Information processing device, program, and information processing system

Abstract

The present invention is provided with: an information acquisition unit capable of acquiring a plurality of pieces of information relating to a vehicle; a plurality of chips provided for each of the plurality of pieces of information and deriving an index value from predetermined information among the plurality of pieces of information; and a driving control unit that performs driving control of the vehicle on the basis of the plurality of index values.

Description

Technical Field

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

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

[0003] According to an embodiment of the present disclosure, an information processing apparatus is provided. The information processing apparatus according to the first aspect includes: an information acquisition unit capable of acquiring a plurality of pieces of information related to a vehicle; a plurality of chips provided for each of the plurality of pieces of information, configured to derive an index value based on predetermined information among the plurality of pieces of information; and a driving control unit configured to perform driving control of the vehicle based on the plurality of index values.

[0004] Regarding the information processing according to the second aspect, in the information processing apparatus according to the first aspect, the chip derives the index value by performing multivariate analysis using integration method using deep learning.

[0005] Regarding the information processing according to the third aspect, in the information processing apparatus according to the first or second aspect, an anchor chip is provided, the anchor chip aggregates the plurality of index values derived by the plurality of chips and derives an index value, and the driving control unit performs driving control of the vehicle based on the index value aggregated by the anchor chip.

[0006] Regarding the information processing according to the fourth aspect, in the information processing apparatus according to the third aspect, the information acquisition unit acquires the plurality of pieces of information in units of one billionth of a second, the chip performs derivation of the index value in units of one billionth of a second using the plurality of pieces of information, the anchor chip performs aggregation of the index value in units of one billionth of a second using the index value, and the driving control unit performs driving control of the vehicle in units of one billionth of a second using the aggregated index value.

[0007] Regarding the information processing according to the fifth aspect, in any one of the information processing apparatuses according to the first to fourth aspects, in response to the load of the plurality of chips, other chips are caused to derive index values.

[0008] Regarding the information processing according to the sixth aspect, in any one of the information processing apparatuses according to the first to fifth aspects, in response to the load of the chip, the processing ability of the process of causing the chip to derive the index value is changed.

[0009] Regarding the information processing of the seventh method, in the information processing apparatus of the third method, the anchoring chip aggregates a plurality of the index values derived by a plurality of the chips based on a weight predetermined for each of the chips.

[0010] Regarding the information processing of the eighth method, in the information processing apparatus of the seventh method, the weight is obtained by inputting the information into a learned model.

[0011] Regarding the information processing of the ninth method, in the information processing apparatus of the third method, the anchoring chip aggregates a plurality of the index values derived by a plurality of the chips based on a weight predetermined for each type of the information from which the chip calculates the index value.

[0012] Regarding the information processing of the tenth method, in the information processing apparatus of the ninth method, the types of the information include information with high priority and information with low priority, and the anchoring chip aggregates a plurality of the index values derived by a plurality of the chips based on a weight predetermined for each priority of the information from which the chip calculates the index value.

[0013] Regarding the information processing of the eleventh method, in the information processing apparatus of the fifth method, the processing of deriving the index value is shared by other chips in such a manner that the loads of the plurality of chips become equal.

[0014] Regarding the information processing of the twelfth method, in the information processing apparatus of the fifth method, the processing of deriving the index value is shared by other chips according to the heat generation state of the chip.

[0015] Regarding the information processing of the thirteenth method, in the information processing apparatus of the fifth method, when the remaining power of the battery mounted on the vehicle becomes equal to or less than a predetermined threshold value, the processing capabilities of the plurality of chips are reduced to maintain the battery.

[0016] Regarding the information processing of the fourteenth method, in the information processing apparatus of the thirteenth method, it is possible for a user to select whether to reduce the processing capability.

[0017] Regarding the information processing of the fifteenth method, an information processing apparatus includes: one or more information acquisition units capable of acquiring information related to a vehicle; one or more chips provided for each of the one or more pieces of information, and deriving an index value based on predetermined information among a plurality of the information; an index value acquisition unit that acquires the index value from the chips provided in other apparatuses different from the vehicle; and a driving control unit that performs driving control of the vehicle based on a plurality of the index values derived from the vehicle and the other apparatuses.

