Information processing device, information processing system, and program

Abstract

This information processing device is provided with: a calculation unit that calculates control variables for controlling the wheel speed and inclination of each of four wheels of a vehicle, and a suspension for supporting the wheels, on the basis of sensor information including road information indicating the road condition of a road on which the vehicle travels; and a control unit that controls automatic driving on the basis of the control variable calculated by the calculation unit.

Description

Technical Field

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

[0002] Japanese Unexamined Patent Application Publication No. 2022-035198 describes a vehicle having an autonomous driving function. Summary of the Invention

[0003] Means for Solving the Problem

[0004] According to one embodiment of the present invention, an information processing device is provided. The information processing device according to the first aspect includes: a calculation unit that calculates control variables for controlling the wheel speed, inclination, and suspension supporting each of the four wheels of the vehicle based on sensor information including road information, the road information indicating the road condition of the road on which the vehicle travels; and a control unit that controls autonomous driving based on the control variables calculated by the calculation unit.

[0005] In the information processing device according to the second aspect, based on the information processing device according to the first aspect, the control unit controls the autonomous driving in units of one billionth of a second based on the control variables calculated by the calculation unit.

[0006] In the information processing device according to the third aspect, based on the information processing device according to the first or second aspect, a determination unit is provided that determines control information for calculating each of the control variables from the sensor information that can be acquired, each of the control variables being used to control the wheel speed of each of the four wheels, the inclination of each of the four wheels, and the suspension supporting each of the four wheels; the calculation unit calculates a plurality of the control variables for the control unit to control the autonomous driving based on the control information determined by the determination unit.

[0007] In the information processing device according to the fourth aspect, based on the information processing device according to the third aspect, the determination unit updates the information determined as the control information from the sensor information based on the control result of the control unit for the autonomous driving.

[0008] In the information processing device according to the fifth aspect, based on the information processing device according to any one of the first to fourth aspects, the calculation unit calculates the control variables based on preference information related to the riding feeling preference of the occupants in the vehicle and the sensor information.

[0009] In the information processing apparatus according to the sixth aspect, on the basis of the information processing apparatus according to the fifth aspect, an acquisition unit is further provided, and the acquisition unit acquires somatosensory information related to the somatosensation of the occupant; the calculation unit calculates the control variable based on the preference information analyzed from the acquired somatosensory information.

[0010] In the information processing apparatus according to the seventh aspect, on the basis of the information processing apparatus according to the sixth aspect, the somatosensory information is at least one of the voice, line of sight, and biological information of the occupant.

[0011] In the information processing apparatus according to the eighth aspect, on the basis of the information processing apparatus according to any one of the first to seventh aspects, the calculation unit calculates control variables for controlling the wheel speeds, tilts, and suspensions supporting the wheels of each of the four wheels of the own vehicle based on the sensor information including the road information and other vehicle information related to other vehicles traveling during driving. The road information represents the road condition of the road on which the own vehicle travels. The control unit controls the autonomous driving of the own vehicle based on the control variables calculated by the calculation unit.

[0012] In the information processing apparatus according to the ninth aspect, on the basis of the information processing apparatus according to the eighth aspect, the other vehicle is a preceding vehicle that travels ahead of the own vehicle on the road.

[0013] In the information processing apparatus according to the tenth aspect, on the basis of the information processing apparatus according to the ninth aspect, an update unit is provided, and the update unit updates the learning model for calculating the control variables of the own vehicle based on the other vehicle information.

[0014] In the information processing apparatus according to the eleventh aspect, on the basis of the information processing apparatus according to any one of the first to tenth aspects, the information processing apparatus is mounted on the vehicle and includes an acquisition unit that acquires first sensor information that is the sensor information of a sensor mounted on the vehicle and second sensor information of other vehicles traveling around the vehicle. The calculation unit calculates the control variable based on the acquired first sensor information and second sensor information. The control unit controls the autonomous driving so that the vehicle travels synchronously with the other vehicles based on the control variable calculated by the calculation unit.

[0015] In the information processing apparatus according to the twelfth aspect, on the basis of the information processing apparatus according to the eleventh aspect, the control unit transmits the calculated control variable to the other vehicle so that the other vehicle travels synchronously with the vehicle.

[0016] In the information processing apparatus according to the thirteenth aspect, based on the information processing apparatus according to the twelfth aspect, the calculation unit further calculates the control variable corresponding to each of the other vehicles, and the control unit sends the control variable to each of the corresponding other vehicles to respectively control the autonomous driving of the other vehicles.

[0017] According to an embodiment of the present disclosure, there is provided an information processing system. The information processing system according to the fourteenth aspect includes the vehicle equipped with the information processing apparatus according to any one of the eleventh to thirteenth aspects and the other vehicles. The vehicle sends the control variable to the other vehicles, and the other vehicles control the autonomous driving to travel synchronously with the vehicle based on the control variable received from the vehicle.

[0018] According to an embodiment of the present disclosure, there is provided a program for causing a computer according to the fifteenth aspect to function as the information processing apparatus according to any one of the first to thirteenth aspects.

[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 be the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0021] Figure 2 FIG. is a diagram schematically showing an example of the network configuration in the vehicle according to the first embodiment.

[0022] Figure 3 FIG. is a flowchart executed by the Central Brain according to the first embodiment.

