Output device, control method, program, and storage medium

Abstract

The present invention provides an output device capable of optimally controlling a vehicle based on information regarding road paint. [Solution] The in-vehicle unit 1 has a vehicle position estimation unit 17 and an automatic driving control unit 18. The vehicle position estimation unit 17 estimates the vehicle's position by comparing the detection results of road paint by the lidar with the map DB 10. The automatic driving control unit 18 acquires road paint information from the map DB 10, including detection accuracy information Idet which indicates the accuracy of the matching for each road paint. Then, based on the detection accuracy information Idet, the automatic driving control unit 18 outputs information to the vehicle's electronic control unit or information output unit 16 for controlling the vehicle so that matching can be performed with an accuracy of a predetermined value or higher.

Description

Technical Field

[0001] The present invention relates to a technique for controlling a vehicle.

Background Art

[0002] Conventionally, a technique for automatically driving a vehicle by detecting features around the vehicle using a radar or a camera and estimating the position of the host vehicle with high accuracy based on the detection result is known. In Patent Document 1, based on the output of an external sensor, the blurring state of the white line on the road during travel is determined, and when there is a lane in which the white line can be detected with higher accuracy than the current lane, a technique for controlling the vehicle to change lanes to the lane is disclosed.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a mode of measuring and monitoring the state of the white line by an external sensor during travel, there are cases where the state of the white line in another lane cannot be accurately determined, such as when there are other vehicles around. Also, the state of the white line outside the measurement range of the external sensor cannot be grasped.

[0005] The present invention has been made to solve the above problems, and a main object thereof is to provide an output device capable of suitably controlling a vehicle based on information related to road paint.

Means for Solving the Problems

[0006] The invention according to claim 1 is an output device, A comparison unit performs a comparison between the position of the road paint based on the amount of reflected light obtained by a detection device that detects an object by receiving reflected light that has been reflected by the object, and the position of the road paint in map information. A first acquisition unit acquires accuracy information indicating the accuracy of the matching for each road paint mark, A detection unit compares the position estimation errors in a first direction and a second direction with respect to the direction of movement of a moving object with a predetermined threshold, and detects the direction in which the position estimation error is greater than the threshold, A second acquisition unit acquires suitability information for each road paint mark, which indicates the degree of suitability when the road paint mark is used, as a criterion for position estimation of the direction detected by the detection unit. The system includes an output unit that, based on the accuracy information and suitability information, identifies high-precision road paints installed along the path of the moving body from among the road paints on which the matching accuracy is above a predetermined value and the suitability is above a predetermined standard, and outputs control information for controlling the moving body to approach the high-precision road paint.

[0007] The invention described in claim 10 is a control method performed by an output device, A comparison step involves comparing the position of the road paint, based on the amount of reflected light obtained by a detection device that detects an object by receiving reflected light that has been reflected by the object, with the position of the road paint in map information. A first acquisition step involves acquiring accuracy information indicating the accuracy of the matching for each road paint mark, A detection step involves comparing the position estimation errors in a first direction and a second direction with respect to the direction of movement of a moving object with a predetermined threshold, and detecting the direction in which the position estimation error is greater than the threshold, A second acquisition step is to acquire suitability information for each road paint, which indicates the degree of suitability when the road paint is used, as a criterion for position estimation of the direction detected by the above detection step, The system includes an output step of outputting control information for controlling the mobile body to approach the high-precision road paint, based on the accuracy information and suitability information, by identifying high-precision road paint provided along the path of the mobile body such that the matching accuracy is above a predetermined value and the suitability is above a predetermined standard, and based on the accuracy information and suitability information, the system identifies high-precision road paint from among the road paint provided along the path of the mobile body.

[0008] The invention described in claim 13 is a program executed by a computer, A comparison unit performs a comparison between the position of the road paint based on the amount of reflected light obtained by a detection device that detects an object by receiving reflected light that has been reflected by the object, and the position of the road paint in map information. A first acquisition unit acquires accuracy information indicating the accuracy of the matching for each road paint mark, A detection unit compares the position estimation errors in the first and second directions with respect to the direction of travel of the moving body with a predetermined threshold, and detects the direction in which the position estimation error is greater than the threshold. A second acquisition unit acquires suitability information for each road paint mark, which indicates the degree of suitability when the road paint mark is used, as a criterion for position estimation of the direction detected by the detection unit. Based on the accuracy information and suitability information, the computer functions as an output unit that identifies high-precision road paints from the road paints installed along the path of the moving object, where the accuracy of the matching is above a predetermined value and the suitability is above a predetermined standard, and outputs control information to control the moving object so that it approaches the high-precision road paint. [Brief explanation of the drawing]

[0009] [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 an example of the data structure of road paint information included in a map database. [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 is a flowchart illustrating the control of the first vehicle based on road paint information. [Figure 7] This is an overhead view of a vehicle traveling on a two-lane road with road markings on the right side, which result in low detection accuracy. [Figure 8] This image shows an overhead view of a vehicle on a single-lane road where road markings with low detection accuracy are present. [Figure 9] Here are other examples of road paint that result in low detection accuracy. [Figure 10] This is a schematic diagram showing the route selection screen. [Figure 11] This is a flowchart illustrating the control of the second vehicle based on road paint information. [Figure 12] This is an overhead view of a vehicle traveling on a two-lane road with road markings on the left side, which result in low detection accuracy. [Modes for carrying out the invention]

[0010] According to a preferred embodiment of the present invention, the output device comprises a matching unit that matches the detection result of road paint by a detection device with map information, a first acquisition unit that acquires accuracy information indicating the accuracy of the matching for each road paint, and an output unit that outputs control information for controlling a moving object so that the matching can be performed with an accuracy of a predetermined value or higher based on the accuracy information. In this embodiment, the output device can suitably control a moving object so that the matching can be performed with an accuracy of a predetermined value or higher by acquiring accuracy information for each road paint when matching the detection result of road paint by a detection device with map information.

