JP2019144041A - Self-position estimation device - Google Patents

Abstract

To provide a self-position estimation device which allows a movable body such as an automatic driving vehicle to travel according to weather.SOLUTION: A vehicle controller 3 includes: a sensor 4 for acquiring point group information indicating the situation of the outside of an automatic driving vehicle C; a communication part 32 for acquiring weather information; and a control part 31 for acquiring map data 33a including weather feature information 13b, so as to estimate a self-position of the automatic driving vehicle C on the basis of the point group information. The control part 31 evaluates the accuracy of the point group information on the basis of the map data 33a and the weather information.SELECTED DRAWING: Figure 1

Description

  The present invention relates to a self-position estimation apparatus that estimates the self-position of a moving object.

  In the automatic driving of a moving body such as a vehicle that is currently being developed, the situation around the own vehicle is obtained by various sensors such as lidar (LiDAR: Light Detection And Ranging), and based on the obtained surrounding situation. To estimate its own position (self-position). Specifically, self-position estimation is often performed using a predetermined feature (especially road surface information) as a surrounding situation (see, for example, Patent Document 1).

JP 2017-78607 A

  When self-position estimation is performed using the road surface information described above, information that can be acquired by a sensor such as a lidar may change depending on weather conditions such as rain and snow. For example, if water accumulates on a road marking or the like drawn on the road surface due to rain, the road marking may not be detected by a sensor such as a lidar.

  As described above, it is desired that a mobile body such as an autonomous driving vehicle can travel according to the weather.

  An example of a problem to be solved by the present invention is to enable traveling according to the weather in a moving body such as an autonomous driving vehicle.

  In order to solve the above-mentioned problem, the invention described in claim 1 is a map including a first acquisition unit that acquires an external situation signal indicating an external situation of a moving object, and information indicating characteristics affected by the weather. A second acquisition unit that acquires data; a third acquisition unit that acquires weather information indicating a weather state; and a self-position estimation unit that estimates a self-position of the mobile body based on the external situation signal. The self-position estimation unit is characterized by evaluating the accuracy of the external situation signal based on the map data and the weather information.

  The invention according to claim 6 is a self-position estimation method executed by a self-position estimation apparatus that estimates a self-position of a moving body, and obtains an external situation signal indicating a situation outside the moving body. A second acquisition step of acquiring map data including information indicating characteristics affected by the process, weather, a third acquisition step of acquiring weather information indicating the state of the weather, and based on the external status signal A self-position estimation step of estimating the self-position of the mobile body, wherein the self-position estimation step evaluates the accuracy of the external situation signal based on the map data and the weather information.

  The invention according to claim 7 is characterized in that the self-position estimation method according to claim 6 is executed by a computer.

  The invention according to claim 8 is a map data structure of map data used for self-position estimation, wherein the map data includes information indicating characteristics relating to weather.

  The invention described in claim 10 is characterized in that the map data structure described in claim 8 or 9 is stored.

It is a schematic block diagram of the system which has the map data storage device concerning the Example of this invention. It is a function block diagram of the server apparatus shown by FIG. It is a functional block diagram of the vehicle control apparatus shown by FIG. It is the figure which showed the image of the weather characteristic information. It is the figure which showed the part concerning the weather characteristic information of the map data structure of the map data shown by FIG. It is a flowchart of the self-position estimation method in the control part shown by FIG. It is a flowchart concerning the modification of the self-position estimation method shown by FIG. It is a flowchart concerning the modification of the self-position estimation method shown by FIG.

  Hereinafter, a self-position estimation apparatus according to an embodiment of the present invention will be described. A self-position estimation apparatus according to an embodiment of the present invention acquires a first acquisition unit that acquires an external situation signal that indicates an external situation of a moving object, and map data that includes information indicating characteristics affected by weather A second acquisition unit, a third acquisition unit for acquiring weather information indicating weather conditions, and a self-position estimation unit for estimating the self-position of the mobile body based on an external situation signal, The unit evaluates the accuracy of the external situation signal based on the map data and the weather information. By doing in this way, it can drive | work by performing the self-position estimation according to the weather which the weather information which the 2nd acquisition part acquired by the information which shows the characteristic received by the weather contained in map data according to the weather.

