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

Abstract

The goal is to obtain the information necessary to predict changes in the distance between vehicles moving at high speed while constantly changing their position and speed. [Solution] The information processing device (2) includes an acquisition unit (231) that acquires a state value indicating the state of the target vehicle (V1), a prediction unit (232) that inputs the state value of the target vehicle to a prediction model (222) that 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, 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. The prediction unit uses a model in which a Singer Model is applied to a Kinematic Bicycle Model as the prediction model, and uses a model that calculates acceleration as the predicted state value.

Description

[Technical Field]

[0001] This disclosure relates to an information processing device, an information provision system, an information processing program, and an information processing method. [Background technology]

[0002] Patent Document 1 describes a server comprising: an information receiving unit that receives location information and speed information of the first vehicle, information of detection results of unresponsive objects located on the road that do not respond to the second communication from the first vehicle, and information of measurement results of the location and speed of unresponsive objects from a first vehicle having a function to perform a first communication with a vehicle and a function to perform a second communication between the vehicle and a moving object; a vehicle information storage unit that stores the location information and speed information of the first vehicle, the information of detection results of unresponsive objects, and the information of measurement results of the location and speed of unresponsive objects received from the first vehicle; and an information processing unit that determines whether the unresponsive object is a vehicle or not based on the information of detection results of unresponsive objects, and if it is determined that the unresponsive object is a vehicle, stores the information of measurement results of the location and speed of the unresponsive object in the vehicle information storage unit as location information and speed information of a vehicle that does not support communication. [Prior art documents] [Patent Documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-160281 [Overview of the project] [Problems that the invention aims to solve]

[0004] Vehicles move at high speeds, and their position and speed are constantly changing. Therefore, a distance between vehicles that was appropriate at the time of measurement may become too close (potentially leading to a rear-end collision) immediately afterward (for example, a few seconds later). However, conventional technology has made it difficult to obtain the information necessary to predict such changes in vehicle distance. [Means for solving the problem]

[0005] To solve the above problems, an information processing device according to one aspect of the present disclosure includes: an acquisition unit that acquires a state value indicating the state of a target vehicle; a prediction unit that, upon inputting a state value, inputs the state value of the target vehicle to a prediction model that 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, and obtains a 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. The prediction unit uses a model in which a Singer Model is applied to a Kinematic Bicycle Model as the prediction model, and uses a model that calculates acceleration as the predicted state value.

[0006] Furthermore, an information provision system according to another aspect of this disclosure is installed near a road on which a target vehicle travels and comprises: an external sensor for detecting the state of the target vehicle; a calculation unit for calculating a state value indicating the state of the target vehicle based on the detection results of the external sensor; a prediction unit for obtaining a predicted state value for the target vehicle calculated by 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; and an output unit for transmitting information based on the predicted state value of the target vehicle to other vehicles traveling on the road, wherein the prediction unit uses a model that applies a Singer Model to a Kinematic Bicycle Model as the prediction model, and uses a model that calculates acceleration as the predicted state value.

[0007] Furthermore, an information provision system according to another aspect of this disclosure is installed on another vehicle traveling on the road on which the target vehicle is traveling, and comprises: an external sensor for detecting the state of the target vehicle; a calculation unit for calculating a state value indicating the state of the target vehicle based on the detection result of the external sensor; a prediction unit for obtaining a predicted state value of the target vehicle calculated by a prediction model for which the generated state value of the target vehicle is input when a state value is input, and the prediction unit for outputting information based on the predicted state value of the target vehicle to at least the other vehicle, wherein the prediction unit uses a model in which a Singer Model is applied to a Kinematic Bicycle Model as the prediction model, and uses a model for calculating acceleration as the predicted state value.

[0008] Furthermore, information processing programs in other aspects of this disclosure cause a computer to perform 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 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, and to obtain 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. In the prediction process, the prediction model is a model that applies a Singer Model to a Kinematic Bicycle Model, and the model used calculates acceleration as the predicted state value. Note that a computer-readable recording medium on which the information processing program is recorded also falls within the scope of this disclosure.

