Information processing device, information providing system, information processing program, and information processing method

Abstract

To obtain information required to predict a change in the distance between vehicles of a vehicle moving at high speed while constantly changing its position and speed. [Solution] The information processing device (2) comprises an acquisition unit (231) that acquires a state value indicating the state of a target vehicle (V1), a prediction unit (232) that inputs the state value of the target vehicle into a prediction model (222) that calculates a predicted state value indicating the state after a predetermined time has elapsed since the state corresponding to the state value was measured when the state value was input, and obtains the predicted state value of the target vehicle calculated by the prediction model, and an output unit (21) that outputs information based on the predicted state value of the target vehicle, and the prediction unit uses a model that applies the Singer Model to the Kinematic Bicycle Model as the prediction model, and calculates acceleration as the predicted state value.

Description

[Technical Field]

[0001] The present disclosure relates to an information processing device, an information providing system, an information processing program, and an information processing method. [Background technology]

[0002] Patent document 1 describes a server that includes an information receiving unit that receives, from a first vehicle having a function of performing first communication with the vehicle and a function of performing second communication between the vehicle and a moving body, position information and speed information of the first vehicle, information on the detection results of a non-responsive object located on the road that does not respond to the second communication from the first vehicle, and information on the measurement results of the position and speed of the non-responsive object; a vehicle information storage unit that stores the position information and speed information of the first vehicle received from the first vehicle, information on the detection results of the non-responsive object, and information on the measurement results of the position and speed of the non-responsive object; and an information processing unit that determines whether the non-responsive object is a vehicle based on the information on the detection results of the non-responsive object, and if it is determined that the non-responsive object is a vehicle, stores the information on the measurement results of the position and speed of the non-responsive object in the vehicle information storage unit as position information and speed information of a vehicle that is not capable of communication. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-160281 Summary of the Invention [Problem to be solved by the invention]

[0004] Vehicles move at high speeds, and their positions and speeds are constantly changing. For this reason, the distance between the vehicles in front and behind may be appropriate at the time of measurement, but it may become too close (leading to a rear-end collision in the worst case) immediately after the measurement (for example, a few seconds later). However, with conventional technology, it was difficult to obtain the information necessary to predict such changes in the distance between vehicles. [Means for solving the problem]

[0005] In order to solve the above problem, an information processing device according to one embodiment of the present disclosure includes an acquisition unit that acquires a state value indicating the state of a target vehicle, a prediction unit that inputs the state value of the target vehicle into a prediction model that, when a state value is input, calculates a predicted state value indicating the state after a predetermined time has elapsed since the state corresponding to the state value was measured, thereby obtaining the predicted state value of the target vehicle calculated by the prediction model, and an output unit that outputs information based on the predicted state value of the target vehicle, wherein the prediction unit uses a model that applies a Singer Model to a Kinematic Bicycle Model as the prediction model, and calculates acceleration as the predicted state value.

[0006] In addition, an information provision system according to another aspect of the present disclosure is provided near a road on which a target vehicle is traveling, and includes an external sensor that detects the state of the target vehicle; a calculation unit that calculates a state value indicating the state of the target vehicle based on the detection result of the external sensor; a prediction unit that inputs the generated state value of the target vehicle into a prediction model that, when a state value is input, calculates a predicted state value that indicates the state after a predetermined time has elapsed since the measurement of the state corresponding to the state value, thereby obtaining the predicted state value of the target vehicle calculated by the prediction model; and an output unit that transmits information based on the predicted state value of the target vehicle to other vehicles traveling on the road, wherein the prediction unit uses as the prediction model a model that applies a Singer Model to a Kinematic Bicycle Model, and calculates acceleration as the predicted state value.

[0007] In addition, an information provision system according to another aspect of the present disclosure is provided in another vehicle traveling on a road on which a target vehicle is traveling, and comprises: an external sensor that detects the state of the target vehicle; a calculation unit that calculates a state value indicating the state of the target vehicle based on the detection result of the external sensor; a prediction unit that inputs the generated state value of the target vehicle into a prediction model that, when a state value is input, calculates a predicted state value that indicates the state after a predetermined time has elapsed since the measurement of the state corresponding to the state value, thereby obtaining the predicted state value of the target vehicle calculated by the prediction model; and an output unit that outputs information based on the predicted state value of the target vehicle to at least the other vehicle, wherein the prediction unit uses as the prediction model a model that applies a Singer Model to a Kinematic Bicycle Model, and calculates acceleration as the predicted state value.

[0008] Furthermore, an information processing program according to another aspect of the present disclosure causes a computer to execute an acquisition process of acquiring a state value indicating a state of a target vehicle, a prediction process of inputting the state value of the target vehicle into a prediction model that, when a state value is input, calculates a predicted state value indicating a state after a predetermined time has elapsed since the measurement of the state corresponding to the state value, thereby obtaining a predicted state value of the target vehicle calculated by the prediction model, and an output process of outputting information based on the predicted state value of the target vehicle, wherein the prediction process uses a model that applies a Singer Model to a Kinematic Bicycle Model and calculates acceleration as the predicted state value. Note that a computer-readable recording medium having an information processing program recorded thereon is also within the scope of the present disclosure.