[0018] Regarding the information processing of the sixteenth mode, in the information processing apparatus of the fifteenth mode, the chip derives the index value through multivariate analysis using integration method that employs deep learning.

[0019] Regarding the information processing of the seventeenth mode, in the information processing apparatus of the fifteenth mode or the sixteenth mode, there is an anchoring chip that aggregates the multiple index values derived by the multiple chips to derive an index value, and the driving control unit performs driving control of the vehicle based on the index value aggregated by the anchoring chip.

[0020] Regarding the information processing of the eighteenth mode, in any one of the information processing apparatuses of the fifteenth mode to the seventeenth mode, in response to the loads of the multiple chips, other chips are made to derive index values.

[0021] Regarding the information processing of the nineteenth mode, in any one of the information processing apparatuses of the fifteenth mode to the eighteenth mode, in response to the load of the chip, the processing ability of the process of making the chip derive an index value is changed.

[0022] Regarding the information processing of the twentieth mode, a program is provided that causes a computer to function as any one of the information processing apparatuses of the first mode to the nineteenth mode.

[0023] Regarding the information processing system of the twenty - first mode, an information processing system is provided. The information processing system includes: the information processing apparatus of any one of the fifteenth mode to the nineteenth mode provided in a vehicle; one or more information acquisition units provided in other apparatuses different from the vehicle and capable of acquiring information related to the vehicle; and one or more chips provided in the other apparatuses for each of the one or more pieces of information, and deriving index values based on predetermined information among the multiple pieces of information.

[0024] Regarding the information processing system of the twenty - second mode, in the information processing system of the twenty - first mode, the other apparatus includes another vehicle, which is a vehicle different from the vehicle.

[0025] Regarding the information processing system of the twenty - third mode, in the information processing system of the twenty - first mode, the other apparatus includes a monitoring device for monitoring a road.

[0026] Regarding the information processing system of the twenty - fourth mode, in the information processing system of the twenty - third mode, as the monitoring device, it includes a monitoring device provided on a road.

[0027] In addition, the above - mentioned summary of the disclosure does not enumerate all the necessary features of the disclosure. In addition, sub - combinations of these feature groups can also be the disclosure. Description of the Drawings

[0028] Figure 1 is a diagram schematically showing the AI risk prediction ability of ultra-high-performance autonomous driving.

[0029] Figure 2 is a schematic diagram showing an example of a vehicle equipped with a Central Brain.

[0030] Figure 3 is a block diagram showing an example of the information processing device according to the first embodiment.

[0031] Figure 4 is a diagram schematically showing an example of driving control using an index value.

[0032] Figure 5 is a diagram schematically showing an example of driving control using an index value.

[0033] Figure 6 is a block diagram showing an example of the hardware structure of a computer that functions as an information processing device.

[0034] Figure 7 is a block diagram showing an example of the information processing device according to the second embodiment.

[0035] Figure 8 is a block diagram showing an example of an information processing system including the information processing device according to the third embodiment.

[0036] Figure 9 is a diagram showing an example of Figure 7 the fourth embodiment of an information processing system different from the one including the information processing device according to the third embodiment.

[0037] Figure 10 is a diagram showing an example of Figure 7 and Figure 8 the fifth embodiment of an information processing system different from the one including the information processing device according to the third embodiment. Detailed Embodiments

[0038] Hereinafter, the present disclosure will be described by way of embodiments. However, the following embodiments do not limit the technical solutions related to the claims. In addition, not all combinations of features described in the embodiments are necessary for the solution means of the present disclosure.

[0039] The information processing device of the present disclosure can accurately obtain the index value required for driving control based on a large amount of information related to vehicle control. Therefore, the information processing device of the present disclosure can be at least partially mounted on a vehicle to achieve vehicle control.

[0040] In addition, the information processing apparatus of the present disclosure can provide a driving system that can achieve autonomous driving in real time based on data obtained from multiple sensor inputs in the edge of AI / multivariate analysis / target search / strategy formulation / optimal probability solution / optimal speed solution / optimal route management at Level 6 (L6), and can be adjusted based on the optimal solution of the delta.