[0009] Furthermore, an information processing method relating to another aspect of this 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 measurement of the state corresponding to the state value, and obtains a 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 the prediction step uses a model in which a Singer Model is applied to a Kinematic Bicycle Model as the prediction model, and the model calculates acceleration as the predicted state value. [Brief explanation of the drawing]

[0010] [Figure 1] This is a schematic diagram showing an example of the schematic configuration of an information provision system according to one embodiment of the present disclosure. [Figure 2] This is a schematic diagram showing another example of the system's general configuration. [Figure 3] An example of the functional configuration of the system is shown in block number. [Figure 4] This diagram illustrates the method used to generate the predictive model employed by the system. [Figure 5] This figure shows the predictive model used by the system. [Figure 6] This flowchart shows an example of the flow of an information provision method according to another embodiment of the present disclosure. [Modes for carrying out the invention]

[0011] <Information Provision System 100> The following describes in detail an information provision system 100 according to one embodiment of this disclosure.

[0012] [Configuration of Information Provision System 100] The information providing system 100 is a system for providing information regarding a target vehicle traveling on a road R to other vehicles also traveling on the road R. As shown in FIG. 1, the information providing system 100 includes an external sensor 1 and an information processing device 2. The information providing system 100 according to the present embodiment further includes a calculation device 3. The other vehicles are so-called connected cars configured to be communicable with the information processing device 2. The target vehicle may be a connected car or a so-called non-connected car that does not have a function of communicating with the information processing device 2. Note that the information providing system 100 may include a plurality of external sensors 1. Also, 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 the present embodiment is provided near the road R on which the target vehicle V1 travels. The vicinity of the road R includes, for example, the side of the road R, above the road R, buildings, fences, etc. facing the road. Note that the external sensor 1 may be provided as an in-vehicle sensor on another vehicle V2 traveling on the road R on which the target vehicle V1 travels, as shown in FIG. 2. Also, the external sensor 1 detects the state of the target vehicle V1. Further, the external sensor 1 repeatedly detects the state of the target vehicle V1. The external sensor 1 according to the present embodiment is a LiDAR (Light Detection And Ranging) that detects the distance from the external sensor 1 to the target vehicle V1. That is, the external sensor 1 according to the present embodiment detects the distance from the external sensor 1 to the target vehicle V1. Note that the external sensor 1 may be a camera, radar, etc. 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 them may detect the state other than the target vehicle V1. When the external sensor 1 according to the present 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 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 the present embodiment calculates at least one of the position, azimuth angle, speed, and vehicle length of the target vehicle V1 as the state value based on the distance. If at least the position of the target vehicle V1 is included in the calculated state value, these can be obtained from the time-series change of the position even if the azimuth angle and speed are not obtained. The calculation device 3 calculates the state value at a predetermined cycle. Each time the calculation device 3 calculates the state value, it transmits the state value to the information processing device 2. Note that the calculation unit 31 may be provided in the external sensor 1 or in the information processing device 2. In this case, the information providing system 100 may not 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 23.

[0016] 〔Communication unit 21〕 The communication unit 21 communicates with the calculation device 3. The communication unit 21 according to the present embodiment also communicates with a vehicle having a communication function. The communication unit 21 according to the present embodiment is composed of 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 the information processing program 221. The information processing program 221 is a program that enables the computer to function as an information processing device 2. In this embodiment, the storage unit 22 stores the 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.) from the calculation unit 23. The storage unit 22 in this embodiment is composed of semiconductor memory, a hard disk drive, etc. Note that the storage unit 22 may be physically separated according to the contents to be stored (information processing program 221, prediction model 222, calculation results).

