Visibility meter erroneous-detection detector and visibility meter erroneous-detection detecting method

Abstract

To provide a visibility meter erroneous-detection detector that can easily detect whether a visibility meter is making an erroneous detection regarding a road condition.SOLUTION: A visibility meter erroneous-detection detector comprises a specifying unit, an acquisition unit, an evaluation value calculating unit, and a detection unit. The specifying unit specifies a visibility meter which indicates an occurrence of reduced visibility as a specified visibility meter based on a comparison between a visibility range distance value provided by the visibility meter and a visibility range threshold. The acquisition unit acquires at least one of imaging information of a road including a setting position and an area around the road, traffic volume information for indicating a traffic situation on the road including the setting position, event information for indicating an event occurring on the road including the setting position, and past weather information for the road including the setting position, as peripheral environment information. The evaluation value calculating unit calculates an evaluation value that indicates the visibility range situation of the road where occurrence of poor visibility is determined, based on a content of the peripheral environment information. The detection unit detects a possibility of erroneous detection of the specified visibility meter based on the evaluation value.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] An embodiment of the present invention relates to a visibility meter false detection detection device and a visibility meter false detection detection method. [Background technology]

[0002] Traffic control systems for roads such as expressways are known. Various information collection devices (terminals) may be installed on roads to detect road conditions. Examples of information collection devices include meteorological observation devices such as thermometers, hygrometers, and visibility meters, as well as CCTV cameras and traffic counters. Collected information from these information collection devices is provided to a control center in real time. Traffic control systems may be configured to notify a control room of an incident, such as a fire, accident, falling object, or natural disaster. For example, when a critical incident such as a fire or accident occurs, an alarm screen accompanied by an audible alarm may pop up (output) on the screen of a control console installed in the control center. This configuration allows for rapid awareness within the control room, enabling controllers to quickly coordinate with relevant parties. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2014-178843 [Patent Document 2] Japanese Patent Application Laid-Open No. 2008-052453 [Patent Document 3] Japanese Patent Application Laid-Open No. 2002-163792 Summary of the Invention [Problem to be solved by the invention]

[0004] As described above, an alarm that is output when a specific event occurs on a road can be generated by a mechanism that outputs a pop-up or alarm when a measurement value provided by an information collection device exceeds a preset threshold, but there are rare cases where an alarm is output due to a false detection. For example, the measurement value from a visibility meter, which is one type of weather observation device (information collection device), is important as an indicator of the degree of visibility on a road, and when the measurement value exceeds a threshold, it becomes necessary to take measures such as speed restrictions or road closures due to poor visibility.

[0005] However, if the measurement area of ​​some visibility meters is blocked by obstacles such as insects or debris, or if smoke temporarily flows into the measurement area, the measurement value may decrease (visibility distance value may decrease). In this case, even though the road is usable with sufficient visibility, the control center may be provided with a measurement value indicating poor visibility, and an alarm may be issued. In order to confirm whether the event that is the subject of the alarm (e.g., poor visibility) is correct, the control center's controller must check the detection values ​​of other information gathering devices, process requests for on-site confirmation, and so on, which increases the workload.

[0006] Therefore, if a visibility meter false detection detection device could be provided that could easily detect whether a visibility meter is making an erroneous detection (false detection) regarding road conditions, it would be useful as it would reduce the workload of control personnel. [Means for solving the problem]

[0007] A visibility meter false detection detection device according to an embodiment includes an identification unit, an acquisition unit, an evaluation value calculation unit, and a detection unit. The identification unit compares visibility distance values ​​provided by multiple visibility meters installed on a road with a visibility threshold used to determine whether poor visibility is occurring on the road, and identifies the visibility meter that provided a visibility distance value indicating the occurrence of poor visibility as a specific visibility meter. The acquisition unit acquires, as surrounding environment information for the installation location of the specific visibility meter, at least one of image information capturing an image of the road including the installation location and its surroundings, traffic volume information indicating traffic conditions on the road including the installation location, event information indicating events occurring on the road including the installation location, and past weather information for the road including the installation location. The evaluation value calculation unit calculates an evaluation value indicating the visibility conditions of the road determined to have poor visibility based on the content of the surrounding environment information. The detection unit detects the possibility of false detection by the specific visibility meter based on the evaluation value. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is an exemplary schematic block diagram showing the configuration of a traffic control system and a traffic control center including a visibility meter false detection detection device (visibility meter false detection detection unit) according to an embodiment, and details of an information collection device managed by a management office. [Figure 2] FIG. 2 is an exemplary schematic block diagram illustrating the configuration of a visibility meter erroneous detection detection device (visibility meter erroneous detection detection unit) according to the embodiment. [Figure 3] FIG. 3 is an exemplary flowchart illustrating processing by the visibility meter erroneous detection detection device (visibility meter erroneous detection detection unit) according to the embodiment. [Figure 4] FIG. 4 is an exemplary flowchart showing a process of calculating an evaluation value based on event information, which is part of the evaluation value calculation process in FIG. [Figure 5] FIG. 5 is an exemplary flowchart showing a process of calculating an evaluation value based on an image from a CCTV camera, which is part of the evaluation value calculation process in FIG. [Figure 6]FIG. 6 is an exemplary flowchart showing the process of calculating an evaluation value based on information from a traffic counter, which is part of the evaluation value calculation process in FIG. [Figure 7] FIG. 7 is an exemplary flowchart showing the process of calculating an evaluation value based on past measurement information (weather information) in the evaluation value calculation process in FIG. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, embodiments of the present invention will be described with reference to the drawings. The configurations of the embodiments described below, as well as the actions and results (effects) brought about by the configurations, are merely examples and are not limited to the following description.

[0010] The visibility meter false detection detection device of this embodiment calculates an evaluation value indicating the visibility conditions of a road where poor visibility is determined to be occurring, based on the content of surrounding environmental information about the road on which the visibility meter is installed.Then, based on whether the evaluation value exceeds a predetermined threshold, it detects the possibility of a false detection by the visibility meter that provided a visibility distance value indicating the occurrence of poor visibility.In other words, the visibility meter false detection detection device is a device that evaluates the reliability of detection by the visibility meter, making it easier for controllers to determine whether the output of an alarm based on the detection result of the visibility meter is appropriate.

[0011] FIG. 1 is an exemplary schematic block diagram showing the configuration of a traffic control system and a traffic control center including a visibility meter false detection detection device (visibility meter false detection detection unit) according to an embodiment, and details of an information collection device managed by a management office.

[0012] As shown in FIG. 1, a traffic control center 100 and a management office 102 are connected via a network W, and are configured to be able to send and receive information to and from each other.

[0013] A traffic control center 100 is provided for each of a plurality of jurisdictional areas that are set up by dividing a national territory into a plurality of areas. The traffic control center 100 performs traffic control processing for roads such as expressways and toll roads that fall within the jurisdictional area that it is responsible for. The traffic control center 100 provides various information, responds to events such as accidents and disasters, and manages roads, etc., to ensure that traffic in the jurisdictional area is carried out efficiently and safely.

