Vehicles and Programs

Abstract

To stabilize the landing of a jumped vehicle.SOLUTION: A vehicle comprises a vehicle body, a first edge provided so as to be extrudable from a bottom surface of the vehicle body, a second edge provided so as to be extrudable from the bottom surface in a position closer to the center of gravity of the vehicle than the first edge in the bottom surface of the vehicle body, and an information processing device. The information processing device comprises an information acquisition unit and a control unit. The information acquisition unit acquires a plurality of pieces of sensor information including obstacle information. When the information acquisition unit acquires obstacle information, the control unit jumps the vehicle by pushing out the first edge to avoid the obstacle, returns the first edge to the original position when the vehicle lands, and avoids the obstacle by controlling the first edge and the second edge so as to land the vehicle from the second edge by pushing out the second edge.SELECTED DRAWING: Figure 17

Description

[Technical Field]

[0001] The present invention relates to a vehicle and a program. [Background technology]

[0002] Patent Document 1 describes a vehicle with an automatic driving function. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-035198 Summary of the Invention [Means for solving the problem]

[0004] According to one embodiment of the present invention, there is provided a vehicle, the vehicle comprising: a vehicle body; a first edge provided so as to be extruded from the bottom surface of the vehicle body; a second edge provided on the bottom surface of the vehicle body at a position closer to the center of gravity of the vehicle than the first edge and capable of being pushed out from the bottom surface; an information processing device, The information processing device includes: an information acquisition unit that acquires a plurality of pieces of sensor information including obstacle information; and a control unit that controls the first edge and the second edge to avoid the obstacle when the information acquisition unit acquires information about an obstacle, so as to push out the first edge to make the vehicle jump in order to avoid the obstacle, return the first edge to its original position when the vehicle lands, and push out the second edge to make the vehicle land on the second edge.

[0005] According to another embodiment of the present invention, there is provided another vehicle, the other vehicle comprising: The vehicle body, an edge provided so as to be extruded from the bottom surface of the vehicle body; an elastic body having higher elasticity than the edge and provided so as to be extruded from the bottom surface of the vehicle; an information processing device, The information processing device includes: an information acquisition unit that acquires a plurality of pieces of sensor information including obstacle information; The vehicle has a control unit that controls the edge and the elastic body to avoid the obstacle when the information acquisition unit acquires information about the obstacle, pushes out the edge to make the vehicle jump in order to avoid the obstacle, returns the edge to its original position when the vehicle lands, and pushes out the elastic body to make the vehicle land from the elastic body.

[0006] In the vehicle according to the present invention, the information acquisition unit detects the obstacle in units of one billionth of a second, The control unit controls the first edge and the second edge in units of one billionth of a second.

[0007] In another vehicle according to the present invention, the information acquisition unit detects the obstacle in units of one billionth of a second, The control unit controls the edge and the elastic body in units of one billionth of a second.

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

[0009] The above summary of the invention does not list all of the necessary features of the present invention, and subcombinations of these features may also constitute inventions. [Brief explanation of the drawings]

