Information processing device, method, and program

Abstract

The invention relates to an information processing apparatus, method, and program. The information processing device (100) can control communication with a plurality of objects (200) to improve the completion degree of a traffic digital twin (130). An information processing device (100) is provided with: a communication unit (110) that acquires object data from a plurality of objects (200) by means of communication; a processing unit (120) that constructs a traffic digital twinning (130) in a virtual space, said traffic digital twinning (130) being synchronized with a real space time, on the basis of the object data acquired by the communication unit (110); and a determination unit (140) that determines, on the basis of object data around the undetermined region, the probability of presence of an object in the undetermined region in which no object data is present in the traffic digital twin (130), and notifies the processing unit (120) of the determined probability of presence of an object in the undetermined region.

Description

Technical Field

[0001] The present disclosure relates to an information processing device and the like that control communications with a plurality of vehicles. Background Art

[0002] Patent Document 1 discloses a system in which a server receives digital data (vehicle data) describing vehicle states and vehicle behaviors from a plurality of vehicles and updates a traffic digital twin constructed in the server based on the received vehicle data.

[0003] Patent Document 1: Japanese Patent Application Publication No. 2020-013557

[0004] In areas without vehicle data, it is sometimes unclear whether an object exists there. If there are such areas where the existence of an object is unclear, the completeness of the traffic digital twin is reduced. Therefore, it is expected to reduce the areas where the existence of an object is unclear to improve the completeness of the traffic digital twin. Summary of the invention

[0005] The present disclosure has been made in view of the above-mentioned problems, and an object thereof is to provide an information processing device and the like that can improve the degree of completeness of a traffic digital twin.

[0006] In order to solve the above-mentioned problems, one method of the disclosed technology is an information processing device that controls communication with multiple objects, comprising: a communication unit, which obtains object data from multiple objects through communication; a processing unit, which constructs a traffic digital twin in virtual space that is synchronized with the real space at all times based on the object data obtained by the communication unit; and a determination unit, which determines the existence probability of objects in an undetermined area in the traffic digital twin that has no object data based on object data around the undetermined area, and notifies the processing unit of the determined existence probability of objects in the undetermined area.

[0007] According to the information processing device disclosed above, the degree of completion of the traffic digital twin can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 This is a schematic diagram of a digital twin system including an information processing device according to an embodiment of the present disclosure.

[0009] Figure 2 This is a flowchart of a process for determining the existence probability of an object executed by an information processing device.

[0010] Figure 3 This is an example of a correspondence map used in the process of determining the existence probability of an object.

[0011] Figure 4This is an example of a correspondence map used in the process of determining the existence probability of an object.

[0012] Figure 5 This is an example of a correspondence map used in the process of determining the existence probability of an object.

[0013] Figure 6 This is a flowchart of a vehicle control instruction process executed by an information processing device.

[0014] Figure 7 It is a specific schematic diagram for explaining the control instruction processing of the vehicle. DETAILED DESCRIPTION

[0015] When there is an undetermined area without direct data in the digital twin constructed based on various data obtained from the object, the information processing device of the present disclosure determines the existence probability of the object in the undetermined area based on the obtained data. By reflecting the determined existence probability of the object in the construction of the digital twin to supplement the undetermined area, the completeness of the digital twin can be improved.

[0016] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings.

[0017] <Implementation Method>

[0018] [constitute]

[0019] Figure 1 1 is a schematic diagram showing an overall configuration example of a digital twin system 10 including an information processing device 100 according to an embodiment of the present disclosure. Figure 1 The digital twin system 10 illustrated in FIG. 1 includes an information processing device 100 and a plurality of objects 200. The information processing device 100 and the plurality of objects 200 are connected to each other so as to be communicable directly or via a communication base station (not shown).