[0014] The management office 102 manages the roads in its assigned area that fall within the jurisdiction of the traffic control center 100, and operates and manages the information collection devices 104 installed in its assigned area. The assigned area of ​​the management office 102 is obtained by subdividing the jurisdiction of the traffic control center 100, for example, based on the places the roads pass through. The management office 102 collects and manages various information detected by the information collection devices 104, and provides the collected information to the traffic control center 100. In addition to managing the information collection devices 104 themselves, the management office 102 (person in charge) also handles various events that may occur on the roads (accidents, disasters, handling of fallen objects, etc.) based on requests from the traffic control center 100 or road users, for example.

[0015] As described above, the information collection device 104 is installed on a road in the area under the control of the management office 102, and acquires various information indicating the road conditions of the roads for which information is collected continuously or at predetermined intervals. The information collection device 104 includes, for example, a weather observation device 106, a CCTV camera 108, a traffic counter 110, etc.

[0016] The meteorological observation device 106 includes, for example, a visibility meter 106a, a thermometer 106b, a hygrometer 106c, a rain gauge 106d, a snow gauge 106e, and a wind direction and speed meter 106f.

[0017] The visibility meters 106a may be installed at predetermined intervals along the roads for which information is to be collected, such as on the roadside. The visibility meters 106a measure the atmospheric extinction rate or light transmittance to measure (detect) information (visibility distance values) for estimating the line of sight at the measurement location. For example, the visibility meters 106a emit a beam such as a laser beam from a projector, and measure the beam that passes through the atmosphere, attenuates, and reaches a receiver. The visibility distance values ​​detected by the visibility meters 106a can be used to detect poor visibility due to, for example, fog, rain, snow, smoke from a fire, etc. The traffic control center 100 can determine the presence and extent of poor visibility by obtaining the visibility distance values ​​as meteorological observation information via the management office 102, and can therefore consider and implement measures, such as speed restrictions and road closures, for sections of the road where poor visibility occurs. As a result, the traffic control center 100 can provide road users (drivers of vehicles traveling on the road) with traffic information based on poor visibility.

[0018] Thermometers 106b and hygrometers 106c may be installed at predetermined intervals along the roads for which information is to be collected, such as on the roadside. Temperature information measured by thermometer 106b and humidity information measured by hygrometer 106c are provided to traffic control center 100 via management office 102 and can be used to determine the weather (climate) conditions and road surface conditions (such as the possibility of slipping due to ice or wetness) in the relevant section. As a result, traffic control center 100 can provide road users with traffic information based on the weather and road surface conditions.

[0019] Rain gauges 106d and snow gauges 106e may be installed at predetermined intervals along the roads for which information is to be collected, such as on the roadside. Rainfall information (e.g., rainfall amount per unit time) measured by rain gauge 106d and snowfall information (e.g., snowfall amount per unit time) measured by snow gauge 106e are provided to traffic control center 100 via management office 102 and may be used to determine the weather (climate) conditions and road surface conditions (such as the possibility of slipping due to rain or snow) for the relevant section, and in the case of snowfall, to determine whether chains should be used. As a result, traffic control center 100 may provide road users with traffic information based on rainfall or snowfall. By considering temperature and humidity information in addition to rainfall and snowfall information, traffic control center 100 may be able to improve the accuracy of determining the weather (climate) conditions and road surface conditions for the relevant section.

[0020] The anemometers 106f may be installed at predetermined intervals along the roads for which information is to be collected, such as on the roadside. Information on wind direction and speed measured by the anemometers 106f is provided to the traffic control center 100 via the management office 102 and may be used to determine the weather (climate) conditions in the relevant section and to warn drivers of driving cautions. As a result, the traffic control center 100 may provide road users with traffic information such as speed limits and driving cautions.

[0021] CCTV cameras 108 (Closed-Circuit Television Cameras) may be installed at predetermined intervals along roads from which information is collected. By processing image information captured by the CCTV cameras 108, situational information about the managed roads and their surrounding areas can be obtained. The image information is provided to the traffic control center 100 via the management office 102 and can be used to confirm and determine the road conditions in the relevant section. As a result, the traffic control center 100 can provide road users with traffic information that is easier to understand. Note that the image information from the CCTV cameras 108 can also be used to confirm the reliability of road condition determinations based on information acquired by other information collection devices 104.

[0022] Traffic counters 110 may be installed at predetermined intervals along the road from which information is collected. The traffic counters 110 count the number of vehicles passing at traffic volume observation points. The traffic counters 110 may also measure the speed of vehicles passing by the traffic counters 110. The measurement information of the traffic counters 110 (sometimes referred to as traffic volume information or traffic information) is provided to the traffic control center 100 via the management office 102 and may be used to determine the road conditions, particularly the congestion situation, of the relevant section. As a result, the traffic control center 100 may provide road users with traffic information such as congestion information, estimated arrival times to destinations, and suggested detours.

[0023] As described above, the information collection device 104 is a device (sensor) that is mainly placed along the road from which information is collected, but also includes an event information acquisition device 112 that acquires information on events that actually occur on or along the road. The event information acquisition device 112 may be placed in, for example, the management office 102, but may also be placed in a location other than the management office 102, for example, on the road like the other information collection devices 104, or in another facility.

[0024] Here, event information refers to, for example, information about accidents, disasters, fires, etc. that may actually occur on or along roads, and is information that may affect traffic on the roads under management. The event information acquisition device 112 can acquire event information, for example, from information provided by road wardens or road users (drivers, etc.), or from information provided by the police, other organizations, or other systems. The event information is provided to the traffic control center 100 via the management office 102 and can be used to check and determine the road conditions in the relevant section. As a result, the traffic control center 100 can provide more specific traffic information to road users.

[0025] The information collection device 104 described above is an example, and the format of the information acquisition does not matter as long as the information described above can be acquired. Also, information collection devices (sensors) with other configurations may be included as long as they collect information about roads under management, and like other information, the information may be provided to the traffic control center 100 via the management office 102 or the like for use in managing and monitoring road conditions.

[0026] Next, the traffic control center 100 will be described in detail.

[0027] As described above, the traffic control center 100 performs control operations to ensure efficient and safe traffic on roads based on road and road-related environmental information (weather information, image information, traffic volume information, event information, etc.) provided by the management office 102.

[0028] The traffic control system K included in the traffic control center 100 is mainly composed of a control console 10, a display device 12, and a traffic control server 14. The traffic control server 14 may be installed in a control room R in which the control console 10 and the display device 12 are installed, or may be installed in a location or facility separate from the control room R.

[0029] The control console 10 is an example of an input / output device that is provided in the control room R and can be operated by a controller. The control console 10 is equipped with a keyboard, a mouse, etc. as input devices. It is also equipped with a display unit 10a, etc. as output devices. The display unit 10a can display operation instructions, operation contents, processing results, etc. of various processes executed by the traffic control server 14. The display unit 10a can also display various information acquired by the information collection device 104 and provided via the management office 102. The display unit 10a may be covered with a transparent operation unit such as a touch panel and integrated with the input device.