[0023] Figure 4 FIG. is a first explanatory diagram for explaining an example of the control of autonomous driving by the Central Brain according to the first embodiment.

[0024] Figure 5 FIG. is a second explanatory diagram for explaining an example of the control of autonomous driving by the Central Brain according to the first embodiment.

[0025] Figure 6 FIG. is a third explanatory diagram for explaining an example of the control of autonomous driving by the Central Brain according to the first embodiment.

[0026] Figure 7It is the fourth explanatory diagram for explaining an example of the control of autonomous driving by the central brain according to the first embodiment.

[0027] Figure 8 It is the fifth explanatory diagram for explaining an example of the control of autonomous driving by the central brain according to the first embodiment.

[0028] Figure 9 It is a diagram schematically showing an example of the hardware configuration of a computer that functions as the central brain.

[0029] Figure 10 It is a block diagram showing an example of the functional configuration of a computer that functions as the central brain according to the second embodiment.

[0030] Figure 11 It is a flowchart executed by the central brain according to the second embodiment.

[0031] Figure 12 It is a block diagram showing an example of the functional configuration of the central brain according to the third embodiment.

[0032] Figure 13 It is a flowchart executed by the central brain according to the third embodiment.

[0033] Figure 14 It is a schematic diagram of the schematic configuration of the control system according to the fourth embodiment.

[0034] Figure 15 It is a flowchart executed by the central brain according to the fourth embodiment.

[0035] Figure 16 It is a block diagram showing an example of the functional configuration of the central brain according to the fifth embodiment.

[0036] Figure 17 It is a flowchart executed by the central brain according to the fifth embodiment.

[0037] Figure 18 It is a timing diagram executed in cooperation by the central brain according to the fifth embodiment. Specific Embodiments

[0038] Hereinafter, embodiments of the present disclosure will be described, but the following embodiments do not limit the present disclosure. In addition, not all combinations of features described in the embodiments are necessary for the solution of the present disclosure.

[0039] (First Embodiment)

[0040] First, the first embodiment according to the present embodiment will be described.

[0041] Figure 1 Schematically shows the danger prediction ability of the AI for ultra-high-performance autonomous driving related to the first embodiment. In this embodiment, various sensor information is digitalized by AI 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.

[0042] 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.

[0043] As Figure 2 shown, a plurality of gateways are communicably connected to the central brain 120. The central brain 120 is connected to the external cloud via the gateway 130. The central brain 120 is configured to be able to access the external cloud via the gateway 130. On the other hand, due to the existence of the gateway 130, it is configured that the central brain 120 cannot be directly accessed from the outside.

[0044] 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.

[0045] Examples of the sensors used in this embodiment include radar, lidar (LiDAR), high-pixel / long-focus / ultra-wide-angle / 360-degree / high-performance cameras, visual recognition, faint sounds, ultrasonic waves, vibrations, infrared rays, ultraviolet rays, electromagnetic waves, temperature, humidity, spot AI weather forecasts, high-precision multi-channel global positioning systems (GPS), low-altitude satellite information, long-tail event AI data, etc. Long-tail event AI data refers to the trip data of a Level 5-equipped vehicle.

[0046] Examples of the sensor information obtained from various sensors include biological information such as the line of sight, voice, heart rate, and body temperature of the occupants sitting in the vehicle 12, 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 up / down / 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 bans, 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, fogs, etc.). In this embodiment, these detections are performed every one billionth of a second.

[0047] In this embodiment, the central brain 120 functions as a computing unit that calculates control variables for controlling the wheel speed, tilt, and suspension of each of the four wheels of the vehicle 12 based on sensor information detected by the above sensors and including road information (e.g., road material, detection of the up / down, lateral, and diagonal tilt angles of slopes, road freezing mode, and road moisture content) indicating the road conditions of the road on which the vehicle 12 travels. It should be noted that the tilt of the wheels includes the tilt of the wheels with respect to an axis horizontal to the road and the tilt of the wheels with respect to an axis perpendicular to the road. Specifically, the central brain 120 calculates a total of 16 control variables for controlling the wheel speed of each of the four wheels, the tilt of each of the four wheels with respect to an axis horizontal to the road, the tilt of each of the four wheels with respect to an axis perpendicular to the road, and the suspensions supporting the four wheels respectively. In this embodiment, the calculation of the above 16 control variables is performed every one billionth of a second. It should be noted that the wheel speed of each of the above four wheels can also be referred to as "the rotation speed (revolution speed) of the in-wheel motors mounted on the four wheels respectively", and the tilt of each of the four wheels with respect to an axis horizontal to the road can also be referred to as "the horizontal angle of each of the four wheels". And, for example, when the vehicle is traveling on a mountain road, the above control variables are values for performing the optimal steering matching the mountain road, and when the vehicle is parked in a parking lot, the above control variables are values for traveling at the optimal angle matching the parking lot.

[0048] In addition, in this embodiment, the central brain 120 functions as a control unit that controls autonomous driving in units of one billionth of a second based on the above calculated control variables. Specifically, the central brain 120 controls the in-wheel motors mounted on the four wheels respectively based on the above 16 control variables, thereby controlling the wheel speed, tilt, and suspensions supporting the four wheels of the vehicle 12 respectively, so as to perform autonomous driving.

[0049] The central brain 120 repeatedly executes Figure 3 the flowchart shown.