[0010] [Figure 1] This is a diagram that shows the risk prediction capabilities of the AI ​​in ultra-high performance autonomous driving. [Figure 2] FIG. 1 is a diagram illustrating an example of a network configuration within a vehicle. [Figure 3] FIG. 10 is a diagram illustrating an example of proportionality constant steps. [Figure 4] FIG. 10 is a diagram illustrating an example of proportionality constant steps. [Figure 5] FIG. 10 is a diagram illustrating an example of proportionality constant steps. [Figure 6] This is a flowchart executed by the Central Brain. [Figure 7] FIG. 1 is a first explanatory diagram illustrating an example of a stopping distance of a vehicle. [Figure 8] FIG. 2 is a second explanatory diagram illustrating an example of the stopping distance of the vehicle 12. [Figure 9] FIG. 10 is a third explanatory diagram illustrating an example of the stopping distance of the vehicle 12. [Figure 10] FIG. 4 is a fourth explanatory diagram illustrating an example of the stopping distance of the vehicle 12. [Figure 11] FIG. 1 is an explanatory diagram illustrating an example of control of autonomous driving by a Central Brain. [Figure 12] 1 is a schematic diagram of Perfect Steering Control and Perfect Steering Control. [Figure 13] FIG. 10 is an explanatory diagram illustrating an edge. [Figure 14] FIG. 10 is an explanatory diagram illustrating an example of edge operation by the Central Brain. [Figure 15] FIG. 2 is an explanatory diagram illustrating a first edge and a second edge. [Figure 16] FIG. 10 is an explanatory diagram illustrating an example of the operation of the first and second edges by the Central Brain. [Figure 17] FIG. 10 is an explanatory diagram illustrating an example of the operation of the first and second edges by the Central Brain. [Figure 18] FIG. 10 is an explanatory diagram illustrating an edge and an elastic body. [Figure 19] 10A and 10B are explanatory diagrams illustrating an example of the operation of an edge and an elastic body by the Central Brain. [Figure 20]10A and 10B are explanatory diagrams illustrating an example of the operation of an edge and an elastic body by the Central Brain. [Figure 21] FIG. 1 is a diagram illustrating an example of a hardware configuration of a computer that functions as a Central Brain. DETAILED DESCRIPTION OF THE INVENTION

[0011] The present invention will be described below through embodiments of the invention, but the following embodiments do not limit the scope of the invention according to the claims. Furthermore, not all of the combinations of features described in the embodiments are necessarily essential to the solution of the invention.

[0012] FIG. 1 shows an overview of the risk prediction capabilities of the AI ​​for ultra-high performance autonomous driving according to this embodiment. In this embodiment, information from multiple types of sensors is 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), optimizing the operation of the vehicle 12. In this embodiment, the vehicle 12 is preferably an electric vehicle.

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

[0014] As shown in Figure 2, the Central Brain 120 is communicatively connected to multiple Gateway Ways. The Central Brain 120 is connected to an external cloud via the Gateway Ways. The Central Brain 120 is configured to be able to access the external cloud via the Gateway Ways. On the other hand, the existence of the Gateway Ways means that the Central Brain 120 cannot be directly accessed from the outside.

[0015] The Central Brain 120 outputs a request signal to the server every time a predetermined time elapses. Specifically, the Central Brain 120 outputs a request signal representing an inquiry to the server every billionth of a second.

[0016] Examples of sensors used in this embodiment include radar, LiDAR, high-resolution, telephoto, ultra-wide-angle, 360-degree, high-performance cameras, vision recognition, subtle sounds, ultrasound, vibration, infrared, ultraviolet, electromagnetic waves, temperature, humidity, spot AI weather forecasts, high-precision multi-channel GPS, low-altitude satellite information, and long-tail incident AI data. Long-tail incident AI data is trip data from a vehicle with Level 5 implementation.

[0017] The sensor information collected from multiple types of sensors includes the shift in the center of gravity of body weight, detection of road material, detection of outside air temperature, detection of outside humidity, detection of the up, down, sideways and diagonal inclination angle of a slope, detection of how frozen the road is, detection of the amount of moisture, detection of the material of each tire, wear condition, air pressure, road width, whether or not there is a no-passing rule, oncoming vehicles, information on the vehicle types of vehicles in front and behind, the cruising status of those vehicles, and surrounding conditions (birds, animals, soccer balls, wrecked vehicles, earthquakes, fires, wind, typhoons, heavy rain, light rain, blizzards, fog, etc.), and in this embodiment, these detections are performed every billionth of a second.

[0018] In this embodiment, when the Central Brain 120 acquires information acquired by the information acquisition unit, such as road information indicating the road conditions of the road on which the vehicle 12 is traveling, including information on obstacles such as broken-down vehicles on the road (e.g., road material, up / down, lateral and diagonal inclination angles of slopes, how the road is frozen, and the amount of moisture on the road), the Central Brain 120 functions as a control unit that controls the vehicle speed to avoid the obstacle.

[0019] In this embodiment, the Central Brain 120 calculates a control variable that controls the vehicle speed, 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 vehicle speed (acceleration / deceleration) using the following relational expression. This allows for perfect cornering without friction.

