Toyota motor corporation

Abstract

To generate simulation map data based on probe data collected by a moving body.SOLUTION: An information processing device is configured to: acquire first map data representing a real-world environment map generated based on probe data sensed by a first moving body in the real world; acquire conversion data for converting the first map data into second map data representing an environment map of a virtual space for simulating the moving body; and convert the first map data into the second map data corresponding to the virtual space using the conversion data.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present disclosure relates to collecting road information. [Background technology]

[0002] There is a technology that uses real-world road map data to generate a virtual space for vehicle simulation. In this regard, for example, Patent Document 1 discloses an information processing device that converts map information acquired from an external medium into a format compatible with the system. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 8-171570 [Patent Document 2] Japanese Patent Application Laid-Open No. 2015-215461 Summary of the Invention [Problem to be solved by the invention]

[0004] The present disclosure aims to generate map data for simulation based on probe data collected by a mobile object. [Means for solving the problem]

[0005] One aspect of an embodiment of the present disclosure is An information processing device having a control unit that executes the following operations: acquiring first map data, which is an environmental map of the real world generated based on probe data sensed by a first moving body in the real world; acquiring conversion data for converting the first map data into second map data, which is an environmental map of a virtual space in which a simulation of the moving body is performed; and using the conversion data, converting the first map data into the second map data corresponding to the virtual space.

[0006] Furthermore, one aspect of the embodiment of the present disclosure is The information processing device has a control unit that performs the following operations: acquiring multiple pieces of position information sensed by a first moving body in the real world; performing a smoothing process on the multiple pieces of position information; acquiring road shape data representing the shape of a road based on the results of the smoothing process; and converting a first road map generated based on the road shape data into a second road map corresponding to a virtual space in which a simulation is performed.

[0007] Other aspects include a method executed by the information processing device, a program for causing a computer to execute the method, or a computer-readable storage medium non-temporarily storing the program. [Effects of the Invention]

[0008] According to the present disclosure, map data for simulation can be generated based on probe data collected by a mobile object. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a diagram for explaining an overview of a system according to an embodiment. [Figure 2] 2 is a diagram illustrating the configuration of a server device 10 and a vehicle 1. FIG. [Figure 3] FIG. 1 is a diagram illustrating the flow of data. [Figure 4] 5A and 5B are diagrams for explaining a process of generating trajectory data from position information. [Figure 5] 5A and 5B are diagrams for explaining a process of generating road shape data from trajectory data. [Figure 6] 4 is a flowchart of a process executed by the server device 10. DETAILED DESCRIPTION OF THE INVENTION

[0010] There is a movement to develop vehicles using digital twins. Digital twins refer to the process of converting real-world events into data and recreating the real world in a virtual space. By using digital twins, it becomes possible to perform driving simulations of the vehicle being developed in a virtual space.

[0011] When simulating vehicle driving in a virtual space, it is preferable to prepare a driving environment that is equivalent to that in the real world. Therefore, it is conceivable to create a vehicle driving environment using real road map data. However, road map data generally used commercially is specialized for applications such as car navigation, and therefore cannot be directly used for simulation purposes. The information processing device according to the present disclosure solves such problems.

[0012] An information processing device according to one aspect of the present disclosure includes: The system has a control unit that executes the following operations: acquiring first map data, which is an environmental map of the real world generated based on probe data sensed by a first moving body in the real world; acquiring conversion data for converting the first map data into second map data, which is an environmental map of a virtual space in which a simulation of the moving body is performed; and converting the first map data into the second map data corresponding to the virtual space using the conversion data.

[0013] The first moving body is typically a vehicle traveling in the real world. The first moving body can provide probe data. The probe data may be, for example, a collection of data indicating the position, direction of travel, speed, etc. of the first moving body. The first map data is a real-world environmental map, which is a two-dimensional or three-dimensional representation of an area in which a mobile object can travel, and is typically a road map showing road areas.