[0041] Figure 1 Schematically shows the AI risk prediction ability of the ultra-high-performance autonomous driving according to the present embodiment. In the present embodiment, multiple sensor information is digitalized by AI and stored in the cloud 2. The AI predicts and judges the optimal mixture of the situation every nanosecond (one billionth of a second) to optimize the driving of the vehicle 12 (refer to Figure 2 ).

[0042] Figure 2 Is a schematic diagram showing an example of the vehicle 12 equipped with the central brain 3. The central brain 3 can be an example of the information processing apparatus 1 according to the present embodiment. As Figure 2 shown, the central brain 3 can be communicatively connected to a plurality of gateways 4. The central brain 3 according to the present embodiment can achieve L6-level autonomous driving based on a plurality of information obtained via the gateway 4.

[0043] "Level 6" represents the level of autonomous driving, which is equivalent to a level higher than Level 5 representing fully autonomous driving. Level 5 represents fully autonomous driving, but it is equivalent to the level of human driving, and even so, there is still a probability of accidents. Level 6 represents a level higher than Level 5, which is equivalent to a level with a lower probability of accidents than Level 5.

[0044] The computing power of Level 6 is about 1000 times that of Level 5. Therefore, it is possible to achieve high-performance driving control that cannot be achieved at Level 5.

[0045] Figure 3 Is a block diagram showing an example of the information processing apparatus 1 according to the embodiment of the present disclosure. The information processing apparatus 1 according to the present embodiment at least includes an information acquisition unit 10, a chip 20, an anchor chip 30, and a driving control unit 40.

[0046] The information acquisition unit 10 can acquire various information related to the vehicle 12. As the information acquisition unit 10, for example, it may include sensors installed in various parts of the vehicle, and communication components for acquiring information that can be obtained via a network from a server (not shown) or the like. As the sensors included in the information acquisition unit 10, there may be mentioned radar, lidar (LiDAR), high-pixel telephoto ultra-wide-angle 360-degree high-performance cameras, visual recognition, microphones, ultrasonic sensors, vibration sensors, infrared sensors, ultraviolet sensors, electromagnetic wave sensors, temperature sensors, humidity sensors, real-time AI weather forecasts, high-precision multi-channel GPS, low-altitude satellite information, etc.

[0047] The information that can be obtained by various sensors includes: vehicle speed, wheel rotation speed, yaw angular velocity, steering and brakes, air resistance, road resistance, road elements (such as garbage), slip coefficient, temperature or material of the ground (such as the road), external air temperature, tilt of the ground, freezing state or moisture content of the road, material or wear condition of each tire, or air pressure, road width, presence or absence of overtaking prohibition, presence or absence of oncoming vehicles, vehicle type information of the front and rear vehicles, cruising conditions of these vehicles, surrounding conditions (birds, animals, footballs, accident vehicles, earthquakes, fires, winds, typhoons, heavy rain, light rain, snowstorms, fog, etc.). In the present embodiment, by utilizing L6-level computing power, these detections can be performed every one billionth of a second (nanosecond).

[0048] The information involved can distinguish the types of information. For example, regarding autonomous driving, it is divided into information with high priority and information with low priority. Information with high priority is information that becomes a factor having a great impact on safe driving, etc. For example, road elements (such as garbage), surrounding conditions (birds, animals, footballs, accident vehicles, earthquakes, fires, winds, typhoons, heavy rain, light rain, snowstorms, fog, etc.). In addition, information with low priority is information that becomes a factor having a relatively small impact on safe driving compared to information with high priority, etc. For example, temperature or material of the ground (such as the road), external air temperature, vehicle type information of the front and rear vehicles, etc. In addition, information with high priority or information with low priority is not limited to the above information, and can also be changed according to the vehicle 12, or can be changed according to the type of the vehicle 12 or the use of the vehicle 12, etc. In addition, the information is not limited to the case of being divided into information with high priority and information with low priority, and can also be divided into other types, such as information with high urgency and information with low urgency, etc., and can also be divided into information acquired by sensors provided in the vehicle 12 and information acquired via a network from a server (not shown) or the like.

[0049] It should be noted that the above-mentioned information acquisition unit 10 includes a vehicle lower sensor, which is provided at the lower part of the vehicle and can detect the temperature, material, and inclination of the ground on which the vehicle is traveling. By using this vehicle lower sensor, independent intelligent tilt control can be performed.