[0030] The display device 12 is a large display device provided in the control room R, and is configured to be able to display, for example, information on the entire area under the jurisdiction of the traffic control center 100, for example, on a map. The display device 12 can also display information from each information collection device 104 provided by each management office 102, the processed results thereof, etc. The display device 12 may also display the actual conditions of each road (actual images captured by CCTV cameras 108), etc. The display unit 10a of the control console 10 can display the same content as that of the display device 12, allowing the controller to check the displayed information locally. The display device 12 can also display the content displayed on the display unit 10a, so that the work content of each controller, i.e., the control content and related information, can be shared throughout the control room R.

[0031] The traffic control server 14 may be configured with, for example, general computer resources. The traffic control server 14 may be configured with, for example, a central processing unit (CPU), read-only memory (ROM), random access memory (RAM), and a storage unit such as a hard disk drive (HDD) or solid-state drive (SSD). The CPU of the traffic control server 14 reads a program installed and stored in a nonvolatile storage unit such as a ROM or SSD, and implements various modules that function in the traffic control server 14 according to the program. The traffic control server 14 implements modules such as a processing unit 20, a visibility meter false detection detection unit 22 (visibility meter false detection detection device), an input processing unit 24, an output processing unit 26, and a display processing unit 28. Note that some or all of these modules may be configured as dedicated hardware. The traffic control server 14 also includes an information storage unit 30 that stores information acquired by the traffic control server 14 and processed content as history information.

[0032] The processing unit 20 includes a communication control unit 20a, an information collection unit 20b, an information processing unit 20c, an alarm processing unit 20d, a memory processing unit 20e, etc. The processing unit 20 has a function of collecting and processing information about roads within its jurisdiction obtained from each management office 102 and other information sources, etc., and providing users who use the roads within its jurisdiction with information that is useful when using the roads.

[0033] The communication control unit 20a controls communications with each management office 102 and other information sources (for example, police-related information sources, weather-related information sources, etc.), and performs information transmission and reception processing.

[0034] The information collection unit 20b performs a process of acquiring information about roads within the jurisdiction that is provided via the communication control unit 20a. The information collection unit 20b may be configured to acquire predetermined information constantly or at predetermined timings, or to acquire specified information as needed in response to a request from a traffic controller.

[0035] The information processing unit 20c compares each type of information collected by the information collecting unit 20b with, for example, a preset threshold, and determines, based on the information, whether or not it is necessary to provide, for example, caution information or warning information. For example, the information processing unit 20c determines, based on weather information collected by the information collecting unit 20b from the weather observation device 106, whether or not measures such as slip warnings, chain restrictions, and speed restrictions are necessary. Furthermore, based on image information collected by the information collecting unit 20b from the CCTV camera 108 and traffic volume information from the traffic counter 110, the information processing unit 20c determines, based on event information collected by the information collecting unit 20b from the event information acquiring device 112, whether or not it is necessary to provide traffic restrictions, speed restrictions, detour information, and the like.

[0036] When it is determined as a result of the processing by the information processing unit 20c that a warning or provision of information is necessary, the alarm processing unit 20d executes processing to output an alarm to the display device 12 in the control room R, the display unit 10a of the control console 10, or the like. The alarm processing unit 20d can, for example, pop up an alarm display on the display device 12 or the display unit 10a, or output an alarm sound or alarm message together with the pop-up display.

[0037] The memory processing unit 20e executes a process of storing various information acquired by the traffic control server 14 together with time information such as the acquisition date and time as history information in the information storage unit 30. The memory processing unit 20e also executes a process of storing, for example, the date and time when an alarm was output, the content of the alarm, road condition information at that time, and the like in the information storage unit 30 in association with each other.

[0038] As described above, the information collection device 104 that collects information for determining road conditions includes the weather observation device 106, the CCTV camera 108, the traffic counter 110, the event information acquisition device 112, and the like. Among these, the value (visibility distance value) detected by the visibility meter 106a is extremely important as it is one of the factors used to determine speed limits and road closures for roads subject to control. Even if the visibility meter 106a itself is functioning normally, the measurement value of the visibility meter 106a may decrease (the visibility distance value may decrease) if an obstacle such as an insect or debris blocks the measurement area of ​​the visibility meter 106a or if smoke temporarily flows into the measurement area. In this case, even if the road is usable with sufficient visibility, the information processing unit 20c may determine that visibility is poor, and the alarm processing unit 20d may execute an alarm output process. In such a case, the traffic controller will need to check the detection values ​​of other information collection devices 104 or request on-site confirmation to confirm whether the judgment of poor visibility in response to the output alarm is correct, which increases the workload. Therefore, the traffic control server 14 of this embodiment is provided with a visibility meter false detection detection unit 22 (visibility meter false detection detection device).

[0039] The visibility meter false detection detection unit 22 evaluates the reliability of the measurement (detection) by the visibility meter 106a, and detects whether the output of an alarm based on the detection result of the visibility meter 106a is appropriate. Based on the detection result, the visibility meter false detection detection unit 22 can, for example, notify the reliability of when an alarm is output, or suppress the output of the alarm itself. The detailed configuration of the visibility meter false detection detection unit 22 will be described later.

[0040] The input processing unit 24 is an interface that accepts input operations by a controller at the control console 10. The output processing unit 26 is an interface that outputs various information collected by the information collection unit 20b, processing results of the information processing unit 20c, etc. to the display unit 10a of the control console 10 or the display device 12.

[0041] The display processing unit 28 adjusts and switches the display mode (information layout, display color, etc.) on the display unit 10a and the display device 12, and controls the display content to be presented in a state that is easy for air traffic controllers to understand.

[0042] The information storage unit 30 is configured with a hard disk drive (HDD), a solid state drive (SSD), or the like, and stores, as described above, the information collected by the information collection unit 20b and the processing results of the information processing unit 20c as history information. The information storage unit 30 may be included in the traffic control server 14 or in another system. Furthermore, the information storage unit 30 may be installed in another facility.

[0043] FIG. 2 is an exemplary schematic block diagram showing the configuration of a visibility meter erroneous detection detection unit 22 (visibility meter erroneous detection detection device) according to this embodiment.

[0044] In order to detect false detection by the visibility meter 106a, the visibility meter false detection detection unit 22 includes detailed modules such as an identification unit 32, an acquisition unit 34, an evaluation value calculation unit 36, and a detection unit 38. Note that some or all of these detailed modules may be configured in hardware.

[0045] The identification unit 32 identifies the visibility meter 106a to be detected for detecting whether or not a false detection has occurred. That is, the identification unit 32 compares visibility distance values ​​provided by multiple visibility meters 106a installed on roads under control with a visibility threshold used to determine whether or not poor visibility is occurring on the road, and identifies the visibility meter 106a that provided a visibility distance value indicating the occurrence of poor visibility as a specific visibility meter. In this case, the visibility threshold may be a value that is set in advance, for example, through testing, to determine whether or not the visibility distance value to be compared corresponds to poor visibility, based on conditions such as season, weather, and time of day for the roads under management. In another embodiment, the identification unit 32 may identify the specific visibility meter using a processing result obtained by the information processing unit 20c of the processing unit 20 processing the visibility distance values ​​acquired from the visibility meters 106a.

