Information processing device, vehicle, and program

Abstract

An information processing device is provided with: an information acquisition unit that acquires a plurality of pieces of sensor information including information about an obstacle; and a control unit that controls the speed of the vehicle so as to avoid the obstacle when the information of the obstacle is acquired by the information acquisition unit.

Description

Technical Field

[0001] The present disclosure relates to an information processing device, a vehicle, and a program. Background Art

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

[0003] Prior Art Documents

[0004] Patent Documents

[0005] Patent Document 1: Japanese Unexamined Patent Application Publication No. 2022-035198 Summary of the Invention

[0006] Means for Solving the Problem

[0007] According to an embodiment of the present disclosure, an information processing device is provided. The information processing device according to the first aspect includes: an information acquisition unit that acquires a plurality of pieces of information; and a control unit that controls the speed of the vehicle to avoid an obstacle when the information acquisition unit acquires information about the obstacle.

[0008] Regarding the information processing device according to the second aspect, in the information processing device according to the first aspect, the control unit calculates a control variable for controlling the speed of the vehicle and controls the autonomous driving of the vehicle in units of one billionth of a second based on the control variable.

[0009] Regarding the information processing device according to the third aspect, in the information processing device according to the first aspect, the control unit avoids the obstacle by controlling an edge provided on the bottom surface of the vehicle that causes the vehicle to jump.

[0010] According to an embodiment of the present disclosure, a vehicle is provided. The vehicle according to the fourth aspect includes: a vehicle body; a first edge provided so as to be able to be pushed out from the bottom surface of the vehicle body; a second edge provided so as to be able to be pushed out from the bottom surface of the vehicle body at a position closer to the center of gravity of the vehicle than the first edge; and an information processing device having: an information acquisition unit that acquires a plurality of sensor information including information about an obstacle; and a control unit that controls the first edge and the second edge to avoid the obstacle, so that when the information acquisition unit acquires information about the obstacle, the first edge is pushed out to make the vehicle jump to avoid the obstacle, the first edge is restored to its original state when the vehicle lands, and the second edge is pushed out to make the vehicle land from the second edge.

[0011] For a vehicle according to the fifth mode, it includes: a vehicle body; an edge provided in a manner capable of being pushed out from the bottom surface of the vehicle body; an elastomer provided in a manner capable of being pushed out from the bottom surface of the vehicle body, having higher elasticity than the edge; and an information processing device having: an information acquisition unit that acquires a plurality of sensor information including information on obstacles; and a control unit that controls the edge and the elastomer to avoid the obstacles, such that when the information acquisition unit acquires information on an obstacle, the edge is pushed out to make the vehicle jump to avoid the obstacle, the edge is restored to its original state when the vehicle lands, and the elastomer is pushed out to make the vehicle land from the elastomer.

[0012] For a vehicle according to the sixth mode, in the vehicle according to the fourth mode, the information acquisition unit detects the obstacle in units of one billionth of a second, and the control unit controls the first edge and the second edge in units of one billionth of a second.

[0013] For a vehicle according to the seventh mode, in the vehicle according to the fifth mode, the information acquisition unit detects the obstacle in units of one billionth of a second, and the control unit controls the edge and the elastomer in units of one billionth of a second.

[0014] For an information processing device according to the eighth mode, it includes: an information acquisition unit that acquires a plurality of sensor information including information on obstacles; and a control unit that avoids the obstacle by controlling an edge provided on the bottom surface of the vehicle to make the vehicle jump, and the control unit calculates a control variable for controlling the vehicle's avoidance action of the obstacle based on the characteristics of the vehicle's occupant.

[0015] For an information processing device according to the ninth mode, in the information processing device according to the eighth mode, the control unit controls the vehicle's autonomous driving in units of one billionth of a second based on the control variable.

[0016] For an information processing device according to the tenth mode, in the information processing device according to the eighth mode, the control unit updates the characteristics based on the reaction of the vehicle's occupant when avoiding the obstacle.

[0017] For an information processing device according to the eleventh mode, in the information processing device according to the eighth mode, the information processing device is set to be able to be pre-selected by the occupant whether to avoid the obstacle by controlling the edge.

[0018] According to an embodiment of the present disclosure, a program is provided, which is used to make a computer function as an information processing device according to any one of the first mode to the eleventh mode.

[0019] It should be noted that the above summary of the present disclosure does not list all the necessary features of the present disclosure. In addition, sub-combinations of these feature groups can also constitute the present disclosure. Description of the Drawings

[0020] Figure 1 It is a diagram schematically showing the danger prediction ability of artificial intelligence (AI) for ultra-high performance autonomous driving.

