Information processing device, method, and program

Abstract

To provide an information processing device which can improve the completion degree of a traffic digital twin.SOLUTION: An information processing device controls communication with a plurality of objects and comprises: a communication unit which acquires object data from the plurality of objects by communication; a processing unit which constructs a traffic digital twin time-synchronized with a real space on a virtual space on the basis of the object data acquired by the communication unit; and a determination unit which determines existence probability of an object in an indeterminate area in which no object data exists in the traffic digital twin on the basis of the object data around the indeterminate area to notify the processing unit of the determined existence probability of the object in the indeterminate area.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to an information processing device that controls communication with a plurality of vehicles. [Background technology]

[0002] Patent document 1 discloses a system in which a server receives digital data (vehicle data) describing the vehicle status and behavior from multiple vehicles, and updates a traffic digital twin constructed within the server based on this received vehicle data. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2020-013557 Summary of the Invention [Problem to be solved by the invention]

[0004] In areas where there is no vehicle data, it may be unclear whether an object exists there or not. The presence of such areas where the presence of objects is unknown reduces the completeness of the transportation digital twin. For this reason, it is desirable to reduce the areas where the presence of objects is unknown and improve the completeness of the transportation digital twin.

[0005] The present disclosure has been made in consideration of the above-mentioned problems, and aims to provide an information processing device and the like that can improve the completeness of transportation digital twins. [Means for solving the problem]

[0006] In order to solve the above problem, one aspect of the disclosed technology is an information processing device that controls communication with multiple objects, and includes: a communication unit that acquires object data from the multiple objects through communication; a processing unit that constructs a traffic digital twin in a virtual space that is time-synchronized with the real space based on the object data acquired by the communication unit; and a determination unit that determines the probability of an object's existence in an undetermined area in the traffic digital twin where there is no object data based on object data surrounding the undetermined area, and notifies the processing unit of the determined probability of an object's existence in the undetermined area. [Effects of the Invention]

[0007] According to the information processing device of the present disclosure, the degree of completion of a transportation digital twin can be improved. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a schematic configuration diagram of a digital twin system including an information processing device according to an embodiment of the present disclosure; [Figure 2] Flowchart of object existence probability determination process executed by information processing device [Figure 3] An example of a correspondence map used in determining the object existence probability [Figure 4] An example of a correspondence map used in determining the object existence probability [Figure 5] An example of a correspondence map used in determining the object existence probability [Figure 6] Flowchart of vehicle control instruction processing executed by an information processing device [Figure 7] A concrete image diagram explaining the vehicle control instruction processing DETAILED DESCRIPTION OF THE INVENTION

[0009] When a digital twin constructed from various data acquired from an object has an undetermined area for which there is no direct data, the information processing device of the present disclosure determines the probability of the object's existence in the undetermined area based on the acquired data. By reflecting this determined probability of the object's existence in the construction of the digital twin and complementing the undetermined area, the completeness of the digital twin can be improved. Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings.

[0010] <Embodiment> [composition] Fig. 1 is a schematic diagram showing an example of the overall configuration of a digital twin system 10 including an information processing device 100 according to an embodiment of the present disclosure. The digital twin system 10 illustrated in Fig. 1 is configured to include the information processing device 100 and a plurality of objects 200. The information processing device 100 and the plurality of objects 200 are communicatively connected to each other directly or via a communication base station (not shown).

[0011] The information processing device 100 is configured to be able to communicate with a plurality of objects 200. This information processing device 100 can provide a predetermined service to, for example, a specific object 200 based on object data including information on the state of the object acquired from each of the plurality of objects 200. Examples of the object 200 include moving objects such as vehicles and smartphones. If the object 200 is a vehicle, for example, a traffic control service can be provided to a specific vehicle 200 based on vehicle data including information on the state of the vehicle acquired from each of the plurality of vehicles 200. An example of the information processing device 100 is a cloud server configured on a cloud.