[0020] y=ax 2 Here, a is the proportionality constant, an example of a control variable. Perfect, frictionless cornering means beautiful, smooth, perfect acceleration. This mathematical formula can express perfect acceleration. The proportionality constant can be smoothly adjusted in any number of steps every billionth of a second. Central Brain 120 calculates the proportionality constant based on sensor information such as driving time, battery depletion, avoid accidents, material condition (e.g., tires), and wind speed. It compares the calculated values ​​with data stored in the cloud to fine-tune the differences. By accurately transmitting the proportionality constant derived through goal seeking to the in-wheel motors and four spin angles installed on each of the four wheels 21, it achieves perfect speed control and perfect steering control for optimal acceleration and deceleration. This is an object-oriented goal-seeking driving system. The role of Level 6 is to realize this driving. Here, "Level 6" represents a level of autonomous driving, and is equivalent to a level higher than Level 5, which represents fully autonomous driving. Although Level 5 represents fully autonomous driving, it is at the same level as human driving, and there is still a chance of accidents occurring. Level 6 represents a level higher than Level 5, and is equivalent to a level with a lower probability of accidents occurring than Level 5.

[0021] 3 to 5 are diagrams for explaining the stages of the proportionality constant. As shown in FIG. 3, for example, if the proportionality constant has four stages, it can be 0, S, M, and L (L is Max performance). In this way, the proportionality constant can be changed to any number of stages every billionth of a second, and acceleration / deceleration is performed by issuing a command to the in-wheel motors mounted on each of the four wheels 21. FIG. 4 is a graph showing the case where the proportionality constant has four stages. Then, as shown in FIG. 5, the vehicle speed can be varied by combining multiple stages of the proportionality constant.

[0022] The Central Brain 120 repeatedly executes the flowchart shown in FIG. In step S10, the Central Brain 120 acquires sensor information including information on the obstacles 13 detected by the sensors and road information, and then the Central Brain 120 proceeds to step S11. In step S11, the Central Brain 120 calculates a proportionality constant based on the sensor information acquired in step S10. Then, the Central Brain 120 proceeds to step S12. In step S12, the Central Brain 120 controls the autonomous driving based on the proportionality constant calculated in step S11. Then, the Central Brain 120 ends the processing of this flowchart.

[0023] 7 to 10 are explanatory diagrams illustrating examples of stopping distances of vehicle 12. FIG. 7 shows an example in which vehicle 12 traveling at a speed of 100 km / h (hours per hour) acquires information about obstacle 13 at a point 115 m (meters) from obstacle 13. In this example, it takes 0.3 seconds from acquiring information about obstacle 13 (position of vehicle A) to recognizing the hazard, and 0.7 seconds from recognizing the hazard to starting to brake, and during this total of 1.0 second, vehicle 12 moves approximately 28 m (position of vehicle B). It also shows that it takes a braking distance of 84 m for vehicle 12 to stop (position of vehicle C), for example.

[0024] Fig. 8 shows an example in which a vehicle 12 traveling at a speed of 100 km / h on a frozen road surface acquires information about an obstacle 13 at a point 115 m (meters) from the obstacle 13. In this example, it takes 0.3 seconds from acquiring information about the obstacle 13 (the position of vehicle A) to recognizing the hazard, and 0.7 seconds from recognizing the hazard to starting to brake, and in this total of 1.0 second, the vehicle 12 moves approximately 28 m (to the position of vehicle B). For example, as shown in Fig. 7 above, it takes a braking distance of 84 m for the vehicle 12 to stop, and then it skids and stops (to the position of vehicle C).

[0025] 9 shows an example in which a stopped vehicle 12 accelerates to a speed of 100 km / h and then acquires information about an obstacle 13. In this example, it takes 1.9 seconds (26 m) for the stopped vehicle 12 (position of vehicle A) to accelerate to 100 km / h, 0.3 seconds from acquiring information about the obstacle 13 (position of vehicle B) to recognizing the danger, and 0.7 seconds (position of vehicle C) from recognizing the danger to starting to brake. During this total of 1.0 second, the vehicle 12 reaches a speed of 200 km / h and travels approximately 42 m (position of vehicle C). It also shows that it takes, for example, a braking distance of 280 m for the vehicle 12 to stop (position of vehicle D).