[0009] In addition, an information processing method according to another aspect of the present disclosure includes an acquisition step in which a computer acquires a state value indicating the state of a target vehicle; a prediction step in which the computer inputs the state value of the target vehicle into a prediction model that calculates a predicted state value indicating the state after a predetermined time has elapsed since the state corresponding to the state value was measured when the state value was input, thereby obtaining the predicted state value of the target vehicle calculated by the prediction model; and an output step in which the computer outputs information based on the predicted state value of the target vehicle, wherein in the prediction step, a model that applies a Singer Model to a Kinematic Bicycle Model and calculates acceleration is used as the predicted state value. [Brief explanation of the drawings]

[0010] [Figure 1] 1 is a schematic diagram illustrating an example of a schematic configuration of an information providing system according to an embodiment of the present disclosure. [Figure 2] FIG. 2 is a schematic diagram showing another example of the schematic configuration of the system. [Figure 3] FIG. 2 is a block diagram showing an example of the functional configuration of the system. [Figure 4] FIG. 10 is a diagram illustrating a method for generating a prediction model used by the system. [Figure 5] FIG. 1 is a diagram showing a prediction model used by the system. [Figure 6] 10 is a flowchart illustrating an example of a flow of an information providing method according to an embodiment of another aspect of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0011] <Information Providing System 100> The information providing system 100 according to an embodiment of the present disclosure will be described in detail below.

[0012] [Configuration of information providing system 100] The information provision system 100 is a system for providing information about a target vehicle traveling on road R to other vehicles also traveling on road R. As shown in FIG. 1 , the information provision system 100 includes an external sensor 1 and an information processing device 2. The information provision system 100 according to this embodiment further includes a calculation device 3. The other vehicle is a so-called connected car configured to be able to communicate with the information processing device 2. The target vehicle may be a connected car, or may be a so-called non-connected car that does not have the function of communicating with the information processing device 2. The information provision system 100 may include a plurality of external sensors 1. The information processing device 2 may be provided outside the vehicle or inside the vehicle.

[0013] [External sensor 1] The external sensor 1 is provided outside the target vehicle V1. The external sensor 1 according to this embodiment is provided near the road R on which the target vehicle V1 is traveling. The vicinity of the road R includes, for example, the side of the road R, above the road R, and buildings and fences facing the road. As shown in FIG. 2, the external sensor 1 may be provided as an on-board sensor on another vehicle V2 traveling on the road R on which the target vehicle V1 is traveling. The external sensor 1 detects the state of the target vehicle V1. The external sensor 1 repeatedly detects the state of the target vehicle V1. The external sensor 1 according to this embodiment is a Light Detection and Ranging (LiDAR) that detects the distance from the external sensor 1 to the target vehicle V1. That is, the external sensor 1 according to this embodiment detects the distance from the external sensor 1 to the target vehicle V1. The external sensor 1 may be a camera, radar, or the like that captures the target vehicle V1. When the information providing system 100 includes a plurality of external sensors 1, all of the external sensors 1 may detect the state of the target vehicle V1, or some of the external sensors 1 may detect the state of a vehicle other than the target vehicle V1. When the external sensor 1 according to this embodiment detects the state (distance) of the target vehicle V1, it transmits the detection result to the calculation device 3.

[0014] [Calculation device 3] The calculation device 3 includes a calculation unit 31. The calculation unit 31 calculates a state value indicating the state of the target vehicle V1 based on the detection result of the external sensor 1. The state value is a numerical value indicating the state of the target vehicle V1. As described above, the external sensor 1 detects the distance from the external sensor 1 to the target vehicle V1. Therefore, the calculation unit 31 according to this embodiment calculates at least one of the position, azimuth angle, speed, and vehicle length of the target vehicle V1 as a state value based on the distance. If the calculated state value includes at least the position of the target vehicle V1, these can be determined from time-series changes in the position even if the azimuth angle and speed are not available. The calculation device 3 calculates the state value at a predetermined interval. The calculation device 3 transmits the state value to the information processing device 2 each time it calculates the state value. The calculation unit 31 may be included in the external sensor 1 or the information processing device 2. In this case, the information provision system 100 does not need to include the calculation device 3.

[0015] [Information processing device 2] As shown in FIG. 3, the information processing device 2 includes a communication unit 21, a storage unit 22, and a calculation unit .

[0016] [Communications Department 21] The communication unit 21 communicates with the calculation device 3. The communication unit 21 according to this embodiment also communicates with a vehicle having a communication function. The communication unit 21 according to this embodiment is configured with a communication module. Note that the communication unit 21 may be configured to communicate with the external sensor 1.

[0017] [Storage unit 22] The storage unit 22 stores an information processing program 221. The information processing program 221 is a program that causes a computer to function as the information processing device 2. The storage unit 22 according to this embodiment stores a prediction model 222. Details of the prediction model 222 will be described later. The storage unit 22 can also store various calculation results (predicted state values, etc.) of the calculation unit 23. The storage unit 22 according to this embodiment is configured with a semiconductor memory, a hard disk drive, etc. The storage unit 22 may be physically divided into sections for storing different contents (information processing program 221, prediction model 222, calculation results).

