Information processing device and program

Abstract

The present invention is provided with: a calculation unit that calculates a combination for each of a plurality of combinations of a predetermined number of sensor information among a plurality of pieces of sensor information provided in a vehicle; calculating the wheel speed and the inclination of each of the four wheels of the vehicle and respective index values of suspensions supporting the wheels for controlling the wheel speed, the inclination and the suspensions, and calculating respective control variables of the wheel speed, the inclination and the suspensions by summarizing the index values; and a control unit that controls automatic driving on the basis of the control variable.

Description

Technical Field

[0001] The present invention relates to an information processing apparatus 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] An object of the present disclosure is to obtain an information processing apparatus and a program that can control autonomous driving based on index values obtained from a combination of sensors mounted on a vehicle.

[0004] According to a first aspect of the present disclosure, there is provided an information processing apparatus including: a calculation unit that calculates, for each of a plurality of combinations of a predetermined number of pieces of sensor information among a plurality of pieces of sensor information possessed by a vehicle, an index value for each of a wheel speed, an inclination, and a suspension that supports the wheel for controlling the wheel speed, the inclination, and the suspension of each of the four wheels of the vehicle, and sums up the index values to calculate a control variable for each of the wheel speed, the inclination, and the suspension; and a control unit that controls autonomous driving based on the control variable.

[0005] According to a second aspect of the present disclosure, there is provided an information processing apparatus, in the information processing apparatus according to the first aspect, the control unit controls the autonomous driving in units of one billionth of a second based on the control variable.

[0006] According to a third aspect of the present disclosure, there is provided an information processing apparatus, in the information processing apparatus according to the first aspect, the calculation unit selects a combination of the plurality of pieces of sensor information for at least an autonomous driving control object including a wheel speed, an inclination, and a suspension, and calculates a predetermined number of index values for each autonomous driving control object through the selected combination of sensor information.

[0007] According to a fourth aspect of the present disclosure, there is provided an information processing apparatus, in the information processing apparatus according to the third aspect, the calculation unit calculates a predetermined number of index values for each autonomous driving control object based on a combination of the plurality of pieces of sensor information that changes in response to a driving state of the vehicle.

[0008] According to a fifth aspect of the present disclosure, there is provided an information processing apparatus including: a calculation unit that calculates, for each of a plurality of combinations of a predetermined number of pieces of sensor information among the plurality of pieces of sensor information possessed by a vehicle, a wheel speed, an inclination, and an index value for each suspension supporting the wheel for controlling an autonomous driving control object including at least the wheel speed, the inclination, and the suspension, and calculates a control variable for each autonomous driving control object by summing up the index values; and a control unit that controls autonomous driving based on the control variables calculated by the calculation unit, wherein the calculation unit uses the control variable calculated for one autonomous driving control object as an index value for calculating the control variable for another autonomous driving control object.

[0009] According to a sixth aspect of the present disclosure, there is provided an information processing apparatus. In the information processing apparatus according to the fifth aspect, the calculation unit weights the control variables of the autonomous driving control objects that mutually utilize each other.

[0010] According to a seventh aspect of the present disclosure, there is provided an information processing apparatus. In the information processing apparatus according to the first aspect or the fifth aspect, the calculation unit calculates the control variable based on the index value through multivariate analysis using an integration method with deep learning.

[0011] According to an eighth aspect of the present disclosure, there is provided an information processing apparatus. In the information processing apparatus according to the first aspect or the fifth aspect, the inclination includes a steering angle and a camber angle.

[0012] According to a ninth aspect of the present disclosure, there is provided an information processing apparatus. In the information processing apparatus according to the fifth aspect, the calculation unit selects a combination of the plurality of pieces of sensor information for the autonomous driving control object, and calculates a predetermined number of index values for each autonomous driving control object through the selected combination of sensor information.

[0013] According to a tenth aspect of the present disclosure, there is provided a program for causing a computer to function as the information processing apparatus.

[0014] In addition, the above summary of the disclosure does not enumerate all the necessary features of the present disclosure. In addition, sub-combinations of these feature groups can also form the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a diagram schematically showing the AI risk prediction ability of ultra-high performance autonomous driving according to the first embodiment.

[0016] Figure 2 is a diagram schematically showing an example of the network structure inside the vehicle according to the first embodiment.