[0018] [Calculation unit 23] The calculation unit 23 comprises an acquisition unit 231 and a prediction unit 232. The calculation unit according to this embodiment further comprises a generation unit 233, a decision unit 234, a second decision unit 235, a correction unit 236, and an output processing unit 237. The calculation unit 23 according to this embodiment is composed of a processor (the information processing device 2 is composed of a computer). Therefore, the functions of each control block 231 to 237 are realized by the calculation unit 23 executing an acquisition process, a prediction process, a generation process, a decision process, a second decision process, and an output process according to 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 status value. In this embodiment, the acquisition unit 231 acquires the status value received by the communication unit 21 from the calculation device 3 each time the calculation device 3 transmits a status value. If multiple received status values ​​are accumulated in the queue, the acquisition unit 231 acquires them in order from the oldest status value indicated by the timestamp. In addition, the acquisition unit 231 in this embodiment further acquires a second status value from an on-board sensor installed on a vehicle traveling on the road R on which the target vehicle V1 is traveling. The second status value is a numerical value indicating the status of the target vehicle V1. The vehicle on which the on-board sensor is installed may be the target vehicle V1 or another vehicle V2. In this embodiment, the acquisition unit 231 acquires the second status value received by the communication unit 21 from the on-board sensor each time the on-board sensor transmits a second status value. If multiple received second status values ​​are accumulated in the queue, the acquisition unit 231 acquires them in order from the oldest time indicated by the timestamp. The second status values ​​acquired by the acquisition unit 231 according to this embodiment include the position (coordinates) of other vehicles V2.

[0020] (Prediction section 232) The prediction unit 232 performs prediction processing. In the prediction processing, the prediction unit 232 inputs the state value of the target vehicle V1 to the prediction model 222 and obtains the predicted state value of the target vehicle V1 calculated by the prediction model 222. The prediction unit 232 obtains the predicted state value each time the acquisition unit 231 acquires a state value.

[0021] The prediction model 222 is designed to calculate a predicted state value that indicates the state after a predetermined time has elapsed since the measurement of the state corresponding to the given state value, once a state value has been input. The predetermined time can be, for example, the elapsed time from the measurement of the state corresponding to the given 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 that indicates the state after a predetermined time has elapsed since the measurement, rather than at the time of acquisition, the effect of delay between the time of prediction, measurement, and acquisition can be eliminated. As a result, the generation unit 233, described later, will be able to reproduce a digital twin with higher accuracy.

[0022] Specifically, the prediction model 222 is a model that applies a Singer Model to a Kinematic Bicycle Model, and calculates acceleration as a predicted state value. Furthermore, because the prediction model 222 is based on the Kinematic Bicycle Model, it can also calculate position coordinates (x, y), etc., as predicted state values. As shown in Figure 4, the prediction model 222 according to this embodiment is obtained by solving the simultaneous equations of the differential equations constituting the Kinematic Bicycle Model and the differential equations constituting the Singer Model, discretizing them, and then linearizing them using the results. Therefore, as shown in Figure 5, the arguments of the sine and cosine functions included in the prediction model 222 according to this embodiment are equations that include the azimuth angle component Ψ or slip angle component β, translational velocity v or translational acceleration α, total length or wheelbase l, and elapsed time t from the time when the vehicle was in the prior state. The time when the vehicle was in the prior state includes, for example, the time when the state value was measured, the time when the prediction model 222 calculated the previous predicted state value, etc. The prediction model 222 may be configured to output a numerical value as a predicted state value that indicates the state after a predetermined time has elapsed since the acquisition of the state value by the acquisition unit 231, which will be described later. Furthermore, the prediction model 222 may be stored in a different storage device (not shown) 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 the state value and the second state value corresponding to the said state value to the prediction model 222 to obtain the predicted state value of the target vehicle V1 calculated by the prediction model 222. The prediction unit 232 may also be configured to input only the second state value to the prediction model 222 to obtain the predicted state value calculated by the prediction model 222.

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

[0025] (Second Judgment Department 235) The second determination unit 235 determines, each time the acquisition unit 231 acquires a status value, whether there is a correspondence between the newly acquired status value and the latest predicted status value calculated by the prediction model 222. The determination of whether there is a correspondence includes at least one of the following: whether the ID of the status value is the same as the ID of the predicted status value, and whether the distance (Euclidean distance, Mahalanobis distance, etc.) between the position of the target vehicle V1 indicated by the status value and the position of the target vehicle V1 indicated by the predicted status value is less than a threshold. If the second determination unit 235 determines that there is a correspondence between the status value and the predicted status value, it then associates the new status value with the latest predicted status value. As described above, the acquisition unit 231 further acquires a second status value. Therefore, in this embodiment, the second determination unit 235 also determines, each time the acquisition unit 231 acquires a second status value, whether there is a correspondence between the newly acquired second status value and the latest predicted status value. Then, if the second judgment 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, if the second judgment 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 transmitted the newly acquired state value is a new vehicle that was not previously targeted.