[0020] The information processing device 100 is configured to communicate with a plurality of objects 200. The information processing device 100 can provide a predetermined service, for example, for a specific object 200 based on object data including information related to the state of the object obtained from each of the plurality of objects 200. As the object 200, a mobile body such as a vehicle and a smartphone can be exemplified. When the object 200 is a vehicle, a traffic control service can be provided, for example, for a specific vehicle 200 based on vehicle data including information related to the state of the vehicle obtained from each of the plurality of vehicles 200. As the information processing device 100, a cloud server configured on the cloud can be exemplified.

[0021] 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. The 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 hard disk drive (HDD), a solid state drive (SSD) and other readable and writable storage media, and an input and output interface, etc. The processor reads out the program stored in the memory and executes it to realize all or part of the functions performed by the communication unit 110, the processing unit 120, the determination unit 140, and the control unit 150.

[0022] The communication unit 110 is a structure for performing communication with multiple objects 200 to receive (acquire) object data including information related to the state of the object, data related to the generation of the digital twin 130, and communication requests related to the specified service from the multiple objects 200. In the case where the object 200 is a vehicle, the communication unit 110 is used to perform communication with multiple vehicles 200 to receive (acquire) vehicle data such as the position, speed, and driving direction of the vehicle as information related to the state of the vehicle, data related to the surroundings of the vehicle as data related to the generation of the digital twin 130, and data related to the communication quality, and communication requests related to the specified service from the multiple vehicles 200. In addition, the communication unit 110 can send information, data, control instructions, etc. required for the specified service to the object 200 that sends the communication request among the multiple objects 200.

[0023] The processing unit 120 is in charge of the overall control of the information processing device 100, including communication with multiple objects 200, management of the digital twin 130, etc. In particular, when constructing a traffic digital twin based on the digital twin 130, the processing unit 120 of this embodiment improves the completeness of the traffic digital twin by considering or reflecting the existence probability of objects in the undetermined area determined by the determination unit 140 described later.

[0024] The digital twin 130 is a database for reproducing a virtual world (virtual space) synchronized with the real world (real space) on a cloud computer by updating and storing the current and past data related to the state of the object obtained (collected) from multiple objects 200 in real time. In the case where the object 200 is a vehicle, a traffic digital twin can be generated in the digital twin 130, which copies all objects (moving objects / stationary objects) and traffic conditions on the driving path in places (roads, parking lots, etc.) where the vehicles participating in the digital twin system 10 including multiple vehicles 200 can travel. As information included in the data stored in the digital twin 130, vehicle information (VIN, etc.), information related to other vehicle traffic (including bicycles, pedestrians, etc.), map information, time information (timestamp), location information (GPS latitude / longitude), and trajectory information (vehicle speed, direction, etc.) as a driving track, etc. can be exemplified.

[0025] When there is an area without direct object data in the traffic digital twin constructed by the processing unit 120, that is, an "uncertain area", the decision unit 140 determines the "object existence probability" that represents the probability of the object 200 existing in the uncertain area. The object existence probability takes a value in the range of 0 to 100%, for example, a parameter that gradually decreases during the movement estimation period until the next object data is received, taking the time when the information processing device 100 receives the object data from the object 200 as the maximum value of 100%. The ratio of the object existence probability reduction can be determined one by one according to the state of each object 200, etc.

[0026] The control unit 150 derives the probability of collision between the multiple objects 200 based on the existence probability of the objects in the traffic digital twin, and determines the object control values ​​indicated to the multiple objects 200 based on the probability of collision. The method of determining the object control value involved in the control unit 150 will be described later.

[0027] The object 200 is a movable body (mobility) such as a vehicle configured to communicate with the information processing device 100. The object 200 can provide information related to 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 prescribed services to the information processing device 100 via a communication device not shown in the figure. When the object 200 is a vehicle, the information related to the state of the vehicle includes the position of the vehicle, the speed of the vehicle, and the direction of travel of the vehicle. When the object 200 is a vehicle, the data related to the generation of the digital twin 130 includes data related to other vehicles, buildings, pedestrians, and other objects existing around the vehicle 200. These information and data can be obtained using various sensors (not shown) mounted on the object (vehicle) 200. The number of objects (vehicles) 200 communicating with the information processing device 100 is not particularly limited.