[0017] Figure 3 It is a flowchart executed by the Central Brain in the first embodiment.

[0018] Figure 4 It is the first explanatory diagram showing a control example of autonomous driving performed by the Central Brain in the first embodiment.

[0019] Figure 5 It is the second explanatory diagram showing a control example of autonomous driving performed by the Central Brain in the first embodiment.

[0020] Figure 6 It is the third explanatory diagram showing a control example of autonomous driving performed by the Central Brain in the first embodiment.

[0021] Figure 7 It is the fourth explanatory diagram showing a control example of autonomous driving performed by the Central Brain in the first embodiment.

[0022] Figure 8 It is the fifth explanatory diagram showing a control example of autonomous driving performed by the Central Brain in the first embodiment.

[0023] Figure 9 A diagram schematically showing an example of the hardware structure of a computer that functions as the Central Brain.

[0024] Figure 10 It is a flowchart executed by the Central Brain in the third embodiment.

[0025] Explanation of Reference Numerals

[0026] 120: Central Brain; 120: Computer; 1210: Main Controller; 1212: CPU; 1214: RAM; 1216: Graphics Controller; 1218: Display Device; 1220: Input / Output Controller; 1222: Communication Interface; 1224: Storage Device; 1230: ROM; 1240: Input / Output Chip. Detailed Embodiment

[0027] Hereinafter, the present disclosure will be described by way of embodiments. However, the following embodiments do not limit the technical solutions involved in the claims. In addition, not all combinations of features described in the embodiments are necessary for the solution means of the present disclosure.

[0028] (First Embodiment)

[0029] Figure 1Schematically shows the AI danger prediction ability of ultra-high performance autonomous driving involved in the first embodiment. In the first embodiment, various sensor information is digitalized by AI and stored in the cloud. The AI predicts and judges the optimal mixture of situations every nanosecond (one billionth of a second) to optimize the driving of vehicle 12.

[0030] Figure 2 It is a diagram for explaining the structure inside vehicle 12 of the central brain 120. The central brain 120 is an example of an information processing device.

[0031] As Figure 2 shown, the central brain 120 can be communicatively connected to a plurality of gateways. The central brain 120 is connected to an external cloud via the gateway. The central brain 120 is configured to be able to access the external cloud via the gateway. On the other hand, due to the presence of the gateway, it is configured that the central brain 120 cannot be directly accessed from the outside.

[0032] 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 billionth of a second.

[0033] Examples of sensors equipped in vehicle 12 used in the first embodiment include radar, lidar (LiDAR), high-pixel telescopic ultra-wide-angle 360-degree high-performance cameras, visual recognition, micro sound, ultrasonic waves, vibration, infrared rays, ultraviolet rays, electromagnetic waves, temperature, humidity, real-time AI weather forecast, high-precision multi-channel GPS, low-altitude satellite information, long-tail event AI data, etc. Long-tail event AI data is the trip data of a vehicle equipped with L5 level.

[0034] Examples of sensor information obtained from various sensors include the movement of the center of gravity of the weight, road material detection, external air temperature detection, external air humidity detection, detection of the up, down, horizontal and oblique inclination angles of the ramp, frozen state of the road, moisture content detection, material and wear condition of each tire, air pressure detection, road width, presence or absence of overtaking prohibition, oncoming vehicle, vehicle type information of the front and rear vehicles, cruising state of these vehicles, surrounding conditions (birds, animals, footballs, accident vehicles, earthquakes, fires, winds, typhoons, heavy rains, light rains, snowstorms, fog, etc.). In the first embodiment, these detections are performed every billionth of a second.

[0035] In the first embodiment, the central brain 120 functions as a computing unit that calculates, for each of a plurality of combinations of a predetermined number of pieces of sensor information among the sensor information detected by the sensors, the wheel speed, the inclination, and the control variables for controlling the wheel speed, the inclination, and the suspension of each of the four wheels of the vehicle. In addition, the inclination of the wheel includes both the inclination of the wheel with respect to the axis horizontal to the road (in other words, the angle at which the front wheel turns right or left with respect to the vehicle body, referred to as the steering angle) and the inclination of the wheel with respect to the axis perpendicular to the road (in other words, the angle between the tire and the ground when looking straight at the vehicle from the front, referred to as the camber angle). In addition, the objects of autonomous driving control may include other objects of autonomous driving control such as braking force, pitch angle, vehicle height, etc., in addition to the above-mentioned wheel speed, inclination, and suspension. In addition, the wheel speed, inclination (steering angle, camber angle), and suspension may be referred to as autonomous driving control elements for controlling autonomous driving.

