server

Abstract

To mitigate the impact on a transportation network caused by the suspension of high-impact groups between a group 1 and a group 2.SOLUTION: When the impact factor IF of a first group GR1 is greater than or equal to the impact factor IF of a second group GR2 (S61: YES), a CPU sends a deceleration request DD to a leading vehicle of the second group GR2 with the lower impact factor IF in the step S63. When the impact factor IF of the first group GR1 is less than the impact factor IF of the second group GR2 (S61: NO), the CPU sends a deceleration request DD to the leading vehicle of the first group GR1 with the lower impact factor IF in the step S72.SELECTED DRAWING: Figure 6

Description

[Technical Field]

[0001] The present invention relates to a server. [Background technology]

[0002] Patent Document 1 describes a communication system including a vehicle and a server. When a group of vehicles travels, the server transmits a control signal to the lead vehicle of the group. When the lead vehicle reaches a bus stop, the server transmits a signal to the lead vehicle as a control signal for selecting a following vehicle. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2017-167669 Summary of the Invention [Problem to be solved by the invention]

[0004] In the communication system described in Patent Document 1, a situation is assumed in which the locations of the first group and the second group overlap after a specified time. In this case, the server may transmit a request to stop traveling to the leading vehicle of the first group and the leading vehicle of the second group. However, depending on the group of leading vehicles to which the request to stop traveling is transmitted, the impact on the transportation network may be excessively large if the group stops traveling. [Means for solving the problem]

[0005] In order to solve the above problem, the present invention provides a server that transmits to a vehicle a control signal based on predicted mobile object information generated based on mobile object information including real-world vehicle position information, and that performs the following operations: based on the predicted mobile object information, identifies a first group and a second group different from the first group as a group in which a plurality of the vehicles travel together; calculates, for each identified group, an impact level indicating the degree of impact that the group being calculated will have on a transportation network when it stops; estimates, for each identified group, a presence range in which the group is estimated to be located after a predetermined specified time in a virtual space that reproduces the positions of the plurality of vehicles and is constructed based on the predicted mobile object information; determines whether the presence range of the first group overlaps with the presence range of the second group; and, if the presence range of the first group overlaps with the presence range of the second group, does not send a request to stop traveling to the lead vehicle of the group with the higher impact level among the first group and the second group, but instead sends a change request to change vehicle speed to the lead vehicle of the group with the lower impact level among the first group and the second group.

[0006] According to the above configuration, the server sends a change request to the leading vehicle of the group with the lower impact of the first group or the second group, so that the server can prevent the range of the first group from overlapping with the range of the second group after a specified time. Furthermore, the server does not send a request to stop traveling to the leading vehicle of the group with the lower impact of the first group or the second group, so that the server can suppress the impact on the transportation network caused by the stopping of the group with the higher impact of the first group or the second group. [Brief explanation of the drawings]

[0007] [Figure 1] FIG. 1 is a schematic diagram showing a communication system. [Figure 2] FIG. 2 is a flowchart showing a series of processes for generating predicted moving object information. [Figure 3] FIG. 3 is a flowchart showing a series of processes for determining a group. [Figure 4] FIG. 4 is a flowchart showing a series of processes for calculating the influence degree. [Figure 5] FIG. 5 is a flowchart showing a series of processes including estimation of the existence range. [Figure 6] FIG. 6 is a flowchart showing a series of processes including transmission of a control signal to the leading car. DETAILED DESCRIPTION OF THE INVENTION

[0008] An embodiment of the server will be described below with reference to the drawings. Note that the following description will be given of a communication system including the server. <Communication system overview> As shown in FIG. 1, the communication system 10 includes a plurality of vehicles 20, a wireless communication network 30, and a server 40.

[0009] The vehicle 20 has a vehicle communication device 21, a vehicle control device 22, and a plurality of information acquisition devices 23. The vehicle communication device 21 communicates with the server 40 by wireless communication via a wireless communication network 30. The vehicle control device 22 controls the communication of the vehicle communication device 21.

[0010] The multiple information acquisition devices 23 acquire various types of information about the vehicle 20. The multiple information acquisition devices 23 are a GPS receiver 24 and a vehicle speed sensor 25. The GPS receiver 24 receives location information indicating the location of the vehicle 20 from the GPS device. The location information is, for example, latitude and longitude coordinate values. The vehicle speed sensor 25 acquires the traveling speed of the vehicle 20 as the vehicle speed. Each information acquisition device 23 outputs the acquired information about the vehicle 20 to the vehicle control device 22.

