Estimation device, method for estimation, computer program, and storage medium

Abstract

To precisely predict the actual measurement performance of a sensor.SOLUTION: An estimation device 10 includes a first acquisition unit 11, a second acquisition unit 12, and an estimation unit 14. The first acquisition unit 11 acquires actual measurement performance information showing the actual measurement performance of a sensor capable of acquiring surrounding information on surroundings of mobile bodies from the mobile bodies, positional information of the mobile bodies, and time information. A second acquisition unit 12 acquires first condition information of a first condition affecting the actual measurement performance of the sensor, which is a factor outside the mobile bodies. The estimation unit 14 estimates the actual measurement performance of the sensor at each time on the basis of the acquired actual performance information, positional information, time information, and the first condition information.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an estimation device, an estimation method, a computer program, and a storage medium. [Background technology]

[0002] Development of autonomous driving, driving assistance, safety devices, etc. for vehicles is underway. Here, autonomous driving, etc. uses sensors to detect surrounding conditions, and if the actual measurement performance of the sensors deteriorates, the autonomous driving, etc. cannot be performed smoothly. For this reason, it is preferable to identify in advance the areas where the actual measurement performance of sensors deteriorates.

[0003] Patent Document 1 describes that a region where the sensor's detection capability is reduced is identified based on the sensor's detection capability evaluated based on sensing information and the position where the sensing information is detected. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2016-95831 Summary of the Invention [Problem to be solved by the invention]

[0005] However, the method of Patent Document 1 was unable to accurately predict in advance the degradation of the sensor's actual measurement performance.

[0006] One example of a problem to be solved by the present invention is to predict the actual measurement performance of a sensor with high accuracy. [Means for solving the problem]

[0007] The first invention is a first acquisition unit that acquires, from a plurality of moving bodies, actual measurement performance information indicating actual measurement performance of a sensor that can acquire surrounding information around the moving bodies, and position information of the moving bodies, together with time information; a second acquisition unit that acquires first condition information that is a factor outside the moving body and that affects the actual measurement performance of the sensor; an estimation unit that estimates the actual performance of the sensor for each time based on the acquired actual performance information, the position information, the time information, and the first condition information; The estimation device includes:

[0008] The second invention is: a first acquisition step of acquiring, from a plurality of moving bodies, actual measurement performance information indicating actual measurement performance of a sensor capable of acquiring peripheral information around the moving bodies and position information of the moving bodies together with time information; a second acquisition step of acquiring first condition information that is a factor outside the moving body and that affects the actual measurement performance of the sensor; an estimation step of estimating the actual performance of the sensor for each time based on the acquired actual performance information, the position information, the time information, and the first condition information; This is an estimation method including:

[0009] The third invention is A computer program for realizing an estimation device, Computer, a first acquisition means for acquiring, from a plurality of moving bodies, actual measurement performance information indicating actual measurement performance of a sensor capable of acquiring peripheral information around the moving bodies, and position information of the moving bodies, together with time information; a second acquisition means for acquiring first condition information that is a factor outside the moving body and that affects the actual measurement performance of the sensor; and The computer program functions as an estimation means for estimating the actual performance of the sensor at each time based on the acquired actual performance information, the position information, the time information, and the first condition information. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 is a block diagram illustrating a configuration of an estimation device according to an embodiment. [Figure 2] FIG. 1 is a diagram illustrating an example of a usage environment of an estimation device according to an embodiment. [Figure 3] 10 is a diagram illustrating an example of information acquired by a first acquisition unit. FIG. [Figure 4] 1 is a flowchart of an estimation method according to an embodiment. [Figure 5] 1 is a block diagram illustrating a configuration of an estimation device according to a first embodiment. [Figure 6] FIG. 2 is a diagram illustrating a hardware configuration of an estimation device. [Figure 7] 3 is a flowchart of an estimation method according to the first embodiment. [Figure 8] FIG. 11 is a diagram illustrating an example of information acquired by a first acquisition unit according to a third embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0011] Hereinafter, embodiments of the present invention will be described with reference to the drawings. In all the drawings, like components are designated by like reference numerals, and the description thereof will be omitted as appropriate.

[0012] In the following description, the first acquisition unit 11, second acquisition unit 12, extraction unit 13, estimation unit 14, and storage unit 16 of the estimation device 10 represent functional blocks rather than hardware-based configurations. The first acquisition unit 11, second acquisition unit 12, extraction unit 13, estimation unit 14, and storage unit 16 of the estimation device 10 are realized by any combination of hardware and software, centered around the CPU of any computer, memory, a program loaded into the memory, a storage medium such as a hard disk that stores the program, and a network connection interface. There are various variations in the realization methods and devices.

[0013] FIG. 1 is a block diagram illustrating the configuration of an estimation device 10 according to an embodiment. The estimation device 10 includes a first acquisition unit 11, a second acquisition unit 12, and an estimation unit 14. The first acquisition unit 11 acquires measured performance information indicating the measured performance of a sensor capable of acquiring peripheral information around a plurality of mobile objects from the mobile objects, and position information of the mobile objects, along with time information. The second acquisition unit 12 acquires first condition information, which is a factor external to the mobile object and affects the measured performance of the sensor. The estimation unit 14 estimates the measured performance of the sensor for each time based on the acquired measured performance information, position information, time information, and first condition information. This will be described in detail below.

[0014] The actual performance of sensors mounted on a moving body such as a vehicle may be degraded due to factors external to the moving body. The estimation unit 14 uses the actual performance information of the sensors of multiple moving bodies 20 to estimate the future actual performance of the sensors. By doing so, it is possible to grasp future degradation of the actual performance of the sensors with higher accuracy, to smoothly operate the automatic driving, safety devices, and driving assistance devices of the moving body, and to avoid areas and times where the actual performance of the sensors is likely to be degraded.

[0015] FIG. 2 is a diagram illustrating an example of a usage environment of the estimation device 10 according to this embodiment. The estimation device 10 acquires measured performance information from multiple moving bodies 20 via a communication network 30. The moving bodies 20 are, for example, vehicles such as automobiles, motorcycles, and trains. Each moving body 20 is equipped with a sensor capable of acquiring peripheral information about the moving body. The sensor is, for example, an optical camera, a millimeter-wave radar, or a LIDAR (Laser Imaging Detection and Ranging, Laser Illuminated Detection and Ranging, or Light Detection and Ranging). Each moving body 20 may be equipped with multiple sensors. Furthermore, each moving body 20 may be equipped with multiple types of sensors. For example, each moving body 20 uses measurement results from one or more sensors to drive safety devices, auxiliary devices, etc., or to perform automatic driving.

[0016] The measurement performance of a sensor mounted on the moving body 20 may be degraded due to factors external to the moving body 20. Factors external to the moving body 20 include, for example, weather, sunlight, and season. Specifically, for a sensor such as a lidar that emits light toward a surrounding object and receives reflected light to measure the distance to the object, snowfall, rain, accumulated snow, blowing snow, fog, and the like can be noise sources. Furthermore, when strong sunlight is directly incident on the light receiving part of the sensor due to, for example, setting sun, this can interfere with measuring the surrounding conditions.

[0017] 3 is a diagram showing an example of information acquired by the first acquisition unit 11. The first acquisition unit 11 acquires measured performance information indicating the measured performance of a sensor from the mobile object 20, and location information of the mobile object 20. The measured performance information is generated based on the results of measurements made using the sensor in the mobile object 20. If the mobile object 20 is equipped with multiple sensors, the measured performance information is generated for each sensor. The measured performance information is, for example, information indicating the measured performance level of a sensor, and is generated, for example, as follows.