[0028] [control]

[0029] Next, refer to Figure 2 to Figure 7 The control performed by the information processing device 100 according to the present embodiment is described. As the processing performed by the information processing device 100, there can be exemplified a process of determining the existence probability of an object in an undetermined area within a traffic digital twin and a process of instructing the control of the vehicle 200 using the existence probability of the object of each vehicle 200 within the traffic digital twin. Hereinafter, the features implemented by the information processing device 100 will be described by taking the case where the object 200 is a vehicle as an example.

[0030] (1) The object's existence probability determines the processing

[0031] Figure 2 This is a flowchart for explaining the steps of the object existence probability determination process performed by each component of the information processing device 100. For example, if the traffic digital twin is started (built), the Figure 2 The existence probability determination process of the exemplified object is repeatedly performed until the activation (construction) of the traffic digital twin is completed.

[0032] (Step S201)

[0033] The communication unit 110 of the information processing device 100 obtains vehicle data from each of the plurality of vehicles 200. The vehicle data may be requested by the information processing device 100 from the vehicle 200 based on a predetermined cycle, a judgment that the degree of completion of the traffic digital twin is reduced, etc., or may be sent by the vehicle 200 to the information processing device 100 at a predetermined cycle or a predetermined timing (occurrence of a specific event, etc.). The vehicle data obtained from the plurality of vehicles 200 is stored in the digital twin 130.

[0034] When the vehicle data is acquired from the plurality of vehicles 200 by the communication unit 110 , the process proceeds to step S202 .

[0035] (Step S202)

[0036] The processing unit 120 of the information processing device 100 refers to the digital twin 130 to construct a traffic digital twin based on vehicle data obtained from multiple vehicles 200. The construction of the traffic digital twin can use a known method. The information processing device 100 of this embodiment is characterized in that, in addition to the known method, the existence probability of objects in the undetermined area determined in step S205 described later is also considered or reflected in the construction of the traffic digital twin.

[0037] If the traffic digital twin is constructed by the processing unit 120, the process proceeds to step S203.

[0038] (Step S203)

[0039] The processing unit 120 of the information processing device 100 determines whether the degree of completion of the constructed traffic digital twin is below a predetermined threshold. This determination is made to determine whether the constructed traffic digital twin has reached a level that can be used in traffic control services for the vehicle 200. Therefore, the predetermined threshold is appropriately set based on whether the traffic digital twin can ensure a level that can remotely control the vehicle 200 safely and securely.

[0040] When the processing unit 120 determines that the degree of completion of the traffic digital twin is below the predetermined threshold (step S203, yes), the process proceeds to step S204. On the other hand, when the processing unit 120 determines that the degree of completion of the traffic digital twin exceeds the predetermined threshold (step S203, no), the process proceeds to step S201.

[0041] (Step S204)

[0042] When there is an area without direct vehicle data in the traffic digital twin constructed by the processing unit 120, that is, an undetermined area, the determination unit 140 of the information processing device 100 determines whether the existence of an object in the undetermined area can be inferred. As a method of inferring the existence of an object in the undetermined area, the following three methods can be exemplified.

[0043] a. Using the vehicle distance method

[0044] Consider a case where there is an undetermined area between a certain vehicle (the front vehicle) and the vehicle following the front vehicle (the rear vehicle). In this case, for example, if the distance between the front vehicle and the rear vehicle is shorter than the size of a specific vehicle (e.g., a light passenger car), it can be determined that there are no other vehicles or other moving objects in the undetermined area. On the other hand, for example, if the distance between the front vehicle and the rear vehicle is longer than the size of a specific vehicle (e.g., a bus or a truck), it can be inferred that there may be other vehicles or other moving objects in the undetermined area.

[0045] Therefore, when using the vehicle-to-vehicle distance technique, if the front vehicle and the rear vehicle located in front and rear of the undetermined area maintain a specified vehicle-to-vehicle distance (e.g., 4m to 6m) for a specified time (e.g., 10 seconds), it can be determined that the presence or absence of an object in the undetermined area can be inferred.