[0036] Here, the predetermined number is, for example, three. Based on three pieces of sensor information, an index value for controlling the wheel speed, the inclination, and the suspension is calculated. The number of index values calculated based on the combination of three pieces of sensor information is, for example, three. The index values for controlling the wheel speed, the inclination, and the suspension include, for example, an index value calculated based on the information related to air resistance in the sensor information, an index value calculated based on the information related to road resistance in the sensor information, an index value calculated based on the information related to the slip coefficient in the sensor information, and the like. Then, the index values calculated for each of the plurality of combinations of different sensor information combinations are aggregated to calculate the control variables for controlling the wheel speed, the inclination, and the suspension. For example, a plurality of index values are calculated through the combination of sensors 1, 2, and 3, a plurality of index values are calculated through the combination of sensors 4, 5, and 6, and a plurality of index values are calculated through the combination of sensors 1, 3, and 7, and the index values are aggregated to calculate the control variables. In this way, while changing the combination of sensor information, a predetermined number, for example, 300 index values are calculated, and the control variables are calculated. Specifically, the computing unit may use machine learning, more specifically, deep learning, to calculate the control variables based on the sensor information. In other words, the computing unit may be composed of AI (Artificial Intelligence).

[0037] The calculation unit uses the computing power of Level 6 for the data collected per nanosecond by a large number of sensor groups, etc. For example, for the wheel speed V, by using the integration method shown in the following formula (1) for multivariate analysis (for example, refer to formula (2)), accurate control variables can be obtained. More specifically, while obtaining the integral value of various ultra-high-resolution delta values with the computing power of Level 6, the indexed values of each variable can be obtained in real time at the edge level, and the highest probability value can be obtained for the results generated in the next nanosecond.

[0038] (Mathematical formula 1)

[0039]

[0040] (Mathematical formula 2)

[0041] V n = DL(f(A, B, C,..., N)(dA n / dt)) (2)

[0042] Formula (2) represents the control variable. In addition, DL in formula (2) represents deep learning, and A, B, C, D,..., N are indexed values calculated based on sensor information, such as indexed values calculated based on air resistance, indexed values calculated based on road resistance, indexed values calculated based on road elements, and indexed values calculated based on slip coefficients, etc. When the indexed values calculated while changing the combination of a predetermined number of sensor information are 300, the indexed values of A to N in the formula are also 300, and the 300 indexed values are summarized.

[0043] In addition, in the above formulas (1) and (2), the wheel speed (V) is calculated, but the control variables for controlling the inclination (steering angle, camber angle) and suspension are also calculated in the same way.

[0044] Specifically, the central brain 120 calculates the wheel speeds of each of the four wheels for control, the inclinations (steering angle, camber angle) of each of the four wheels relative to the axis horizontal to the road, the inclinations (steering angle, camber angle) of each of the four wheels relative to the axis perpendicular to the road, and a total of 16 control variables for the suspension supporting each of the four wheels. In this embodiment, the calculation of the above 16 control variables is performed once every billionth of a second.

[0045] In addition, the wheel speed of each of the four wheels described above can be referred to as the "rotation speed (number of rotations) of the in-wheel motor mounted on each of the four wheels". The inclination (steering angle) of each of the four wheels with respect to the axis horizontal to the road can be referred to as the "horizontal angle of each of the four wheels". The inclination (camber angle) of each of the four wheels with respect to the axis perpendicular to the road can be referred to as the "vertical angle of each of the four wheels". The suspension (coil spring, shock absorber) that determines the position of the wheel with respect to the road can be referred to as the "attenuation amount that absorbs the impact received by each of the four wheels from the road".

[0046] Moreover, the above control variables are, for example, values for performing optimal steering matching the mountain road when the vehicle is traveling on a mountain road, and values for traveling at an optimal angle matching the parking lot when the vehicle is parked in the parking lot.