[0011] The vehicle control device 22 controls the following running of the vehicle 20. The vehicle control device 22 controls following running of another vehicle 20 running ahead, in response to a user operation. When the vehicle control device 22 is in following running, it generates following information FD indicating that the vehicle is in following running.

[0012] The vehicle control device 22 acquires, as moving object information VI, various types of information about the vehicle 20 acquired from multiple information acquisition devices 23, identification information indicating the vehicle 20, the time when the various types of information were acquired, and following information FD of the vehicle 20. The moving object information VI is information about the vehicle 20 in the real world. The identification information indicating the vehicle 20 includes information indicating whether the vehicle 20 is a large vehicle LV and whether it is an emergency vehicle EV. A large vehicle LV is a vehicle 20 whose total vehicle weight is equal to or greater than a predetermined weight, or whose passenger capacity is equal to or greater than a predetermined number of people. Examples of large vehicles LV include large trucks and route buses. Examples of emergency vehicles EV include ambulances.

[0013] The vehicle control device 22 outputs the moving object information VI to the vehicle communication device 21. Then, the vehicle communication device 21 transmits the moving object information VI to the server 40. Note that in FIG. 1, one vehicle 20 out of the multiple vehicles 20 is illustrated in detail, and the details of the other vehicles 20 are omitted. Each vehicle 20 transmits its own moving object information VI to the server 40.

[0014] The server 40 is capable of communicating with a plurality of vehicles 20. The server 40 acquires a plurality of pieces of mobile object information VI from the plurality of vehicles 20. The server 40 is capable of transmitting a change request CD as a control signal to the vehicles 20 based on predicted mobile object information FI generated based on the mobile object information VI, which will be described later. The server 40 includes a communication device 50, an information processing device 60, and a data center 70.

[0015] The communication device 50 communicates with a plurality of vehicles 20. The communication device 50 receives mobile object information VI transmitted from the vehicles 20. The communication device 50 outputs the received mobile object information VI to the information processing device 60. The communication device 50 also transmits information acquired from the information processing device 60 to the vehicles 20.

[0016] The information processing device 60 includes a CPU 61, which is an execution device, a peripheral circuit 62, a data storage unit 63, a program storage unit 64, and a bus 65. The bus 65 connects the CPU 61, the peripheral circuit 62, the data storage unit 63, and the program storage unit 64 so that they can communicate with one another. The peripheral circuit 62 includes a circuit that generates a clock signal that regulates internal operation, a power supply circuit, a reset circuit, etc. The data storage unit 63 stores data generated in accordance with the operation of the CPU 61. The program storage unit 64 stores a generation program P1 for predicted moving object information FI, a determination program P2 for a group GR, a calculation program P3 for an influence degree IF, a calculation program P4 for an existence range AR, and a control program P5 for a leading vehicle. The CPU 61 performs information processing by executing various programs stored in the program storage unit 64.

[0017] The data center 70 stores predicted mobile object information FI. The predicted mobile object information FI is information generated based on mobile object information VI of multiple vehicles 20, and includes multiple pieces of mobile object information VI from the time the mobile object information VI in a specified area was acquired. The specified area may be, for example, an area including one country, an area including only a portion of one country, or an area including the entire world. In other words, the predicted mobile object information FI is a so-called digital twin. The data center 70 also stores time series data of the predicted mobile object information FI generated by the information processing device 60. The data center 70 acquires the predicted mobile object information FI generated by the information processing device 60 multiple times over time. As a result, the data center 70 stores time series data of the predicted mobile object information FI.

[0018] <Generation of predicted moving object information> Next, the generation of the predicted moving object information FI performed by the information processing device 60 will be described. The CPU 61 repeatedly executes the generation program P1 for the predicted moving object information FI at a predetermined cycle, thereby repeatedly generating the predicted moving object information FI. The predetermined cycle is set to, for example, one minute.

[0019] As shown in FIG. 2, when the CPU 61 starts executing the program P1 for generating the predicted moving object information FI, it first performs the process of step S11. In step S11, the CPU 61 acquires the moving object information VI of each vehicle 20 in the communication system 10. When acquiring a plurality of pieces of moving object information VI, the CPU 61 acquires the moving object information VI of each vehicle 20 based on the identification information of the vehicle 20 included in the moving object information VI. Thereafter, the CPU 61 proceeds to step S12.