[0026] (Correction section 236) The correction unit 236 corrects the predicted state value associated with the state value based on the state value so that it approaches (matches) the state value. At the same time, the correction unit 236 also corrects the timestamp of the predicted state value so that it approaches (matches) the timestamp of the state value. In this embodiment, the correction unit 236 performs the correction using a Kalman filter. 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 generation processing. In the generation processing, the generation unit 233 generates a digital twin that reproduces the target vehicle V1 after a predetermined time has elapsed since the measurement of the state corresponding to the state value based on the predicted state value, and the surrounding environment of the target vehicle V1 in the virtual space. 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 the present embodiment generates a digital twin based on the predicted state value 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 arithmetic unit 23 may not include the generation unit 233.

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

[0029] Then the determination unit 234 calculates tau dot If the value is less than a predetermined value (for example, -0.5), it is determined that there is a high probability of a rear-end collision, and if it is -0.5 or greater, it is determined that there is a low probability of a rear-end collision. In this way, the determination unit 234 can compare the state of the target vehicle V1 and the state of the other vehicle V2 at the same time by making a determination based on the digital twin. dot Instead of calculating it, the system may be configured to determine whether or not there is a possibility of a rear-end collision using the second-order predicted value of TTC. 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 does not need to include the determination unit 234.

[0030] (Output processing unit 237) The output processing unit 237 executes output processing. In output processing, the output processing unit 237 according to this embodiment controls the communication unit 21. As a result, the communication unit 21 transmits (outputs) information based on the predicted state value of the target vehicle V1 to at least another vehicle V2. In other words, the communication unit 21 according to this embodiment also functions as an output unit. The information based on the predicted state value includes the digital twin generated by the generation unit 233. As described above, the calculation unit 23 according to this embodiment includes a determination unit 234. Therefore, the output processing unit 237 according to this embodiment also controls the communication unit 21 when the determination unit 234 determines that the target vehicle V1 may rear-end another vehicle V2. As a result, the communication unit 21 transmits information indicating that collision avoidance action will be taken to the other vehicle V2 as information based on the predicted state value. The output processing unit 237 may also be configured to transmit information based on the predicted state value to something other than the other vehicle V2 (the target vehicle V1, a remote monitoring device, etc.). Furthermore, if the information processing device 2 is installed inside the vehicle, the output processing unit 237 may be configured to supply information based on predicted state values ​​to the vehicle's display unit or the like. In this case, the terminals or the like connected to the vehicle in the information processing device become the output units.

[0031] [Effects of the Information Processing Device 2 (Information Provisioning System 100)] The information processing device 2 (information providing system 100) described above uses a prediction unit 232 that applies a Singer Model to a Kinematic Bicycle Model as its prediction model 222, 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) for the target vehicle V1 from the state values ​​of the target vehicle V1 obtained from the calculation unit 31. Using these predicted state values, it becomes possible to predict, for example, how the distance between the target vehicle V1 and another vehicle V2 traveling in the same direction ahead of the target vehicle V1 will change (whether there is a possibility of a rear-end collision). Therefore, according to the information processing device 2 (information providing system 100), it is possible to obtain the information (predicted acceleration values) necessary to predict changes in the distance between vehicles moving at high speed while constantly changing their position and speed.

[0032] By using this disclosure, safer autonomous driving technologies (autonomous vehicles, control devices, remote monitoring devices, etc.) can be realized. Therefore, by using this disclosure, it is possible to contribute to achieving Sustainable Development Goals (SDGs) such as Goal 9 "Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation" and Goal 11 "Make cities and human settlements inclusive, safe, resilient and sustainable."