[0047] In addition, in the present embodiment, the central brain 120 calculates a total of 16 control variables for controlling the wheel speed of each of the four wheels, the inclination (steering angle) of each of the four wheels with respect to the axis horizontal to the road, the inclination (camber angle) of each of the four wheels with respect to the axis perpendicular to the road, and the suspension that supports each of the four wheels. However, this calculation does not have to be performed by the central brain 120, and a dedicated anchor chip for calculating the above control variables may be provided separately. In this case, DL in equation (2) represents deep learning, and A, B, C, D,..., N represent value index values calculated from sensor information. When the number of aggregated indexes is 300 as described above, the number of indexes in this equation is also 300.

[0048] In addition, in the present embodiment, the central brain 120 functions as a control unit, and this control unit controls autonomous driving in units of one billionth of a second based on the calculated control variables. Specifically, the central brain 120 controls the wheel speed, inclination, and suspension that supports each of the four wheels of the vehicle 12 by controlling the in-wheel motors mounted on the four wheels respectively based on the above 16 control variables 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 road information detected by sensors. Then, the central brain 120 proceeds to step S11.

[0051] In step S11, the central brain 120 calculates the above-mentioned 16 control variables based on the sensor information obtained 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 It is an explanatory diagram showing a control example of autonomous driving by the central brain 120. Additionally, Figures 4 to 6 It is an explanatory diagram from the perspective of observing the vehicle 12 from the front, Figure 7 and Figure 8 It is an explanatory diagram from the perspective of observing the vehicle 12 from below.

[0054] Figure 4 It shows a situation where the vehicle 12 is 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-mentioned 16 control variables calculated in combination with the road R1, thereby controlling the wheel speed, inclination (steering angle, camber angle) of each of the four wheels 30, and the suspension 32 that supports each of the four wheels 30 to perform autonomous driving.

[0055] Figure 5 It shows a situation where the vehicle 12 is 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-mentioned 16 control variables calculated in combination with the mountain road R2, thereby controlling the wheel speed, inclination (steering angle, camber angle) of each of the four wheels 30, and the suspension 32 that supports each of the four wheels 30 to perform autonomous driving.

[0056] Figure 6 It shows a situation where the vehicle 12 is traveling in a puddle R3. 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 in combination with the puddle R3, thereby controlling the wheel speed, inclination, and the suspension 32 that supports each of the four wheels 30 to perform autonomous driving.

[0057] Figure 7 It shows a situation where the vehicle 12 is bending 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-mentioned 16 control variables calculated in combination with the incoming curved road, thereby controlling the wheel speed, inclination (steering angle, camber angle) of each of the four wheels 30, and the suspension 32 (not shown) that supports each of the four wheels 30 to perform autonomous driving.

[0058] Figure 8 This shows a situation where the vehicle 12 moves parallel in the direction indicated by the arrow A2. 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 in combination with the parallel movement in the direction indicated by the arrow A2, thereby controlling the wheel speed, inclination (steering angle, camber angle) of each of the four wheels 30, and the suspension 32 (not shown) that supports each of the four wheels 30 to perform autonomous driving.

[0059] In addition, Figures 4 to 8 The states of the wheels 30 and the suspension 32 shown (the inclination (steering angle, camber angle) is only an example, and of course, states of the wheels 30 and the suspension 32 different from those shown in each figure can also be generated.

[0060] Here, although the in-wheel motors mounted on existing vehicles can independently control their respective drive wheels, in this vehicle, it is not possible to analyze road conditions, etc. to control the in-wheel motors. Therefore, in this vehicle, for example, when driving on a mountain road or in a puddle, etc., it is not possible to perform appropriate autonomous driving based on road conditions, etc.

[0061] However, according to the vehicle 12 according to the present embodiment, based on the above-described configuration, it is possible to perform autonomous driving that controls speed, steering, etc. suitable for the environment such as road conditions.

[0062] (Second Embodiment)

[0063] The following will describe the second embodiment of the present disclosure. In addition, the basic configuration, etc. of the second embodiment are the same as Figures 1 to 8 those of the first embodiment shown.

[0064] In the second embodiment, the calculation unit uses the computing power of L6 for the data collected per nanosecond by a large number of sensor groups, etc. For example, for the wheel speed V, by performing multivariate analysis (for example, referring to Equation (4)) using the integration method shown in the following Equation (3), accurate control variables can be obtained. More specifically, while obtaining the integrated value of various ultra-high-resolution delta values with the computing power of L6, the indexed values of each variable can be obtained in real time at the edge level, and the highest probability value can be obtained for the result generated in the next nanosecond.