[0014] By collecting probe data (for example, a collection of location information) acquired by the first moving body, map data representing an area (for example, a road area) in which the first moving body can travel can be generated.

[0015] However, the first map data generated by such a method cannot be used for simulation purposes as it is. For example, the location information acquired by the first moving body by GPS or the like is expressed as coordinates using latitude and longitude (geographic coordinate system). The latitude and longitude are expressed based on an ellipsoid that approximates the Earth. Therefore, the first map data generated using the latitude and longitude is also a projection of roads arranged on the ellipsoid onto a plane. On the other hand, the virtual space used for the simulation is a distortion-free planar coordinate system. Therefore, the first map data cannot be used for the purpose of the simulation as is.

[0016] To address this, in an information processing device according to a first aspect of the present disclosure, a control unit acquires conversion data for converting first map data into second map data, which is an environment map of a virtual space, and converts the first map data into second map data using the conversion data. The second map data is, for example, a road map for simulating a second moving object, and is a road map expressed in a planar coordinate system.

[0017] The transformation data is typically data for mapping latitude and longitude expressed in a predetermined geodetic system (geographic coordinate system) to plane coordinates. The transformation data may include, for example, data indicating the source geodetic system, data indicating the geometric properties of the corresponding Earth ellipsoid, a scale factor, the position of the origin, eccentricity, etc.

[0018] The probe data may include a set of position information, and the control unit may further perform a smoothing process on the set of position information, and obtain road shape data representing the shape of the road based on the result of the smoothing process.

[0019] When sensing position information using a mobile object, depending on the accuracy of the sensing (for example, the measurement accuracy of a GPS module), the shape and area of ​​the road may not be accurately represented. Therefore, the control unit may perform a smoothing process on the set of position information acquired from the first mobile object. Smoothing refers to a process of smoothing multiple pieces of position information to eliminate singular points and noise contained in the set of position information. Examples of smoothing processes include a process of generating a spline curve that interpolates multiple pieces of position information, and a curve fitting process. By performing such processes, road shape data representing the shape of the road can be acquired. The road shape data is data that represents the shape of roads on which a vehicle can travel. The road shape data may be, for example, data that represents the positions of center lines of roads and lanes, or data that represents the positions of boundary lines of roads and lanes.

[0020] The control unit may generate the first map data based on the road shape data. For example, the control unit estimates a road area from the road shape data and includes the estimated road area in the first map data. By converting the first map data thus generated into second map data, it is possible to obtain an environment map with less noise for simulation.

[0021] In addition, an information processing device according to a second aspect of the present disclosure has a control unit that performs the following operations: acquiring multiple pieces of position information sensed by a first moving body in the real world; performing a smoothing process on the multiple pieces of position information; acquiring road shape data representing the shape of a road based on the results of the smoothing process; and converting a first road map generated based on the road shape data into a second road map corresponding to a virtual space in which a simulation is performed.

[0022] Similarly, the information processing device according to the second aspect can obtain the effect of acquiring an environment map with less noise for use in simulation.

[0023] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. The configurations of the following embodiments are examples, and the present disclosure is not limited to the configurations of the embodiments.

[0024] (First embodiment) An overview of a vehicle system according to a first embodiment will be described. The vehicle system according to this embodiment includes a vehicle (vehicle 1) that supplies probe data to a server device 10, and the server device 10 that generates a road map based on the probe data.

[0025] An overview of the processing performed by the system will be described with reference to FIG. The server device 10 is configured to be able to communicate with a plurality of vehicles 1, and acquires probe data collected by each of the plurality of vehicles 1. In this embodiment, the plurality of vehicles 1 periodically collect probe data including position information and transmit it to the server device 10. The probe data may include information other than position information. The information can be said to be information that discretely represents the traveling position of the vehicle 1.