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

[0034] [Flow of Information Processing Method S100] As shown in Figure 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 decision step S5, a second decision step S6, a correction step S7, and a third decision step S8.

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

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

[0037] [Second decision step S6] After acquiring the status value, the process moves to the second decision step S6. In the second decision step S6, each time the computer acquires a status value in the acquisition step S1, it determines whether there is a correspondence between the newly acquired status value and the latest predicted status value calculated by the prediction model 222. If it determines that there is a correspondence between the status value and the predicted status value (step S6: YES), the computer performs the association between the new status value and the latest predicted status value. As described above, in the acquisition step S1, the computer acquires a second status value. Therefore, each time the second status value is acquired in the acquisition step S1, in the second decision step S6 according to this embodiment, the computer also determines whether there is a correspondence between the newly acquired second status value and the latest predicted status value. If it determines that there is a correspondence between the second status value and the predicted status value, the computer also performs the association between the new second status value and the latest predicted status value. The computer performing the association may be the information processing device 2 or another device.

[0038] On the other hand, if in the second judgment step S6 it is determined that there is no correspondence between the newly acquired status value and the latest predicted status value (the newly acquired status value could not be associated with the latest predicted status value) (step S6: NO), the computer determines that the vehicle that transmitted the newly acquired status value is a new vehicle that had not been targeted before. In this case, the computer starts the information processing method (steps S1 to S8) for the target vehicle V1 for the new vehicle. After determining in the second judgment step S6 that there is no correspondence between the status value and the predicted status value, it may further determine whether the determination that there is no correspondence has been repeated a predetermined number of times. If it is determined that the determination that there is no correspondence has been repeated a predetermined number of times, the computer may determine that the vehicle that transmitted the newly acquired status value is a new vehicle that had not been targeted before.

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

[0040] [Generation Step S4] After obtaining the predicted state values, the process moves to generation step S4. In generation step S4, the computer generates a digital twin in virtual space that reproduces the target vehicle V1 and its surrounding environment after a predetermined time has elapsed since the measurement of the state corresponding to the state value, based on the predicted state values. In generation step S4, a digital twin is generated each time the prediction model 222 generates predicted state values. In generation step S4 according to this embodiment, the 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 or another device.

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

[0042] [Output step S3] After obtaining the predicted state value, the process moves to output step S3. In output step S3, the computer outputs information based on the predicted state value of the target vehicle V1. In output step S3 according to this embodiment, the computer transmits information based on the predicted state value of the target vehicle V1 (digital twin, information indicating that collision avoidance action will be taken, etc.) to at least another vehicle V2. The computer that outputs the information based on the predicted state value may be the information processing device 2 described above, or it may be another device. In addition, in output step S3, the information based on the predicted state value may be output to something other than the other vehicle V2 (target vehicle V1, remote monitoring device, etc.).

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

[0044] [Effects and 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 information (predicted acceleration values) necessary to predict changes in the distance between vehicles moving at high speed while constantly changing their position and velocity.

[0045] <Variation> This disclosure is not limited to the embodiments described above, 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 this disclosure.

[0046] For example, the information provision system 100 according to the above embodiment generates predicted state values ​​using state values ​​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 predicted state values ​​using state values ​​of itself (the target vehicle V1) generated by the built-in sensors of the target vehicle V1.

[0047] Furthermore, the above-mentioned information processing program may be recorded on one or more computer-readable recording media, rather than on a temporary basis. Each unit may or may not have such recording media. 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 each control block 231 to 237 provided by the above-mentioned arithmetic unit can also be realized by logic circuits. For example, an integrated circuit in which logic circuits functioning as each control block 231 to 235 are formed is also included in the scope of this disclosure. In addition, it is also possible to realize the functions of each part by, for example, a quantum computer.