[0018] In the mobile object 20, a sensor measures a known specific object. Based on the measurement results, information indicating the accuracy of the sensor, such as signal strength and S / N ratio, is obtained. The specific object may be, for example, a dedicated object provided for determining the actual measurement performance of the sensor, or a general object. In the latter case, the specific object may be, for example, an object installed on the road, such as a traffic light, delineator, guardrail, road sign, or directional sign, or a road marking, such as a regulatory sign or an instruction sign. The accuracy of the sensor to be used as a reference (e.g., signal strength or S / N ratio) is pre-stored in the mobile object 20 or a memory unit provided outside the mobile object 20. The accuracy of the sensor to be used as a reference is, for example, the accuracy of the sensor at the time of shipment in a good environment. The accuracy of the sensor to be used as a reference read from the memory unit is then compared with the accuracy of the measured sensor, and the actual performance level of the sensor is calculated. For example, the ratio (%) of the measured signal strength to the reference signal strength is derived as the actual performance level.

[0019] Here, we will explain in more detail the case where the sensor is a LIDAR as an example. LIDAR emits pulsed laser light and measures the distance to a surrounding object based on the time it takes for the laser light to reflect off the surrounding object and return. The three-dimensional position of the point where the laser light is reflected relative to the LIDAR can be calculated based on the irradiation angle of the laser light and the measured time.

[0020] In addition to distance, the intensity of reflected light can also be obtained as data. The longer the distance to the surrounding object, the weaker the reflected light becomes, but it can be further attenuated depending on environmental conditions such as the influence of raindrops in the atmosphere. When the reflected light attenuates beyond a certain level, it becomes difficult to measure distance due to being overwhelmed by noise.

[0021] Then, by scanning, measurements are taken across the surface. Here, the data obtained from one scan (one revolution) is called one frame, and hereafter, unless otherwise specified, we will consider one frame as a single block of data. It is assumed that the effects of ambient light can be ignored.

[0022] Each moving body 20 detects an object, such as a signboard, from one frame of point cloud data. Object detection is performed using a known algorithm. The relative position (distance) and orientation of the object with respect to the sensor are calculated from the three-dimensional coordinates of the point (points) corresponding to the detected object.

[0023] Among the objects in the environment, there are known objects whose information, such as position, orientation, size, shape, and surface reflection characteristics, is included in map information necessary for autonomous driving, for example. The intensity of light emitted from a LIDAR in a certain position and orientation and reflected by a known object can be calculated using information on the relative distance and angle to the object and the reflection characteristics of the object's surface. The intensity calculated in this way is the intensity in an ideal environment, with no attenuation due to external factors. We will call this the ideal intensity.

[0024] However, as mentioned above, the actual light intensity received by a lidar is attenuated by the influence of particles in the atmosphere, etc. By comparing the actual light intensity received from a certain object (called the "measured intensity") with the ideal intensity calculated for the same object, it is possible to understand the intensity attenuation in that environment.

[0025] Let's say an object is detected from point cloud data acquired by a LIDAR at a certain position and orientation, and the ideal intensity at that time is calculated. Multiple point data corresponding to one object are extracted from the point cloud data acquired by the LIDAR, and the average intensity of their reflected light is calculated. The attenuation rate of this average value relative to the ideal intensity is then calculated. Based on this attenuation rate, it is possible to express the actual measured performance and performance degradation rate for an object, for example, a certain distance away from the sensor, in the environmental conditions at that time.

[0026] The extent of the influence of the environment changes depending on the distance the irradiated light passes through the atmosphere, so even in the same environment, the attenuation rate differs depending on the distance to the object.

[0027] It is also possible to determine a representative distance, for example, "the attenuation rate of reflection intensity from an object 20 m away from the LIDAR is the measured performance information," detect an object at that distance from the LIDAR, and obtain the measured performance. Alternatively, it is also possible to collect measured intensity data for objects at various distances, approximate the relationship between distance and measured intensity with a function, and use the parameters of that function as the measured performance information in that environment, which is not limited to a certain distance.

[0028] The collection of measured intensity data may be carried out over multiple frames within a time range of, for example, several minutes.

[0029] In addition to the method using the intensity of reflected light, a method using the S / N ratio may also be adopted. That is, the point data acquired by the LIDAR also includes "likelihood information," such as S / N ratio information. The average likelihood of points corresponding to a certain object may then be used as actual measurement performance information when measuring an object at that distance in that environment.

[0030] In this embodiment, the mobile object 20 is equipped with, for example, a GPS (Global Positioning System) receiver and can acquire its own location information at each time. The mobile object 20 associates the generated measured performance information with location information indicating the location where the measured performance was measured.

[0031] Furthermore, the first acquisition unit 11 acquires time information simultaneously with the measured performance information and the position information. With regard to this time information, for example, the time information of the time when the measured performance of the sensor in the mobile object 20 is measured is linked to the measured performance information. Furthermore, if the measurement time of the measured performance and the time when the first acquisition unit 11 acquires the measured performance information are substantially the same, the time information of the time when the first acquisition unit 11 acquires the measured performance information may be linked to the measured performance information.

[0032] The moving body 20 may generate measured performance information every time the moving body 20 approaches a specific object, or may generate measured performance information at every predetermined time T1. Furthermore, the first acquiring unit 11 may acquire measured performance information, position information, and time information every time the moving body 20 generates measured performance information, or may acquire measured performance information, position information, and time information at every predetermined time T2.

[0033] The first condition information is information about factors external to the moving body 20 that affect the actual measurement performance of the sensor. Examples of the first condition information include weather information indicating the weather and information indicating the direction of sunlight irradiation. When rain, snow, snow accumulation, fog, or the like occurs outside the moving body 20, these factors can cause light to be diffused or absorbed, which can reduce signal strength. Furthermore, when sunlight enters the light receiving element of the sensor, it becomes noise. The second acquisition unit 12 can acquire, as the first condition information, weather information indicating the weather and information indicating the direction of sunlight irradiation (for example, information indicating the direction and height of the sun) from, for example, a weather information service server.

[0034] In this embodiment, the estimation unit 14 estimates the measured performance of the sensor for each position and time based on the acquired measured performance information, position information, time information, and first condition information. By doing so, it is possible to grasp the deterioration of the measured performance of the sensor in advance with high accuracy.

[0035] 4 is a flowchart of the estimation method according to this embodiment. This estimation method includes a first acquisition step S10, a second acquisition step S20, and an estimation step S30. In the first acquisition step S10, measured performance information indicating the measured performance of a sensor capable of acquiring peripheral information around the mobile objects 20 and position information of the mobile objects 20 are acquired together with time information from multiple mobile objects 20. In the second acquisition step S20, first condition information, which is a factor external to the mobile objects 20 and affects the measured performance of the sensor, is acquired. In the estimation step S30, the measured performance of the sensor is estimated for each time based on the acquired measured performance information, position information, time information, and first condition information.

[0036] This estimation method is realized by, for example, the above-described estimation device 10.

[0037] As described above, according to this embodiment, the estimation unit 14 estimates the measured performance of the sensor for each time based on the acquired measured performance information, location information, time information, and first condition information. By doing so, it is possible to identify with high accuracy the area and time when the measured performance of the sensor is expected to deteriorate.

[0038] Example 1 5 is a block diagram illustrating the configuration of the estimation device 10 according to Example 1. The estimation device 10 according to this example has the configuration of the estimation device 10 according to the embodiment.

[0039] Moreover, the estimation device 10 according to this embodiment further includes an extraction unit 13. The estimation unit 14 estimates the measured performance of the sensor using the measured performance information extracted by the extraction unit 13. The extraction unit 13 extracts the measured performance information to be used by the estimation unit 14, thereby reducing the processing load on the estimation unit 14.

[0040] The extracting unit 13 extracts, from the plurality of pieces of measured performance information acquired by the first acquiring unit 11, measured performance information corresponding to the position information to be estimated.

