Scenario data generation system, method, and program

Abstract

To enable the creation of desired scenario data for simulations such as autonomous driving with minimal effort. [Solution] The scenario data generation system comprises a memory device and a processor. The memory device stores scenario data containing information about the actions of one or more objects at each sample time in a scene in which the objects are operating, and prompt data that instructs the generative artificial intelligence to perform a desired action on the object. The processor starts a step-by-step simulation at each sample time based on the scenario data. In each step, the generative artificial intelligence generates a next state, which is the state of the object at the next sample time, based on a desired action on the state of the object in the scenario data. The system updates the state of the object at the next sample time in the scenario data to the next state, and outputs the scenario data when the simulation is completed.

Description

Technical Field

[0001] This disclosure relates to a technique for generating scenario data.

Background Art

[0002] In the development of an automated driving system or a driving support system, the operation of the system is verified by simulating the scenes around the vehicle (see Patent Document 1). Since it is necessary to verify various scenes encountered during driving of a vehicle, it is required to prepare a large amount of scenario data.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The work of creating scenario data for simulating vehicle driving is not easy, and a great deal of labor is required to create a large amount of scenario data. Further, in the development of an automated driving system or a driving support system, scenario data for scenes with few chances of encounter such as dangerous scenes is also desired. However, when creating scenario data based on images captured by a drive recorder or the like, images of a desired scene are not always obtained by the drive recorder or the like.

[0005] One object included in this disclosure is to provide a technique that enables creation of desired scenario data with less labor.

Means for Solving the Problems

[0006] <00000​​The storage device stores scenario data containing information about the actions of one or more objects at each sample time in a scene in which the objects are operating, and prompt data that instructs the generative artificial intelligence to perform a desired action of the object. The aforementioned processor, Start a step-by-step simulation for each sample time based on the aforementioned scenario data. In each step, The generative artificial intelligence is instructed to generate the next state, which is the state of the object at the next sample time, based on the desired action performed on the state of the object in the scenario data. The state of the object at the next sample time in the scenario data is updated to the next state. The scenario data is output when the simulation is completed.

[0007] One aspect of the scenario data generation method included in this disclosure is a scenario data generation method using a device having a storage device and a processor, The storage device stores scenario data containing information about the actions of one or more objects at each sample time in a scene in which the objects are operating, and prompt data that instructs the generative artificial intelligence to perform a desired action of the object. The aforementioned processor, Start a step-by-step simulation for each sample time based on the aforementioned scenario data. In each step, The generative artificial intelligence is instructed to generate the next state, which is the state of the object at the next sample time, based on the desired action performed on the state of the object in the scenario data. The state of the object at the next sample time in the scenario data is updated to the next state. The scenario data is output when the simulation is completed.

[0008] A scenario data generation program according to one aspect of this disclosure is a scenario data generation program that causes a device having a storage device and a processor to generate scenario data, The storage device stores scenario data containing information about the actions of one or more objects at each sample time in a scene in which the objects are operating, and prompt data that instructs the generative artificial intelligence to perform a desired action of the object. By executing the scenario data generation program, the processor will Start a step-by-step simulation for each sample time based on the aforementioned scenario data. In each step, The generative artificial intelligence is instructed to generate the next state, which is the state of the object at the next sample time, based on the desired action performed on the state of the object in the scenario data. The state of the object at the next sample time in the scenario data is updated to the next state. The scenario data is output when the simulation is completed. [Effects of the Invention]

[0009] According to one aspect of this disclosure, it is possible to create desired scenario data with minimal effort. [Brief explanation of the drawing]