[0046] The acquisition unit 34 may acquire various pieces of information required to detect false detection by the specific visibility meter (visibility meter 106a) via, for example, the information collection unit 20b. In another embodiment, the acquisition unit 34 may acquire various pieces of information directly from the management office 102 or other information sources via the network W.

[0047] The acquisition unit 34 includes, for example, a surrounding environment information acquisition unit 34a, a road characteristics acquisition unit 34b, and the like.

[0048] The surrounding environment information acquisition unit 34a acquires surrounding environment information about the installation position where the specific visibility meter (visibility meter 106a) is installed. The surrounding environment information acquisition unit 34a may acquire, for example, image information (image information from CCTV camera 108) capturing an image of a road including the installation position of the specific visibility meter and its surroundings. The surrounding environment information acquisition unit 34a may also acquire, for example, traffic volume information (measurement information from traffic counter 110) indicating the traffic conditions of a road including the installation position of the specific visibility meter. The surrounding environment information acquisition unit 34a may also acquire, for example, event information (information acquired by event information acquisition device 112) indicating an event occurring on a road including the installation position of the specific visibility meter. The surrounding environment information acquisition unit 34a may also acquire, for example, past weather information (information acquired by weather observation devices 106 other than visibility meter 106a) for a road including the installation position of the specific visibility meter. That is, the surrounding environment information acquisition unit 34a acquires at least one of these pieces of information.

[0049] The road characteristics acquisition unit 34b acquires road characteristics of the road on which the specific visibility meter is installed. As will be described later, by taking road characteristics into consideration when evaluating the reliability of detection by the specific visibility meter, the reliability of the evaluation can be improved. Here, the road characteristics include at least one of the following: terrain information on the road through which the road passes, tunnel passage information indicating whether the road passes through a tunnel, and curve sequence information on the road. Specifically, the terrain information includes, for example, information on whether the road passes through a plain, mountainous area, or coastal area, as well as terrain undulation information. Furthermore, the tunnel information includes information on whether the specific visibility meter is at the entrance or exit of a tunnel. The curve sequence information includes information on the number of curves and the shape (curvature) of each curve. Note that the road characteristics described above are merely examples, and any information that can be used when evaluating the reliability of detection by the specific visibility meter may be added as appropriate.

[0050] The evaluation value calculation unit 36 ​​calculates an evaluation value indicating the visibility condition of a road where poor visibility has been determined to occur, based on the content of the surrounding environment information acquired by the surrounding environment information acquisition unit 34a. The evaluation value calculation unit 36 ​​includes a summation processing unit 36a and a weighting processing unit 36b.

[0051] The evaluation value calculation unit 36, for example, scores information (surrounding environment information) acquired by other information collection devices 104 at substantially the same timing on a road where a specific visibility meter is installed.

[0052] For example, consider a case where the surrounding environment information is image information (image information from CCTV camera 108) capturing an image of a road including the installation location of a specific visibility meter and its surroundings. The target CCTV camera 108 can be one that exists within a specific range including the installation location of the specific visibility meter (for example, a CCTV camera 108 that exists within kilometer post A). For example, if the measurement value of visibility meter 106a exceeds the visibility threshold and is identified as a specific visibility meter, image processing is performed on the acquired captured image, for example, to digitize it to indicate visibility. Then, based on the digitized value, it is possible to determine what the actual visibility situation is.

[0053] For example, if the quantified value exceeds a preset threshold, it can be estimated that the visibility in the vicinity is actually poor due to the occurrence of fog, etc., and that the measurement result of the specific visibility meter is likely to be correct (no false detection factors). In this case, the evaluation value calculation unit 36 ​​does not add points to the evaluation value indicating the visibility condition of the road for the image information (for example, adds 0 points).

[0054] On the other hand, if the quantified value is below a preset threshold (in the case of good visibility), it can be estimated that there is a high possibility that visibility around the specific visibility meter is poor. In this case, the evaluation value calculation unit 36 ​​adds a score to the evaluation value indicating the visibility condition of the road for the image information (for example, the added score is set to 0). For example, it can be estimated that an obstacle such as an insect or debris is blocking the measurement area of ​​the specific visibility meter, which may result in a decrease in the visibility distance value.

[0055] Next, consider a case where the surrounding environment information is traffic volume information (measurement information from a traffic counter 110) indicating the traffic conditions of a road including the installation location of a specific visibility meter. The target traffic counter 110 can be one that exists within a specific range including the installation location of the specific visibility meter (for example, a traffic counter 110 that exists within B kilometer posts). For example, when the measurement value of the visibility meter 106a exceeds the visibility threshold and is identified as a specific visibility meter, traffic volume information (for example, average speed) measured T time ago is compared with the current average speed, and the average speed is quantified. The interval (time T) for comparing the average speeds can be set as appropriate.

[0056] When the quantified average speed has dropped by, for example, E% (e.g., 50%, which can be set appropriately) or more, it is highly likely that the average speed has dropped due to actual reduced visibility caused by, for example, fog, and it can be estimated that the measurement result of the specific visibility meter is highly likely to be correct (there is no cause for erroneous detection). In this case, the evaluation value calculation unit 36 ​​does not add points to the evaluation value indicating the visibility condition of the road for the traffic volume information (for example, add 0 points).

[0057] On the other hand, if the reduction in the quantified average speed is, for example, less than E%, it is considered that the visibility has not deteriorated enough to exceed the visibility threshold. In other words, it can be estimated that there is a high possibility that the visibility around the specific visibility meter is poor. In this case, the evaluation value calculation unit 36 ​​adds a score to the evaluation value indicating the visibility condition of the road for the traffic volume information (for example, adding a score > 0). For example, it can be estimated that an obstacle such as an insect or trash is blocking the measurement area of ​​the specific visibility meter, which may be indicating a reduction in the visibility distance value.

[0058] Next, consider a case where the surrounding environment information is event information indicating an event occurring on a road including the installation location of a specific visibility meter. The target event information can be information that exists within a specific range including the installation location of the specific visibility meter (for example, an event occurring on a road or along a railway line within C kilometer posts). In this case, the event is a fire (including an accident fire, a facility fire, etc.) accompanied by the generation of smoke or the like that affects changes in visibility. Then, for example, when the measurement value of the visibility meter 106a exceeds the visibility threshold and is identified as a specific visibility meter, the event is quantified according to the content of the event (for example, whether or not there is a fire).

[0059] If there is no event information (0 items), scoring is not possible, so the evaluation value calculation unit 36 ​​does not add points to the evaluation value indicating the road visibility conditions for the event information (for example, adds 0 points).

[0060] If event information exists but is not fire information, it is highly likely that localized visibility deterioration due to smoke from a fire or the like has not occurred, and it can be estimated that visibility is actually poor due to the occurrence of fog, for example, and that the measurement results of the specific visibility meter are highly likely to be correct (there is no cause for erroneous detection). In this case, the evaluation value calculation unit 36 ​​does not add points to the evaluation value indicating the road visibility conditions for the imaging information (for example, adds 0 points).

