ P (x), σ P (y), σ P (z)), Rider measurement accuracy σ L (σ L (x), σ L (y), σ LWhen the position (z) is obtained, the in-vehicle device 10 evaluates the difference value using the position estimation accuracy and the lidar measurement accuracy. That is, by looking at the ratio of the difference value with respect to the error range 40 of the estimated vehicle position and the lidar error range 43, the validity of the difference between the ground object measurement value 42 and the ground object prediction value 41 is judged. Specifically, the in-vehicle device 10 calculates evaluation values Ex, Ey, and Ez by the following evaluation formula (8) (step S34).

Number

[0077] Next, the in-vehicle device 10 compares the evaluation values Ex, Ey, and Ez with a predetermined value (step S35). When at least one of the evaluation values Ex, Ey, and Ez is greater than the predetermined value (step S35: Yes), the in-vehicle device 10 determines that the above (A) or (C) is considered as the cause of the increase in the difference value, that is, there is a possibility of (C). Therefore, the in-vehicle device 10 uses, as error information, the date and time information at that time, the estimated vehicle position at that time, the ground object ID of the target ground object, the difference value (dx, dy, dz), the position estimation accuracy σ P (σ P (x), σ P (y), σ P (z)), the lidar measurement accuracy σ L (σ L (x), σ L (y), σ L (z)) and transmits them to the server device 20 (step S36). Then, the process ends. Including the date and time information in the error information transmitted to the server device 20 is important because when the actual position coordinates of the ground object change due to environmental changes of the ground object or the like, the date and time information can be used to know when the change has occurred.

[0078] On the other hand, if all of the evaluation values ​​Ex, Ey, and Ez are smaller than the predetermined values ​​(step S35: No), the in-vehicle device 10 terminates processing. In the case of (B) above, the denominator of evaluation formula (8) is large, so even if the numerators |dx|, |dy|, and |dz| are large, the evaluation values ​​Ex, Ey, and Ez will not be large. In this way, the difference value evaluation processing in the in-vehicle device 10 detects that the cause of the large difference value may be (C) above, and the error information at that time is sent to the server device 20.

[0079] Next, the map correction process performed by the server device 20 will be described. Figure 13 is a flowchart of the map correction process. This process is actually performed by the control unit 25 of the server device 20 executing a pre-prepared program. First, the server device 20 acquires error information from multiple vehicles (step S41). Next, the server device 20 determines whether or not it has acquired a predetermined number of error information points for the same feature (step S42).

[0080] If a predetermined number of error information points have not been obtained for the same feature (Step S42: No), the server device 20 determines that the reason the difference value increased on the vehicle side is (A), that is, the difference value increased by chance due to occlusion or the like, and terminates the process.

[0081] On the other hand, if a predetermined number of error information points are obtained for the same feature (Step S42: Yes), the server device 20 determines that the reason for the large difference value on the vehicle side is (C), that is, the position coordinates of the feature in the map data do not match the position coordinates of the actual feature. Since the difference value is the amount of correction in the direction of travel, left-right direction and vertical direction of the vehicle in the body coordinate system shown in Figure 11, the server device 20 converts the difference values ​​(dx, dy, dz) included in the error information received from multiple in-vehicle devices 10 into the amount of deviation in the map coordinate system. Then, the server device 20 determines a correction difference value that indicates the amount of correction for the feature position in the map data by weighted statistical processing (Step S43). Specifically, the server device 20 determines the position estimation accuracy σ of each vehicle included in the error information received from the in-vehicle devices 10.P and rider measurement accuracy σ L The difference values ​​are statistically processed using weighting based on the following: for example, the position estimation accuracy σ P and rider measurement accuracy σ L Error information with a small sum is judged to be highly accurate, and its weight is increased. In this way, the server device 20 determines the difference value for correction.

[0082] Furthermore, if the server device 20 has not acquired more than a predetermined number of error information for the same feature (step S42: No), it determines that the reason the difference value has increased on the vehicle side is (A), and if it has acquired more than a predetermined number of error information for the same feature (step S42: Yes), it determines that the reason the difference value has increased on the vehicle side is (C). However, it is not necessary to determine whether the reason the difference value has increased is either (A) or (C). In other words, if it has not acquired more than a predetermined number of error information for the same feature (step S42: No), the server device 20 terminates processing, and if it has acquired more than a predetermined number of error information for the same feature (step S42: Yes), the server device 20 may convert the difference values ​​(dx, dy, dz) included in the error information received from multiple in-vehicle devices 10 into a shift amount in the map coordinate system, and determine a correction difference value indicating the amount of correction for the feature position in the map data by weighted statistical processing (step S43).