[0011] In one embodiment of the output device described above, the output unit identifies low-precision road paint from among the road paints provided along the path of the moving body in which the matching accuracy is less than a predetermined value, and outputs control information for controlling the moving body to move away from the low-precision road paint. In this embodiment, the output device can suitably move the vehicle so as not to detect road paint in which the matching accuracy is less than a predetermined value.

[0012] In another embodiment of the output device described above, the output unit searches for a route to the destination based on the accuracy information and outputs the information of the searched route as control information. In this embodiment, the output device can determine the route to be traveled by taking into account in advance the accuracy of matching the road paint detection results with the map information.

[0013] In another embodiment of the output device described above, the output unit outputs control information for displaying on the display unit a route that can be matched with an accuracy of a predetermined value or higher as a recommended route. In this embodiment, the output device can suitably present to the user a route that can be matched with map information with an accuracy of a predetermined value or higher as a recommended route.

[0014] In another embodiment of the output device described above, the output unit identifies a road mark from among the road markings provided along the path of the moving body that provides a matching accuracy of at least a predetermined value, and outputs control information for controlling the moving body to approach that road marking. In this embodiment, the output device can suitably move the vehicle to approach a road marking that provides a matching accuracy of at least a predetermined value, in order to enable more accurate matching.

[0015] In another embodiment of the output device described above, the output device includes a detection unit that compares the position estimation errors in a first direction and a second direction with respect to the direction of travel of the moving body with a predetermined threshold and detects the direction in which the position estimation error is greater than the threshold, and a second acquisition unit that acquires suitability information for each road paint, indicating the degree of suitability when the road paint is used as a reference for position estimation in the direction detected by the detection unit, and the output unit identifies the high-precision road paint based on the accuracy information and the suitability information. In this embodiment, the output device can suitably move the vehicle towards a road paint that has a high degree of suitability as a reference for position estimation in the direction in which the position estimation error is determined to be greater than the threshold.

[0016] In another embodiment of the output device described above, the output device further comprises a position estimation unit that estimates the position of the moving object based on the results of the comparison. In this embodiment, the output device can use the accuracy information of the comparison to control the vehicle so as to maintain the position estimation accuracy at a predetermined level.

[0017] In another embodiment of the output device described above, the output unit determines, based on the accuracy of the position estimation by the position estimation unit, whether it is necessary to control the moving object away from road paint where the matching accuracy is less than a predetermined value. In this embodiment, the output device can accurately determine whether it is necessary to move the vehicle to avoid road paint where the matching accuracy is less than a predetermined value.

[0018] In another embodiment of the output device described above, the position estimation unit reduces the weighting of the matching result in the position estimation when road paint with a matching accuracy below a predetermined value is included in the detection range of the detection device. In this embodiment, the output device can suitably reduce the decrease in position estimation accuracy based on matching results for road paint with a matching accuracy below a predetermined value.

[0019] In another embodiment of the output device described above, the road markings in the accuracy information for which the accuracy of the matching is less than the predetermined value are lane markings represented by complex lines. Lane markings represented by complex lines are prone to errors between the detection result by the detection device and the map information. Therefore, preferably, in the accuracy information, lane markings represented by complex lines are recorded as road markings for which the accuracy of the matching is less than the predetermined value.

[0020] In another embodiment of the output device described above, road paint in which the accuracy of the matching is less than the predetermined value in the accuracy information is road paint that is faded. Since the detection accuracy of road paint that is faded is reduced, errors are likely to occur between the detection result of the detection device and the map information. Therefore, preferably, in the accuracy information, road paint that is faded is recorded as road paint in which the accuracy of the matching is less than the predetermined value.

[0021] According to another preferred embodiment of the present invention, the output device performs a control method comprising: a matching step of matching the detection result of road paint by a detection device with map information; a first acquisition step of acquiring accuracy information indicating the accuracy of the matching for each road paint; and an output step of outputting control information for controlling a moving object so that the matching can be performed with an accuracy of a predetermined value or higher based on the accuracy information. By performing this control method, the output device can suitably control the moving object so that the matching can be performed with an accuracy of a predetermined value or higher.

[0022] According to another preferred embodiment of the present invention, a computer is configured to function as a program executed by a computer, comprising: a matching unit that matches the detection results of road paint by a detection device with map information; a first acquisition unit that acquires accuracy information indicating the accuracy of the matching for each road paint; and an output unit that outputs control information for controlling a moving object so that the matching can be performed with an accuracy of a predetermined value or higher based on the accuracy information. By executing this program, the output device can suitably control the moving object so that the matching can be performed with an accuracy of a predetermined value or higher. Preferably, the program is stored in a storage medium. [Examples]

[0023] Hereinafter, preferred embodiments of the present invention will be described with reference to the drawings. [Schematic configuration]

[0024] Figure 1 is a schematic diagram of the driver assistance system according to this embodiment. The driver assistance system shown in Figure 1 includes an on-board unit 1 mounted on the vehicle that performs control related to driver assistance, 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.

[0025] The on-board unit 1 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 on-board unit 1 is mounted (also called "vehicle position") based on the outputs of these sensors. Based on the estimated vehicle position, the on-board unit 1 performs automatic driving control of the vehicle so that it travels along a set route to a destination. The on-board unit 1 stores a map database (DB: Database) 10 that stores road data and information about landmarks on the road. The aforementioned landmarks include, for example, three-dimensional objects such as kilometer posts and signs that are periodically lined up along the side of the road, as well as road markings such as lane lines and road markings. The on-board unit 1 then estimates the vehicle position by comparing the output of the lidar 2, etc. with the information registered in the map DB 10.

[0026] LIDA 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, LIDA 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 (point cloud 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, which is determined based on the received signal described above. Generally, the accuracy of the LIDA's distance measurement is higher the closer the distance to the object, and lower the accuracy the further the distance. Note that since road paint has a different reflectivity from other areas of the road surface, it is possible to identify the point cloud data of road paint based on the level of the received signal corresponding to the amount of reflected light. In this embodiment, LIDA 2 is assumed to be installed to scan at least the road surface of a road being driven on. The rider 2, gyro sensor 3, vehicle speed sensor 4, and GPS receiver 5 each supply output data to the in-vehicle unit 1. The in-vehicle unit 1 is an example of an "output device" in this invention, and the rider 2 is an example of a "detection device" in this invention.