[0061] On the other hand, if the event information indicates a fire, it is possible that the visibility of only the specific visibility meter has deteriorated due to localized deterioration in visibility caused by smoke from the fire, etc. In other words, it can be estimated that there is a high possibility that visibility around the entire specific visibility meter is poor. In this case, the evaluation value calculation unit 36 ​​adds points to the evaluation value indicating the visibility condition of the road for the event information (for example, adding points > 0 points).

[0062] Next, consider a case where the surrounding environment information is past weather information for a road including the installation location of a specific visibility meter. For example, if the measurement value of the visibility meter 106a exceeds the visibility threshold and is identified as a specific visibility meter, the information is quantified according to the past weather information. In other words, when the measurement value of the visibility meter 106a exceeds the threshold (if it is identified as a specific visibility meter), past measurement information (past weather information) is referenced. Past weather information includes, for example, the thermometer 106b, the hygrometer 106c, the rain gauge 106d, the snow gauge 106e, and the wind direction and speed meter 106f. If all of the values ​​are within ±F%, data from the visibility meter 106a at a similar time (hour and minute) (similar past data) is considered to exist and referenced. Note that in this case, the data is assumed to be dynamic, and data from the past G days is referenced. Note that it is assumed here that the reliability of the similar past data referenced is high. The values ​​of F and G can be set as appropriate.

[0063] If there is no similar past data to refer to, scoring is not possible, so the evaluation value calculation unit 36 ​​does not add points to the evaluation value indicating the road visibility conditions for past weather information (for example, adds 0 points).

[0064] If similar past data exists and the visibility distance value for a similar time (hour and minute) is equal to or greater than the visibility threshold, it can be estimated that the data from a time when weather conditions other than visibility are very similar is likely to indicate that visibility actually worsened. In other words, it can be estimated that the measurement results of the specific visibility meter are likely to be correct (there is no cause for erroneous detection). In this case, the evaluation value calculation unit 36 ​​does not add points to the evaluation value indicating the road visibility condition for the past weather information (for example, it adds 0 points).

[0065] On the other hand, if similar past data exists and the visibility distance value for the same time (hour / minute) is below the visibility threshold, it is unlikely that the visibility value alone will be similar even if the weather conditions other than visibility are very similar. In other words, it can be estimated that there is a high possibility that visibility around the specific visibility meter is poor. In this case, the evaluation value calculation unit 36 ​​adds points to the evaluation value indicating the road visibility condition for the past weather information (for example, adding points > 0). For example, it can be estimated that the visibility distance value may be reduced as a result of obstacles such as insects or debris blocking the detection area of ​​the specific visibility meter.

[0066] In this way, the evaluation value indicating the visibility conditions of a road where poor visibility is determined to be occurring, calculated based on the content of each surrounding environment information, can be estimated to be higher the higher the possibility that the visibility meter 106a (specific visibility meter) being detected is making a false detection.

[0067] The summing processing unit 36a sums the evaluation values ​​for each piece of surrounding environment information calculated by the evaluation value calculation unit 36. As a result, the summed evaluation value is a value that takes into account multiple pieces of information, and is a value that improves the detection reliability of false detection by the visibility meter 106a (specific visibility meter) that is the detection target.

[0068] Incidentally, the evaluation values ​​calculated based on each type of surrounding environment information may have different reliability. For example, there may be a difference in reliability between an evaluation value based on image information acquired by CCTV camera 108 and an evaluation value based on past weather information. Therefore, the evaluation value calculation unit 36 ​​may weight the evaluation values ​​according to the type of surrounding environment information and modify the evaluation values.

[0069] The weighting processor 36b assigns weights to each piece of surrounding environment information. For example, if it is estimated based on image information that the measurement by the specific visibility meter is likely to be a false positive, the reliability of the estimation (evaluation value) can be considered to be the highest. On the other hand, if it is estimated based on a comparison with past weather information that the measurement by the specific visibility meter is likely to be a false positive, the reliability of the estimation (evaluation value) may be low because similar past data is used as the weather information. Therefore, the weighting processor 36b sets the weight of past weather information (measurement information) to, for example, "x1.0," the weight of event information to, for example, "x1.5," the weight of traffic volume information to, for example, "x2.0," and the weight of image information to, for example, "x4.0." As a result, the reliability of the evaluation value calculated based on each piece of surrounding environment information is corrected and averaged according to the content of the surrounding environment information.

[0070] The detection unit 38 detects the possibility of a false detection by the specific visibility meter based on the calculated evaluation value. For example, if the evaluation value (total value) is equal to or greater than a predetermined judgment threshold, the detection unit 38 determines that the alarm judgment based on the measurement (detection) result of the specific visibility meter is a false judgment. Note that the detection unit 38 may be able to set multiple judgment thresholds.

[0071] For example, stepwise determination thresholds may be set such that the first determination threshold < the second determination threshold < the third determination threshold. For example, if the evaluation value < the first determination threshold, the alarm output based on the detection result of the specific visibility meter is unlikely to be a false determination. In other words, the alarm output is likely to be normal, and it can be assumed that a prompt alarm response is required. Furthermore, if the first determination threshold ≦ the evaluation value < the second determination threshold, the alarm output based on the detection result of the specific visibility meter is likely to be a false determination approximately "low." In other words, the alarm output is generally normal, and it can be assumed that an appropriate response to the alarm is required. Furthermore, if the second determination threshold ≦ the evaluation value < the third determination threshold, the alarm output based on the detection result of the specific visibility meter is likely to be a false determination approximately "medium." In other words, the specific visibility meter may be temporarily making a false detection, and it can be assumed that continued monitoring of the alarm is required for a predetermined period of time. Furthermore, if the third determination threshold ≦ the evaluation value, the alarm output based on the detection result of the specific visibility meter is likely to be a false determination approximately "high." In other words, there is a possibility that the specific visibility meter is temporarily making a false detection, and it can be assumed that the alarm should be checked again after a predetermined period of time, for example.

[0072] The detection unit 38 may calculate an evaluation value (total value) each time the visibility distance value exceeds the visibility threshold and detect (determine) whether or not there is a possibility of a false detection. Alternatively, the detection unit 38 may calculate the evaluation value (total value) multiple times within a predetermined period and detect (determine) whether or not there is a possibility of a false detection. That is, the detection unit 38 may calculate the evaluation value (total value) for, for example, a predetermined period (e.g., once per minute, M times (e.g., 10 times) or N minutes (e.g., 10 minutes)), and detect (determine) a false detection when the evaluation value exceeds the determination threshold a predetermined percentage of the time (J% or more times (e.g., 70%)). In this way, by calculating the evaluation value multiple times and comparing it with the determination threshold multiple times, and finally detecting whether or not there is a possibility of a false detection, the reliability of false detection can be improved.

[0073] Note that the longer the predetermined period (M times or N minutes) for calculating the evaluation value multiple times, the more reliable the calculation will be, but on the other hand, the longer the period (time) until it can be determined whether the alarm is due to a false positive. Therefore, it is desirable to set the predetermined period while balancing reliability and speed.