[0049] 〔summary〕 The information processing device according to Embodiment 1 of the present disclosure comprises: an acquisition unit that acquires a state value indicating the state of the target vehicle; a prediction unit that, upon inputting the state value of the target vehicle, inputs the state value of the target vehicle to a prediction model that 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, and obtains a 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. The prediction unit is configured to use a model in which a Singer Model is applied to a Kinematic Bicycle Model as the prediction model, and to use a model that calculates acceleration as the predicted state value. In the information processing device according to Embodiment 2 of the present disclosure, the prediction unit may be configured such that, as the prediction model, the arguments of the sine function and cosine function included in the prediction model are formulas that include the azimuth angle component or slip angle component, translational speed or translational acceleration, wheelbase, and elapsed time from the time in the prior state for the target vehicle. The information processing device according to Embodiment 3 of the present disclosure may further include, in Embodiment 1 or 2 above, a generation unit that generates a digital twin in a virtual space which reproduces the target vehicle and the surrounding environment of the target vehicle after a predetermined time has elapsed since the measurement of the state corresponding to the state value, based on the predicted state value. The information processing device according to Embodiment 4 of the present disclosure may further include, in Embodiment 1 or 2 above, a determination unit that determines, based on the acceleration, whether or not there is a possibility that the target vehicle will rear-end another vehicle when there is another vehicle traveling in the same direction as the target vehicle in front of the target vehicle, and the output unit may be configured to output information to the other vehicle indicating that it will take action to avoid the collision when the determination unit determines that there is a possibility that the target vehicle will rear-end the other vehicle. The information processing device according to Embodiment 5 of the present disclosure may be configured such that, in Embodiment 1 above, the acquisition unit acquires a second state value indicating the state of the target vehicle from an on-board sensor provided on a vehicle traveling on a road on which the target vehicle is traveling, and the prediction unit inputs the state value and the second state value to the prediction model to obtain a predicted state value of the target vehicle calculated by the prediction model. The information provision system according to aspect 6 of the present disclosure is installed near a road on which a target vehicle travels and comprises: an external sensor for detecting the state of the target vehicle; a calculation unit for calculating a state value indicating the state of the target vehicle based on the detection results of the external sensor; a prediction unit for obtaining a predicted state value for the target vehicle calculated by 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; and an output unit for transmitting information based on the predicted state value of the target vehicle to other vehicles traveling on the road. The prediction unit is configured to use a model in which a Singer Model is applied to a Kinematic Bicycle Model as the prediction model, and to use a model that calculates acceleration as the predicted state value. The information provision system according to aspect 7 of the present disclosure is installed on another vehicle traveling on the road on which the target vehicle is traveling, and comprises: an external sensor for detecting the state of the target vehicle; a calculation unit for calculating a state value indicating the state of the target vehicle based on the detection result of the external sensor; a prediction unit for obtaining a predicted state value of the target vehicle calculated by a prediction model for which the generated state value of the target vehicle is input when a state value is input, and the prediction unit for outputting information based on the predicted state value of the target vehicle to at least the other vehicle, wherein the prediction unit is configured to use a model in which a Singer Model is applied to a Kinematic Bicycle Model as the prediction model, and to use a model that calculates acceleration as the predicted state value. The information processing program according to aspect 8 of this disclosure causes a computer to perform an acquisition process to acquire a state value indicating the state of a 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 the 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; and an output process that outputs information based on the predicted state value of the target vehicle. In the prediction process, the prediction model is a model in which a Singer Model is applied to a Kinematic Bicycle Model, and the model used calculates acceleration as the predicted state value. The information processing method according to aspect 9 of this 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 measurement of the state corresponding to the state value, and obtains a 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 in which a Singer Model is applied to a Kinematic Bicycle Model, and the model used calculates acceleration as the predicted state value. [Explanation of Symbols]

[0050] 100 Information Provision System 1. External sensor 2. Information Processing Device 21. Communication section (output section) 22 Memory section 221 Information Processing Programs 222 Predictive Models 23 Arithmetic section 231 Acquisition Department 232 Prediction Section 233 Generation part 234 Judgment Department 235 Second Judgment Department 236 Correction section 237 Output Processing Unit 3. Calculation device 31 Calculation Section S100 Information Processing Method S1 Acquisition Steps S2 Prediction Step S3 Output Step S4 Generation Step S5 Decision Step S6 Second Decision Step S7 Correction Step S8 Third Decision Step V1 Applicable Vehicles V2 Other vehicles