[0020] In step S12, the CPU 61 generates predicted mobile object information FI based on the acquired multiple pieces of mobile object information VI. Specifically, the CPU 61 performs the following process to synchronize the mobile object information VI of each vehicle 20, thereby generating the predicted mobile object information FI. First, the CPU 61 references information indicating the time of acquisition for each piece of acquired mobile object information VI. Next, the CPU 61 predicts the mobile object information VI at the reference time by using the time of the most recently acquired mobile object information VI as the reference time and correcting the other mobile object information VI by the time difference. For example, the CPU 61 performs the correction based on the mobile object information VI, such as past vehicle speed.

[0021] Then, the CPU 61 generates various types of information of the predicted moving object information VI as predicted moving object information FI. As a result, the CPU 61 acquires the moving object information VI of the multiple vehicles 20 synchronized at the reference time as predicted moving object information FI. Thereafter, the CPU 61 proceeds to step S13.

[0022] In step S13, the CPU 61 stores the generated predicted mobile object information FI in the data center 70. After that, the CPU 61 ends the series of processes. As a result, the data center 70 stores the acquired predicted mobile object information FI. As the CPU 61 repeatedly executes the generation program P1 for the predicted mobile object information FI, the data center 70 acquires and stores the predicted mobile object information FI at predetermined intervals. Therefore, the data center 70 stores the time series data of the predicted mobile object information FI.

[0023] <Group decision> Next, the determination of the group GR traveling together performed by the information processing device 60 will be described.

[0024] The CPU 61 repeatedly executes the group GR determination program P2 at a predetermined cycle. The predetermined cycle is set to, for example, one minute. As a result, the CPU 61 determines which vehicles 20 in a predetermined area belong to the group GR and which vehicles 20 do not belong to the group GR.

[0025] 3, when the CPU 61 starts execution of the judgment program P2 for the group GR, it first starts processing in step S21. In step S21, the CPU 61 acquires time-series data of the predicted moving object information FI in the data center 70 for a predetermined past period. The predetermined past period is, for example, 10 minutes. Thereafter, the CPU 61 proceeds to processing in step S22.

[0026] In step S22, the CPU 61 extracts multiple vehicles 20 that have been present within a specified range for the past specified period based on the time-series data of the predicted moving object information FI acquired in step S21. The specified range is, for example, a range in which the distance between multiple vehicles 20 is within 100 meters. The CPU 61 then proceeds to step S23. Note that if the CPU 61 is unable to extract multiple vehicles 20 in step S22, the CPU 61 adds non-component information indicating that all vehicles 20 do not constitute a group GR to the predicted moving object information FI, and ends this series of processes.

[0027] In step S23, the CPU 61 determines whether or not a specified percentage or more of the vehicles 20 are currently being followed among the plurality of vehicles 20 extracted in step S22. The specified percentage is set to, for example, 80%. Specifically, the CPU 61 determines whether or not the moving object information VI of the plurality of vehicles 20 extracted in step S22 includes following information FD. Then, the CPU 61 compares the number of moving object information VI that includes following information FD with the number extracted in step S22.

[0028] If the number of following information FDs is equal to or greater than the specified ratio (S23: YES), the CPU 61 proceeds to step S24. In step S24, the CPU 61 determines that the plurality of vehicles 20 extracted in step S22 are a group GR traveling together. Thereafter, the CPU 61 proceeds to step S25.

[0029] In step S25, the CPU 61 adds, to the predicted moving object information FI, configuration information indicating that the vehicles 20 are in one group GR and group identification information identifying the group GR, for the plurality of vehicles 20 determined to be in one group GR in step S24. Thereafter, the CPU 61 ends the series of processes.

[0030] On the other hand, if the number of pieces of following information FD does not exceed the specified ratio (S23: YES), the CPU 61 proceeds to step S31. In step S31, the CPU 61 does not determine that the multiple vehicles 20 extracted in step S22 form one group. Thereafter, the CPU 61 proceeds to step S32.

[0031] In step S32, the CPU 61 adds non-constitution information indicating that the vehicles 20 do not constitute a group GR to the predicted moving object information FI for the vehicles 20 that were not determined to constitute a group GR in step S31. The CPU 61 then terminates the series of processes. By executing the group GR determination program P2 in this manner, the predicted moving object information FI comes to include information indicating whether or not the vehicles 20 constitute a group GR.