[0012] The information processing device 100 includes a communication unit 110, a processing unit 120, a digital twin 130, a determination unit 140, and a control unit 150. This information processing device 100 is typically configured to include a processor such as a CPU (Central Processing Unit), a memory such as a RAM (Random Access Memory), a readable and writable storage medium such as a hard disk drive (HDD) or a solid state drive (SSD), an input / output interface, and the like, and realizes all or part of the functions executed by the communication unit 110, the processing unit 120, the determination unit 140, and the control unit 150 by the processor reading and executing a program stored in the memory.

[0013] The communication unit 110 is configured to communicate with a plurality of objects 200 and receive (acquire) object data including information about the state of the object and data related to the generation of the digital twin 130, as well as communication requests related to predetermined services, from the plurality of objects 200. When the object 200 is a vehicle, the communication unit 110 communicates with the plurality of vehicles 200 and receives (acquires) vehicle data including information about the state of the vehicle, such as the vehicle's position, speed, and driving direction, and data related to the vehicle's surroundings and communication quality as data related to the generation of the digital twin 130, as well as communication requests related to predetermined services, from the plurality of vehicles 200. Furthermore, the communication unit 110 can transmit information, data, control instructions, and the like required for the predetermined service to an object 200 from among the plurality of objects 200 that has transmitted a communication request.

[0014] The processing unit 120 is responsible for overall control of the information processing device 100, including communication with multiple objects 200 and management of the digital twin 130. In particular, the processing unit 120 of this embodiment improves the completeness of a transportation digital twin by considering or reflecting the probability of objects existing in an undetermined area determined by the determination unit 140, which will be described later, when constructing a transportation digital twin based on the digital twin 130.

[0015] The digital twin 130 is a database that recreates a virtual world (virtual space) on a cloud computer that is time-synchronized with the real world (real space) by updating and storing data on the current and past states of multiple objects 200 acquired (collected) in real time. If the objects 200 are vehicles, the digital twin 130 can generate a traffic digital twin that replicates all of the objects (moving and stationary) and traffic conditions on the roads in locations (roads, parking lots, etc.) where vehicles participating in the digital twin system 10, including multiple vehicles 200, can travel. Examples of information included in the data stored in the digital twin 130 include vehicle information (such as VIN), information on other vehicle traffic (including bicycles, pedestrians, etc.), map information, time information (timestamps), location information (GPS latitude / longitude), and trajectory information (vehicle speed, direction, etc.).

[0016] When an "uncertain area" that is an area without direct object data exists in the traffic digital twin constructed by the processing unit 120, the determination unit 140 determines an "object presence probability" that indicates the probability that the object 200 exists in the uncertain area. This object presence probability takes a value in the range of 0 to 100%, and is a parameter that, for example, has a maximum value of 100% when the information processing device 100 receives object data from the object 200 and gradually decreases during the movement estimation period until the next time the object data is received. The rate at which the object presence probability decreases can be determined individually depending on the state of each object 200, etc.

[0017] The control unit 150 derives the probability of a collision occurring between the multiple objects 200 based on the existence probability of the objects in the traffic digital twin, and determines the object control values ​​to be instructed to the multiple objects 200 based on the probability of the collision occurring, etc. The method by which the control unit 150 determines the object control values ​​will be described later.

[0018] The object 200 is a mobility such as a vehicle configured to be able to communicate with the information processing device 100. The object 200 can provide the information processing device 100 with information about the state of the object, data related to the generation of the digital twin 130 constructed in the information processing device 100, and communication requests related to predetermined services via a communication device (not shown). When the object 200 is a vehicle, the information about the state of the vehicle itself includes the vehicle's position, vehicle speed, and vehicle traveling direction. When the object 200 is a vehicle, the data related to the generation of the digital twin 130 includes data about objects other than the vehicle itself, such as other vehicles, buildings, and pedestrians, which are objects present around the vehicle 200. Various sensors (not shown) mounted on the object (vehicle) 200 can be used to acquire this information and data. There is no particular limitation on the number of objects (vehicles) 200 that communicate with the information processing device 100.