[0010] [Figure 1] This is a conceptual diagram showing an example of the computer configuration included in the scenario data generation system according to the embodiment of this disclosure. [Figure 2] This is a conceptual diagram showing an example of the processing of a scenario data generation system according to the embodiment of this disclosure. [Figure 3] This figure illustrates the data structure of a configuration file according to the embodiment of this disclosure. [Figure 4] This is a conceptual diagram showing a road defined by a configuration file according to an embodiment of this disclosure. [Figure 5]A diagram illustrating the data structure of a configuration file according to an embodiment of the present disclosure. [Figure 6] A conceptual diagram showing a camera defined by a configuration file according to an embodiment of the present disclosure. [Figure 7] A diagram illustrating an image of a camera according to an embodiment of the present disclosure. [Figure 8] A conceptual diagram showing the situation on a road in a scene of a driving simulation according to an embodiment of the present disclosure. [Figure 9] A diagram illustrating a vehicle driving scenario file according to an embodiment of the present disclosure. [Figure 10] A conceptual diagram showing the situation on a road in a scene of a driving simulation according to an embodiment of the present disclosure. [Figure 11] A diagram illustrating a generated AI vehicle driving scenario file according to an embodiment of the present disclosure. [Figure 12] A diagram showing a configuration example of a vehicle control prompt according to an embodiment of the present disclosure. [Figure 13] A diagram showing a configuration example of a vehicle detection prompt according to an embodiment of the present disclosure. [Figure 14] A flowchart illustrating an execution process of a simulation including a generated AI according to an embodiment of the present disclosure. [Figure 15] A flowchart illustrating an execution process of a simulation including a generated AI according to an embodiment of the present disclosure.

Embodiments for Carrying Out the Invention

[0011] Hereinafter, embodiments of the present invention will be described with reference to the drawings.

[0012] FIG. 1 is a conceptual diagram showing a configuration example of a computer included in a scenario data generation system according to an embodiment of the present disclosure.

[0013] The computer 10 includes an input unit 11, a display unit 12, a communication unit 13, a CPU (Central Processing Unit) 14, memory 15, and a storage device 16.

[0014] The input unit 11 receives information input from an external device such as a keyboard or touch panel (not shown) connected to the computer 10. The display unit 12 displays the information processed by the CPU 14. The communication unit 13 communicates with external devices via a network 30, such as the Internet. The CPU 14 reads and executes programs or information stored in the memory 15 or storage device 16. Temporary data, for example, is stored in the memory 15.

[0015] The storage device 16 stores the vehicle driving scenario file 161, the configuration file 162, the generated AI vehicle driving scenario file 163, and the prompt file 164. The storage device 16 also stores the program data for the generated AI scenario extension system 5.

[0016] The storage device 16 stores scenario data containing information about the actions of one or more objects at each sample time in a scene in which they are operating, and prompt data that instructs the generative artificial intelligence to perform the desired actions of the objects. The scenario data is, for example, information describing the appearance of the moving object in a scene in which an object is moving. The initial scenario data may be created from video footage of a vehicle in motion taken by a dashcam. The scenario data is stored as a generative AI vehicle driving scenario file 163. Objects will be described later. The prompt data is stored as a prompt file 164.

[0017] The generation AI scenario extension system 5 has a simulator 51. The simulator 51 includes a simulation execution unit 511, a generation AI execution unit 512, and a communication processing unit 513. The CPU 14 reads and executes the software program of the generation AI scenario extension system 5, thereby enabling the simulation execution unit 511, the generation AI execution unit 512, and the communication processing unit 513 to function.

[0018] The simulation execution unit 511 has the function of executing simulations.

[0019] The generative AI execution unit 512 has the function of executing or causing the generative artificial intelligence to execute processing based on a prompt. In this specification, generative artificial intelligence may be abbreviated as generative AI. The generative artificial intelligence referred to here is a Large Language Model (LLM), which is an artificial intelligence that generates information corresponding to a prompt by taking a prompt written in text or the like as input, such as chatGPT (registered trademark).

[0020] The communication processing unit 513 has the function of communicating with external devices.

[0021] The CPU 14 reads and executes the software program of the generation AI scenario extension system 5 stored in the memory device 16, and performs the following processing: The simulation execution unit 511 starts a step-by-step simulation for each sample time based on the scenario data. At each step for each sample time, the simulation execution unit 511 causes the generation artificial intelligence to generate the next state, which is the state of the object at the next sample time, based on a desired action based on the state of the object in the scenario data, updates the state of the object at the next sample time in the scenario data to the next state, and outputs the scenario data when the simulation is completed.

[0022] An object may be a virtual object in a virtual space. For example, an object may be a virtual moving object. While an object is typically a virtual vehicle, it may also be other objects such as a virtual building or a virtual pedestrian.