[0074] The detection unit 38 may use different values ​​for the judgment threshold to be referenced when detecting the possibility of a false detection by the specific visibility meter based on the calculated evaluation value, depending on the characteristics of the road to be controlled. For example, it may be desirable to change the accuracy of the alarm depending on the topography through which the road to be controlled passes, whether the road passes through a tunnel, the degree of continuity of curves on the road, etc.

[0075] For example, in mountainous areas prone to fog, road sections with many curves, road sections with many tunnels, road sections with rugged terrain, etc., even if the possibility of a false positive is detected (determined) as low, it may be more appropriate to notify the false positive. For example, in mountainous areas, the determination threshold may be lowered so that false positive determinations are made more frequently, while in plain areas, false positive determinations are made less frequently. As a result, more appropriate false positive detection processing can be performed depending on the area the road passes through.

[0076] The processing of the visibility meter erroneous detection detection device (visibility meter erroneous detection detection unit 22) configured as above will be described with reference to the flowcharts of FIGS.

[0077] FIG. 3 is an exemplary flowchart showing the processing of the visibility meter erroneous detection detection device (visibility meter erroneous detection detection unit 22).

[0078] In step S100, the identification unit 32 checks whether the measurement value of the visibility meter 106a exceeds the visibility threshold, that is, whether the visibility meter 106a can be identified as a specific visibility meter.

[0079] If the result of step S100 is "No," that is, if there is no visibility meter 106a indicating poor visibility, this flow is temporarily ended.

[0080] If the result of step S100 is "Yes," that is, if there is a visibility meter 106a indicating poor visibility, the visibility meter erroneous detection detection unit 22 starts the evaluation value calculation process based on each piece of surrounding environment information.

[0081] In S102, the evaluation value calculation unit 36 ​​executes the individual processes shown in FIGS.

[0082] FIG. 4 is an exemplary flowchart showing a process of calculating an evaluation value based on event information, which is part of the evaluation value calculation process in FIG.

[0083] In step S200, the evaluation value calculation unit 36 ​​checks whether the visibility meter 106a has been identified.

[0084] If the answer is "No" in step S200, that is, if there is no specific visibility meter and no visibility meter 106a indicating poor visibility, the flow ends.

[0085] If the answer is "Yes" in step S200, that is, if a specific visibility meter is present and a visibility meter 106a indicating poor visibility is present, the process proceeds to step S202.

[0086] In step S202, the surrounding environment information acquisition unit 34a refers to event information in the vicinity of the relevant visibility meter 106a (specific visibility meter), and then the process proceeds to step S204.

[0087] If the answer is "Yes" in step S204, that is, if an event exists near the specific visibility meter, the process proceeds to step S206.

[0088] In step S206, the evaluation value calculation unit 36 ​​checks the event content, and then the process proceeds to step S208.

[0089] In step S208, it is determined whether the event content includes fire information. If the answer is "No" in step S208, that is, if the event content does not include fire information. For example, if the event content is about something falling on the road, the event is deemed not to be related to poor visibility. In this case, the process proceeds to step S210.

[0090] In step S210, the evaluation value calculation unit 36 ​​does not add points to the evaluation value indicating the visibility condition of the road (for example, adds 0 points). In other words, if event information exists but is not fire information, it is highly likely that there has been no local deterioration in visibility due to smoke from the fire, etc. In this case, it can be estimated that there is a high possibility that visibility is actually poor due to the occurrence of fog, etc., and that the measurement results of the specific visibility meter are correct (there is no cause for erroneous detection). Therefore, the evaluation value calculation unit 36 ​​does not add points to the evaluation value indicating the visibility condition of the road for the imaging information. Then, this flow is temporarily terminated.

[0091] If the answer is "Yes" in step S208, that is, if the event content includes fire information, the process proceeds to step S212.

[0092] In step S212, the evaluation value calculation unit 36 ​​adds a point to the evaluation value indicating the visibility condition of the road (for example, sets the added point to 0). In other words, if the event information indicates a fire, there is a possibility that the visibility of only the specific visibility meter has deteriorated due to localized deterioration in visibility caused by smoke from the fire, etc. In other words, it can be estimated that there is a high possibility that the visibility around the entire specific visibility meter is suspiciously poor. In this case, the evaluation value calculation unit 36 ​​adds a point to the evaluation value indicating the visibility condition of the road for the event information. Then, this flow is temporarily terminated.

[0093] If the answer is "No" in step S204, that is, if there is no event near the specific visibility meter, the process proceeds to step S214.

[0094] In step S214, the evaluation value calculation unit 36 ​​does not add points to the evaluation value indicating the visibility condition of the road (for example, adds 0 points). In other words, if there is no event information (0 events), scoring is not possible, so no points are added to the evaluation value indicating the visibility condition of the road for the event information. Then, this flow is temporarily ended.

[0095] Next, an example of calculating an evaluation value based on image information will be described with reference to the flowchart of FIG.

[0096] FIG. 5 is an exemplary flowchart showing a process of calculating an evaluation value based on an image from a CCTV camera, which is part of the evaluation value calculation process in FIG.

[0097] In step S300, the evaluation value calculation unit 36 ​​checks whether the visibility meter has been identified.

[0098] If the answer is "No" in step S300, that is, if there is no specific visibility meter and no visibility meter 106a indicating poor visibility, the flow is temporarily ended.

[0099] If the answer is "Yes" in step S300, that is, if a specific visibility meter is present and a visibility meter 106a indicating poor visibility is present, the process proceeds to step S302.

[0100] In step S302, the surrounding environment information acquisition unit 34a acquires an image from a CCTV camera 108 near the corresponding visibility meter 106a (specific visibility meter), and the evaluation value calculation unit 36 ​​processes the image to calculate a visibility value indicating the visibility. Then, the process proceeds to step S304.

[0101] In step S304, the evaluation value calculation unit 36 ​​checks whether the quantified visibility value exceeds a preset threshold value. If the answer is "Yes" in step S304, that is, if the visibility value exceeds the preset threshold value, the process proceeds to step S306.

[0102] In step S306, the evaluation value calculation unit 36 ​​does not add points to the evaluation value indicating the visibility condition of the road (for example, adds 0 points). For example, this is the case when the visibility in the vicinity is actually poor due to the occurrence of fog or the like, and it can be estimated that the measurement result of the specific visibility meter is highly likely to be correct (no factors for erroneous detection). In this case, the evaluation value calculation unit 36 ​​does not add points to the evaluation value indicating the visibility condition of the road for the imaging information (for example, adds 0 points). Then, this flow is temporarily ended.

[0103] If the answer is "No" in step S304, that is, if the visibility value does not exceed the preset threshold, the process proceeds to step S308.

[0104] In step S308, the evaluation value calculation unit 36 ​​adds a point to the evaluation value indicating the visibility condition of the road (for example, sets the added point to > 0). In other words, if the quantified visibility value is equal to or less than a preset threshold (in the case of good visibility), it can be estimated that there is a high possibility that visibility around the specific visibility meter is poor. In this case, the evaluation value calculation unit 36 ​​adds a point to the evaluation value indicating the visibility condition of the road for the image information. For example, it can be estimated that an obstacle such as an insect or debris is blocking the measurement area of ​​the specific visibility meter, which may result in a decrease in the visibility distance value. Then, this flow is temporarily terminated.