[0026] Furthermore, the server device 10 estimates the shape of the road on which the vehicle 1 has traveled based on the collected set of position information. The position information contained in the probe data is discrete and includes errors. Therefore, the server device 10 performs predetermined processing on the collected set of position information to estimate the continuous trajectory of the vehicle 1, and estimates the road shape based on this. The server device 10 may estimate the center line of the road on which the vehicle 1 has traveled, or may estimate the boundary line of the road on which the vehicle 1 has traveled, as the road shape.

[0027] The server device generates a road map based on the estimated road shape. The road map is a map showing road areas on which a vehicle can travel. The location information contained in the probe data includes latitude and longitude acquired by the GPS module using a predetermined geodetic system. Therefore, a road map generated using this data is also expressed in the same geodetic system (e.g., latitude and longitude using WGS84) as the geodetic system used by the GPS module. However, since the virtual space in which the simulation is performed is expressed in a plane coordinate system (e.g., a system that numerically represents the distance from the origin), a road map generated based on the probe data cannot be applied to the virtual space in which the simulation is performed.

[0028] Therefore, the server device 10 according to this embodiment acquires predetermined conversion data and then uses the conversion data to convert the coordinate system of the generated road map into a planar coordinate system, thereby making it possible to generate an environment for performing a vehicle simulation in a virtual space based on the probe data.

[0029] [Device configuration] FIG. 2 is a diagram showing an example of the configuration of the server device 10 and the vehicle 1. As shown in FIG. First, the server device 10 will be described. The server device 10 is a computer such as a personal computer, a smartphone, a mobile phone, a tablet computer, a personal digital assistant, etc. The server device 10 includes a control unit 11, a storage unit 12, a communication unit 13, and an input / output unit 14.

[0030] The server device 10 can be configured as a computer having a processor (CPU, GPU, etc.), a main memory device (RAM, ROM, etc.), and an auxiliary memory device (EPROM, hard disk drive, removable media, etc.). The auxiliary memory device stores an operating system (OS), various programs, various tables, etc., and by executing the programs stored therein, various functions (software modules) that meet predetermined purposes, as described below, can be realized. However, some or all of the functions may be realized as hardware modules using hardware circuits such as ASICs and FPGAs.

[0031] The control unit 11 is a computing unit that executes predetermined programs to realize various functions of the server device 10. The control unit 11 can be realized by a hardware processor such as a CPU. The control unit 11 may also be configured to include RAM, ROM (Read Only Memory), cache memory, etc.

[0032] The control unit 11 is configured to have three software modules: an acquisition unit 111, a first generation unit 112, and a second generation unit 113. Each software module may be realized by the control unit 11 (such as a CPU) executing a program stored in the storage unit 12, which will be described later.

[0033] The acquisition unit 111 acquires probe data (hereinafter referred to as vehicle data) from a plurality of vehicles 1 under its management. The acquisition unit 111 periodically acquires vehicle data (hereinafter referred to as "vehicle data") from a plurality of vehicles 1. The vehicle data is data related to travel generated by the vehicle 1, and includes an identifier (vehicle ID) of the vehicle 1, a trip ID (described later), and location information. The acquisition unit 111 periodically acquires vehicle data from a plurality of vehicles 1 and stores it in the storage unit 12 (described later). By referring to the stored vehicle data, the history of location information for each vehicle can be obtained.

[0034] The processes executed by the first generating unit 112 and the second generating unit 113 will be described with reference to Fig. 3. Fig. 3 is a diagram showing the flow of data in the server device 10. As shown in the figure, the first generation unit 112 generates trajectory data by smoothing multiple pieces of position information included in the vehicle data acquired by the acquisition unit 111. The first generation unit 112 executes this process for each of the multiple vehicles.