[0018] [Calculation unit 23] The calculation unit 23 includes an acquisition unit 231 and a prediction unit 232. The calculation unit according to this embodiment further includes a generation unit 233, a determination unit 234, a second determination unit 235, a correction unit 236, and an output processing unit 237. The calculation unit 23 according to this embodiment is configured with a processor (the information processing device 2 is configured with a computer). Therefore, the functions of the control blocks 231 to 237 are realized by the calculation unit 23 executing an acquisition process, a prediction process, a generation process, a determination process, a second determination process, and an output process in accordance with the information processing program 221 stored in the storage unit 22.

[0019] (Acquisition part 231) The acquisition unit 231 executes an acquisition process. In the acquisition process, the acquisition unit 231 acquires a state value. The acquisition unit 231 according to this embodiment acquires a state value received by the communication unit 21 from the calculation device 3 every time the calculation device 3 transmits the state value. When multiple received state values ​​are accumulated in the queue, the acquisition unit 231 acquires the state value in order from the oldest state value indicated by the timestamp. Furthermore, the acquisition unit 231 according to this embodiment further acquires a second state value from an on-board sensor provided on a vehicle traveling on the road R on which the target vehicle V1 is traveling. The second state value is a numerical value indicating the state of the target vehicle V1. The vehicle on which the on-board sensor is provided may be the target vehicle V1 or another vehicle V2. The acquisition unit 231 according to this embodiment acquires the second state value received by the communication unit 21 from the on-board sensor every time the on-board sensor transmits the second state value. When a plurality of received second state values ​​are accumulated in the queue, the acquisition unit 231 acquires the second state values ​​in order from the oldest time indicated by the time stamp. The second state values ​​acquired by the acquisition unit 231 according to this embodiment include the position (coordinates) of the other vehicle V2.

[0020] (Prediction unit 232) The prediction unit 232 executes a prediction process. In the prediction process, the prediction unit 232 inputs a state value of the target vehicle V1 into the prediction model 222 and obtains a predicted state value of the target vehicle V1 calculated by the prediction model 222. The prediction unit 232 obtains a predicted state value every time the acquisition unit 231 obtains a state value.

[0021] The prediction model 222 is configured to, when a state value is input, calculate a predicted state value indicating the state after a predetermined time has elapsed since the measurement of the state corresponding to the state value. The predetermined time can be, for example, the elapsed time from the measurement of the state corresponding to the state value to the measurement of the state corresponding to the next state value. In this way, by having the prediction model 222 calculate a predicted state value indicating the state after a predetermined time has elapsed since the measurement, rather than the acquisition, the influence of delays between the measurement and acquisition times can be eliminated from the prediction. As a result, the generation unit 233, described later, can reproduce a digital twin with higher accuracy.

[0022] Specifically, prediction model 222 is a model that applies the Singer Model to the Kinematic Bicycle Model and calculates acceleration as a predicted state value. Furthermore, because prediction model 222 is based on the Kinematic Bicycle Model, it can also calculate position coordinates (x, y) and the like as predicted state values. As shown in FIG. 4 , prediction model 222 according to this embodiment is obtained by solving simultaneous equations, including the differential equations that make up the Kinematic Bicycle Model and the differential equations that make up the Singer Model, discretizing the equations, and linearizing the results. Therefore, as shown in FIG. 5 , the arguments of the sine function and cosine function included in prediction model 222 are equations that include the azimuth angle component Ψ or slip angle component β, translational velocity v or translational acceleration α, overall length or wheelbase l, and elapsed time t from the time in the prior state of target vehicle V1. The time in the prior state includes, for example, the time when the state value was measured, the time when prediction model 222 calculated the previous predicted state value, etc. The prediction model 222 may be configured to output, as a predicted state value, a numerical value indicating a state after a predetermined time has elapsed since the state value was acquired by the acquisition unit 231, which will be described later. The prediction model 222 may also be stored in another storage device (not shown) different from the information processing device 2.

[0023] As described above, the acquisition unit 231 further acquires a second state value. Therefore, the prediction unit 232 according to this embodiment inputs a state value and a second state value corresponding to the state value to the prediction model 222, and obtains a predicted state value of the target vehicle V1 calculated by the prediction model 222. Note that the prediction unit 232 may be configured to input only the second state value to the prediction model 222 and obtain a predicted state value calculated by the prediction model 222.

