Information processing device and program

Abstract

To provide an information processing device and a program.SOLUTION: An information processing device comprises an information acquisition unit which acquires a plurality of pieces of sensor information including obstacle information, and a control unit which avoids the obstacle by controlling an edge that is provided in a bottom surface of a vehicle and allows the vehicle to jump. The control unit calculates a control variable, which controls an avoidance action of the obstacle of the vehicle, on the basis of characteristics of a crew of the vehicle. The control unit also controls an automatic operation of the vehicle in billionths of a second on the basis of the control variable.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to an information processing apparatus and a program.

Background Art

[0002] Patent Document 1 describes a vehicle having an automatic driving function.

Prior Art Document

Patent Document

[0003]

Patent Document 1

Summary of the Invention

Means for Solving the Problems

[0004] According to an embodiment of the present invention, an information processing apparatus is provided. The information processing apparatus includes an information acquisition unit that acquires a plurality of sensor information including obstacle information, and a control unit that is provided on the bottom surface of the vehicle and controls an edge that jumps the vehicle to avoid the obstacle. The control unit calculates a control variable for controlling the obstacle avoidance behavior of the vehicle based on the characteristics of the occupants of the vehicle.

[0005] In the information processing apparatus, the control unit controls the automatic driving of the vehicle in units of one billionth of a second based on the control variable.

[0006] In the information processing apparatus, the control unit updates the characteristics based on the reaction of the occupants of the vehicle when avoiding the obstacle.

[0007] In the information processing apparatus, it is possible for the occupant to pre-select whether to avoid the obstacle by controlling the edge.

[0008] According to an embodiment of the present invention, a program for causing a computer to function as the information processing apparatus is provided.

[0009] It should be noted that the above summary of the invention does not enumerate all the necessary features of the present invention. Furthermore, subcombinations of these features may also constitute an invention. [Brief explanation of the drawing]

[0010] [Figure 1] This diagram provides a schematic overview of the hazard prediction capabilities of AI in ultra-high-performance autonomous driving systems. [Figure 2] This diagram schematically shows an example of a network configuration within a vehicle. [Figure 3] This diagram schematically shows an example of the stages of the proportionality constant. [Figure 4] This diagram schematically shows an example of the stages of the proportionality constant. [Figure 5] This diagram schematically shows an example of the stages of the proportionality constant. [Figure 6] This is a flowchart executed by Central Brain. [Figure 7] This is the first explanatory diagram illustrating an example of vehicle stopping distance. [Figure 8] This is a second explanatory diagram illustrating an example of the stopping distance of vehicle 12. [Figure 9] This is a third explanatory diagram illustrating an example of the stopping distance of vehicle 12. [Figure 10] This is the fourth explanatory diagram illustrating an example of the stopping distance of vehicle 12. [Figure 11] This is an explanatory diagram illustrating an example of autonomous driving control using the Central Brain. [Figure 12] This is a diagram illustrating Perfect Steering Control and its overview. [Figure 13] This is an explanatory diagram illustrating edges. [Figure 14] This is an explanatory diagram illustrating an example of edge operation using Central Brain. [Figure 15] This diagram schematically shows an example of a computer hardware configuration that functions as a central brain.

Best Mode for Carrying Out the Invention

[0011] Hereinafter, the present invention will be described through embodiments of the invention. However, the following embodiments do not limit the invention according to the claims. Also, not all combinations of features described in the embodiments are essential for the solution means of the invention.

[0012] FIG. 1 schematically shows the ability of the AI for ultra-high performance autonomous driving according to this embodiment to predict risks. In this embodiment, multiple types of sensor information are converted into AI data and stored in the cloud. The AI predicts and determines the best mix of situations every nanosecond (one billionth of a second) and optimizes the operation of the vehicle 12. In this embodiment, it is desirable for the vehicle 12 to be an electric vehicle.

[0013] FIG. 2 is a diagram for explaining the configuration inside the vehicle 12 of the Central Brain 120. The Central Brain 120 is an example of an information processing device.