[0032] <Calculation of impact> Next, the calculation of the influence IF of the group GR performed by the information processing device 60 will be described. The CPU 61 repeatedly executes the impact IF calculation program P3 at a predetermined cycle. The predetermined cycle is set to, for example, one minute. The impact IF is a value indicating the degree of impact on the transportation network of a predetermined area when the group GR to be calculated stops. In this way, the CPU 61 calculates the impact IF for each group GR made up in the predetermined area.

[0033] Specifically, the CPU 61 identifies the first group GR1 and the second group GR2 by referring to information for identifying the groups GR included in the predicted moving object information FI. The CPU 61 calculates the influence IF of the first group GR1 and the influence IF of the second group GR2.

[0034] 4, when the CPU 61 starts execution of the calculation program P3 for the influence degree IF, the CPU 61 first starts the processing of step S41. In step S41, the CPU 61 calculates the number NM of vehicles 20 that constitute the group GR to be calculated. Specifically, the CPU 61 refers to the predicted moving object information FI to calculate the number NM of vehicles 20 that have information that identifies the group GR to be calculated. Thereafter, the CPU 61 proceeds to the processing of step S42.

[0035] In step S42, the CPU 61 calculates the average speed AV of the vehicles 20 included in the group GR. Specifically, the CPU 61 refers to the predicted moving object information FI of the vehicles 20 included in the group GR to be calculated, and acquires the speed of the vehicle 20 having information that identifies the group GR to be calculated. Next, the CPU 61 calculates the average value of the acquired speeds of the multiple vehicles 20 as the average speed AV of the vehicles 20 included in the group GR. Thereafter, the CPU 61 proceeds to step S43.

[0036] In step S43, the CPU 61 determines whether or not there is an emergency vehicle EV included in the group GR that is the calculation target. Specifically, the CPU 61 determines whether or not the various information in the predicted moving object information FI of the vehicle 20 included in the group GR includes information indicating that the vehicle is an emergency vehicle EV. Thereafter, the CPU 61 proceeds to step S44.

[0037] In step S44, the CPU 61 calculates whether or not a large vehicle LV is present in the group GR that is the calculation target. The CPU 61 determines whether or not the various types of information in the predicted moving object information FI of the vehicle 20 included in the group GR include information indicating that the vehicle is a large vehicle LV. Thereafter, the CPU 61 proceeds to step S45.

[0038] In step S45, the CPU 61 calculates the influence IF of the group GR to be calculated. Specifically, the greater the number of vehicles 20 constituting the group GR to be calculated, the greater the calculated influence IF. The CPU 61 calculates the influence IF to be greater the greater the average speed AV of the vehicles 20 constituting the group GR to be calculated. The CPU 61 calculates the influence IF to be greater when an emergency vehicle EV is included in the group GR to be calculated, than when an emergency vehicle EV is not included. The CPU 61 calculates the influence IF to be greater when a large vehicle LV is included in the group GR to be calculated, than when a large vehicle LV is not included. Thereafter, the CPU 61 adds information indicating the influence IF of the group GR to the predicted moving object information FI, and ends the series of processes.

[0039] <Calculation of the range of existence> Next, the calculation of the existence range AR of the group GR performed by the information processing device 60 will be described. The presence range AR is a range in which the target vehicle 20 or group GR is estimated to exist in a virtual space constructed based on the predicted moving object information FI. The virtual space is constructed based on the predicted moving object information FI and is a space that reproduces the positions of multiple vehicles 20.

[0040] The CPU 61 repeatedly executes the existence range AR calculation program P4 at a predetermined cycle. The predetermined cycle is set to, for example, one minute. This allows the CPU 61 to calculate the existence range AR of each group GR and determine whether the existence range AR of the first group GR1 overlaps with the existence range AR of the second group GR2.

[0041] As shown in FIG. 5, when the CPU 61 starts executing the presence range AR calculation program P4, it first executes the process of step S51. In step S51, the CPU 61 calculates the presence range AR of each vehicle 20, where the vehicle 20 will be located after a predetermined specified time, based on the predicted moving object information FI. The presence range AR of a vehicle 20 is a range where the probability that the vehicle 20 will be located after the specified time is equal to or greater than a predetermined probability. The width of the presence range AR varies depending on the measurement uncertainty included in each piece of moving object information VI. The measurement uncertainty is based on, for example, errors resulting from the accuracy of the measurement method for position information and vehicle speed. The larger the error, the greater the uncertainty. Furthermore, the faster the vehicle speed, the greater the uncertainty. The greater the uncertainty, the wider the presence range AR. Note that the specified time is determined in advance through tests or simulations as a time that can prevent the presence ranges AR of the groups GR from overlapping by controlling their travel. For example, the specified time is one minute. After calculating the existence range AR of each vehicle 20, the CPU 61 advances the process to step S52.

