Data collection device, road state evaluation support device, and program

Abstract

To provide a data collection device or the like capable of collecting data or the like to be a basis for determining repair necessity or the like of a road or a road accessory or the like without installing a measuring device such as a laser range finder in a measuring place in advance.SOLUTION: The data collection device is mounted on a vehicle and used. The data collection device has a processing means for collecting determination basic data which is data to be a basis for determining the repair necessity of a road or a road accessory. The determination basic data includes image data of surroundings of the vehicle.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] The present invention relates to, for example, a data collection device, a road condition evaluation support device, a program, and the like. [Background technology]

[0002] A laser distance measurement method is used to measure displacement such as the amount of subsidence of a road surface (Patent Document 1). In this laser distance measurement method, the amount of displacement of the surface to be measured is calculated using a laser distance measurement means that has the function of setting the irradiation angle of the laser light to a specified azimuth angle and / or vertical angle. A specific location on the surface to be measured obtained in the previous or previous-previous survey is set as the displacement reference point, and the three-dimensional coordinates of the displacement reference point, or the distance and sighting direction from the surveying instrument to the displacement reference point, are stored. In the current survey, a point at a position corresponding to the displacement reference point is set as the sighting point using the stored sighting direction of the displacement reference point from the surveying instrument or the three-dimensional coordinates of the displacement reference point. The instrument is aimed at the target surface, and the point on the target surface detected in the aiming direction is designated as the detection point. The three-dimensional coordinates of this detection point or the distance from the instrument to the detection point are obtained, and the displacement of the detection point relative to the displacement reference point is calculated. Based on the detected displacement, it is possible to predict when the road surface needs repair. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2011-7657 Summary of the Invention [Problem to be solved by the invention]

[0004] In order to measure displacement such as the amount of subsidence of a road surface using the laser ranging method, a laser ranging device must be installed at the measurement location in advance. By installing a laser ranging device in advance at a location where there is a high risk of subsidence, the amount of subsidence of the road surface at that location can be measured, but the amount of subsidence of the road surface cannot be measured at a location where a laser ranging device is not installed.

[0005] An object of the present invention is to provide a data collection device or the like that can collect data that serves as a basis for determining whether or not a road or road appendages need repair, without installing a measurement device such as a laser distance measuring device in advance at the measurement location. Another object of the present invention is to provide a road condition evaluation support device or the like that determines whether or not repair is needed based on the collected data. [Means for solving the problem]

[0006] (1) The data collection device may be mounted on a vehicle and have a processing means for collecting basic judgment data, which is the data that forms the basis for determining whether or not a road or road accessories need repair, and the basic judgment data may include image data of the area around the vehicle.

[0007] Based on the collected image data, it is possible to accurately determine whether or not roads or road accessories need repairs. It is preferable to have a function to display the collected basic data for determination, and it is particularly preferable to have a function to display an image of the collected image data. In this way, it is possible to accurately determine whether or not roads or road accessories need repairs by looking at the displayed image.

[0008] The necessity of road repairs (whether repair work is necessary) is determined based on, for example, the presence or absence of unevenness such as bumps and depressions in the road surface, ruts, cracks, areas where expressway joints need repair, subsidence of the road surface, etc. As basic data for determining the presence or absence of road surface unevenness, it is preferable to use, for example, acceleration data that indicates the acceleration applied to a vehicle when passing over unevenness in the road surface, image data of the surroundings of the vehicle, etc. As basic data for determining the presence or absence of ruts or cracks, it is preferable to use, for example, image data of the area ahead of the vehicle, etc. As basic data for determining the presence or absence of road surface subsidence, it is preferable to use, for example, data that indicates the inclination of the vehicle when passing over an area where there is subsidence, etc.

[0009] Examples of road accessories include fall prevention fences, fences separating roadways from sidewalks, rows of trees on roads, road signs, road markings, road information display devices, vehicle monitoring devices, utility ducts, etc. Images of the vehicle's surroundings and other data can be used as basic data for determining whether or not these items need repair.

[0010] The processing means may have a function to transfer the collected image data to an external device having a display function, or a function to display the image data on a device having a display function. The entity that makes the judgment on the necessity of repairs may be, for example, a road administrator who has viewed the image data included in the judgment basic data. The entity that makes the judgment on the necessity of repairs may be, for example, an image analysis program that analyzes the image data included in the judgment basic data, and artificial intelligence (AI) that makes a judgment based on the image analysis results. That's fine.

[0011] The collected image data may be composed of a plurality of images acquired at a fixed sampling period. The sampling period may be short enough to capture images of the surroundings of the route traveled by the vehicle without interruption along the route from the plurality of images included in the collected image data. It is particularly preferred that the processing means collect video data as the image data.

[0012] The processing means may have a function of recording the collected basis data for judgment in a recording device, which may be, for example, a removable memory card. In this way, the basis data for judgment can be transferred from the data collection device to another device via the memory card.

[0013] For example, acceleration data that quantifies the acceleration applied to the vehicle may be included in the basic judgment data. The processing means may have a function to transfer the acceleration data included in the basic judgment data to an external device with a display function, or a function to display the acceleration data on a device with a display function. The acceleration data may be displayed, for example, as a change in acceleration over time (time waveform). The entity that makes the judgment on the need for repairs may be, for example, a road administrator who views the time waveform of acceleration displayed on a computer screen. The entity that makes the judgment on the need for repairs may also be, for example, a judgment program that makes a judgment based on the shape of the time waveform of acceleration.

[0014] (2) The determination basis data may include sensing data acquired by a sensor mounted on the vehicle and different from image data of the surroundings of the vehicle.

[0015] As sensing data, for example, position data indicating the current position of the vehicle, image data different from the image data obtained by photographing the surroundings of the vehicle in (1) above, acceleration data indicating the magnitude of acceleration applied to the vehicle, etc. may be used.

[0016] A GPS receiver, for example, may be used as a sensor for acquiring the position data. The processing means may have a function for detecting, for example, using the position data, that a vehicle has approached a location where a determination as to whether a road or road-related property needs repair should be made, or that a vehicle has entered an area where a determination as to whether a road or road-related property needs repair should be made. The processing means may have a function for collecting basis data for determination at the location or area where a determination as to whether a road or road-related property needs repair should be made.

[0017] As the image data obtained by photographing the surroundings of the vehicle in (1) above and the sensor for acquiring other image data, different imaging devices, such as a CMOS camera or a CCD camera, may be used. For example, one imaging device may be used to photograph the road surface and road accessories ahead of the vehicle, and the other imaging device may be used to photograph the road surface directly below the vehicle. From these image data, the processing means can obtain information such as the condition of the road surface and the shape and surface condition of road accessories located around the road on which the vehicle has traveled.

[0018] The sensor for acquiring acceleration data may be, for example, an acceleration sensor that detects acceleration applied to the vehicle. The processing means can detect the magnitude of an impact applied to the vehicle, for example, from the acceleration data. Depending on the difference in the magnitude of the impact, the processing means can distinguish, for example, whether the vehicle has received an impact due to a collision or whether the vehicle has received an impact due to passing over an uneven road surface.

[0019] The processing means has a function of associating the image data obtained by photographing the surroundings of the vehicle in (1) with the sensing data in (2) and recording the data in a database. For example, the two data may be associated by the date and time of acquisition. In this way, it is possible to extract one piece of data from the other piece of data that corresponds to it. The processing means may have a function to transfer the image data and sensing data to an external device. The external device may have a function to associate the transferred image data and sensing data with each other, create a database, and record the data.

[0020] It is particularly advantageous if the sensing data is data other than image data. By associating the image data obtained by photographing the surroundings of the vehicle in (1) with the sensing data other than image data in (2), it is possible to more accurately determine whether or not repairs to the road or roadside structures are necessary.

[0021] (3) the sensing data includes acceleration data representing a magnitude of acceleration applied to the vehicle; The data collection device may have a function in which, when the processing means detects that a collision has occurred in the vehicle based on the acceleration data, the processing means records the image data and acceleration data collected during a period before and after the time of detection in a recording device.

[0022] A data collection device having the above function (1) can be used as a general drive recorder that records images of the surroundings at the time a collision accident occurs (hereinafter referred to as event recording).

[0023] (4) When the processing means detects that the vehicle has passed over an uneven road surface based on the acceleration data, the data collection device may record the image data and acceleration data collected during a period before and after the detection time in the recording device.

[0024] When it is difficult to determine whether or not road repairs are required based on acceleration data alone, the image of the road surface shown in the image data can be used as information for determining whether or not repairs are required.

[0025] For example, if the peak value of the measured acceleration exceeds a threshold, it may be determined that the vehicle has passed over an uneven road surface. This threshold may be determined by actually driving on a road with various uneven surfaces and measuring the acceleration applied to the vehicle. Road surface unevenness may include, for example, cracks in the pavement, ruts, steps, potholes, etc.

[0026] For a typical drive recorder that records events, the peaks in the acceleration waveform generated by the impact of a vehicle passing over a road irregularity are considered a nuisance (garbage data) when detecting the occurrence of a collision accident. Generally, the peaks in the acceleration waveform generated when passing over a road irregularity are smaller than the peaks in the acceleration waveform generated during a collision accident. By utilizing this difference in peak magnitude, a typical drive recorder distinguishes between the occurrence of a collision accident and the passing over a road irregularity. It is recommended that a typical drive recorder be equipped with a function to detect (pick up) the location of a road irregularity by using a relatively small acceleration time waveform peak, which was previously treated as a nuisance, as a trigger (detection opportunity). This allows a typical drive recorder to be easily converted into a data collection device that collects basic data for determining whether road or roadside features require repair. Furthermore, by providing both the function of detecting the occurrence of a collision accident and the function of detecting the passing over a road irregularity, the drive recorder can be used in common with this data collection device.

[0027] (5) The processing means determines that a collision has occurred with the vehicle when the magnitude of acceleration indicated by the acceleration data is equal to or greater than a first determination threshold, and determines that the vehicle has collided with a depression in the road surface when the magnitude of acceleration indicated by the acceleration data is equal to or greater than a second determination threshold that is smaller than the first determination threshold. It is preferable to use a data collection device that determines that a protrusion has been passed.

[0028] The collected acceleration data can be used to distinguish between impacts received when a vehicle passes over an uneven road surface and impacts received due to a collision accident. This allows, for example, a drive recorder that records events to be used as a data collection device that collects basic data for determining whether roads and other structures require repairs. For example, a typical drive recorder has a function for comparing the magnitude of acceleration with a first determination threshold. By adding a function for comparing the magnitude of acceleration with a second determination threshold to such a typical drive recorder, the typical drive recorder can be used as a data collection device that collects basic data for determining whether roads and other structures require repairs. When the acceleration is equal to or greater than the first determination threshold, it is determined that the vehicle has collided and that it has passed over an uneven road surface. This allows for accurate determination that the vehicle has passed over an uneven road surface when the vehicle passes over an uneven road surface with a large difference in elevation that would cause the vehicle to receive an impact as great as that caused by a collision.

[0029] (6) The data collection device may further include a microphone, and the processing means may also use sound information collected by the microphone to detect when the vehicle has passed over an uneven road surface.

[0030] By using sound information in combination, it is possible to determine with greater accuracy whether or not the road surface is uneven.

[0031] (7) The processing means may analyze the image data to determine whether the vehicle is traveling through a section where unevenness has occurred due to factors other than road surface deterioration, and if it determines that the vehicle is traveling through a section where unevenness has occurred due to factors other than road surface deterioration, the data collection device may skip the process of detecting whether the vehicle has passed through an uneven road surface.

[0032] It is possible to exclude irregularities caused by factors other than deterioration that are not subject to repair from the detection target. This makes it possible to prevent excessive detection of points where irregularities have occurred. As a result, it becomes possible to easily detect points where irregularities have occurred that should truly be considered as points subject to repair.

[0033] It is preferable to determine whether or not sections where unevenness has occurred due to factors other than road surface deterioration include at least sections where unevenness has occurred due to construction work, sections where steps have occurred at joints where open-cut work has been performed, steps due to the edges of manholes, steps at joints of bridges, etc. For example, if a construction sign is detected from the image data, it is preferable to determine that the road currently being traveled is under construction. Joints where open-cut work has been performed, edges of manholes, joints of bridges, etc. can be detected by analyzing the image data.

[0034] (8) The processing means may be a data collection device that determines the type of road surface on which the vehicle is traveling by analyzing the image data, and changes the criteria for determining that the vehicle has passed over an uneven road surface depending on the type of road surface.

[0035] The acceleration (impact) that a vehicle receives depends on the type of road surface that the vehicle is traveling on. By changing the above-mentioned determination criteria depending on the type of road surface, it is possible to make an appropriate determination depending on the type of road surface.

[0036] The types of road surfaces include, for example, asphalt pavement, concrete pavement, block pavement, etc. The processing means should have a function to determine the type of these road surfaces. Concrete pavement has joints of about 1 cm at intervals of, for example, 5 to 10 m. Block Pavement surfaces have a unique pattern made up of multiple blocks laid out. Asphalt pavement surfaces do not have seams or unique patterns. The processing means may analyze the image data of the road surface and determine the type of road surface by detecting the presence or absence of seams or unique patterns.

[0037] (9) The processing means may be a data collection device that detects intentionally created uneven paving on the road surface by analyzing the image data, and at a point where an uneven paving is detected, skips the process of detecting whether the vehicle has passed over an uneven road surface.

[0038] This can prevent points where intentionally created road surface step paving has been made from being raised as candidates for points where steps that need to be repaired have occurred.

[0039] An example of intentionally created step paving on a road surface is one that applies sound and vibration to the driver of a traveling vehicle to reduce the driving speed or to alert the driver. Step paving is achieved, for example, by applying aggregate such as ceramic to the pavement surface using a resin-based adhesive. The processing means may detect patterns of aggregate such as ceramic from image data of the road surface to determine whether or not intentional step paving has been created on the road surface.

[0040] (10) The processing means may have a function of acquiring current location data indicating the current location, storing collection location data specifying the location where the judgment basis data should be collected, determining whether or not to collect the judgment basis data based on the current location data and the collection location data, and, if it determines that the judgment basis data should be collected, collecting the judgment basis data and recording it in the recording device.