[0021] Figure 2 It is a diagram schematically showing an example of the network structure inside a vehicle.

[0022] Figure 3 It is a diagram schematically showing an example of a series of proportional constants.

[0023] Figure 4 It is a diagram schematically showing an example of a series of proportional constants.

[0024] Figure 5 It is a diagram schematically showing an example of a series of proportional constants.

[0025] Figure 6 It is a flowchart executed by the Central Brain.

[0026] Figure 7 It is the first explanatory diagram explaining an example of the stopping distance of a vehicle.

[0027] Figure 8 It is the second explanatory diagram explaining an example of the stopping distance of vehicle 12.

[0028] Figure 9 It is the third explanatory diagram explaining an example of the stopping distance of vehicle 12.

[0029] Figure 10 It is the fourth explanatory diagram explaining an example of the stopping distance of vehicle 12.

[0030] Figure 11 It is an explanatory diagram explaining a control example of autonomous driving based on the Central Brain.

[0031] Figure 12 It is a schematic diagram of perfect steering control and perfect steering control.

[0032] Figure 13 It is an explanatory diagram explaining the edge.

[0033] Figure 14 It is an explanatory diagram explaining an operation example of the edge based on the Central Brain.

[0034] Figure 15 It is an explanatory diagram for explaining the first edge and the second edge.

[0035] Figure 16 It is an explanatory diagram for explaining an operation example of the first edge and the second edge based on the central brain.

[0036] Figure 17 It is an explanatory diagram for explaining an operation example of the first edge and the second edge based on the central brain.

[0037] Figure 18 It is an explanatory diagram for explaining the edge and the elastomer.

[0038] Figure 19 It is an explanatory diagram for explaining an operation example of the edge and the elastomer based on the central brain.

[0039] Figure 20 It is an explanatory diagram for explaining an operation example of the edge and the elastomer based on the central brain.

[0040] Figure 21 It is a diagram schematically showing an example of the hardware structure of a computer that functions as a central brain. Detailed implementation mode

[0041] Hereinafter, the present disclosure will be described by way of embodiments of the present disclosure. However, the following embodiments do not limit the invention described in the claims. In addition, not all of the feature combinations described in the embodiments are essential for the solution of the present disclosure.

[0042] (First embodiment)

[0043] First, the first embodiment according to the present embodiment will be described.

[0044] Figure 1 Schematically shows the ability of the AI for danger prediction of the ultra-high-performance autonomous driving according to the present embodiment. In the present embodiment, a variety of sensor information is digitalized into AI data and stored in the cloud. The AI predicts and judges the optimal mixture of the situation every nanosecond (one billionth of a second) to optimize the operation of the vehicle 12. In the present embodiment, the vehicle 12 is preferably an electric vehicle.

[0045] Figure 2 It is a diagram for explaining the structure inside the vehicle 12 of the central brain 120. The central brain 120 is an example of an information processing device.

[0046] As Figure 2As shown, a plurality of gateways are communicably connected to the central brain 120. The central brain 120 is connected to an external cloud via the gateways. The central brain 120 is configured to be able to access the external cloud via the gateways. On the other hand, due to the existence of the gateways, it is configured that the central brain 120 cannot be directly accessed from the outside.

[0047] The central brain 120 outputs a request signal to the server every predetermined time. Specifically, the central brain 120 outputs a request signal indicating an inquiry to the server every one billionth of a second.

[0048] Examples of the sensors used in this embodiment include radar, lidar (LiDAR), high-pixel / long-focal-length / ultra-wide-angle / 360-degree / high-performance cameras, visual recognition, faint sound, ultrasonic waves, vibration, infrared rays, ultraviolet rays, electromagnetic waves, temperature, humidity, spot AI weather forecast, high-precision multi-channel global positioning system (GPS), low-altitude satellite information, long-tail event AI data, etc. Long-tail event AI data refers to the trip data of a vehicle equipped with level 5.

[0049] Examples of the sensor information obtained from various sensors include the movement of the center of gravity of the weight, the detection of the road material, the detection of the external air temperature, the detection of the external air humidity, the detection of the uphill / downhill / horizontal / oblique inclination angles of the ramp, the freezing mode of the road, the detection of the moisture content, the material, wear condition, and air pressure of each tire, the road width, the presence or absence of overtaking prohibition, oncoming vehicles, the vehicle type information of the front and rear vehicles, the cruising states of these vehicles, the surrounding conditions (birds, animals, footballs, accident vehicles, earthquakes, fires, winds, typhoons, heavy rains, light rains, snowstorms, fog, etc.). In this embodiment, these detections are performed every one billionth of a second.