[0041] In this embodiment, the estimation unit 14 estimates the measured performance of the sensor using weather information indicating the weather as the first condition information. In this case, the estimation unit 14 particularly effectively uses measured performance information from the relatively recent past to the present. This is because the current and recent weather conditions are relatively similar in time periods close to the present. Furthermore, the external environment other than the weather is also relatively similar in time periods close to the present, so the estimation unit 14 particularly effectively uses measured performance information from the relatively recent past to the present. Therefore, the extraction unit 13 extracts measured performance information for a predetermined time period from the multiple pieces of measured performance information acquired by the first acquisition unit 11. Specifically, the extraction unit 13 extracts measured performance information from a predetermined time period T3 or later from the present. The predetermined time period T3 is, for example, at least 5 minutes or at least 10 minutes, and is within 12 hours, 6 hours, or 3 hours.

[0042] In this embodiment, the weather information is, for example, precipitation amount, snow depth, visibility due to fog, etc. Furthermore, the weather information is a forecast value for the future, and is actual observed values, analyzed values, or a combination of observed values and analyzed values for the past.

[0043] In this embodiment, the second acquisition unit 12 acquires weather information at the time to be estimated as the first condition information. Furthermore, in this embodiment, the second acquisition unit 12 further acquires weather information at the time when the measured performance indicated by the measured performance information was measured as the first condition information. The estimation unit 14 then derives a relationship between the weather information at the time when the measured performance indicated by the measured performance information was measured and the measured performance, and estimates the measured performance at the time to be estimated using the weather information at the time to be estimated and the derived relationship. The second acquisition unit 12 acquires the weather information at the time to be estimated and the weather information at the time when the measured performance indicated by the measured performance information was measured, and the estimation unit 14 uses this information, thereby enabling the measured performance of the sensor to be estimated with higher accuracy.

[0044] 6 is a diagram illustrating an example of the hardware configuration of the estimation device 10. In this diagram, the estimation device 10 is implemented using an integrated circuit 40. The integrated circuit 40 is, for example, an SoC (System On Chip).

[0045] The integrated circuit 40 has a bus 402, a processor 404, a memory 406, a storage device 408, an input / output interface 410, and a network interface 412. The bus 402 is a data transmission path through which the processor 404, the memory 406, the storage device 408, the input / output interface 410, and the network interface 412 transmit and receive data to and from each other. However, the method of connecting the processor 404 and other components to each other is not limited to bus connection. The processor 404 is an arithmetic processing unit implemented using a microprocessor or the like. The memory 406 is a memory implemented using a RAM (Random Access Memory) or the like. The storage device 408 is a storage device implemented using a ROM (Read Only Memory), a flash memory, or the like.

[0046] The input / output interface 410 is an interface for connecting the integrated circuit 40 to peripheral devices. In the example shown in the figure, the input / output interface 410 is connected to a monitor 420 for checking the operating status of the estimating device 10 and an input panel 421 for inputting instructions to the estimating device 10.

[0047] The network interface 412 is an interface for connecting the integrated circuit 40 to a communication network. This communication network is, for example, a CAN (Controller Area Network) communication network. The method for connecting the network interface 412 to the communication network may be wireless connection or wired connection.

[0048] The storage device 408 stores program modules for realizing the functions of the first acquisition unit 11, the second acquisition unit 12, the extraction unit 13, and the estimation unit 14. The processor 404 reads these program modules into the memory 406 and executes them to realize the functions of the first acquisition unit 11, the second acquisition unit 12, the extraction unit 13, and the estimation unit 14.

[0049] The hardware configuration of the integrated circuit 40 is not limited to the configuration shown in this figure. For example, the program module may be stored in the memory 406. In this case, the integrated circuit 40 may not include the storage device 408.

[0050] 7 is a flowchart of the estimation method according to this embodiment. This estimation method is implemented using the estimation device 10 according to this embodiment as described above. This estimation method will be described in detail below.

[0051] The estimation method according to this embodiment includes a first acquisition step S10, an extraction step S15, a second acquisition step S20, and an estimation step S30.

[0052] In a first acquisition step S10, the first acquisition unit 11 acquires measured performance information from a plurality of moving objects 20. The first acquisition unit 11 also acquires time information and location information linked to the measured performance information. The acquired measured performance information, time information, and location information are stored in a storage unit 16 provided in the estimation device 10.

[0053] Next, in extraction step S15, the extraction unit 13 extracts measured performance information to be used for estimation by the estimation unit 14 from the plurality of pieces of measured performance information acquired by the first acquisition unit 11 and stored in the storage unit 16. In this embodiment, the extraction unit 13 extracts measured performance information for which time information linked to each piece of measured performance information indicates a time before time T3 or later. Furthermore, the extraction unit 13 further extracts, from the extracted plurality of pieces of measured performance information, measured performance information for which the position indicated by the position information linked to each piece of measured performance information corresponds to the target position of estimation by the estimation unit 14, as measured performance information to be used for estimation by the estimation unit 14. Note that the extraction unit 13 may also extract measured performance information for which the position indicated by the position information linked to each piece of measured performance information corresponds to a predetermined range (for example, within 1 kilometer) from the target position, as measured performance information to be used for estimation by the estimation unit 14.

[0054] In the second acquisition step S20, the second acquisition unit 12 acquires weather information as first condition information. Specifically, the second acquisition unit 12 acquires weather information for the time and location at which the measured performance indicated by each piece of measured performance information was measured, based on the time information and location information linked to the measured performance information extracted in the extraction step S15 as the measured performance information to be used for estimation by the estimation unit 14. The second acquisition unit 12 also acquires weather information for the time and location that is the target of estimation. Note that the location at which the measured performance was measured and the location of the acquired weather information do not need to be exactly the same location. The estimation unit 14 may acquire, from among the available weather information, weather information for the location closest to the location at which the measured performance was measured, or weather information for the area including the location at which the measured performance was measured, as weather information for the location corresponding to the location at which the measured performance was measured.

[0055] Next, in estimation step S30, the estimation unit 14 estimates the measured performance of the sensor at the target location and time. Specifically, the estimation unit 14 first derives a relationship between the measured performance information extracted in extraction step S15 and the weather information at the time and location at which the measured performance information was measured. This relationship is, for example, the amount of decrease in the measured performance level per unit amount of the numerical value indicated by the weather information. The amount of decrease in the measured performance level is, for example, the amount of decrease from 100% of the measured performance level described above.

[0056] Next, the estimation unit 14 estimates the measured performance of the sensor at the target location and time using the relationship between the measured performance information and the weather information for the time and location at which the measured performance information was measured, as well as the weather information for the target location and time. Specifically, for example, the estimation unit 14 estimates the amount of degradation in the measured performance level at the target location and time by multiplying the numerical value indicated by the weather information by the amount of degradation in the measured performance level per unit amount indicated by the weather information. This allows the estimation to take into account the effects of factors other than weather information on the sensor using the measured performance information, thereby improving the accuracy of the estimation.

[0057] The estimation unit 14 may use any one or more of the precipitation amount, snow depth, and visibility due to fog as weather information. When using two or more, for example, among the precipitation amount, snow depth, and visibility due to fog, the one that satisfies a predetermined condition for weather information at a target time and a target location may be identified and used as the main cause of the deterioration of the sensor's actual measurement performance.

[0058] In this embodiment, the estimation unit 14 estimates the actual performance of the sensor for each time for multiple target locations and generates information for each time indicating a degradation area where the degradation of the sensor's actual performance is expected. Specifically, the estimation unit 14 extracts, from the multiple target locations, target locations where the estimated actual performance of the sensor for the target time falls below a predetermined standard, and sets the information indicating the extracted target locations as information indicating a degradation area. The multiple target locations are, for example, multiple locations along roads within a predetermined area. This makes it possible to determine where the degradation of the sensor's actual performance is expected at the target time. The information indicating a degradation area may be generated when the estimation device 10 receives a request signal, or may be generated repeatedly at predetermined time intervals. Note that if information indicating a degradation area is generated multiple times for the same time, it is updated to the latest information.