[0065] (Mathematical Equation 3)

[0066]

[0067] (Mathematical Equation 4)

[0068] V n = DL(f(A, B,..., S, C, R,..., N)(dA n / dt)) (4)

[0069] Equation (4) represents a control variable. Additionally, in Equation (4), DL represents deep learning, and A, B, C, D, ..., N are index values calculated based on sensor information, such as index values calculated based on air resistance, index values calculated based on road resistance, index values calculated based on road elements, and index values calculated based on slip coefficients, etc. When the number of index values calculated while changing the combination of a predetermined number of sensor information is 300, the index values of A to N in the equation are also 300, and the 300 index values are summarized.

[0070] Here, in Equation (4), in addition to the index values of A to N, it also includes variables labeled as "S", "C", and "R".

[0071] Equation (10) is a control variable calculated for the suspension S (refer to Equation (9) described later), the variable "C" is a control variable calculated for the camber angle C (refer to Equation (7) described later), and the variable "R" is a control variable calculated for the steering angle R (refer to Equation (5) described later).

[0072] Additionally, in the above Equations (3) and (4), the wheel speed (V) is calculated, but the control variables for controlling the inclination (steering angle, camber angle) and the suspension are calculated in the same way.

[0073] That is, regarding the inclination (steering angle R), by performing multivariate analysis using the integration method shown in Equation (5) above (for example, refer to Equation (6)), an accurate control variable can be obtained.

[0074] (Mathematical formula 5)

[0075]

[0076] (Mathematical formula 6)

[0077] R n = DL(f(A, B,..., V, S, C,..., N)(dA n / dt)) (6)

[0078] Here, Equation (6) represents a control variable. Additionally, in Equation (6), in addition to the index values of A to N, it also includes variables labeled as "V", "S", and "C".

[0079] Equation (4) is a control variable calculated for the wheel speed V (refer to Equation (3)), Equation (10) is a control variable calculated for the suspension S (refer to Equation (9)), and Equation (8) is a control variable calculated for the camber angle C (refer to Equation (7)).

[0080] Regarding the inclination (camber angle C), by performing multivariate analysis using the integration method shown in the following formula (7) (for example, refer to formula (8)), an accurate control variable can be obtained.

[0081] (Mathematical formula 7)

[0082]

[0083] (Mathematical formula 8)

[0084] C n = DL(f(A, B,..., V, S, R,..., N)(dA n / dt)) (8)

[0085] Here, in formula (8), in addition to the index values from A to N, variables labeled "V", "S", "R" are also included.

[0086] Formula (4) is the control variable calculated for the wheel speed V (refer to formula (3)), formula (10) is the control variable calculated for the suspension S (refer to formula (9)), and formula (5) is the control variable calculated for the steering angle R (refer to formula (4)).

[0087] Furthermore, regarding the suspension S, by performing multivariate analysis using the integration method shown in the following formula (9) (for example, refer to formula (10)), an accurate control variable can be obtained.

[0088] (Mathematical formula 9)

[0089]

[0090] (Mathematical formula 10)

[0091] S n = DL(f(A, B,..., V, C, R,..., N)(dA n / dt)) (10)

[0092] Here, in formula (10), in addition to the index values from A to N, variables labeled "V", "C", "R" are also included.

[0093] Formula (4) is the control variable calculated for the wheel speed V (refer to formula (3)), formula (8) is the control variable calculated for the camber angle C (refer to formula (7)), and formula (6) is the control variable calculated for the steering angle R (refer to formula (5)).

[0094] (Third Embodiment)

[0095] Hereinafter, the third embodiment of the present disclosure will be described. In addition, the basic configuration of the third embodiment is the same as that of Figure 1and Figure 2 The configuration of the first embodiment shown is the same, so reference is appropriately made to the drawings shown in the first embodiment.

[0096] The third embodiment is characterized by the selection of sensor information when obtaining the index value.

[0097] In the first embodiment, based on a predetermined number (for example, three) of sensor information, an index value for controlling the wheel speed, inclination (steering angle, camber), and suspension is calculated.

[0098] That is, the index values calculated for each of a plurality of combinations of sensor information with different combinations of sensor information are aggregated, and a control variable for controlling the wheel speed, inclination (steering angle, camber), and suspension is calculated. Thus, while changing the combination of sensor information, a predetermined number, for example, 300 index values are calculated, and the control variable is calculated.