[0050] In this embodiment, when the information acquisition unit acquires road information (for example: the road material, the uphill / downhill / horizontal / oblique inclination angles of the ramp, the freezing mode of the road, and the moisture content of the road, etc.) indicating the road conditions of the road on which the vehicle 12 travels, including information on obstacles such as a vehicle that has broken down on the road, detected by the above sensors, the central brain 120 functions as a control unit for controlling the speed of the vehicle to avoid the obstacle.

[0051] In addition, in the present embodiment, the central brain 120 functions as a control unit that calculates a control variable for controlling the speed of the vehicle and controls the autonomous driving of the vehicle in units of one billionth of a second based on the control variable. Specifically, the central brain 120 controls the speed (acceleration / deceleration) of the vehicle through the following relational expression. Also, a perfect turn without friction is achieved.

[0052] [Calculation formula 1]

[0053] y = ax 2

[0054] Here, a is a proportionality constant, which is an example of a control variable. A perfect turn without friction means beautiful, smooth, and perfect acceleration. Perfect acceleration can be represented by this formula. The proportionality constant can be smoothly changed to any series in units of one billionth of a second. The central brain 120 calculates based on sensor information such as driving time, battery depletion, sudden crisis avoidance (such as avoid accidents), the state of materials such as tires (material condition), and variables such as wind speed (wind speed), compares with the data stored in the cloud, fine-tunes the differences, and correctly transmits the value of the proportionality constant derived through goal seek to the in-wheel motors respectively mounted on the four wheels 21 and the four spin angles, thereby achieving perfect speed control and perfect steering control for optimal acceleration and deceleration. This is an object-oriented goal-seeking driving system. Also, achieving such driving is a task of level 6. Here, "level 6" refers to the level representing autonomous driving, which is equivalent to a level higher than level 5 representing full autonomous driving. Although level 5 represents full autonomous driving, it is at the same level as human driving, and there is still a probability of accidents and the like. Level 6 represents a level higher than level 5, which is equivalent to a lower probability of accidents than level 5.

[0055] Figures 3 to 5 It is a diagram for explaining the series of the proportionality constant.

[0056] As Figure 3 shown, for example, when the series of the proportionality constant is 4 levels, it may be 0, S, M, L (L is the maximum (Max) performance). In this way, the proportionality constant can be changed to any level in units of one billionth of a second and instructs the in-wheel motors respectively mounted on the four wheels 21 to perform acceleration and deceleration. Figure 4 It is a diagram that charts the case where the proportionality constant is 4 levels. Also, as Figure 5 shown, by combining proportionality constants of multiple levels, the speed of the vehicle can be made variable.

[0057] The central brain 120 repeatedly executes Figure 6 the flowchart shown.

[0058] In step S10, the central brain 120 acquires sensor information including information on the obstacle 13 or road information detected by the sensor. Then, the central brain 120 proceeds to step S11.

[0059] In step S11, the central brain 120 calculates a proportionality constant based on the sensor information acquired in step S10. After that, the central brain 120 proceeds to step S12.

[0060] 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.

[0061] Figures 7 to 10 is an explanatory diagram for explaining an example of the stopping distance of the vehicle 12.

[0062] Figure 7 This is an example where the vehicle 12 traveling at a speed of 100 km / h (kilometers per hour) acquires information on the obstacle 13 at a location 115 m (meters) away from the obstacle 13. In this example, it takes 0.3 seconds from the acquisition of the information on the obstacle 13 (the position of vehicle A) to the danger recognition, and 0.7 seconds from the danger recognition to the start of braking. During this total of 1.0 second, the vehicle 12 moves approximately 28 m (the position of vehicle B). And, for example, it shows that the braking distance reaches 84 m until the vehicle 12 stops (the position of vehicle C).

[0063] Figure 8 This is an example where the vehicle 12 traveling at a speed of 100 km / h on an icy road surface acquires information on the obstacle 13 at a location 115 m (meters) away from the obstacle 13. In this example, it takes 0.3 seconds from the acquisition of the information on the obstacle 13 (the position of vehicle A) to the danger recognition, and 0.7 seconds from the danger recognition to the start of braking. During this total of 1.0 second, the vehicle 12 moves approximately 28 m (the position of vehicle B). And, for example, as described above Figure 7 as shown, it shows that the vehicle 12 further skids and stops (the position of vehicle C) at a position where the braking distance reaches 84 m until it stops.