[0059] The estimation device 10 transmits the generated information indicating the degraded area to one or more mobile bodies 20. The mobile body 20 that has acquired the information indicating the degraded area can, for example, set a route to the destination that avoids the degraded area. By doing so, the mobile body 20 can arrive at the destination smoothly by automatic driving or the like.

[0060] The estimation unit 14 may record locations where the sensor's actual measurement performance frequently degrades in the map information. For example, the estimation device 10 collects sensor's actual measurement performance information over a long period of time and detects locations where the sensor's actual measurement performance frequently degrades due to the external environment. Information indicating the detected locations is then added to the map information. For example, a road maintenance engineer may refer to such a map to install many landmarks that can be used to estimate the self-position of the mobile object 20 and are easy for the sensor to detect at locations where the sensor's actual measurement performance frequently degrades. The estimation unit 14 may also record locations where the sensor's actual measurement performance frequently degrades in the map information along with environmental information indicating the environment of the locations. By referring to such a map, the cause of the performance degradation can be estimated and measures such as the installation of landmarks can be taken more effectively.

[0061] According to this example, similar to the embodiment, the estimation unit 14 estimates the measured performance of the sensor for each time based on the acquired measured performance information, location information, time information, and first condition information. By doing so, it is possible to identify with high accuracy the area and time when the measured performance of the sensor is expected to deteriorate.

[0062] Moreover, according to this embodiment, the estimation device 10 further includes an extraction unit 13, and the estimation unit 14 estimates the measured performance of the sensor using the measured performance information extracted by the extraction unit 13. Therefore, the amount of measured performance information processed by the estimation unit 14 can be reduced, and the processing load of the estimation device 10 can be reduced.

[0063] Example 2 The estimation device 10 according to the second embodiment is similar to the estimation device 10 according to the first embodiment, except that the second acquisition unit 12 acquires weather information associated with measured performance information. This will be described in detail below.

[0064] In this embodiment, the second acquisition unit 12 acquires weather information associated with the measured performance information, instead of acquiring weather information at the time when the measured performance indicated by the measured performance information was measured. In this embodiment, the mobile object 20 is equipped with a weather sensor for measuring weather such as precipitation and visibility. The weather sensor is, for example, a rainfall sensor or a camera. Then, in each mobile object 20, the measured performance of the sensor is measured, and the weather sensor measures the weather at the time of the measurement. Then, in the mobile object 20, weather information is generated based on the measured weather and associated with the measured performance information.

[0065] Weather information provided by weather information services has a certain degree of variation in location and time, and there may be errors in the situation when the measured performance of the sensor is actually measured. On the other hand, according to the estimation device 10 of this embodiment, the first acquisition unit 11 acquires measured performance information, the second acquisition unit 12 acquires weather information associated with the measured performance information, and the estimation unit 14 estimates the measured performance of the sensor using the weather information acquired from the second acquisition unit 12, thereby making it possible to estimate the measured performance of the sensor with higher accuracy based on the actual weather.

[0066] The estimation method according to this embodiment will be described below. In this method, in a first acquisition step S10, measured performance information and weather information associated with the measured performance information are acquired. Then, an extraction step S15 is performed in the same manner as in the first embodiment.

[0067] Next, in a second acquisition step S20, the second acquisition unit 12 acquires weather information for the target time and target location to be estimated.

[0068] Next, in an estimation step S30, the estimation unit 14 estimates the measured performance of the sensor at the target location and time. Specifically, the estimation unit 14 first derives a relationship between the measured performance information extracted in the extraction step S15 and the weather information associated with the measured performance information. This relationship is, for example, the amount of degradation in the measured performance level per unit amount of the numerical value indicated by the weather information.

[0069] Next, the estimation unit 14 estimates the measured performance of the sensor at the target location and time using the relationship between the measured performance information and the weather information associated with that measured performance information, and the weather information for the target location and time. Specifically, for example, the estimation unit 14 estimates the amount of degradation in the measured performance level at the target location and time by multiplying the numerical value indicated by the weather information by the amount of degradation in the measured performance level per unit amount indicated by the weather information. This allows the estimation to take into account the effects of factors other than weather information on the sensor using the measured performance information, thereby improving the accuracy of the estimation.

[0070] Also in this embodiment, similarly to the first embodiment, the estimation unit 14 may generate information indicating a region where the concentration has decreased.

[0071] According to this example, similar to the embodiment, the estimation unit 14 estimates the measured performance of the sensor for each time based on the acquired measured performance information, location information, time information, and first condition information. By doing so, it is possible to identify with high accuracy the area and time when the measured performance of the sensor is expected to deteriorate.

[0072] Furthermore, according to this embodiment, the first acquisition unit 11 acquires weather information associated with the measured performance information, and therefore the measured performance of the sensor can be estimated with higher accuracy based on the actual weather.

[0073] Example 3 The estimation device 10 according to the third embodiment is the same as the estimation device 10 according to the first or second embodiment, except that the first acquisition unit 11 further acquires information indicating the type of the moving body 20, and the estimation unit 14 estimates the actual measurement performance of the sensor for each type of the moving body 20.

[0074] The information indicating the type of the moving body 20 may be, for example, a number indicating the vehicle model. The information indicating the type of the moving body 20 may also be information indicating the type of vehicle, such as a motorcycle, a light vehicle, a standard-sized vehicle, or a large vehicle. Even in the same environment, the degree of degradation of the sensor's actual measurement performance varies depending on the sensor type, the sensor position, the orientation of the sensor's light receiving unit, and the like. The sensor type is based on the sensing principle, such as an optical camera, millimeter-wave radar, or LIDAR. The sensor type may also be the sensor model number. The structure and specifications of the sensor vary depending on the sensor model number. For example, depending on the sensor principle and model number, some sensors are resistant to rain and snow, while others are not. Furthermore, when the sensor's actual measurement performance deteriorates due to sunlight, the degree of degradation depends on the sensor position and the orientation of the light receiving unit. Furthermore, the degree of degradation of the sensor's actual measurement performance due to snow accumulation varies depending on the sensor's mounting position on the moving body 20. The number, type, position, orientation of the light receiving unit, and other factors of the sensors mounted on the moving body 20 vary depending on the type of moving body 20. Therefore, the first acquisition unit 11 acquires information indicating the type of moving body 20 for which the measured performance information was generated, and the estimation unit 14 estimates the measured performance of the sensor for each type of moving body 20, thereby providing more accurate sensor performance prediction information for each moving body 20.

[0075] 8 is a diagram illustrating an example of information acquired by the first acquisition unit 11 according to this embodiment. In this embodiment, the first acquisition unit 11 acquires information indicating the type of the moving object 20 in addition to measured performance information, location information, and time information. Furthermore, the first acquisition unit 11 acquires, from each moving object 20, measured performance information about a plurality of sensors mounted on the moving object 20 in association with information indicating the type of each sensor.

[0076] Then, the estimation unit 14 estimates the measured performance of the sensor for each type of sensor. The estimation of the measured performance of the sensor is performed by the same process as in Example 1 or 2. Then, the estimation unit 14 compiles the measured performance information estimated for the multiple sensors mounted on each type of moving body 20, and generates an estimation result for each type of moving body 20.

[0077] For example, the estimation unit 14 further generates information indicating a degradation area for each type of mobile object 20. Specifically, the estimation unit 14 extracts, from among a plurality of target locations, target locations whose estimation results for a target time satisfy predetermined conditions, and sets information indicating the extracted target locations as information indicating a degradation area. Here, for example, the estimation unit 14 can determine that the estimation results satisfy the predetermined conditions when the measured performance levels of a predetermined number or more of sensors mounted on the mobile object 20 are below a predetermined threshold. Furthermore, the estimation unit 14 may determine that the estimation results satisfy the predetermined conditions when the measured performance level of a particular sensor with particularly high importance is below a predetermined threshold.

[0078] The mobile object 20 can then obtain and use information indicating the low-energy area corresponding to its type from the estimation device 10.