[0024] When the acquisition unit 231 acquires a state value, the prediction unit 232 inputs the state value from one cycle before (the most recent state value) among the state values ​​held by the information processing device 2 up to that point into the prediction model 222 configured as described above, thereby obtaining a predicted state value at the time the state value was acquired. When attempting to reproduce multiple target vehicles V1, or the target vehicle V1 and another vehicle V2, in a single digital twin, the prediction unit 232 inputs the state values ​​(second state values) acquired from each vehicle V1, V2 into the prediction model to obtain a predicted state value for each vehicle V1, V2. In this case, the time of the corrected predicted state value generally differs for each vehicle. Therefore, to reproduce multiple target vehicles V1, or the target vehicle V1 and another vehicle V2, in a single digital twin, it is necessary to align the time of the predicted state value of each vehicle with the time of the digital twin. Therefore, in this case, the prediction model 222 according to this embodiment sets the predetermined time as the elapsed time from each time indicated by the state value of each vehicle to the time of the digital twin.

[0025] (Second Judgment Department 235) The second determination unit 235 determines whether there is a correspondence between the newly acquired state value and the latest predicted state value calculated by the prediction model 222 each time the acquisition unit 231 acquires a state value. The presence or absence of the correspondence includes at least one of whether the ID of the state value is the same as the ID of the predicted state value and whether the distance (Euclidean distance, Mahalanobis distance, etc.) between the position of the target vehicle V1 indicated by the state value and the position of the target vehicle V1 indicated by the predicted state value is less than a threshold. Then, when the second determination unit 235 determines that there is a correspondence between the state value and the predicted state value, it associates the new state value with the latest predicted state value. As described above, the acquisition unit 231 further acquires a second state value. Therefore, the second determination unit 235 according to this embodiment also determines whether there is a correspondence between the newly acquired second state value and the latest predicted state value each time the acquisition unit 231 acquires a second state value. Then, when the second determination unit 235 determines that there is a correspondence between the second state value and the predicted state value, it also associates the new second state value with the latest predicted state value. On the other hand, when the second determination unit 235 determines that there is no correspondence between the state value (second state value) and the predicted state value, it determines that the vehicle that has transmitted the newly acquired state value is a new vehicle that has not been targeted so far.

[0026] (correction unit 236) The correction unit 236 corrects the predicted state value associated with the state value based on the state value so that the predicted state value approaches (matches) the state value. At that time, the correction unit 236 also corrects the timestamp of the predicted state value so that the timestamp approaches (matches) the timestamp of the state value. The correction unit 236 according to this embodiment performs the correction using a Kalman filter. Note that the correction unit 236 may be configured to adjust the degree of correction taking into account the reliability of the state value. The reliability can be set based on the characteristics of the external sensor 1. Furthermore, the correction of the predicted state value may be performed by a device other than the information processing device 2. In this case, the calculation unit 23 does not need to include the correction unit 236.

[0027] (Generation unit 233) The generation unit 233 executes a generation process. In the generation process, the generation unit 233 generates a digital twin that reproduces, in a virtual space, the target vehicle V1 and the surrounding environment of the target vehicle V1 after a predetermined time has elapsed since the state corresponding to the state value was measured, based on the predicted state value. The generation unit 233 generates a digital twin each time the prediction model 222 generates a predicted state value. The generation unit 233 according to this embodiment generates a digital twin based on the predicted state values ​​generated by the prediction model 222 and corrected by the correction unit 236. Note that the generation of the digital twin may be performed by a device other than the information processing device 2. In this case, the calculation unit 23 does not need to include the generation unit 233.

[0028] (Judgment unit 234) When there is another vehicle V2 traveling ahead of the target vehicle V1 in the same direction as the target vehicle V1, the determination unit 234 executes a determination process. In the determination process, the determination unit 234 determines whether there is a possibility that the target vehicle V1 will collide with the other vehicle V2 from the acceleration (predicted state value). As described above, the calculation unit 23 according to this embodiment includes a generation unit 233 that generates a digital twin based on the predicted state value. Therefore, the determination unit 234 according to this embodiment determines whether there is a possibility of a collision from the digital twin generated by the generation unit 233. Specifically, the determination unit 234 first calculates the relative distance x of the target vehicle V1 with respect to the other vehicle V2 from the positions, speeds, and accelerations of the target vehicle V1 and the other vehicle V2, respectively. r , relative velocity v r , and relative acceleration a r Then, the determination unit 234 calculates the calculated relative distance x r , relative velocity v r , and relative acceleration a r Using this, the time derivative value tau of TTC (Time-To-Collision) dot Calculate the time derivative value tau dot can be calculated, for example, using the following formula (1). tau dot =-1+x r ×a r / v r 2 (1)

[0029] Then, the determination unit 234 calculates tau dot If the value of tau is less than a predetermined value (for example, -0.5), it is determined that the possibility of a rear-end collision is high, and if it is -0.5 or more, it is determined that the possibility of a rear-end collision is low. In this way, by making a determination based on the digital twin, the determination unit 234 can compare the state of the target vehicle V1 with the state of the other vehicle V2 at the same time. Note that the determination unit 234 determines that tau is less than a predetermined value (for example, -0.5), it is determined that the possibility of a rear-end collision is low. dot Alternatively, the information processing device 2 may be configured to determine whether or not there is a possibility of a rear-end collision using a secondary predicted value of TTC instead of calculating the above. Furthermore, the determination of whether or not there is a possibility of a rear-end collision may be performed by a device other than the information processing device 2. In this case, the calculation unit 23 may not be provided with the determination unit 234.