[0026] 10 shows an example in which vehicle 12, stopped on a frozen road surface, accelerates to a speed of 100 km / h and then acquires information about obstacle 13. In this example, it takes 1.9 seconds (26 m) for vehicle 12 to accelerate from the stopped state (position of vehicle A) to 100 km / h, 0.3 seconds from acquiring information about obstacle 13 (position of vehicle B) to recognizing the hazard, and 0.7 seconds (position of vehicle C) from recognizing the hazard to starting to brake. During this total of 1.0 second, vehicle 12 reaches a speed of 200 km / h and travels approximately 42 m (position of vehicle C). As shown in FIG. 9 above, for example, it takes a braking distance of 280 m for vehicle 12 to stop, and then it skids and stops (position of vehicle D).

[0027] 11 is an explanatory diagram illustrating an example of autonomous driving control by the Central Brain 120. Fig. 11 shows an example in which the Central Brain 120 avoids an obstacle 13 by controlling the speed of the vehicle 12 based on a proportionality constant calculated by the Central Brain 120 from sensor information such as driving time, battery depletion, situations such as avoiding accidents, the material condition of materials such as tires, and variables such as wind speed. The example shows a vehicle 12 stopped on an icy road surface accelerating to a speed of 100 km / h and then acquiring information about the obstacle 13. This example shows that the vehicle 12 accelerates from the stopped vehicle 12 (the position of vehicle A) to 100 km / h in 1.9 seconds (26 m), and immediately (one billionth of a second) acquires information about the obstacle 13 (the position of vehicle B), decelerating or accelerating the vehicle by controlling its autonomous driving.

[0028] Fig. 12 shows an outline of the perfect speed control and perfect steering control realized by the information processing device according to this embodiment. The principle shown in Fig. 12 calculates the vehicle speed and realizes the perfect speed control and perfect steering control from the Input and Cloud Date.

[0029] Conventional self-driving vehicles are capable of cornering while taking road conditions into account to a certain extent, but they are unable to perform cornering accurately to the nearest billionth of a second. Furthermore, it takes a certain distance for a self-driving vehicle to detect an obstacle, brake, and stop. Therefore, conventional self-driving vehicles are unable to accurately control cornering to the nearest billionth of a second, resulting in accidents such as slippage during cornering. Even if theoretically accurate cornering could be calculated, it was not possible to perform cornering while taking into account various actual frictional conditions (tires, road conditions, air temperature, humidity, wind speed, etc.). The vehicle 12 according to this embodiment can improve the safety of autonomous driving based on the configuration described above.

[0030] Furthermore, in this embodiment, even when the obstacle 13 cannot be avoided by controlling the speed of the vehicle 12, or even when the obstacle 13 can be avoided by controlling the speed of the vehicle 12, the Central Brain 120 functions as a control unit that avoids the obstacle 13 by pushing an edge 22 provided on the bottom surface 20 of the vehicle body of the vehicle 12 from the bottom surface 20 so as to make the vehicle 12 jump. Specifically, as shown in FIG. 13 , the vehicle 12 has an edge 22 that protrudes from the bottom surface 20 toward the road and can be pushed out toward the road, between the front wheel 21A and the rear wheel 21B of the wheels 21 on the bottom surface 20. Note that the shape of the edge 22 is not limited to that shown in FIG. 13 . Furthermore, the number of edges is not limited to one on each side of the vehicle 12, but multiple edges may be provided, and the edges may be provided in various locations on the vehicle 12.