[0014] As shown in FIG. 2, a plurality of Gate Ways are communicably connected to the Central Brain 120. The Central Brain 120 is connected to an external cloud via the Gate Way. The Central Brain 120 is configured to be able to access an external cloud via the Gate Way. On the other hand, due to the presence of the Gate Way, it is configured so that direct access from the outside to the Central Brain 1​​​​​​​Examples of sensors used in this embodiment include radar, LiDAR, high-pixel, telephoto, ultra-wide-angle, 360-degree, high-performance cameras, vision recognition, fine sound, ultrasonic waves, vibration, infrared rays, ultraviolet rays, electromagnetic waves, temperature, humidity, spot AI weather forecast, high-precision multi-channel GPS, low-altitude satellite information, long-tail incident AI data, etc. Long-tail incident AI data is Trip data of a Level 5 implemented vehicle.

[0017] As sensor information incorporated from multiple types of sensors, detection of the movement of the center of gravity of the weight, detection of the road material, detection of the outside air temperature, detection of the outside air humidity, detection of the vertical, horizontal, and diagonal inclination angles of slopes, the freezing condition of the road, detection of the moisture content, the material of each tire, wear condition, air pressure, road width, presence or absence of overtaking prohibition, oncoming vehicles, vehicle type information of the front and rear vehicles, their cruising states, surrounding conditions (birds, animals, soccer balls, accident vehicles, earthquakes, fires, wind, typhoons, heavy rain, light rain, snow, fog, etc.), etc. are included. In this embodiment, these detections are performed every 1 billionth of a second.

[0018] In this embodiment, when the information acquisition unit of the Central Brain 120 acquires information indicating the road conditions of the road on which the vehicle 12 travels, including information on obstacles such as vehicles that are malfunctioning on the road, detected by the above sensors (e.g., road material, vertical, horizontal, and diagonal inclination angles of slopes, the freezing condition of the road, and the moisture content of the road, etc.), it functions as a control unit that controls the speed of the vehicle to avoid the obstacle.

[0019] Also, in this embodiment, the Central Brain 120 calculates a control variable for controlling the speed of the vehicle and functions as a control unit that controls the automatic driving of the vehicle in units of 1 billionth of a second based on the control variable. Specifically, the Central Brain 120 controls the speed (acceleration and deceleration) of the vehicle according to the following relational expressions. And it realizes perfect cornering without friction.

Equation

[0020] Figures 3 through 5 are diagrams illustrating the stages of the proportionality constant.

[0021] As shown in Figure 3, for example, if the proportionality constant has four stages, the possible settings are 0, S, M, and L (where L is the maximum performance). In this way, the proportionality constant can be changed in any number of stages every one billionth of a second, and acceleration and deceleration are controlled by instructing the in-wheel motors mounted on each of the four wheels 21. Figure 4 is a graph showing the case where the proportionality constant has four stages. Furthermore, as shown in Figure 5, the vehicle speed can be varied by combining multiple stages of proportionality constants.

[0022] The Central Brain 120 repeatedly executes the flowchart shown in Figure 6.

[0023] In step S10, the Central Brain 120 acquires sensor information, including information about obstacles 13 detected by the sensors and road information. Then, the Central Brain 120 proceeds to step S11.

[0024] In step S11, the Central Brain 120 calculates the proportionality constant based on the sensor information acquired in step S10. Then, the Central Brain 120 proceeds to step S12.

[0025] In step S12, the Central Brain 120 controls the automatic driving based on the proportionality constant calculated in step S11. Then, the Central Brain 120 terminates the processing of the flowchart.

[0026] Figures 7 to 10 are explanatory diagrams illustrating examples of stopping distances for vehicle 12.

[0027] Figure 7 shows an example where a vehicle 12 traveling at a speed of 100 km / h acquires information about an obstacle 13 at a point 115 m (meters) away from the obstacle 13. In this example, it takes 0.3 seconds from acquiring information about the obstacle 13 (position of vehicle A) to recognizing the danger, and 0.7 seconds from recognizing the danger to starting the brakes. During this total of 1.0 second, vehicle 12 moves approximately 28 m (position of vehicle B). It also shows that, for example, it takes 84 m of braking distance for vehicle 12 to come to a stop (position of vehicle C).