[0079] The first acquisition unit 11 may further acquire information indicating the traveling direction of the moving object 20, which is associated with the measured performance information. Since the measured performance of the sensor may vary depending on the traveling direction, for example, when the influence of sunlight is large, the estimation unit 14 may further estimate the measured performance of the sensor for each traveling direction. In this way, even when the measured performance of the sensor depends on the direction of the light receiving part of the sensor, the measured performance of the sensor can be estimated with high accuracy.

[0080] According to this example, similar to the embodiment, the estimation unit 14 estimates the measured performance of the sensor for each time based on the acquired measured performance information, location information, time information, and first condition information. By doing so, it is possible to identify with high accuracy the area and time when the measured performance of the sensor is expected to deteriorate.

[0081] Furthermore, according to this embodiment, the first acquisition unit 11 further acquires information indicating the type of the moving body 20, and the estimation unit 14 estimates the actual measurement performance of the sensor for each type of the moving body 20. Therefore, estimation is performed that reflects the type of sensor mounted on the moving body 20, and the impact on autonomous driving, etc. can be grasped with higher accuracy.

[0082] Example 4 The estimation device 10 according to the fourth embodiment is the same as the estimation device 10 according to at least one of the first to third embodiments, except that the first acquisition unit 11 further acquires information indicating characteristics of sensors mounted on the moving object 20, and the estimation unit 14 estimates the performance of the sensor for each of the sensor characteristics. This will be described in detail below.

[0083] The information indicating the sensor characteristics is, for example, information indicating the type and installation method of the sensor. Even in the same environment, the degree of degradation of the sensor's actual measurement performance varies depending on the sensor type, sensor position, and orientation of the sensor's light receiving element. The sensor type is based on the sensing principle, such as an optical camera, millimeter-wave radar, or LIDAR. The sensor type may also be the sensor model number. The structure and specifications of the sensor vary depending on the sensor model number. For example, depending on the sensor principle and model number, some sensors are resistant to rain and snow, while others are not. Furthermore, when the sensor's actual measurement performance deteriorates due to sunlight, the degree of degradation depends on the sensor's position and orientation of the light receiving element. Furthermore, the degree of performance deterioration due to snow accumulation varies depending on the sensor's installation position on the mobile object 20. Therefore, the first acquisition unit 11 acquires information indicating the sensor characteristics of the mobile object 20 for which actual measurement performance information was generated, and the estimation unit 14 estimates the sensor performance for each sensor, thereby providing more accurate sensor performance prediction information for each mobile object 20.

[0084] The estimation unit 14 estimates the measured performance of the sensor for each sensor feature indicated by the second condition information. The estimation unit 14 may also estimate the measured performance for each combination of sensor features, such as "XX-type LIDAR installed facing right with respect to the direction of travel." The estimation of the measured performance of the sensor is performed using the same processing as in at least one of Examples 1 to 3. Then, the estimation unit 14 compiles the measured performances estimated for the multiple sensors mounted on the multiple moving bodies 20 for each sensor, and generates an estimation result for each sensor.

[0085] According to this example, similar to the embodiment, the estimation unit 14 estimates the measured performance of the sensor for each time based on the acquired measured performance information, location information, time information, and first condition information. By doing so, it is possible to identify with high accuracy the area and time when the measured performance of the sensor is expected to deteriorate.

[0086] In addition, according to this embodiment, the first acquisition unit 11 further acquires information indicating the characteristics of the sensors, and the estimation unit 14 estimates the performance of the sensors for each of the sensor characteristics. Therefore, estimation is performed that reflects the characteristics of the sensors mounted on the moving object 20, and the impact on autonomous driving, etc. can be grasped with higher accuracy.

[0087] Example 5 The estimation device 10 according to the fifth embodiment is the same as the estimation device 10 according to at least one of the first to third embodiments, except for the points described below.

[0088] In the estimation device 10 according to this embodiment, the extraction unit 13 extracts measured performance information for which the time information acquired by the first acquisition unit 11 falls within a predetermined date and time range.

[0089] In sensing involving the detection of light, sunlight incident on a light receiving unit can become noise. In this embodiment, the estimation device 10 can estimate a degradation in the actual measurement performance of a sensor caused by the influence of sunlight incident on the light receiving unit of the sensor.

[0090] An estimation device 10 according to this embodiment will be described in detail below. Note that, hereinafter, an example will be described in which the estimation device 10 has the same configuration as in the first embodiment. However, in the estimation device 10 according to this embodiment, similar to the third embodiment, the first acquisition unit 11 may further acquire information indicating the type of the moving object 20, and the estimation unit 14 may estimate the measured performance of the sensor for each type of moving object 20. Furthermore, the first acquisition unit 11 may further acquire information indicating the traveling direction of the moving object 20, which is associated with the measured performance information, and the estimation unit 14 may estimate the measured performance of the sensor for each traveling direction. Furthermore, the first acquisition unit 11 may further acquire information indicating the orientation of the sensor of the moving object 20, which is associated with the measured performance information, and the estimation unit 14 may estimate the performance of the sensor for each traveling direction.

[0091] In this embodiment, in a first acquisition step S10, the first acquisition unit 11 acquires measured performance information from a plurality of moving objects 20. The first acquisition unit 11 also acquires time information and location information linked to the measured performance information. The acquired measured performance information, time information, and location information are stored in a storage unit 16 provided in the estimation device 10.

[0092] Next, in extraction step S15, the extraction unit 13 extracts measured performance information to be used for estimation by the estimation unit 14 from the multiple pieces of measured performance information acquired by the first acquisition unit 11 and stored in the storage unit 16. In this embodiment, the extraction unit 13 extracts measured performance information for which the time information associated with each piece of measured performance information falls within a predetermined date and time range. The predetermined date range is, for example, N days before to N days after the target date. Here, N is an integer, for example, 2 to 5. The predetermined time range is, for example, M minutes before to M minutes after the target time. Here, M is, for example, 10 to 30. The year is not particularly important. In this way, the estimation unit 14 extracts, from the multiple pieces of measured performance information, information from the same time and at the same time in the past for the target date. As a result, measured performance information of measured performance measured under sunlight conditions similar to those of the target date can be extracted.

[0093] By the estimation unit 14 extracting information on dates and times close to the dates and times to be estimated in this manner, the processing load on the estimation unit 14 is reduced when the estimation unit 14 estimates a deterioration in the actual measurement performance of the sensor due to sunlight.

[0094] Furthermore, the extraction unit 13 further extracts, from the extracted plurality of pieces of measured performance information, pieces of measured performance information in which the position indicated by the position information linked to each piece of measured performance information corresponds to the target position of estimation by the estimation unit 14, as measured performance information to be used for estimation by the estimation unit 14. Note that the extraction unit 13 may also extract, as measured performance information to be used for estimation by the estimation unit 14, pieces of measured performance information in which the position indicated by the position information linked to each piece of measured performance information corresponds to a position within a predetermined range from the target position.

[0095] In the second acquisition step S20, the second acquisition unit 12 acquires information indicating the irradiation direction of sunlight (for example, information indicating the direction and height of the sun) as the first condition information. Specifically, the second acquisition unit 12 acquires information indicating the irradiation direction at the time and position at which the measured performance indicated by each piece of measured performance information was measured, based on the time information and position information linked to the measured performance information extracted in the extraction step S15 as the measured performance information to be used for estimation by the estimation unit 14. The second acquisition unit 12 also acquires information indicating the irradiation direction at the time and position to be estimated. Note that the position at which the measured performance was measured and the position of the acquired information indicating the irradiation direction do not need to be exactly the same position. The estimation unit 14 may acquire, from among the information indicating the irradiation direction that can be acquired, information indicating the irradiation direction of the position closest to the position at which the measured performance was measured, or information indicating the irradiation direction of the area including the position at which the measured performance was measured, as the information indicating the irradiation direction of the position corresponding to the position at which the measured performance was measured.