[0041] It is possible to collect basic judgment data only where it is necessary, and not collect it in other locations. This reduces the amount of basic judgment data to be analyzed compared to collecting basic judgment data at every location the vehicle has traveled. This reduces the amount of memory required to store the basic judgment data, and eliminates the time wasted in analyzing unnecessary basic judgment data.

[0042] The current position data may be acquired by a GPS receiver. The GPS receiver may be built into the data collection device. Alternatively, the data collection device may receive the current position data from a GPS receiver installed in the vehicle. The sensing data may include current position data indicating the current position. It is particularly preferable to associate the current position data included in the sensing data with image data, acceleration data, etc., and record the data in a database. In this way, the location where the image data and the acceleration data were collected can be identified.

[0043] (11) The collection location data includes data specifying a measurement point where the determination basis data should be collected, The data collection device may preferably have a function in which the processing means notifies the user that the vehicle is approaching the measurement point when the distance between the current position of the vehicle and the measurement point becomes equal to or less than a reference distance.

[0044] The driver of a vehicle equipped with a data collection device can be notified when the vehicle approaches a measurement point, allowing him or her to begin preparations for collecting the decision-making data.

[0045] The measurement points from which basic data for judgment should be collected are selected from multiple points where irregularities, etc., that do not currently require immediate repair are detected through analysis of basic data for judgment that has been measured in the past. By continuously collecting basic data for judgment at such measurement points at intervals of several days, it is possible to determine whether repairs are necessary, taking into account the rate at which deterioration of the road surface progresses.

[0046] (12) The processing means may store a standard value of the traveling speed when passing through the measurement point, and may have a function of notifying the driver of the vehicle that the vehicle is approaching the measurement point and urging the driver of the vehicle to travel at the standard traveling speed.

[0047] The driver of a vehicle equipped with a data collection device can be made aware that he or she needs to drive at the standard value before driving through the measurement point. This can prevent the driver from driving through the measurement point at a speed that deviates significantly from the standard value, which would result in the collected basic data for judgment being unable to be used effectively. For example, a voice message can be output to urge the driver to drive at the standard speed.

[0048] (13) The processing means may be a data collection device having a function of constantly recording the collected basic judgment data in the recording device.

[0049] It is possible to collect basic judgment data for determining whether roads or roadside features need repairs for all routes traveled by a vehicle equipped with a data collection device. This allows basic judgment data to be collected over a wider area. If basic judgment data collected in the past when no abnormality was found is accumulated at a point where an abnormality is found in a road or roadside feature, it is possible to analyze the time and cause of the abnormality by using the basic judgment data collected in the past.

[0050] The period for continuous recording may be, for example, the period from when the vehicle's accessory key is turned on to when it is turned off. Alternatively, a section of the driving route for collecting the judgment basis data may be predetermined, and continuous recording may be performed within the section. For example, the processing means may store position data for the start and end points of the section for collecting the judgment basis data, and start collecting the judgment basis data when the current position of the vehicle coincides with the start point, and stop collecting the judgment basis data when it coincides with the end point.

[0051] (14) The processing means may be a data collection device having a function of analyzing the image data to determine whether or not repairs are required for roads or road accessories.

[0052] This reduces the burden on road administrators in checking image data. In addition, even if an abnormality is overlooked by a human eye, the processing means can detect the abnormality, thereby reducing the chance of the abnormality being overlooked.

[0053] By analyzing image data, it would be possible to detect cracks in the road surface, deterioration of road markings, poor drainage of roads after rain, corrosion and deformation of road accessories such as road signs and guardrails, signs of landslides, and damage caused by street trees.

[0054] (15) The data collection device may further include a communication means, and the processing means may have a function of transmitting the basic judgment data collected while the vehicle is traveling to an external device via the communication means.

[0055] When a vehicle equipped with a data collection device is a road maintenance vehicle that departs from a vehicle depot, travels a desired route, and then returns to the vehicle depot, the collected basic data for judgment can be sent to an external device before the vehicle returns to the vehicle depot. If this function is installed in a consumer drive recorder, basic data for judgment can be collected not only from vehicles for road maintenance work, but also from general vehicles equipped with drive recorders. By collecting and accumulating more basic data for judgment from the datacenter, the accuracy of judgment on the need for repairs can be improved.

[0056] (16) The road condition evaluation support device may have a processing means for displaying, on a display means, images of the image data acquired at a specific point on multiple different dates and times in chronological order, based on a database in which image data of the surroundings of the vehicle collected by a data collection device mounted on the vehicle, acceleration data representing the magnitude of the acceleration applied to the vehicle, vehicle position data, and collection date and time data are associated and stored.

[0057] Road managers can check the progress of deterioration of roads or road accessories by looking at the images displayed in chronological order. This allows them to determine whether repairs are necessary and to predict when repairs will be required. They can also look at the displayed images to determine whether or not it is necessary to continue to monitor the deterioration state of the displayed point. If it is determined that continued monitoring is necessary, the images displayed in chronological order provide useful information for determining the frequency of monitoring.

[0058] The specific points for which images are to be displayed on the display means may be points where it has been determined that the road surface is uneven based on acceleration data, points that are the subject of continuous observation, and the like.

[0059] This database may be stored in a storage device of the road condition evaluation support device. Furthermore, it is particularly preferable to store this database on a server connected to a data communication network. In this way, by connecting multiple road condition evaluation support devices to the data communication network, the database can be accessed from multiple road condition evaluation support devices.

[0060] (17) The road condition evaluation support device may be configured such that the processing means causes the display means to display, together with the image of the image data, the time waveform of the corresponding acceleration data.

[0061] By looking at the time waveform of the acceleration data, road administrators can identify the magnitude of the impact that was applied to the vehicle when it passed that point. The magnitude of the impact can be used as information to identify, for example, the difference in elevation of unevenness occurring on the road surface. From the time waveform of the acceleration data displayed in chronological order, it is possible to know the degree of deterioration of unevenness occurring on the road surface.

[0062] (18) The road condition evaluation support device may be configured such that the processing means has a function of displaying, on the display means, a time waveform of the acceleration data showing a peak waveform as the acceleration data collected at the specific point.

[0063] By detecting the peaks in the time waveforms of acceleration data, it is possible to accurately match the points at which the time waveforms of multiple acceleration data displayed in chronological order were acquired, thereby enabling the time-series evaluation of impacts caused by road surface irregularities at the same point.

[0064] Acceleration data collected at a specific point can be found by referring to the position data associated with the acceleration data. This position data is obtained, for example, by a GPS receiver, and position measurement errors occur due to various factors. For this reason, it is difficult to accurately match the positions at which multiple time waveforms of acceleration data collected at different dates and times were acquired from the position data alone. By detecting peaks in the time waveforms of the acceleration data, it is possible to identify the positions at which multiple time waveforms of acceleration data collected at different dates and times were acquired. The positions at which the acceleration data is acquired can be matched with high precision, and as a result, image data for a specific location can be extracted with high precision based on the date and time at which the acceleration data was collected.

[0065] (19) The road condition evaluation support device may have a function in which the processing means calculates an evaluation value that depends on the unevenness of the road surface at the specific point from each of the plurality of acceleration data collected at the specific point on a plurality of different dates and times, and displays the change over time of the calculated evaluation value on the display means.

[0066] The degree of future deterioration can be estimated from the change over time in the evaluation value that depends on the unevenness of the road surface at a specific point.The estimated degree of deterioration can be used to predict when repairs should be made.As an evaluation value that depends on the unevenness of the road surface, for example, the height of the peak that appears in the time waveform of the acceleration data can be used.

[0067] The processing means may display the evaluation values ​​from the past to the present in a graph format. By viewing the changes in the evaluation values ​​in a graph format, road managers can visually recognize the rate at which the evaluation values ​​are deteriorating. The processing means may predict future changes in the evaluation values ​​from the changes in the evaluation values ​​from the past to the present, and display the changes in the evaluation values ​​from the past to the present and the predicted future changes in the evaluation values ​​in a graph format. The predicted future changes in the evaluation values ​​can be used as useful information for determining the timing of repairs to roads or road accessories. (20) The processing means may be a road condition evaluation support device having a function of extracting, from the time waveforms of the acceleration data stored in the database, a time waveform that suggests that the vehicle has passed over an uneven road surface.

[0068] Based on the acceleration data stored in the database, points where bumps or irregularities are likely to exist on the road surface can be extracted without manual intervention. This makes it possible to process a huge amount of acceleration data. Extraction of time waveforms that suggest that a bump or irregularity has been passed over can be performed based on, for example, the height of peaks that appear in the time waveform of the acceleration data, the shape of the time waveform, and the magnitude relationship of acceleration in the three directions (front / back, left / right, up / down).

[0069] For example, when a vehicle is suddenly braked, a large acceleration occurs in the longitudinal direction. When a driver performs a sudden steering operation, a large acceleration occurs in the lateral direction. In contrast, when a vehicle passes over an uneven road surface, a large acceleration occurs in the vertical direction. The processing means may extract points where unevenness is likely to exist on the road surface by utilizing the difference in the direction of the generated acceleration. Furthermore, the time waveform of acceleration data corresponding to sudden braking, sudden steering, etc. changes more gradually than the time waveform of acceleration data corresponding to passing over an uneven road surface. The processing means may extract points where unevenness is likely to exist on the road surface by utilizing the difference in the shape of this time waveform.

[0070] The processing means preferably uses information other than acceleration data to extract a time waveform that indicates that the vehicle has passed over an uneven road surface. The processing means preferably uses, for example, the vehicle's traveling speed as information other than acceleration data. For example, a peak appears in the time waveform of acceleration data when a vehicle door is opened or closed, but the traveling speed at this time is approximately zero. By using traveling speed data in combination to determine whether the vehicle has passed over an uneven road surface, it is possible to exclude from detection the peak in the time waveform of acceleration data that occurs due to the opening or closing of the door.

[0071] (21) The database includes vehicle type data indicating the type of vehicle in which the data collection device was installed when the acceleration data was collected, The road condition evaluation support device may preferably have a function in which the processing means normalizes the acceleration data based on vehicle type data.

[0072] Normalizing the acceleration data based on vehicle type data enables comparison of acceleration data collected by data collection devices installed in multiple vehicles of different vehicle types. Here, "normalizing based on vehicle type data" means converting acceleration data actually collected by data collection devices installed in various vehicles into acceleration data that would have been obtained if the data collection devices had been installed in a standard vehicle. The acceleration data to be converted may be, for example, a feature that characterizes the time waveform of the acceleration data. The feature that characterizes the time waveform of the acceleration data may be, for example, the height of peaks appearing in the time waveform, the distribution of frequency components of the time waveform, or the like.

[0073] (22) The database stores vehicle speed data in association with the data collection date and time, The road condition evaluation assistance device may preferably have a function in which the processing means normalizes the acceleration data based on the traveling speed data.

[0074] Normalizing the acceleration data based on the driving speed data makes it possible to compare acceleration data collected at different driving speeds. Here, "normalizing based on the driving speed data" means converting acceleration data actually collected at various different driving speeds into acceleration data that would have been obtained if the vehicle had been traveling at a standard speed. The standard speed may be, for example, the legal speed of the route being traveled, or a speed slightly slower than the legal speed. The acceleration data to be converted may be, for example, a feature that characterizes the time waveform of the acceleration data. The feature that characterizes the time waveform of the acceleration data may be, for example, the height of peaks appearing in the time waveform, the distribution of frequency components of the time waveform, or the like.

[0075] (23) The road condition evaluation support device may have the function of having the processing means determine the degree to which road surface repair is necessary based on the image data stored in the database, and register the determination result in the database in association with the image data, the acceleration data, the position data, and the collection date and time data.

[0076] The degree of repair needs can be determined without human intervention, making it possible to handle huge amounts of image data. The degree of road surface repair needs can be determined using artificial intelligence (AI), for example.

[0077] (24) The road condition evaluation support device may be configured such that the processing means has a function of displaying information specifying points where road surface repair is required on the display means in a manner that enables the degree of repair required to be recognized.

[0078] By viewing the information displayed on the display means, the road administrator can easily find the most urgent points among multiple points requiring repair. For example, the road names, addresses, and location information of points requiring repair may be sorted by the degree of need for repair and displayed on the display means in a list format. Alternatively, a map may be displayed on the display means, and different icons may be displayed at points requiring repair depending on the degree of need for repair. Alternatively, instead of icons, different colors may be used to indicate the degree of need for repair on the road.

[0079] (25) The processing means may be a road condition evaluation support device having a function of displaying on the display means location information of points where road surface repairs are required in a manner that allows the extent of reduction in the life cycle cost of the road due to repairs to be identified.

[0080] Road administrators can easily find repair locations that will result in a large reduction in life cycle costs. For example, the road names, addresses, and location information of locations that require repairs can be sorted by the reduction in life cycle costs and displayed in a list format on a display means. A map may be displayed on the display means, and an icon corresponding to the magnitude of the reduction in life cycle cost may be displayed at the point where repair is required. Alternatively, instead of an icon, a color corresponding to the magnitude of the reduction in life cycle cost may be applied to the point on the road where repair is required.

[0081] (26) The processing means may be a road condition evaluation support device that determines whether or not repairs to road accessories are required by analyzing the image data stored in the database.

[0082] This makes it possible to determine whether road features need repair without manual intervention, making it possible to process a huge amount of image data. Examples of road feature repair issues include deterioration of road markings, poor drainage after rain, corrosion and deformation of road features such as road signs and guardrails, signs of landslides, and damage caused by roadside trees.

[0083] (27) The processing means may be a road condition evaluation support device having a function of determining whether or not repairs are required for road accessories based on the differences between multiple pieces of image data collected at the same location on multiple different dates and times.

[0084] The difference in image data allows the rate at which deterioration of road accessories progresses to be recognized. By taking into account the rate at which deterioration of road accessories progresses, it becomes possible to more appropriately determine whether or not repairs are necessary for road accessories. For example, it becomes possible to more appropriately determine whether or not repairs are necessary based on the rate at which road markings fade, the rate at which roadside trees grow, etc.

[0085] (28) The road condition evaluation support device may further include a communication means, and the processing means may receive the image data, acceleration data, location data, collection date and time data, and identification data for identifying the data collection devices via the communication means, and associate these data with each other and store them in the database.