[0028] Figure 8 shows an example where a vehicle 12 traveling at a speed of 100 km / h on an icy road surface acquires information about an obstacle 13 at a point 115 m (meters) away from the obstacle 13. In this example, it takes 0.3 seconds from acquiring information about the obstacle 13 (position of vehicle A) to recognizing the danger, and 0.7 seconds from recognizing the danger to starting the brakes. During this total of 1.0 second, vehicle 12 moves approximately 28 m (position of vehicle B). Furthermore, as shown in Figure 7 above, for example, it is shown that although the braking distance required for vehicle 12 to stop is 84 m, it also slips and stops further (position of vehicle C).

[0029] Figure 9 shows an example where a stationary vehicle 12 accelerates to a speed of 100 km / h and then acquires information about an obstacle 13. In this example, the vehicle accelerates from a stationary position (vehicle A) to 100 km / h in 1.9 seconds (26 m), takes 0.3 seconds from acquiring information about the obstacle 13 (vehicle B) to recognizing the hazard, and takes 0.7 seconds from recognizing the hazard to starting the brakes (vehicle C). During this total of 1.0 second, vehicle 12 reaches a speed of 200 km / h and moves approximately 42 m (vehicle C). It also shows that, for example, the braking distance until vehicle 12 comes to a stop (vehicle D) is 280 m.

[0030] Figure 10 shows an example where a stationary vehicle 12 accelerates to a speed of 100 km / h on an icy road surface and then acquires information about an obstacle 13. In this example, the vehicle accelerates from a stationary position (vehicle A) to 100 km / h in 1.9 seconds (26 m), takes 0.3 seconds from acquiring information about the obstacle 13 (vehicle B) to recognizing the hazard, and takes 0.7 seconds from recognizing the hazard to starting to brake (vehicle C). During this total of 1.0 second, vehicle 12 reaches a speed of 200 km / h and moves approximately 42 m (vehicle C). Furthermore, as shown in Figure 9 above, for example, it is shown that the braking distance required for vehicle 12 to stop is 280 m, and that it then slips and stops (vehicle D).

[0031] Figure 11 is an explanatory diagram illustrating an example of autonomous driving control using Central Brain 120.

[0032] Figure 11 shows an example of how Central Brain 120 avoids obstacles 13 by controlling the speed of vehicle 12 based on a proportionality constant calculated by Central Brain 120 from sensor information such as driving time, battery depletion, avoidance accidents, material condition (including tires), and wind speed. This example shows a stationary vehicle 12 accelerating to a speed of 100 km / h on an icy road surface before acquiring information about obstacle 13. In this example, vehicle 12 accelerates from a stationary position (vehicle A) to 100 km / h in 1.9 seconds (26 m), and immediately (in one billionth of a second) controls the vehicle's automatic driving to decelerate and accelerate after acquiring information about obstacle 13 (vehicle B).

[0033] Figure 12 schematically shows the perfect speed control and perfect steering control realized by the information processing device according to this embodiment. The principle shown in Figure 12 calculates the vehicle speed and realizes perfect speed control and perfect steering control from input and cloud data.

[0034] While conventional autonomous vehicles can corner to some extent, taking road conditions into account, they cannot corner with precision down to one billionth of a second. Furthermore, autonomous vehicles require a certain distance to detect obstacles, apply the brakes, and come to a complete stop. Therefore, conventional autonomous vehicles could not precisely control cornering to within a billionth of a second, resulting in accidents such as skidding during cornering. Furthermore, even if theoretically accurate cornering could be calculated, it was impossible to take into account various real-world frictional conditions (tires, road conditions, temperature, humidity, wind speed, etc.) when performing cornering. According to the vehicle 12 of this embodiment, the safety of autonomous driving can be enhanced based on the configuration described above.

[0035] Furthermore, in this embodiment, the Central Brain 120 functions as a control unit that avoids the obstacle by controlling an edge 22 provided on the bottom surface 20 of the vehicle 12, which causes the vehicle 12 to jump, even if it is not possible to avoid the obstacle 13 by controlling the speed of the vehicle 12, or even if it is possible to avoid the obstacle 13 by controlling the speed of the vehicle 12. The Central Brain 120 calculates control variables that control the obstacle avoidance behavior of the vehicle 12 based on the characteristics of the occupants of the vehicle 12.