[0096] Next, in estimation step S30, estimation unit 14 estimates the measured performance of the sensor at the target position and the target time. Specifically, from the plurality of pieces of measured performance information extracted in extraction step S15, estimation unit 14 first selects measured performance information in which the direction and height of the sun at the position and time at which the measured performance indicated by the measured performance information was measured falls within a predetermined range based on the direction and height of the sun at the target position and the target time. Then, estimation unit 14 estimates the measured performance indicated by the selected measured performance information as the measured performance of the sensor at the target position and the target time.

[0097] According to this example, similar to the embodiment, the estimation unit 14 estimates the measured performance of the sensor for each time based on the acquired measured performance information, location information, time information, and first condition information. By doing so, it is possible to identify with high accuracy the area and time when the measured performance of the sensor is expected to deteriorate.

[0098] Furthermore, according to this embodiment, the extracting unit 13 extracts measured performance information for which the time information acquired by the first acquiring unit 11 is within a predetermined date and time range. Therefore, it is possible to efficiently estimate the effect of sunlight on the measured performance of the sensor.

[0099] Example 6 The estimation device 10 according to the sixth embodiment is the same as the estimation device 10 according to at least one of the first to fifth embodiments, except that the first acquisition unit 11 further acquires request information indicating a position and a time to be estimated from the moving object 20, and the estimation unit 14 estimates the actual measurement performance of the sensor based on the request information. This will be described in detail below.

[0100] In this embodiment, for example, when a route to a destination is set for a mobile body 20, the mobile body 20 inquires of the estimation device 10 whether a decrease in the actual measurement performance of the sensor is expected at each position on the route at the scheduled time of passing through, and the estimation device 10 makes an estimation based on the content of the inquiry.

[0101] In this embodiment, a route is set in the mobile body 20 for navigation or automatic driving. To set the route, first, route candidates between the current location and the destination are created. Then, the mobile body 20 selects multiple positions on the created candidates and specifies the estimated time of passage at each selected position. The mobile body 20 generates request information that sets the selected multiple positions as target positions for estimation and the estimated time of passage at each position as the target time. Note that the request information may further include information indicating the type of the mobile body 20, the external situation of the mobile body 20, the conditions of the mounted sensors, etc.

[0102] The first acquisition unit 11 acquires request information from the mobile object 20. Then, the first acquisition unit 11 estimates the actual measurement performance of the sensor for conditions such as the target position and target time indicated by the request information, and transmits the estimation result to the mobile object 20.

[0103] The mobile unit 20 that received the estimation result determines whether to adopt the route based on the estimation result. Specifically, if the estimation result includes a measured performance level below a predetermined reference level, the route is not adopted and a different candidate is created. On the other hand, if the estimation result does not include a measured performance level below a predetermined reference level, the route is adopted.

[0104] According to this example, similar to the embodiment, the estimation unit 14 estimates the measured performance of the sensor for each time based on the acquired measured performance information, location information, time information, and first condition information. By doing so, it is possible to identify with high accuracy the area and time when the measured performance of the sensor is expected to deteriorate.

[0105] Furthermore, according to this embodiment, the first acquisition unit 11 further acquires request information indicating the position and time to be estimated from the moving object 20, and the estimation unit 14 estimates the actual measurement performance of the sensor based on the request information. Therefore, it is possible to perform estimations necessary for the travel of the moving object 20, and the processing load of the estimation device 10 can be reduced.

[0106] Example 7 The estimation device 10 according to the seventh embodiment is the same as the estimation device 10 according to at least one of the first to sixth embodiments, except that the second acquisition unit 12 acquires, as first condition information, history information that associates past measured performance of the sensor with factors outside the moving object 20 when the measured performance was measured. This will be described in detail below.

[0107] In this embodiment, the second acquisition unit 12 acquires history information that associates the past measured performance of the sensor with the conditions under which the measured performance indicated by the measured performance information was measured. Here, the conditions under which the measured performance was measured include factors external to the mobile object 20. Examples of factors external to the mobile object 20 include the location of the mobile object 20, the time, the amount of rainfall around the mobile object 20, and the intensity of sunlight around the mobile object 20. The conditions under which the measured performance was measured may also include conditions internal to the mobile object 20. Examples of conditions internal to the mobile object 20 include the number of years since the sensor was manufactured, the model number of the sensor, the time elapsed since the sensor was activated, the internal temperature of the sensor, and the total operating time since the sensor was manufactured. The second acquisition unit 12 may acquire the history information from the memory unit 16 of the estimation device 10 or from a server external to the estimation device 10. In this embodiment, multiple pieces of history information are stored in the memory unit 16 or the server.

[0108] The extraction unit 13 extracts history information similar to the conditions to be estimated as history information to be used for estimation. Specifically, for example, the extraction unit 13 extracts history information to be used for estimation using extraction condition information indicating extraction conditions previously stored in the estimation unit 14 through user settings, etc. The extraction condition information defines a range for determining similarity for each item, such as location and time, and the extraction unit 13 determines whether the content of each item of the history information is similar to the target condition. The extraction condition information further indicates the number of items to be determined as similar. The extraction unit 13 determines whether the number of items determined as similar is equal to or greater than the number of items indicated in the extraction condition information. If the number of items determined as similar is equal to or greater than the number of items indicated in the extraction condition information, the extraction unit 13 determines that the history information is similar to the conditions to be estimated and extracts it.

[0109] The estimation unit 14 estimates the performance of the sensor using the extracted history information. Specifically, for example, the estimation unit 14 determines, as an estimation result, the average of the average value of the actual measured performance indicated by the actual measured performance information included in the extracted one or more pieces of history information and the estimated value obtained by at least one of the methods of the above-mentioned first to sixth embodiments.

[0110] According to this example, similar to the embodiment, the estimation unit 14 estimates the measured performance of the sensor for each time based on the acquired measured performance information, location information, time information, and first condition information. By doing so, it is possible to identify with high accuracy the area and time when the measured performance of the sensor is expected to deteriorate.

[0111] According to this embodiment, the second acquisition unit 12 acquires, as the first condition information, history information that associates the past measured performance of the sensor with factors outside the moving body 20 when the measured performance was measured. Therefore, the past history can be further reflected to obtain highly accurate estimation results.

[0112] Example 8 The estimation device 10 according to the eighth embodiment is the same as the estimation device 10 according to at least one of the first to seventh embodiments, except for the points described below.

[0113] In this embodiment, the second acquisition unit 12 further acquires second condition information, which is information related to the sensor and affects the actual measurement performance of the sensor. The estimation unit 14 then further uses the second condition information to estimate the actual measurement performance of the sensor, as will be described in detail below.

[0114] In this embodiment, the second acquisition unit 12 acquires second condition information in addition to the first condition information. The second condition information may include numerical values and symbols indicating the sensor installation state (height, orientation, etc.), sensor specifications, sensor status (internal temperature, etc.), and sensor status (number of years since manufacture, continuous operating time after startup, total operating time since manufacture, etc.).

[0115] In this embodiment, the estimation unit 14 further uses second condition information to estimate the measured performance of the sensor. For example, when the first acquisition unit 11 acquires measured performance information or the like from the mobile object 20, the second acquisition unit 12 acquires second condition information related to the sensor of the mobile object 20 from the mobile object 20 and associates the second condition information with the measured performance information. Also, in this embodiment, the conditions to be estimated include sensor information. The extraction unit 13 extracts measured performance information associated with second condition information indicating conditions similar to sensor information included in the conditions of the sensor to be estimated.

[0116] The estimation unit 14 estimates the performance of the sensor using the extracted measured performance information. Specifically, for example, the estimation unit 14 determines, as an estimation result, the average of the average value of the measured performance indicated by the one or more pieces of extracted measured performance information and the estimated value obtained by at least one of the methods of the above-mentioned first to seventh embodiments.