[0086] Since data is received via a communication means, the effort required to obtain the data can be reduced. Furthermore, since data is received from multiple data collection devices, a larger amount of data can be collected than when data is obtained from a single data collection device. For example, it is preferable to install data collection devices not only in the work vehicles of road management companies, but also in the personal vehicles of road management company employees, etc., to obtain this data. It is even more preferable to install data collection devices in unspecified general vehicles to obtain this data.

[0087] (29) The road condition evaluation support device may be configured such that the processing means assigns points to each data collection device in accordance with the received image data and acceleration data, and stores the accumulated value of the points.

[0088] It will be possible to offer rewards to owners of data collection devices according to the points. Owners of vehicles equipped with data collection devices will be motivated to send more data to the road condition assessment support device. Owners of vehicles without data collection devices will be motivated to install data collection devices. This will make it possible to obtain a larger amount of data. The rewards could be, for example, discounts on tolls on toll roads or discounts on product purchases at roadside stations.

[0089] (30) This specification discloses an invention of a program for causing a mobile terminal to realize the function of the data collection device described in any one of (1) to (15) above.

[0090] (31) This specification discloses an invention of a program for causing a computer to realize the function of the road condition evaluation support device described in any one of (16) to (29) above.

[0091] Furthermore, this specification discloses an invention of a road condition evaluation support system including the data collection device described in any one of (1) to (15) above and the road condition evaluation support device described in any one of (16) to (29) above. Furthermore, this specification discloses an invention of a device having both the functions of the data collection device described in any one of (1) to (15) above and the functions of the road condition evaluation support device described in any one of (16) to (29) above. Such a device may be realized, for example, by a tablet terminal or the like that is detachably mounted on a vehicle. [Effects of the Invention]

[0092] For example, it is possible to collect data that will be the basis for determining whether or not repairs are necessary for roads or road accessories, etc., without installing a measurement device such as a laser distance measuring device in advance at the measurement location. For example, the collected data becomes useful information for determining whether or not repairs are necessary. [Brief explanation of the drawings]

[0093] [Figure 1]FIG. 1A is a perspective view of a data collection device according to a first embodiment, seen obliquely from behind, and FIG. 1B is a diagram showing the data collection device, windshield, dashboard, etc., when mounted on a vehicle. [Figure 2] FIG. 2 is a block diagram of the data collection device 1 according to the first embodiment. [Figure 3] FIG. 3A is a diagram showing, in a table format, an example of the configuration of various sensing data other than image data collected by a data collection device, and FIG. 3B is a diagram showing, in a table format, the configuration of image data. [Figure 4] FIG. 4 is a flowchart of a data recording process executed by the controller of the data collecting device according to the first embodiment. [Figure 5] FIG. 5 is a diagram showing an example of a time waveform of acceleration data, a first determination threshold value At1, a second determination threshold value At2, and image data to be recorded. [Figure 6] FIG. 6A is a flowchart of a process executed by a controller of a data collection device according to a second modified example of the first embodiment, and FIG. 6B is a diagram showing an example of an image acquired by a camera. [Figure 7] FIG. 7A is a flowchart of processing executed by a controller of a data collection device according to a third variant of the first embodiment, and FIGS. 7B, 7C, and 7D are figures showing examples of images acquired while traveling on asphalt pavement, concrete pavement, and block pavement, respectively. [Figure 8] FIG. 8A is a flowchart of processing executed by a controller of a data collection device according to a fourth modified example of the first embodiment, and FIG. 8B is a diagram showing an example of an image when traveling on a road surface with uneven paving. [Figure 9] FIG. 9A is a flowchart of processing executed by a controller of a data collection device according to a fifth variant of the first embodiment, FIG. 9B is a diagram showing an example of a data collection section, and FIG. 9C is a diagram showing an example of a data collection point. [Figure 10]FIG. 10A is a flowchart of a process executed by a controller of a data collecting device according to a sixth modified example of the first embodiment, and FIG. 10B is a diagram showing an example of a path for recording basic determination data. [Figure 11] FIG. 11A is a flowchart of processing executed by a controller of a data collection device according to the seventh variant of the first embodiment, and FIGS. 11B and 11C are diagrams showing examples of images in which the controller has determined that repairs to the road surface or road accessories are required. [Figure 12] FIG. 12 is a block diagram of a road condition evaluation assisting device according to the second embodiment. [Figure 13] FIG. 13 is a diagram showing an example of an image displayed on the display of the road condition evaluation assisting device according to the second embodiment. [Figure 14] FIG. 14 is a diagram showing an example of an image of the change over time in the evaluation value displayed on the display of the road condition evaluation assisting device according to the second embodiment. [Figure 15] FIG. 15 is a diagram showing the relationship between the time waveform of acceleration data and the date, time, and position at which the time waveform was acquired. [Figure 16] FIG. 16A is a diagram showing an example of a main menu screen displayed on a display by a controller, and FIG. 16B is a diagram showing an example of an image displayed on a display by a controller of a road condition evaluation assistance device according to a first variant of the second embodiment. [Figure 17] Figure 17A is a diagram showing an example of the data structure of judgment basis data accumulated in a road condition evaluation support device according to a second variant of the second embodiment, and Figure 17B is a diagram showing the correspondence between vehicle types and conversion coefficients. [Figure 18] FIG. 18 is a diagram showing an example of the correspondence relationship between vehicle type, traveling speed, and conversion coefficient applied in a road condition evaluation assisting device according to a third modified example of the second embodiment. [Figure 19] FIG. 19A is a diagram showing an example of an image displayed on a display of a road condition evaluation assistance device according to a fourth modified example of the second embodiment; [Figure 20]FIG. 20 is a diagram showing another example of an image displaying points determined to require repair. [Figure 21] FIG. 21 is a diagram showing an example of an image displayed on the display of a road condition evaluation assisting device according to the fifth modified example of the second embodiment. [Figure 22] 22A to 22C are diagrams showing examples of image data collected at the same location on different dates and times. [Figure 23] FIG. 23A is a schematic diagram of a system including a road condition evaluation support device according to a seventh variant of the second embodiment and multiple vehicles equipped with a data collection device 1, and FIG. 23B is a diagram showing an example of the cumulative value of points assigned to each data collection device. DETAILED DESCRIPTION OF THE INVENTION

[0094] [First Example] A data collection device according to a first embodiment will be described with reference to FIGS. 1A to 5. FIG.

[0095] FIG. 1A is a perspective view of a data collection device according to a first embodiment, seen from diagonally behind. A display 11 and a plurality of operation buttons 12 are arranged on the surface facing the rear (inside the vehicle cabin) of the housing of the data collection device 1 that is mounted and used in a vehicle. An SD card insertion slot 10 is arranged on the side of the housing of the data collection device 1. A joint rail 13 is provided on the top surface of the housing. Although not shown in FIG. 1A, a camera lens is attached to the front of the housing. A DC jack is arranged on the side that is not shown in FIG. 1A, and a speaker is arranged on the bottom.

[0096] The camera, which includes a lens, captures images of, for example, the area in front of the vehicle. The DC jack is connected to the vehicle's DC power supply via a power cable. A user, such as a driver, inserts an SD card into the data collection device 1 through the SD card insertion port 10. The speaker outputs sound and audio. A joint for mounting the data collection device 1 on the vehicle is attached to the joint rail 13. The display 11 displays various images. The user operates the operation buttons 12 to input various commands to the data collection device 1.

[0097] FIG. 1B shows the data collection device 1, windshield 3, dashboard, etc., mounted on a vehicle. The data collection device 1 is attached to the upper part of the windshield 3 of the vehicle, near the center in the left-right direction, on the passenger side adjacent to the rearview mirror 4. The data collection device 1 is attached and fixed to the windshield 3 with a mounting member such as double-sided tape. The DC jack of the data collection device 1 is connected to a cigarette lighter socket 5 via a power cable 6. When the vehicle's accessory power is turned on, power is supplied to the data collection device 1 from the cigarette lighter socket 5. When the vehicle passes over an uneven road surface and acceleration (or impact) is applied to the vehicle, a similar acceleration (or impact) is also applied to the data collection device 1.

[0098] 2 is a block diagram of a data collection device 1 according to a first embodiment. A controller 20 serving as a processing means includes a central processing unit (CPU) 20a, a read-only memory (ROM) 20b, and a random access memory (RAM) 20c. The ROM 20b stores an operating system (OS), programs for implementing various functions of the data collection device 1, and the like. The CPU 20a executes the programs stored in the ROM 20b to implement various functions of the data collection device 1. The RAM 20c is used as a temporary storage area when the CPU 20a executes the programs.

[0099] The speaker 16 and the display 11 function as notification means for informing the user of various information. The controller 20 causes the speaker 16 to emit sound or voice alerts and various information. Furthermore, the controller 20 causes the display 11 to display various information as images. The operation button 12 functions as input means for the user to give various commands to the data collection device 1.

[0100] Sensing data acquired by various sensors is input to a controller 20. As sensors, a GPS receiver 14, a camera 15, an acceleration sensor 17, and a gyro sensor 18 are prepared.

[0101] The GPS receiver 14 calculates position data indicating the current position of the vehicle based on signals received from GPS satellites. Furthermore, the GPS receiver 14 calculates the current time based on signals received from GPS satellites. The controller 20 can obtain position data indicating the current position of the vehicle and date and time data indicating the current date and time from the GPS receiver 14. The controller 20 has an internal timer, and can also obtain the current date and time from the internal timer.

[0102] The camera 15 captures images of the surroundings of the vehicle and acquires image data of the surroundings. For example, the camera 15 may capture images of the area in front of the vehicle. The camera 15 may be an imaging device such as a CMOS camera or a CCD camera.

[0103] The acceleration sensor 17 measures accelerations applied to the vehicle in the longitudinal direction (X-axis direction), lateral direction (Y-axis direction), and vertical direction (Z-axis direction), and acquires acceleration data. The gyro sensor 18 measures angular velocities in three axial directions of the vehicle, i.e., the rotation direction around the vertical axis (yawing direction), the rotation direction around the horizontal axis (pitching direction), and the rotation direction around the longitudinal axis (rolling direction), and acquires angular velocity data. The controller 20 captures the acceleration data and angular velocity data at a fixed sampling period, for example, a sampling period of 10 ms.

[0104] An SD card 22 is inserted into the SD card reader 19 through the SD card insertion slot 10 (FIG. 1A). The SD card reader 19 writes data to the inserted SD card 22 or reads data from the SD card 22 under the control of the controller 20. Other removable recording media may be used instead of the SD card 22, and a card reader that reads and writes data from and to the removable recording media may be used instead of the SD card reader 19.

[0105] The communication circuit 21 performs data communication with other devices via the data communication network 40 under the control of the controller 20. As the communication circuit 21, it is preferable to use a communication circuit that complies with a short-range wireless communication standard such as the WiFi standard or Bluetooth (registered trademark), or a mobile communication system standard such as LTE or 4G. The short-range wireless communication standard can be applied to communication with a device outside the vehicle when the vehicle is stopped, for example. Alternatively, it can be applied to communication with a device inside the vehicle, such as a personal computer with a large storage capacity. The mobile communication system standard can be applied to communication between a vehicle moving within a wider area and an external device, for example.

[0106] The data collection device 1 has a function of recording sensing data such as position data, image data, acceleration data, and angular velocity data acquired by the GPS receiver 14, camera 15, acceleration sensor 17, and gyro sensor 18 onto an SD card 22.

[0107] Next, the data recording function of the data collecting device 1 will be described. FIG. 3A is a diagram showing, in table form, an example of the configuration of various sensing data other than image data collected by the data collection device 1. The controller 20 collects date and time data, traveling speed data, position data, acceleration data, and angular velocity data at a fixed sampling period. The date and time data includes date information and time information. The controller 20 can obtain the date and time data from the GPS receiver 14 or an internal timer.

[0108] The driving speed data represents the vehicle's driving speed. The controller 20 acquires the driving speed data from the vehicle's electronic control unit (ECU) via the OBD connector. The position data is composed of latitude and longitude information. The acceleration data represents acceleration on the X-axis (front-rear direction), Y-axis (left-right direction), and Z-axis (up-down direction). The angular velocity data represents angular velocity in the rotation direction about the X-axis, Y-axis, and Z-axis.

[0109] 3B is a diagram showing the configuration of image data in table form. The controller 20 compresses the image data input from the camera 15 in accordance with a video compression standard such as H.264 or H.265. The image data is made up of multiple frames F, and some frames FI contain date and time data and position data when the image was captured. The date and time of image capture for a frame F that does not contain date and time data can be calculated based on the date and time frame FI was captured, the number of frames from frame FI, and the frame rate.

[0110] Image data and other sensing data are associated with each other based on date and time data. For example, acceleration data and image data at the time the acceleration data was acquired are associated with each other via the date and time data.

[0111] The date and time data, traveling speed data, position data, acceleration data, angular acceleration data, and image data shown in Figures 3A and 3B are the basis for determining whether road repairs are necessary. In this specification, these data are referred to as determination basis data.

[0112] Fig. 4 is a flowchart of the data recording process executed by the controller 20. When the accessory key of the vehicle is turned on and power is supplied to the data collection device 1, the controller 20 starts the process shown in Fig. 4. When the process starts, the controller 20 starts a process of constantly recording the determination basis data in a ring buffer secured in the RAM 20c.

[0113] The controller 20 acquires acceleration data from the acceleration sensor 17 and determines whether a collision has occurred based on the acceleration data (step ST01). For example, if the magnitude (absolute value) of the measured acceleration is equal to or greater than a first determination threshold, the controller 20 determines that a collision has occurred. The acceleration data to be compared with the first determination threshold may be, for example, acceleration data for three axes, namely, the X-axis, the Y-axis, and the Z-axis, and if the acceleration data for any one of the axes is equal to or greater than the first determination threshold, it may be determined that a collision has occurred.

[0114] When the controller 20 determines that a collision has occurred, the controller 20 performs a process of recording the determination basis data collected during the period before and after the collision occurrence in a recording area for a collision event on the SD card 22. The process starts (step ST03). The data recording period should be a period sufficient to analyze the cause of the collision. For example, it should be a period of two minutes in total, with one minute before and one minute after the collision. The controller 20 reads the basic data for judgment before the collision from the ring buffer and records it on the SD card 22.