[0036] Specifically, the Central Brain 120 controls the edge 22 in addition to, or separately from, the vehicle's speed (acceleration / deceleration) based on the aforementioned number 1. The Central Brain 120 calculates from sensor information such as occupant characteristics, driving time, battery depletion, avoid accidents, material condition (including tires), and wind speed, compares this data with data stored in the cloud, fine-tunes the differences, and correctly transmits the number of proportionality constants derived from goal seeking to the motor that pushes the edge 22 towards the road, thereby achieving perfect speed control and perfect steering control for optimal jumps.

[0037] Here, the steps of the proportionality constant can be changed in any number of steps, every billionth of a second, similar to the case of the vehicle speed described above. In other words, the speed and amount at which edge 22 is pushed toward the road can be changed in any number of steps.

[0038] Furthermore, the characteristics of the occupants include, for example, their tolerance for actions such as jumping in the vehicle 12, their susceptibility to motion sickness, and their fear of jumping in the vehicle 12. Other characteristics of the occupants may also be included, such as the occupants' age, gender, medical history, and driving skills. In other words, even though it is an action to avoid obstacles, it is expected that some occupants will feel afraid of jumping in the vehicle 12, and the proportionality constant can be changed according to the characteristics of the occupants. Such occupant characteristics can be determined by having the occupants input data, or by the Central Brain 120 memorizing the occupants and acquiring sensor information on each occupant's state while riding. Specifically, for occupants who have input that they are afraid of jumping in the vehicle 12, the Central Brain 120 will take actions such as setting the proportionality constant to reduce the jump or avoiding the obstacle without jumping.

[0039] Furthermore, the Central Brain 120 may update the occupant's characteristics based on the occupant's reaction when avoiding obstacles in the vehicle 12. That is, the Central Brain 120 memorizes the occupant and updates the occupant's characteristics by acquiring sensor information (including heart rate, stress level, images, etc.) such as whether the occupant was frightened during the obstacle avoidance action taken against that occupant. With this configuration, it becomes possible to set a proportional constant that reduces the jump or to avoid the obstacle without jumping if the occupant is frightened. Conversely, it becomes possible to set a proportional constant that increases the jump if the occupant is not frightened.

[0040] Furthermore, the occupants may pre-select whether to avoid obstacles by controlling the edge 22. In other words, it is anticipated that some occupants may be afraid of jumping in the vehicle 12, even though it is an action to avoid obstacles, so the occupants may choose to prioritize evasive actions other than jumping.

[0041] Alternatively, Central Brain120 may input the occupant's characteristics into a pre-trained model that has learned the occupant's characteristics and the response to the obstacle avoidance actions performed, and then determine a proportionality constant based on the output results.

[0042] As shown in Figure 13, the vehicle 12 has edges 22 that protrude from the bottom surface 20 toward the road and can be pushed toward the road between the front wheels 21A and the rear wheels 21B of the wheels 21 on the bottom surface 20. Note that the shape of the edges 22 is not limited to that shown in Figure 13. Also, there may be multiple edges, not just one on each side of the vehicle 12, and they may be provided in various locations on the vehicle 12. Then, as shown in Figure 14(A) from the normal state and Figure 14(B), the Central Brain 120 pushes the edges 22 toward the road to make the vehicle 12 jump when avoiding an obstacle 13. Here, the motor and other mechanisms that push the edges 22 toward the road are not shown. Also, the Central Brain 120 may push both edges 22, not just one of the left or right edges 22, or the timing of pushing the left and right edges 22 may be staggered. Furthermore, the Central Brain 120 may control the extrusion of the edge 22 and the speed of the vehicle 12 to rotate the vehicle 12. After such a jump, the Central Brain 120 enables the vehicle 12 to land smoothly through perfect speed control and perfect steering control. In the past, when an obstacle 13 (for example, a burning truck on the road) was detected, the vehicle 12 could not stop depending on its speed and the distance to the obstacle 13, and a collision between the vehicle 12 and the obstacle 13 could not be avoided. However, by configuring it in this way, the safety of autonomous driving can be improved. In addition, by jumping over the obstacle 13, it becomes possible to avoid the obstacle 13 even when it would not be possible to avoid the obstacle 13 by controlling the speed of the vehicle 12.