[0050] The chips 20 (20A, 20B, 20C, 20D) are provided for each piece of information acquired by the multiple information acquisition units 10 (10A, 10B, 10C, 10D). And, an index value is derived according to predetermined information among the multiple pieces of information. For example, when the information acquired by the information acquisition unit 10 is air resistance (A), the chip 20A calculates the index value (refer to Equation (2)); when the information is road resistance (B), the chip 20B calculates the index value (refer to Equation (3)); when the information is road element (C), the chip 20C calculates the index value (refer to Equation (4)); when the information is slip coefficient (D), the chip 20D calculates the index value (refer to Equation (5)). Here, in the present embodiment, the chip 20 can perform these derivations every one billionth of a second (nanosecond) by using the computing power of L6 level.

[0051] In addition, the chip 20 can use machine learning, and more specifically, deep learning, to derive an indexed value related to the control of the vehicle according to the multiple pieces of information acquired by the information acquisition unit 10. In other words, the chip 20 can be composed of AI (Artificial Intelligence). Here, the index value is, for example, the rotational speed of the wheels, that is, the speed (V) of the vehicle. In addition, the index value is not limited to the rotational speed of the wheels and can also be other elements required for the driving control of the vehicle 12.

[0052] The chip 20 uses the computing power of L6 level to perform multivariate analysis on the data collected every nanosecond by multiple sensor groups, etc. in the information acquisition unit 10 by using the integration method shown in the following Equation (1) (for example, refer to Equations (2) to (5)), so as to be able to obtain an accurate index value. More specifically, while obtaining the integral value of various ultra-high-resolution Delta values with the computing power of L6 level, it can obtain the indexed values of each variable in real time at the edge level and obtain the highest probability value for the result generated in the next nanosecond.

[0053] (Mathematical formula 1)

[0054]

[0055] (Mathematical formula 2)

[0056] V a= DL(f(A)(dA / dt)) (2)

[0057] (Mathematical formula 3)

[0058] V b = DL(f(B)(dB / dt)) (3)

[0059] (Mathematical formula 4)

[0060] V c = DL(f(C)(dC / dt)) (4)

[0061] (Mathematical formula 5)

[0062] V d = DL(f(D)(dD / dt)) (5)

[0063] Wherein, DL in the formula represents deep learning, and A, B, C, and D represent air resistance, road resistance, road elements (such as garbage), and slip coefficient, etc.

[0064] In addition, by increasing the number of deep learning times, the indexed values of each variable obtained in the chip 20 can be further refined. For example, a large amount of data such as tires, motor rotation, steering angle, road material, weather, the influence of garbage or quadratic curve deceleration, skidding, balance breakdown, or steering or speed control methods for re-acquisition can be used to calculate more accurate index values.

[0065] The chips 20 do not exchange data with each other and only calculate the functions assigned to each chip 20. That is to say, for example, when the chip 20A is assigned to derive the index value according to the air resistance (A), only this is calculated, and when the chip 20B is assigned to derive the index value according to the road resistance (B), only this is calculated. Thus, the calculation load of each chip 20 can be greatly reduced, and the amount of data output from the chip 20 can also be greatly reduced. Then, the anchor chip 30 aggregates the multiple index values derived by the chip 20 to derive the index value. Specifically, the index value derived by the chip 20A according to the air resistance (A), the index value derived by the chip 20B according to the road resistance (B), the index value derived by the chip 20C according to the road element (C), and the index value derived by the chip 20D according to the slip coefficient (D) are aggregated to calculate the index value (refer to formula 6).

[0066] (Mathematical formula 6)

[0067] V n = DL(f(A, B, C,..., N)(dA n / dt)) (6)

[0068] Among them, DL in the formula represents deep learning, and A, B, C, D, ..., N represent index values calculated based on air resistance, index values calculated based on road resistance, index values calculated based on road elements, index values calculated based on slip coefficients, and the like. f(A, B, C, D, ..., N) represents a function representing the behaviors of A, B, C, D, ..., N, and Vn represents a value (i.e., an index value) output by a deep learning model optimized by performing deep learning on a neural network.