  Further, the self-position estimation unit may not use the signal for self-position estimation when the evaluation of the external situation signal is lower than a predetermined reference. By doing so, it is judged that the reliability of the self-position estimation is lowered due to the influence of the weather, and the external situation signal is not used for the self-position estimation, thereby preventing an erroneous estimation of the self-position. it can.

  In the map data, information indicating characteristics affected by weather may be set for each section subdivided into a predetermined size. By doing so, for example, since the features affected by the weather are subdivided and set, the map data can more accurately reflect the features of the area indicated by the section.

  In addition, the information indicating the characteristics affected by the weather may include at least one of information that water is likely to accumulate due to rain, snow is likely to accumulate due to snow, and freezing. By doing in this way, it is possible to perform self-position estimation corresponding to the case where there is a high possibility that the road surface state will change due to rainfall, snowfall or freezing.

  In addition, a route search unit that searches for a route to a predetermined destination based on the map data acquired by the second acquisition unit may be provided, and the route search unit may further search for a route based on weather information. . By doing in this way, it becomes possible to perform a route search according to the weather.

  In addition, the self-position estimation method according to an embodiment of the present invention includes a first acquisition step of acquiring an external situation signal indicating an external situation of a mobile object, and map data including information indicating characteristics affected by weather A second acquisition step of acquiring the weather information, a third acquisition step of acquiring the weather information, and a self-position estimation step of estimating the self-position of the mobile body based on the external situation signal, Evaluate the accuracy of external status signals based on map data and weather information. By doing in this way, it can drive | work by performing the self-position estimation according to the weather which the weather information which the 2nd acquisition part acquired by the information which shows the characteristic received by the weather contained in map data according to the weather.

  Further, the above-described self-position estimation method may be executed by a computer. By doing in this way, self-position estimation according to the weather which the weather information which the 3rd acquisition part acquired by the information which shows the feature which receives the influence of the weather contained in map data using a computer can be performed. it can.

  A map data structure according to an embodiment of the present invention is a map data structure of map data used for self-position estimation, and the map data includes information indicating characteristics affected by weather. By doing in this way, the self-position estimation according to the weather can be performed by the information indicating the characteristics affected by the weather.

  Further, the map data may include information indicating the characteristics affected by the weather and information on a result of evaluating a signal indicating a situation outside the predetermined moving body based on the weather information. By doing in this way, when the situation that the feature could not be detected by a sensor such as a lidar mounted on a moving body such as a vehicle, the result of evaluating whether or not the situation was affected by the weather Can be included. Therefore, the history of weather and evaluation results can be accumulated, and information indicating characteristics affected by the weather can be effectively used with reference to this history.

  A map data storage device according to an embodiment of the present invention stores the map data structure described above. By doing in this way, the self-position estimation according to the weather can be performed by the information indicating the feature regarding the weather.

  A self-position estimation device, a map data structure, and a map data storage device according to an embodiment of the present invention will be described with reference to FIGS. As shown in FIG. 1, the vehicle control device 3 mounted on the autonomous driving vehicle C as a self-position estimation device can communicate with the server device 1 and the server device 2 via a network N such as the Internet. Yes.

  A functional configuration of the server device 1 as a map data storage device is shown in FIG. The server device 1 includes a control unit 11, a communication unit 12, and a storage unit 13.

  The control unit 11 functions as a CPU (Central Processing Unit) of the server device 1 and controls the server device 1 as a whole. In response to a request from the vehicle control device 3, the control unit 11 reads out map data of a necessary area from the map data 13 a stored in the storage unit 13 and distributes the map data to the vehicle control device 3 via the communication unit 12.

  The communication unit 12 functions as a network interface of the server device 1 and receives request information output from the vehicle control device 3. Further, the control unit 11 transmits the map data read from the map data 13 a to the vehicle control device 3.

  The storage unit 13 functions as a storage device such as a hard disk of the server device 1 and stores map data 13a. The map data 13a is a map that includes detailed information (lane network, position of features, etc.) that allows the autonomous driving vehicle C to travel autonomously. The map data 13a includes weather feature information 13b (see FIG. 5) as information indicating features affected by the weather. The weather feature information 13b will be described later.

  The autonomous driving vehicle C includes a vehicle control device 3 and a sensor 4. The vehicle control device 3 causes the autonomous driving vehicle C to autonomously travel (automatic driving) based on the result detected by the sensor 4 and the map data 33a of the vehicle control device 3.