[0046] When the front vehicle and / or the rear vehicle are equipped with a millimeter wave radar, the presence of vehicles behind the front vehicle and / or in front of the rear vehicle can be detected by the millimeter wave radar. In this case, the inter-vehicle distance between the front vehicle and the rear vehicle, which is determined to be capable of inferring the presence of an object in the uncertain area, can be expanded compared to when the millimeter wave radar is not installed.

[0047] b. Using the distance from the stop line

[0048] Consider the case where an undetermined area exists in a specific place with a stop line drawn, such as an intersection with a traffic light or a railway crossing. In this case, for example, if a traveling vehicle stops at a position close to the stop line, it can be determined that there are no other moving objects such as other vehicles in the undetermined area between the center of the intersection and the vehicle or between the track and the vehicle. On the other hand, for example, if a traveling vehicle stops at a position far from the stop line, it can be inferred that there may be other moving objects such as other vehicles in the undetermined area.

[0049] Therefore, in the method using the distance to the stop line, if the distance from the stop line to the vehicle in the uncertain area of ​​a specific location is within a specified distance range (for example, 4m to 6m), it can be determined that the presence of an object in the uncertain area can be inferred.

[0050] c. Use of evasive action by vehicles

[0051] Consider the case where there is an undetermined area where a vehicle that is traveling performs a steering operation such as changing lanes to take evasive action. In this case, for example, when the number of vehicles taking evasive action is small, or when the period during which multiple vehicles take evasive action is short, it can be determined that there are no stationary objects such as buildings and parked vehicles in the undetermined area. On the other hand, for example, when the number of vehicles taking evasive action is large, or when the period during which multiple vehicles take evasive action is long (or continuing), it can be inferred that there may be stationary objects such as buildings and parked vehicles in the undetermined area.

[0052] Therefore, in the method using the evasive action of the vehicle, if the number of vehicles taking evasive action with respect to the undetermined area is greater than a specified number and the evasive action of the vehicle continues for a specified time, it can be determined that the presence or absence of an object in the undetermined area can be inferred.

[0053] If the determination unit 140 determines that the presence or absence of an object in the undetermined area can be estimated (step S204, yes), the process proceeds to step S205. On the other hand, if the determination unit 140 determines that the presence or absence of an object in the undetermined area cannot be estimated (step S204, no), the process proceeds to step S201.

[0054] (Step S205)

[0055] The determination unit 140 of the information processing device 100 determines the existence probability of an object in the undetermined region. The determination unit 140 determines the existence probability of an object in the undetermined region, for example, as follows.

[0056] In the case of the above-mentioned method using the vehicle distance, for example, it is possible to use Figure 3 The corresponding map shown in FIG. 1 determines the probability of existence of an object in the uncertain area according to the time T during which the vehicle distance is maintained. In addition, in the case of the above-mentioned method using the distance from the stop line, for example, Figure 4 The corresponding map shown in FIG. 1 determines the probability of existence of an object in the uncertain area according to the distance D from the stop line to the position where the vehicle stops. In addition, in the case of the above-mentioned method of using the vehicle's evasive action, for example, Figure 5 The correspondence map shown determines the probability of existence of an object in the uncertain area based on the number N of vehicles that have taken evasive actions and the time t during which the evasive actions have been continuously taken.

[0057] Such a corresponding map can be created in advance based on past performance of determining personal characteristics and road environment-related features through AI, etc. In addition, since the distance between vehicles and the like changes dynamically depending on driving conditions (day and night, weather, etc.), multiple corresponding maps can be prepared in advance according to various driving conditions.

[0058] When the determination unit 140 determines the existence probability of an object in the undetermined area, the process proceeds to step S206 .

[0059] (Step S206)

[0060] The determination unit 140 of the information processing device 100 reflects the determined existence probability of the object in the undetermined area in the construction of the traffic digital twin. This reflection is performed by the determination unit 140 notifying the processing unit 120 of the determined existence probability of the object in the undetermined area.