[0027] Figure 2 is a block diagram showing the functional configuration of the in-vehicle unit 2. The in-vehicle unit 2 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.

[0028] 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). The signals transmitted from the control unit 15 to the vehicle's electronic control unit via Interface 11 are an example of "control information" in this invention.

[0029] The storage unit 12 stores programs to be 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 stores a map DB 10 that includes road paint information.

[0030] Figure 3 shows an example of the data structure of road paint information. Road paint information is, for example, information associated with road data of a road where road paint is installed. In the example in Figure 3, the road paint information includes identification information for identifying individual road paints, location information indicating the position of the road paint, information regarding the detection accuracy when detecting road paints by external sensors such as lidar 2 (also called "detection accuracy information Idet"), and information indicating the suitability of the road paint as a criterion for estimating the vehicle's position in the direction of travel and in a direction perpendicular to the direction of travel (also called "lateral direction") (also called "suitability direction information Sdi").

[0031] Here, the detection accuracy of the road paint indicated by the detection accuracy information Idet represents the accuracy of matching the location of the road paint identified based on the output of LIDA2 with the location of the road paint identified based on map DB10. Examples of road paints with poor detection accuracy indicated by the detection accuracy information Idet include not only road paints that are faded and difficult to detect, but also road paints that have exceptional shapes represented by complex lines. As will be described later, the latter type of road paint is prone to discrepancies between the location of the road paint identified based on the output of LIDA2 and the location of the road paint identified based on map DB10. The detection accuracy information Idet may be flag information indicating whether or not the road paint has low detection accuracy, or it may be numerical information indicating the degree of detection accuracy in stages. In the former case, the detection accuracy information Idet may be included only in the road paint information of road paints with low detection accuracy. Furthermore, the detection accuracy information Idet may not be limited to information that directly indicates detection accuracy, but may also be information that indirectly indicates detection accuracy. In the latter case, the detection accuracy information may be information on the type of road paint indicating whether or not it has a complex shape.

[0032] Furthermore, the appropriate direction information Sdi is information indicating the suitability of the road paint when the in-vehicle unit 2 estimates the vehicle's position by comparing the position of the road paint detected by the lidar 2 with the position information of the road paint registered in the map DB 10, and uses it as a reference for estimating the vehicle's position in the direction of travel and the lateral direction. This information is pre-set based on the shape of the road surface, such as the direction of extension of the road paint. Specifically, for example, a stop line extends in the lateral direction of the vehicle, making it optimal as a reference for estimating the vehicle's position in the direction of travel, but unsuitable as a reference for estimating the vehicle's position in the lateral direction. Similarly, a solid lane marking extends continuously in the direction of travel, making it optimal as a reference for estimating the vehicle's position in the lateral direction, but unsuitable as a reference for estimating the vehicle's position in the direction of travel. Furthermore, dashed lane markings, although inferior to solid lane markings because they extend discontinuously in the direction of vehicle travel, are suitable as a reference for estimating the vehicle's position in the lateral direction, and the ends of the dashed lines can be used as a reference for estimating the vehicle's position in the direction of travel, making them suitable as a reference for estimating the vehicle's position in the direction of travel. As in these examples, the appropriate direction information Sdi stores information indicating the degree of suitability as a reference for estimating the vehicle's position in the direction of travel and the lateral direction for each road paint. The appropriate direction information Sdi may be flag information indicating whether or not the road paint has a low degree of suitability, or it may be numerical information indicating the degree of suitability in stages. In the former case, the appropriate direction information Sdi may be included only in the road paint information for road paints with a low degree of suitability. Also, the appropriate direction information Sdi may not be limited to information that directly indicates the degree of suitability, but may also be information that indirectly indicates the degree of suitability. In the latter case, the appropriate direction information Sdi may be information that indicates the type of road paint.

[0033] The map DB10 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 that manages map information via a communication unit (not shown), and reflects it in the map DB10.

[0034] Referring again to Figure 2, the configuration of the in-vehicle unit 2 will be explained. 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 is, for example, a display or speaker that outputs based on the control of the control unit 15. The information output unit 16 is an example of a "display unit" in the present invention.

[0035] The control unit 15 includes a CPU that executes the program and controls the entire in-vehicle unit 1. In this embodiment, the control unit 15 has a vehicle position estimation unit 17 and an automatic driving control unit 18. The control unit 15 is an example of the "matching unit," "first acquisition unit," "second acquisition unit," "position estimation unit," "output unit," "detection unit," and "computer" that executes the program in the present invention.

[0036] 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 features such as road paint, and location information of features extracted from the map DB 10. In this embodiment, as an example, the vehicle position estimation unit 17 alternately performs a prediction step in which it estimates the vehicle position from the 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 in which it corrects the estimated value of the vehicle position calculated in the previous prediction step.

[0037] The automatic driving control unit 18 refers to the map DB 10 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.

[0038] Here, a supplementary explanation will be given for the estimation process of the vehicle position by the vehicle position estimation unit 17. The vehicle position estimation unit 17 sequentially repeats the prediction step and the measurement update step to estimate the vehicle position. Various state estimation filters developed to perform Bayesian estimation can be used in these steps. For example, an extended Kalman filter, an unscented Kalman filter, a particle filter, etc. are applicable. Thus, various methods have been proposed for position estimation based on Bayesian estimation. Hereinafter, as an example, the vehicle position estimation using an extended Kalman filter will be briefly described.

[0039] FIG. 4 is a diagram showing the state variable vector x in two-dimensional orthogonal coordinates. As shown in FIG. 4, the vehicle position on the plane defined in the two-dimensional orthogonal coordinates of xy is represented by the coordinates "(x, y)" and the orientation "θ" of the vehicle. Here, the orientation θ is defined as the angle formed by the traveling direction of the vehicle and the x-axis. The coordinates (x, y) indicate an absolute position corresponding to, for example, a combination of latitude and longitude.