[0023] If the object is a moving object, the prompt data includes text instructing the generative artificial intelligence to perform a desired movement of the moving object. The scenario data includes information on the moving object's position, which represents the position of the moving object in the scene in which it moves, and the moving object's state, which represents the state of the moving object. In this case, at each step of the sample time, the CPU 14 causes the generative artificial intelligence to generate the next moving object state, which is the state of the moving object at the next sample time, based on the desired movement according to the moving object's position and state in the scenario data. At each step of the sample time, the CPU 14 calculates the next moving object position, which is the position of the moving object at the next sample time, based on the moving object's position and the next moving object state. At each step of the sample time, the CPU 14 updates the next moving object position and next moving object state with the moving object's position and state at the next sample time in the scenario data.

[0024] If the moving object is a vehicle, more specifically a virtual vehicle virtually defined within the scenario data, the scenario data includes, for example, vehicle position information representing the location of the vehicle at each sample time in a scenario where one or more vehicles are traveling on a road, and vehicle state information representing the state of the vehicle. The prompt data includes text instructing the generative artificial intelligence to simulate a vehicle driver and give instructions for the desired driving.

[0025] The vehicle's moving state may include the vehicle's speed and yaw angle. The prompt data may include an instruction to output the angular velocity at which the vehicle's steering wheel is turned.

[0026] The desired driving action may be a lane change operation in which a vehicle traveling in the second lane adjacent to the first lane moves from the second lane to the first lane. In this case, the prompt data includes an instruction to output the angular velocity resulting from steering maneuvers to move from the second lane to the first lane, simulating the actions of the vehicle's driver.

[0027] The scenario data includes video footage from an onboard camera installed in the vehicle. If the vehicle is a virtually defined virtual vehicle, then the onboard camera is also a virtually defined camera. The desired driving action may be to change lanes from the second lane to the first lane when a vehicle traveling in the first lane behind appears in the video footage.

[0028] Figure 2 is a conceptual diagram showing an example of processing of a scenario data generation system according to the embodiment of this disclosure.

[0029] Computer 10 reads the prompt file 164 (St101). Computer 10 reads the configuration file 162 (St102). Computer 10 reads the vehicle driving data from the vehicle driving scenario file 161 (St103). The simulation execution unit 511 of computer 10 executes the simulation (St104).

[0030] The generation AI processing server 20 generates instructions (St201). The generation AI processing server 20 sends the generated instructions to the computer 10 (St202). The computer 10 receives the instructions (St105).

[0031] The AI ​​generation execution unit 512 of the computer 10 executes prompts based on the received instructions (St106). The computer 10 outputs the vehicle driving data as the AI ​​vehicle driving scenario file 163 (St107).

[0032] Figure 3 is a diagram illustrating the data structure of a configuration file according to the embodiment of this disclosure.

[0033] Figure 3 shows road data saved as configuration file 162. The road data includes information items such as road ID, coordinates of the road center, number of lanes, and lane width. The road data may include other information items. For a straight road, the coordinates of the road center may be expressed as a pair of 3D coordinates of the start and end points that define the road, such as [0,0,0;200,0,0]. Information defining the road may be defined in other ways. Figure 4 is a conceptual diagram showing a road defined by the configuration file shown in Figure 3, according to an embodiment of this disclosure. Referring to Figure 4, a straight two-lane road with the road center being the straight line connecting [0,0,0] and [200,0,0] extends for 200 meters in the x direction.

[0034] Figure 5 is a diagram illustrating the data structure of a configuration file according to the embodiment of this disclosure.

[0035] Figure 5 shows the camera data saved as configuration file 162. The camera is an on-board camera installed in the vehicle. Here, the vehicle may refer to other vehicles driving around the user's vehicle, for example, when performing a driving simulation. In this example, the intention is to control other objects, such as other vehicles around the user's vehicle, using a generative AI.

[0036] The camera data includes the following information items: camera height, view position, yaw, pitch, and roll. The view position is two-dimensional coordinate data indicating the relative position from the vehicle. Yaw, pitch, and roll are data indicating the camera's rotation angle. Figure 6 is a conceptual diagram showing a camera defined by the configuration file shown in Figure 5, according to an embodiment of this disclosure. The camera is installed at the relative position [0,0] with the center of the vehicle as the origin. The camera is oriented in a direction rotated 140 degrees along the yaw axis. In the figure, the x direction is roll and the y direction is pitch. If the vertical direction perpendicular to the x and y directions is the z direction, then the z direction is yaw.

[0037] Figure 7 is a diagram illustrating the image from a camera according to an embodiment of the present disclosure.