[0099] However, for each element (autopilot control element) of the wheel speed, inclination (steering angle, camber), and suspension, the information of all sensors is not necessarily required, and among the 300 index values obtained, there may sometimes be data with a low contribution degree.

[0100] Therefore, in the third embodiment, for each autopilot control element of the wheel speed, inclination (steering angle, camber), and suspension, the sensors are selected and discarded, and a predetermined number, for example, 300 index values are calculated through the combination of the selected sensors. For example, for each vehicle control, it is determined whether there is a contribution degree to the vehicle control (wheel speed, inclination, and suspension), and for each vehicle control, the sensors required for calculating the index value are determined.

[0101] That is, for the control variable of the wheel speed, 300 index values required for calculating the wheel speed are prepared, for the control variable of the inclination (steering angle, camber), 300 index values required for calculating the inclination (steering angle, camber) are prepared, and for the control variable of the suspension, 300 index values required for calculating the suspension are prepared.

[0102] The central brain 120 according to the third embodiment repeatedly executes Figure 10 the flowchart shown. In Figure 10 it, as described in the processing flow using a software program, it is assumed that this flowchart can be executed in one billionth of a second.

[0103] In addition, not limited to the software program, in order to reliably execute in one billionth of a second Figure 10For the flowchart shown, semiconductor integrated circuits such as ASIC (Application Specific Integrated Circuit), FPGA (Field Programmable Gate Array), and CPLD (Complex Programmable Logic Device) are preferably used.

[0104] In step 100, a selection process for sensors useful for calculating the index value V of the wheel speed, which is one of the elements of autonomous driving control, is performed, and the process proceeds to step 102.

[0105] In step 102, information from the sensors selected in step 100 (sensor information) is acquired, and the process proceeds to step 104.

[0106] In step 104, based on the sensor information, the index value Vn (n = approximately 300) of the wheel speed is calculated, the process proceeds to step 106, and the calculated index value of the wheel speed is temporarily stored, and the process proceeds to step 108.

[0107] In step 108, a selection process for sensors useful for calculating the index value R of the inclination (steering angle), which is one of the elements of autonomous driving control, is performed, and the process proceeds to step 110.

[0108] In step 110, information from the sensors selected in step 108 (sensor information) is acquired, and the process proceeds to step 112.

[0109] In step 112, based on the sensor information, the index value Rn (n = approximately 300) of the inclination (steering angle) is calculated, the process proceeds to step 114, and the calculated index value of the inclination (steering angle) is temporarily stored, and the process proceeds to step 116.

[0110] In step 116, a selection process for sensors useful for calculating the index value C of the inclination (camber angle), which is one of the elements of autonomous driving control, is performed, and the process proceeds to step 118.

[0111] In step 118, information from the sensors selected in step 116 (sensor information) is acquired, and the process proceeds to step 120.

[0112] In step 120, based on the sensor information, the index value Cn (n = approximately 300) of the inclination (camber angle) is calculated, the process proceeds to step 122, and the calculated index value of the inclination (camber angle) is temporarily stored, and the process proceeds to step 124.

[0113] In step 124, a process of selecting and rejecting sensors useful for calculating the index value S of the suspension, which is one of the automatic driving control elements, is performed, and the process proceeds to step 126.

[0114] In step 126, information from the sensors selected in step 124 (sensor information) is acquired, and the process proceeds to step 128.

[0115] In step 128, based on the sensor information, the index value Sn of the suspension (n = approximately 300) is calculated, and the process proceeds to step 130.

[0116] In step 130, the index value Vn of the wheel speed temporarily stored in step 106, the index value Rn of the inclination (steering angle) temporarily stored in step 114, and the index value Cn of the inclination (camber) temporarily stored in step 122 are read, and the process proceeds to step 132. The control variable is calculated including the index value Sn of the suspension calculated in step 128, and then the process proceeds to step 134.

[0117] In step 134, based on the control variable calculated in step 132, the automatic driving is controlled. Also, the criteria for sensor selection and rejection can be appropriately changed according to the vehicle condition.