[0042] In step S52, the CPU 61 identifies the leading vehicle of each group GR based on the predicted moving object information FI and the presence range AR calculated in step S51. For example, the CPU 61 first estimates a line of vehicles 20 lining up along a road from the presence range AR of the vehicles 20 constituting the group GR. Next, the CPU 61 estimates the direction in which the positions of the vehicles 20 in the predicted moving object information FI will move toward the presence range AR. Then, the CPU 61 identifies the vehicle 20 at the front of the estimated line in the estimated direction among the vehicles 20 at both ends of the estimated line as the leading vehicle. After that, the CPU 61 proceeds to step S53.

[0043] In step S53, the CPU 61 estimates the existence range AR of each group GR after a specified time has elapsed. The existence range AR of the group GR is the smallest range that includes the existence ranges AR of all the vehicles 20 that make up the group GR. Thereafter, the CPU 61 proceeds to step S54.

[0044] In step S54, the CPU 61 determines whether the existence ranges AR of multiple groups GR overlap each other in virtual space. Specifically, the CPU 61 determines whether the existence ranges AR of each group GR estimated in step S53 overlap each other in virtual space. Then, the CPU 61 identifies two groups GR whose existence ranges AR are determined to overlap each other as the first group GR1 and the second group GR2. Therefore, in step S54, the CPU 61 determines whether the existence range AR of the first group GR1 after a specified time overlaps with the existence range AR of the second group GR2 after a specified time. Then, the CPU 61 ends this series of processes.

[0045] <Sending control signals to the lead vehicle> Next, the transmission of a control signal to the lead vehicle of a group GR performed by the information processing device 60 will be described. When the CPU 61 determines in the processing of step S54 that the existence ranges AR of two groups GR, the first group GR1 and the second group GR2, overlap, the CPU 61 starts execution of the control program P5 for the lead vehicle. That is, when the CPU 61 determines that the existence range AR of the first group GR1 overlaps with the existence range AR of the second group GR2 in the virtual space, the CPU 61 starts execution of the control program P5 for the lead vehicle.

[0046] As shown in FIG. 6, when the CPU 61 starts to execute the control program P5 for the leading vehicle, it first starts the processing of step S61. In step S61, the CPU 61 determines whether the influence IF of the first group GR1 is equal to or greater than the influence IF of the second group GR2. If the influence IF of the first group GR1 is equal to or greater than the influence IF of the second group GR2 (S61: YES), the CPU 61 proceeds to step S62.

[0047] In step S62, the CPU 61 calculates the deceleration DE2 of the second group GR2 to enable the first group GR1 to continue traveling. Specifically, the CPU 61 calculates the minimum deceleration DE2 of the second group GR2 at which the range of existence AR of the first group GR1 does not overlap the range of existence AR of the second group GR2 if the first group GR1 continues traveling in the current state. Note that the minimum deceleration is the deceleration with the smallest absolute value of the smallest change in vehicle speed. Thereafter, the CPU 61 proceeds to step S63.

[0048] In step S63, the CPU 61 transmits a change request CD to the leading vehicle of the second group GR2, which is a deceleration request DD requesting deceleration at the deceleration rate DE2 of the second group GR2. That is, the deceleration request DD is a request to stop or reduce the vehicle speed of the leading vehicle of the second group GR2. Then, the CPU 61 proceeds to step S64.

[0049] In step S64, the CPU 61 determines whether the deceleration DE2 of the second group GR2 calculated in step S62 is equal to or greater than the limit value LD. The limit value LD is the maximum deceleration at which the vehicle can be decelerated. If the deceleration DE2 of the second group GR2 is less than the limit value LD (S64: NO), the CPU 61 ends the current series of processes. If the deceleration DE2 of the second group GR2 is equal to or greater than the limit value LD (S64: YES), the CPU 61 proceeds to step S65.