  The server device 2 has the same functional configuration as the server device 1. In the server device 2, weather data 2 a that is information indicating the weather condition of each place is stored in the storage unit 13. The weather data 2a includes not only the weather condition but also information such as precipitation, snowfall, and temperature.

  FIG. 3 shows a functional configuration of the vehicle control device 3. The vehicle control device 3 includes a control unit 31, a communication unit 32, and a storage unit 33.

  As a result of detection by the sensor 4, the control unit 31 estimates the self-position of the autonomous driving vehicle C based on the weather data acquired from the server device 2 and the map data 33 a stored in the storage unit 33. And the control part 31 controls the handle | steering-wheel (steering device), accelerator, brake, etc. of the autonomous driving vehicle C, and makes the autonomous driving vehicle C drive autonomously. That is, the control unit 31 acquires the detection result (recognition result) of the sensor 4 as the external recognition unit. In addition, the control unit 31 requests the server device 1 to distribute map data in an area serving as a travel route via the communication unit 32, and the map data distributed from the server device 1 is stored in the storage unit 33 as map data 33a. As well as searching for a route to the destination.

  The communication unit 32 transmits the request information output by the control unit 31 to the server device 1. In addition, the map data distributed from the server device 1 and the weather data distributed from the server device 2 are received.

  The storage unit 33 as a map data storage device stores map data 33a. The map data 33a is a map that includes detailed information that allows the autonomous driving vehicle C to travel autonomously. The map data 33a includes weather feature information 13b as with the map data 13a.

  The sensor 4 is a sensor for recognizing the information of the own vehicle such as the own vehicle position and the surrounding environment (features present in the vicinity), and includes a camera, a lidar, a GPS (Global Positioning System) receiver, and the like . In addition to these sensors, an acceleration sensor for detecting the acceleration of the vehicle, a speed sensor for detecting the speed of the vehicle, or inertia for recognizing the posture (orientation, etc.) of the vehicle and correcting the acquired data of other sensors. You may provide a measuring device (IMU: Inertial Measurement Unit), a gyro sensor, etc.

  The camera included in the sensor 4 captures an image representing the external environment of the autonomous driving vehicle C. The lidar included in the sensor 4 discretely measures the distance to an object existing in the outside world, and recognizes the position, shape, and the like of the object as a three-dimensional point group. Information acquired by this rider is output as point cloud information. The GPS receiver included in the sensor 4 generates and outputs position information of latitude and longitude representing the current position of the vehicle.

  Next, the map data structure which has the weather characteristic information 13b concerning a present Example is demonstrated. As shown in FIG. 4, the weather feature information 13b included in the map data according to the present embodiment is subdivided into meshes (partitions) M having a predetermined size on the map. It shows the ease. FIG. 4 shows that water tends to accumulate as the hatching density increases. In FIG. 4, the mesh M exists also in the portion without hatching, but the display is omitted for easy understanding of the drawing (that is, the portion without hatching indicates an area where water does not easily collect). The shape of the mesh M is not limited to a square, but may be a rectangle or a polygon such as a hexagon or an octagon. Moreover, although the magnitude | size of the mesh M can be determined arbitrarily, the magnitude | size which can be utilized for the self-position estimation and route search of the autonomous driving vehicle C is desirable.

  In addition, as shown in FIG. 5, mesh ID, position information, ease of water accumulation, and the like are set for each mesh M as the weather feature information 13b. The position information may be an absolute position such as latitude and longitude, or a relative position (how many rows and columns) from a reference position where the absolute position is set. The ease of water accumulation is information indicating the ease of water accumulation during rainfall in the mesh M. This may be shown in stages such as large, medium, and small as shown in FIG. In the case of a numerical value, for example, a parameter such as a coefficient that can predict the amount of accumulated water by multiplying the amount of precipitation may be used.

  What is necessary is just to set this ease of water accumulation based on the performance in the area which the said mesh M concerned shows in the past. Alternatively, it may be set in consideration of the characteristics of the area such as topography and geology (many asphalt roads and buildings, or many unpaved roads, good drainage, etc.).