[0118] Figure 9 An example of the hardware configuration of the computer 1200 that functions as the central brain 120 is schematically shown. The program installed in the computer 1200 enables the computer 1200 to function as one or more "parts" of the devices according to the first embodiment, the second embodiment, and the third embodiment, or enables the computer 1200 to perform operations associated with the devices according to the first embodiment, the second embodiment, and the third embodiment or the one or more "parts", and / or enables the computer 1200 to execute the processes according to the first embodiment, the second embodiment, and the third embodiment or the stages of the processes. Such a program can be executed by the CPU 1212 to enable the computer 1200 to perform specific operations associated with some or all of the blocks in the flowcharts and block diagrams described in this specification.

[0119] In the first, second, and third embodiments, the computer 1200 includes a CPU 1212, a RAM 1214, and a graphics controller 1216, which are interconnected by a main 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 main controller 1210 via an input / output controller 1220. The DVD drive can be a DVD-ROM drive, a DVD-RAM drive, or the like. The storage device 1224 can be a hard disk drive, a solid state drive, or the like. The computer 1200 also includes legacy input / output units such as a ROM 1230 and a keyboard, which are connected to the input / output controller 1220 via an input / output chip 1240.

[0120] The CPU 1212 controls each unit by operating in accordance with programs stored in the ROM 1230 and the RAM 1214. The graphics controller 1216 acquires image data generated by the CPU 1212 in a frame buffer or the like provided in the RAM 1214 or in itself, and displays the image data on the display device 1218.

[0121] 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.

[0122] The ROM 1230 stores therein a boot program or the like executed by the computer 1200 at activation 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, or the like.

[0123] 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 and installed in the storage device 1224, the RAM 1214, or the ROM 1230, which are examples of computer-readable storage media, and is executed by the CPU 1212. The information processing described in these programs is read by the computer 1200 and brings about cooperation between the programs and various types of hardware resources. The device or method can be configured to perform operations or processing of information by accompanying the use of the computer 1200.

[0124] For example, when communication is performed between the computer 1200 and an external device, the CPU 1212 executes a communication program loaded into the RAM 1214, and instructs 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 can read transmission data stored in a transmission buffer area provided in a recording medium such as the RAM 1214, the storage device 1224, the DVD-ROM, or the IC card, transmit the read transmission data to the network, or write the reception data received from the network into a reception buffer area provided on the recording medium, etc.

[0125] In addition, the CPU 1212 can allow 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. Then, the CPU 1212 can write the processed data back to the external recording medium.

[0126] Various types of information such as various types of programs, data, tables, and databases can be stored in the recording medium and undergo information processing. The CPU 1212 performs various types of processing on the data read from the RAM 1214, including various types of operations, information processing, conditional judgment, conditional branch, unconditional branch, information retrieval / replacement, etc. specified by an instruction sequence of a program described anywhere in the present disclosure, and can write the result back to the RAM 1214. In addition, the CPU 1212 can retrieve information in a file, a database, etc. in the recording medium. For example, when a plurality of entries are stored in the recording medium and each of the entries has an attribute value of a first attribute associated with an attribute value of a second attribute, the CPU 1212 can search for an entry in the plurality of entries whose attribute value of the first attribute matches a specified condition, 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.

[0127] The above-described program or software module can be stored in a computer-readable storage medium in 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 can be used as a computer-readable storage medium, whereby the program is provided to the computer 1200 through the network.

[0128] In the flowcharts and block diagrams in the first, second, and third embodiments, the blocks can represent stages of a process of performing operations or "parts" of a device having a function of performing operations. The specific stages and "parts" can be implemented by dedicated circuits, programmable circuits provided together with computer-readable instructions stored on a computer-readable storage medium, and / or processors provided 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 reconfigurable hardware circuits that include, for example, logical products, logical sums, exclusive ORs, negative logical products, negative logical sums, and other logical operations, flip-flops, registers, and storage devices such as field programmable gate arrays (FPGAs) and programmable logic arrays (PLAs).

[0129] A computer-readable storage medium can include any tangible device capable of storing instructions executable by a suitable device. As a result, a computer-readable storage medium having instructions stored therein has an article of manufacture that includes instructions that can be executed to create elements for performing the specified operations in the 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 disks, magnetic disks, hard disks, random access memories (RAMs), read-only memories (ROMs), erasable programmable read-only memories (EPROMs or flash memories), electrically erasable programmable read-only memories (EEPROMs), static random access memories (SRAMs), compact disc read-only memories (CD-ROMs), digital versatile discs (DVDs), Blu-ray discs, memory sticks, integrated circuit cards, etc.