[0050] In step S65, the CPU 61 calculates the acceleration AC1 of the first group GR1 to enable the first group GR1 to continue traveling. Specifically, when the second group GR2 starts decelerating at the limit value LD, the CPU 61 calculates the minimum acceleration at which the range of presence AR of the first group GR1 does not overlap the range of presence AR of the second group GR2 as the acceleration AC1 of the first group GR1. Note that the minimum acceleration is the acceleration at which the absolute value of the vehicle speed changes significantly is the smallest. Thereafter, the CPU 61 proceeds to step S66.

[0051] In step S66, the CPU 61 transmits an acceleration request AD to the leading vehicle of the first group GR1, requesting acceleration at the acceleration AC1. That is, the CPU 61 does not transmit a request to stop the leading vehicle of the first group GR1. Thereafter, the CPU 61 ends this series of processes.

[0052] Meanwhile, when the influence IF of the first group GR1 is less than the influence IF of the second group GR2 (S61: NO), the CPU 61 advances the process to step S71. In step S71, the CPU 61 calculates the deceleration DE1 of the first group GR1 to enable the second group GR2 to continue traveling. Specifically, the CPU 61 calculates the minimum deceleration DE1 of the first group GR1 that prevents the range of presence AR of the first group GR1 from overlapping the range of presence AR of the second group GR2 if the second group GR2 were to continue traveling in its current state. Thereafter, the CPU 61 proceeds to step S72.

[0053] In step S72, the CPU 61 transmits to the leading vehicle of the first group GR1 a change request CD that is a deceleration request DD requesting deceleration at the deceleration DE1 of the first group GR1. Thereafter, the CPU 61 proceeds to step S73.

[0054] In step S73, the CPU 61 determines whether the deceleration DE1 of the first group GR1 is equal to or greater than the limit value LD. If the deceleration DE1 of the first group GR1 is less than the limit value LD (S73: NO), the CPU 61 ends the current series of processes. If the deceleration DE1 of the first group GR1 is equal to or greater than the limit value LD (S73: YES), the CPU 61 proceeds to step S74.

[0055] In step S74, the CPU 61 calculates an acceleration AC2 for the second group GR2 to enable the second group GR2 to continue traveling. Specifically, when the first group GR1 starts decelerating at the limit value LD, the CPU 61 calculates the minimum acceleration at which the existence range AR of the first group GR1 does not overlap with the existence range AR of the second group GR2 as the acceleration AC2 for the second group GR2. Thereafter, the CPU 61 proceeds to step S75.

[0056] In step S75, the CPU 61 transmits an acceleration request AD to the leading vehicle of the second group GR2, requesting acceleration at the acceleration AC2. That is, the CPU 61 does not transmit a request to stop the leading vehicle of the second group GR2. Thereafter, the CPU 61 ends this series of processes.

[0057] <Effects of the embodiment> (1) When the CPU 61 determines that the presence range AR of the first group GR1 overlaps with the presence range AR of the second group GR2, it transmits a deceleration request DD to the leading vehicle of the first group GR1, which has a low influence IF. This prevents the presence range AR of the first group GR1 from overlapping with the presence range AR of the second group GR2 after a specified time. Furthermore, the CPU 61 does not transmit a request to stop traveling to the leading vehicle of the second group GR2, which has a high influence IF. This eliminates the need to stop traveling of the second group GR2, which has a high influence IF. As a result, the server 40 can reduce the impact on the transportation network caused by the stopping of the group GR with a high influence IF out of the first group GR1 and the second group GR2.

[0058] (2) The CPU 61 calculates a larger influence IF as the number of vehicles 20 constituting the group GR to be calculated increases. Therefore, it is possible to prevent the influence of a group GR with a large number of vehicles 20 stopping traveling from becoming large.

[0059] (3) The CPU 61 calculates the influence IF to be larger as the average speed AV of the vehicles 20 that make up the group GR being the calculation target is larger. Therefore, it is possible to suppress the influence of a group GR with a large average speed AV stopping its travel from becoming large.

[0060] (4) When the CPU 61 determines that the range of travel AR of the first group GR1 overlaps with the range of travel AR of the second group GR2, it transmits an acceleration request AD to the leading vehicle of the second group GR2, which has a high influence IF. This allows a larger difference in the traveling speed between the second group GR2 and the first group GR1.

[0061] <Other embodiments> This embodiment can be modified as follows: This embodiment and the following modifications can be combined and implemented within the scope of technical compatibility.