[0115] If no collision is detected in step ST01, the controller 20 determines whether or not the vehicle has passed over an uneven road surface (step ST02). For example, if the magnitude of the measured acceleration is equal to or greater than a second threshold value that is smaller than the first threshold value, the controller 20 determines that the vehicle has passed over an uneven road surface. The acceleration data compared with the second threshold value may be, for example, acceleration data for three axes, namely, the X-axis, the Y-axis, and the Z-axis. If the acceleration data for any one of the axes is equal to or greater than the second threshold value, it may be determined that the vehicle has passed over an uneven road surface. Note that, since acceleration is mainly applied in the vertical direction when passing over an uneven road surface, only the acceleration data for the Z-axis may be compared with the second threshold value. Road surface unevenness includes, for example, cracks in the pavement, ruts, steps, potholes, etc.

[0116] When the controller 20 determines that the vehicle has passed over a road surface irregularity, it records the determination basis data collected during a period before and after the time point when the vehicle passed over the road surface irregularity in a recording area for road condition evaluation on the SD card 22 (step ST04). The period for recording the data may be, for example, a period sufficient to record an image of the road surface irregularities. For example, the period may be a total of 40 seconds, with 20 seconds before and after the time point when the vehicle passed over the road surface irregularity.

[0117] If the passage of an uneven surface is not detected in step ST02, or if the recording of the determination basic data is completed in steps ST03 and ST04, the controller 20 determines whether or not to end the data collection process (step ST05). For example, if the supply of power from the vehicle to the data collection device 1 is stopped, the controller 20 ends the data collection process. The data collection device 1 has a built-in battery with a capacity capable of supplying power for executing the data collection stop process even if the power supply from the vehicle is stopped, and after the power supply from the vehicle is stopped, the data collection device 1 operates using power from the built-in battery. If data collection is to continue, the controller 20 repeats the process from step ST01. In step ST01, the controller 20 makes a determination on the newly collected acceleration data.

[0118] Next, the recording process of the basic data for judgment in steps ST03 and ST04 will be described with reference to FIG. 5 is a diagram showing an example of a time waveform of acceleration data, a first determination threshold At1, a second determination threshold At2, and image data to be recorded. The first determination threshold At1 is set to a value greater than the maximum acceleration experienced by a vehicle when passing over a normal road surface with irregularities. The second determination threshold At2 is set to a value greater than the maximum acceleration experienced by a vehicle when traveling on a normal road surface without irregularities.

[0119] When the vehicle passes over an uneven road surface, a peak P1 greater than the second determination threshold At2 appears in the time waveform of the acceleration data. When the controller 20 detects this peak P1, the controller 20 records the image data 31, acceleration data, etc. acquired during the period T1 before and after the time when the peak P1 appeared in the SD card 22.

[0120] When a vehicle collision occurs, a peak P2 greater than the first determination threshold At1 appears in the time waveform of the acceleration data. When the controller 20 detects this peak P2, it records onto the SD card 22 the image data 32 and acceleration data etc. acquired during a period T2 before and after the time when the peak P2 appears.

[0121] Next, we will explain the image display function of the data collection device 1. The controller 20 has a function of displaying on the display 11 an image of image data recorded on the SD card 22. For example, when the user operates the operation button 12 to input the date and time data or location data when the image to be displayed was acquired, the controller 20 causes the display 11 to display an image of the image data associated with the input date and time data or location data.

[0122] Furthermore, the controller 20 has a function of displaying basic judgment data other than image data recorded on the SD card 22 in numerical or graphical form on the display 11. For example, when the user operates the operation button 12 to input the basic judgment data (e.g., acceleration data and date and time data) that the user wants to display, the controller 20 causes the input basic judgment data to be displayed in numerical or graphical form on the display 11.

[0123] Next, we will explain the data transmission function of the data collection device 1. The controller 20 has the function of reading out the judgment basis data recorded on the SD card 22 and transmitting it from the communication circuit 21 to other devices via the data communication network 40. For example, when the user operates the operation button 12 to instruct transmission of the judgment basis data, the controller 20 starts transmitting the judgment basis data. The controller 20 may also be configured to transmit the collected judgment basis data to other devices in real time. @ Instead of recording the judgment basis data on the SD card 22, the controller 20 may transfer the judgment basis data to an in-vehicle device, such as a PC with a large-capacity storage device, via the communication circuit 21. After the judgment basis data is temporarily stored in the in-vehicle device, it may be transmitted to an external server or management PC (e.g., a road condition evaluation support device described below) via a device capable of long-distance communication, such as a mobile terminal, or by using the communication function of the data collection device 1 that complies with mobile communication system standards, such as LTE or 4G. Wireless communication between the data collection device 1 and the in-vehicle device is more stable than long-distance communication. Therefore, the in-vehicle device can be used as a large-capacity storage device for buffering the judgment basis data, similar to the SD card 22. @ Furthermore, the determination basis data once recorded on the SD card 22 may be read from the SD card 22 and transferred to an in-vehicle device via the communication circuit 21. In this way, even in a situation where communication with an external server, management computer, etc. is not possible, a large amount of determination basis data can be stored without being limited by the recording capacity of the SD card 22. When communication with an external server, etc. becomes possible, the data may be transmitted from the in-vehicle device to the external server, management computer, etc. via a device capable of long-distance communication, such as a mobile terminal.

[0124] [Effects of the first embodiment] Next, the advantageous effects of the first embodiment will be described. A road administrator can view the image displayed on the display 11 and check the road surface condition and the shape and surface condition of road accessories contained in the image. Furthermore, by inserting the SD card 22 into a personal computer or the like, the image data and other basic data for judgment recorded on the SD card 22 can be checked. Based on this basic data for judgment, the road administrator can determine whether or not repairs are required for the road or road accessories.

[0125] The necessity of road repairs (whether repair work is necessary or not) can be determined based on, for example, the presence or absence of unevenness such as bumps and depressions in the road surface, ruts, cracks, repair locations at highway joints, subsidence of the road surface, etc. The presence or absence of road surface unevenness can be determined based on, for example, acceleration data that indicates the acceleration applied to a vehicle when passing over unevenness in the road surface, image data of the surroundings of the vehicle, etc. The presence or absence of ruts and cracks can be determined based on, for example, image data of the area ahead of the vehicle, image data of the road surface, etc. The presence or absence of subsidence of the road surface can be determined based on, for example, image data of the vehicle when passing over a subsided area. The vehicle inclination can be calculated from the angular velocity of the vehicle.

[0126] Examples of road accessories include fall prevention fences, fences separating roadways from sidewalks, rows of trees on roads, road signs, road markings, road information display devices, vehicle monitoring devices, utility ducts, etc. The need for repairs of these can be determined based on data such as images of the vehicle's surroundings.

[0127] The image data collected by the data collection device 1 should be composed of multiple images acquired at a fixed sampling period. This sampling period should be short enough to allow the multiple images included in the collected image data to be used to acquire images of the surroundings of the route traveled by the vehicle along the route without interruption. In this way, information on the deterioration state of the road and roadside features can be obtained for the entire section for which image data is acquired.

[0128] In the first embodiment, image data and other data, such as acceleration data, are obtained as information on the road surface irregularities. By using these two or more types of data with different properties in combination, it is possible to more accurately determine whether road repairs are necessary.

[0129] The data collection device 1 according to the first embodiment uses collected acceleration data to distinguish between impacts received when a vehicle passes over road irregularities and impacts received due to a collision accident. This allows, for example, a drive recorder that records images (event recording) of a collision to be used as a data collection device that collects basic data for determining whether road repairs are required. For example, a typical drive recorder has a function for comparing the magnitude of acceleration with a first threshold for detecting a collision. By adding a function for comparing the magnitude of acceleration with a second threshold for detecting road irregularities to such a typical drive recorder, the typical drive recorder can be used as the data collection device 1 that collects basic data for determining whether road repairs are required. When the acceleration is equal to or greater than the first threshold, it is determined that the vehicle has collided and has passed over a road irregularity. This allows for accurate determination of whether the vehicle has passed over a road irregularity when the vehicle passes over a road irregularity with a large difference in elevation that would cause the vehicle to be subjected to an impact similar to that caused by a collision.

[0130] In this way, by adding simple functions to a general consumer drive recorder, it can be used as a business data collection device 1 that collects basic data for determining whether or not roads or road accessories need repairs. This makes it possible to reduce the cost of the data collection device 1. Conversely, the data collection device 1 according to the first embodiment can be used as a drive recorder.

[0131] For a typical drive recorder that records events, the peaks of the acceleration time waveform generated by the impact applied when the vehicle passes over an uneven road surface are a nuisance (garbage data) for detecting the occurrence of a collision accident. Generally, the peaks of the acceleration time waveform generated when passing over an uneven road surface are smaller than the acceleration peaks generated in the event of a collision accident. By utilizing the difference in the magnitude of these peaks, a typical drive recorder distinguishes between the occurrence of a collision accident and passing over an uneven road surface. In the data collection device 1 according to the first embodiment, the relatively small peaks of the acceleration time waveform that are treated as a nuisance in the drive recorder can be treated as useful information for finding (picking up) points where unevenness occurs in the road surface.

[0132] In the first embodiment, as shown in Fig. 3, the image data is associated with the basic data for determination other than the image data via date and time data indicating the date and time when the data was collected. Therefore, it is possible to extract image data corresponding to the basic data other than image data. For example, it is possible to extract image data corresponding to the peak of the waveform of acceleration data.

[0133] The controller 20 can transmit the basis judgment data recorded on the SD card 22 to an external device via the communication circuit 21 while the vehicle is traveling. By equipping a consumer drive recorder with a function for collecting basis judgment data, it is possible to collect basis judgment data not only from vehicles used for road maintenance work, but also from ordinary vehicles equipped with drive recorders. By collecting and accumulating a larger amount of basis judgment data from drive recorders installed in many ordinary vehicles, the accuracy of repair necessity determination can be improved.

[0134] If the vehicle speed is extremely slow, a large acceleration peak that exceeds the second determination threshold At2 will not occur even if the vehicle passes over an uneven surface. Therefore, if the vehicle speed is extremely slow, valid acceleration data for determining whether or not there is an uneven surface will not be obtained. In order to determine whether or not there is an uneven surface based on valid acceleration data, it is recommended that the process of detecting whether or not the vehicle is passing over an uneven surface (step ST02) in FIG. 4 be performed after the vehicle speed exceeds a predetermined threshold.

[0135] [First Modification of the First Embodiment] Next, a first modification of the first embodiment will be described. The data collection device 1 according to the first modification of the first embodiment further includes a microphone as a sensor for collecting basic data for determination. The controller 20 has a function of determining whether the vehicle has passed over an uneven road surface by using both the acceleration data and the sound data collected by the microphone.

[0136] By using both acceleration data and sound information, it is possible to determine with higher accuracy whether or not there are road irregularities. Furthermore, information about the shape and size of road irregularities can be obtained from the volume and frequency (spectrum) of the sound.

[0137] [Second Modification of the First Embodiment] Next, a second modified example of the first embodiment will be described with reference to FIGS. 6A and 6B. 6A is a flowchart of processing executed by the controller 20 of the data collecting device 1 according to the second modified example of the first embodiment. Below, differences from the flowchart of processing executed by the controller 20 of the data collecting device 1 according to the first embodiment shown in FIG. 4 will be described.

[0138] In the second modified example, between steps ST01 and ST02 of the first embodiment, a process (step ST11) is added in which the collected image data is analyzed to determine whether the section in which the vehicle is traveling is a section in which unevenness has occurred due to factors other than road surface deterioration. If the section in which the vehicle is traveling is not a section in which unevenness has occurred due to factors other than road surface deterioration, it is determined whether the passage of unevenness in the road surface has been detected (step ST02). This determination process and the subsequent processes are the same as those in the first embodiment.

[0139] If the section in which the vehicle is traveling is a section where unevenness has occurred due to factors other than road surface deterioration, the controller 20 skips the process of determining whether or not the vehicle has passed through an uneven road surface (step ST02), and performs a process of determining whether or not the process has ended (step ST05).

[0140] FIG. 6B is a diagram showing an example of an image acquired by the camera 15 (FIG. 2). A sign saying "Under Construction" has been installed on the side of the road. When the controller 20 analyzes the acquired image data and detects the under construction sign, it recognizes that the road ahead is under construction. Whether or not the section under construction has been passed can be determined from the road surface condition in the image data. The controller 20 From the time the vehicle approaches the section under construction until the time it leaves the section under construction, it is determined that the vehicle is traveling through a section where unevenness has occurred due to factors other than deterioration of the road surface.

[0141] Next, the excellent effects of the data collection device 1 according to the second modified example of the first embodiment will be described.

[0142] In sections of roads under construction, unevenness occurs on the road surface due to, for example, the surface layer of the road being cut away, exposing the sub-surface. These unevennesses are caused by factors other than road surface deterioration, and even if the data collection device 1 detects these unevennesses, it is not necessary to determine whether repairs are necessary. In the second modification, by excluding unevennesses that do not require a repair determination from the detection targets, it is possible to prevent excessive detection of unevenness. As a result, it is possible to prevent the storage capacity of the SD card 22 from being consumed by unnecessary determination basic data. Because the total amount of data recorded is reduced, the amount of data to be analyzed when determining whether unevennesses require repairs based on the recorded data is also reduced. As a result, it is possible to shorten the processing time required for analysis.

[0143] Sections where unevenness has occurred due to factors other than road surface deterioration may include sections where unevenness has occurred due to ongoing construction, as shown in Figure 6B. Other sections may also include sections where steps have occurred at joints where open-cut construction work has been performed, steps due to the edges of manholes, steps at bridge joints, etc. Joints where open-cut construction work has been performed, manhole edges, bridge joints, etc. may be detected by analyzing image data.

[0144] In the third modified example shown in FIG. 6A, when the controller 20 determines that the vehicle is traveling through a section where unevenness has occurred due to factors other than road surface deterioration, the unevenness detection process is skipped. However, the unevenness detection process may be executed, and the process of recording the determination basic data (step ST04) may be skipped.