[0038] Figure 7 shows the images captured by the in-vehicle cameras shown in Figures 5 and 6. Figure 7 also shows two examples: camera footage without a vehicle and camera footage with a vehicle. The vehicle shown in the camera footage is, for example, the vehicle used in a driving simulation.

[0039] Figure 8 is a conceptual diagram showing the road conditions in a scene of a driving simulation according to an embodiment of this disclosure.

[0040] In step St104 of Figure 2, the simulation execution unit 511 of the computer 10 is executing the simulation. In this driving simulation, the own vehicle and another vehicle are driving on the road with road ID=1 shown in Figures 3 and 4. The own vehicle's VehicleID is 1. The other vehicle's VehicleID is 2. The onboard camera of the other vehicle is as described above, referring to Figures 5 to 7. The own vehicle is traveling at 60 km / h. The other vehicle is traveling at 40 km / h.

[0041] Figure 9 is a diagram illustrating a vehicle driving scenario file according to an embodiment of the present disclosure. The vehicle driving scenario file 161 is a scenario file that serves as the basis for generating a desired vehicle driving scenario.

[0042] Figure 9 shows the data structure of the vehicle driving scenario file 161 shown in Figure 1. The vehicle driving scenario file 161 has the following information items: Time(s), VechicleID, Position, Speed, and Yaw. Time(s) is the elapsed time from a certain reference time in the simulation. VechicleID is an ID for uniquely identifying the vehicle. Position is 3D coordinate data indicating the relative position of the vehicle with respect to a predetermined reference position. Speed ​​is the speed of the vehicle. Yaw indicates the orientation of the vehicle on the Yaw axis, not the orientation of the onboard camera. Speed ​​and Yaw indicate the state of the vehicle.

[0043] Figure 10 is a conceptual diagram showing road conditions in a scene of a driving simulation according to an embodiment of this disclosure.

[0044] Figure 11 is a diagram illustrating an example of a generated AI vehicle driving scenario file according to an embodiment of the present disclosure. The generated AI vehicle driving scenario file 163 is a file of a vehicle driving scenario generated based on the vehicle driving scenario file 161.

[0045] As shown in Figure 2, the AI ​​generation execution unit 512 of the computer 10 executes the instructions received from the AI ​​generation processing server 20 in the driving simulation started in step St104 (St106). The computer 10 outputs the vehicle driving data when the instructions are executed as the AI ​​generation vehicle driving scenario file 163 (St107). Figure 11 shows the state of the AI ​​generation vehicle driving scenario file 163 output in this way.

[0046] As shown in Figure 10, the AI ​​processing server 20 generates dangerous scenes for the user's vehicle. In this example, it generates a dangerous scene in which the user's vehicle is traveling in the left lane at 60 km / h when another vehicle traveling in the right lane at 40 km / h suddenly changes lanes to the left lane. The sudden lane change by the other vehicle is represented, as shown in Figure 11, by the other vehicle with VehicleID=2 changing its Yaw from 0 (turning the steering wheel left or right) at a time (Time(s) of 11.6 seconds.

[0047] Figure 12 shows an example of the configuration of a vehicle control prompt according to an embodiment of the present disclosure. The vehicle control prompt is recorded in a prompt file 164. The prompt data stored in the prompt file 164 includes text that instructs the generative artificial intelligence to simulate a vehicle driver and give instructions for a desired driving action. The generative AI execution unit 512 reads the prompt from the prompt file 164 (St101) and executes the prompt based on the instructions received from the generative AI processing server 20 (St106). Figure 12 shows an example of a prompt that instructs the generative artificial intelligence to simulate the driver of another vehicle and instruct another vehicle to change lanes. When the generative AI execution unit 512 executes the prompt, the yaw angular velocity is output. In step St107, the computer 10 outputs a generative AI vehicle driving scenario file 163 (see Figure 11) in which the output yaw angular velocity is described in the information item Yaw.

[0048] Figure 13 is a diagram showing an example of the configuration of a vehicle detection prompt according to an embodiment of the present disclosure. The vehicle detection prompt is recorded in a prompt file 164. The generation AI execution unit 512 reads the prompt from the prompt file 164 (St101) and executes the prompt based on the instructions received from the generation AI processing server 20 (St106). Figure 13 shows an example of a prompt that detects whether or not a vehicle is visible in the image captured by an on-board camera mounted on another vehicle. When the generation AI execution unit 512 executes the prompt, 0 is output if no vehicle is detected, and 1 is output if a vehicle is detected.