[0069] For example, when the anchoring chip 30 outputs 15 rotations considering air resistance (A), 16 rotations considering road resistance (B), 14 rotations considering road elements (C), etc., the anchoring chip 30 calculates how many rotations of the wheels each of the motors of the four wheels should advance (accelerate, decelerate).

[0070] In addition, the anchoring chip 30 may not equally process the multiple index values derived by each of the multiple chips 20, but may aggregate them based on a predetermined weighting for each chip 20. For example, when the weighting weight of the index value derived by the chip 20C based on road elements (C) is higher than that of the index value derived by the chip 20A based on air resistance (A), it is possible to derive an index value that evaluates the index value obtained by aggregating based on road elements (C) higher than the index value derived based on air resistance (A). The importance of multiple pieces of information required for autonomous driving is not necessarily equal. By using information with higher importance to derive index values, appropriate autonomous driving can be achieved. In addition, the aggregation based on the involved weighting may be performed not only frequently but also in a predetermined situation. For example, when the vehicle 12 is in a turning situation, the weight of the index value derived based on road resistance may be increased compared to the index value derived based on air resistance (A) to use the road friction coefficient information.

[0071] In addition, the involved weighting may also be set by the user. In addition, the weighting weight may be output by inputting the information acquired by the sensor into the learned model. Here, the learned model is, for example, a model that has performed machine learning using the information and the derived index value as teacher data. In addition, a neural network is used to generate the learned model. In addition, the generation of the learned model is not limited to the case of using a neural network, and other methods may also be used. In addition, the generation of the learned model may be performed by the information processing device 1 or by other devices.

[0072] In addition, the anchoring chip 30 can also aggregate a plurality of index values derived from a plurality of chips 20 based on weights predetermined for each type of information used to calculate the index values for the chips 20. For example, as described above, when the types of information are classified into high-priority information and low-priority information, the weight of the weight can be set higher for the index value derived from the information included in the high-priority information than for the index value derived from the information included in the low-priority information. In addition, when the types of information are classified into high-urgency information and low-urgency information, the weight of the weight can be set higher for the index value derived from the information included in the high-urgency information than for the index value derived from the information included in the low-urgency information.

[0073] The driving control unit 40 performs driving control of the vehicle 12 based on the index values aggregated by the anchoring chip 30. The driving control unit 40 can implement autonomous driving control of the vehicle. Specifically, the highest probability value of the result occurring in the next nanosecond can be obtained from the plurality of index values, and driving control of the vehicle can be implemented in consideration of this probability value. Here, in the present embodiment, the driving control unit 40 can implement these driving controls every one billionth of a second (nanosecond) by using L6-level computing power.

[0074] Figure 4 and Figure 5 is a diagram schematically showing an example of driving control using index values.

[0075] According to the information processing apparatus 1 having the above-described configuration, it is possible to perform analysis or derivation of information using L6-level computing power, which is much greater than L5-level computing power, and thus it is possible to perform precise analysis at a level that has not been possible in the past. As a result, it is possible to perform vehicle control for safe autonomous driving. Moreover, through the above-described multivariate analysis using AI, a value difference of 1000 times can be generated compared to the L5-level world.

[0076] In addition, regarding which chip 20 calculates what index value, it is not limited to having one chip 20 calculate one index. For example, when it is known at the design stage that both the index value of air resistance (A) and the index value of road resistance (B) can be calculated by chip A, one chip 20A can calculate multiple indexes. Further, when it is known at the design stage that the computing power of only chip 20A is not sufficient to calculate the index value of air resistance (A), multiple chips such as chip 20A and chip 20B can be used to calculate one index value to be responsible for the calculation of the index value of air resistance (A).

[0077] In addition, the chip 20 is not limited to the case of deriving the index value according to the predetermined information. In response to the load of the chip 20, other chips 20 may derive the index value. For example, when the load of the chip 20A is heavy, in addition to the chip 20A, the chip 20B may also derive the index value of the air resistance (A) that the chip 20A is responsible for processing. In addition, when the load of the chip 20 is light, one chip 20 may also derive multiple index values. In addition, other chips 20 may be made to share the process of deriving the index value to equalize the loads of the multiple chips 20. By configuring in this way, it is possible to prevent a load from being imposed only on a part of the chips 20. In addition, when other chips 20 share the process of deriving the index value, it is not limited to the case where the process is performed by two chips 20 in cooperation with other chips 20, and the process may also be shared by three or more chips 20.