[0145] [Third Modification of the First Embodiment] Next, a third modified example of the first embodiment will be described with reference to FIGS. 7A to 7D. 7A is a flowchart of processing executed by the controller 20 of the data collection device 1 according to the third modified example of the first embodiment. When power is supplied to the data collection device 1, the controller 20 analyzes image data acquired by the camera 15 (FIG. 2) to determine the type of road surface on which the vehicle is currently traveling (step ST21). The controller 20 changes the second determination threshold At2 (FIG. 5) for detecting unevenness in the road surface depending on the type of road surface.

[0146] The types of road surfaces include, for example, asphalt pavement, concrete pavement, and block pavement. If the controller 20 determines that the type of road surface is asphalt pavement, it sets the second determination threshold value At2 to a value for asphalt pavement (step ST22), if the controller 20 determines that the type of road surface is concrete pavement, it sets the second determination threshold value At2 to a value for concrete pavement (step ST23), and if the controller 20 determines that the type of road surface is block pavement, it sets the second determination threshold value At2 to a value for block pavement (step ST24).

[0147] After setting the second determination threshold At2, the controller 20 determines whether or not to end the process (step ST25). For example, if the supply of power to the data collection device 1 is stopped, the controller 20 stops the process. If the process is to be continued, the controller 20 repeats the process from step ST21. This repeated process may be performed, for example, at a fixed cycle, for example, every second.

[0148] 7B, 7C, and 7D are diagrams showing examples of images acquired while traveling on asphalt, concrete, and block pavement, respectively. Concrete pavement has seams of approximately 1 cm width at intervals of, for example, 5 m to 10 m, as shown in FIG. 7C. Block pavement has a unique pattern consisting of, for example, multiple blocks laid out, as shown in FIG. 7D. Asphalt pavement has no seams or unique patterns, as shown in FIG. 7B. The controller 20 can determine the type of road surface by detecting the presence or absence of seams or unique patterns through image analysis.

[0149] Next, the excellent effects of the third modified example of the first embodiment will be described. The acceleration (impact) experienced by a vehicle depends on the type of road surface on which the vehicle is traveling. By changing the second determination threshold At2 according to the type of road surface, it is possible to appropriately determine the presence or absence of unevenness according to the type of road surface. For example, when traveling on a block-paved road, the vehicle is continuously subjected to impacts due to the unique pattern of the blocks. It is advisable to set the second determination threshold At2 so that the magnitude of the impact due to this unique pattern is excluded from the unevenness detection target. When the road surface is concrete pavement, it is advisable to set the second determination threshold At2 so that the magnitude of the impact applied to the vehicle by the joints is excluded from the unevenness detection target. By setting the second determination threshold At2 in this way, it is possible to prevent excessive detection of unevenness that does not require repair.

[0150] In the third modification of the first embodiment, the second determination threshold At2 is varied depending on the type of road surface. However, the second determination threshold At2 may also be varied depending on the road type, for example, between an ordinary road and an expressway. This makes it possible to more appropriately determine the presence or absence of unevenness on ordinary roads and expressways. Furthermore, it is preferable to collect determination basis data when the vehicle's traveling speed exceeds a threshold depending on the type of road on which the vehicle is traveling.

[0151] Instead of determining the type of road surface by image analysis, the type of road surface for each road may be stored in advance in the SD card 22, etc. The controller 20 can identify the type of road surface of the road on which the vehicle is currently traveling based on the relationship between the current position of the vehicle and the pre-stored road surface type for each road.

[0152] [Fourth Modification of the First Embodiment] Next, a fourth modified example of the first embodiment will be described with reference to FIGS. 8A and 8B. 8A is a flowchart of processing executed by the controller 20 of the data collecting device 1 according to the fourth modified example of the first embodiment. Below, differences from the flowchart of processing executed by the controller 20 of the data collecting device 1 according to the first embodiment shown in FIG. 4 will be described.

[0153] In the fourth modified example, between step ST01 and step ST02, the controller 20 analyzes image data acquired by the camera 15 (FIG. 2) to determine whether or not the road surface has intentionally created uneven paving (step ST31). If it is determined that the road surface has uneven paving, the controller 20 skips the unevenness detection process (step ST02) and determines whether or not the process is finished (step ST05). If it is determined that the road surface does not have uneven paving, the controller 20 executes the unevenness detection process (step ST02).

[0154] FIG. 8B is a diagram showing an example of an image when traveling on a road surface with stepped pavement. An example of intentionally created stepped pavement is one that applies sound or vibration to the driver of a traveling vehicle to reduce the traveling speed or to draw attention. Step pavement is achieved, for example, by applying aggregate such as ceramic to the pavement surface using a resin adhesive. As shown in FIG. 8B, areas 94 where aggregate is applied are periodically arranged in the traveling direction. By detecting the area 94 where aggregate has been applied from the image data, the troller 20 can determine whether or not the road surface has intentionally been paved with uneven surfaces.

[0155] In the third modified example, it is possible to prevent a point where intentionally created uneven pavement has been installed from being raised as a candidate point where an unevenness that needs to be repaired has occurred.

[0156] Information specifying a location where intentionally installed uneven pavement is provided may be registered in advance in the SD card 22, etc. The controller 20 may skip the unevenness detection process (step ST02) when the vehicle passes a location where intentionally installed uneven pavement is provided that has been registered in advance.

[0157] It is preferable to display the image data on an external device such as a personal computer, and allow the user to specify the section for which the unevenness detection process (step ST02) is to be skipped while viewing the image. For example, it is preferable to use a pointing device to specify on the image the start and end points of the section for which the unevenness detection process (step ST02) is to be skipped.

[0158] [Fifth Modification of the First Embodiment] Next, a fifth modified example of the first embodiment will be described with reference to FIGS. 9A to 9C. In a fifth modification of the first embodiment, the controller 20 stores collection location data that identifies a location where basic determination data, which is the basis for determining whether or not a road or road appendage needs repair, should be collected. The collection location data may be acquired by the controller 20 receiving it from an external device via the communication circuit 21 (FIG. 2), for example. Alternatively, the controller 20 may acquire the collection location data by reading it out from the SD card 22. The controller 20 stores the acquired collection location data in the RAM 20c (FIG. 2).

[0159] The collection position data includes at least one of data in a format that specifies a point where basic judgment data should be collected (data collection point) and data in a format that specifies the start and end points of a section where basic judgment data should be collected (data collection section). The controller 20 periodically determines whether the current position of the vehicle is near a data collection point and whether it is within the data collection section. If the current position of the vehicle is near a data collection point or within the data collection section, the controller 20 sets a flag (data collection flag) that indicates that the current position is a location where basic judgment data should be collected. If the current position of the vehicle is neither near a data collection point nor within the data collection section, the controller 20 resets the data collection flag.

[0160] 9A is a flowchart of processing executed by the controller 20 of the data collecting device 1 according to the fifth modified example of the first embodiment. Below, differences from the flowchart of processing executed by the controller 20 of the data collecting device 1 according to the first embodiment shown in FIG. 4 will be described.

[0161] In the fifth modified example, instead of the processing of step ST02 in Fig. 4, the controller 20 determines whether the current position of the vehicle is a location where basic judgment data should be collected (step ST41). For example, if the data collection flag is set, the controller 20 determines that the current position of the vehicle is a location where basic judgment data should be collected, and if the data collection flag is reset, the controller 20 determines that the current position of the vehicle is not a location where basic judgment data should be collected.

[0162] If it is determined that the current position of the vehicle is a location where basic data for judgment should be collected, the controller 20 records the basic data for judgment in a recording area for road condition evaluation of the SD card 22 (step ST42). Thereafter, it is determined whether or not to end the process (step ST05). If it is determined that the current position of the vehicle is not a location where basic determination data should be collected, the controller 20 skips the process of recording the basic determination data and determines whether or not to end the process (step ST05).

[0163] FIG. 9B is a diagram showing an example of a data collection section. In FIG. 9B, the data collection section 80 is indicated by a thick solid line. A vehicle departs from a vehicle depot 83, travels a route including a predetermined data collection section 80, and then returns to the vehicle depot. When the vehicle reaches a start point 81 of the data collection section 80, the controller 20 sets a data collection flag, and when the vehicle reaches an end point 82, the controller 20 resets the data collection flag. As a result, while the vehicle is traveling through the data collection section 80, the judgment basis data is constantly recorded on the SD card 22.

[0164] FIG. 9C is a diagram showing an example of data collection points. At least one data collection point 85 is set in advance. A vehicle departs from a vehicle depot 83, passes through the data collection point 85, and then returns to the vehicle depot 83. When the vehicle is located in a neighborhood 86 of the data collection point 85 (the portion indicated by the thick solid line in FIG. 9C), the controller 20 sets a data collection flag. Therefore, the controller 20 records the judgment basis data in the neighborhood 86 of the data collection point 85 in the SD card 22.

[0165] Next, the excellent effects of the fifth modification of the first embodiment will be described. In a fifth modification of the first embodiment, at locations where basic determination data should be collected, the basic determination data is recorded on the SD card 22 regardless of whether or not there is a peak in the acceleration data. Therefore, when road surface deterioration occurs such that no significant peak appears in the acceleration data, image data of the location where the deterioration occurs can be recorded. The presence or absence of road surface deterioration can be determined by displaying the image data on a screen and having the road administrator visually inspect the image. Alternatively, the presence or absence of road surface deterioration can be automatically determined by performing image analysis using image analysis software. The presence or absence of road surface irregularities can also be determined based on the magnitude of the acceleration data.

[0166] In addition, the judgment basis data is recorded on the SD card 22 at locations where it should be collected, and is not recorded at other locations on the SD card 22. This reduces the amount of memory space consumed on the SD card 22 compared to when the judgment basis data is recorded on the SD card 22 for all routes traveled by the vehicle. Furthermore, when analyzing the collected judgment basis data, the amount of judgment basis data to be analyzed is reduced, so the time required for the analysis process can be shortened.

[0167] It is a good idea to register points where road surface deterioration is observed but where repairs are not immediately necessary as data collection points 85 (Figure 9C). By periodically collecting basic data for judgment at these points, the progression of deterioration can be continuously monitored.

[0168] The vicinity area 86 of the data collection point 85 (Figure 9C) should be defined so that the road surface at the data collection point 85 can be photographed, taking into account the position error determined by the GPS receiver 14 (Figure 2) and the difference between the current position of the vehicle and the position of the road surface photographed by the camera 15.

[0169] The controller 20 may have a function of notifying the driver that the vehicle is approaching the data collection point 85 when the distance between the current position of the vehicle and the data collection point 85 becomes equal to or less than a reference distance. The driver of the vehicle can know that the vehicle is approaching the data collection point 85. This allows the driver to start preparations for collecting the determination basis data.

[0170] Furthermore, the controller 20 preferably stores a standard value of the traveling speed when passing through the data collection point 85, and has a function of informing the driver of the vehicle that the vehicle is approaching the data collection point 85 and urging the driver of the vehicle to travel at the standard traveling speed. It is possible to realize that it is necessary to drive at the standard value before driving through the data collection point 85. This makes it possible to prevent the occurrence of a situation in which the vehicle drives through the data collection point 85 at a value that is significantly different from the standard value, making it impossible to effectively use the collected basic data for judgment.

[0171] The approach of the vehicle to the data collection point 85 and the standard value of the traveling speed may be notified by a sound, for example, from the speaker 16. This allows the driver to notice the notified information without looking away.

[0172] [Sixth Modification of the First Embodiment] Next, a sixth modified example of the first embodiment will be described with reference to FIGS. 10A and 10B.

[0173] In the first embodiment, the data collection device 1 records the determination basis data on the SD card 22 when it detects road surface irregularities (step ST04 in FIG. 4). In a fifth modification of the first embodiment, the determination basis data is recorded on the SD card 22 only at locations where the determination basis data should be collected (step ST42 in FIG. 9). In a sixth modification, the data collection device 1 has a function of constantly recording the determination basis data.

[0174] The mode in which the basic judgment data is constantly recorded is referred to as the "constant recording mode," and the mode in which the basic judgment data is recorded when an unevenness is detected is referred to as the "event recording mode." The controller 20 stores one of the constant recording mode and the event recording mode as the default mode. If the user does not perform a mode switching operation, the data collection device 1 operates in the default mode. If the user operates the operation button 12 (FIGS. 1 and 2) to select a mode different from the default mode, the data collection device 1 operates in the mode selected by the user.

[0175] 10A is a flowchart of processing executed by the controller 20 of the data collecting device 1 according to the sixth modified example of the first embodiment. Below, differences from the processing executed by the controller 20 of the data collecting device 1 according to the first embodiment shown in FIG. 4 will be described.

[0176] In the sixth modified example, between step ST01 and step ST02, the controller 20 determines the current mode (step ST51). If the current mode is the constant recording mode, the controller 20 records the determination basis data in a recording area for road condition evaluation on the SD card 22 (step ST04). If the current mode is the event recording mode, the controller 20 executes the unevenness detection process (step ST02).

[0177] 10B is a diagram showing an example of a route for recording the basis data for judgment. A vehicle departs from a vehicle depot 83, travels along a route 87, and then returns to the vehicle depot 83. In the sixth modified example, when the continuous recording mode is selected, the basis data for judgment is recorded for the entire section of the route traveled from the time the vehicle departs from the vehicle depot 83 until the time the vehicle returns.

[0178] Next, the advantageous effects of the sixth modification will be described. By setting the data collection device 1 to the constant recording mode, it is possible to collect basic judgment data for determining whether or not repairs are necessary for roads or road accessories for all routes traveled by a vehicle. This allows basic judgment data to be collected over a wider area. If basic judgment data collected at a time in the past when no abnormality was found is accumulated at a point where an abnormality is found in a road or road accessory, it is possible to analyze the time when the abnormality occurred, the cause of the abnormality, etc., by using the basic judgment data collected at the time in the past in combination.

[0179] The period for constant recording may be, for example, the period from when the accessory key of the vehicle is turned on to when it is turned off.

[0180] [Seventh Modification of the First Embodiment] Next, a seventh modified example of the first embodiment will be described with reference to FIGS. 11A to 11C.