[0083] Next, the server device 20 determines whether the variation in each difference value during the calculation process of the correction difference value is less than or equal to a predetermined value (step S44). If the variation in the correction difference value is less than or equal to a predetermined value (step S44: Yes), the server device 20 uses the correction difference value to correct the position coordinates of the feature in the map data (step S45). Since the correction difference value is the amount of correction in the direction of travel, left-right direction, and vertical direction of the vehicle in the body coordinate system shown in Figure 11, the server device 20 converts these into the amount of deviation in the map coordinate system before correcting the position coordinates of the feature in the map data. In this way, the position coordinates of the feature in the map data are corrected based on error information obtained from multiple vehicles. The correction difference value used here is obtained by weighting the difference values ​​included in the error information obtained from multiple in-vehicle devices 10 using position estimation accuracy and lidar measurement accuracy, so the position coordinates in the map data can be corrected without being affected by the position estimation accuracy or lidar measurement accuracy of each in-vehicle device 10.

[0084] On the other hand, if the variation in the correction difference values ​​is not below a predetermined value (step S44: No), it is difficult to accurately determine the amount of correction needed for the position coordinates of the feature in the map data. Therefore, the server device 20 reduces the position accuracy information of that feature in the map data (step S46). That is, the server device 20 adds information to the feature in the map data indicating that its position accuracy is low. Then the processing ends. As a result, the in-vehicle unit 10 can see that the position accuracy is low when using the information of that feature in the map data, and can take measures such as reducing the weight used in the self-position estimation process or not using it at all. In practice, such features are often measured again by a dedicated measurement vehicle, but as a temporary measure, adding information indicating that the position accuracy is low prevents the in-vehicle unit 10 on the vehicle 1 side from using low-accuracy position information to perform driving assistance or autonomous driving.

[0085] [Specific example] Next, we will explain the effects of the vehicle position estimation process based on the above embodiment.

[0086] Figure 14(A) is a graph showing the changes in the x and y directions of the vehicle's position when it is driven on a test course while the vehicle's position estimation process according to the embodiment is performed. Figure 14(B) is an enlarged view of the graph in the driving section within frame 91 of Figure 14(A). In Figure 14(B), the positions of the measured features are plotted as circles. Figure 14(C) is a graph showing the changes in the difference value dx in the driving section of frame 91. The difference value dx is close to zero ("0").

[0087] Here, as a simulation, the position of the feature indicated by symbol 92 in Figure 14(B) was intentionally shifted 80 cm forward, and the vehicle position estimation process was performed. As a result, the difference value dx was calculated to be 0.749 [m]. Figure 14(D) shows the change in the difference value dx obtained during the driving section of frame 91. As shown by circle 93, at the point when the feature indicated by symbol 92, whose position was intentionally shifted, was detected, a difference value close to the position shift of that feature was obtained.

[0088] The values ​​at the time of detecting the shifted object with code 92 are: x-direction difference value dx = 0.749 [m], and vehicle position estimation accuracy σ p (x) = 0.048 [m], Lida measurement accuracy σ L (x) = 0.058 [m]. Using these values, the evaluation value Ex is calculated using equation (8).

[0089]

number

[0090] As can be seen from these simulation results, the vehicle position estimation process in the embodiment calculates a value close to the positional displacement of the terrain features. Therefore, as mentioned above, by collecting data from multiple vehicles in the server device 20, it becomes possible to obtain a value close to the true amount of displacement of the terrain features.

[0091] [Differentiation] The following describes suitable modifications for the examples. The following modifications may be applied in combination to the examples. (Variation 1) In the above embodiment, a large difference between the measured feature value and the predicted feature value indicates that the location coordinates of the feature in the map data do not match the actual location coordinates. However, even if the location coordinates of the feature in the map data are correct, the difference value can become large due to the type and shape of the feature. For example, when using road signs as a feature, if multiple road signs are installed side by side as shown in Figure 15, the in-vehicle unit 10 may detect them as the same feature. Even if the predicted feature value calculated based on the map data distinguishes between the two signs, if the measured feature value does not distinguish between the two signs, the difference value will become large.