[0043] Figure 15 schematically shows an example of the hardware configuration of a computer 1200 that functions as a Central Brain 120. A program installed on the computer 1200 can cause the computer 1200 to function as one or more "parts" of the apparatus according to this embodiment, or to cause the computer 1200 to execute operations associated with the apparatus according to this embodiment or such one or more "parts", and / or to cause the computer 1200 to execute a process or a stage of such process according to this embodiment. Such a program may be executed by the CPU 1212 to cause the computer 1200 to execute specific operations associated with some or all of the blocks in the flowcharts and block diagrams described herein.

[0044] The computer 1200 according to this embodiment includes a CPU 1212, RAM 1214, and a graphics controller 1216, which are interconnected by a host controller 1210. The computer 1200 also includes input / output units such as a communication interface 1222, a storage device 1224, a DVD drive, and an IC card drive, which are connected to the host controller 1210 via an input / output controller 1220. The DVD drive may be a DVD-ROM drive and a DVD-RAM drive, etc. The storage device 1224 may be a hard disk drive and a solid-state drive, etc. The computer 1200 also includes legacy input / output units such as a ROM 1230 and a keyboard, which are connected to the input / output controller 1220 via an input / output chip 1240.

[0045] The CPU 1212 operates according to the programs stored in the ROM 1230 and RAM 1214, thereby controlling each unit. The graphics controller 1216 acquires the image data generated by the CPU 1212 and stores it in the frame buffer provided in RAM 1214 or within itself, so that the image data is displayed on the display device 1218.

[0046] The communication interface 1222 communicates with other electronic devices via a network. The storage device 1224 stores programs and data used by the CPU 1212 in the computer 1200. The DVD drive reads programs or data from a DVD-ROM or the like and provides them to the storage device 1224. The IC card drive reads programs and data from an IC card and / or writes programs and data to an IC card.

[0047] The ROM 1230 stores boot programs and / or programs that depend on the computer 1200's hardware, which are executed by the computer 1200 when activated. The input / output chip 1240 may also connect various input / output units to the input / output controller 1220 via USB ports, parallel ports, serial ports, keyboard ports, mouse ports, etc.

[0048] The program is provided on a computer-readable storage medium such as a DVD-ROM or IC card. The program is read from the computer-readable storage medium and installed on a storage device 1224, RAM 1214, or ROM 1230, which are examples of computer-readable storage media, and executed by the CPU 1212. The information processing described within these programs is read by the computer 1200, resulting in coordination between the program and the various types of hardware resources described above. The apparatus or method may be configured to realize the operation or processing of information in accordance with the use of the computer 1200.

[0049] For example, when communication is performed between a computer 1200 and an external device, the CPU 1212 may execute a communication program loaded into RAM 1214 and, based on the processing described in the communication program, instruct the communication interface 1222 to perform communication processing. Under the control of the CPU 1212, the communication interface 1222 reads transmission data stored in a transmission buffer area provided in a recording medium such as RAM 1214, storage device 1224, DVD-ROM, or IC card, transmits the read transmission data to the network, or writes received data received from the network to a reception buffer area provided on the recording medium.

[0050] Furthermore, the CPU 1212 may read all or necessary parts of a file or database stored on an external recording medium such as the storage device 1224, a DVD drive (DVD-ROM), or an IC card into the RAM 1214, and perform various types of processing on the data in the RAM 1214. The CPU 1212 may then write the processed data back to the external recording medium.

[0051] Various types of information, such as various types of programs, data, tables, and databases, may be stored on the recording medium and subjected to information processing. The CPU 1212 may perform various types of processing on the data read from RAM 1214, including various types of operations, information processing, conditional judgments, conditional branching, unconditional branching, information retrieval / replacement, etc., as described throughout this disclosure and specified by the program instruction sequence, and write the results back to RAM 1214. The CPU 1212 may also retrieve information in files, databases, etc., within the recording medium. For example, if multiple entries are stored in the recording medium, each having an attribute value of a first attribute associated with an attribute value of a second attribute, the CPU 1212 may search among the multiple entries for an entry that matches the specified condition for the attribute value of the first attribute, read the attribute value of the second attribute stored in that entry, and thereby obtain the attribute value of the second attribute associated with the first attribute that satisfies the predetermined condition.

[0052] The program or software module described above may be stored on or near the computer 1200 in a computer-readable storage medium. Alternatively, a recording medium such as a hard disk or RAM provided within a server system connected to a dedicated communication network or the Internet can be used as a computer-readable storage medium, thereby providing the program to the computer 1200 via the network.