[0181] FIG. 11A is a flowchart of a process executed by the controller 20 of the data collection device 1 according to a seventh modification of the first embodiment. This process is executed, for example, between step ST02, step ST03, step ST04, and step ST05 of the first embodiment shown in FIG. 4. In the seventh modification, after step ST02, step ST03, and step ST04 of the first embodiment shown in FIG. 4, the controller 20 analyzes image data acquired by the camera 15 (FIG. 2) (step ST61). For example, the controller 20 analyzes the condition of the road surface in the image data and the condition of roadside features in the image data. Based on the analysis results, the controller 20 determines whether the road surface and roadside features require repair (step ST62). For example, the controller 20 detects cracks in the road surface, deterioration of road markings, poor drainage of roads after rain, corrosion or deformation of roadside features such as road signs and guardrails, signs of landslides, and damage caused by roadside trees.

[0182] If the controller 20 determines that repairs to the road surface or road appendages are necessary, it records the image data and other basic data for determination on the SD card 22 (step ST63). After recording the basic data for determination, it executes step ST05 of the flowchart shown in Fig. 4. If the controller 20 determines that repairs to the road surface or road appendages are not necessary, it executes step ST05 of the flowchart shown in Fig. 4 without recording the basic data for determination on the SD card 22.

[0183] 11B and 11C are diagrams showing examples of images in which the controller 20 has determined that the road surface or roadside features need repair. In the example shown in Fig. 11B, a broken portion 90 is observed in part of the guardrail. In the example shown in Fig. 11C, part of a road sign 91 is hidden by a roadside tree 92, making it invisible to the driver.

[0184] Next, the advantageous effects of the seventh modification of the first embodiment will be described. In the seventh modification of the first embodiment, the amount of images that the road administrator needs to view is smaller than when the road administrator views all images acquired during driving to find areas that need repair. This reduces the burden on the road administrator in checking images. Furthermore, even if an abnormality is overlooked by a human visual inspection, the controller 20 can detect the area that needs repair, thereby reducing the chance of overlooking the area that needs repair.

[0185] [Eighth Modification of the First Embodiment] Next, an eighth modification of the first embodiment will be described. In the first embodiment, one camera 15 (FIG. 2) is mounted on the data collection device 1. In the eighth modification, a second camera is mounted in addition to the camera 15. The second camera captures an image in a direction different from the direction captured by the first camera 15. For example, the first camera 15 captures an image in front of the vehicle, and the second camera captures an image of the road surface directly below the vehicle.

[0186] In steps ST03 and ST04 (FIG. 4) of the first embodiment, the controller 20 records the image data acquired by the first camera 15 and the image data acquired by the second camera on the SD card 22.

[0187] Next, the excellent effects of the eighth modification of the first embodiment will be described. This allows not only the area in front of the vehicle but also the road surface directly below the vehicle to be photographed. Based on the image data of the road surface directly below the vehicle recorded on the SD card 22, it becomes possible to observe the condition of the road surface in more detail.

[0188] The second camera may also be configured to capture an image of the area behind the vehicle. In this way, the first camera 15 and the second camera can capture images of roadside objects from different directions. This can improve the accuracy of determining whether roadside objects need repair.

[0189] [Second Example] Next, a road condition evaluation assisting device 50 according to a second embodiment will be described with reference to FIGS.

[0190] FIG. 12 is a block diagram of a road condition evaluation assistance device 50 according to a second embodiment. The road condition evaluation assistance device 50 includes a controller 60, a display 61, an input device 62, an SD card reader 63, a communication device 64, a database 65, and an external storage device 66. The controller 60, which serves as processing means, includes a central processing unit (CPU) 60a and a random access memory (RAM) 60c. The external storage device 66 stores various programs, such as an operating system (OS) and application programs. The CPU 60a transfers the programs stored in the external storage device 66 to the RAM 60c and executes them, thereby realizing various functions of the road condition evaluation assistance device 50. Furthermore, the RAM 60c is used as a temporary storage area when the CPU 60a executes the programs.

[0191] An SD card 22 on which basic judgment data collected by the data collection device 1 (FIGS. 1 and 2) is recorded is inserted into an SD card reader 63. The controller 60 has a function of reading the basic judgment data recorded on the SD card 22 and storing it in a database 65. Furthermore, the controller 60 has a function of storing in the database 65 the basic judgment data collected by the data collection device 1 and received by the communication device 64 via the data communication network 40. The database 65 stores the date and time data, traveling speed data, position data, acceleration data, angular velocity data, and image data shown in FIG. 3A in mutually associated relation.

[0192] The controller 60 displays a dialog box on the display 61 that prompts the user to input a command. A user of the road condition evaluation assistance device 50, for example, a road administrator, looks at the dialog box displayed on the display 61 and operates the input device 62 to give various commands to the controller 60. The controller 60 executes processing in response to the command given by the user and displays the processing results on the display 61.

[0193] Next, the processing executed by the road condition evaluation assistance device 50 will be described. When the user performs an operation to display acceleration data and image data of a specific point, the controller 60 causes the display 61 to display in chronological order images of the image data acquired at the specific point on multiple different dates and times.

[0194] FIG. 13 is a diagram showing an example of an image displayed on the display 61. The controller 60 extracts image data of a specific location specified by the user from the database 65, and displays images 70 of image data acquired by the camera 15 (FIG. 2) at multiple dates and times in chronological order on the display 61, along with information 71 indicating the dates and times of acquisition. The vertical direction of the displayed graphic corresponds to the chronological order. FIG. 13 shows, as an example, images 70 of the same location acquired at 10:00:07 on July 5, 2016, 11:12:47 on July 30, 10:15:33 on August 26, 2016, and 10:43:09 on October 8, 2016.

[0195] Furthermore, the controller 60 extracts acceleration data associated with the displayed image data from the database 65, and displays a time waveform 72 of the extracted acceleration data on the display 61 so that its correspondence with the image 70 can be identified. For example, the time waveform 72 of the acceleration data may be displayed next to the image 70. As the time waveform of the acceleration data to be displayed, for example, acceleration data in the vertical direction of the vehicle may be used. Alternatively, the time waveforms of acceleration data in the vertical, longitudinal, and lateral directions of the vehicle may be displayed in different colors and superimposed. An identification marker 73 is displayed on the time axis of the time waveform 72 at a position corresponding to the time point when the displayed image 70 was acquired. As the identification marker 73, for example, a dashed line extending vertically may be used.

[0196] When the road administrator moves the identification sign 73 in the direction of the time axis, the controller 60 switches the displayed image to an image corresponding to the time specified by the identification sign 73.

[0197] The controller 60 further displays an evaluation value button 75 on the display 61. When the user selects the evaluation value button 75, the controller 60 calculates an evaluation value that depends on the unevenness of the road surface at a specific point from each of a plurality of acceleration data collected at a specific point on a plurality of different dates and times. Furthermore, the controller 60 displays the change over time of the calculated evaluation value on the display 61.

[0198] The evaluation value may be, for example, the maximum height of the peaks appearing in the time waveform of the acceleration data. Alternatively, the evaluation value may be, for example, the maximum amplitude (peak-to-peak) of the time waveform. Alternatively, the evaluation value may be, for example, the average value of the acceleration waveform that exceeds a certain threshold.

[0199] FIG. 14 is a diagram showing an example of an image of the change in the evaluation value over time displayed on the display 61. The controller 60 causes the display 61 to display the change in the evaluation value over time in graph form. The horizontal axis of the graph represents date and time, and the vertical axis represents the evaluation value. The evaluation value from the past to the present is displayed with a solid line, and the predicted change in the evaluation value in the future is displayed with a dashed line. The evaluation value when the difference in elevation of the unevenness reaches a level that requires repair is represented as TE. The controller 60 displays a dashed line at the position where the evaluation value becomes TE so that the position where the evaluation value becomes TE can be recognized. In the example shown in FIG. 14, the evaluation value increases over time. This means that the difference in elevation of the unevenness of the road surface is increasing over time. The controller 20 predicts future changes in the evaluation value based on the slope of the past changes in the evaluation value.

[0200] Next, with reference to FIG. 15, a process for displaying the image 70 and the time waveform 72 of the acceleration data shown in FIG. 13 will be described.

[0201] FIG. 15 shows the relationship between the time waveform of acceleration data and the date, time, and location at which the time waveform was acquired. When a user inputs location data for a location L0 to be displayed, the controller 60 extracts acceleration data acquired at the input specific location L0 or a location nearby the input location L0 from the location data stored in the database 65. The date and time at which the specific location L0 was passed is t0. A peak P is detected from the extracted time waveform A of the acceleration data. Image data (e.g., one frame F of a video) corresponding to the date and time t1 at which the acceleration peak P was detected is extracted from the database 65, and an image 70 of the image data corresponding to the date and time t1 and a time waveform 72 for a period before and after the acceleration peak P are displayed on the display 61. Due to errors in the position data calculation by the GPS receiver 14, the location L1 at which the peak P appears may not necessarily coincide with the location L0 input by the user.

[0202] The frame F at the time t1 when the peak P appears corresponds to an image ahead of the unevenness that caused the peak P. The image 70 displayed on the display 61 is the image of the area ahead of the unevenness that caused the peak P. It is advisable to extract a frame F slightly before the date and time t1 so as to include the irregularities.

[0203] [Effects of the second embodiment] By looking at the images 70 (FIG. 13) displayed in chronological order, road managers can check the progress of deterioration of roads or road accessories. This allows them to determine whether repairs are necessary and predict when repairs will be required. Also, by looking at the displayed images 70, they can determine whether or not it is necessary to continue to monitor the deterioration state of the displayed point. If it is determined that continued monitoring is necessary, the images 70 displayed in chronological order provide useful information for determining the frequency of monitoring.

[0204] The road administrator may input specific points for which images will be displayed on the display 61, such as points where the road surface has been determined to be uneven based on acceleration data, points that are being monitored continuously, or points that are predicted to require repair due to various factors.

[0205] By looking at the time waveform of the acceleration data displayed on the display 61, the road administrator can identify the magnitude of the impact that was applied to the vehicle when it passed that point. The magnitude of the impact can be used as information for identifying the height difference of the unevenness occurring on the road surface. From the time waveform of the acceleration data displayed in chronological order, the progress of deterioration of the unevenness occurring on the road surface can be known.

[0206] Acceleration data collected at specific points designated by a road administrator can be found by referencing the location data associated with the acceleration data. This location data is obtained, for example, by a GPS receiver 14, and location measurement errors occur due to various factors. For this reason, it is difficult to accurately match the locations where multiple time waveforms of acceleration data collected during driving at different dates and times were acquired using only the location data.

[0207] In the second embodiment, the controller 60 detects peak P (FIG. 15) of the time waveform of the acceleration data, thereby enabling the positions at which multiple time waveforms of acceleration data collected at different dates and times were acquired to be accurately matched. As a result, it is possible to evaluate impacts caused by unevenness at the same point in chronological order, and to accurately extract image data of a specific point based on the date and time at which the acceleration data was collected.

[0208] The degree of deterioration can be estimated from the time-dependent change in the evaluation value (Fig. 14), which depends on the unevenness of the road surface at a specific point. The estimated degree of deterioration can then be used to predict when repairs should be carried out.

[0209] The change in the evaluation value over time is displayed in graph form (Figure 14), allowing road managers to visually recognize the rate at which the evaluation value is deteriorating. The future change in evaluation value shown in the graph in Figure 14 can be used as useful information for determining the timing of repairs to roads or road accessories.

[0210] The controller 60 may have a function of predicting the timing when repairs are likely to be required for multiple candidate repair locations, averaging the timing so that repair work is not concentrated, and displaying the recommended timing for repair work on the display 61. In this case, the controller 60 may determine the recommended timing for repair work so that the overall life cycle cost of the road is minimized.

[0211] The controller 60 may receive the determination basis data in real time from the data collecting device 1. The controller 60 analyzes the determination basis data received in real time. If it is determined that recollection is necessary, a signal urging recollection may be transmitted to the data collection device 1. For example, if the driving speed at the point where it is determined that unevenness exists is outside the range of a specified speed, a signal urging the driver of the vehicle equipped with the data collection device 1 to drive again at the specified speed may be transmitted.

[0212] [First Modification of the Second Embodiment] Next, a road condition evaluation assisting device 50 according to a first modified example of the second embodiment will be described with reference to FIGS. 16A and 16B.

[0213] In the second embodiment, the road administrator designated specific points where image data should be displayed, but in the first modified example of the second embodiment, the controller 60 has a function of extracting points where unevenness occurs.

[0214] 16A is a diagram showing an example of a main menu screen displayed on the display 61 (FIG. 12) by the controller 60. Within the menu screen, an uneven point extraction button 76 is displayed for instructing extraction of uneven points. By selecting the uneven point extraction button 76, the road administrator causes the controller 60 to execute processing to extract uneven points.

[0215] When the unevenness point extraction button 76 (FIG. 16A) is selected, the controller 60 extracts a time waveform suggesting that the vehicle has passed over an unevenness in the road surface from the time waveform of the acceleration data stored in the database 65. For example, it is preferable to compare the peak value of the time waveform of the acceleration data with a judgment threshold, and extract a time waveform including a peak value exceeding the judgment threshold as a time waveform suggesting that the vehicle has passed over an unevenness in the road surface.

[0216] 16B is a diagram showing an example of an image displayed on the display 61 by the controller 60. Time waveforms suggesting that unevenness was passed over at different dates and positions are extracted, and the extracted acceleration time waveforms, as well as the date and time data and position data corresponding to the peaks of the time waveforms, are displayed. In addition, a time series display button 77A for instructing time series display and a detailed inspection registration button 77B for registering the point as a target for continued observation are displayed.

[0217] When a road administrator designates one of the multiple acceleration time waveforms and selects the time series display button 77A, the controller 60 causes the display 61 to display in chronological order the time waveforms of acceleration data acquired at other dates and times at the point where the peak of the designated time waveform was detected, as well as image data, etc. The image to be displayed is similar to the image shown in Fig. 13, for example.

[0218] When the road administrator designates one of the multiple acceleration time waveforms and selects the detailed inspection registration button 77B, the controller 60 registers the point where the peak of the designated time waveform appears as a detailed inspection target point. The controller 60 stores information indicating the point registered as a detailed inspection target point in, for example, the external storage device 66.