[0049] Figure 14 is a flowchart illustrating the execution process of a simulation including a generative AI according to an embodiment of this disclosure. Figure 15 is a flowchart illustrating the execution process of a simulation including a generative AI according to an embodiment of this disclosure.

[0050] Figures 14 and 15 show an example where a generating AI detects a vehicle, and then the generating AI further causes another vehicle to change lanes.

[0051] Computer 10 reads the vehicle control prompts and vehicle detection prompts contained in the prompt file 164 and sends them to the generation AI processing server 20 (St301).

[0052] Computer 10 reads data from configuration file 162 and sets it in simulator 51 (St302). Computer 10 reads data from vehicle driving scenario file 161 and sets it in simulator 51 (St303).

[0053] Computer 10 sets initial values ​​for dist and yaw, and sets the initial value of detect to 0 (St304). Computer 10 starts running the simulation (St305). dist is the position in the y-direction from the center of the left lane. yaw is the current yaw angle. detect is the result of detecting whether or not there is a vehicle in the image.

[0054] Computer 10 determines whether it is time to finish the simulation (St306). If it is time to finish (St306: Yes), the processes shown in Figures 14 and 15 are terminated. If it is not time to finish (St306: No), the process proceeds to step St307.

[0055] In step St307, the computer 10 determines whether the value of detect is 1 or not. If the value of detect is 1 (St307: Yes), the process proceeds to step St314. If the value of detect is not 1 (St307: No), the process proceeds to step St308.

[0056] In step St308, computer 10 acquires the camera image as an image file. Computer 10 sends the acquired image file to the generation AI processing server 20 along with the string "Is there a car in the image?" (St309).

[0057] Computer 10 receives the data {"detect":"XXX"} from the generation AI processing server and sets the value of XXX to detect (St310).

[0058] Computer 10 calculates the position of VechicleID:2 (another vehicle) after one sample time from the vehicle speed and yaw, and sets it in the simulation (St311). Computer 10 runs the simulation for one sample time (St312). Computer 10 writes the data to the generated AI vehicle driving scenario file 163 (St313). Then the process returns to step St306.

[0059] In step St314, computer 10 obtains the location of VechicleID:2 (another vehicle).

[0060] Computer 10 calculates the distance (YYY) from the position of VechicleID:2 (another vehicle) to the center of the left lane and sets the calculated value to dist (St315).

[0061] Computer 10 sends "dist=YYY,yaw=ZZZ" as a string to the generation AI processing server 20 (St316).

[0062] Computer 10 receives the data {"yawrate":"WWW"} from the generation AI processing server and sets the value of WWW to yawrate (St317). Computer 10 adds the acquired value of yawrate to yaw (St318).

[0063] Computer 10 obtains the location of VechicleID:2 (another vehicle) (St319). Then the process transitions to step St311.

[0064] The embodiments described above are illustrative for explaining the present invention and are not intended to limit the scope of the invention to those embodiments only. Those skilled in the art can implement the present invention in various other forms without departing from its scope. Furthermore, these embodiments include the following; however, the contents of these embodiments are not limited to those described below.

[0065] (Item 1) As described above, the scenario data generation system has a storage device and a processor, The storage device stores scenario data containing information about the actions of one or more objects at each sample time in a scene in which the objects are operating, and prompt data that instructs the generative artificial intelligence to perform a desired action of the object. The aforementioned processor, Start a step-by-step simulation for each sample time based on the aforementioned scenario data. In each step, The generative artificial intelligence is instructed to generate the next state, which is the state of the object at the next sample time, based on the desired action performed on the state of the object in the scenario data. The state of the object at the next sample time in the scenario data is updated to the next state. Output the scenario data when the simulation is completed. Scenario data generation system. This allows for the creation of desired scenario data with minimal effort. The generative artificial intelligence calculates and updates the object's state as the simulation progresses step-by-step, and then calculates the next state based on the updated state. Therefore, the desired scenario can be created through intuitive instructions, even if the prompts do not necessarily require mathematical rigor.