[0030] (output processing unit 237) The output processing unit 237 executes output processing. In the output processing, the output processing unit 237 according to the present embodiment controls the communication unit 21. As a result, the communication unit 21 transmits (outputs) information based on the predicted state values ​​of the target vehicle V1 to at least the other vehicle V2. That is, the communication unit 21 according to the present embodiment also functions as an output unit. The information based on the predicted state values ​​includes the digital twin generated by the generation unit 233. As described above, the calculation unit 23 according to the present embodiment includes the determination unit 234. Therefore, the output processing unit 237 according to the present embodiment controls the communication unit 21 even when the determination unit 234 determines that there is a possibility that the target vehicle V1 will rear-end the other vehicle V2. As a result, the communication unit 21 transmits information to the other vehicle V2 that an action to avoid a rear-end collision will be taken as information based on the predicted state values. Note that the output processing unit 237 may be configured to transmit information based on the predicted state values ​​to something other than the other vehicle V2 (such as the target vehicle V1 or a remote monitoring device). Furthermore, when the information processing device 2 is provided in a vehicle, the output processing unit 237 may be configured to supply information based on the predicted state value to a display unit or the like of the vehicle. In this case, a terminal or the like of the information processing device connected to the vehicle serves as the output unit.

[0031] [Operational Effects of Information Processing Device 2 (Information Providing System 100)] In the information processing device 2 (information provision system 100) described above, the prediction unit 232 uses, as the prediction model 222, a model that applies the Singer Model to the Kinematic Bicycle Model and calculates acceleration as a predicted state value. This prediction model 222 can also calculate position coordinates (x, y) as a predicted state value. The prediction unit 232 then obtains predicted state values ​​(predicted acceleration values ​​and predicted position coordinate values) of the target vehicle V1 from the state values ​​of the target vehicle V1 acquired from the calculation unit 31. Using these predicted state values, it becomes possible to predict, for example, how the inter-vehicle distance between the target vehicle V1 and another vehicle V2 traveling ahead of the target vehicle V1 in the same direction as the target vehicle V1 will change (whether there is a possibility of a rear-end collision). Therefore, the information processing device 2 (information provision system 100) can obtain information (predicted acceleration values) necessary to predict changes in the inter-vehicle distance between vehicles moving at high speed while constantly changing their positions and speeds.

[0032] Use of this disclosure can realize safer autonomous driving technology (autonomous vehicles, control devices, remote monitoring devices, etc.), which can contribute to achieving Sustainable Development Goals (SDGs), such as Goal 9 "Build resilient infrastructure, promote inclusive and sustainable industrialization, promote innovation and build resilient infrastructure," and Goal 11 "Make cities and towns inclusive and sustainable."

[0033] <Information processing method S100> Next, an information processing method S100 according to an embodiment of another aspect of the present disclosure will be described in detail.

[0034] [Flow of information processing method S100] 6, the information processing method S100 includes an acquisition step S1, a prediction step S2, and an output step S3. The information processing method S100 according to this embodiment further includes a generation step S4, a determination step S5, a second determination step S6, a correction step S7, and a third determination step S8.

[0035] [Acquisition step S1] In the first acquisition step S1, a computer acquires a state value. In the acquisition step S1 according to this embodiment, the computer further acquires a second state value from an on-board sensor provided on a vehicle traveling on the road R on which the target vehicle V1 is traveling. The computer that acquires the state value may be the information processing device 2 or another device.

[0036] [Prediction step S2] After acquiring the state values, the process proceeds to the prediction step S2. In the prediction step S2, the computer inputs the state values ​​of the target vehicle V1 into the prediction model 222 to obtain a predicted state value of the target vehicle V1 calculated by the prediction model 222. In the prediction step S2, the computer uses, as the prediction model 222, a model that applies the Singer Model to the Kinematic Bicycle Model and calculates acceleration as the predicted state value. The prediction model 222 can also calculate position coordinates (x, y) and the like as predicted state values. As described above, in the acquisition step S1, a second state value is further acquired. Therefore, in the prediction step S2 according to this embodiment, the computer inputs the state values ​​and the second state values ​​associated with the state values ​​into the prediction model 222 to obtain a predicted state value of the target vehicle V1 calculated by the prediction model 222. The computer that acquires the predicted state value may be the information processing device 2 or another device.

[0037] [Second decision step S6] After acquiring the state value, the process proceeds to the second determination step S6. In the second determination step S6, each time the computer acquires a state value in the acquisition step S1, the computer determines whether or not there is a correspondence between the newly acquired state value and the latest predicted state value calculated by the prediction model 222. If it is determined that there is a correspondence between the state value and the predicted state value (step S6: YES), the computer associates the new state value with the latest predicted state value. As described above, in the acquisition step S1, the computer further acquires a second state value. Therefore, in the second determination step S6 according to this embodiment, each time the computer acquires a second state value in the acquisition step S1, the computer also determines whether or not there is a correspondence between the newly acquired second state value and the latest predicted state value. If it is determined that there is a correspondence between the second state value and the predicted state value, the computer also associates the new second state value with the latest predicted state value. The computer that performs the association may be the information processing device 2 or another device.