[0078] In addition, a sensor capable of measuring the temperature of each chip 20 may be provided, and other chips 20 may be made to share the process of deriving the index value according to the heat generation condition of the chip 20. For example, when the temperature of the chip 20A exceeds a predetermined threshold value, other chips 20 (for example, the chip 20B) may also process the derivation of the index value that the chip 20A processes. By configuring in this way, it is possible to prevent a part of the chips 20 from experiencing thermal runaway, processing degradation caused by heat generation, etc.

[0079] In addition, for the chip 20 that is predetermined to derive important information that has a great impact on safe driving, the process of deriving the index value of the information that has been predetermined to be derived by other chips 20 may not be shared. Thereby, the processing ability of the chip 20 that is predetermined to derive important information can be ensured.

[0080] In addition, in response to the load of the chip 20, the processing ability of the process of deriving the index value by the chip 20 may be changed. Specifically, when the vehicle 12 is moving forward on a flat road or traveling in a place where there are significantly fewer dangers, etc., and the load is light, each chip 20 does not need to perform calculations every billionth of a second, and can perform calculations at a slower speed by reducing the workload, etc. However, when a crisis is detected (for example, when a burning truck is found on a highway, etc.), an instruction is issued to make the derivation of the necessary index value run at full load. In addition, the rhythm of the calculation may also be allocated, and respective strengths may be given to the chip 20A that calculates the air resistance (A), the chip 20B that calculates the road resistance (B), etc. as needed.

[0081] In addition, when the remaining power of the battery equipped in the vehicle 12 is equal to or lower than a predetermined threshold, the processing capabilities of the multiple chips 20 can also be reduced to maintain the battery. For example, when the remaining power of the battery decreases, the vehicle 12 can also switch to a power-saving mode with reduced processing capabilities of the chips 20 to suppress battery consumption. This example is particularly effective when the vehicle 12 is a car powered by a battery such as an electric vehicle (EV). With such a configuration, even when the remaining power of the battery decreases, the driving distance that can be achieved can be ensured. Here, the processing capabilities of the chips 20 are changed by changing the operating frequency of the chips 20, etc.

[0082] In addition, based on the driving position of the vehicle 12, the distance to the destination or the next charging station can be calculated, for example. If it is predicted that the remaining power of the battery will run out during the journey with the normal processing capabilities of the chips 20, the processing capabilities of the chips 20 can be reduced to maintain the battery.

[0083] In addition, when the remaining power of the battery is equal to or lower than a predetermined threshold, the user can choose whether to reduce the processing capabilities of the chips 20. This is considering that even when the remaining power of the battery decreases, there are cases where the user does not want the processing capabilities of the chips 20 to decline, or there are driving plans with scheduled charging, etc., and it is best to leave the choice to the user. The user selection involved can preferably be made before the remaining power of the battery reaches the predetermined threshold, or after the processing capabilities of the chips 20 are reduced due to the remaining power of the battery reaching the predetermined threshold. Therefore, when reducing the processing capabilities of the chips 20, it is preferable to notify the user of the reduction in the processing capabilities of the chips 20.

[0084] In addition, the theoretical values of the index values can also be materialized and stored. For example, when multiple vehicles 12 travel extremely long distances, a vast amount of driving data is accumulated and deep learning is performed on it. As a result, it is possible to obtain the theoretical value index values such as what the index value is like under the condition of air resistance (A), what the index value is like under the condition of road resistance (B), what the index value is like under the condition of road elements (C), and what the index value is like under the condition of the slip coefficient (D) for the instantaneous information. This is called materialization (crystallization).

[0085] Figure 6FIG. 0 schematically shows an example of the hardware configuration of a computer 1200 that functions as the above-described information processing apparatus (central brain 3). Programs installed in the computer 1200 can cause the computer 1200 to function as one or more "units" of the apparatus according to the above-described embodiments, or cause the computer 1200 to execute operations associated with the apparatus according to each embodiment or the one or more "units", and / or cause the computer 1200 to execute the processes according to each embodiment or stages of the processes. Such programs 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.