[0019] [control] Next, the control executed by the information processing device 100 according to this embodiment will be described with further reference to Figures 2 to 7. Examples of the processing executed by the information processing device 100 include a process of determining the presence probability of an object in an undetermined area within the traffic digital twin, and a process of issuing an instruction to control the vehicle 200 using the presence probability of the object for each vehicle 200 within the traffic digital twin. Below, the features realized by the information processing device 100 will be described using the case where the object 200 is a vehicle as an example.

[0020] (1) Object existence probability determination process Fig. 2 is a flowchart illustrating the procedure of the object existence probability determination process executed by each component of the information processing device 100. The object existence probability determination process illustrated in Fig. 2 is started, for example, when the transportation digital twin is started (constructed), and is repeatedly performed until the traffic digital twin is started (constructed) and finished being started (constructed).

[0021] (Step S201) The communication unit 110 of the information processing device 100 acquires vehicle data from each of the multiple vehicles 200. The information processing device 100 may request this vehicle data from the vehicles 200 at a predetermined cycle or based on a determination of a decrease in the degree of completion of the traffic digital twin, or the vehicle 200 may transmit the vehicle data to the information processing device 100 at a predetermined cycle or at a predetermined timing (such as the occurrence of a specific event). The vehicle data acquired from the multiple vehicles 200 is stored in the digital twin 130.

[0022] Once the communication unit 110 has acquired the vehicle data from the plurality of vehicles 200, the process proceeds to step S202.

[0023] (Step S202) The processing unit 120 of the information processing device 100 references the digital twin 130 and constructs a transportation digital twin based on vehicle data acquired from multiple vehicles 200. Well-known techniques can be used to construct a transportation digital twin. In addition to using these well-known techniques, the information processing device 100 of this embodiment is characterized in that it takes into account or reflects the probability of an object's presence in the undetermined area determined in step S205, which will be described later, in constructing the transportation digital twin.

[0024] Once the transportation digital twin is constructed by the processing unit 120, the process proceeds to step S203.

[0025] (Step S203) The processing unit 120 of the information processing device 100 determines whether the degree of completion of the constructed transportation digital twin is equal to or lower than a predetermined threshold. This determination is made to determine whether the constructed transportation digital twin has reached a level at which it can be used for traffic control services for the vehicle 200. Therefore, the predetermined threshold is appropriately set based on factors such as whether the transportation digital twin has ensured a level at which the vehicle 200 can be remotely controlled safely and securely.

[0026] If the processing unit 120 determines that the degree of completion of the transportation digital twin is equal to or less than the predetermined threshold (step S203, Yes), the process proceeds to step S204. On the other hand, if the processing unit 120 determines that the degree of completion of the transportation digital twin exceeds the predetermined threshold (step S203, No), the process proceeds to step S201.

[0027] (Step S204) When an uncertain area, which is an area without direct vehicle data, exists in the traffic digital twin constructed by the processing unit 120, the determination unit 140 of the information processing device 100 determines whether it is possible to estimate the presence or absence of an object in the uncertain area. The following three methods can be given as examples of methods for estimating the presence or absence of an object in this uncertain area.

[0028] a. Method using inter-vehicle distance Consider a case where an uncertain area exists between a certain vehicle (a leading vehicle) and a vehicle (a trailing vehicle) following the leading vehicle. In this case, for example, if the distance between the leading vehicle and the trailing vehicle is shorter than the size of a specific vehicle (e.g., a light passenger car), it can be determined that no moving object such as another vehicle exists in this uncertain area. On the other hand, for example, if the distance between the leading vehicle and the trailing vehicle is longer than the size of a specific vehicle (e.g., a bus or a truck), it can be estimated that there is a possibility that a moving object such as another vehicle exists in this uncertain area.

[0029] Therefore, in this method using the inter-vehicle distance, if the leading vehicle and the trailing vehicle located in front and behind the uncertain area maintain a predetermined inter-vehicle distance (for example, 4m to 6m) for a predetermined period of time (for example, 10 seconds), it can be determined that it is possible to estimate the presence or absence of an object in the uncertain area.