[0038] On the other hand, if it is determined in the second determination step S6 that there is no correspondence between the newly acquired state value and the latest predicted state value (the newly acquired state value cannot be matched with the latest predicted state value) (step S6: NO), the computer determines that the vehicle that has transmitted the newly acquired state value is a new vehicle that has not been considered as a target vehicle until now. In this case, the computer starts the information processing method (steps S1 to S8) for the new vehicle, target vehicle V1. Note that after determining in the second determination step S6 that there is no correspondence between the state value and the predicted state value, it may further determine whether the determination that there is no correspondence has been repeated a predetermined number of times. Then, if it is determined that the determination that there is no correspondence has been repeated a predetermined number of times, it may determine that the vehicle that has transmitted the newly acquired state value is a new vehicle that has not been considered as a target vehicle until now.

[0039] [Correction step S7] After associating the state value with the predicted state value, the process proceeds to a correction step S7. In the correction step S7, the computer corrects the predicted state value associated with the state value based on the state value so that the predicted state value approaches (matches) the state value. At this time, in the correction step S7, the timestamp of the predicted state value is also corrected so that the timestamp approaches (matches) the timestamp of the state value. The computer that corrects the predicted state value may be the information processing device 2 or another device.

[0040] [Generation step S4] After obtaining the predicted state values, the process proceeds to generation step S4. In generation step S4, the computer generates a digital twin in a virtual space based on the predicted state values, which reproduces the target vehicle V1 and the surrounding environment of the target vehicle V1 after a predetermined time has elapsed since the state corresponding to the state value was measured. In generation step S4, a digital twin is generated each time the prediction model 222 generates a predicted state value. In generation step S4 according to this embodiment, a digital twin is generated based on the predicted state values ​​generated by the prediction model 222 and corrected in correction step S7. The computer that generates the digital twin may be the information processing device 2 described above, or may be another device.

[0041] [Decision step S5] If there is another vehicle V2 traveling ahead of the target vehicle V1 in the same direction as the target vehicle V1, the process proceeds to a determination step S5. In the determination step S5, the computer determines whether or not there is a possibility that the target vehicle V1 will collide with the other vehicle V2 from the rear based on the acceleration (predicted state value). The computer that determines whether or not there is a possibility of a collision may be the information processing device 2 or another device.

[0042] [Output step S3] After obtaining the predicted state values, the process proceeds to output step S3. In output step S3, the computer outputs information based on the predicted state values ​​of the target vehicle V1. In output step S3 according to this embodiment, the computer transmits information based on the predicted state values ​​of the target vehicle V1 (such as a digital twin or information indicating that action to avoid a rear-end collision will be taken) to at least another vehicle V2. The computer that outputs the information based on the predicted state values ​​may be the information processing device 2 or another device. Note that in output step S3, the information based on the predicted state values ​​may be output to something other than the other vehicle V2 (such as the target vehicle V1 or a remote monitoring device).

[0043] [Third decision step S8] After outputting the information based on the predicted state values, the process proceeds to a third determination step S8. In the third determination step S8, the computer determines whether or not it is necessary to continue generating the predicted state values ​​of the target vehicle V1. If it is determined that it is not necessary to continue (step S8: NO), the information processing method S100 ends. On the other hand, if it is determined that it is necessary to continue (step S8: YES), the process returns to step S1. The computer that determines whether or not to continue may be the information processing device 2 or another device.

[0044] [Effects of information processing method S100] According to the information processing method S100 described above, similar to the information processing device 2 (information provision system 100), it is possible to obtain the information (predicted acceleration value) necessary to predict changes in the inter-vehicle distance of a vehicle moving at high speed while constantly changing its position and speed.

[0045] <Modification> The present disclosure is not limited to the above-described embodiments, and various modifications are possible within the scope of the claims. Embodiments obtained by appropriately combining the technical means disclosed in different embodiments are also included in the technical scope of the present disclosure.

[0046] For example, the information provision system 100 according to the above embodiment generates a predicted state value using a state value calculated by the calculation unit 31 based on the state of the target vehicle V1 detected by the external sensor 1. However, the information provision system 100 may be configured to generate a predicted state value using a state value of the target vehicle V1 itself (the target vehicle V1) generated by an internal sensor provided in the target vehicle V1.

[0047] Furthermore, the information processing program may be stored not temporarily but on one or more computer-readable storage media. Each unit may or may not have a storage medium. In the latter case, the information processing program may be supplied to each unit via any wired or wireless transmission medium.

[0048] Furthermore, some or all of the functions of the control blocks 231 to 237 included in the arithmetic unit can be realized by logic circuits. For example, the scope of the present disclosure also includes integrated circuits in which logic circuits that function as the control blocks 231 to 235 are formed. In addition, the functions of each unit can also be realized by, for example, a quantum computer.