[0092] In such cases, vehicle 1, which is equipped with a camera, transmits the error information, including the image captured by the camera, to the server device 20. In this case, the position coordinates of the feature in the map data are correct, so there is no need to correct the position coordinates. However, this feature can be said to be one that is prone to errors when used for estimating the vehicle's position. Therefore, the server device 20 analyzes the captured image, for example, using image recognition by artificial intelligence (AI), and if it determines that the feature is prone to errors, it adds information (for example, a "deprecated flag") indicating that its use in position estimation is not recommended. This allows the onboard device 10 of vehicle 1 to take measures such as not actively using features with the deprecated flag in the position estimation process.

[0093] The same problem can occur when using road markings such as white lines as geographic features. Even neatly painted road markings can easily lead to matching errors when compared with the results detected by the LIDA. For example, complex white lines as illustrated in Figures 16(A) to (C), or white lines with road studs 50 as illustrated in Figure 16(D), require processing to determine the center of the white line in relation to the LIDA measurement data. A method that simply identifies the LIDA detection data by reflectivity will result in the white line becoming thicker or distorted, making accurate matching impossible.

[0094] Therefore, when the onboard unit 10 of vehicle 1 detects a location with a large difference when matching the lidar detection data with the map data, it sends information indicating this to the server device 20, including this information in the error information. The server device 20 can collect error information from multiple vehicles 1 and add information to each feature (road surface paint) indicating whether it is easy or difficult to match. The onboard unit 10 of vehicle 1 can also refer to this information and take measures such as reducing the weight of that feature in position estimation or not using it at all.

[0095] (Modification 2) In the above embodiment, the difference value evaluation process, which evaluates the difference value, is performed on the vehicle-mounted device 10 of vehicle 1, and the map correction process is performed on the server device 20. Alternatively, the vehicle-mounted device 10 may transmit data on all features to the server device 20, and the server device 20 may perform the difference value evaluation process and the map correction process. Specifically, the vehicle-mounted device 10 transmits error information on all features (including date and time information, estimated vehicle position, feature ID, difference value, position estimation accuracy, and lidar measurement accuracy) to the server device 20 without performing evaluation using evaluation values ​​Ex, Ey, and Ez. The server device 20 first calculates the evaluation values ​​Ex, Ey, and Ez using the received error information, and then executes the map correction process shown in Figure 8 or Figure 13 for error information where these values ​​exceed predetermined values.

[0096] (Variation 3) Conversely, the difference value evaluation process and map correction process may be performed on the vehicle-mounted unit 10 of vehicle 1. In this case, the vehicle-mounted unit 10 first executes the difference value evaluation process shown in Figure 7 or Figure 12, generates error information, and stores it in the storage unit 12. If vehicle 1 travels to the same location multiple times, multiple pieces of error information for the same feature will be obtained. Therefore, the vehicle-mounted unit 10 uses the multiple pieces of error information for the same feature stored in the storage unit 12 to perform map correction processing as shown in Figure 8 or Figure 13. That is, instead of using error information obtained from many vehicles, it uses multiple pieces of error information obtained from multiple trips of its own vehicle. In this way, the vehicle-mounted unit 10 corrects the features stored in the map DB 13 using correction difference values, or performs processing to reduce the positional accuracy information for those features. If necessary, the corrected map data may also be sent to the server device 20.

[0097] (Modification 4) Regarding the evaluation formula, you may use the following formula (10) instead of the above formula (8).

number

[0098] (Variation 5) Alternatively, the position of the object can be calculated based on measurements by adding the distance measurement to the object to the estimated position of the vehicle. Then, the calculated position of the object based on measurements can be compared with the position of the object in the map information to determine if there is any discrepancy.

[0099] (Experimental variation 6) In the above embodiment, the server device 20 collects error information from the in-vehicle unit 10. However, if the vehicle measures a predetermined feature multiple times, the in-vehicle unit 10 may calculate a weighted mean and variance for the multiple difference values ​​(dy, dy), and transmit the error information to the server device 20 if the variance is small.