[0066] (Item 2) In the scenario data generation system described in item 1, The aforementioned object is a moving object, The prompt data includes text that instructs the generative artificial intelligence to perform the desired movement of the moving object. The scenario data includes information on the position of the moving object in a scene where the moving object is moving, and information on the state of the moving object. The aforementioned processor, In the above step, The generative artificial intelligence is instructed to generate the next moving body state, which is the moving body state at the next sample time, based on the desired movement according to the moving body position and moving body state in the scenario data. Based on the aforementioned moving body position and the aforementioned next moving body state, the next moving body position, which is the moving body position at the next sample time, is calculated. The position and state of the moving object at the next sample time in the scenario data are updated to the next position and state of the moving object. This allows for the creation of scenario data for desired mobile object movement with minimal effort.

[0067] (Item 3) In the scenario data generation system described in item 2, The aforementioned moving object is a vehicle. The scenario data includes vehicle position information representing the position of the vehicle at each sample time in a scene in which one or more vehicles are driving, and vehicle state information representing the state of the vehicle. The prompt data includes text instructing the generative artificial intelligence to simulate a vehicle driver and give instructions for a desired driving action. This allows for the creation of desired vehicle driving scenario data with minimal effort.

[0068] (Item 4) In the scenario data generation system described in item 3, The aforementioned vehicle is a virtual vehicle virtually defined within the scenario data. This makes it easy to create scenario data for driving a virtual vehicle that is both natural and intentionally desired, by having a virtually defined vehicle driven moment by moment through step-by-step decisions made by generating artificial intelligence.

[0069] (Item 5) In the scenario data generation system described in item 3, The moving state of the vehicle includes the vehicle's speed and its yaw angle. The prompt data includes an instruction to output the angular velocity at which the vehicle's steering wheel is turned. This allows a vehicle virtually defined within the scenario data to be controlled by the generating artificial intelligence through step-by-step decisions, in a manner that simulates the generating artificial intelligence steering the vehicle.

[0070] (Item 6) In the scenario data generation system described in item 5, The desired operation is a lane change operation in which the vehicle traveling in the second lane adjacent to the first lane moves from the second lane to the first lane. The prompt data includes an instruction to output the angular velocity resulting from steering maneuvers to move from the second lane to the first lane, simulating the driver of the vehicle. This allows the generating artificial intelligence to perform lane changes for a vehicle virtually defined within the scenario data, based on step-by-step decisions made by the generating artificial intelligence.

[0071] (Item 7) In the scenario data generation system described in item 6, The aforementioned scenario data includes video footage from an on-board camera mounted on the vehicle. The desired driving method is to change lanes from the second lane to the first lane when a vehicle traveling in the first lane behind is captured in the video. This makes it easy to create scenarios where a vehicle suddenly changes lanes from an adjacent lane.

[0072] (Item 8) In a method for generating scenario data using a device having a memory device and a processor, The storage device stores scenario data containing information about the actions of one or more objects at each sample time in a scene in which the objects are operating, and prompt data that instructs the generative artificial intelligence to perform a desired action of the object. The aforementioned processor, Start a step-by-step simulation for each sample time based on the aforementioned scenario data. In each step, The generative artificial intelligence is instructed to generate the next state, which is the state of the object at the next sample time, based on the desired action performed on the state of the object in the scenario data. The state of the object at the next sample time in the scenario data is updated to the next state. The scenario data is output when the simulation is completed. This allows for the creation of desired scenario data with minimal effort. The generative artificial intelligence calculates and updates the object's state as the simulation progresses step-by-step, and then calculates the next state based on the updated state. Therefore, the desired scenario can be created through intuitive instructions, even if the prompts do not necessarily require mathematical rigor.

[0073] (Item 9) In a scenario data generation program that causes a device having a memory device and a processor to generate scenario data, The storage device stores scenario data containing information about the actions of one or more objects at each sample time in a scene in which the objects are operating, and prompt data that instructs the generative artificial intelligence to perform a desired action of the object. By executing the scenario data generation program, the processor will Start a step-by-step simulation for each sample time based on the aforementioned scenario data. In each step, The generative artificial intelligence is instructed to generate the next state, which is the state of the object at the next sample time, based on the desired action performed on the state of the object in the scenario data. The state of the object at the next sample time in the scenario data is updated to the next state. The scenario data is output when the simulation is completed. This allows for the creation of desired scenario data with minimal effort. The generative artificial intelligence calculates and updates the object's state as the simulation progresses step-by-step, and then calculates the next state based on the updated state. Therefore, the desired scenario can be created through intuitive instructions, even if the prompts do not necessarily require mathematical rigor. [Explanation of symbols]