[0035] Here, the relationship between position information and trajectory data will be explained. FIG. 4(A) is a diagram showing multiple pieces of position information acquired by a vehicle 1 traveling on a certain road. As shown in the figure, the position information acquired by the vehicle has large errors and may deviate from the road, so even if they are connected (shown by dotted lines), it does not necessarily result in an accurate representation of the road shape. Therefore, when a road map is generated based on such data, areas that are not roads may be represented as roads, or areas where roads exist may be represented as outside the road.

[0036] Therefore, in this embodiment, the first generating unit 112 performs a smoothing process on the position information acquired from the vehicle 1. The smoothing process is a process of converting a set of discrete position information into a continuous curve. The smoothing process can be performed, for example, by generating a spline curve that interpolates multiple points. In addition, the first generating unit 112 can convert a set of discrete position information into a smooth continuous curve by linear interpolation or the like. FIG. 4(B) is an example in which the set of position information shown in FIG. 4(A) is converted into a curve by the smoothing process. The generated curve can also be said to be an estimate of the traveling trajectory of the vehicle 1. In the following description, data representing the estimated traveling trajectory will be referred to as trajectory data.

[0037] Simply estimating the travel trajectory of a single vehicle does not allow for accurate determination of the road position. For example, because GPS positioning has errors, smoothing alone may result in the travel trajectory extending beyond the road. Therefore, in this embodiment, the first generating unit 112 integrates multiple travel trajectories (trajectory data) corresponding to multiple vehicles, as shown in FIG. 3, and generates data (road shape data) that estimates the shape of the road based on the integration result.

[0038] 5A and 5B show examples of estimating road shape based on travel trajectories. Illustrated reference numerals 501, 502, and 503 represent travel trajectories of different vehicles, respectively. These travel trajectories may be estimated using the method described above. FIG. 5(A) is a diagram showing an example of estimating road boundary lines (boundaries between roads and areas outside the road) as the shape of a road, and FIG. 5(B) is a diagram showing an example of estimating road center lines as the shape of a road.

[0039] One method for estimating road boundary lines and road centerlines based on multiple driving trajectories is to use a machine learning model. For example, a machine learning model is trained using the driving trajectories of a vehicle that has traveled on a known road segment as input data and the position information of the road boundary lines and road centerlines on that road segment as training data. This makes it possible to obtain a machine learning model that has learned the positional relationship between the driving trajectory and the road shape (the positions of road boundary lines and road center lines). By inputting the driving trajectory into such a machine learning model, it is possible to obtain position information on the road boundary lines and road center lines. The driving trajectory that serves as input data may be obtained by driving on a road segment other than the one used for learning.

[0040] If the target road has multiple lanes, the driving trajectory may be classified by lane using a method such as clustering, and estimation processing may be performed for each lane. In this case, lane centerlines and lane boundary lines can be estimated.

[0041] Note that the positions of road boundary lines and road center lines can also be generated by methods other than those using machine learning models. For example, the position of the road center line may be generated by averaging multiple driving trajectories, or the position of the road boundary line may be generated based on the distribution of multiple driving trajectories.

[0042] In this embodiment, the first generating unit 112 generates road shape data including road boundary lines and road centerlines by the above-described method. The first generating unit 112 may generate the road shape data in units of multiple road segments. The road shape data may also include two or more of road boundary lines, road center lines, lane boundary lines, and lane center lines.

[0043] The second generation unit 113 executes a process of generating first map data based on the road shape data generated by the first generation unit 112, and a process of converting the first map data to generate second map data. The second generation unit 113 generates first map data by, for example, integrating the road shape data generated for each road segment. The first map data is a map representing a road area, i.e., an area in which a vehicle can travel.

[0044] If the road shape data includes position information of road boundary lines, the road boundary lines become the edges of the road, and therefore the first map data can be generated without additional processing.

[0045] On the other hand, the road shape data does not necessarily represent the edges of the road. In such a case, the second generation unit 113 estimates the road area based on the road shape data and then generates the first map data. For example, if the road shape data is represented by a road centerline or lane centerline, an area of ​​a predetermined width centered on the road centerline or lane centerline may be assumed to be the road area. The width of the road may be estimated based on the variation in the trajectories of each vehicle. For example, if the positional variation in the trajectories is greater, the road width can be estimated to be wider.