[0100] Specifically, if the evaluation values ​​Ex and Ey are greater than predetermined values, and the reason for the large difference value is not "(B) The estimated vehicle position used to determine the feature prediction value is incorrect," then the onboard unit 10 calculates the position estimation accuracy σ for the difference values ​​(dx, dy) calculated N times. P (σ P (x), σ P (y) and rider measurement accuracy σ L (σ L (x), σ L The weighted mean and variance are calculated using (y). That is, the weight of (dy,dy) is increased when the accuracy is good, and decreased when the accuracy is poor.

[0101] The weighted average of the difference value dx is calculated using the following equation (11). The weighted average of the difference value dy is calculated using the same equation for the difference value dy as in equation (11), that is, by replacing "x" with "y" in equation (11).

number

[0102] The variance of the difference value dx is calculated using the following equation (12). The variance of the difference value dy is calculated using the same equation for the difference value dy as in equation (12), that is, by replacing "x" with "y" in equation (12).

number

[0103] In this calculation result, if the variance is large, it can be seen that there is a lot of variation in the difference value, so it is determined that the most likely cause of the large difference value is "(A) There is an error in the feature measurement value," and the in-vehicle unit 10 terminates processing without sending error information to the server device 20. On the other hand, if the variance is small, it can be seen that there is little variation in the difference value, so it is determined that the most likely cause of the large difference value is "(C) The position coordinates of the features in the map data used to determine the feature prediction value are shifted," and the in-vehicle unit 10 sends the weighted average value of the difference value (dy, dy) and the accuracy of the weighted average value to the server device 20.

[0104] Here, the precision of the weighted mean of the difference value dx is given by equation (13) below. The precision of the weighted mean of the difference value dy is calculated by an equation similar to equation (13) for the difference value dy, that is, by replacing "x" with "y" in equation (13).

number

[0105] Furthermore, the above-mentioned in-vehicle device 10 has N difference values ​​(dy, dy) and position estimation accuracy σ. P (σ P (x), σ P (y) and rider measurement accuracy σ L (σ L (x), σ L (y)) may be sent to the server device 20, and the server device may use the above formula to calculate the weighted mean and variance, determine (A) and (C) based on the variance, and calculate the accuracy of the weighted mean.

[0106] By performing this processing on the in-vehicle device 10, only data that is highly likely to be caused by "(C) the position coordinates of the features in the map data used to determine the feature prediction values ​​are shifted" is sent to the server device 20. This reduces the frequency of data transmission from the in-vehicle device 10 to the server device 20, thereby reducing the processing load on the server device 20.

[0107] Furthermore, the server device 20 may decide whether or not to perform the above processing on the in-vehicle unit 10, and if processing is to be performed, the server device 20 may send request information to the in-vehicle unit 10 to perform the above processing. Specifically, the server device 20 determines whether or not it is necessary to scrutinize the positions of features stored in the map DB 13 based on the last update date, the accuracy of the map information, etc. If scrutiny of the position of a feature is necessary, the server device 20 does not send request information to perform the above processing, but is set to send error information from the in-vehicle unit 10 to the server device 20. On the other hand, if scrutiny of the position of a feature is not necessary, the server device 20 sends request information to the in-vehicle unit 10 to perform the above processing. This makes it possible to perform processing according to the need for scrutiny for each feature. [Explanation of Symbols]

[0108] 1 Onboard device 2 Riders 3. Gyroscope 4. Vehicle speed sensor 5 GPS receivers 10 Onboard equipment 13 Map Database 15, 25 Control Unit 20 Server Devices 23 Map Database

Claims

1. A first acquisition unit acquires first distance information indicating the distance from the moving object to the target object, which is measured by the measurement unit, and second distance information indicating the distance from the position of the moving object to the position of the target object in the map information, which is estimated by the estimation unit. A second acquisition unit acquires measurement accuracy information indicating the measurement accuracy of the object's position by the measurement unit, and estimation accuracy information indicating the estimation accuracy of the moving body's position by the estimation unit. A determination unit that determines whether there is a discrepancy of a predetermined value or more between the position of the object in the map information and the position of the object in the actual environment, based on the difference in distance indicated by the first distance information and the second distance information, the measurement accuracy information, and the estimation accuracy information, A determination device characterized by comprising:

2. A third acquisition unit acquires error information, including the difference value, the estimated accuracy information, and the measurement accuracy information, from the determination unit for an object that is determined to have a deviation greater than or equal to the predetermined value. When the third acquisition unit acquires a predetermined number or more of error information for the same object, the correction unit determines a correction difference value indicating the amount of correction required for the object's position in the map information by statistical processing of the difference values ​​included in the acquired error information, and corrects the object's position in the map information using the correction difference value. The determination device according to claim 1, characterized by comprising:

3. The first distance information and the second distance information are vectors in a coordinate system of the moving body with respect to the moving body, and the difference value is a difference value vector in the coordinate system of the moving body. The determination device according to claim 2, characterized in that the correction unit converts the difference value vector from moving object coordinates to a map coordinate system and performs the statistical processing.

4. The determination device according to claim 2, characterized in that when the third acquisition unit acquires error information from a predetermined number of vehicles for the same object, and the variation in the difference values ​​included in the acquired error information is greater than or equal to a predetermined value, the correction unit reduces the level of positional accuracy information assigned to the object in the map information.

5. The aforementioned determination device is mounted on the vehicle, The determination device according to claim 1, further comprising a transmission unit that transmits error information, including the identification information of the object, the difference value, the estimated accuracy information, and the measurement accuracy information, to an external server when the determination unit determines that there is a deviation greater than or equal to the predetermined value.

6. The determination device according to claim 5, characterized in that when the transmitting unit acquires multiple pieces of error information for the same object, and the variance value based on the multiple pieces of error information is less than or equal to a predetermined value, it transmits the multiple pieces of error information or information obtained using the multiple pieces of error information to the external server.

7. The determination device according to claim 2 or 5, characterized in that the error information includes date and time information.

8. The measurement unit and the estimation unit are mounted on the vehicle. The determination device is installed on the server, The determination device according to claim 1, characterized in that the first acquisition unit acquires the first distance information and the second distance information from the vehicle.

9. A determination method performed by a determination device, A first acquisition step involves acquiring first distance information, which is measured by a measurement unit and indicates the distance from the moving object to the target object, and second distance information, which is estimated by an estimation unit and indicates the distance from the position of the moving object to the position of the target object in the map information. A second acquisition step involves acquiring measurement accuracy information indicating the measurement accuracy of the object's position by the measurement unit, and estimation accuracy information indicating the estimation accuracy of the moving body's position by the estimation unit. A determination step of determining whether there is a discrepancy of a predetermined value or more between the position of the object in the map information and the position of the object in the actual environment, based on the difference in distance indicated by the first distance information and the second distance information, the measurement accuracy information, and the estimation accuracy information, A determination method characterized by comprising:

10. A program executed by a determination device equipped with a computer, A first acquisition unit acquires first distance information indicating the distance from the moving object to the target object, which is measured by the measurement unit, and second distance information indicating the distance from the position of the moving object, which is estimated by the estimation unit, to the position of the target object in the map information. A second acquisition unit acquires measurement accuracy information indicating the measurement accuracy of the object's position by the measurement unit, and estimation accuracy information indicating the estimation accuracy of the moving body's position by the estimation unit. A determination unit determines whether there is a discrepancy of a predetermined value or more between the position of the object in the map information and the position of the object in the actual environment, based on the difference in distance indicated by the first distance information and the second distance information, the measurement accuracy information, and the estimation accuracy information. A program characterized by causing the aforementioned computer to function as such.

11. A storage medium storing the program described in claim 10.

12. A first acquisition unit acquires first distance information indicating the distance from the moving object to the target object, which is measured by the measurement unit, and second distance information indicating the distance from the position of the moving object to the position of the target object in the map information, which is estimated by the estimation unit. A second acquisition unit acquires measurement accuracy information indicating the measurement accuracy of the object's position by the measurement unit, and estimation accuracy information indicating the estimation accuracy of the moving body's position by the estimation unit. A determination unit that determines whether there is a deviation of a predetermined value or more between the position of the object on the map information and the position of the object based on the first distance, based on the difference in distance indicated by the first distance information and the second distance information, the measurement accuracy information, and the estimation accuracy information, A determination device characterized by comprising:

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