[0064] Figure 9This is an example where information about the obstacle 13 is obtained after the stopped vehicle 12 accelerates to a speed of 100 km / h. In this example, it takes 1.9 seconds (26 m) to accelerate from the stopped vehicle 12 (the position of vehicle A) to 100 km / h. It takes 0.3 seconds from the acquisition of the information about the obstacle 13 (the position of vehicle B) until danger recognition, and 0.7 seconds from danger recognition until the start of braking (the position of vehicle C). During this total of 1.0 second, vehicle 12 reaches a speed of 200 km / h and moves approximately 42 m (the position of vehicle C). Also, for example, it is shown that the braking distance reaches 280 m until vehicle 12 stops (the position of vehicle D).

[0065] Figure 10 This is an example where information about the obstacle 13 is obtained after the vehicle 12 stopped on the frozen road surface accelerates to a speed of 100 km / h. In this example, it takes 1.9 seconds (26 m) to accelerate from the stopped vehicle 12 (the position of vehicle A) to 100 km / h. It takes 0.3 seconds from the acquisition of the information about the obstacle 13 (the position of vehicle B) until danger recognition, and 0.7 seconds from danger recognition until the start of braking (the position of vehicle C). During this total of 1.0 second, vehicle 12 reaches a speed of 200 km / h and moves approximately 42 m (the position of vehicle C). And, for example, as described above Figure 9 As shown, it means that vehicle 12 further skids and stops at a position where the braking distance reaches 280 m until it stops (the position of vehicle D).

[0066] Figure 11 This is an explanatory diagram of a control example for autonomous driving based on the central brain 120.

[0067] Figure 11 This is the following example, that is, the central brain 120 controls the speed of vehicle 12 by a proportional constant calculated by the central brain 120 based on sensor information such as driving time, battery depletion, sudden crisis avoidance situations, the state of materials such as tires, and variables such as wind speed, so as to avoid the obstacle 13. And it is an example where information about the obstacle 13 is obtained after the vehicle 12 stopped on the frozen road surface accelerates to a speed of 100 km / h. In this example, it is shown that it takes 1.9 seconds (26 m) to accelerate from the stopped vehicle 12 (the position of vehicle A) to 100 km / h. If the information about the obstacle 13 is obtained (the position of vehicle B), then immediately (one billionth of a second), the autonomous driving of the vehicle is controlled to perform deceleration / acceleration.

[0068] Figure 12Schematically shows the perfect speed control and perfect steering control achieved by the information processing apparatus according to the present embodiment. Figure 12 The principle shown is to calculate the speed of the vehicle and achieve perfect speed control and perfect steering control based on the input and cloud data.

[0069] Here, in existing autonomous vehicles, although it is possible to turn while taking into account the road conditions to a certain extent, it is impossible to accurately turn in units of one billionth of a second. In addition, it takes a certain distance until the autonomous vehicle detects an obstacle and applies the brakes to stop.

[0070] Therefore, in existing autonomous vehicles, since it is impossible to accurately control the turn in units of one billionth of a second, accidents such as skidding occur during turning. In addition, even if it is possible to calculate the theoretically accurate turn, it is impossible to turn while taking into account various real-world friction conditions (such as tires, road conditions, temperature, humidity, wind speed, etc.).

[0071] According to the vehicle 12 according to the present embodiment, based on the structure described above, the safety of autonomous driving can be improved.

[0072] In addition, in the present embodiment, when it is impossible to avoid the obstacle 13 by controlling the speed of the vehicle 12, or even when it is possible to avoid the obstacle 13 by controlling the speed of the vehicle 12, the central brain 120 also functions as a control unit that pushes out the edge 22 from the bottom surface 20 to avoid the obstacle 13. The edge 22 is provided on the bottom surface 20 of the vehicle body of the vehicle 12 and causes the vehicle 12 to jump. Specifically, as Figure 13 shown, between the front wheel 21A and the rear wheel 21B of the wheels 21 on the bottom surface 20 of the vehicle 12, there is an edge 22 that protrudes from the bottom surface 20 toward the road and can be pushed out to the road side. It should be noted that the edge 22 is not limited to Figure 13 the shape shown. In addition, it is not limited to being provided one on each side of the vehicle 12, and multiple edges can also be provided. In addition, it can also be provided at various positions of the vehicle 12.