[0030] If the front vehicle and / or rear vehicle are equipped with a millimeter wave radar or the like, the presence or absence of a vehicle behind the front vehicle and / or in front of the rear vehicle can be determined by the detection of the millimeter wave radar. In this case, the inter-vehicle distance between the front vehicle and the rear vehicle at which it is determined that it is possible to estimate the presence or absence of an object in the uncertain area can be extended compared to when the front vehicle and / or rear vehicle are not equipped with a millimeter wave radar or the like.

[0031] b. Method using distance from stop line Consider a case where there is an uncertain area at a specific location where a stop line is drawn, such as an intersection with a signal or a railroad crossing. In this case, for example, if a traveling vehicle stops at a position close to the stop line, it can be determined that no moving object, such as another vehicle, exists between the center of the intersection and the vehicle or between the tracks and the vehicle in this uncertain area. On the other hand, for example, if a traveling vehicle stops at a position away from the stop line, it can be estimated that there is a possibility that a moving object, such as another vehicle, exists in this uncertain area.

[0032] Therefore, in this method using the distance from the stop line, if the distance from the stop line to the vehicle in the uncertain area of ​​a specific location is within a predetermined distance range (for example, 4m to 6m), it can be determined that it is possible to estimate the presence or absence of an object in the uncertain area.

[0033] c. Methods using vehicle evasive actions Consider a case where there is an undetermined area where traveling vehicles are taking evasive action by steering, such as changing lanes. In this case, for example, if the number of vehicles that have taken evasive action is small or if the period during which multiple vehicles have taken evasive action is short, it is possible to determine that no stationary objects, such as buildings or parked vehicles, exist in this undetermined area. On the other hand, for example, if the number of vehicles that have taken evasive action is large or if the period during which multiple vehicles have taken evasive action is long (or is continuing), it can be estimated that there is a possibility that stationary objects, such as buildings or parked vehicles, exist in this undetermined area.

[0034] Therefore, in this method using vehicle evasive behavior, if there is a predetermined number or more of vehicles taking evasive behavior in an uncertain area and if the vehicles continue to take evasive behavior for a predetermined period of time, it can be determined that it is possible to estimate the presence or absence of an object in the uncertain area.

[0035] If the determination unit 140 determines that it is possible to estimate the presence or absence of an object in the undetermined area (step S204, Yes), the process proceeds to step S205. On the other hand, if the determination unit 140 determines that it is impossible to estimate the presence or absence of an object in the undetermined area (step S204, No), the process proceeds to step S201.

[0036] (Step S205) The determination unit 140 of the information processing device 100 determines the probability of an object existing in an undetermined area. The determination unit 140 determines the probability of an object existing in this undetermined area, for example, as follows.

[0037] In the case of the method using the inter-vehicle distance described above, the probability of an object existing in the uncertain area can be determined according to the time T during which the inter-vehicle distance is maintained, for example, using the correspondence map shown in Fig. 3. In the case of the method using the distance from the stop line described above, the probability of an object existing in the uncertain area can be determined according to the distance D from the stop line to the position where the vehicle has stopped, for example, using the correspondence map shown in Fig. 4. In the case of the method using the evasive behavior of the vehicle described above, the probability of an object existing in the uncertain area can be determined according to the number N of vehicles that have taken evasive behavior and the time t during which that evasive behavior is continuously performed, for example, using the correspondence map shown in Fig. 5.

[0038] Such correspondence maps can be created in advance based on past performance in which personal characteristics and characteristics of road environments have been determined using AI, etc. Furthermore, since inter-vehicle distances and the like change dynamically depending on driving conditions (day / night, weather, etc.), multiple correspondence maps may be prepared in advance to accommodate various driving conditions.

[0039] Once the determination unit 140 has determined the probability of an object existing in the undetermined area, the process proceeds to step S206.

[0040] (Step S206) The determination unit 140 of the information processing device 100 reflects the existence probability of an object in the determined undetermined area in the construction of a traffic digital twin. This reflection is performed by the determination unit 140 notifying the processing unit 120 of the existence probability of an object in the determined undetermined area, for example.