[0031] Then, when avoiding the obstacle 13, the Central Brain 120 moves from the normal state shown in FIG. 14(A) to the state shown in FIG. 14(B), pushing the edge 22 toward the road, causing the vehicle 12 to jump. Here, mechanisms such as a motor for pushing the edge 22 toward the road are not shown. Furthermore, the Central Brain 120 may push both edges 22 instead of pushing only one of the left and right edges 22, or may push the left and right edges 22 at different timings. Furthermore, the Central Brain 120 may control the pushing of the edge 22 and the speed of the vehicle 12 so as to rotate the vehicle 12. After the jump, the Central Brain 120 enables the vehicle 12 to land smoothly through perfect speed control and perfect steering control. In the past, even when an obstacle 13 (for example, a truck on fire on the road) was detected, the vehicle 12 was unable to stop depending on the speed of the vehicle 12 and the distance to the obstacle 13, and it was not possible to avoid a collision between the vehicle 12 and the obstacle 13. However, by configuring in this way, it is possible to improve the safety of automated driving. Also, by jumping over the obstacle 13, it becomes possible to avoid the obstacle 13 even in cases where it is not possible to avoid the obstacle 13 by controlling the speed of the vehicle 12.

[0032] In addition to the edge 22, as shown in Fig. 15, another edge 24 may be provided at a position on the underside 20 of the vehicle 12 closer to the center of gravity of the vehicle 12 than the edge 22. Specifically, as shown in Fig. 15, an edge 24 (hereinafter referred to as the second edge 24) may be provided on the inner side of the edge 22 (hereinafter referred to as the first edge 22) in the width direction of the vehicle 12. The second edge 24 has a shorter length in the front-rear direction of the vehicle 12 than the first edge 22. Furthermore, the number of second edges 24 is not limited to one on each side of the vehicle 12, and multiple second edges 24 may be provided.

[0033] In this case, as shown in FIG. 16(B), the Central Brain 120 jumps the vehicle 12 by pushing only one of the left and right first edges 22 toward the road to avoid the obstacle 13, from the normal state shown in FIG. 16(A). By pushing the left or right first edge 22, the vehicle 12 rotates in the air around axis X passing through its center of gravity, as shown in FIG. 17(A). Upon landing, as shown in FIG. 17(A), the Central Brain 120 returns the first edge 22 to its original position and pushes both second edges 24 from the bottom surface 20 of the vehicle 12. Here, mechanisms such as a motor that push the second edge 24 toward the road are not shown. As a result, the vehicle 12 rotates and lands on the second edge 24 before the wheels 21, as shown in FIG. 17(B). After the vehicle 12 lands and stops, the Central Brain 120 returns the second edge 24 to its original position. As a result, the state of the vehicle 12 returns to the normal state shown in FIG. 16(A).

[0034] In this way, when the vehicle 12 rotates and jumps, the second edge 24, which is located closer to the center of gravity of the vehicle 12 than the first edge 22, lands first, thereby stabilizing the landing of the vehicle 12.

[0035] 18, an elastic body 26 having higher elasticity than the edge 22 may be provided on the bottom surface of the vehicle 12. Specifically, as shown in FIG. 18, the elastic body 26 may be provided on the inner side of the edge 22 in the width direction of the vehicle 12. The number of elastic bodies 26 is not limited to one on each side of the vehicle 12, but multiple elastic bodies 26 may be provided, and they may also be provided in various locations on the vehicle 12.

[0036] In this case, as shown in FIG. 19(B), the Central Brain 120 pushes the edge 22 toward the road to avoid the obstacle 13, causing the vehicle 12 to jump from the normal state shown in FIG. 19(A). When landing, as shown in FIG. 20(A), the Central Brain 120 returns the edge 22 to its original position and pushes both elastic bodies 26 out from the bottom surface 20 of the vehicle 12. Here, mechanisms such as motors that push the elastic bodies 26 toward the road are not shown. As a result, as shown in FIG. 20(B), the vehicle 12 lands on the elastic bodies 26 before the wheels 21. After landing, the Central Brain 120 returns the elastic bodies 26 to their original positions. As a result, the state of the vehicle 12 returns to the normal state shown in FIG. 19(A).

[0037] In this way, after the vehicle 12 jumps, the elastic body 26, which has a higher elastic modulus than the edge 22, lands first, thereby stabilizing the landing of the vehicle 12.