[0073] And, starting from the normal state of (A) in Figure 14 , as Figure 14As shown in (B), when the central brain 120 avoids an obstacle, it pushes the edge 22 toward the road side, causing the vehicle to jump. Here, the illustration of mechanisms such as the motor that pushes the edge 22 toward the road side is omitted. In addition, the central brain 120 can push not only the edge 22 on one side (left or right) but also the edges 22 on both sides, and can also stagger the timing of pushing the edges 22 on the left and right. Moreover, the central brain 120 can control the pushing of the edge 22 and the speed of the vehicle 12 to make the vehicle 12 rotate. The central brain 120 can smoothly land the vehicle 12 through perfect speed control and perfect steering control after this jump. Even when an obstacle 13 (for example, a burning truck on the road) is detected, due to the speed of the vehicle 12 or the distance to the obstacle 13, it is impossible to stop and avoid the collision between the vehicle 12 and the obstacle 13. However, with this configuration, the safety of autonomous driving can be improved. In addition, by jumping over the obstacle 13, even when it is impossible to avoid the obstacle 13 by controlling the speed of the vehicle 12, the obstacle 13 can be avoided.

[0074] (Second Embodiment)

[0075] Next, for the second embodiment related to this embodiment, the parts that overlap with the above embodiment will be omitted or simplified in the description.

[0076] In the second embodiment, as Figure 15 shown, in addition to the edge 22, other edges 24 can also be provided at positions on the bottom surface 20 of the vehicle 12 that are closer to the center of gravity of the vehicle 12 than the edge 22. Specifically, as Figure 15 shown, an edge 24 (referred to as the second edge 24) can be provided inside the vehicle 12 in the width direction of the vehicle 12 with respect to the edge 22 (hereinafter referred to as the first edge 22). The length of the second edge 24 in the front-rear direction of the vehicle 12 is shorter than that of the first edge 22. In addition, regarding the second edge 24, it is not limited to being provided one on each side (left and right) of the vehicle 12, and multiple can also be provided.

[0077] In this case, starting from the normal state shown in (A) of Figure 16 , as shown in (B) of Figure 16 , when the central brain 120 avoids an obstacle 13, it only pushes the first edge 22 on one side (left or right) toward the road side, causing the vehicle 12 to jump. By pushing the first edge 22 on one side, as shown in (A) of Figure 17 , the vehicle 12 rotates in the air around the axis X passing through its center of gravity. When landing, as shown in (A) of Figure 17 , the central brain 120 returns the first edge 22 to its original state and pushes the second edges 24 on both sides out from the bottom surface 20 of the vehicle 12. Here, the illustration of mechanisms such as the motor that pushes the second edge 24 toward the road side is omitted. Thus, as Figure 17As shown in (B), while the vehicle 12 is rotating, it lands from the second edge 24 before the wheel 21. After the vehicle 12 lands and stops, the central brain 120 restores the second edge 24 to its original state. Thus, the state of the vehicle 12 returns to Figure 16 the normal state shown in (A).

[0078] In this way, when the vehicle 12 rotates and jumps, by making the vehicle 12 land from the second edge 24 provided at a position closer to the center of gravity of the vehicle 12 than the first edge 22, the landing of the vehicle 12 can be stabilized.

[0079] In addition, as Figure 18 shown, in addition to the edge 22, an elastic body 26 having a higher elasticity than the edge 22 can be provided on the bottom surface of the vehicle 12. Specifically, as Figure 18 shown, the elastic body 26 can be provided inside the vehicle 12 in the width direction of the edge 22. The elastic body 26 is not limited to being provided one on each side of the vehicle 12, and multiple elastic bodies can also be provided. In addition, the elastic body 26 can be provided at various positions of the vehicle 12.

[0080] In this case, starting from the normal state of (A) of Figure 19 , as shown in (B) of Figure 19 , when the central brain 120 avoids the obstacle 13, it pushes the edge 22 toward the road side to make the vehicle 12 jump. When landing, as Figure 20 shown in (A), the central brain 120 restores the edge 22 to its original state and pushes out the bilateral elastic bodies 26 from the bottom surface 20 of the vehicle 12. Here, the illustration of mechanisms such as motors for pushing out the elastic bodies 26 toward the road side is omitted. Thus, as Figure 20 shown in (B), the vehicle 12 lands from the elastic body 26 before the wheel 21. After landing, the central brain 120 restores the elastic body to its original state. Thus, the state of the vehicle 12 returns to Figure 19 the normal state shown in (A).