[0041] Once the determination unit 140 reflects the existence probability of objects in the undetermined area in the construction of the traffic digital twin, the process proceeds to step S201.

[0042] This object existence probability determination process makes it possible to effectively estimate the existence probability of an object in an undetermined area within a traffic digital twin. Therefore, by taking this object existence probability into consideration or reflecting it in the construction of the traffic digital twin, the completeness of the traffic digital twin can be improved.

[0043] (2) Vehicle control instruction processing Fig. 6 is a flowchart illustrating the procedure of the vehicle control instruction process executed by the control unit 150 of the information processing device 100. Fig. 7 is a specific image diagram for easily explaining the vehicle control instruction process. The vehicle control instruction process illustrated in Fig. 6 is started, for example, when it is time to instruct the target vehicle 200 on a vehicle control value related to traffic control.

[0044] (Step S601) The control unit 150 of the information processing device 100 acquires the object existence probability for each vehicle 200 in the traffic digital twin by referring to the digital twin 130. In the example of Fig. 7, the control unit 150 acquires the object existence probability of vehicle A as 70%, the object existence probability of vehicle B as 90%, the object existence probability of vehicle C as 30%, and the object existence probability of vehicle D as 60%.

[0045] When the control unit 150 acquires the object presence probability for each vehicle 200 in the traffic digital twin, the process proceeds to step S602.

[0046] (Step S602) The control unit 150 of the information processing device 100 acquires the longest predicted time and the collision grace period for each vehicle 200 in the traffic digital twin. Here, the longest predicted time is the longest time that one or more applications that provide traffic control services or the like to the vehicle 200 can predict the behavior of the vehicle 200. Furthermore, the collision grace period is the time that one or more applications that provide traffic control services or the like to the vehicle 200 predict that it will take for one vehicle 200 to collide with another vehicle 200. The longest predicted time and the collision grace period are variable values ​​that vary depending on the speed of the vehicle 200, and are determined in advance by each application. The one or more applications that provide traffic control services or the like may be configured within the information processing device 100 or on a cloud external to the information processing device 100.

[0047] When the control unit 150 acquires the longest predicted time and collision grace time for each vehicle 200 in the traffic digital twin, the process proceeds to step S603.

[0048] (Step S603) The control unit 150 of the information processing device 100 derives a grace period coefficient for each vehicle 200 based on the longest predicted time and the collision grace period acquired in step S602. This grace period coefficient is a parameter indicating the ratio of the collision grace period to the longest predicted time, and is calculated for each vehicle 200 by the following formula [1]. Grace factor = (collision grace time) / (longest predicted time) … [1]

[0049] In the example of FIG. 7, if the longest predicted time for vehicle A is "10 seconds" and the collision grace period for vehicle A is "4 seconds", the grace period coefficient for vehicle A is "0.4 (=4 / 10)".

[0050] Once the control unit 150 has derived the grace coefficient for each vehicle 200, the process proceeds to step S604.

[0051] (Step S604) The control unit 150 of the information processing device 100 derives the probability of collision between each of the vehicles 200 based on the object presence probability of each of the vehicles 200 acquired in step S601 and the grace coefficient of each of the vehicles 200 derived in step S603. This collision probability is a parameter indicating the probability of a collision between two vehicles 200, and is calculated for each combination of any two vehicles 200 using the following equation [2]. Collision probability = (probability of existence of an object in the first vehicle) × (probability of existence of an object in the second vehicle) × (1 - first vehicle's grace factor) … [2]

[0052] In the example of Figure 7, the probability of an object existing in vehicle A is "0.7", the probability of an object existing in vehicle C is "0.3", and the grace period coefficient for vehicle A is "0.4", so the probability of a collision occurring with vehicle C from the perspective of vehicle A is "0.126 (= 0.7 × 0.3 × (1 - 0.4))".

[0053] Once the control unit 150 has derived the collision probability between each of the vehicles 200, the process proceeds to step S605.