[0046] The first map data may include information on lanes and intersections in addition to information indicating road areas. For example, if the road shape data includes information on the positions of lane boundaries and lane centerlines, this information may be added to the first map data. Furthermore, if the road shape data includes information on the connections between roads and structures such as overpasses and tunnels, this information may be added to the first map data.

[0047] The first map data is expressed in a geographic coordinate system (for example, latitude and longitude expressed using a predetermined geographic coordinate system such as WGS84). An example of the first map data is map data in NDS (Navigation Data Standard) format. NDS is a data format standardized for car navigation systems.

[0048] Furthermore, the second generation unit 113 converts the first map data corresponding to the geographic coordinate system into second map data, which is map data corresponding to the plane coordinate system, using the conversion data stored in the storage unit 12. The conversion data is data for mapping latitude and longitude expressed in a predetermined geodetic system (geographic coordinate system) to plane coordinates. The conversion data includes, for example, data indicating the original geodetic system, parameters related to projection (for example, the geometry of the corresponding Earth ellipsoid), and the like. The second map data may include parameters related to the road network (data indicating the geometric properties of the road network), parameters related to coordinate transformation (for example, data related to the scale factor and the position of the origin), etc. An example of the second map data is map data in the OpenDrive format. OpenDrive is an open format for representing road networks that can be used in various simulation tools. The second generating unit 113 can output the second map data obtained by the conversion to the storage unit 12 or an external device.

[0049] The second generation unit 113 may update the already generated second map data based on the first map data. For example, if a new road segment that is not included in the second map data is generated in the first map data, the second generation unit 113 may perform a partial conversion process on the road segment and add data corresponding to the road segment to the second map data.

[0050] The storage unit 12 is a means for storing information, and is configured with storage media such as RAM, a magnetic disk, a flash memory, etc. The storage unit 12 stores programs executed by the control unit 11, data used by the programs, etc.

[0051] The storage unit 12 stores the vehicle data, trajectory data, road shape data, conversion data, machine learning models, and the like described above.

[0052] The communication unit 13 is a wireless communication interface for connecting the server device 10 to a network. The communication unit 13 is configured to be able to communicate with the vehicle 1 via, for example, a wireless LAN or a mobile communication service such as 3G, 4G, or 5G.

[0053] The input / output unit 14 is a unit that accepts input operations performed by the operator of the device and presents information to the operator. In this embodiment, it is composed of a single touch panel display. That is, it is composed of a liquid crystal display and its control means, and a touch panel and its control means.

[0054] The specific hardware configuration of the server device 10 may include omissions, substitutions, and additions of components as appropriate depending on the embodiment. For example, the control unit 11 may include multiple hardware processors. The hardware processor may be configured with a microprocessor, FPGA, GPU, etc. Furthermore, input / output devices other than those illustrated (for example, an optical drive, etc.) may be added. Furthermore, the server device 10 may be configured with multiple computers. In this case, the hardware configurations of the computers may or may not be the same.

[0055] Next, the in-vehicle device 20 will be described. The in-vehicle device 20 includes a control unit 21, a storage unit 22, a communication unit 23, and a location information acquisition unit 24.

[0056] The control unit 21 is a computing unit that executes predetermined programs to realize various functions of the in-vehicle device 20. The control unit 21 can be realized by, for example, a hardware processor such as a CPU. The control unit 21 may also be configured to include RAM, ROM (Read Only Memory), cache memory, etc.

[0057] The control unit 21 is configured to have a message transmission unit 211 as a software module. The software module may be realized by the control unit 21 (CPU or the like) executing a program stored in the storage unit 22, which will be described later.