[0050] In step S10, the central brain 120 acquires sensor information including the road information detected by the sensors. Then, the central brain 120 proceeds to step S11.

[0051] In step S11, the central brain 120 calculates the above 16 control variables based on the sensor information acquired in step S10. Then, the central brain 120 proceeds to step S12.

[0052] In step S12, the central brain 120 controls the autonomous driving based on the control variables calculated in step S11. Then, the central brain 120 ends the processing of this flowchart.

[0053] Figures 4 to 8 FIG. is an explanatory diagram for explaining an example of the control of the central brain 120 over the autonomous driving. It should be noted that Figures 4 to 6 is an explanatory diagram from the perspective of observing the vehicle 12 from the front, Figure 7 and Figure 8 is an explanatory diagram from the perspective of observing the vehicle 12 from below.

[0054] Figure 4 FIG. shows the vehicle 12 traveling on a flat road R1. The central brain 120 controls the in-wheel motors 31 respectively mounted on the four wheels 30 based on the above 16 control variables calculated according to the road R1, thereby controlling the wheel speed, inclination of each of the four wheels 30, and the suspension 32 that supports each of the four wheels 30, so as to perform autonomous driving.

[0055] Figure 5 FIG. shows the vehicle 12 traveling on a mountain road R2. The central brain 120 controls the in-wheel motors 31 respectively mounted on the four wheels 30 based on the above 16 control variables calculated according to the mountain road R2, thereby controlling the wheel speed, inclination of each of the four wheels 30, and the suspension 32 that supports each of the four wheels 30, so as to perform autonomous driving.

[0056] Figure 6 FIG. represents the vehicle 12 traveling in a waterlogged section R3. The central brain 120 controls the in-wheel motors 31 respectively mounted on the four wheels 30 based on the above 16 control variables calculated according to the waterlogged section R3, thereby controlling the wheel speed, inclination of each of the four wheels 30, and the suspension 32 that supports each of the four wheels 30, so as to perform autonomous driving.

[0057] Figure 7 FIG. shows the vehicle 12 turning in the direction indicated by the arrow A1. The central brain 120 controls the in-wheel motors 31 respectively mounted on the four wheels 30 based on the above 16 control variables calculated according to the incoming bend, thereby controlling the wheel speed, inclination of each of the four wheels 30, and the suspension 32 (not shown) that supports each of the four wheels 30, so as to perform autonomous driving.

[0058] Figure 8The case where the vehicle 12 moves parallel in the direction shown by the arrow A2 is shown. The central brain 120 controls the in-wheel motors 31 respectively mounted on the four wheels 30 based on the above-mentioned 16 control variables calculated according to the parallel movement in the direction shown by the arrow A2, thereby controlling the wheel speeds, tilts of the four wheels 30 respectively, and the suspensions 32 (not shown) that support the four wheels 30 respectively, so as to perform autonomous driving.

[0059] It should be noted that Figures 4 to 8 The states (tilts) of the wheels 30 and the suspensions 32 shown are only examples, and of course, there will be states of the wheels 30 and the suspensions 32 different from those shown in each figure.

[0060] Among them, although the existing in-wheel motors mounted on vehicles can independently control their respective drive wheels, in this vehicle, the in-wheel motors cannot be controlled by analyzing road conditions, etc. Therefore, in this vehicle, for example, when driving on mountain roads or in waterlogged sections, etc., appropriate autonomous driving based on road conditions, etc. cannot be performed.

[0061] However, through the vehicle 12 according to the present embodiment, based on the structure described above, autonomous driving that can control speed, steering, etc. according to the environment such as road conditions can be performed.

[0062] Figure 9 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 can cause the computer 1200 to function as one or more "parts" of the device according to the present embodiment, or cause the computer 1200 to execute operations associated with the device according to the present embodiment or the one or more "parts", and / or can cause 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 to cause the computer 1200 to execute specific operations associated with some or all of the blocks in the flowcharts and block diagrams described in this specification.

[0063] The computer 1200 according to this 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 conventional input / output units such as a keyboard, which are connected to the input / output controller 1220 via an input / output chip 1240.

[0064] The CPU 1212 operates according to programs stored in the ROM 1230 and the RAM 1214, thereby controlling each unit. The graphics controller 1216 obtains 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.

[0065] 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.

[0066] The ROM 1230 stores therein a boot program or the like 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.

[0067] 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 causes cooperation between the programs and the various types of hardware resources described above. The apparatus or method can be configured by performing operations or processing of information according to the use of the computer 1200.

[0068] For example, when communication is performed between the computer 1200 and an external device, the CPU 1212 may 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 reception data received from the network into the reception buffer provided on the recording medium and the like.

[0069] In addition, the CPU 1212 may cause all or a necessary part of a file or a 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 may write the processed data back to the external recording medium.

[0070] Various types of information such as various types of programs, data, tables, and databases may be stored in the recording medium to undergo information processing. The CPU 1212 may perform various types of processing on the data read from the RAM 1214, and write the result back to the RAM 1214. The various types of processing include various types of operations, information processing, conditional judgment, conditional branch, unconditional branch, information retrieval / replacement, etc. described throughout this disclosure and specified by an instruction sequence of a program. In addition, the CPU 1212 may retrieve information in files, databases, etc. within the recording medium. For example, in a case where there are a plurality of entries each having an attribute value of a first attribute associated with an attribute value of a second attribute stored in the recording medium, the CPU 1212 may 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.