Claims

1. An acquisition unit that acquires a status value indicating the status of the target vehicle, A prediction unit inputs the status value of the target vehicle to a prediction model that calculates a predicted status value indicating the state after a predetermined time has elapsed since the measurement of the state corresponding to the status value, and obtains the predicted status value of the target vehicle calculated by the prediction model. An output unit that outputs information based on the predicted status values ​​of the target vehicle, Equipped with, The prediction unit uses a model that applies a Singer Model to a Kinematic Bicycle Model as the prediction model, and uses a model that calculates acceleration as the predicted state value. Information processing device.

2. The prediction unit uses a prediction model in which the arguments of the sine and cosine functions included in the prediction model are formulas that include the azimuth angle component or slip angle component, translational speed or translational acceleration, wheelbase, and elapsed time from the time when the vehicle was in the prior state. The information processing apparatus according to claim 1.

3. The system further includes a generation unit that generates a digital twin in a virtual space, which reproduces the target vehicle and its surrounding environment after a predetermined time has elapsed since the measurement of the state, based on the predicted state value. The information processing apparatus according to claim 1 or 2.

4. The system further includes a determination unit that, when another vehicle is traveling in the same direction as the target vehicle in front of the target vehicle, determines, based on the acceleration, whether or not there is a possibility that the target vehicle will rear-end the other vehicle. The output unit outputs information to the other vehicle indicating that it will take action to avoid a collision when the determination unit determines that the target vehicle is likely to rear-end the other vehicle. The information processing apparatus according to claim 1 or 2.

5. The acquisition unit acquires a second status value indicating the status of the target vehicle from an on-board sensor installed on a vehicle traveling on the 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 the predicted state value of the target vehicle calculated by the prediction model. The information processing apparatus according to claim 1.

6. An external sensor is installed near the road on which the target vehicle travels and detects the status of the target vehicle, A calculation unit calculates a state value indicating the state of the target vehicle based on the detection results of the external sensor, A prediction unit inputs the generated state value of the target vehicle to a prediction model that 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, and obtains the predicted state value of the target vehicle calculated by the prediction model. An output unit that transmits information based on the predicted status values ​​of the target vehicle to other vehicles traveling on the road, Equipped with, The prediction unit uses a model that applies a Singer Model to a Kinematic Bicycle Model as the prediction model, and uses a model that calculates acceleration as the predicted state value. Information provision system.

7. An external sensor is installed on another vehicle traveling on the same road as the target vehicle, and detects the status of the target vehicle. A calculation unit calculates a state value indicating the state of the target vehicle based on the detection results of the external sensor, A prediction unit inputs the generated state value of the target vehicle to a prediction model that 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, 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 status value of the target vehicle to at least the other vehicle, Equipped with, The prediction unit uses a model that applies a Singer Model to a Kinematic Bicycle Model as the prediction model, and uses a model that calculates acceleration as the predicted state value. Information provision system.

8. On the computer, A process to obtain status values ​​that indicate the status of the target vehicle, When a state value is input, the system inputs the state value of the target vehicle to a prediction model that calculates a predicted state value indicating the state after a predetermined time has elapsed since the measurement of the state corresponding to that state value, and performs a prediction process to obtain the predicted state value of the target vehicle calculated by the prediction model. Output processing that outputs information based on the predicted status values ​​of the aforementioned target vehicle, Make it run, In the prediction process described above, the prediction model is a model that applies a Singer Model to a Kinematic Bicycle Model, and the model used calculates acceleration as the predicted state value. Information processing program.

9. The computer obtains a status value indicating the status of the target vehicle in an acquisition step, 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 the 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, The computer outputs information based on the predicted state values ​​of the target vehicle in an output step, Includes, In the prediction step, the prediction model is a model that applies a Singer Model to a Kinematic Bicycle Model, and the model used calculates acceleration as the predicted state value. Information processing methods.

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