  If a puddle occurs due to rain, the road marking may not be correctly recognized by a rider or the like, and it may be determined that there is no road marking even though there is actually a road marking. Therefore, by having the weather feature information 13b as shown in FIGS. 4 and 5, it is possible to recognize that the detection result by the lidar or the like may be incorrect as will be described later. 4 and 5, the ease of water accumulation due to rain has been described, but the ease of snow accumulation and the ease of freezing due to snow may be added to the information of the mesh M in the same manner. Snow cover may also be unable to be correctly recognized by a lidar or the like because the road marking is hidden, and even if it is frozen, it may not be correctly recognized by a lidar or the like due to a change in reflectance or irregular reflection. Therefore, it is useful to set these pieces of information as the weather feature information 13b.

  Next, a method for estimating the self-position of the autonomous driving vehicle C (self-position estimation method) in the vehicle control device 3 having the above-described configuration will be described with reference to the flowchart of FIG. The flowchart shown in FIG. 6 is executed by the control unit 31. Moreover, you may comprise the flowchart of FIG. 6 as a self-position estimation program run with computers, such as CPU which comprises the control part 31. FIG.

  First, in step S <b> 101, the control unit 31 acquires a result (sensor information) detected by the sensor 4. This sensor information is, for example, point cloud information acquired by a lidar, image information taken by a camera, or the like. That is, in this embodiment, the point group information and the image information become an external situation signal indicating a situation outside the moving body, and the control unit 31 functions as a first acquisition unit. In the following description, the external status signal will be mainly described with point cloud information.

  Next, in step S <b> 102, the control unit 31 acquires map data 33 a from the storage unit 33. The map data acquired in this step may be map data around the current position of the autonomous driving vehicle C. That is, the control unit 31 functions as a second acquisition unit that acquires map data including weather feature information.

  Next, in step S <b> 103, the control unit 31 acquires weather data from the server device 2 via the communication unit 32. This weather data may be in a range corresponding to the map data acquired in step S102.

  Next, in step S104, the control unit 31 evaluates the result detected by the sensor 4. This evaluation is, for example, determining the reliability (accuracy) of road markings or the like included in the point cloud information acquired by the lidar. For example, if it is found that it is raining from the weather data acquired in step S103, the weather feature information corresponding to the position detected by the sensor 4 is read from the map data 33a. Next, from the amount of precipitation up to now and the ease of water accumulation shown in FIG. 5, it is estimated whether or not there is a water accumulation on the road surface. And when it is estimated that the puddle has occurred, the result detected by the sensor 4 is highly likely that the road marking or the like is not correctly detected due to the puddle, and the road included in the point cloud information. Evaluate that the reliability of markings is low. That is, the control unit 31 evaluates the accuracy of the point cloud information based on the map data 33a and the weather data.

  Referring to the example of FIG. 5, for example, when the amount of precipitation in the past hour is 10 mm or more and less than 30 mm, and the ease of water pooling is “large”, the mesh M has a water pool. It is highly likely that road markings included in the acquired point cloud information are not detected, and it is evaluated that the reliability of road markings included in the point cloud information is low. In other words, in this example, the predetermined criterion for evaluation is whether or not road markings can be detected, and there is a high possibility that road markings are not detected and the reliability of road markings is low. Is considered lower than a predetermined standard.

  This evaluation may be performed by calculating a numerical value. For example, it is possible to obtain a probability that a road marking or the like can be detected by a mathematical formula or a graph, and to evaluate that it cannot be detected if it is less than a predetermined probability.

  Next, in step S105, if the result of the evaluation in step S104 is equal to or greater than a predetermined reference, the control unit 31 performs self-test based on the road marking included in the sensor information acquired in step S101 in step S106. Perform position estimation. If it is an example mentioned above, it is a case where it is evaluated that the reliability of the road marking included in the point cloud information is high. In that case, the road marking and the map data 33a included in the sensor information acquired in step S101 are displayed. The self-position is estimated by matching the included road markings. That is, the control unit 31 functions as a self-position estimating unit that estimates the self-position of the autonomous driving vehicle C based on the point cloud information.

  On the other hand, if the result evaluated in step S104 is less than the predetermined reference in step S105, self-position estimation is performed in step S107 using, for example, road markings included in the point cloud information detected by the lidar. Absent. In this case, for example, self-position estimation is performed using features other than road markings. Alternatively, self-position estimation may be performed by another method such as GPS without performing self-position estimation by the lidar, or self-position estimation may not be performed at the position.