[0071] The programs or software modules described above may 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 a RAM provided in a server system connected to a dedicated communication network or the Internet may be used as a computer-readable storage medium, thereby providing a program to the computer 1200 via the network.

[0072] (Second Embodiment)

[0073] Next, a second embodiment related to the present embodiment will be described, while omitting or simplifying the repetitive parts with the above-described embodiment.

[0074] Figure 10 It is a block diagram showing an example of the functional configuration of the computer 1200 that functions as the central brain 120.

[0075] As Figure 10 shown, as a functional configuration, the CPU 1212 of the computer 1200 has a determination unit 1212A, an acquisition unit 1212B, a calculation unit 1212C, and a control unit 1212D. Each function is realized by the CPU 1212 reading and executing a program installed in the computer 1200.

[0076] The determination unit 1212A determines, from the sensor information that can be acquired, control information for respectively calculating a total of 16 control variables for controlling the wheel speed of each of the four wheels, the inclination of each of the four wheels with respect to an axis horizontal to the road, the inclination of each of the four wheels with respect to an axis perpendicular to the road, and the suspensions respectively supporting the four wheels. As an example, when the vehicle 12 stops, the determination unit 1212A acquires the sensor information that can be detected by each sensor used in the vehicle 12, and determines the control information for calculating each control variable from the sensor information.

[0077] The acquisition unit 1212B acquires the sensor information detected by the above-described respective sensors every 1 nanosecond during the autonomous driving of the vehicle 12.

[0078] During the autonomous driving of the vehicle 12, the calculation unit 1212C calculates, every 1 nanosecond, a plurality of control variables for the control unit 1212D to control the autonomous driving, based on the control information previously determined by the determination unit 1212A from the sensor information acquired by the acquisition unit 1212B. Specifically, the calculation unit 1212C calculates a total of 16 control variables for controlling the wheel speed of each of the four wheels, the inclination of each of the four wheels with respect to an axis horizontal to the road, the inclination of each of the four wheels with respect to an axis perpendicular to the road, and the suspensions respectively supporting the four wheels.

[0079] Based on the control variables calculated by the calculation unit 1212C, the control unit 1212D controls the autonomous driving of the vehicle 12 every 1 nanosecond.

[0080] In addition, based on the control result of the automatic driving by the control unit 1212D, the determination unit 1212A updates the information determined as control information from the sensor information during the automatic driving of the vehicle 12. For example, when, as a control result of the automatic driving, it is predicted that it will take more time than expected to park in a parking lot, the determination unit 1212A updates the control information used to calculate the control variables for controlling the automatic driving related to parking in the parking lot.

[0081] Next, the processing flow executed by the computer 1200 that functions as the central brain 120 will be described. In the computer 1200, the CPU 1212 reads the program installed in the computer 1200, expands and executes it in the RAM 1214, thereby executing Figure 11 the processing of the flowchart shown. It should be noted that as a prerequisite for this processing, the CPU 1212 determines, from the sensor information that can be acquired, the control information for calculating a total of 16 control variables respectively, where the total 16 control variables are used to control the wheel speed of each of the four wheels, the inclination of each of the four wheels with respect to the axis horizontal to the road, the inclination of each of the four wheels with respect to the axis perpendicular to the road, and the suspensions that support each of the four wheels respectively.

[0082] In step S20, the CPU 1212 acquires sensor information including road information detected by the sensor. Then, the CPU 1212 proceeds to step S21.

[0083] In step S21, the CPU 1212 calculates the above-mentioned 16 control variables based on the control information determined in advance from the sensor information acquired in step S20. Then, the CPU 1212 proceeds to step S22.

[0084] In step S22, the CPU 1212 controls the automatic driving based on the control variables calculated in step S21. Then, the CPU 1212 proceeds to step S23.

[0085] In step S23, the CPU 1212 updates the information determined as control information from the sensor information based on the control result of the automatic driving performed in step S22. Then, the CPU 1212 ends the processing of this flowchart.

[0086] As described above, in the computer 1200 that functions as the central brain 120 according to the second embodiment, the CPU 1212 determines, from the sensor information that can be obtained, control information for respectively calculating a total of 16 control variables for controlling the wheel speed of each of the four wheels, the inclination of each of the four wheels with respect to the axis horizontal to the road, the inclination of each of the four wheels with respect to the axis perpendicular to the road, and the suspensions that respectively support the four wheels. Then, the CPU 1212 calculates the above-mentioned 16 control variables for controlling autonomous driving based on the control information determined when the vehicle 12 stops. Thus, in this computer 1200, compared with the case where information for respectively calculating a plurality of control variables is determined during autonomous driving, the processing load on the CPU 1212 during autonomous driving can be reduced.

[0087] In addition, in the above computer 1200, the CPU 1212 updates the information determined as control information from the sensor information based on the control result of autonomous driving. Thus, in this computer 1200, the information for respectively calculating a plurality of control variables during autonomous driving can be optimized.

[0088] (Third Embodiment)

[0089] Next, the third embodiment according to the present embodiment will be described, while omitting or simplifying the parts that overlap with the above embodiments.