[0058] The message sending unit 211 periodically generates vehicle data and sends it to the server device 10. The vehicle data is data related to the traveling of the vehicle 1, and includes, for example, an identifier of the vehicle 1, the speed, direction of travel, and location information of the vehicle 1. The location information of the vehicle 1 can be acquired via the location information acquisition unit 24, which will be described later.

[0059] In addition, if the same vehicle travels the same road segment multiple times, it is impossible to determine which points need to be connected to form a correct trajectory. For this reason, in this embodiment, the message sending unit 211 includes an identifier (trip ID) assigned to each trip (referred to as a trip) in the vehicle data. This allows the server device 10 to estimate the vehicle trajectory for each trip.

[0060] The storage unit 22 is a means for storing information, and is configured with storage media such as RAM, a magnetic disk, a flash memory, etc. The storage unit 22 stores programs executed by the control unit 21, data used by the programs, etc.

[0061] The communication unit 23 is a device that performs wireless communication with a predetermined network. In this embodiment, the communication unit 23 is configured to be connectable to a predetermined cellular communication network. The communication unit 23 is configured to have an eUICC (Embedded Universal Integrated Circuit Card) for identifying a user. The eUICC may be a physical SIM card, an eSIM, or the like.

[0062] The position information acquisition unit 24 acquires position information of the vehicle 1. The position information acquisition unit 24 includes a GPS antenna and a positioning module for determining the position information. The GPS antenna is an antenna that receives positioning signals transmitted from positioning satellites (also referred to as GNSS satellites). The positioning module is a module that calculates position information based on the signals received by the GPS antenna.

[0063] [Processing flow] Next, a description will be given of the flow of processing executed by the server device 10. Figures 6(A) and 6(B) are flowcharts of processing executed by the server device 10. It is assumed that the server device 10 (acquisition unit 111) acquires vehicle data from a plurality of vehicles 1 under its management and stores the vehicle data in the storage unit 12 before the illustrated process starts.

[0064] FIG. 6(A) is a flowchart of a process in which the server device 10 generates first map data based on vehicle data acquired from the vehicle 1. The processing in steps S11 to S12 is processing for generating trajectory data for each trip taken by multiple vehicles. These steps are executed for each trip taken by multiple vehicles under the control of the system, i.e., for each combination of vehicle ID and trip ID included in the vehicle data.

[0065] First, in step S11, the acquisition unit 111 acquires a set of location information from the vehicle data stored in the storage unit 12. In this step, the acquisition unit 111 first extracts records having a combination of the vehicle ID and trip ID to be processed from the vehicle data. Note that, if map data is generated periodically, only records generated after the previous processing time may be acquired. Then, the acquisition unit 111 extracts location information from the multiple acquired records. By executing this step, multiple pieces of location information corresponding to a specific trip are acquired.

[0066] Next, in step S12, the first generation unit 112 generates trajectory data for the target trip. In this step, the first generation unit 112 performs smoothing processing on the set of location information acquired in step S11 as described with reference to Fig. 4. This makes it possible to obtain trajectory data corresponding to one trip.

[0067] By executing steps S11 and S12 for a plurality of trips to be processed, a plurality of pieces of locus data can be obtained.

[0068] Next, in step S13, the first generating unit 112 integrates the generated plurality of trajectory data to generate road shape data. As described above, the road shape data is data that represents the shape of the road on which the vehicle traveled. The road shape data may include position information of the center lines of roads and lanes, or position information of boundary lines of roads and lanes.

[0069] Next, in step S14, the second generation unit 113 generates first map data based on the road shape data. In this step, the second generation unit 113 generates or estimates a road area based on the road shape data, and generates a map including the road area. For example, if the road shape data represents the positions of road boundary lines, the road area can be generated based on the positions of the road boundary lines. Also, if the road shape data represents the positions of road centerlines or lane boundary lines, the road area may be generated after estimating the positions of the road boundary lines.