[0053] In this embodiment, blocks in the flowchart and block diagram may represent a stage in a process in which an operation is performed or a "part" of a device that has the role of performing an operation. A particular stage and "part" may be implemented by a dedicated circuit, a programmable circuit supplied with computer-readable instructions stored on a computer-readable storage medium, and / or a processor supplied with computer-readable instructions stored on a computer-readable storage medium. The dedicated circuit may include digital and / or analog hardware circuits, and may include integrated circuits (ICs) and / or discrete circuits. The programmable circuit may include reconfigurable hardware circuits, such as field-programmable gate arrays (FPGAs) and programmable logic arrays (PLAs), which include logical AND, logical OR, exclusive OR, negated AND, negated OR, and other logical operations, flip-flops, registers, and memory elements.

[0054] A computer-readable storage medium may include any tangible device capable of storing instructions to be executed by a suitable device, and as a result, a computer-readable storage medium having instructions stored therein will comprise a product that includes instructions that can be executed to create means for performing operations specified in a flowchart or block diagram. Examples of computer-readable storage media may include electronic storage media, magnetic storage media, optical storage media, electromagnetic storage media, semiconductor storage media, etc. More specific examples of computer-readable storage media may include floppy disks, diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), electrically erasable programmable read-only memory (EEPROM), static random access memory (SRAM), compact disk read-only memory (CD-ROM), digital multipurpose disc (DVD), Blu-ray® disc, memory stick, integrated circuit card, etc.

[0055] Computer-readable instructions may include assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk®, Java®, C++, and traditional procedural programming languages ​​such as the C programming language or similar programming languages.

[0056] Computer-readable instructions may be provided to a general-purpose computer, a special-purpose computer, or a programmable circuit, either locally or via a wide area network (WAN) such as a local area network (LAN) or the internet, so that the computer-readable instructions may be executed by the processor or programmable circuit of a general-purpose computer, a special-purpose computer, or other programmable data processing device, in order to generate means for performing operations specified in a flowchart or block diagram. Examples of processors include computer processors, processing units, microprocessors, digital signal processors, controllers, microcontrollers, and the like.

[0057] Although the present invention has been described above using embodiments, the technical scope of the present invention is not limited to the scope described in the above embodiments. It will be apparent to those skilled in the art that various modifications or improvements can be made to the above embodiments. It will be clear from the claims that such modified or improved forms may also be included in the technical scope of the present invention.

[0058] It should be noted that the execution order of operations, procedures, steps, and stages in the apparatus, systems, programs, and methods shown in the claims, specifications, and drawings is not explicitly stated as "before" or "prior to," and that these can be implemented in any order unless the output of a previous process is used in a later process. Even if the operation flow in the claims, specifications, and drawings is described using phrases such as "first," and "next," for convenience, this does not mean that it is essential to perform the operations in that order. [Explanation of Symbols]

[0059] 120 Central Brain, 1200 Computer, 1210 Host Controller, 1212 CPU, 1214 RAM, 1216 Graphics Controller, 1218 Display Device, 1220 Input / Output Controller, 1222 Communication Interface, 1224 Storage Device, 1230 ROM, 1240 Input / Output Chip

Claims

1. An information acquisition unit that acquires multiple sensor information, including information about obstacles, The vehicle includes a control unit that controls an edge provided on the underside of the vehicle, which is pushed toward the road side to cause the vehicle to jump and leap over the obstacle, thereby avoiding the obstacle. The control unit is an information processing device that calculates a control variable, which is the speed or amount at which the edge is pushed toward the road, based on the characteristics of the vehicle occupant.

2. The information processing apparatus according to claim 1, wherein the control unit controls the extrusion of the edge in units of one billionth of a second based on the control variable.

3. The information processing apparatus according to claim 1, wherein the control unit updates the characteristics based on a response obtained from sensor information including the heart rate or stress level of the occupant of the vehicle when avoiding the obstacle.

4. The information processing device according to claim 1, which allows the occupant to pre-select whether to avoid the obstacle by controlling the edge or to prioritize evasive actions other than jumping.

5. A program for causing a computer to function as an information processing device according to any one of claims 1 to 4.

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