[0219] When the road administrator operates the input device 62 to input a command to display a list of detailed inspection target points, the controller 60 displays information about the detailed inspection target points in a list format on the display 61. When the road administrator selects one point from the displayed detailed inspection target points, the controller 60 displays the date and time data information, image data image, and time waveform of acceleration data for the selected detailed inspection target point in chronological order on the display 61, as shown in Fig. 13 .

[0220] Furthermore, the controller 60 may store information specifying the detailed inspection target point, such as location data. The data collection device 1 (FIGS. 1 and 2) has a function of transferring the data to the data collection device 1. The data specifying the detailed inspection target points may be transferred, for example, via the SD card 22. Alternatively, the data may be transferred via the data communication network 40 (FIG. 12).

[0221] The data collection device 1 that has acquired the information specifying the detailed inspection point may have a function of notifying the driver when the vehicle approaches the detailed inspection point. For example, it may output a voice message from the speaker 16 (FIG. 2) saying, "The detailed inspection point is 1 km ahead. Please perform a detailed inspection."

[0222] Next, the excellent effects of the first modification of the second embodiment will be described. In the first modification of the second embodiment, points where unevenness is likely to exist on the road surface can be extracted without human intervention based on the acceleration data stored in the database 65. This makes it possible to process a huge amount of acceleration data.

[0223] In the first modification of the second embodiment, unevenness is detected based on the peak value of the time waveform of the acceleration data. However, a time waveform suggesting that the vehicle has passed over an uneven road surface may be extracted based on the shape of the time waveform, the magnitude relationship of acceleration in the three directions (front / back, left / right, up / down), etc. For example, when the vehicle is suddenly braked, a large acceleration occurs in the front / back direction. When the driver performs an abrupt steering operation, a large acceleration occurs in the left / right direction. In contrast, when the vehicle passes over an uneven road surface, a large acceleration occurs in the up / down direction. The controller 60 may extract a point where an uneven road surface is likely to exist by utilizing the difference in the direction of the generated acceleration. Furthermore, the time waveform of acceleration data corresponding to sudden braking, abrupt steering, etc. changes more gradually than the time waveform of acceleration data corresponding to passing over an uneven road surface. The controller 60 may extract a point where an uneven road surface is likely to exist by utilizing the difference in the shape of the time waveform.

[0224] It is particularly preferable for the controller 60 to use information other than acceleration data to extract a time waveform that suggests that the vehicle has passed over an uneven road surface. The controller 60 may use, for example, the vehicle's traveling speed as information other than acceleration data. For example, a peak appears in the time waveform of the acceleration data due to the impact of opening or closing a vehicle door, but the traveling speed at this time is approximately zero. By using the traveling speed data in combination to determine whether the vehicle has passed over an uneven road surface, it is possible to exclude the peak in the time waveform of the acceleration data that occurs due to the opening or closing of the door from the detection target.

[0225] When the controller 60 detects a point where unevenness is likely to exist, if no peak is detected in the waveform of the acceleration data at the same point on the dates before and after the point, it is advisable not to extract the point as an unevenness occurrence point, since such a point is likely to be a location where unevenness has temporarily occurred due to a small obstacle such as a pebble.

[0226] The controller 60 may have a function of determining the vehicle inclination based on the angular velocity data and detecting points where an abnormal inclination occurs on the road surface. The points where the angular velocity data is analyzed may be determined based on the underground conditions of the road. For example, the points where the angular velocity data is analyzed may be determined based on information such as the existence of a large underground cavity during subway construction. By performing a process to detect the occurrence of an abnormal inclination on the road surface while taking into account the underground conditions of the road, the amount of angular velocity data to be analyzed can be reduced.

[0227] The controller 60 may display, within the image shown in FIG. 13, date and time information of events that are expected to cause significant changes in the image, such as road cleaning or snow removal work. There will be a significant difference in the images collected before and after road cleaning, or before and after snow removal work. If a road administrator observes the images without knowing whether cleaning or snow removal work has been performed, he or she will be unable to distinguish between the two images. There is a concern that this could lead to incorrect judgments about whether repairs are necessary. By letting road managers notice the display of information such as the date and time of road cleaning and the date and time of snow removal work, it is possible to prevent incorrect judgments about whether repairs are necessary.

[0228] [Second Modification of the Second Embodiment] Next, a road condition evaluation assisting device 50 according to a second modified example of the second embodiment will be described with reference to FIGS. 17A and 17B.

[0229] In the second embodiment, the judgment basis data was obtained from a single data collection device 1 (Figures 1A, 1B, and 2) mounted on a specific vehicle, such as a road maintenance company's work vehicle, but in the second variant, the judgment basis data is obtained from data collection devices 1 mounted on multiple vehicles.

[0230] 17A is a diagram showing an example of the data structure of the determination basis data. Date and time data, traveling speed data, position data, acceleration data, and angular velocity data are stored in database 65 in association with vehicle type data. Furthermore, the image data shown in FIG. 3B is also associated with the vehicle type data.

[0231] Fig. 17B is a diagram showing the correspondence relationship between vehicle types and conversion coefficients. This correspondence relationship is stored in advance in, for example, the external storage device 66. In the example shown in Fig. 17B, vehicle types A01, A02, and B01 are associated with conversion coefficients x1, x2, and x3, respectively.

[0232] Even if the shape and depth of road surface irregularities are the same, the magnitude of acceleration measured by the data collection device 1 will differ depending on the vehicle in which the data collection device 1 is installed. In a second modification of the second embodiment, the controller 60 has a function of normalizing acceleration data based on vehicle type data. "Normalization" refers to converting acceleration data actually collected by the data collection device 1 installed in various vehicles into acceleration data that would have been obtained if the data collection device 1 had been installed in a standard vehicle. The acceleration data to be converted may be, for example, a feature quantity characterizing the time waveform of the acceleration data. The feature quantity characterizing the time waveform of the acceleration data may be, for example, the height of peaks appearing in the time waveform or the distribution of frequency components of the time waveform. The controller 60 normalizes the feature quantity based on the vehicle type data, for example, by multiplying the feature quantity of the time waveform of the actually measured acceleration data by a conversion coefficient for the vehicle type in which the data collection device 1 that acquired the acceleration data is installed.

[0233] By normalizing the acceleration data acquired by the data collection device 1 mounted on various vehicles based on the vehicle type data, it becomes possible to compare the acceleration data acquired by the data collection device 1 mounted on multiple vehicles of different vehicle types.

[0234] The conversion coefficient can be calculated in advance by mounting the data collection device 1 on various vehicles, driving them over uneven surfaces, and collecting acceleration data.

[0235] The magnitude of acceleration data acquired when traveling over bumps may be affected by weather. For example, tire temperature and suspension temperature differ between summer and winter, and these parameters are thought to affect the acceleration data. For example, it is advisable to normalize the acquired acceleration data based on the air temperature at the time of data acquisition. This makes it possible to compare acceleration data acquired in summer with acceleration data acquired in winter.

[0236] [Third Modification of the Second Embodiment] Next, a road condition evaluation assistance device 50 according to a third modified example of the second embodiment will be described with reference to FIG. 18. In the second modified example of the second embodiment, a conversion factor is set for each vehicle type as shown in FIG. 17B. In contrast, in the third modified example of the second embodiment, a conversion factor is set for each vehicle type and each traveling speed. The controller 60 has a function of normalizing acceleration data based on the vehicle type and traveling speed. "Normalization" means converting acceleration data actually collected at various different traveling speeds into acceleration data that would have been obtained if the vehicle had been traveling at a standard speed. The standard speed may be, for example, the legal speed of the route being traveled, or a speed slightly slower than the legal speed.

[0237] 18 is a diagram showing an example of the correspondence relationship between vehicle type, traveling speed, and conversion coefficient. For example, a conversion coefficient x11 is associated with vehicle type A01 and traveling speed v1. The controller 60 normalizes the acceleration data by multiplying the feature amount of the time waveform of the acceleration data acquired when the data collection device 1 is mounted on a vehicle of vehicle type A01 and traveling at traveling speed v1 by the conversion coefficient x11.

[0238] Even if the shape and depth of unevenness on the road surface are the same, the magnitude of acceleration measured by the data collection device 1 will differ if the traveling speed of the vehicle equipped with the data collection device 1 differs. The controller 60 normalizes the acceleration data based on the vehicle type and traveling speed, making it possible to compare acceleration data collected at different traveling speeds.

[0239] The conversion coefficient can be calculated in advance by mounting the data collection device 1 on a vehicle and collecting acceleration data while passing over uneven surfaces at various travel speeds.

[0240] When the actual traveling speed is significantly different from the standard speed, it may be difficult to determine an appropriate conversion coefficient. Such traveling speeds are not associated with conversion coefficients in the correspondence table shown in Fig. 18. The controller 60 may exclude acceleration data obtained at traveling speeds for which no appropriate coefficients are associated from the determination of the presence or absence of unevenness.

[0241] [Fourth Modification of the Second Embodiment] Next, a road condition evaluation assisting device 50 according to a fourth modified example of the second embodiment will be described with reference to FIGS. 19A to 20. FIG.

[0242] In a fourth modification of the second embodiment, the controller 60 has a function of determining the degree to which road surface repair is necessary based on image data stored in the database 65, and registering the determination result in the database 65 in association with image data, acceleration data, position data, date and time data, etc. When the road administrator operates the input device 62 (FIG. 12) to instruct execution of a process for determining the degree to which repair is necessary, the controller 60 calculates the degree to which repair is necessary at various points by analyzing the image data stored in the database 65. The degree to which repair is necessary may be calculated using artificial intelligence technology such as deep learning.

[0243] The controller 60 displays the position data of the point where it is determined that repair is necessary on the display 61 (FIG. 12) so that the degree of repair necessity can be recognized.

[0244] FIG. 19A is a diagram showing an example of an image displayed on the display 61. Information specifying the points determined to require repair and the degree of need for repair are arranged and displayed in a list format on the display 61. As information specifying the points determined to require repair, for example, road names, addresses, and latitude and longitude information may be displayed. Furthermore, an image display button 78 is displayed corresponding to each point. In FIG. 19A, for example, repair is required at point △△△ on XX street. This shows an example in which an abnormality has occurred and the degree of need for repair is 10. When the road administrator selects the image display button 78 corresponding to this point, the controller 60 displays an image of the point △△△ on XX street.

[0245] 19B is a diagram showing an example of an image of point △△△ on XX street. From this image, irregularities 93 can be seen on the road surface.

[0246] Fig. 20 is a diagram showing another example of an image displaying locations determined to require repair. The controller 60 displays a map including multiple locations determined to require repair, and displays an icon on the map at each location determined to require repair, indicating the degree to which the repair is required. For example, a number in a circle may be used as the icon. The number generated in the circle indicates the degree to which the repair is required. The locations requiring repair may be colored differently depending on the degree to which the repair is required.

[0247] Next, the excellent effects of the fourth modification of the second embodiment will be described. In the fourth modification of the second embodiment, the degree of need for repairs to roads and road accessories can be determined without manual intervention, making it possible to handle a huge amount of image data. By viewing the information displayed on the display 61, a road administrator can easily find out which of multiple locations needing repairs has the highest urgency.

[0248] Using data from the road maintenance management plan, it is a good idea to exclude locations where the road itself is close to due for renewal (repair) from the list of locations that need repair.

[0249] [Fifth Modification of the Second Embodiment] Next, a road condition evaluation assisting device 50 according to a fifth modified example of the second embodiment will be described with reference to FIG.

[0250] In the fourth variant of the second embodiment, the controller 60 displays the location information of points requiring repair on the display 61 so that the degree to which repair is required at the point can be recognized, whereas in the fifth variant, the controller 60 displays the points requiring repair on the display 61 so that the extent to which the life cycle cost of the road will be reduced by the repair can be recognized.

[0251] FIG. 21 is a diagram showing an example of an image displayed on the display 61. Information identifying points determined to require repair is associated with the reduction in the life cycle cost of the road due to the repair, and the information is sorted by the reduction in life cycle cost and displayed on the display 61 in a list format. Information identifying points determined to require repair may include, for example, the road name, address, and latitude and longitude information. Furthermore, an image display button 79 is displayed corresponding to each point. For example, FIG. 21 shows that the reduction in the life cycle cost of the road due to repair at point △△△ on XX street is 20,000,000 yen. When the road administrator selects the image display button 79 corresponding to this point, the controller 60 causes the display 61 to display an image of this point.

[0252] Next, the excellent effect of the fifth modified example of the second embodiment will be explained. The road administrator can easily find the repair locations that will result in the greatest reduction in life cycle costs by looking at the image displayed on the display 61. By repairing the locations in order of the greatest reduction in the life cycle costs of the road as a whole, it is possible to suppress the increase in the life cycle costs of the road.

[0253] For example, it is useful to detect when the degree of damage to road accessories is rapidly increasing, and repairs can be made while the damage is still minor, thereby preventing repair costs from increasing. It can be controlled.

[0254] While Fig. 21 shows an example in which points determined to require repairs are displayed in a list format together with the extent of reduction in life cycle cost, as shown in Fig. 20, a map may be displayed on the display 61, and icons corresponding to the extent of reduction in life cycle cost may be displayed at points requiring repairs. Alternatively, instead of icons, points on the road requiring repairs may be colored according to the extent of reduction in life cycle cost.

[0255] [Sixth Modification of the Second Embodiment] Next, a road condition evaluation assisting device 50 according to a sixth modified example of the second embodiment will be described with reference to FIGS. 22A to 22C.

[0256] In a sixth modified example of the second embodiment, the controller 60 has a function of determining whether or not road accessories need repair by analyzing image data stored in the database 65. When a road administrator operates the input device 62 (FIG. 12) to instruct execution of a process for determining whether or not road accessories need repair, the controller 60 analyzes the image data stored in the database 65 to determine whether or not road accessories at various points need repair. The controller 60 displays the result on the display 61. Furthermore, the controller 60 registers the determination result in the database 65 in association with image data, acceleration data, position data, date and time data, etc.

[0257] Examples of road accessory repair targets include deterioration of road markings, poor road drainage after rain, corrosion or deformation of road accessories such as road signs and guardrails, signs of landslides, and damage caused by roadside trees. AI technology, such as deep learning, can be used to determine whether repairs are necessary. If the color of a portion of a guardrail differs from the surrounding color, for example, if a portion of a white guardrail has turned brown, it can be determined that corrosion is likely occurring in that area. In this way, it is possible to determine whether road accessory repairs are necessary based on spatial color changes.