[0081] In this way, after the vehicle 12 jumps, by making the elastic body 26 with a higher elastic modulus land first, the landing of the vehicle 12 can be stabilized.

[0082] (Third Embodiment)

[0083] Next, for the third embodiment related to the present embodiment, the repetitive parts with the above embodiments will be omitted or simplified for description.

[0084] In the third embodiment, the central brain 120 calculates a control variable for controlling the avoidance action of the vehicle 12 with respect to an obstacle based on the characteristics of the occupants of the vehicle 12. Specifically, the central brain 120 controls the edge 22 in addition to or independently of the speed (acceleration / deceleration) of the vehicle according to the above calculation formula 1. In addition to the characteristics of the occupants, the central brain 120 calculates based on sensor information such as driving time, battery depletion, sudden crisis avoidance (such as avoid accidents), the state of materials such as tires (material condition), and wind speed (wind speed), and compares it with the data stored in the cloud, fine-tunes the differences, and correctly transmits the value of the proportional constant derived by goal seek to the motor that pushes the edge 22 towards the road side, thereby achieving perfect speed control and perfect steering control for making an optimal jump.

[0085] Here, the order of the proportional constant is similar to the case of the speed of the vehicle described above and can be changed to any order in billionths of a second. That is, the speed or amount of pushing the edge 22 towards the road side can be changed to any order.

[0086] In addition, the characteristics of the occupants include, for example, the tolerance for actions such as jumping of the vehicle 12, the ease of motion sickness, the fear of jumping of the vehicle 12, etc. The characteristics of the occupants can also include other characteristics, such as the age, gender, medical history, or driving skills of the occupants. That is, although it is an action for avoiding an obstacle, it is anticipated that there are occupants who are afraid of jumping through the vehicle 12, and the proportional constant is set to be changeable according to the characteristics of the occupants. In addition to the occupants inputting data, the characteristics of the occupants can also be judged by the central brain 120 storing the occupants and acquiring sensor information on the state of each occupant during riding. Specifically, for an occupant who has input a fear of jumping through the vehicle 12, the central brain 120 executes a proportional constant that makes the jump smaller, or avoids the obstacle without making a jump, etc.

[0087] It should be noted that the central brain 120 can update the characteristics of the occupants based on the reactions of the occupants of the vehicle 12 when avoiding obstacles. That is, the central brain 120 stores the occupants, and for the avoidance action of the obstacle for that occupant, acquires sensor information such as the fear of the occupant (including heart rate, pressure value, image, etc.), and updates the characteristics of the occupants. With such a configuration, in the case where the occupant is afraid, etc., measures such as setting a proportional constant that makes the jump smaller, or avoiding the obstacle without making a jump can be executed. In addition, in the case where the occupant is not afraid, etc., a proportional constant that increases the jump can be set, etc.

[0088] In addition, it can be set so that the occupant can pre-select whether to avoid an obstacle by controlling the edge 22. That is, although it is an action for avoiding an obstacle, it is anticipated that there are occupants who are afraid of jumping the vehicle 12, and it can be set so that the occupant can select to preferentially execute avoidance actions other than jumping.

[0089] In addition, the central brain 120 can input the characteristics of the occupant into the learned model, and determine the proportionality constant based on the output result. The learned model has learned the characteristics of the occupant and the reaction to the executed avoidance action for the obstacle.

[0090] Figure 21 An example of the hardware configuration of the computer 1200 in which the central brain 120 functions is schematically shown. The program installed in the computer 1200 enables the computer 1200 to function as one or more "parts" of the device according to the present embodiment, or enables the computer 1200 to perform operations associated with the device according to the present embodiment or the one or more "parts", and / or enables the computer 1200 to execute the process according to the present embodiment or a stage of the process. Such a program can be executed by the CPU 1212 so that the computer 1200 performs specific operations associated with some or all of the blocks in the flowcharts and block diagrams described in this specification.

[0091] The computer 1200 according to the present embodiment includes a CPU 1212, a RAM 1214, and a graphics controller 1216 that are interconnected via 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 can be a DVD-ROM drive, a DVD-RAM drive, etc. The storage device 1224 can be a hard disk drive, a solid state drive, etc. The computer 1200 also includes a ROM 1230 and traditional input / output units such as a keyboard, which are connected to the input / output controller 1220 via an input / output chip 1240.