  Next, in step S108, the control unit 31 determines whether or not the autonomous driving vehicle C has arrived at the destination. If the autonomous driving vehicle C has arrived at the destination, the control unit 31 ends the flowchart. If not, the control unit 31 returns to step S101. The flowchart is executed again.

  In the above description of the flowchart, the road marking is described. However, the present invention is not limited to the road marking, and may be a manhole or the like as long as it is provided on the road.

  As is clear from the above description, step S101 functions as the first acquisition process, step S102 functions as the second acquisition process, step S103 functions as the third acquisition process, and step S106 functions as the self-position estimation process.

  Next, a modification of the flowchart shown in FIG. 6 will be described. The flowchart in FIG. 6 shows the self-position estimation operation during the traveling of the autonomous driving vehicle C, but the flowchart shown in FIG. 7 evaluates the prediction of the detection result in each route in advance at the stage of the route search before traveling. Thus, the route search is performed again based on the evaluation result.

  The flowchart of FIG. 7 will be described. First, the control unit 31 performs a route search in step S201. In this route search, a route to a destination input by a user or the like is searched for by a known method based on map data. The map used for route search may be map data 33a, or map data may be separately stored in the storage unit 33 for route search, or may be acquired from the server device 1. That is, the control unit 31 functions as a route search unit.

  The next steps S202 and S203 are the same as steps S102 and S103 in the flowchart of FIG. However, the map data to be acquired is the range of the route searched in step S101. Weather data is also within the range of the route.

  Next, in step S204, the control unit 31 evaluates the result detected by the sensor 4. This evaluation is basically performed in the same way as described in step S104. However, in the case of the flowchart in FIG. 7, since it is before the actual traveling, the point cloud information is obtained by traveling from now on. In this case, the degree of reliability (accuracy) of road markings is predicted and determined in advance. For example, when it is found that the route searched for from the weather data acquired in step S203 is raining, the weather feature information corresponding to the route is read from the map data 33a. Then, from the amount of precipitation up to now and the ease of water accumulation shown in FIG. 5, it is estimated whether there is a water pool on the road surface of the route, and it is estimated that the water pool has been generated. Evaluates that the result detected by the sensor 4 is highly likely that a road marking or the like cannot be detected correctly due to the effect of a water pool.

  Next, in step S205, when the result evaluated in step S204 is equal to or greater than a predetermined reference, the flowchart ends. This means that it is determined that there is no point in the searched route that has a high possibility that a road marking or the like cannot be detected correctly due to the influence of a water pool, and the vehicle travels on the route searched in step S101 without performing a re-search. To start.

  On the other hand, in step S205, when the result evaluated in step S204 is less than the predetermined reference, a route to the destination is re-searched in step S206. That is, the search is performed again avoiding a point where there is a high possibility that the road marking or the like cannot be correctly detected due to the influence of the water pool.

  Note that, before the autonomous driving vehicle C travels, the flow chart is not limited to the flowchart shown in FIG. The flowchart shown in FIG. 8 efficiently searches for a route by performing evaluation during the route search.

  The flowchart of FIG. 8 will be described. Steps S301 and S302 are the same as steps S202 and S203 in FIG. In step S303, evaluation and route search are performed. For example, when the weather data is rain, the route is searched so as not to pass through the area where the water is easy to collect “large” by referring to the weather feature information when searching for the route.

  According to the present embodiment, the vehicle control device 3 includes the sensor 4 that acquires the point cloud information indicating the situation outside the autonomous driving vehicle C, the communication unit 32 that acquires the weather information, and the weather feature information 13b. And a control unit 31 that acquires map data 33a and estimates the self-position of the autonomous driving vehicle C based on the point cloud information. Then, the control unit 31 evaluates the accuracy of the point cloud information based on the map data 33a and the weather information. In this way, the autonomous driving vehicle C can travel by performing self-position estimation according to the weather indicated by the weather information acquired by the communication unit 32 based on the weather feature information 13b included in the map data 33a. .

  In addition, when there is a high possibility that the road marking included in the point cloud information cannot be detected correctly, the control unit 31 does not perform self-position estimation using the road marking included in the point cloud information. By doing so, it is judged that the reliability of self-position estimation using road marking etc. has been reduced due to the influence of weather, so that point cloud information is not used for self-position estimation, and self-position estimation False estimation can be prevented.