[0090] In the third embodiment, the central brain 120 acquires, in addition to the preference information related to the riding feeling preferences of the occupants riding in the vehicle 12 preset by the occupants, the sensor information detected by the above sensors, the sensor information including the somatosensory information (e.g., biological information such as the occupants' line of sight, voice, heart rate, and body temperature) felt by the occupants riding in the vehicle 12, and the road information (e.g., the material of the road, the detection of the up / down direction, lateral and oblique inclination angles of the slope, the freezing mode of the road, and the moisture content of the road) indicating the road conditions of the road on which the vehicle 12 travels. The central brain 120 functions as a calculation unit that calculates control variables for controlling the wheel speed, inclination, and the suspensions supporting the wheels of each of the four wheels of the vehicle 12 based on the sensor information including the preference information, road information, and somatosensory information.

[0091] Figure 12 It is a block diagram for explaining the functional configuration of the central brain 120. As an example, as Figure 12 shown, the central brain 120 functions as an acquisition unit 200, a calculation unit 210, and a control unit 220 by executing an information processing program.

[0092] The acquisition unit 200 acquires sensor information including preference information, road information, and somatosensory information. For example, as the preference information, the acquisition unit 200 acquires the preferences of the occupant's riding feeling such as whether it is possible to drive on an inclined surface, whether it is possible to drive in a waterlogged section, and whether it is possible to drive by parallel movement, which are preset by the occupant in advance. In addition, the acquisition unit 200 acquires road information such as the material of the road, the vertical, horizontal, and diagonal inclination angles of the slope, the freezing mode of the road, and the moisture content of the road, which are detected by the sensors mounted on the vehicle 12. Further, as the somatosensory information, the acquisition unit 200 acquires biological information such as the occupant's line of sight, voice, heart rate, and body temperature.

[0093] The calculation unit 210 uses the acquired road information, preference information, and somatosensory information to calculate control variables for controlling the wheel speeds, inclination, and suspension that supports the wheels of each of the four wheels of the vehicle 12. For example, the calculation unit 210 calculates control variables for performing control to obtain the riding feeling preferred by the occupant based on the preference information and road information including whether it is possible to drive on an inclined surface, whether it is possible to drive in a waterlogged section, and whether it is possible to drive by parallel movement.

[0094] In addition, the calculation unit 210 analyzes the somatosensory information and calculates preference information indicating the passenger's preference, and uses the calculated preference information and road information to calculate control variables. For example, when the heart rate and body temperature of the occupant included in the somatosensory information increase, the calculation unit 210 determines that the current driving is not the preference of the occupant, and thus calculates control variables for performing control to make the front-rear direction of the vehicle 12 face the traveling direction and to decelerate the wheel speed of the vehicle 12.

[0095] In addition, the calculation unit 210 calculates preference information based on the line of sight of the occupant included in the somatosensory information and calculates control variables. For example, when the line of sight of the occupant is 90 degrees to the left with respect to the traveling direction, the calculation unit 210 calculates control variables for performing control to rotate the front-rear direction of the vehicle 12 90 degrees to the left with respect to the traveling direction and for the vehicle 12 to continue traveling in the traveling direction (for example, crab driving).

[0096] In addition, the calculation unit 210 calculates preference information based on the voice and line of sight of the occupant included in the somatosensory information and calculates control variables. For example, the calculation unit 210 analyzes the voice of the occupant's "want to observe carefully" included in the somatosensory information and extracts the feature of "want to observe". The calculation unit 210 calculates control variables for controlling the suspension to decelerate the wheel speed of the vehicle 12 and to tilt the vehicle body in the direction of the occupant's line of sight based on the extracted feature.

[0097] The control unit 220 controls the autonomous driving of the vehicle 12 based on the control variables calculated by the calculation unit 210.

[0098] The central brain 120 repeatedly executes Figure 13 the flowchart shown.

[0099] In step S30, the central brain 120 acquires the preference information set by the occupant. Then, the central brain 120 proceeds to step S31.

[0100] In step S31, the central brain 120 acquires the somatosensory information detected by the sensors. Then, the central brain 120 proceeds to step S32.

[0101] In step S32, the central brain 120 acquires the sensor information including the road information detected by the sensors. Then, the central brain 120 proceeds to step S33.

[0102] In step S33, the central brain 120 calculates the above-mentioned 16 control variables based on the acquired preference information, somatosensory information, and sensor information. Then, the central brain 120 proceeds to step S34.

[0103] In step S34, the central brain 120 controls the autonomous driving based on the control variables calculated in step S33. Then, the central brain 120 ends the processing of the flowchart.

[0104] (Fourth Embodiment)

[0105] Next, the fourth embodiment related to the present embodiment will be described, and the overlapping parts with the above embodiments will be omitted or simplified.

[0106] Figure 14 is a schematic diagram of the schematic configuration of the control system 10 related to the fourth embodiment.

[0107] As Figure 14 shown, the control system 10 includes a plurality of vehicles 12A, 12B, and 12C. In addition, the number of vehicles 12 in the control system 10 is not limited to 3, and can be more than 3 or less than 3.

[0108] Each of the vehicles 12A, 12B, 12C is equipped with a central brain 120A, 120B, 120C, respectively. Each of the central brains 120A, 120B, 120C is communicably connected to each other via the network N. And each of the central brains 120A, 120B, 120C sends the sensor information detected by each sensor used in each of the vehicles 12A, 12B, 12C to each other every one billionth of a second.