[0061] When the existence probability of the object in the undetermined area is reflected in the construction of the traffic digital twin by the decision unit 140 , the process proceeds to step S201 .

[0062] The processing is determined based on the existence probability of the object. Since the existence probability of the object in the undetermined area within the traffic digital twin can be effectively inferred, the completeness of the traffic digital twin can be improved by taking the existence probability of the object into consideration or reflecting it in the construction of the traffic digital twin.

[0063] (2) Vehicle control instruction processing

[0064] Figure 6 This is a flowchart for explaining the procedure of vehicle control instruction processing executed by the control unit 150 of the information processing device 100 . Figure 7 This is a schematic diagram for explaining the control instruction processing of the vehicle in an easy-to-understand specific manner. Figure 6 The illustrated vehicle control instruction process is started, for example, when a timing for instructing the target vehicle 200 on vehicle control values ​​related to traffic control has arrived.

[0065] (Step S601)

[0066] The control unit 150 of the information processing device 100 refers to the digital twin 130 to obtain the existence probability of the object for each vehicle 200 in the traffic digital twin. Figure 7 In the example, the control unit 150 obtains a 70% existence probability of the object being vehicle A, a 90% existence probability of the object being vehicle B, a 30% existence probability of the object being vehicle C, and a 60% existence probability of the object being vehicle D.

[0067] When the control unit 150 obtains the existence probability of the object for each vehicle 200 in the traffic digital twin, the process proceeds to step S602 .

[0068] (Step S602)

[0069] The control unit 150 of the information processing device 100 obtains the longest predicted time and collision margin time of 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 to the vehicle 200 can predict the behavior of the vehicle 200. In addition, the collision margin time is the time predicted by one or more applications that provide traffic control services to the vehicle 200 until a certain vehicle 200 collides with another vehicle 200. The maximum prediction time and collision margin time are variable values ​​that change according to the speed of the vehicle 200 and are predetermined by each application. One or more applications that provide traffic control services can be configured within the information processing device 100 or on a cloud outside the information processing device 100.

[0070] When the longest predicted time and the collision margin time of each vehicle 200 in the traffic digital twin are obtained by the control unit 150 , the process proceeds to step S603 .

[0071] (Step S603)

[0072] The control unit 150 of the information processing device 100 derives the margin coefficient of each vehicle 200 based on the maximum predicted time and the collision margin time obtained in step S602. The margin coefficient is a parameter indicating the ratio of the collision margin time to the maximum predicted time, and is calculated for each vehicle 200 using the following formula [1].

[0073] Margin factor = (collision margin time) / (maximum predicted time)…[1]

[0074] exist Figure 7 In the example of , when the longest predicted time of car A is "10 seconds" and the collision margin time of car A is "4 seconds", the margin coefficient of car A is "0.4 (=4 / 10)".

[0075] When the margin coefficient of each vehicle 200 is derived by the control unit 150 , the process proceeds to step S604 .

[0076] (Step S604)

[0077] The control unit 150 of the information processing device 100 derives the probability of collision between each vehicle 200 based on the object existence probability of each vehicle 200 obtained in the above step S601 and the margin coefficient of each vehicle 200 derived in the above step S603. The collision probability is a parameter that represents the possibility of collision between two vehicles 200 by probability, and is calculated by the following formula [2] for each combination of any two vehicles 200.

[0078] Probability of collision = (Probability of existence of the object of the first vehicle) × (Probability of existence of the object of the second vehicle) × (1 - margin factor of the first vehicle)…[2]

[0079] exist Figure 7 In the example, since the probability of the object of car A is "0.7", the probability of the object of car C is "0.3" and the margin coefficient of car A is "0.4", the probability of a collision with car C as seen from car A is "0.126 (=0.7×0.3×(1-0.4))".

[0080] When the control unit 150 derives the collision occurrence probability between each of the vehicles 200 , the process proceeds to step S605 .