[0130] Computer-readable instructions can include either source code or object code described by any combination of one or more programming languages, the programming languages including assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state-setting data, and object-oriented programming languages such as Smalltalk (registered trademark), JAVA (registered trademark), C++, etc., and conventional procedural programming languages such as the "C" programming language or similar programming languages.

[0131] 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 via a local area network (LAN), a wide area network (WAN) such as the Internet, etc., so that the processor or programmable circuit of the general-purpose computer, special-purpose computer, or other programmable data processing device executes to generate the computer-readable instructions for performing the elements specified in the flowchart or block diagram. As examples of the processor, include a computer processor, a processing unit, a microprocessor, a digital signal processor, a controller, a microcontroller, etc.

[0132] As described above, the first embodiment, the second embodiment, and the third embodiment have been described, but the technical scope of the present disclosure is not limited to the scope described in the above embodiments. It is obvious that various changes or improvements can be made to the above embodiments by those skilled in the art. It is obvious from the description of the claims that the modes incorporating such changes or improvements can also be included in the technical scope of the present disclosure.

[0133] It should be noted that the execution order of each process such as operations, 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", "preceding", etc., and as long as the subsequent process does not use the output of the preceding process, it can be implemented in any order. For the operation flowcharts in the claims, the specification, and the drawings, even if they are described using "first", "then", etc. for convenience, it does not mean that they must be implemented in that order.

[0134] The disclosures of Japanese Patent Application No. 2022-182131 filed on November 14, 2022, Japanese Patent Application No. 2023-062335 filed on April 6, 2023, and Japanese Patent Application No. 2023-063765 filed on April 10, 2023 are incorporated herein by reference in their entirety.

Claims

1. An information processing device, comprising: a calculation unit that calculates, for each of a plurality of combinations of a predetermined number of pieces of sensor information among the plurality of pieces of sensor information possessed by a vehicle, the wheel speed, inclination, and index value for each of the suspensions supporting the wheels, respectively, for controlling the wheel speed, the inclination, and the suspension of each of the four wheels of the vehicle, and sums up the index values to calculate control variables for the wheel speed, the inclination, and the suspension, respectively; and a control unit that controls autonomous driving based on the control variables.

2. The information processing device according to claim 1, wherein the control unit controls the autonomous driving in units of one-billionth of a second based on the control variables.

3. The information processing device according to claim 1, wherein the calculation unit selects a combination of the plurality of pieces of sensor information for at least autonomous driving control objects including wheel speed, inclination, and suspension, and calculates a predetermined number of index values for each autonomous driving control object through the selected combination of sensor information.

4. The information processing device according to claim 3, wherein the calculation unit calculates a predetermined number of index values for each autonomous driving control object based on a combination of the plurality of pieces of sensor information that changes in response to the driving condition of the vehicle.

5. An information processing device, comprising: a calculation unit that calculates, for each of a plurality of combinations of a predetermined number of pieces of sensor information among the plurality of pieces of sensor information possessed by a vehicle, the wheel speed, inclination, and index value for each of the suspensions supporting the wheels, respectively, for controlling autonomous driving control objects including at least the wheel speed, the inclination, and the suspension, and sums up the index values to calculate control variables for each of the autonomous driving control objects; and a control unit that controls autonomous driving based on the control variables calculated by the calculation unit, wherein the calculation unit uses the control variable calculated for one autonomous driving control object as an index value for calculating the control variable for another autonomous driving control object.

6. The information processing device according to claim 5, wherein the calculation unit weights the control variables of the autonomous driving control objects that are mutually utilized.

7. The information processing device according to claim 1 or claim 5, wherein the calculation unit calculates the control variables based on the index values through multivariate analysis using integration method with deep learning.

8. The information processing device according to claim 1 or claim 5, wherein the inclination includes a steering angle and a camber angle.

9. The information processing device according to claim 5, wherein the calculation unit selects a combination of the plurality of pieces of sensor information for the autonomous driving control objects, and calculates a predetermined number of index values for each autonomous driving control object through the selected combination of sensor information.

10. A program for causing a computer to function as the information processing device according to any one of claims 1 to 9.

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