[0070] FIG. 6(B) is a flowchart of the process in which the server device 10 converts the first map data to generate the second map data. First, in step S21, the second generation unit 113 acquires the conversion data stored in the storage unit 12. Next, in step S22, the second generator 113 converts the first map data into second map data using the conversion data.

[0071] As described above, the server device 10 according to the first embodiment performs a smoothing process on the position information acquired from a plurality of vehicles to estimate their trajectories, and generates first map data expressed in a geographic coordinate system based on the estimated trajectories. Furthermore, the server device 10 converts the first map data into a planar coordinate system to generate second map data, which is map data for simulation. According to this configuration, it becomes possible to generate map data for performing simulations in a virtual space based on probe data collected in the real world.

[0072] (Modification of the first embodiment) In the first embodiment, the smoothing process is performed on the position information collected from the vehicle to generate the trajectory data, and the first map data is generated based on the trajectory data. However, the method for generating the first map data is not limited to this. For example, the road area may be derived directly from multiple pieces of position information without generating a vehicle travel trajectory. For example, the first map data may be generated using a machine learning model that learns the positional relationship between the vehicle's position information and the road area of ​​the road on which the vehicle traveled.

[0073] (Other variations) The above-described embodiment is merely an example, and the present disclosure can be modified and implemented as appropriate within the scope that does not deviate from the gist of the disclosure. For example, the processes and means described in this disclosure can be freely combined and implemented as long as no technical contradiction occurs.

[0074] In addition, the processing described as being performed by one device may be shared and executed by multiple devices. Alternatively, processes that have been described as being performed by different devices may be executed by a single device. In a computer system, the hardware configuration (server configuration) by which each function is realized can be flexibly changed.

[0075] The present disclosure can also be realized by providing a computer program implementing the functions described in the above embodiments to a computer, and having one or more processors in the computer read and execute the program. Such a computer program may be provided to the computer via a non-transitory computer-readable storage medium connectable to the computer's system bus or via a network. Non-transitory computer-readable storage media include, for example, any type of disk, such as a magnetic disk (e.g., a floppy disk, a hard disk drive (HDD), etc.), an optical disk (e.g., a CD-ROM, a DVD disk, a Blu-ray disk), a read-only memory (ROM), a random access memory (RAM), an EPROM, an EEPROM, a magnetic card, a flash memory, an optical card, or any type of medium suitable for storing electronic instructions. [Explanation of symbols]

[0076] 1. Vehicle 10. Server equipment 11 Control section 12...Storage section 13. Communications Department 14...Input / output section 20...In-vehicle equipment

Claims

1. Acquiring first map data, which is an environmental map of the real world, generated based on probe data sensed by a first moving object in the real world; acquiring conversion data for converting the first map data into second map data, which is an environmental map of a virtual space in which a simulation of a moving object is performed; converting the first map data into the second map data corresponding to the virtual space using the conversion data; An information processing device having a control unit that executes the above.

2. the second map data is a road map used in a simulation of a second moving object; The information processing device according to claim 1 .

3. the first map data is a road map expressed in a predetermined geodetic system; the second map data is a road map represented by a planar coordinate system; The conversion data is data for mapping latitude and longitude expressed in the predetermined geodetic system onto plane coordinates. The information processing device according to claim 1 .

4. the probe data includes a set of location information; the control unit further performs a smoothing process on the set of position information, and acquires road shape data representing a shape of a road based on a result of the smoothing process. The information processing device according to claim 1 .

5. the control unit generates the first map data based on the road shape data. The information processing device according to claim 4 .

6. acquiring a plurality of pieces of position information sensed by a first moving object in the real world; performing a smoothing process on the plurality of pieces of position information, and acquiring road shape data representing the shape of a road based on a result of the smoothing process; converting a first road map generated based on the road shape data into a second road map corresponding to a virtual space in which a simulation is performed; An information processing device having a control unit that executes the above.

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