[0074] 10...Computer, 11...Input unit, 12...Display unit, 13...Communication unit, 14...CPU, 15...Memory, 16...Storage device, 161...Vehicle driving scenario file, 162...Configuration file, 163...Generated AI vehicle driving scenario file, 164...Prompt file, 20...Generated AI processing server, 30...Network, 5...Generated AI scenario expansion system, 51...Simulator, 511...Simulation execution unit, 512...Generated AI execution unit, 513...Communication processing unit

Claims

1. It has a storage device and a processor, The storage device stores scenario data containing information about the operation of one or more objects at each sample time in a scene in which the objects are operating, and prompt data that instructs the generative artificial intelligence to perform a desired operation of the objects. The aforementioned processor, Start a step-by-step simulation for each sample time based on the aforementioned scenario data. In each step, The generative artificial intelligence is instructed to generate the next state, which is the state of the object at the next sample time, based on the desired action performed on the state of the object in the scenario data. The state of the object at the next sample time in the scenario data is updated to the next state. Output the scenario data when the simulation is completed. Scenario data generation system.

2. The aforementioned object is a moving object, The prompt data includes text that instructs the generative artificial intelligence to perform the desired movement of the moving object. The aforementioned scenario data includes information on the mobile body position, which represents the position of the mobile body in a scene where the mobile body is moving, and information on the mobile body state, which represents the state of the mobile body. The aforementioned processor, In the above step, The generative artificial intelligence is instructed to generate the next moving body state, which is the moving body state at the next sample time, based on the desired movement according to the moving body position and moving body state in the scenario data. Based on the aforementioned moving body position and the aforementioned next moving body state, the next moving body position, which is the moving body position at the next sample time, is calculated. The position and state of the moving object at the next sample time in the scenario data are updated to the next position and state of the moving object. The scenario data generation system according to claim 1.

3. The aforementioned moving object is a vehicle. The scenario data includes vehicle position information representing the position of the vehicle at each sample time in a scene in which one or more vehicles are driving, and vehicle state information representing the state of the vehicle. The prompt data includes text instructing the generative artificial intelligence to simulate a vehicle driver and give instructions for the desired driving. The scenario data generation system according to claim 2.

4. The aforementioned vehicle is a virtual vehicle virtually defined within the scenario data. The scenario data generation system according to claim 3.

5. The moving state of the vehicle includes the vehicle's speed and its yaw angle. The prompt data includes an instruction to output the angular velocity at which the vehicle's steering wheel is turned. The scenario data generation system according to claim 3.

6. The desired operation is a lane change operation in which the vehicle traveling in the second lane adjacent to the first lane moves from the second lane to the first lane. The prompt data includes an instruction to output the angular velocity resulting from steering maneuvers to move from the second lane to the first lane, simulating the driver of the vehicle. The scenario data generation system according to claim 5.

7. The aforementioned scenario data includes video footage from an on-board camera mounted on the vehicle. The desired driving is to change lanes from the second lane to the first lane when a vehicle traveling in the first lane behind is captured in the video. The scenario data generation system according to claim 6.

8. A method for generating scenario data using a device having a memory device and a processor, The storage device stores scenario data containing information about the operation of one or more objects at each sample time in a scene in which the objects are operating, and prompt data that instructs the generative artificial intelligence to perform a desired operation of the objects. The aforementioned processor, Start a step-by-step simulation for each sample time based on the aforementioned scenario data. In each step, The generative artificial intelligence is instructed to generate the next state, which is the state of the object at the next sample time, based on the desired action performed on the state of the object in the scenario data. The state of the object at the next sample time in the scenario data is updated to the next state. Output the scenario data when the simulation is completed. Scenario data generation method.

9. A scenario data generation program that causes a device having a memory device and a processor to generate scenario data, The storage device stores scenario data containing information about the operation of one or more objects at each sample time in a scene in which the objects are operating, and prompt data that instructs the generative artificial intelligence to perform a desired operation of the objects. By executing the scenario data generation program, the processor will Start a step-by-step simulation for each sample time based on the aforementioned scenario data. In each step, The generative artificial intelligence is instructed to generate the next state, which is the state of the object at the next sample time, based on the desired action performed on the state of the object in the scenario data. The state of the object at the next sample time in the scenario data is updated to the next state. Output the scenario data when the simulation is completed. Scenario data generation program.

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