[0105] Next, an example of calculating an evaluation value based on measurement information (traffic volume information) from the traffic counter 110 will be described with reference to the flowchart of FIG.

[0106] FIG. 6 is an exemplary flowchart showing the process of calculating an evaluation value based on information from a traffic counter, which is part of the evaluation value calculation process in FIG.

[0107] In step S400, the evaluation value calculation unit 36 ​​checks whether the visibility meter 106a has been identified.

[0108] If the answer is "No" in step S400, that is, if there is no specific visibility meter and no visibility meter 106a indicating poor visibility, the flow is temporarily ended.

[0109] If the answer is "Yes" in step S400, that is, if a specific visibility meter is present and a visibility meter 106a indicating poor visibility is present, the process proceeds to step S402.

[0110] In step S402, the surrounding environment information acquisition unit 34a acquires traffic volume information from the traffic counter 110 near the relevant visibility meter 106a (specific visibility meter), and the evaluation value calculation unit 36 ​​compares the traffic volume information (e.g., average speed) measured T time ago with the current average speed. Then, the process proceeds to step S404.

[0111] In step S404, the evaluation value calculation unit 36 ​​checks whether the average speed has dropped by, for example, E% or more (e.g., 50%: can be set appropriately) as a result of the comparison. If the answer is "Yes" in step S404, that is, if the average speed has dropped by E% or more, the process proceeds to step S406.

[0112] In step S406, the evaluation value calculation unit 36 ​​does not add points to the evaluation value indicating the visibility condition of the road (for example, adds 0 points). For example, this is the case when it is highly likely that the average speed has decreased due to the actual deterioration of visibility caused by fog or the like, and it can be estimated that the measurement results of the specific visibility meter are highly likely to be correct (there is no cause for erroneous detection). In this case, the evaluation value calculation unit 36 ​​does not add points to the evaluation value indicating the visibility condition of the road for the traffic volume information. Then, this flow is temporarily terminated.

[0113] If the answer is "No" in step S404, that is, if the average speed has not dropped by E% or more, the process proceeds to step S408.

[0114] In step S408, the evaluation value calculation unit 36 ​​adds a point to the evaluation value indicating the visibility condition of the road (for example, sets the added point to 0). In other words, if the decrease in the quantified average speed is, for example, less than E%, it is considered that the visibility has not deteriorated enough to exceed the visibility threshold. In this case, it can be estimated that there is a high possibility that the visibility around the specific visibility meter is poor. Therefore, the evaluation value calculation unit 36 ​​adds a point to the evaluation value indicating the visibility condition of the road for the traffic volume information. In this case, it can be estimated that, for example, an obstacle such as an insect or debris is blocking the detection area of ​​the specific visibility meter, resulting in a decrease in the visibility distance value. Then, this flow is temporarily terminated.

[0115] Next, an example of calculating an evaluation value based on past measurement information (weather information) will be described using the flowchart of FIG.

[0116] FIG. 7 is an exemplary flowchart showing the process of calculating an evaluation value based on past measurement information (weather information) in the evaluation value calculation process in FIG.

[0117] In step S500, the evaluation value calculation unit 36 ​​checks whether the visibility meter 106a has been identified.

[0118] If the answer is "No" in step S500, that is, if there is no specific visibility meter and no visibility meter 106a indicating poor visibility, the flow is temporarily ended.

[0119] If the answer is "Yes" in step S500, that is, if a specific visibility meter is present and a visibility meter 106a indicating poor visibility is present, the process proceeds to step S502.

[0120] In step S502, the surrounding environment information acquisition unit 34a refers to past measurement information (weather information), and then the process proceeds to step S504.

[0121] If the answer is "Yes" in step S504, that is, if there is past weather information (similar information) similar to the weather information at the time (hour / minute) when the specific visibility meter was identified, the process proceeds to step S506.

[0122] In step S506, the evaluation value calculation unit 36 ​​refers to the visibility distance value of the similarity information, and the process proceeds to step S508.

[0123] In step S508, the evaluation value calculation unit 36 ​​checks whether the visibility distance value at the same referenced time (hour / minute) exceeds the visibility threshold. If the answer is "Yes" in step S508, that is, if the visibility distance value at the same referenced time (hour / minute) exceeds the visibility threshold, the process proceeds to step S510.

[0124] In step S510, the evaluation value calculation unit 36 ​​does not add points to the evaluation value indicating the visibility condition of the road (for example, adds 0 points). In other words, if the visibility distance value for the same referenced time (hour and minute) exceeds the visibility threshold, it can be estimated that there is a high possibility that the data for a time when weather conditions other than visibility are very similar actually indicate poor visibility. In other words, it can be estimated that there is a high possibility that the measurement results of the specific visibility meter are correct (there is no cause for erroneous detection). In this case, the evaluation value calculation unit 36 ​​does not add points to the evaluation value indicating the visibility condition of the road for past weather information. Then, this flow is temporarily terminated.

[0125] If the answer is "No" in step S508, that is, if the visibility distance value at the same referenced time (hour and minute) does not exceed the visibility threshold value, the process proceeds to step S512.

[0126] In step S512, the evaluation value calculation unit 36 ​​adds a point to the evaluation value indicating the visibility condition of the road (for example, sets the added point to 0). In other words, if similar past data exists and the visibility distance value at the same time (hour / minute) is less than the visibility threshold, it is unlikely that the visibility value alone will not be similar even though the weather conditions other than visibility are very similar. In other words, this is a case where it can be estimated that there is a high possibility that visibility around the specific visibility meter is poor. In this case, the evaluation value calculation unit 36 ​​adds a point to the evaluation value indicating the visibility condition of the road for the past weather information. For example, it can be estimated that obstacles such as insects or debris are blocking the measurement area of ​​the specific visibility meter, resulting in a decrease in the visibility distance value. Then, this flow is temporarily terminated.

[0127] If the answer is "No" in step S504, that is, if there is no past weather information (similar information) similar to the weather information at the time (hour / minute) when the specific visibility meter was identified (cannot be confirmed), the process proceeds to step S514.

[0128] In step S514, the evaluation value calculation unit 36 ​​does not add points to the evaluation value indicating the visibility condition of the road (for example, adds 0 points). In other words, if there is no similar information in the past (0 cases), scoring is not possible, so the evaluation value calculation unit 36 ​​does not add points to the evaluation value indicating the visibility condition of the road for the past weather information. Then, this flow is temporarily ended.

[0129] When the evaluation value calculation process based on each piece of surrounding environment information is completed, the process returns to the flowchart of FIG. 3 and the process of step S104 is executed.

[0130] In step S104, the weighting processing unit 36b weights the evaluation value calculated for each piece of surrounding environment information. That is, the reliability of the evaluation value calculated based on each piece of surrounding environment information is corrected and averaged. Then, the process proceeds to step S106.