[0092] The CPU 1212 operates according to the programs stored in the ROM 1230 and the RAM 1214, thereby controlling each unit. The graphics controller 1216 acquires the image data generated by the CPU 1212 from a frame buffer or the like provided in the RAM 1214 or within itself, and causes the image data to be displayed on the display device 1218.

[0093] 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 within 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 the IC card.

[0094] The ROM 1230 stores therein a boot program executed by the computer 1200 at startup and / or a program dependent on the hardware of the computer 1200. The input / output chip 1240 can 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.

[0095] The program is provided by a computer-readable storage medium such as a DVD-ROM or an IC card. The program is read from the computer-readable storage medium, installed in the storage device 1224, the RAM 1214 or the 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 enables cooperation between the programs and the various types of hardware resources described above. The apparatus or method may be configured by operating or processing information in accordance with the use of the computer 1200.

[0096] For example, when communication is performed between the computer 1200 and an external device, the CPU 1212 can execute a communication program loaded into the RAM 1214 and command the communication interface 1222 to perform communication processing based on the processing described in the communication program. Under the control of the CPU 1212, the communication interface 1222 reads the transmission data stored in the transmission buffer provided in a recording medium such as the RAM 1214, the storage device 1224, the DVD-ROM or the IC card, and transmits the read transmission data to the network, or writes the received data received from the network to the reception buffer provided on the recording medium.

[0097] In addition, the CPU 1212 can cause all or a necessary part of a file or database stored in an external recording medium such as the storage device 1224, the DVD drive (DVD-ROM), the IC card, etc. to be read into the RAM 1214 and perform various types of processing on the data on the RAM 1214. Next, the CPU 1212 can write the processed data back to the external recording medium.

[0098] Various types of information such as various types of programs, data, tables, and databases can be stored in a recording medium to undergo information processing. The CPU 1212 can perform various types of processing on the data read from the RAM 1214 and write the results back to the RAM 1214. The various types of processing include various types of operations, information processing, conditional judgments, conditional branches, unconditional branches, information retrieval / replacement, etc. described throughout this disclosure and specified by the instruction sequences of the programs. In addition, the CPU 1212 can retrieve information in files, databases, etc. within the recording medium. For example, in the case where a plurality of entries each having an attribute value of a first attribute associated with an attribute value of a second attribute are stored in the recording medium, the CPU 1212 can retrieve an entry that matches the condition specifying the attribute value of the first attribute from the plurality of entries and read the attribute value of the second attribute stored in the entry, thereby obtaining the attribute value of the second attribute associated with the first attribute that satisfies a predetermined condition.

[0099] The programs or software modules described above can be stored in a computer-readable storage medium on or near the computer 1200. In addition, 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.

[0100] The blocks in the flowcharts and block diagrams in this embodiment can represent stages of a process of performing operations or "parts" of a device having the function of performing operations. Specific stages and "parts" can be implemented by dedicated circuits, programmable circuits supplied together with computer-readable instructions stored on a computer-readable storage medium, and / or processors supplied together with computer-readable instructions stored on a computer-readable storage medium. The dedicated circuits can include digital and / or analog hardware circuits and can also include integrated circuits (ICs) and / or discrete circuits. The programmable circuits can include, for example, reconfigurable hardware circuits such as field programmable gate arrays (FPGAs) and programmable logic arrays (PLAs). The reconfigurable hardware circuits include logical AND, logical OR, logical XOR, logical NAND, logical NOR, and other logical operations, flip-flops, registers, and storage elements.

[0101] A computer-readable storage medium may include any tangible device that can store instructions executable by an appropriate device. As a result, a computer-readable storage medium having instructions stored therein constitutes a product comprising instructions that can be executed to generate units for performing the 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 (registered trademark) disks, magnetic disks, 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 (registered trademark) disk, memory stick, integrated circuit card, etc.

[0102] Computer-readable instructions may include any one of assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state-setting data, or source code or object code described in any combination of one or more programming languages, the one or more programming languages including object-oriented programming languages such as Smalltalk (registered trademark), JAVA (registered trademark), C++, etc. and traditional procedural programming languages such as the "C" programming language or similar programming languages.

[0103] Computer-readable instructions can be provided to a processor or programmable circuit of a general-purpose computer, a special-purpose computer, or other programmable data processing device locally or via a local area network (LAN), a wide area network (WAN) such as the Internet, etc., causing the processor or programmable circuit of the general-purpose computer, special-purpose computer, or other programmable data processing device to execute the computer-readable instructions to generate units for performing the operations specified in the flowchart or block diagram. Examples of the processor include a computer processor, a processing unit, a microprocessor, a digital signal processor, a controller, a microcontroller, etc.