[0040] FIG. 5 is a diagram showing the schematic relationship between the prediction step and the measurement update step. As shown in FIG. 5, by repeating the prediction step and the measurement update step, the calculation and update of the estimated value of the state variable vector X are sequentially executed. Here, the state variable vector at the reference time (i.e., the current time) "t" to be calculated is denoted as "X T , t , t , t , t , t , ,

[0041] , - , ^ , t " or "X ^ t ". (It is denoted as "the state variable vector X t =(x t , y t , θ t ) T ".) Note that a tilde " - " is attached above the character representing the tentative estimated value estimated in the prediction step, and a hat " ^ " is attached above the character representing the more accurate estimated value updated in the measurement update step.

[0041] In the prediction step, the vehicle position estimation unit 17 uses the state variable vector X at time t-1, which was calculated in the previous measurement update step. ^ t-1 In contrast, the vehicle's speed "v" and angular velocity "ω" (these are collectively called the "control value u") t =(v t , ω t ) T This is written as ". By applying this, the estimated value of the vehicle's position at time t (also called the "prior estimate") X - t The system calculates the following. At the same time, the vehicle position estimation unit 17 calculates the prior estimated value X. - t The covariance matrix "Σ" corresponds to the error distribution. - t The covariance matrix at time t-1 calculated in the previous measurement update step, "Σ ^ t-1 It is calculated from ".

[0042] Furthermore, in the measurement update step, the vehicle position estimation unit 17 associates the position vectors of features registered in the map DB 10 with the scan data of the lidar 2. Then, if this association is successful, the vehicle position estimation unit 17 records the measured value "Z" of the associated feature by the lidar 2. t " and the prior estimate X - t Furthermore, the measurement estimate of the feature "Z" was obtained by modeling the measurement process by LIDA2 using the position vectors of the features registered in map DB10. ^ t Obtain the following values ​​respectively. Measured value Z t This is a two-dimensional vector representing the distance and scan angle of the feature measured by the rider 2 at time t. The vehicle position estimation unit 17 then uses the measured value Z as shown in equation (1) below. t and the measured estimated value Z ^ t The difference is the Kalman gain "K t Multiply this by the prior estimate X - t By adding this to the updated state variable vector (also called the "posterior estimate") X ^ t Calculate. X ^ t =X - t +K t (Z t -Z ^ t ) Formula (1)

[0043] Furthermore, in the measurement update step, the vehicle position estimation unit 17, similar to the prediction step, estimates the post-prediction value X ^ t The covariance matrix Σ corresponds to the error distribution. ^ t The prior covariance matrix Σ - t We will find the Kalman gain K from this. t These parameters can be calculated in a similar manner to known self-localization techniques using, for example, extended Kalman filters.

[0044] Here, using the road paint with low detection accuracy indicated by the detection accuracy information Idet as a reference, the posterior estimate X is obtained from equation (1). ^ t When calculating, the measured value Z is based on output 2 of rider 2. t The measured estimated value Z was obtained using the position vectors of features registered in map DB10. ^ t The difference between the two becomes large. That is, in this case, the Kalman gain K in equation (1) t The difference multiplied by "Z" t -Z ^ t The value of " becomes larger. In this case, the posterior estimate X obtained by equation (1) becomes larger. ^ t The estimation accuracy will decrease.

[0045] Taking the above into consideration, the automatic driving control unit 18 controls the vehicle to avoid performing position estimation based on road paint with low detection accuracy indicated by the detection accuracy information Idet. In this way, the automatic driving control unit 18 effectively suppresses a decrease in position estimation accuracy. The specific control method will be explained in detail in the following sections: [First Vehicle Control Based on Road Paint Information] and [Second Vehicle Control Based on Road Paint Information].

[0046] [First vehicle control based on road paint information] The first vehicle control system, based on road marking information, corrects the target trajectory to change lanes to a different lane when the vehicle is traveling in a lane where road markings with low detection accuracy are present.

[0047] (1-1) Processing flow Figure 6 is a flowchart showing the first vehicle control based on road paint information performed by the automated driving control unit 18. In the flowchart of Figure 6, the automated driving control unit 18 corrects the pre-set target trajectory of the vehicle when it determines that road paint with low detection accuracy exists near the target trajectory based on the detection accuracy information Idet of the road paint information. It is assumed that when the flowchart of Figure 6 is executed, the automated driving control unit 18 has set a target trajectory for the vehicle along the route to the set destination.

[0048] First, the automatic driving control unit 18 determines whether the current position estimation accuracy is worse than a predetermined value (step S100). For example, the automatic driving control unit 18 determines that the current position estimation accuracy is worse than a predetermined value if the major axis of the error ellipse, which is determined based on the error covariance matrix obtained in the calculation process of position estimation based on the extended Kalman filter, is longer than a predetermined length. If the current position estimation accuracy is worse than a predetermined value (step S100; Yes), the automatic driving control unit 18 proceeds to step S101. On the other hand, if the current position estimation accuracy is not worse than a predetermined value (step S100; No), the automatic driving control unit 18 terminates the flowchart process.

[0049] If the current position estimation accuracy is worse than a predetermined value, the automatic driving control unit 18 obtains road paint information associated with the road data of the roads that make up the route to the destination from the map DB 10 (step S101). In this case, the automatic driving control unit 18 obtains, for example, road paint information from the map data 10 that corresponds to roads on the route that are within a predetermined distance from the current position.

[0050] The automatic driving control unit 18 then determines whether there is a road mark in the vicinity of the target trajectory (for example, in the same lane as the target trajectory) where the detection accuracy indicated by the detection accuracy information Idet is lower than a predetermined threshold (also simply called "low detection accuracy") (step S102). The threshold mentioned above is predetermined based on experiments, etc., taking into account whether or not there is a decrease in the accuracy of position estimation by the self-vehicle position estimation unit 17, and is stored in advance in the memory unit 12, etc.

[0051] Then, if the automatic driving control unit 18 determines that there is a road paint with low detection accuracy, as indicated by the detection accuracy information Idet, near the target trajectory (step S102; Yes), it reduces the weighting of the comparison result with the map DB10 when reflecting it in the self-position estimation (step S103). For example, in a driving section where there is a possibility that the road paint with low detection accuracy is within the measurement range of the rider 2, the automatic driving control unit 18 reduces the weighting of the "K" in equation (1). t (Z t -Z ^ t The value is multiplied by a predetermined coefficient less than 1. In this way, in step S103, the automatic driving control unit 18 can suitably reduce the decrease in position estimation accuracy even if the road paint with low detection accuracy falls within the measurement range of the lidar 2.