[0109] In the fourth embodiment, the central brain 120 functions as a calculation unit that calculates the above-mentioned 16 control variables of the own vehicle (e.g., vehicle 12A) based on sensor information including road information indicating the road conditions of the road on which the own vehicle travels and other vehicle information related to other vehicles (e.g., vehicle 12B and vehicle 12C) traveling during driving. The above-mentioned other vehicles are preceding vehicles that travel ahead of the own vehicle on the road. In addition, the above-mentioned other vehicle information is, for example, sensor information detected by various sensors used in other vehicles.

[0110] Further, in the fourth embodiment, in the storage device 1224 of the computer 1200 that functions as the central brain 120, a learning model for respectively calculating the above-mentioned 16 control variables is stored. The learning model is a machine learning model that outputs control variables by inputting the sensor information of the own vehicle and other vehicle information. Moreover, the central brain 120 calculates the above-mentioned 16 control variables by inputting the sensor information of the own vehicle and other vehicle information acquired every one billionth of a second into the learning model. That is, it can be said that the central brain 120 is composed of AI (Artificial Intelligence) that can use machine learning, and more specifically, deep learning, to calculate control variables based on the sensor information of the own vehicle and other vehicle information.

[0111] In addition, in the fourth embodiment, the central brain 120 functions as an update unit that updates the learning model for calculating the control variables of the own vehicle based on the acquired other vehicle information. It should be noted that the data for updating the learning model is not limited to other vehicle information, and other information such as the sensor information of the own vehicle can also be used.

[0112] Moreover, in the fourth embodiment, the central brain 120 functions as a control unit that controls the autonomous driving of the own vehicle based on the calculated above-mentioned 16 control variables.

[0113] Next, the process flow executed by the central brain 120 will be described. In the computer 1200 that functions as the central brain 120, the CPU 1212 reads the program installed in the computer 1200, expands it in the RAM 1214, and executes it, thereby executing Figure 15 the processing of the flowchart shown.

[0114] In step S40, the central brain 120 obtains sensor information including road information detected by the sensors of the host vehicle. Then, the central brain 120 proceeds to step S41.

[0115] In step S41, the central brain 120 obtains other vehicle information. Then, the central brain 120 proceeds to step S42.

[0116] In step S42, the central brain 120 calculates the above 16 control variables based on the sensor information obtained in step S40 and the other vehicle information obtained in step S41. Then, the central brain 120 proceeds to step S43.

[0117] In step S43, the central brain 120 controls the autonomous driving based on the control variables calculated in step S42. Then, the central brain 120 ends the processing of this flowchart. It should be noted that although it is omitted in Figure 15 , in the processing of this flowchart, the following processing can also be performed: updating the learning model for calculating the control variables of the host vehicle based on the other vehicle information obtained in step S41.

[0118] As described above, the central brain 120 according to the fourth embodiment calculates the above 16 control variables of the host vehicle based on the sensor information of the host vehicle and the other vehicle information. Then, the central brain 120 controls the autonomous driving of the host vehicle based on the calculated 16 control variables. Thus, according to this central brain 120, since the autonomous driving of the host vehicle can be controlled based on the information of the host vehicle and the information of other vehicles, it is possible to expect an improvement in the accuracy of autonomous driving compared with the case of controlling the autonomous driving of the host vehicle only based on the information of the host vehicle.

[0119] In addition, in the fourth embodiment, the other vehicle is a preceding vehicle that travels ahead of the host vehicle on the road. And the central brain 120 according to the fourth embodiment updates the learning model for calculating the control variables of the host vehicle based on the other vehicle information. Thus, according to this central brain 120, since the learning model for calculating the control variables of the host vehicle can be updated based on the measured data when the preceding vehicle travels on the road, an improvement in the accuracy of autonomous driving when the host vehicle travels on the road where the preceding vehicle travels can be expected.

[0120] (Fifth Embodiment)

[0121] Next, the fifth embodiment according to this embodiment will be described, and the repeated parts with the above embodiments will be omitted or simplified.

[0122] In addition, in the fifth embodiment, the central brain 120 obtains sensor information detected by other vehicles traveling around the vehicle 12, and calculates a control variable based on the sensor information of the vehicle 12 and the other vehicles. It should be noted that hereinafter, when differentiating between the vehicle 12 and other vehicles, they are described as "vehicle 12" and "other vehicle 14" for differentiation. Similarly, when differentiating between the central brains installed on the vehicle 12 and the other vehicle 14, they are described as "central brain 120" in the vehicle 12 and "central brain 120A" in the other vehicle 14 for differentiation.

[0123] Figure 16 is a block diagram for explaining the functional configurations of the central brain 120 and the central brain 120A.

[0124] As an example, as Figure 16 shown, the central brain 120 of the vehicle 12 functions as an acquisition unit 200, a calculation unit 210, a control unit 220, and a transmission unit 230. In addition, the central brain 120A of the other vehicle 14 functions as an acquisition unit 300, a transmission unit 310, a reception unit 320, and a control unit 330.

[0125] The acquisition unit 200 in the vehicle 12 obtains sensor information from sensors installed on the vehicle 12 and obtains sensor information transmitted from the other vehicle 14.