[0081] (Step S605)

[0082] The control unit 150 of the information processing device 100 derives the impact degree of the collision between each vehicle 200 based on the collision occurrence probability between each vehicle 200 and the relative speed between each vehicle 200 derived in the above step S604. The impact degree of the collision is a parameter that quantifies the impact when two vehicles 200 collide, and is calculated by the following formula [3] for each combination of any two vehicles 200. The relative speed between the vehicles 200 is a vector with direction information, and can be obtained based on vehicle data received from the target vehicle 200.

[0083] Impact degree at the time of collision = (Probability of collision between vehicles) × (Relative speed between vehicles)…[3]

[0084] exist Figure 7 In the example, when the relative speed of car C as seen from car A is +50 m / s, since the probability of collision with car C as seen from car A is "0.126", the impact degree of the collision between car A and car C is "6.3 (=0.126×50)".

[0085] When the control unit 150 derives the degree of influence at the time of the collision between the vehicles 200 , the process proceeds to step S606 .

[0086] (Step S606)

[0087] The control unit 150 of the information processing device 100 determines the vehicle control value to be instructed to each vehicle 200 based on the impact degree when the collision between each vehicle 200 is derived in the above step S605 and the traffic conditions (surrounding conditions) around the vehicle 200. The vehicle control value is a value for performing control required to avoid collision between the vehicles 200, and is determined according to the following formula [4] for a specific vehicle 200 to be instructed.

[0088] Vehicle control value = (acceleration and deceleration control value) × (impact degree during collision)…[4]

[0089] exist Figure 7 In the example, the vehicle control value when instructing car A to stop in order to avoid a collision between car A and car C is determined by the following formula [5] as "-0.63 m / s 2 The vehicle control value when instructing vehicle C to pass is determined as +0.315 m / s according to the following formula [6]. 2 ”.

[0090] Vehicle control value of vehicle A = (deceleration required to stop at the intersection start position before the collision margin time) × (impact degree of collision between vehicle A and vehicle C)…[5]

[0091] =-10m / s 2 ×6.3

[0092] Vehicle control value of car C = (acceleration that allows it to pass the end position of the intersection before the collision margin time) × (impact degree of collision between car A and car C)…[6]

[0093] =+5m / s 2 ×6.3

[0094] As an example, the traffic conditions (surrounding conditions of objects) considered when determining vehicle control values ​​include infrastructure information, vehicle information, and environmental information. Infrastructure information includes: "traffic rules" information including data such as signs (temporary stop, caution), traffic light information (lighting color, remaining time), and speed limit, "road structure" information including data such as road width, number of lanes, crosswalks, road surface conditions, intersection locations (distance to intersections), surrounding obstacles, left and right turn lanes, and "temporary / temporary" information including data such as construction and lane restrictions. Vehicle information includes: "motion" information including data such as position, (on digital twin) existence probability, speed, acceleration, etc., "driver operation" information including data such as turn signal status, steering wheel steering angle, navigation settings (destination / route), accelerator opening, brake pedal force, etc., "product status" information including data such as vehicle type (light / ordinary / large / other), vehicle type (general vehicle / emergency vehicle), powertrain type, tire status, etc., "personality" information including data such as driving tendency (habits, etc.) and driver's response speed, etc. Environmental information includes: "natural elements" information including weather, humidity, temperature and other data, "human elements" information including data such as crowd flow, whether events are held, the location of pedestrians / bicycles, the location of other vehicles, and traffic congestion conditions.

[0095] When the vehicle control values ​​to be instructed to each vehicle 200 are determined by the control unit 150, the control instruction processing of the host vehicle is completed.

[0096] According to the control instruction processing of the vehicle, since the vehicle control value indicated to each vehicle 200 is controlled based on the existence probability of objects assigned to the multiple vehicles 200 that constitute the traffic digital twin, traffic conditions, etc., it is possible to effectively avoid the occurrence of collisions between the vehicles 200 and achieve smooth traffic flow.

[0097] The vehicle control value indicated to each vehicle 200 is determined to be appropriate content in accordance with the policy of compliance with traffic regulations, prevention of dangerous phenomena, and realization of smooth traffic flow based on the traffic regulations and actual traffic conditions of the country or region where the control is implemented. The range within which the vehicle control value should be determined can be set uniformly based on the content of the highest safety and security, or can be set independently for each country or region.