[0258] Road inspections include an initial inspection carried out when a road is opened to traffic, a daily inspection carried out as part of normal operations, a periodic inspection carried out about once a year, and an emergency inspection carried out after a disaster occurs, etc. The controller 60 may carry out inspections of the daily inspection items based on the basic judgment data accumulated in the database 65, for example.

[0259] Furthermore, the controller 60 has a function of determining whether or not repairs to road accessories are necessary based on the differences between a plurality of image data collected at the same location on different dates and times.

[0260] 22A to 22C are diagrams showing examples of image data collected at the same location on different dates and times. In the example shown in FIG. 22A, a speed limit sign 95 is visible without being obscured by roadside trees. In the example shown in FIG. 22B, roadside trees 96 have grown since the time of FIG. 22A, so part of the speed limit sign 95 is obscured by the roadside trees 96, but it is still possible to confirm that the maximum speed is 40 km / h. In the example shown in FIG. 22C, roadside trees 96 have grown even further, so most of the speed limit sign 95 is obscured by the roadside trees 96, making it impossible to confirm that the maximum speed is 40 km / h.

[0261] By calculating the difference between the images of Figures 22A, 22B, and 22C, the controller 60 can detect the growth of the road trees 96 and the way the speed limit sign 95 is gradually hidden by the road trees 96.

[0262] It was also observed that small scratches on road accessories grow larger over time. In this way, it is possible to determine whether or not repairs to road accessories are necessary based on changes in images over time.

[0263] Next, we will explain the excellent effects of the sixth modified example of the second embodiment. In the sixth modified example of the second embodiment, it is possible to determine whether or not road accessories need repair without manual intervention. This makes it possible to process a huge amount of image data.

[0264] The controller 60 can recognize the rate at which deterioration of road accessories progresses based on the difference in image data. By taking into account the rate at which deterioration of road accessories progresses, it is possible to more appropriately determine whether repairs are necessary for road accessories. For example, it becomes possible to more appropriately determine whether repairs are necessary based on the rate at which road markings fade, the rate at which scratches on road accessories grow, the rate at which roadside trees grow, etc.

[0265] Regarding road accessories, it is a good idea to decide in advance which areas to focus on in analyzing to determine whether repairs are necessary. For example, guardrails, guard cables, guardrail support bases and anchors, guardrail bolts and nuts, etc. can be registered as key analysis areas. For example, it is a good idea to have artificial intelligence using deep learning technology focus on analyzing images of key analysis areas and determine whether repairs are necessary.

[0266] By analyzing image data acquired after the rain has stopped, it is possible to easily detect poor drainage function of the road, the occurrence of ruts, etc. Whether or not it has rained can be determined by the operation of the wipers captured in the image data. For example, it is possible to determine that it has rained if the wipers change from an operating state to a stopped state.

[0267] After a sudden downpour, rainwater on the road surface may not be able to be drained even if the drainage function is normal. Therefore, after a sudden downpour, it is not necessary to determine whether the drainage function is normal. Whether a sudden downpour is occurring can be determined based on the wiper operating speed, the degree of smokyness in the distant scenery, etc.

[0268] It is advisable to store as big data the image data, date and time data, and location data collected from many data collection devices 1. Based on this big data, it is possible to know the time periods and areas where it rained.

[0269] It is a good idea to register locations where disasters such as landslides have occurred in the past as data collection locations 85 (Figure 9C). This allows images of disaster-prone locations to be continuously accumulated. By observing these images over time, it becomes possible to detect signs of disasters such as landslides in advance.

[0270] Furthermore, by evaluating the images in chronological order, it is possible to detect whether or not liquefaction has occurred due to an earthquake. When liquefaction occurs, objects with relatively low specific gravity rise up, while objects with high specific gravity sink. The controller 60 should have the function of comparing current images with past images to detect objects that have risen up and objects that have sunk, thereby determining whether or not liquefaction has possibly occurred. In particular, it is advisable to determine whether or not liquefaction has occurred by comparing images taken before and after the earthquake.

[0271] In particular, it is advisable to determine areas where liquefaction is likely to occur from information from the Advanced Land Observing Satellite "Daichi" and register these areas as targets for image data collection (data collection points 85 in Figure 9C).In addition to information from the Advanced Land Observing Satellite, areas where liquefaction is likely to occur can also be determined based on a groundwater level height map.

[0272] Furthermore, it is advisable to register areas where landslides are likely to occur as targets for image data collection based on information from Advanced Land Observing Satellites, etc.

[0273] [Seventh Modification of the Second Embodiment] Next, a road condition evaluation assisting device 50 according to a seventh modification of the second embodiment will be described with reference to FIGS. 23A and 23B.

[0274] In a seventh variant of the second embodiment, the road condition evaluation support device 50 receives, via a communication device 64 (Figure 12), determination basis data that serves as the basis for determining whether or not repairs are required for roads or road accessories, and identification data for identifying the data collection devices 1 from multiple data collection devices 1.

[0275] 23A is a schematic diagram of a system including a road condition evaluation assistance device 50 according to a seventh modification of the second embodiment, and a plurality of vehicles 51 equipped with a data collection device 1. The road condition evaluation assistance device 50 communicates data with the data collection devices 1 equipped in each of the plurality of vehicles 51 via a data communication network 40 and a wireless base station 55. Each of the data collection devices 1 transmits the collected judgment basis data and identification data for identifying the data collection device to the road condition evaluation assistance device 50. A controller 60 of the road condition evaluation assistance device 50 receives the judgment basis data and the identification data from each of the data collection devices 1, associates these data, and stores them in a database 65.

[0276] Furthermore, the controller 60 assigns points to each data collecting device 1 according to the received basic judgment data, and stores the accumulated points in the external storage device 66, for example.

[0277] 23B is a diagram showing an example of the cumulative value of points assigned to each data collection device 1. For example, 1,000 points are assigned to the data collection device 1 identified by the identification data ABC00001.

[0278] Next, the excellent effects of the seventh modified example of the second embodiment will be described. In the seventh modified example of the second embodiment, the controller 60 receives data via the communication device 64 (FIG. 12), which eliminates the need to spend time obtaining data. Furthermore, since data is received from multiple data collection devices 1, a larger amount of data can be collected compared to obtaining data from a single data collection device 1. For example, it is preferable to install a data collection device 1 not only in the work vehicles of the road management company, but also in the private vehicles of employees of the road management company, to obtain this data. It is even more preferable to install a data collection device 1 in unspecified general vehicles to obtain this data.

[0279] Furthermore, in the seventh modification of the second embodiment, it is possible to provide benefits to owners of data collection devices 1 according to the points awarded to each data collection device 1. This motivates owners of vehicles equipped with a data collection device 1 to transmit more data to the road condition evaluation support device 50. It also motivates owners of vehicles not equipped with a data collection device 1 to install a data collection device 1. This makes it possible to accumulate a larger amount of data in the road condition evaluation support device 50. The benefits may be, for example, a discount on tolls on toll roads, a discount on the purchase of products at roadside stations, etc.

[0280] Points may be awarded to groups of ordinary citizens such as neighborhood associations and local community associations, and groups that accumulate a large number of points may be given a reward for providing information.

[0281] The controller 60 may have a function of transmitting information specifying an area that requires emergency inspection due to a disaster or the like (an area requiring emergency inspection) to the plurality of data collection devices 1. The area requiring inspection may be input by the road administrator by operating the input device 62.

[0282] The data collection device 1, which has received information specifying the area requiring urgent inspection, has a function of collecting image data and transmitting it in real time to the road condition evaluation support device 50 when the vehicle is located in the area requiring urgent inspection. The road condition evaluation support device 50 has a function of storing the image data received from the data collection device 1 in a database 65 and displaying it on a display 61 in response to an instruction from a road administrator.

[0283] If a vehicle equipped with the data collection device 1 is in an area requiring emergency inspection, the road administrator can obtain images of the area requiring emergency inspection without going to the area, which enables a quick response to disasters.

[0284] It is even more preferable to mount the data collection device 1 on a flying device such as a multicopter operated by a general user, and transmit aerial images of areas requiring urgent inspection to the road condition evaluation support device 50.

[0285] It is advisable to include data indicating vehicle classifications (vehicle classification data), such as standard vehicles, medium-sized vehicles, and large vehicles, in the data transmitted from the data collection device 1 to the road condition evaluation support device 50. It is said that the impact of large vehicles on pavement is approximately 10,000 times greater than the impact of small vehicles. If the ratio of vehicle classifications of vehicles passing through a road differs, the degree of deterioration of the road surface will also differ. If the data collection device 1 is installed in almost all vehicles in Japan, it will be possible to calculate the number of vehicles passing through each road by vehicle classification based on the data obtained from the data collection device 1.

[0286] When estimating the impact of passing vehicles on road surfaces, it is a good idea to reflect the number of vehicles of each vehicle category. For example, the impact on road surfaces can be estimated by converting the passage of one large vehicle into the equivalent of 10,000 standard-sized vehicles. The impact on road surfaces that reflects the tolls for each vehicle category is useful information for predicting when road repairs are necessary.

[0287] Various aspects of the present invention have been described above using examples and modifications. However, it should be noted that the descriptions of these examples and modifications are provided to aid in understanding the present invention, and are not intended to limit the technical scope of the present invention. The technical scope of the present invention is not limited to the configurations explicitly described in the specification, and this specification also discloses other inventions obtained by combining various aspects of the present invention described. The applicant of this application has specified the configuration of the invention for which a patent is sought in the appended claims, but intends to include in the claims in the future inventions that are not currently specified in the claims but include at least one component disclosed in this specification.

[0288] The present invention is not limited to the configuration described in the "Mode for Carrying Out the Invention" above. It is possible to arbitrarily select and combine elements included in each of the above-described embodiments and variations to create a new invention. Furthermore, any element of each embodiment or variation can be arbitrarily combined with any element described in the "Summary of the Problem" or with any element embodying any element described in the "Summary of the Problem" to create a new invention. The applicant of the present application intends to obtain rights to these new inventions through amendments to the present application or divisional applications, etc.

[0289] For example, this specification discloses a program invention for causing a mobile terminal to realize the functions of the data collection device 1 according to the first embodiment and various modifications of the first embodiment. Furthermore, this specification discloses a program invention for causing a computer to realize the functions of the road condition evaluation support device 50 according to the second embodiment and various modifications of the second embodiment. .

[0290] Furthermore, this specification discloses an invention of a road condition evaluation support system including a data collection device 1 according to a first embodiment and various modifications of the first embodiment, and a road condition evaluation support device 50 according to a second embodiment and various modifications of the second embodiment. Furthermore, this specification discloses an invention of a device having both the functions of the data collection device 1 according to the first embodiment and various modifications of the first embodiment, and the functions of the road condition evaluation support device 50 according to the second embodiment and various modifications of the second embodiment. Such a device may be realized, for example, by a tablet terminal or the like that is detachably mounted on a vehicle. [Explanation of symbols]

[0291] 1. Data collection equipment 3. Windshield 4. Rearview mirror 5 cigarette lighter socket 6 Power Cable 10 SD card slot 11 Display 12 Operation buttons 13 Joint Rail 14 GPS receiver 15 Camera 16 speakers 17 Acceleration Sensor 18 Gyro sensor 19 SD card reader 20 Controller 20a CPU 20b ROM 20c RAM 21 Communication Circuit 22 SD card 31, 32 Image data 40 Data Communication Network 50 Road condition evaluation support device 51 vehicles 60 Controller 60a CPU 60c RAM 61 Display 62 Input Device 63 SD card reader 64 Communication Equipment 65 databases 66 External storage device 70 images 71 Information indicating the date and time the image was acquired 72 Time waveform of acceleration data 73 Identification mark indicating the time of acquisition of the displayed image 75 Rating button 76 Unevenness point extraction button 77A Time series display button 77B Detailed inspection registration button 78, 79 Image display button 80 Data Collection Section 81 Starting point of data collection section 82 End of data collection section 83 Vehicle Depot 85 data collection points 86 Areas surrounding the data collection points 87 Routes 90 Broken guardrail 91 road signs 92 Street trees 93 Unevenness of the road surface 94 Aggregate-applied area 95 Maximum speed limit sign 96 Street trees

Claims

1. A data collection device that acquires data about a road surface from a traveling vehicle, a first camera for capturing an image of the front; an acceleration sensor that measures the acceleration of the vehicle; a control unit that acquires and processes image data acquired by the first camera and acceleration data acquired by the acceleration sensor; a recording unit that records the image data and the acceleration data, the control unit has a function of, when detecting an event indicating passing over an uneven road surface during traveling based on the acceleration data, associating and recording the acceleration data with image data of the road surface ahead captured by the first camera during a predetermined period before and after the event; Furthermore, a second camera is provided to capture an image of the area directly below or behind the vehicle, and road surface image data of the area directly below or behind the vehicle acquired by the second camera is also recorded, making it possible to observe the road surface condition or road accessories from different directions based on the image. A data collection device comprising:

2. 2. The data collection device according to claim 1, wherein the control unit sets a second judgment threshold according to the type of road surface or the type of road, and excludes from detection targets continuous impacts caused by block pavement or impacts caused by joints in concrete pavement as factors other than deterioration that do not require repair.

3. 3. The data collection device according to claim 2, wherein the control unit sets different second determination thresholds for ordinary roads and expressways.

4. 4. A data collection device according to claim 1, wherein the control unit applies a first judgment threshold and a second judgment threshold to the acceleration data, judges that a collision has occurred when the measured acceleration is equal to or greater than the first judgment threshold, and judges that an unevenness in the road surface has been passed over when the measured acceleration is equal to or greater than the second judgment threshold and less than the first judgment threshold, and the recording unit records image data and acceleration data acquired during a predetermined period before and after the event for which the judgment was made.

5. 5. The data collection device according to claim 4, wherein the first and second determination thresholds are applied based only on a component of the acceleration data in the Z-axis direction.

6. 6. The data collection device according to claim 4 or claim 5, wherein the recording unit records at least one of the image data acquired by the first camera and the second camera and the acceleration data in association with a predetermined period before and after the event.

7. A program that causes a computer to function as a control unit of the data collection device according to any one of claims 1 to 6.

Images