[0052] Next, the automatic driving control unit 18 modifies the vehicle's target trajectory to avoid road paint that results in low detection accuracy (step S104). Specifically, the automatic driving control unit 18 modifies the target trajectory to change lanes to a different lane from the lane where the road paint with low detection accuracy is located. In another example, if a lane marking with low detection accuracy is located on one side of a single-lane road being driven on, the automatic driving control unit 18 modifies the target trajectory within the lane to shift the driving position toward the lane marking on the opposite side. In this way, the automatic driving control unit 18 can effectively prevent position estimation based on the road paint with low detection accuracy by controlling the vehicle to move away from the road paint with low detection accuracy.

[0053] (1-2) Specific example Figure 7 is an overhead view of a vehicle traveling on a two-lane road 50 with a complex road marking on the right side that results in low detection accuracy. In the example in Figure 7, there are single lane markings 61 and complex lane markings 62 that separate road 50 from road 51 in the opposite direction. Here, single lane markings 61 are made up of only white lines, while complex lane markings 62 are complex lane markings that include orange lines and white lines. In complex lane markings 62, comb-shaped white lines are provided on both sides of the orange line to emphasize the orange line. The dashed line "Lt" indicates the target trajectory of the vehicle set by the automatic driving control unit 18.

[0054] In the case of the composite lane marking 62, since comb-shaped white lines are provided on both sides of the orange line, when the composite lane marking 62 is detected by the lidar 2, in addition to the point cloud data of the orange line, point cloud data of the comb-shaped white lines on both sides is also obtained. Furthermore, generally, when determining the measurement position of a feature (measured value z in Figure 5) by the lidar 2 in the position estimation process, the vehicle position estimation unit 17 calculates the centroid coordinates of the 2D coordinate system based on the vehicle indicated by the point cloud data of the target feature. In this case, when estimating the position using the composite lane marking 62, point cloud data with a lot of variation in the road width direction is obtained from the lidar 2, and the measurement position (measured value z in Figure 5) is determined from point cloud data with a lot of variation in the road width direction. In this case, the measured value z indicating the position coordinates of the composite lane marking 62 measured by the lidar 2 will have relatively low accuracy. Therefore, in the example in Figure 7, the detection accuracy indicated by the detection accuracy information Idet for the road paint information corresponding to the composite lane marking 62 is set to a lower detection accuracy than the detection accuracy indicated by the detection accuracy information Idet for the road paint information corresponding to the single lane marking 61 and the other lane markings 63 and 64.

[0055] In this case, the automatic driving control unit 18 refers to road paint information associated with the road 50 on which the vehicle is traveling from the map DB 10 and determines that the detection accuracy indicated by the detection accuracy information Idet of the road paint information corresponding to the composite lane marking 62 is low detection accuracy, which is lower than a predetermined threshold. Therefore, in this case, in order to avoid performing position estimation based on the composite lane marking 62, the automatic driving control unit 18 sets a target trajectory (see dashed line Lt) to change lanes to the left lane of road 50 that is not adjacent to the composite lane marking 62. When the vehicle is driven according to the target trajectory indicated by dashed line Lt, lane markings 63 and 64 become the closest lane markings when passing alongside the composite lane marking 62. Therefore, in this case, the vehicle position estimation unit 17 can perform position estimation based on lane marking 63 and / or lane marking 64 and maintain a high level of position accuracy in the lateral direction of the vehicle.

[0056] Figure 8 shows an overhead view of a vehicle on a single-lane road when low-detection-accuracy road markings are provided. In the example in Figure 8, a single lane 53 and a composite lane 67 are provided between the single lane 53 and the road 54 in the opposite direction. The single lane 66 consists only of white lines, and the composite lane 67 consists of white lines and orange lines.

[0057] In Figure 8, when the composite lane markings 67 are detected by the lidar 2, point cloud data for both the white and orange lines is obtained. In this case, the point cloud data for the composite lane markings 67 obtained from the lidar 2 will have a large variation in the width direction of the road, and the measured value z for the composite lane markings 67 will have relatively low accuracy. Therefore, in the example in Figure 8, the detection accuracy indicated by the detection accuracy information Idet for the road paint information corresponding to the composite lane markings 67 is set to low detection accuracy.

[0058] In this case, the automatic driving control unit 18 refers to road paint information associated with the road 53 on which the vehicle is traveling from the map DB 10 and determines that the detection accuracy indicated by the detection accuracy information Idet of the road paint information corresponding to the composite lane marking 67 is low detection accuracy, which is lower than a predetermined threshold. Therefore, in this case, in order to avoid performing position estimation based on the composite lane marking 67, the automatic driving control unit 18 sets the target trajectory (see dashed line Lt) so that the vehicle travels in a position that is biased towards the lane marking 68 rather than the composite lane marking 67 within the road 53. When the vehicle is driven according to the target trajectory indicated by dashed line Lt, the lane marking 68 becomes the closest lane marking when passing alongside the composite lane marking 67. Therefore, in this case, the vehicle position estimation unit 17 can perform position estimation based on the lane marking 68 and maintain a high level of position accuracy in the lateral direction of the vehicle.

[0059] Figure 9(A) shows another example of road paint where the detection accuracy information Idet is low. In Figure 9(A), a composite lane marking 69 is provided between road 55 and the opposite lane, road 56. The composite lane marking 69 gradually widens along road 55. For such a composite lane marking 69, the detection accuracy indicated by the road paint detection accuracy information Idet is also set to low detection accuracy.