[0054] (Step S605) The control unit 150 of the information processing device 100 derives the impact of a collision between each of the vehicles 200 based on the collision probability between each of the vehicles 200 derived in step S604 and the relative speed between each of the vehicles 200. This impact of a collision is a parameter that quantifies the impact when two vehicles 200 collide, and is calculated for each combination of any two vehicles 200 using the following equation [3]. Note that the relative speed between the vehicles 200 is a vector quantity accompanied by directional information, and can be calculated based on vehicle data received from the target vehicles 200, etc. Impact of collision = (Probability of collision between vehicles) x (Relative speed between vehicles) … [3]

[0055] In the example of Figure 7, if the relative speed of car C as seen from car A is +50 m / s, the probability of a collision with car C as seen from car A is "0.126", so the impact of a collision between car A and car C is "6.3 (= 0.126 x 50)".

[0056] Once the control unit 150 has calculated the degree of impact of a collision between each of the vehicles 200, the process proceeds to step S606.

[0057] (Step S606) The control unit 150 of the information processing device 100 determines a vehicle control value to be instructed to each vehicle 200 based on the impact of a collision between the vehicles 200 calculated in step S605 above and the traffic conditions (surrounding conditions) around the vehicles 200. This vehicle control value is a value for performing control necessary to avoid a collision between the vehicles 200, and is determined for a specific vehicle 200 that requires instruction according to the following equation [4]. Vehicle control value = (acceleration / deceleration control value) × (impact of collision) … [4]

[0058] In the example of Figure 7, to avoid a collision between cars A and C, the vehicle control value when instructing car A to stop is "-0.63 m / s" according to the following formula [5]. 2 " and the vehicle control value when instructing car C to pass is "+0.315 m / s according to the following formula [6]. 2 " was decided. Vehicle control value for vehicle A = (deceleration that allows the vehicle to stop at the intersection start point within the collision grace period) × (impact of collision between car A and car C) … [5] =-10m / s 2 ×6.3 Vehicle control value for vehicle C = (acceleration that allows it to pass the intersection end position within the collision grace time) × (impact of collision between car A and car C) … [6] =+5m / s 2 ×6.3

[0059] Examples of traffic conditions (surrounding conditions of an object) that are taken into consideration when determining vehicle control values ​​include infrastructure information, vehicle information, and environmental information. Infrastructure information includes "traffic rules" information, which includes data on signs (stop, caution), traffic light information (light color, remaining time), and speed limits; "road structure" information, which includes data on road width, number of lanes, crosswalks, road surface conditions, intersection locations (distance to intersections), surrounding obstacles, and turn lanes; and "temporary" information, which includes data on construction work, lane restrictions, and so on. Vehicle information includes "motion" information such as location, existence probability (on the digital twin), speed, and acceleration, "driver operation" information such as turn signal status, steering angle, navigation settings (destination / route), accelerator position, and brake force, "product condition" information such as vehicle type (light / standard / large / other), vehicle type (general vehicle / emergency vehicle), powertrain type, and tire condition, and "personality" information such as driving tendencies (habits, etc.) and driver response speed. Environmental information includes "natural requirements" information such as weather, humidity, and temperature, and "human requirements" information such as people flow, whether or not an event is being held, pedestrian / bicycle positions, other vehicle positions, and traffic congestion status.

[0060] When the control unit 150 determines the vehicle control values ​​to be instructed to each vehicle 200, the vehicle control instruction process ends.

[0061] According to this vehicle control instruction processing, the vehicle control values ​​instructed to each vehicle 200 are controlled based on the probability of object presence and traffic conditions assigned to the multiple vehicles 200 that make up the traffic digital twin, thereby effectively avoiding collisions between these vehicles 200 and realizing smooth traffic flow.

[0062] The vehicle control values ​​to be instructed to each vehicle 200 are determined to be appropriate in accordance with policies such as compliance with traffic rules, prevention of dangerous events, and realization of smooth traffic flow, taking into consideration the traffic rules and actual traffic conditions of the country or region where this control is implemented. The range within which the vehicle control values ​​should be determined may be set uniformly to match the most safe and secure content, or may be set individually for each country or region.