[0117] According to this example, similar to the embodiment, the estimation unit 14 estimates the measured performance of the sensor for each time based on the acquired measured performance information, location information, time information, and first condition information. By doing so, it is possible to identify with high accuracy the area and time when the measured performance of the sensor is expected to deteriorate.

[0118] According to this embodiment, the second acquisition unit 12 further acquires second condition information, which is information about the sensor itself and affects the actual measurement performance of the sensor. The estimation unit 14 then further uses the second condition information to estimate the actual measurement performance of the sensor. Therefore, a highly accurate estimation result that further reflects the characteristics of the sensor can be obtained.

[0119] Example 9 The estimation device 10 according to the ninth embodiment is the same as the estimation device 10 according to at least one of the first to eighth embodiments, except that the estimation unit 14 estimates the measured performance of a sensor at a third location based on measured performance information about the first and second locations. The first, second, and third locations are different from each other. This will be described in detail below.

[0120] In this embodiment, the first acquisition step S10 and the second acquisition step S20 are This is performed in the same manner as at least one of Examples 1 to 8. In this embodiment, the extraction unit 13 extracts measured performance information within a specific distance range from the target position of estimation, i.e., the third point. Then, the extraction unit 13 extracts measured performance information of the first point and the second point, which are symmetrical points with respect to the third point, from the extracted measured performance information, as measured performance information to be used for estimation. In this embodiment, the extraction unit 13 does not need to extract measured performance information corresponding to the target position.

[0121] The estimation unit 14 estimates the actual measurement performance of the sensors at the first and second locations at the target time in the same manner as in at least one of the methods according to Examples 1 to 8. Then, the average value of the obtained estimation results for the first location and the second location is set as the estimation result for the third location.

[0122] The extraction unit 13 may set two points that are equidistant from each other on opposite sides of a road with respect to the third point as the first point and the second point, instead of the two points that are symmetrical with respect to the third point. The third point is a point on a road.

[0123] Furthermore, the extracting unit 13 may extract a plurality of pieces of actually measured performance information in order of proximity to the third point, weight the pieces of actually measured performance information by proximity, and take the average of the extracted pieces of actually measured performance information.

[0124] According to this example, similar to the embodiment, the estimation unit 14 estimates the measured performance of the sensor for each time based on the acquired measured performance information, location information, time information, and first condition information. By doing so, it is possible to identify with high accuracy the area and time when the measured performance of the sensor is expected to deteriorate.

[0125] According to this embodiment, the estimation unit 14 estimates the measured performance of the sensor at the third location based on the measured performance information for the first and second locations. Therefore, it is not necessary to use the measured performance information for the target location for the estimation.

[0126] Although the embodiments and examples have been described above with reference to the drawings, these are merely examples of the present invention, and various other configurations may be adopted. For example, in the sequence diagrams and flowcharts used in the above description, multiple steps (processes) are described in order, but the order of execution of the steps performed in each example is not limited to the order described. In each example, the order of the steps shown in the drawings may be changed to the extent that the content is not affected. Furthermore, the above-described embodiments and examples may be combined to the extent that the content is not contradictory.

[0127] Below, examples of reference forms are given. 1-1. A first acquisition unit that acquires actual measurement performance information indicating the actual measurement performance of a sensor that can acquire surrounding information around a plurality of moving bodies from the moving bodies and position information of the moving bodies together with time information; a second acquisition unit that acquires first condition information that is a factor outside the moving body and that affects the actual measurement performance of the sensor; an estimation unit that estimates the actual performance of the sensor for each time based on the acquired actual performance information, the position information, the time information, and the first condition information; An estimation device comprising: 1-2. In the estimation device according to 1-1, The second acquisition unit is an estimation device that further acquires second condition information, which is information about the sensor and affects actual measurement performance of the sensor. 1-3. In the estimation device according to 1-1 or 1-2, The second acquisition unit is an estimation device that acquires, as the first condition information, historical information that associates the past measured performance of the sensor with factors external to the moving body at the time the measured performance was measured. 1-4. The estimation device according to any one of 1-1 to 1-3, The estimation unit is an estimation device that estimates the actual measurement performance of the sensor using weather information indicating weather as the first condition information. 1-5. In the estimation device according to 1-4, The second acquisition unit is an estimation device that acquires, as the first condition information, the weather information at a time to be estimated. 1-6. The estimation device according to 1-4. or 1-5. The second acquisition unit is an estimation device that further acquires, as the first condition information, the weather information at the time when the actual performance indicated by the actual performance information was measured. 1-7. The estimation device according to any one of 1-4 to 1-6, The second acquisition unit is an estimation device that further acquires the weather information associated with the measured performance information. 1-8. The estimation device according to any one of 1-1 to 1-7, further comprising an extraction unit that extracts the measured performance information corresponding to the position information to be estimated from the plurality of pieces of measured performance information acquired by the first acquisition unit, The estimation unit is an estimation device that estimates the actual performance of the sensor using the actual performance information extracted by the extraction unit. 1-9. The estimation device according to 1-8, The extraction unit is an estimation device that extracts the measured performance information for a predetermined time period from the plurality of pieces of measured performance information acquired by the first acquisition unit. 1-10. The estimation device according to 1-8, The extraction unit is an estimation device that extracts the measured performance information in which the time information acquired by the first acquisition unit is within a predetermined date and time range. 1-11. The estimation device according to any one of 1-1 to 1-10, the first acquisition unit further acquires information indicating a type of the moving object; The estimation unit is an estimation device that estimates the actual measurement performance of the sensor for each type of the moving object. 1-12. The estimation device according to any one of 1-1 to 1-11, The estimation unit is an estimation device that estimates the actual measurement performance of the sensor for each time period for a plurality of target locations and generates information for each time period that indicates a degradation area where the actual measurement performance of the sensor is expected to degrade. 1-13. The estimation device according to any one of 1-1 to 1-11, the first acquisition unit further acquires request information indicating a position to be estimated and a time to be estimated from the moving object; The estimation unit is an estimation device that estimates the actual measurement performance of the sensor based on the request information. 2-1. A first acquisition step of acquiring actual measurement performance information indicating the actual measurement performance of a sensor capable of acquiring peripheral information around a plurality of moving bodies from the moving bodies and position information of the moving bodies together with time information; a second acquisition step of acquiring first condition information that is a factor outside the moving body and that affects the actual measurement performance of the sensor; an estimation step of estimating the actual performance of the sensor for each time based on the acquired actual performance information, the position information, the time information, and the first condition information; Estimation methods including: 2-2. In the estimation method described in 2-1, In the second obtaining step, second condition information that is information about the sensor and that affects actual measurement performance of the sensor is further obtained. 2-3. In the estimation method described in 2-1. or 2-2., In the second acquisition step, the estimation method acquires, as the first condition information, historical information that associates the past measured performance of the sensor with factors external to the moving body at the time the measured performance was measured. 2-4. In the estimation method according to any one of 2-1 to 2-3, In the estimating step, weather information indicating weather is used as the first condition information to estimate the actual measurement performance of the sensor. 2-5. In the estimation method described in 2-4, In the second obtaining step, the weather information at a time to be estimated is obtained as the first condition information. 2-6. In the estimation method described in 2-4. or 2-5., In the second obtaining step, the weather information at the time when the actual performance indicated by the actual performance information was measured is further obtained as the first condition information. 2-7. In the estimation method according to any one of 2-4 to 2-6, In the first obtaining step, the weather information associated with the measured performance information is further obtained. 2-8. In the estimation method according to any one of 2-1 to 2-7, further comprising an extraction step of extracting the measured performance information corresponding to the position information to be estimated from the plurality of pieces of measured performance information acquired in the first acquisition step, In the estimation step, the actual performance of the sensor is estimated using the actual performance information extracted in the extraction step. 2-9. In the estimation method described in 2-8., In the extracting step, the actually measured performance information for a predetermined time period is extracted from the plurality of pieces of actually measured performance information acquired in the first acquiring step. 2-10. In the estimation method described in 2-8., In the extracting step, the measured performance information obtained in the first obtaining step is within a predetermined date and time range. 2-11. The estimation method according to any one of 2-1 to 2-10, In the first acquisition step, information indicating a type of the moving object is further acquired; In the estimation step, the actual measurement performance of the sensor is estimated for each type of the moving body. 2-12. The estimation method according to any one of 2-1 to 2-11, In the estimation step, the actual measurement performance of the sensor is estimated for each time for a plurality of target positions, and information is generated for each time indicating a degradation area where degradation of the actual measurement performance of the sensor is expected. 2-13. The estimation method according to any one of 2-1 to 2-11, In the first acquisition step, request information indicating a position and a time to be estimated is further acquired from the moving object; In the estimation step, the actual performance of the sensor is estimated based on the request information. 3-1. A computer program for realizing an estimation device, Computer, a first acquisition means for acquiring, from a plurality of moving bodies, actual measurement performance information indicating actual measurement performance of a sensor capable of acquiring peripheral information around the moving bodies, and position information of the moving bodies, together with time information; a second acquisition means for acquiring first condition information that is a factor outside the moving body and that affects the actual measurement performance of the sensor; and a computer program that functions as an estimation means for estimating the actual performance of the sensor at each time based on the acquired actual performance information, the position information, the time information, and the first condition information; 3-2. In the computer program described in 3-1, The second acquisition means further acquires second condition information, which is information about the sensor and affects actual measurement performance of the sensor. 3-3. In the computer program according to 3-1. or 3-2., The second acquisition means is a computer program that acquires, as the first condition information, historical information that associates the past measured performance of the sensor with factors external to the moving body at the time the measured performance was measured. 3-4. In the computer program according to any one of 3-1 to 3-3, The estimation means is a computer program that estimates the actual measurement performance of the sensor using weather information indicating weather as the first condition information. 3-5. In the computer program described in 3-4, The second acquisition means is a computer program that acquires the weather information at a time to be estimated as the first condition information. 3-6. In the computer program according to 3-4. or 3-5., The second acquisition means further acquires, as the first condition information, weather information at the time when the actual performance indicated by the actual performance information was measured. 3-7. In the computer program according to any one of 3-4 to 3-6, The second acquisition means is a computer program that further acquires the weather information associated with the measured performance information. 3-8. In the computer program according to any one of 3-1 to 3-7, further comprising an extraction means for extracting the measured performance information corresponding to the position information to be estimated from the plurality of pieces of measured performance information acquired by the first acquisition means, The estimation means is a computer program that estimates the actual performance of the sensor using the actual performance information extracted by the extraction means. 3-9. In the computer program according to 3-8, The extraction means is a computer program that extracts the measured performance information for a predetermined time period from the plurality of pieces of measured performance information acquired by the first acquisition means. 3-10. In the computer program according to 3-8, The extraction means is a computer program that extracts the measured performance information whose time information acquired by the first acquisition means is within a predetermined date and time range. 3-11. In the computer program according to any one of 3-1. to 3-10., the first acquisition means further acquires information indicating a type of the moving object; The estimation means is a computer program that estimates the actual measurement performance of the sensor for each type of moving body. 3-12. In the computer program according to any one of 3-1 to 3-11, The estimation means is a computer program that estimates the actual measurement performance of the sensor at each time for a plurality of target locations and generates information at each time that indicates areas where the actual measurement performance of the sensor is expected to deteriorate. 3-13. The computer program according to any one of 3-1 to 3-11, the first acquisition means further acquires request information indicating a position to be estimated and a time to be estimated from the moving object; The estimation means is a computer program that estimates the actual measurement performance of the sensor based on the request information. [Explanation of symbols]