[0104] As described above, the technology of the present disclosure has been described using embodiments, but the technical scope of the present disclosure is not limited to the scope described in the above embodiments. Those skilled in the art should understand that various changes or improvements can be made to the above embodiments. As can be seen from the claims, embodiments with such changes or improvements are also included in the technical scope of the present disclosure.

[0105] It should be noted that the execution order of each process such as the actions, sequences, steps, and stages in the devices, systems, programs, and methods shown in the claims, the specification, and the drawings is not particularly specified as "before...", "earlier than...", etc., or as long as the output of the previous process is not used in the subsequent process, it can be implemented in any order. Regarding the action flow in the claims, the specification, and the drawings, even if it is described using "first", "then", etc. for convenience, it does not mean that it must be implemented in that order.

[0106] The entire disclosures of Japanese Patent Application No. 2022-169078 filed on October 21, 2022, Japanese Patent Application No. 2022-201402 filed on December 16, 2022, and Japanese Patent Application No. 2022-201614 filed on December 16, 2022 are incorporated herein by reference in their entireties.

[0107] All documents, patent applications, and technical standards described in this specification are also incorporated herein by reference to the same extent as if each document, patent application, and technical standard was specifically and individually described as being incorporated by reference.

Claims

1. An information processing device, wherein, the information processing device includes: an information acquisition unit that acquires a plurality of sensor information including information on an obstacle; and a control unit that controls the speed of the vehicle to avoid the obstacle when the information on the obstacle is acquired by the information acquisition unit.

2. The information processing device according to claim 1, wherein, the control unit calculates a control variable for controlling the speed of the vehicle and controls the autonomous driving of the vehicle in units of one billionth of a second based on the control variable.

3. The information processing device according to claim 1, wherein, the control unit avoids the obstacle by controlling an edge provided on the bottom surface of the vehicle that enables the vehicle to jump.

4. A vehicle, wherein, the vehicle includes: a vehicle body; a first edge provided so as to be able to be pushed out from the bottom surface of the vehicle body; a second edge provided so as to be able to be pushed out from the bottom surface of the vehicle body at a position closer to the center of gravity of the vehicle than the first edge; and an information processing device, the information processing device having: an information acquisition unit that acquires a plurality of sensor information including information on an obstacle; and a control unit that controls the first edge and the second edge to avoid the obstacle, so that when the information on the obstacle is acquired by the information acquisition unit, the first edge is pushed out to make the vehicle jump to avoid the obstacle, the first edge is restored to its original state when the vehicle lands, and the second edge is pushed out to make the vehicle land from the second edge.

5. A vehicle, wherein, the vehicle includes: a vehicle body; an edge provided so as to be able to be pushed out from the bottom surface of the vehicle body; an elastic body provided so as to be able to be pushed out from the bottom surface of the vehicle body and having higher elasticity than the edge; and an information processing device, the information processing device having: an information acquisition unit that acquires a plurality of sensor information including information on an obstacle; and a control unit that controls the edge and the elastic body to avoid the obstacle, so that when the information on the obstacle is acquired by the information acquisition unit, the edge is pushed out to make the vehicle jump to avoid the obstacle, the edge is restored to its original state when the vehicle lands, and the elastic body is pushed out to make the vehicle land from the elastic body.

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

7. The vehicle according to claim 5, wherein, the information acquisition unit detects the obstacle in units of one billionth of a second, and the control unit controls the edge and the elastic body in units of one billionth of a second.

8. An information processing device, wherein, the information processing device includes: An information acquisition unit that acquires a plurality of sensor information including information on obstacles; and A control unit that avoids the obstacles by controlling an edge provided on the bottom surface of the vehicle to make the vehicle jump, The control unit calculates a control variable for controlling the avoidance action of the vehicle with respect to the obstacles based on the characteristics of the occupants of the vehicle.

9. The information processing device according to claim 8, wherein, The control unit controls the autonomous driving of the vehicle in units of one billionth of a second based on the control variable.

10. The information processing device according to claim 8, wherein, The control unit updates the characteristics based on the reaction of the occupants of the vehicle when avoiding the obstacles.

11. The information processing device according to claim 8, wherein, The information processing device is configured to be able to be pre-selected by the occupant whether to avoid the obstacles by controlling the edge.

12. A program, wherein, The program causes a computer to function as the information processing device according to any one of claims 1 to 11.

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