[0060] Figure 9(B) shows a two-lane road 57 with road markings 70-73. In the example in Figure 9(B), road markings 70 and 71 on the left lane of road 57 are not faded, while road markings 72 and 73 on the right lane of road 57 are faded. Furthermore, the road paint information for road markings 72 and 73 has detection accuracy information Idet registered, indicating low detection accuracy. Therefore, in the example in Figure 9(B), when the vehicle passes through road 57, the automatic driving control unit 18 determines, based on the detection accuracy information Idet of the road paint information for road markings 70-73, that the lane with road markings 72 and 73 should be avoided. The automatic driving control unit 18 then sets a target trajectory to travel in the left lane with road markings 70 and 71, where the detection accuracy indicated by the detection accuracy information Idet is above a predetermined threshold.

[0061] (1-3) Application to pathfinding The automated driving control unit 18 may, in searching for a route to a destination, refer to the detection accuracy information Idet of road paint information and perform route searching in a way that avoids road paint with low detection accuracy.

[0062] In this case, the automated driving control unit 18 searches for a route that minimizes the total link cost calculated for each road based on factors such as travel time and distance, for example, using Dijkstra's algorithm. At this time, the automated driving control unit 18 adds a cost based on the detection accuracy indicated by the detection accuracy information Idet to the link cost, in addition to the travel time and distance. In this case, the cost added based on the detection accuracy information Idet is set higher, for example, the lower the detection accuracy indicated by the detection accuracy information Idet. By doing this, the automated driving control unit 18 can suitably search for a route consisting of roads with high detection accuracy indicated by the detection accuracy information Idet. Furthermore, the automated driving control unit 18 can also search for a route that substantially avoids roads with road markings that result in low detection accuracy by adding a cost significantly larger than the cost based on travel time and distance to the link cost corresponding to roads with road markings that result in low detection accuracy.

[0063] Figure 10 is a schematic diagram showing the route selection screen displayed by the information output unit 16. In the example in Figure 10, the automatic driving control unit 18 searches for a recommended route 83 based on a normal route search that does not take into account the detection accuracy information Idet, based on the destination specified based on user input (also called the "route without considering detection accuracy"), and a recommended route 84 based on a route search that takes the detection accuracy information Idet into consideration (also called the "route with considering detection accuracy"), and displays them on the route search screen. In Figure 10, the automatic driving control unit 18 also indicates low detection accuracy sections, which are road sections containing road paint with low detection accuracy, with dashed lines on the route without considering detection accuracy 83. In Figure 10, mark 81 indicates the destination, and mark 82 indicates the current location.

[0064] In the example shown in Figure 10, the route 83, which does not consider detection accuracy, includes a low detection accuracy section, which is a road section containing road paint that results in low detection accuracy, and the position estimation accuracy may be low in that section. On the other hand, the route 84, which considers detection accuracy, does not have a low detection accuracy section, so if the route 84 is selected as the route to travel, it is possible to suitably perform position estimation based on road paint. In this way, the automatic driving control unit 18 can suitably allow the user to select a route that avoids road sections containing road paint that results in low detection accuracy by using road paint information in route search.

[0065] [Second vehicle control based on road paint information] The second vehicle control system, based on road paint information, searches for a road paint suitable as a reference for estimating the vehicle's position in the direction of the large error, based on the detection accuracy information Idet and appropriate direction information Sdi included in the road paint information, when the position estimation error in the direction of travel or the lateral direction is greater than a predetermined threshold, and corrects the target trajectory to approach the road paint suitable as a reference for estimating the vehicle's position.

[0066] (2-1) Processing flow Figure 11 is a flowchart showing the second vehicle control based on road paint information performed by the automated driving control unit 18. In the flowchart of Figure 11, the automated driving control unit 18 searches for a road paint suitable as a reference for estimating the vehicle's position in a direction where the error in estimating the vehicle's position is large, based on the detection accuracy information Idet and the appropriate direction information Sdi of the road paint information. If such a suitable road paint exists near the target trajectory, the automated driving control unit 18 corrects the pre-set target trajectory of the vehicle. It is assumed that when the flowchart of Figure 11 is executed, the automated driving control unit 18 has set a target trajectory for the vehicle along the route to the set destination.

[0067] First, the automatic driving control unit 18 identifies the errors in the vehicle's position estimation in the direction of travel and the lateral direction (step S201). For example, the automatic driving control unit 18 identifies the errors in the vehicle's position estimation in the direction of travel and the lateral direction, respectively, by performing a transformation on the covariance matrix of the error obtained in the calculation process of position estimation based on the extended Kalman filter using a rotation matrix with the vehicle's orientation θ.

[0068] Next, the automatic driving control unit 18 monitors the position estimation accuracy of the vehicle position estimation unit 17 in the direction of travel and in the lateral direction, respectively. The automatic driving control unit 18 then determines whether or not there is a direction with low position estimation accuracy (also called a "low position accuracy direction Dtag") (step S202). For example, the automatic driving control unit 18 compares the position estimation errors in the direction of travel and the lateral direction identified in step S201 with predetermined thresholds and detects directions where the position estimation error is greater than the predetermined threshold as low position accuracy direction Dtags. It then determines whether or not a low position accuracy direction Dtag has been detected. Here, the direction of travel and the lateral direction of the vehicle are examples of the "first direction" and "second direction" in this invention.

[0069] In step S202, if the automated driving control unit 18 determines that there are no low-position accuracy directional Dtags (step S202; No), it determines that there is no need to correct the vehicle's target trajectory and terminates the flowchart processing. On the other hand, if the automated driving control unit 18 determines that there are low-position accuracy directional Dtags (step S202; Yes), it obtains road paint information associated with the road data of the roads that constitute the route to the destination from the map DB 10 (step S203). In this case, the automated driving control unit 18 obtains, for example, road paint information from the map DB 10 corresponding to roads on the route that are within a predetermined distance from the current position.

[0070] Then, the automatic driving control unit 18 determines whether or not there is road paint suitable as a criterion for estimating the vehicle's position for low position accuracy direction Dtag in the vicinity of the target trajectory (step S204). In this case, the automatic driving control unit 18 searches for road paint suitable as a criterion for estimating the vehicle's position for low position accuracy direction Dtag by excluding road paint that is determined to have lower detection accuracy than a predetermined standard and road paint that is determined to be less suitable than a predetermined standard as a criterion for estimating the vehicle's position for low position accuracy direction Dtag from among the road paints in the vicinity of the target trajectory, based on the detection accuracy information Idet and the appropriate direction information Sdi included in the road paint information, and determines whether or not there is road paint suitable as a criterion for estimating the vehicle's position for low position accuracy direction Dtag in the vicinity of the target trajectory as a result of the search.