[0038] 21 schematically illustrates an example of the hardware configuration of a computer 1200 functioning as the Central Brain 120. A program installed on the computer 1200 can cause the computer 1200 to function as one or more "parts" of an apparatus according to the present embodiment, or to perform operations associated with the apparatus according to the present embodiment or one or more "parts," and / or to perform a process according to the present embodiment or steps of the process. Such a program can be executed by the CPU 1212 to cause the computer 1200 to perform specific operations associated with some or all of the blocks in the flowcharts and block diagrams described herein.

[0039] The computer 1200 according to this embodiment includes a CPU 1212, a 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 communications 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, a DVD-RAM drive, or the like. The storage device 1224 may be a hard disk drive, a solid-state drive, or the like. The computer 1200 also includes a ROM 1230 and legacy input / output units such as a keyboard, which are connected to the input / output controller 1220 via an input / output chip 1240.

[0040] The CPU 1212 operates according to programs stored in the ROM 1230 and the RAM 1214, thereby controlling each unit. The graphics controller 1216 acquires image data generated by the CPU 1212 into a frame buffer or the like provided in the RAM 1214 or into the graphics controller itself, and causes the image data to be displayed on the display device 1218.

[0041] 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.

[0042] The ROM 1230 stores therein a boot program or the like that is executed by the computer 1200 upon activation, and / or programs that depend on the hardware of the computer 1200. The input / output chip 1240 may also connect various input / output units to the input / output controller 1220 via a USB port, a parallel port, a serial port, a keyboard port, a mouse port, etc.

[0043] The programs are provided by a computer-readable storage medium such as a DVD-ROM or an IC card. The programs are read from the computer-readable storage medium, installed in the storage device 1224, RAM 1214, or ROM 1230, which are also examples of computer-readable storage media, and executed by the CPU 1212. The information processing described in these programs is read by the computer 1200, and causes cooperation between the programs and the various types of hardware resources described above. An apparatus or a method may be configured by implementing operations or processing of information in accordance with the use of the computer 1200.

[0044] For example, when communication is performed between computer 1200 and an external device, CPU 1212 may execute a communication program loaded into RAM 1214 and instruct communication interface 1222 to perform communication processing based on the processing described in the communication program. Under the control of CPU 1212, communication interface 1222 reads transmission data stored in a transmission buffer area provided in RAM 1214, storage device 1224, a DVD-ROM, or a recording medium such as an IC card, and transmits the read transmission data to the network, or writes received data received from the network to a reception buffer area or the like provided on the recording medium.

[0045] Furthermore, the CPU 1212 may cause all or a necessary portion of a file or database stored in an external recording medium such as the storage device 1224, a DVD drive (DVD-ROM), an IC card, etc. to be read into the RAM 1214, and may perform various types of processing on the data on the RAM 1214. The CPU 1212 may then write back the processed data to the external recording medium.

[0046] Various types of information, such as various types of programs, data, tables, and databases, may be stored on the recording medium and may undergo information processing. CPU 1212 may perform various types of processing on data read from RAM 1214, including various types of operations, information processing, conditional judgment, conditional branching, unconditional branching, information search / replacement, etc., as described throughout this disclosure and specified by the instruction sequences of the programs, and write the results back to RAM 1214. CPU 1212 may also search for information in files, databases, etc. on the recording medium. For example, if multiple entries, each having an attribute value of a first attribute associated with an attribute value of a second attribute, are stored on the recording medium, CPU 1212 may search for an entry whose attribute value of the first attribute matches a specified condition from among the multiple entries, read the attribute value of the second attribute stored in the entry, and thereby obtain the attribute value of the second attribute associated with the first attribute that satisfies a predetermined condition.

[0047] The above-described programs or software modules may be stored in a computer-readable storage medium on or near the computer 1200. A recording medium such as a hard disk or RAM provided in a server system connected to a dedicated communication network or the Internet can also be used as a computer-readable storage medium, thereby providing the programs to the computer 1200 via the network.