[0098] [Specific example]

[0099] For example, in Figure 7 In the example of FIG. 1 , several situations in which vehicle control values ​​may be indicated for vehicle A and vehicle C (or vehicle D) that are predicted to collide in the future are described, and specific examples of traffic conditions used to determine these situations are described.

[0100] (Scenario 1)

[0101] A case where a driver instructs vehicle A to slow down but does not instruct vehicle C (or vehicle D) to slow down

[0102] 1-1. Cases using the existence probability of an object

[0103] For vehicle C, which has a lower probability of existence than vehicle A, it is considered that the communication quality between the vehicle C and the information processing device 100 is poor, and the vehicle data is updated less frequently than that of vehicle A. That is, vehicle A is more likely to receive instructions related to vehicle control than vehicle C. Therefore, in order to give priority to the prevention of dangerous phenomena and more effectively reduce the possibility of collision between vehicles 200, vehicle A, which has a higher probability of existence, is instructed to decelerate.

[0104] 1-2. Use of infrastructure information (priority roads, traffic light status)

[0105] When the road on which car C is traveling has priority over the road on which car A is traveling (priority road), or when the traffic light on the road on which car C is traveling is green, etc., in order to achieve smooth traffic flow, car A is instructed to slow down, and car C is instructed to maintain its speed or accelerate.

[0106] (Scenario 2)

[0107] Situation where the driver does not indicate to slow down vehicle A but indicates to slow down vehicle C (or vehicle D)

[0108] 2-1. Use of vehicle information (body information, operation information)

[0109] If car A is an emergency vehicle and is predicted to enter the intersection, car C is instructed to slow down in order to give priority to the travel of car A. Furthermore, if car A is predicted to turn right based on the driver's operation (turn signal, etc.), car D is also instructed to slow down.

[0110] 2-2. Use of infrastructure information (road information) and vehicle information (location)

[0111] Even if the road on which vehicle C is traveling is a priority road and vehicle A is a general vehicle, when vehicle A has entered the intersection, in order to prevent priority dangerous phenomena and reduce the possibility of vehicles 200 colliding with each other, vehicle C (and vehicle D depending on the direction of travel) is instructed to slow down.

[0112] 2-3. Use of traffic information (vehicle parking time)

[0113] When a large number of vehicles are parked behind car A and car A itself has been parked for a long time, priority is given to achieving smooth traffic flow, and even if the road car C is traveling on is a priority road, car C (and car D depending on the direction of travel) is temporarily instructed to slow down.

[0114] 2-4. Use of Personal Information

[0115] In the case where a large number of vehicles are parked behind car A and car A itself is parked for a long time, emotion inference is performed based on the personality information of the drivers of each vehicle. In the case where the driver of car A feels great pressure, in order to prevent the driver of car A from becoming distracted due to pressure, even if the road where car C is traveling is a priority road, car C (and car D depending on the direction of travel) is temporarily instructed to slow down. However, in the case where it is better to make car A wait in consideration of the personality information of the drivers of cars C and D, other measures can be considered.

[0116] <Function / Effects, etc.>

[0117] As described above, according to the information processing device 100 involved in one embodiment of the present disclosure, when the degree of completion of the traffic digital twin is low, the existence probability of objects in the undetermined area that do not have direct object data in the traffic digital twin is determined based on the indirect object data that can be obtained from objects in other areas whose existence is determined. Moreover, the information processing device 100 considers or reflects the existence probability of objects in the determined undetermined area in the construction of the traffic digital twin to supplement the information of the undetermined area. In this way, the degree of completion of the traffic digital twin can be improved.

[0118] In addition, according to the information processing device 100 involved in one embodiment of the present disclosure, information related to collision is calculated based on the existence probability, traffic conditions, etc. given to multiple objects 200 constituting the traffic digital twin, and control of each object 200 is instructed based on the calculated information. As a result, vehicle control that effectively reflects the existence probability of the object can be implemented, and prevention of dangerous phenomena such as collision avoidance between objects 200 can be prioritized and smooth traffic flow can be achieved.