[0049] 〔summary〕 An information processing device according to aspect 1 of the present disclosure includes an acquisition unit that acquires a state value indicating the state of the target vehicle, a prediction unit that inputs the state value of the target vehicle into a prediction model that, when a state value is input, calculates a predicted state value indicating the state after a predetermined time has elapsed since the state corresponding to the state value was measured, thereby obtaining the predicted state value of the target vehicle calculated by the prediction model, and an output unit that outputs information based on the predicted state value of the target vehicle, wherein the prediction unit is configured to use a model that applies a Singer Model to a Kinematic Bicycle Model as the prediction model, and that calculates acceleration as the predicted state value. An information processing device according to aspect 2 of the present disclosure may be configured such that, in aspect 1 above, the prediction unit uses as the prediction model an equation in which the arguments of the sine function and cosine function included in the prediction model include an azimuth angle component or a slip angle component, a translational velocity or a translational acceleration, a wheelbase, and an elapsed time from a time in a prior state for the target vehicle. The information processing device according to aspect 3 of the present disclosure may be configured in the above-mentioned aspect 1 or 2, further comprising a generation unit that generates a digital twin that reproduces the target vehicle and the surrounding environment of the target vehicle in a virtual space after a predetermined time has elapsed since the measurement of the state corresponding to the predicted state value, based on the predicted state value. The information processing device according to aspect 4 of the present disclosure may be configured in the above-mentioned aspect 1 or 2 such that, when there is another vehicle traveling in the same direction as the target vehicle ahead of the target vehicle, the information processing device further includes a judgment unit that judges whether or not there is a possibility that the target vehicle will rear-end the other vehicle based on the acceleration, and the output unit may be configured to output information to the other vehicle indicating that action to avoid the rear-end collision will be taken when the judgment unit judges that there is a possibility that the target vehicle will rear-end the other vehicle. An information processing device according to aspect 5 of the present disclosure may be configured such that, in aspect 1 above, the acquisition unit acquires a second state value indicating the state of the target vehicle from an onboard sensor provided on a vehicle traveling on the road on which the target vehicle is traveling, and the prediction unit inputs the state value and the second state value into the prediction model to obtain a predicted state value of the target vehicle calculated by the prediction model. An information provision system according to a sixth aspect of the present disclosure is provided near a road on which a target vehicle is traveling, and includes an external sensor that detects the state of the target vehicle; a calculation unit that calculates a state value indicating the state of the target vehicle based on the detection result of the external sensor; a prediction unit that inputs the generated state value of the target vehicle into a prediction model that, when a state value is input, calculates a predicted state value indicating the state after a predetermined time has elapsed since the measurement of the state corresponding to the state value, thereby obtaining the predicted state value of the target vehicle calculated by the prediction model; and an output unit that transmits information based on the predicted state value of the target vehicle to other vehicles traveling on the road, wherein the prediction unit uses, as the prediction model, a model that applies a Singer Model to a Kinematic Bicycle Model, and calculates acceleration as the predicted state value. An information provision system according to aspect 7 of the present disclosure is provided in another vehicle traveling on the road on which a target vehicle is traveling, and comprises: an external sensor that detects the state of the target vehicle; a calculation unit that calculates a state value indicating the state of the target vehicle based on the detection result of the external sensor; a prediction unit that inputs the generated state value of the target vehicle into a prediction model that, when a state value is input, calculates a predicted state value indicating the state after a predetermined time has elapsed since the measurement of the state corresponding to the state value, thereby obtaining the predicted state value of the target vehicle calculated by the prediction model; and an output unit that outputs information based on the predicted state value of the target vehicle to at least the other vehicle, wherein the prediction unit uses, as the prediction model, a model that applies the Singer Model to a Kinematic Bicycle Model, and calculates acceleration as the predicted state value. An information processing program according to aspect 8 of the present disclosure causes a computer to execute an acquisition process to acquire a state value indicating the state of a target vehicle; a prediction process to input the state value of the target vehicle into a prediction model that, when a state value is input, calculates a predicted state value indicating the state after a predetermined time has elapsed since the state corresponding to the state value was measured, thereby obtaining the predicted state value of the target vehicle calculated by the prediction model; and an output process to output information based on the predicted state value of the target vehicle, wherein the prediction process uses a model that applies a Singer Model to a Kinematic Bicycle Model as the prediction model, and calculates acceleration as the predicted state value. An information processing method according to aspect 9 of the present disclosure includes an acquisition step in which a computer acquires a state value indicating the state of a target vehicle; a prediction step in which the computer inputs the state value of the target vehicle into a prediction model that calculates a predicted state value indicating the state after a predetermined time has elapsed since the state corresponding to the state value was measured when the state value was input, thereby obtaining the predicted state value of the target vehicle calculated by the prediction model; and an output step in which the computer outputs information based on the predicted state value of the target vehicle, wherein in the prediction step, the prediction model is a model that applies the Singer Model to a Kinematic Bicycle Model, and uses a model that calculates acceleration as the predicted state value. [Explanation of symbols]