[0126] The calculation unit 210 uses the sensor information obtained from the vehicle 12 and the other vehicle 14 to calculate control variables for controlling the wheel speed, inclination, and suspension supporting the wheels of each of the four wheels of the vehicle 12. For example, when the other vehicle 14 is traveling in front of the vehicle 12 in the traveling direction, the calculation unit 210 detects sensor information including road information and calculates control variables for controlling the wheel speed, inclination, and suspension supporting the wheels of each of the four wheels of the vehicle 12 to follow the other vehicle 14. Among them, the calculation unit 210 calculates the control variable by considering, for example, the influence of the road conditions around the other vehicle 14 using the sensor information obtained from the other vehicle 14.

[0127] In addition, when another vehicle 14 travels in parallel on the right side of the vehicle 12, the calculation unit 210 detects the condition of the other vehicle 14, calculates a control variable to travel according to the action of the other vehicle 14, and performs autonomous driving of the vehicle 12. For example, the calculation unit 210 calculates a control variable such that when the other vehicle 14 travels close to the vehicle 12 due to road conditions, it moves the same distance as the distance by which the other vehicle approaches the vehicle 12 to the opposite side of the position of the other vehicle. That is, the calculation unit 210 calculates a control variable according to the action of the other vehicle 14 traveling around the vehicle 12, and performs autonomous driving of the vehicle 12 in such a way as to maintain a fixed interval from the other vehicle 14.

[0128] The control unit 220 controls the autonomous driving of the vehicle 12 based on the control variable calculated by the calculation unit 210.

[0129] The transmission unit 230 transmits the calculated control variable to the other vehicle 14.

[0130] The acquisition unit 300 in the other vehicle 14 acquires sensor information from sensors installed on the other vehicle 14.

[0131] The transmission unit 310 transmits the acquired sensor information to the vehicle 12.

[0132] The reception unit 320 receives the control variable transmitted from the vehicle 12.

[0133] The control unit 330 controls the autonomous driving of the other vehicle 14 based on the control variable received from the vehicle 12.

[0134] That is, in the above description, the vehicle 12 and the other vehicle 14 perform autonomous driving based on the control variable calculated by the vehicle 12. For example, in the case where multiple vehicles 12 travel in a column, the vehicle 12 among the multiple vehicles 12 transmits the control variable calculated based on the sensor information acquired from the vehicle 12 and the other vehicle 14 to the other vehicle 14 other than the vehicle 12, and the vehicle 12 and the other vehicle 14 perform autonomous driving based on the control variable. The other vehicle 14 performs autonomous driving based on the control variable calculated by the vehicle 12, thereby performing autonomous driving to synchronize the actions of the vehicle 12 and the other vehicle 14.

[0135] In addition, in the fifth embodiment, a method in which the vehicle 12 and the other vehicle 14 respectively perform autonomous driving based on the same control variable calculated by the vehicle 12 is described. However, it is not limited to this. It may also be that the vehicle 12 calculates a control variable corresponding to each of the other vehicles 14, and the vehicle 12 and the other vehicle 14 perform autonomous driving based on their respective corresponding control variables.

[0136] For example, vehicle 12 obtains sensor information from each of the other vehicles 14, and uses the obtained sensor information to calculate the control variables of vehicle 12 and each of the other vehicles 14. Vehicle 12 separately sends the calculated control variables to the corresponding other vehicles 14, and vehicle 12 and the other vehicles 14 perform autonomous driving based on the respective control variables.

[0137] The central brain 120 repeatedly executes Figure 17 the flowchart shown.

[0138] In step S50, the central brain 120 obtains sensor information including road information detected by the sensors. Then, the central brain 120 proceeds to step S51.

[0139] In step S51, the central brain 120 obtains sensor information including road information detected by the other vehicles 14. Then, the central brain 120 proceeds to step S52.

[0140] In step S52, the central brain 120 calculates the above 16 control variables based on the sensor information of vehicle 12 and the other vehicles 14 obtained in step S50 and step S51. Then, the central brain 120 proceeds to step S53.

[0141] In step S53, the central brain 120 sends the control variables calculated in step S52 to the other vehicles 14. Then, the central brain 120 proceeds to step S54.

[0142] In step S54, the central brain 120 controls autonomous driving based on the control variables calculated in step S52. Then, the central brain 120 ends the processing of this flowchart.

[0143] The central brain 120 in vehicle 12 and the central brain 120A in the other vehicles 14 cooperatively and repeatedly execute Figure 18 the processing in the timing diagram shown.

[0144] In step S60, the central brain 120 and the central brain 120A obtain sensor information including road information detected by the sensors.

[0145] In step S61, the central brain 120A sends the sensor information to vehicle 12.

[0146] In step S62, the central brain 120 obtains sensor information from the other vehicles 14.

[0147] In step S63, the central brain 120 uses the obtained sensor information to obtain the control variables.

[0148] In step S64, the central brain 120 sends the calculated control variable to the other vehicle 14.

[0149] In step S65, the central brain 120A receives the control variable sent from the vehicle 12.

[0150] In step S66, the central brain 120 and the central brain 120A use the control variable to control the autonomous driving. Then, the central brain 120 and the central brain 120A end the processing of this timing diagram.

[0151] As described above, the technology of the present disclosure has been described using the 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, the embodiments with such changes or improvements can also be included in the technical scope of the present disclosure.