[0048] The blocks in the flowcharts and block diagrams in the present embodiments may represent stages of a process in which an operation is performed or "parts" of an apparatus responsible for performing the operation. Particular stages and "parts" may be implemented by dedicated circuitry, programmable circuitry provided with computer-readable instructions stored on a computer-readable storage medium, and / or a processor provided with computer-readable instructions stored on a computer-readable storage medium. The dedicated circuitry may include digital and / or analog hardware circuits, including integrated circuits (ICs) and / or discrete circuits. The programmable circuitry may include reconfigurable hardware circuits, such as field programmable gate arrays (FPGAs) and programmable logic arrays (PLAs), including AND, OR, XOR, NAND, NOR, and other logical operations, flip-flops, registers, and memory elements.

[0049] A computer-readable storage medium may include any tangible device capable of storing instructions that are executed by an appropriate device, such that a computer-readable storage medium having instructions stored thereon comprises an article of manufacture, including instructions that can be executed to create means for performing the operations specified in the flowcharts or block diagrams. Examples of computer-readable storage media may include electronic, magnetic, optical, electromagnetic, and semiconductor storage media. 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 disc read-only memory (CD-ROM), digital versatile disc (DVD), Blu-ray disc, memory stick, integrated circuit card, and the like.

[0050] The computer readable instructions may include either assembler instructions, Instruction Set Architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state-setting data, or source or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk®, JAVA®, C++, etc., and conventional procedural programming languages ​​such as the “C” programming language or similar programming languages.

[0051] Computer-readable instructions may be provided locally or over a wide area network (WAN) such as a local area network (LAN), the Internet, etc. to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, or programmable circuitry, such that the processor or programmable circuitry executes the computer-readable instructions to generate means for performing the operations specified in the flowcharts or block diagrams. Examples of processors include computer processors, processing units, microprocessors, digital signal processors, controllers, microcontrollers, etc.

[0052] 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 and improvements can be made to the above embodiments. It is clear from the claims that such modifications and improvements can also be included within the technical scope of the present invention.

[0053] It should be noted that the order of execution of each process, such as operations, procedures, steps, and stages, in the devices, systems, programs, and methods shown in the claims, specifications, and drawings is not specifically stated as "before," "prior to," etc., and that the processes can be performed in any order unless the output of a previous process is used in a subsequent process. Even if the operational flow in the claims, specifications, and drawings is described using "first," "next," etc. for convenience, this does not mean that the processes must be performed in this order. [Explanation of symbols]

[0054] 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. A vehicle, The vehicle body, a first edge provided so as to be extruded from the bottom surface of the vehicle body; a second edge provided on the bottom surface of the vehicle body at a position closer to the center of gravity of the vehicle than the first edge and capable of being pushed out from the bottom surface; an information processing device; The information processing device includes: an information acquisition unit that acquires a plurality of pieces of sensor information including obstacle information; a control unit that controls the first edge and the second edge to avoid the obstacle when the information acquisition unit acquires information about the obstacle, so as to push out the first edge to cause the vehicle to jump in order to avoid the obstacle, return the first edge to its original position when the vehicle lands, and push out the second edge to cause the vehicle to land on the second edge.

2. A vehicle, The vehicle body, an edge provided so as to be extruded from the bottom surface of the vehicle body; an elastic body that is provided so as to be extruded from the bottom surface of the vehicle body and has higher elasticity than the edge; an information processing device; The information processing device includes: an information acquisition unit that acquires a plurality of pieces of sensor information including obstacle information; a control unit that controls the edge and the elastic body to avoid the obstacle when the information acquisition unit acquires information about the obstacle, pushes out the edge to make the vehicle jump in order to avoid the obstacle, returns the edge to its original position when the vehicle lands, and pushes out the elastic body to make the vehicle land from the elastic body.

3. the information acquisition unit detects the obstacle in units of one billionth of a second, The vehicle according to claim 1 , wherein the control unit controls the first edge and the second edge in units of one billionth of a second.

4. the information acquisition unit detects the obstacle in units of one billionth of a second, The vehicle according to claim 2 , wherein the control unit controls the edge and the elastic body in units of one billionth of a second.

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

Images