[0063] [Specific example] For example, in the example of Figure 7, we will explain specific examples of traffic conditions used to determine several cases in which vehicle control values ​​may be instructed for vehicle A and vehicle C (or vehicle D), which are likely to collide in future predictions.

[0064] (Case 1) A case in which car A is instructed to slow down, but car C (or car D) is not instructed to slow down.

[0065] 1-1. Using the probability of object existence It is thought that vehicle C, which has a lower probability of existence than vehicle A, has poor communication quality with the information processing device 100 and updates its vehicle data less frequently than vehicle A. In other words, vehicle A is more likely to be able to receive instructions regarding vehicle control than vehicle C. Therefore, in order to prioritize prevention of dangerous events and more effectively reduce the possibility of the vehicles 200 colliding with each other, an instruction to decelerate is given to vehicle A, which has a higher probability of existence.

[0066] 1-2. When using infrastructure information (priority roads, traffic light status) When it is necessary to ensure smooth traffic flow, such as when the road on which vehicle C is traveling has priority over the road on which vehicle A is traveling (priority road) or when the traffic light on the road on which vehicle C is traveling is green, vehicle A is instructed to slow down and vehicle C is instructed to maintain its speed or accelerate.

[0067] (Case 2) A case in which car A is not instructed to slow down, and car C (or car D) is instructed to slow down

[0068] 2-1. When using vehicle information (vehicle information, operation information) If vehicle A is an emergency vehicle and is predicted to enter the intersection, vehicle C is instructed to slow down to give priority to vehicle A. Furthermore, if vehicle A is predicted to turn right due to driver operation (such as blinker), vehicle D is also instructed to slow down.

[0069] 2-2. When using infrastructure information (road information) and vehicle information (location) Even if the road on which vehicle C is traveling is a priority road and vehicle A is a general vehicle, if vehicle A has already entered the intersection, priority is given to preventing dangerous incidents, and an instruction is given to vehicle C (and vehicle D, depending on the direction of travel) to slow down in order to reduce the possibility of the vehicles 200 colliding with each other.

[0070] 2-3. When using traffic information (vehicle stop time) If there are many vehicles parked behind vehicle A and vehicle A itself has been stopped for a long time, smooth traffic flow will be prioritized, and even if the road on which vehicle C is traveling is a priority road, vehicle C (and vehicle D, depending on the direction of travel) will be temporarily instructed to slow down.

[0071] 2-4. When using personality information When there are many vehicles parked behind vehicle A and vehicle A itself has been parked for a long time, emotions are estimated from the personality information of the drivers of each vehicle, and if the driver of vehicle A is feeling great stress, an instruction is given to vehicle C (and vehicle D, depending on the direction of travel) to temporarily slow down in order to prevent the driver of vehicle A from becoming distracted due to stress, even if the road on which vehicle C is traveling is a priority road. However, if personality information of the drivers of vehicles C and D is taken into consideration and it is better to make vehicle A wait, a different response can be considered.

[0072] <Actions, effects, etc.> As described above, according to an information processing device 100 according to an embodiment of the present disclosure, when the degree of completion of a transportation digital twin is low, the existence probability of an object in an area in the transportation digital twin where there is no direct object data and the presence or absence is uncertain is determined based on indirect object data that can be obtained from objects in other areas where the presence is confirmed. The information processing device 100 then takes into account or reflects the determined existence probability of the object in the uncertain area in the construction of the transportation digital twin, thereby complementing the information of the uncertain area. This makes it possible to improve the degree of completion of the transportation digital twin.

[0073] Furthermore, according to the information processing device 100 according to an embodiment of the present disclosure, collision-related information is calculated based on the presence probabilities assigned to the multiple objects 200 constituting the traffic digital twin, traffic conditions, and the like, and control of each object 200 is instructed based on this calculated information. This makes it possible to implement vehicle control that effectively reflects the presence probabilities of objects, and realize smooth traffic flow while prioritizing the prevention of dangerous events such as avoiding collisions between objects 200.