[0152] In the flowchart and block diagram of this embodiment, the blocks 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 circuit can include digital and / or analog hardware circuits, and can also include integrated circuits (ICs) and / or discrete circuits. The programmable circuit can include, for example, reconfigurable hardware circuits such as field programmable gate arrays (FPGAs) and programmable logic arrays (PLAs). The reconfigurable hardware circuit includes logical AND, logical OR, logical exclusive OR, logical NAND, logical NOR, and other logical operations, flip-flops, registers, and storage elements.

[0153] A computer-readable storage medium can include any tangible device that can store instructions executable by an appropriate device. As a result, a computer-readable storage medium having instructions stored in the tangible device comprises a product including 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 can 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 can 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.

[0154] Computer-readable instructions can 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.

[0155] Computer-readable instructions may be provided to a processor or programmable circuitry of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus locally or via a local area network (LAN), a wide area network (WAN) such as the Internet, etc., causing the processor or programmable circuitry of the general-purpose computer, special-purpose computer, or other programmable data processing apparatus 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.

[0156] It should be noted that the execution order of each process such as actions, sequences, steps, and stages in the apparatus, system, program, and method 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.

[0157] 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 clearly understand that various changes or improvements can be made to the above embodiments. It can be seen from the description in the claims that the embodiments with such changes or improvements can also be included within the technical scope of the present disclosure.

[0158] It should be noted that the execution order of each process such as actions, sequences, steps, and stages in the apparatus, system, program, and method 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.

[0159] The entire disclosures of Japanese Patent Application No. 2022-168190, filed on October 20, 2022, Japanese Patent Application No. 2022-192162, filed on November 30, 2022, Japanese Patent Application No. 2022-192163, filed on November 30, 2022, Japanese Patent Application No. 2022-193627, filed on December 2, 2022, and Japanese Patent Application No. 2022-192164, filed on November 30, 2022, are incorporated herein by reference in their entirety.

[0160] All documents, patent applications, and technical standards cited in this specification are incorporated herein by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually incorporated by reference.

Claims

1. An information processing device, in, The information processing device comprises: a calculation unit that calculates control variables for controlling a wheel speed, a tilt, and a suspension supporting each of four wheels of a vehicle based on sensor information including road information indicating a road condition of a road on which the vehicle is traveling; and A control unit controls automatic driving based on the control variable calculated by the calculation unit.

2. The information processing device according to claim 1, in, The control unit controls the automatic driving in units of billionths of a second based on the control variable calculated by the calculation unit.

3. The information processing device according to claim 1, in, The information processing device includes a determination unit that determines control information for calculating each of the control variables from the sensor information that can be acquired, wherein each of the control variables is used to control the wheel speed of each of the four wheels, the inclination of each of the four wheels, and the suspension that supports each of the four wheels. The calculation section calculates the plurality of control variables used by the control section to control the automatic driving based on the control information determined by the determination section.

4. The information processing device according to claim 3, in, The determination unit updates the information determined as the control information from the sensor information based on a control result of the automatic driving by the control unit.

5. The information processing device according to claim 1, in, The calculation unit calculates the control variable based on preference information related to a riding feeling of an occupant riding the vehicle and the sensor information.

6. The information processing device according to claim 5, in, The information processing device further includes an acquisition unit that acquires physical sensation information related to the physical sensation of the occupant. The calculation unit calculates the control variable based on the preference information analyzed from the acquired body sensory information.

7. The information processing device according to claim 6, in, The body sensory information is at least one of voice, sight, and biological information of the occupant.

8. The information processing device according to claim 1, in, The calculation unit calculates the control variables for controlling the wheel speed, tilt, and suspension supporting the wheels of each of four wheels of the host vehicle based on the sensor information including the road information indicating the road condition of the road on which the host vehicle is traveling, and other vehicle information related to other vehicles traveling. The control unit controls the automatic driving of the host vehicle based on the control variable calculated by the calculation unit.

9. The information processing device according to claim 8, in, The other vehicle is a preceding vehicle that is traveling ahead of the host vehicle on the road.

10. The information processing device according to claim 9, in, The information processing device includes an updating unit configured to update a learning model for calculating the control variable of the host vehicle based on the other vehicle information.

11. The information processing device according to claim 1, in, The information processing device is mounted on the vehicle. The information processing device includes an acquisition unit that acquires first sensor information, which is the sensor information of a sensor mounted on the vehicle, and second sensor information of other vehicles traveling around the vehicle. The calculation unit calculates the control variable based on the acquired first sensor information and the acquired second sensor information. The control unit controls automatic driving so that the vehicle travels in synchronization with the other vehicle based on the control variable calculated by the calculation unit.

12. The information processing device according to claim 11, in, The control unit transmits the calculated control variable to the other vehicle so that the other vehicle travels in synchronization with the vehicle.

13. The information processing device according to claim 12, in, The calculation unit further calculates the control variable corresponding to each of the other vehicles. The control unit transmits the control variable to each of the corresponding other vehicles to control the automatic driving of each of the other vehicles.

14. An information processing system, in, The information processing system includes the vehicle and the other vehicle equipped with the information processing device according to any one of claims 11 to 13. The vehicle sends the control variable to the other vehicle, The other vehicles control automatic driving to travel in synchronization with the vehicle based on the control variables received from the vehicle.

15. A procedure, in, The program is for causing a computer to function as the information processing device according to any one of claims 1 to 13.

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