[0119] An embodiment of the present disclosure has been described above, 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 the method, a computer-readable non-temporary storage medium storing the program, and a system having an information processing device and a vehicle.

[0120] Industrial Application Possibility

[0121] The present disclosure is useful in cases where it is desired to improve the degree of completeness of a traffic digital twin in an information processing device.

[0122] Description of Reference Numerals

[0123] 10…digital twin system; 100…information processing device; 110…communication unit; 120…processing unit; 130…digital twin; 140…determination unit; 150…control unit; 200…object (vehicle).

Claims

1. An information processing device for controlling communication with a plurality of objects, wherein: have: a communication unit that acquires object data from the plurality of objects through communication; A processing unit, which constructs a traffic digital twin in virtual space that is synchronized with the real space at all times based on the object data obtained by the communication unit; as well as The determination unit determines the existence probability of the object in the undetermined area based on the object data around the undetermined area where the object data is not in the traffic digital twin, and notifies the processing unit of the determined existence probability of the object in the undetermined area.

2. The information processing device according to claim 1, wherein: The determination unit determines the existence probability of the object in the undetermined area of ​​the traffic digital twin when the degree of completion of the traffic digital twin is equal to or less than a predetermined threshold.

3. The information processing device according to claim 2, wherein: The object is a vehicle, The determination unit determines the existence probability of the object in the uncertain area based on an inter-vehicle distance between vehicles located in front and behind the uncertain area and a time for which the inter-vehicle distance is maintained.

4. The information processing device according to claim 2, wherein: The object is a vehicle, The determination unit determines the existence probability of the object in the uncertain area based on the distance from a stop line to the vehicle when the vehicle located behind the uncertain area stops at a specific location including an intersection and a railway crossing.

5. The information processing device according to claim 2, wherein: The object is a vehicle, The determination unit determines the existence probability of the object in the uncertain area based on the number of vehicles traveling to avoid the uncertain area and the duration of the avoiding action of the vehicles.

6. The information processing device according to claim 1, wherein: It also has a control unit that performs the following processing: The probability of collision between the plurality of objects is derived based on the existence probability of the objects possessed by the traffic digital twin, and the control values ​​indicated to the plurality of objects are determined based on the probability of collision between the plurality of objects.

7. The information processing device according to claim 6, wherein: The control unit derives the probability of collision between the plurality of objects based on a longest time that the behavior of the object can be predicted by one or more applications providing services to the object and a time that the one or more applications predict will take until two objects collide.

8. The information processing device according to claim 7, wherein: The control unit derives the degree of influence when the plurality of objects collide based on the probability that the plurality of objects collide with each other and the relative speeds between the plurality of objects.

9. The information processing device according to claim 8, wherein: The control unit determines the control values ​​to be indicated to the plurality of objects based on the influence degrees when the plurality of objects collide and the surrounding conditions of the objects.

10. A method, performed by a computer of an information processing device controlling communications with a plurality of objects, wherein: include: The step of acquiring object data from the plurality of objects through communication; A step of constructing a traffic digital twin in virtual space that is synchronized with real space at all times based on the object data obtained; A step of determining the existence probability of an object in the undetermined area based on the object data around the undetermined area for which there is no object data in the traffic digital twin; as well as The step of reflecting the determined existence probability of the object in the undetermined area in the construction of the traffic digital twin.

11. A program executed by a computer of an information processing device that controls communication with a plurality of objects, wherein: include: The step of acquiring object data from the plurality of objects through communication; A step of constructing a traffic digital twin in virtual space that is synchronized with real space at all times based on the object data obtained; A step of determining the existence probability of an object in the undetermined area based on the object data around the undetermined area for which there is no object data in the traffic digital twin; as well as The step of reflecting the determined existence probability of the object in the undetermined area in the construction of the traffic digital twin.

Images