[0131] In step S106, the summation processing unit 36a sums the evaluation values ​​for each piece of surrounding environment information. As a result, the summed evaluation value takes into account multiple pieces of information and becomes a value with improved reliability. Then, the process proceeds to step S108.

[0132] In step S108, the detection unit 38 checks whether the evaluation value (total value) has been calculated multiple times within a predetermined period. For example, the detection unit 38 checks whether the evaluation value (total value) has been calculated M times (e.g., 10 times) or N minutes (e.g., 10 minutes) at a time of once per minute.

[0133] If the result of step S108 is "No," that is, if the predetermined period has not yet elapsed, the process proceeds to step S102, and the evaluation value calculation unit 36 ​​executes the evaluation value calculation process again based on the surrounding environment information.

[0134] If the answer in step S108 is "Yes," that is, if a predetermined period of time has elapsed, the process proceeds to step S110. In this case, if the calculation process has been performed M times (for example, 10 times), the answer will be "Yes" even before N minutes (for example, 10 minutes) have elapsed. Conversely, even if the calculation process has not been performed M times (for example, 10 times), the answer will be "Yes" after N minutes (for example, 10 minutes) have elapsed.

[0135] In step S110, the detection unit 38 detects (determines) whether there is a possibility of erroneous detection by comparing the evaluation value with a determination threshold. In this case, the detection unit 38 determines whether the evaluation value has exceeded the determination threshold a predetermined percentage (J% or more times (e.g., 70%)). If the determination is "Yes" in step S110, the process proceeds to step S112.

[0136] In step S112, the detection unit 38 determines that the alarm output based on the detection result of the specific visibility meter is likely to be a false detection because the evaluation value exceeds the judgment threshold by a predetermined percentage or more, and executes a procedure to notify the user of the possibility of a false detection. For example, the detection unit 38 notifies the alarm processing unit 20d of the traffic control server 14 that the alarm judgment result is likely to be a false detection. Then, this flow is temporarily terminated.

[0137] The alarm processing unit 20d may determine the alarm output mode depending on, for example, the degree of possibility of the notified erroneous detection. For example, if the possibility that the alarm output is an erroneous judgment is "high," the alarm processing unit 20d provides the control console 10 or the display device 12 with information indicating that the alarm is an alarm that can be rechecked after a predetermined period of time. Also, if the possibility that the alarm output is an erroneous judgment is "medium" or "low," the alarm processing unit 20d provides the control console 10 or the display device 12 with information indicating that the alarm is an alarm and the corresponding response information depending on the degree of possibility.

[0138] If the answer at step S110 is "No," that is, if the result of comparing the evaluation value with the determination threshold does not exceed the predetermined ratio, the process proceeds to step S114.

[0139] In step S114, the detection unit 38 determines that the alarm output based on the detection result of the specific visibility meter is likely to be correct because the evaluation value does not exceed the judgment threshold by a predetermined percentage or more, and executes a notification process indicating that the alarm output is normal. For example, the detection unit 38 notifies the alarm processing unit 20d of the traffic control server 14 that the alarm judgment result is likely to be correct. Then, this flow is temporarily terminated.

[0140] The alarm processor 20d may, for example, output an alarm as usual, or may output this alarm together with a message indicating that there is no possibility of erroneous detection by the visibility meter 106a or that the possibility is extremely low.

[0141] In this way, the visibility meter false detection detection device (visibility meter false detection detection unit 22) of this embodiment can notify the controller of the possibility of a false detection by the visibility meter 106a. As a result, even when an alarm is output, the controller can reduce the frequency of requesting on-site confirmation or checking other information. Therefore, the visibility meter false detection detection device can reduce the workload of the controller while allowing appropriate alarm processing.

[0142] Although several embodiments of the present invention have been described above, the above-described embodiments and modifications are merely examples and are not intended to limit the scope of the invention. The above-described embodiments can be implemented in various forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. The above-described embodiments and modifications are included within the scope and spirit of the invention, and are also included in the inventions and their equivalents as set forth in the claims. [Explanation of symbols]

[0143] 10 Control console 12 Display device 14 Traffic Control Server 20 Processing section 22 Visibility meter false detection detection unit (visibility meter false detection detection device) 32 Specific part 34 Acquisition Department 34a Surrounding environment information acquisition unit 34b Road characteristic acquisition part 36 Evaluation value calculation unit 36a Addition processing section 36b Weighting processing section 38 Detector 100 Traffic Control Center 102 Management Office 104 Information Gathering Device 106 Weather Observation Equipment 106a Visibility meter 106b Thermometer 106c hygrometer 106d Rain gauge 106e Snowfall gauge 106f Wind direction anemometer 108 CCTV cameras 110 Traffic Counter 112 Event information acquisition device K Traffic Control System R control room

Claims

1. an identification unit that identifies a visibility meter that has provided a visibility distance value indicating the occurrence of poor visibility as a specific visibility meter based on a comparison between a visibility distance value provided by a plurality of visibility meters installed on a road and a visibility threshold value used to determine whether poor visibility is occurring on the road; an acquisition unit that acquires, as surrounding environment information for the installation position where the specific visibility meter is installed, at least one of image information obtained by capturing an image of the road including the installation position and its surroundings, traffic volume information indicating a traffic situation on the road including the installation position, event information indicating an event occurring on the road including the installation position, and past weather information for the road including the installation position; an evaluation value calculation unit that calculates an evaluation value indicating a visibility condition of the road where poor visibility is determined to occur based on the content of the surrounding environment information; a detection unit that detects the possibility of erroneous detection by the specific visibility meter based on the evaluation value; Equipped with Visibility meter false detection detection device.

2. When the acquisition unit acquires a plurality of types of the surrounding environment information, the evaluation value calculation unit performs a different weighting set in advance for each type of the surrounding environment information and calculates the evaluation value. The visibility meter false detection detection device according to claim 1 .

3. the detection unit changes a detection criterion for determining the possibility of erroneous detection for the evaluation value in accordance with road characteristics of the road on which the specific visibility meter is installed. The visibility meter false detection detection device according to claim 1 .

4. The road characteristics are determined based on at least one of topographical information on the road, tunnel passage information on whether the road passes through a tunnel, and curve continuity information on the road. The visibility meter false detection detection device according to claim 3.

5. an identifying step in which the identifying unit identifies the visibility meter that provided the visibility distance value indicating the occurrence of poor visibility as a specific visibility meter based on a comparison between the visibility distance value provided by a plurality of visibility meter installed on a road and a visibility threshold value used to determine whether poor visibility occurs on the road; an acquisition step in which an acquisition unit acquires, as surrounding environment information for an installation position where the specific visibility meter is installed, at least one of image information obtained by capturing an image of the road including the installation position and its surroundings, traffic volume information indicating a traffic situation on the road including the installation position, event information indicating an event occurring on the road including the installation position, and past weather information for the road including the installation position; an evaluation value calculation step in which an evaluation value calculation unit calculates an evaluation value indicating a visibility condition of the road on which it is determined that poor visibility occurs, based on the content of the surrounding environment information; a detection step in which a detection unit detects a possibility of erroneous detection by the specific visibility meter based on the evaluation value; Equipped with Visibility meter false positive detection method.

Images