[0050] 100 Information Provision System 1 External Sensor 2. Information processing equipment 21 Communication unit (output unit) 22 Memory section 221 Information Processing Program 222 Predictive Model 23 Arithmetic section 231 Acquisition Department 232 Prediction Department 233 Generation part 234 Judgment Department 235 Second Judgment Department 236 Correction Unit 237 Output Processing Unit 3. Calculation device 31 Calculation section S100 Information processing method S1 Acquisition step S2 Prediction step S3 Output Step S4 Generation Step S5 Decision Step S6 Second decision step S7 Correction step S8 Third decision step V1 Target vehicle V2 Other vehicles

Claims

1. an acquisition unit that acquires a state value indicating a state of the target vehicle; a prediction unit that inputs the state value of the target vehicle into a prediction model that calculates, when a state value is input, a predicted state value that indicates a state after a predetermined time has elapsed since the measurement of the state corresponding to the state value, and obtains the predicted state value of the target vehicle calculated by the prediction model; an output unit that outputs information based on the predicted state value of the target vehicle; Equipped with The prediction unit uses, as the prediction model, a model in which a Singer Model is applied to a Kinematic Bicycle Model, and a model that calculates acceleration as the predicted state value. Information processing device.

2. the prediction unit uses, as the prediction model, an equation in which arguments of a sine function and a cosine function included in the prediction model include an azimuth angle component or a slip angle component, a translational velocity or a translational acceleration, a wheelbase, and an elapsed time from a time in a prior state of the target vehicle; The information processing device according to claim 1 .

3. a generation unit that generates a digital twin that reproduces the target vehicle and the surrounding environment of the target vehicle in a virtual space after a predetermined time has elapsed since the state was measured based on the predicted state value; 3. The information processing device according to claim 1.

4. a determination unit that, when there is another vehicle traveling ahead of the target vehicle in the same direction as the target vehicle, determines whether or not there is a possibility that the target vehicle will collide with the other vehicle based on the acceleration; the output unit outputs, when the determination unit determines that there is a possibility that the target vehicle will collide with the other vehicle, information to the other vehicle indicating that an action to avoid a rear-end collision will be taken.

3. The information processing device according to claim 1.

5. the acquisition unit acquires a second state value indicating a state of the target vehicle from an on-board sensor provided in a vehicle traveling on a road on which the target vehicle is traveling; the prediction unit inputs the state value and the second state value into the prediction model to obtain a predicted state value of the target vehicle calculated by the prediction model; The information processing device according to claim 1 .

6. an external sensor provided near a road on which a target vehicle travels and configured to detect a state of the target vehicle; a calculation unit that calculates a state value indicating a state of the target vehicle based on the detection result of the external sensor; a prediction unit that inputs the generated state value of the target vehicle into a prediction model that, when a state value is input, calculates a predicted state value that indicates a state after a predetermined time has elapsed since the measurement of the state corresponding to the state value, and obtains a predicted state value of the target vehicle calculated by the prediction model; an output unit that transmits information based on the predicted state value of the target vehicle to other vehicles traveling on the road; Equipped with The prediction unit uses, as the prediction model, a model in which a Singer Model is applied to a Kinematic Bicycle Model, and a model that calculates acceleration as the predicted state value. Information provision system.

7. an external sensor provided on another vehicle traveling on the road on which the target vehicle is traveling, the external sensor detecting the state of the target vehicle; a calculation unit that calculates a state value indicating a state of the target vehicle based on the detection result of the external sensor; a prediction unit that inputs the generated state value of the target vehicle into a prediction model that, when a state value is input, calculates a predicted state value that indicates a state after a predetermined time has elapsed since the measurement of the state corresponding to the state value, and obtains a predicted state value of the target vehicle calculated by the prediction model; an output unit that outputs information based on the predicted state value of the target vehicle to at least the other vehicles; Equipped with The prediction unit uses, as the prediction model, a model in which a Singer Model is applied to a Kinematic Bicycle Model, and a model that calculates acceleration as the predicted state value. Information provision system.

8. On the computer, an acquisition process for acquiring a state value indicating a state of the target vehicle; a prediction process in which, when a state value is input, the state value of the target vehicle is input to a prediction model that calculates a predicted state value indicating a state after a predetermined time has elapsed since the state corresponding to the state value was measured, and the predicted state value of the target vehicle calculated by the prediction model is obtained; an output process for outputting information based on the predicted state value of the target vehicle; Execute In the prediction process, a model in which a Singer Model is applied to a Kinematic Bicycle Model is used as the prediction model, and a model that calculates acceleration as the predicted state value is used. Information processing program.

9. an acquisition step in which the computer acquires a state value indicating a state of the target vehicle; a prediction step in which a computer inputs the state value of the target vehicle into a prediction model that calculates a predicted state value indicating a state after a predetermined time has elapsed since the state corresponding to the state value was measured, and obtains a predicted state value of the target vehicle calculated by the prediction model; an output step in which a computer outputs information based on the predicted state value of the target vehicle; Including, In the prediction step, a model in which a Singer Model is applied to a Kinematic Bicycle Model is used as the prediction model, and a model that calculates acceleration as the predicted state value is used. Information processing methods.

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