  In the map data 33a, weather feature information 13b is set for each mesh M subdivided into a predetermined size. By doing so, for example, since the feature affected by the weather is subdivided and set, the map data reflecting the feature of the region indicated by the mesh more accurately can be obtained.

  Further, the weather feature information 13b may include at least one of information that water is likely to accumulate due to rain, that snow is likely to accumulate due to snow, and that it is likely to freeze. By doing in this way, it is possible to perform self-position estimation corresponding to a case where there is a high possibility that the road surface state will change due to rainfall, snowfall or freezing.

  The control unit 31 also has a function as a route search unit that searches for a route to a predetermined destination based on the map data 33a, and the control unit 31 further searches for a route based on weather information. Also good. By doing in this way, it becomes possible to perform the route search according to the weather.

  Moreover, the map data structure concerning the map data 33a is a map data structure of map data used for self-position estimation. The map data 33a includes weather feature information 13b. By doing in this way, the self-position estimation according to the weather can be performed by the weather feature information 13b.

  Moreover, the memory | storage part 33 has memorize | stored the map data 33a which has the map data structure mentioned above. By doing in this way, the self-position estimation according to the weather can be performed by the information indicating the feature regarding the weather.

  The map data 33a may include information on the result of evaluating the point cloud information based on the weather feature information 13b and the weather information. By doing in this way, when the situation that the feature such as the road marking could not be detected by the sensor such as the lidar mounted on the autonomous driving vehicle C, whether or not the situation was affected by the weather. Can be included. Accordingly, the history of weather and evaluation results can be accumulated, and weather feature information can be used more effectively by referring to this history.

  Further, the present invention is not limited to the above embodiment. That is, those skilled in the art can implement various modifications in accordance with conventionally known knowledge without departing from the scope of the present invention. Of course, such modifications are included in the scope of the present invention as long as they include the self-position estimation apparatus, the map data structure, and the feature data storage apparatus of the present invention.

1 Server device (map data storage device)
3 Vehicle control device 4 Sensor (rider)
13 storage unit 13a map data (map data structure)
31 Control Unit 32 Communication Unit 33 Storage Unit (Map Data Storage Device)
33a Map data (map data structure)

Claims (10)

A first acquisition unit for acquiring an external situation signal indicating a situation outside the mobile body;
A second acquisition unit for acquiring map data including information indicating characteristics affected by the weather;
A third acquisition unit for acquiring weather information indicating a weather state;
A self-position estimating unit that estimates the self-position of the mobile body based on the external situation signal,
The self-position estimation unit evaluates the accuracy of the external situation signal based on the map data and the weather information.
The self-position estimation apparatus characterized by the above-mentioned.   The self-position estimation apparatus according to claim 1, wherein the self-position estimation unit is configured not to use the signal for self-position estimation when the evaluation of the external situation signal is lower than a predetermined reference. .   3. The self-position estimation apparatus according to claim 1, wherein the map data includes information indicating characteristics affected by the weather for each section subdivided into a predetermined size. 4.   The information indicating the characteristics affected by the weather includes at least one of information that water is likely to accumulate due to rain, snow is likely to accumulate due to snow, and freezing. The self-position estimation apparatus according to claim 1. A route search unit for searching for a route to a predetermined destination based on the map data acquired by the second acquisition unit;
The route search unit further searches for a route based on the weather information.
The self-position estimation apparatus according to any one of claims 1 to 4, wherein A self-position estimation method executed by a self-position estimation apparatus that estimates a self-position of a moving object,
A first acquisition step of acquiring an external situation signal indicating an external situation of the moving body;
A second acquisition step of acquiring map data including information indicating characteristics affected by the weather;
A third acquisition step of acquiring weather information indicating the weather condition;
A self-position estimation step of estimating the self-position of the mobile body based on the external situation signal,
The self-position estimation step evaluates the accuracy of the external situation signal based on the map data and the weather information.
A self-position estimation method characterized by the above.   A self-position estimation program that causes a computer to execute the self-position estimation method according to claim 6. A map data structure of map data used for self-position estimation,
The map data structure characterized in that the map data includes information indicating characteristics affected by weather.   9. The map data includes information indicating a result affected by the weather and information on a result of evaluating a signal indicating a situation outside a predetermined moving body based on the weather information. Map data structure described in.   A map data storage device that stores the map data structure according to claim 8 or 9.

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