[0062] The method of calculating the impact IF is not limited to the example of the above embodiment. For example, when calculating the impact IF, the CPU 61 may calculate the impact IF of a group GR that includes an emergency vehicle EV to be greater than the impact IF of a group GR that does not include an emergency vehicle EV. Furthermore, when calculating the impact IF, the CPU 61 may calculate a larger impact IF the larger the average size of the vehicles 20 that make up the group GR. In other words, the CPU 61 may calculate the impact IF without depending on some or all of the number NM of vehicles 20, the average speed AV of the vehicles 20, the presence or absence of emergency vehicles EV, and the presence or absence of large vehicles LV.

[0063] The CPU 61 may omit the processes of steps S64 to S66 and steps S73 to S75, that is, the CPU 61 may not transmit the acceleration request AD.

[0064] The CPU 61 does not have to determine whether the existence ranges AR of all groups GR overlap with each other. For example, the CPU 61 may identify a first group GR1 and a second group GR2, and determine whether the existence range AR of the identified first group GR1 overlaps with the existence range AR of the identified second group GR2. Furthermore, the CPU 61 may determine whether the existence range of a group GR having an influence IF greater than a specified value overlaps with the existence range AR of another group GR.

[0065] When the CPU 61 does not transmit the acceleration request AD, it may transmit a change request CD requesting a large change in vehicle speed. In this case, if a request to stop traveling is not transmitted to the group GR with a high influence level IF, overlapping of the existence range AR of the first group GR1 and the existence range AR of the second group GR2 can be avoided, and the group GR with a high influence level IF can continue traveling.

[0066] The method for generating the predicted moving object information FI is not limited to the example of the above embodiment. For example, the reference time may be set to a time that is later than the acquisition times of all vehicles 20. Even in this case, the CPU 61 can generate the predicted moving object information FI after acquiring the moving object information VI.

[0067] The mobile object information VI is not limited to information acquired from the vehicle 20. For example, the mobile object information VI may be information about the vehicle 20 acquired by a camera installed in a transportation network, which information is acquired by the server 40 from the camera.

[0068] The information processing device 60 may be configured as a circuit including one or more processors that execute various processes according to a computer program (software). The information processing device 60 may also be configured as a circuit including one or more dedicated hardware circuits, such as an application-specific integrated circuit (ASIC), that execute at least some of the various processes, or a combination thereof. The processor includes a CPU and memory such as RAM and ROM. The memory stores program code or instructions configured to cause the CPU to execute processes. The memory, i.e., computer-readable medium, includes any available medium that can be accessed by a general-purpose or dedicated computer. [Explanation of symbols]

[0069] 10...Communication system 20...Vehicle 30...Wireless communication network 40...Server 60...Information processing device 61...CPU 64...Program storage unit 70...Data center AD...Acceleration request AR...Existence range AV...Average speed CD...Change request DD...Deceleration request FI...Predicted moving object information GR...Group GR1...First group GR2...Second group IF...Influence level VI...Moving object information

Claims

1. a server that transmits a control signal to a vehicle based on predicted moving object information generated based on moving object information including real-world vehicle position information, Identifying a first group and a second group different from the first group as groups in which the plurality of vehicles will travel together based on the predicted moving object information; Calculating an impact degree indicating the degree of impact on the transportation network when the group to be calculated stops for each of the identified groups; In a virtual space that is constructed based on the predicted moving object information and that reproduces the positions of the plurality of vehicles, estimating, for each of the identified groups, a presence range that is a range in which the group is estimated to be present after a predetermined specified time; determining whether the range of existence of the first group overlaps with the range of existence of the second group; When the range of presence of the first group overlaps with the range of presence of the second group, a request to stop traveling is not transmitted to the leading vehicle of the group having the higher influence degree out of the first group and the second group, and a change request to change vehicle speed is transmitted to the leading vehicle of the group having the lower influence degree out of the first group and the second group. server.

2. When calculating the degree of influence, the greater the number of vehicles constituting the group to be calculated, the greater the degree of influence is calculated to be. The server of claim 1 .

3. When calculating the degree of influence, the higher the average speed of the vehicles constituting the group to be calculated, the greater the degree of influence is calculated to be. The server of claim 1 .

4. The change request is a deceleration request to change the vehicle speed slightly. The server of claim 1 .

5. When the range of presence of the first group overlaps with the range of presence of the second group, an acceleration request for significantly changing the vehicle speed is transmitted to a leading vehicle of the group having the greater influence out of the first group and the second group. The server of claim 4.

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