[0128] 10 Estimation device 11 First acquisition part 12 Second acquisition part 13 Extraction part 14 Estimation part 16 Memory section 20 Mobile 30 Communication Network 40 Integrated Circuits 402 Bus 404 processor 406 memory 408 Storage Devices 410 Input / Output Interface 412 Network Interface 420 monitor 421 Input Panel

Claims

1. a first acquisition unit that acquires, from a plurality of moving objects, actual measurement performance information indicating actual measurement performance of a sensor that can acquire surrounding information around the moving objects, and position information of the moving objects, together with time information; a second acquisition unit that acquires first condition information that is a factor external to the moving object and that affects actual measurement performance of the sensor; an estimation unit that estimates the actual performance of the sensor at each future time based on the acquired actual performance information, the position information, the time information, and the first condition information; An estimation device comprising:

2. 2. The estimation device according to claim 1, The second acquisition unit is an estimation device that further acquires second condition information, which is information about the sensor and affects actual measurement performance of the sensor.

3. 3. The estimation device according to claim 1, The second acquisition unit is an estimation device that acquires, as the first condition information, history information that associates the past measured performance of the sensor with factors external to the moving body at the time the measured performance was measured.

4. The estimation device according to any one of claims 1 to 3, The estimation unit is an estimation device that estimates the actual measurement performance of the sensor using weather information indicating weather as the first condition information.

5. 5. The estimation device according to claim 4, The second acquisition unit is an estimation device that acquires, as the first condition information, the weather information at a time to be estimated.

6. 6. The estimation device according to claim 4, The second acquisition unit is an estimation device that further acquires, as the first condition information, the weather information at the time when the actual performance indicated by the actual performance information was measured.

7. The estimation device according to any one of claims 4 to 6, The second acquisition unit is an estimation device that further acquires the weather information associated with the measured performance information.

8. The estimation device according to any one of claims 1 to 7, an extracting unit that extracts the measured performance information corresponding to the position information to be estimated from the plurality of pieces of measured performance information acquired by the first acquiring unit, The estimation unit is an estimation device that estimates the actual performance of the sensor using the actual performance information extracted by the extraction unit.

9. 9. The estimation device according to claim 8, The extraction unit is an estimation device that extracts the measured performance information for a predetermined time period from the plurality of pieces of measured performance information acquired by the first acquisition unit.

10. 9. The estimation device according to claim 8, The extraction unit is an estimation device that extracts the measured performance information when the time information acquired by the first acquisition unit is within a predetermined date and time range.

11. The estimation device according to any one of claims 1 to 10, the first acquisition unit further acquires information indicating a type of the moving object; The estimation unit is an estimation device that estimates the actual measurement performance of the sensor for each type of the moving object.

12. The estimation device according to any one of claims 1 to 11, The estimation unit is an estimation device that estimates the actual measurement performance of the sensor for each time period for a plurality of target locations and generates information for each time period that indicates a degradation area where the actual measurement performance of the sensor is expected to degrade.

13. The estimation device according to any one of claims 1 to 11, the first acquisition unit further acquires request information indicating a position to be estimated and a time to be estimated from the moving object; The estimation unit is an estimation device that estimates the actual measurement performance of the sensor based on the request information.

14. a first acquisition step of acquiring, from a plurality of moving bodies, actual measurement performance information indicating actual measurement performance of a sensor capable of acquiring peripheral information around the moving bodies and position information of the moving bodies together with time information; a second acquisition step of acquiring first condition information that is a factor external to the moving body and that affects actual measurement performance of the sensor; an estimation step of estimating the actual performance of the sensor at each future time based on the acquired actual performance information, the position information, the time information, and the first condition information; Estimation methods including:

15. A computer program for realizing an estimation device, Computer, a first acquiring means for acquiring, from a plurality of moving bodies, actual measurement performance information indicating actual measurement performance of a sensor capable of acquiring peripheral information around the moving bodies, and position information of the moving bodies, together with time information; a second acquisition means for acquiring first condition information that is a factor external to the moving body and that affects the actual measurement performance of the sensor; and a computer program that functions as an estimation means for estimating the actual performance of the sensor at each future time based on the acquired actual performance information, the position information, the time information, and the first condition information;

16. A storage medium storing the computer program according to claim 15.

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