[0071] Then, if the automated driving control unit 18 determines that a road marking suitable as a reference for estimating the vehicle's position using low position accuracy direction Dtag exists near the target trajectory (step S204; Yes), it modifies the vehicle's target trajectory to approach the road marking (step S205). Specifically, the automated driving control unit 18 modifies the target trajectory to change lanes to a lane where a road marking suitable as a reference is provided. If there are multiple road markings suitable as a reference for estimating the vehicle's position, the automated driving control unit 18 may modify the target trajectory to change lanes to a lane where a road marking with a higher degree of suitability is provided, or it may modify the target trajectory to change lanes to a lane where the road marking closer to the original target trajectory is provided. In another example, if a lane marking suitable as a reference is provided on one side of a single-lane road being driven on, the automated driving control unit 18 modifies the target trajectory within the lane to shift the driving position towards the lane marking. On the other hand, in step S204, if the automatic driving control unit 18 determines that there are no low position accuracy direction Dtags (step S204; No), it determines that there is no need to correct the vehicle's target trajectory and terminates the flowchart processing.

[0072] In this way, the automatic driving control unit 18 can suitably perform position estimation based on the road paint when a road paint suitable as a reference for low position accuracy direction Dtag exists near the target trajectory, by controlling the vehicle to approach the road paint.

[0073] Furthermore, in the second vehicle control based on road paint information, similar to the first vehicle control based on road paint information, if the automatic driving control unit 18 determines that there is road paint with low detection accuracy indicated by the detection accuracy information Idet near the target trajectory, it may reduce the weighting of the comparison result with the map DB10 when reflecting it in the vehicle position estimation, thereby suitably reducing the decrease in position estimation accuracy even if the road paint with low detection accuracy is within the measurement range of the rider 2.

[0074] (2-2) Specific example Figure 12 is an overhead view of a vehicle traveling on a two-lane road 90 with low detection accuracy road paint on the left side. In the example in Figure 12, there is a solid line 93 that separates road 90 from the road 91 in the opposite direction, a dashed line 92 that defines the area within road 90, and a solid line 94 that marks the left edge of road 90. Here, the solid line 94 is assumed to be faded. The dashed line "Lt" indicates the target trajectory of the vehicle set by the automatic driving control unit 18.

[0075] Because solid line 94 is blurred, the detection accuracy indicated by the detection accuracy information Idet for road paint information corresponding to solid line 94 is set to a lower detection accuracy than the detection accuracy indicated by the detection accuracy information Idet for road paint information corresponding to dashed line 92 and solid line 93. Furthermore, because solid line 93 and solid line 94 extend continuously in the direction of vehicle travel, the appropriate direction information Sdi for road paint information corresponding to solid line 93 and solid line 94 contains information indicating that the suitability as a criterion for estimating the vehicle's position in the lateral direction is optimal, but the suitability as a criterion for estimating the vehicle's position in the direction of vehicle travel is unsuitable. Furthermore, because dashed line 92 extends discontinuously in the direction of vehicle travel, the appropriate direction information Sdi for road paint information corresponding to dashed line 92 contains information indicating that the suitability as a criterion for estimating the vehicle's position in the lateral direction is suitable, and the suitability as a criterion for estimating the vehicle's position in the direction of vehicle travel is also suitable.

[0076] Here, the automatic driving control unit 18 determines that the error in estimating the vehicle's position in the lateral direction is greater than a predetermined threshold. In other words, it determines that the lateral direction of the vehicle is a low-position-accuracy direction Dtag. In this case, the automatic driving control unit 18 refers to the road paint information corresponding to dashed line 92, solid line 93, and solid line 94 from the map DB 10. First, the automatic driving control unit 18 excludes solid line 94 from the candidates for position estimation because the detection accuracy information Idet corresponding to solid line 94 indicates low detection accuracy. Next, it compares the suitability of the appropriate direction information Sdi corresponding to the remaining candidates, dashed line 92 and solid line 93, for estimating the vehicle's position in the lateral direction Dtag, which is a low-position-accuracy direction. Since the suitability of solid line 93 is higher, it decides to use solid line 93 as the position estimation criterion and modifies the target trajectory to approach solid line 93. When the vehicle is driven according to the target trajectory indicated by the dashed line Lt, the vehicle position estimation unit 17 can maintain a high level of positional accuracy in the lateral direction of the vehicle by estimating the position based on the solid line 93. Furthermore, if the vehicle's direction of travel is subsequently determined to be a low-position-accuracy direction Dtag, the vehicle can maintain a high level of positional accuracy in the direction of travel by slightly adjusting the target trajectory to approach the dashed line 92 within that lane.

[0077] Alternatively, instead of the above, the automatic driving control unit 18 may score the detection accuracy indicated by the detection accuracy information Idet corresponding to the dashed line 92, solid line 93, and solid line 94, and the suitability indicated by the suitability direction information Sdi, respectively, according to a predetermined standard, and determine a road paint suitable for the criteria for estimating the vehicle's position for the low position accuracy direction Dtag by making a comprehensive determination based on the detection accuracy score and the suitability score.

[0078] In the above example, we described the case where the error in estimating the vehicle's position in the lateral direction is greater than a predetermined threshold. However, if the error in estimating the vehicle's position in the direction of travel is greater than a predetermined threshold, then the road paint with a high degree of suitability for estimating the vehicle's position in the direction of travel among the appropriate direction information Sdi should be determined as the basis for position estimation. Furthermore, if both the error in estimating the vehicle's position in the direction of travel and the error in estimating the vehicle's position in the lateral direction are greater than a predetermined threshold, then the road paint with an appropriate degree of suitability for estimating the vehicle's position in the direction of travel and an appropriate degree of suitability for estimating the vehicle's position in the lateral direction among the appropriate direction information Sdi should be determined as the basis for position estimation.