[0074] The above describes one embodiment of the present disclosure, but the present disclosure can be understood not only as an information processing device, but also as a method executed by an information processing device having a processor and a memory, a program for executing this method, a computer-readable non-transitory storage medium storing the program, and a system including an information processing device and a vehicle. [Industrial Applicability]

[0075] The present disclosure is useful in cases where it is desired to improve the completeness of transportation digital twins in information processing devices. [Explanation of symbols]

[0076] 10 Digital Twin System 100 Information processing device 110 Communications Department 120 Processing section 130 Digital Twin 140 Decision Section 150 control section 200 objects (vehicles)

Claims

1. An information processing device for controlling communication with a plurality of objects, a communication unit that acquires object data from the plurality of objects through communication; a processing unit that constructs a transportation digital twin in a virtual space that is time-synchronized with a real space based on the object data acquired by the communication unit; a determination unit that determines the existence probability of an object in an uncertain area in the traffic digital twin where no object data exists, based on the object data around the uncertain area, and notifies the processing unit of the determined existence probability of the object in the uncertain area; The determination unit is an information processing device that determines the probability of an object existing in the undetermined area of ​​the traffic digital twin when the degree of completion of the traffic digital twin is below a predetermined threshold.

2. the object is a vehicle; The information processing device according to claim 1 , wherein the determination unit determines the probability of an object being present in the uncertain area based on an inter-vehicle distance between vehicles located in front of and behind the uncertain area and a time period during which the inter-vehicle distance is maintained.

3. the object is a vehicle; The information processing device according to claim 1 , wherein the determination unit determines the probability of an object being present in the uncertain area based on the distance from a stop line to a vehicle located behind the uncertain area when the vehicle stops at a specific location including an intersection or a railroad crossing.

4. the object is a vehicle; The information processing device according to claim 1 , wherein the determination unit determines the probability of an object being present in the uncertain area based on the number of vehicles traveling while avoiding the uncertain area and the duration of the avoidance behavior by the vehicles.

5. 2. The information processing device according to claim 1, further comprising: a control unit that derives a probability of a collision occurring between the plurality of objects based on the existence probability of the objects possessed by the traffic digital twin, and determines control values ​​to be instructed to the plurality of objects based on the probability of a collision occurring between the plurality of objects.

6. 6. The information processing device according to claim 5, wherein the control unit derives the probability of a collision occurring between the plurality of objects based on the longest time that one or more applications providing services to the objects can predict the behavior of the objects and the time that the one or more applications predict it will take for two of the objects to collide.

7. The information processing device according to claim 6 , wherein the control unit derives a degree of influence when the plurality of objects collide with each other based on a probability of occurrence of a collision between the plurality of objects and a relative speed between the plurality of objects.

8. The information processing device according to claim 7 , wherein the control unit determines the control values ​​to be instructed to the plurality of objects based on the degree of influence when the plurality of objects collide and a surrounding situation of the objects.

9. 1. A computer-implemented method for controlling communication with a plurality of objects, comprising: acquiring object data from the plurality of objects through communication; constructing a transportation digital twin in a virtual space that is time-synchronized with a real space based on the acquired object data; determining a probability of an object's presence in an uncertain area in the traffic digital twin where the object data is not present, based on the object data in the vicinity of the uncertain area; and reflecting the determined probability of the object's presence in the uncertain area in the construction of the traffic digital twin; The method, wherein the determining step determines the probability of an object's presence in the uncertain area of ​​the traffic digital twin when the completeness of the traffic digital twin is below a predetermined threshold.

10. A program executed by a computer of an information processing device that controls communication with a plurality of objects, acquiring object data from the plurality of objects through communication; constructing a transportation digital twin in a virtual space that is time-synchronized with a real space based on the acquired object data; determining a probability of an object's presence in an uncertain area in the traffic digital twin where the object data is not present, based on the object data in the vicinity of the uncertain area; and reflecting the determined probability of the object's presence in the uncertain area in the construction of the traffic digital twin; The determining step determines the probability of an object existing in the uncertain area of ​​the traffic digital twin when the degree of completion of the traffic digital twin is below a predetermined threshold.

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