[0079] As described above, the in-vehicle unit 1 in this embodiment includes a vehicle position estimation unit 17 and an automatic driving control unit 18. The vehicle position estimation unit 17 estimates the vehicle's position by comparing the detection results of road paint by the lidar 2 with the map DB 10. The automatic driving control unit 18 acquires road paint information from the map DB 10, including detection accuracy information Idet which indicates the accuracy of the comparison for each road paint. The automatic driving control unit 18 then outputs information to the vehicle's electronic control unit or information output unit 16 that controls the vehicle so that the comparison can be performed with an accuracy of at least a predetermined value, based at least on the detection accuracy information Idet. With this configuration, the in-vehicle unit 1 can suitably improve the accuracy of vehicle position estimation.

[0080] [Differentiation] The following describes suitable modifications for the examples. The following modifications may be applied in combination to the examples.

[0081] (Variation 1) Instead of storing the map DB10 in the storage unit 12, the in-vehicle unit 1 may have a server device (not shown) that holds the map DB10. In this case, the in-vehicle unit 1 obtains necessary road surface paint information, etc., by communicating with the server device via a communication unit (not shown).

[0082] (Modification 2) The configuration of the driver assistance system shown in Figure 1 is an example, and the configuration of a driver assistance system to which the present invention can be applied is not limited to the configuration shown in Figure 1. For example, instead of having an in-vehicle unit 1, the driver assistance system may have the vehicle's electronic control unit perform processing such as the vehicle position estimation unit 17 and the automatic driving control unit 18 of the in-vehicle unit 1. In this case, the map DB 10 may be stored in a storage unit in the vehicle, for example, and the vehicle's electronic control unit may receive update information for the map DB 10 from a server device (not shown). [Explanation of Symbols]

[0083] 1 On-vehicle device 2 Riders 3. Gyroscope sensor 4. Vehicle speed sensor 5 GPS receivers 10 Map Database

Claims

1. A comparison unit compares the position of the road paint, based on the amount of reflected light obtained by a detection device that detects an object by receiving reflected light that has been reflected by the object, with the position of the road paint in map information. A first acquisition unit acquires accuracy information indicating the accuracy of the matching for each road paint mark, A detection unit compares the position estimation errors in a first direction and a second direction with respect to the direction of movement of a moving object with a predetermined threshold, and detects the direction in which the position estimation error is greater than the threshold, A second acquisition unit acquires suitability information for each road paint mark, which indicates the degree of suitability when the road paint mark is used, as a criterion for position estimation of the direction detected by the detection unit. Based on the accuracy information and suitability information, an output unit identifies high-precision road paints from the road paints provided along the path of the moving body in which the matching accuracy is above a predetermined value and the suitability is above a predetermined standard, and outputs control information for controlling the moving body to approach the high-precision road paints. An output device having

2. The output unit identifies low-precision road paints from the road paints provided along the path of the moving body, such that the accuracy of the matching is less than the predetermined value. The output device according to claim 1, which outputs control information for controlling the moving body to move it away from the low-precision road paint.

3. The output unit searches for a route to the destination based on the accuracy information. The output device according to claim 1 or 2, which outputs information about the explored path as control information.

4. The output device according to claim 3, wherein the output unit outputs control information for displaying route information that can be matched with an accuracy of a predetermined value or higher as a recommended route on the display unit.

5. The output device according to any one of claims 1 to 4, further comprising a position estimation unit that estimates the position of the moving body based on the results of the aforementioned comparison.

6. The output device according to claim 5, wherein the output unit determines, based on the accuracy of the position estimation by the position estimation unit, whether or not it is necessary to control the moving object away from the road paint where the accuracy of the matching is less than the predetermined value.

7. The output device according to claim 5 or 6, wherein the position estimation unit reduces the weighting of the reflection of the matching result into the position estimation when the road paint in which the matching is less accurate than the predetermined value is included in the detection range of the detection device.

8. The output device according to any one of claims 1 to 7, wherein the road paint in the accuracy information for which the accuracy of the matching is less than the predetermined value is a lane marking represented by a complex line.

9. The output device according to any one of claims 1 to 7, wherein the road paint in which the accuracy of the matching is less than the predetermined value in the accuracy information is road paint that has faded.

10. A control method performed by an output device, A comparison step involves comparing the position of the road paint, based on the amount of reflected light obtained by a detection device that detects an object by receiving reflected light that has been reflected by the object, with the position of the road paint in map information. A first acquisition step involves acquiring accuracy information indicating the accuracy of the matching for each road paint mark, A detection step involves comparing the position estimation errors in a first direction and a second direction with respect to the direction of travel of a moving object with a predetermined threshold, and detecting the direction in which the position estimation error is greater than the threshold, A second acquisition step is to acquire suitability information for each road paint, which indicates the degree of suitability when the road paint is used, as a criterion for position estimation of the direction detected by the above detection step, Based on the accuracy information and suitability information, the output step identifies high-precision road paints from among the road paints provided along the path of the moving body such that the matching accuracy is above a predetermined value and the suitability is above a predetermined standard, and outputs control information for controlling the moving body to approach the high-precision road paint. A control method having

11. A program that is executed by a computer, A comparison unit compares the position of the road paint, based on the amount of reflected light obtained by a detection device that detects an object by receiving reflected light that has been reflected by the object, with the position of the road paint in map information. A first acquisition unit acquires accuracy information indicating the accuracy of the matching for each road paint mark, A detection unit compares the position estimation errors in a first direction and a second direction with respect to the direction of movement of a moving object with a predetermined threshold, and detects the direction in which the position estimation error is greater than the threshold, A second acquisition unit acquires suitability information for each road paint mark, which indicates the degree of suitability when the road paint mark is used, as a criterion for position estimation of the direction detected by the detection unit. A program that causes a computer to function as an output unit that, based on the accuracy information and suitability information, identifies high-precision road paints installed along the path of the moving body from among the road paints whose matching accuracy is above a predetermined value and whose suitability is above a predetermined standard, and outputs control information for controlling the moving body to approach the high-precision road paint.

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

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