[0086] The computer 1200 according to each embodiment may include a CPU 1212, a RAM 1214, and a graphics controller 1216, which may be interconnected via a main controller 1210. The computer 1200 may also include input / output units such as a communication interface 1222, a storage device 1224, a DVD drive, and an IC card drive, which may be connected to the main controller 1210 via an input / output controller 1220. The DVD drive may be a DVD-ROM drive, a DVD-RAM drive, or the like. The storage device 1224 may be a hard disk drive, a solid state drive, or the like. The computer 1200 may also include legacy input / output units such as a ROM 1230 and a keyboard, which may be connected to the input / output controller 1220 via an input / output chip 1240.

[0087] The CPU 1212 may control each unit by operating in accordance with programs stored in the ROM 1230 and the RAM 1214. The graphics controller 1216 may acquire image data generated by the CPU 1212 in a frame buffer or the like provided in the RAM 1214 or itself, and display the image data on the display device 1218.

[0088] The communication interface 1222 may communicate with other electronic devices via a network. The storage device 1224 may store programs and data used by the CPU 1212 in the computer 1200. The DVD drive may read programs or data from a DVD-ROM or the like and provide them to the storage device 1224. The IC card drive may read programs and data from an IC card, and / or write programs and data to the IC card.

[0089] The ROM 1230 may store therein a boot program or the like executed by the computer 1200 at activation, and / or programs dependent on the hardware of the computer 1200. The input / output chip 1240 may also connect various input / output units to the input / output controller 1220 via a USB port, a parallel port, a serial port, a keyboard port, a mouse port, or the like.

[0090] The program can be provided by a computer-readable storage medium such as a DVD-ROM or an IC card. The program can be read from the computer-readable storage medium, installed in the storage device 1224, the RAM 1214, or the ROM 1230, which are 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 can bring about cooperation between the programs and various types of hardware resources. The apparatus or method can be configured to implement the operation or processing of information by accompanying the use of the computer 1200.

[0091] For example, in the case of performing communication between the computer 1200 and an external device, the CPU 1212 can execute a communication program loaded into the RAM 1214 and instruct 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 the transmission data stored in the 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 received data received from the network into the reception buffer area provided on the recording medium, etc.

[0092] 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, a DVD drive (DVD-ROM), an 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.

[0093] 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 can perform 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 the instruction sequence of the 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 files, databases, etc. in the recording medium. For example, in the case where a plurality of entries are stored in the recording medium and each entry has an attribute value of a first attribute associated with an attribute value of a second attribute, the CPU 1212 can search the plurality of entries for an entry in which the attribute value of the first attribute is consistent with a specified condition, read the attribute value of the second attribute stored in the entry, and thereby obtain the attribute value of the second attribute associated with the first attribute that satisfies a predetermined condition.

[0094] The above-described program or software module can be stored in a computer-readable storage medium in or near the computer 1200. Additionally, a recording medium such as a hard disk or RAM provided within a server system connected to a dedicated communication network or the Internet can be used as the computer-readable storage medium, whereby the program can be provided to the computer 1200 via the network.

[0095] In the flowcharts and block diagrams in the present embodiment, the blocks can represent stages of a process of performing operations or "parts" of a device having a function of performing operations. 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).

[0096] The 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 operations specified in the flowchart or block diagram. Examples of the computer-readable storage medium can include electronic storage media, magnetic storage media, optical storage media, electromagnetic storage media, semiconductor storage media, etc. More specific examples of the computer-readable storage medium 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.

[0097] The computer-readable instructions can include either source code or object code described by any combination of one or more programming languages, which programming languages include assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, and object-oriented programming languages such as Smalltalk (registered trademark), JAVA (registered trademark), C++, etc., and conventional program programming languages such as the "C" programming language or similar programming languages.

[0098] 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.

[0099] Next, a second embodiment will be described. In the second embodiment, the same symbols as those in the first embodiment are given to the same elements, components, etc. as those in the first embodiment, and the detailed description thereof is omitted.

[0100] Figure 7 FIG. is a block diagram showing an example of an information processing apparatus 1 according to a second embodiment of the present disclosure. The information processing apparatus 1 according to the present embodiment includes at least an information acquisition unit 10, a chip 20, an anchor chip 30, a driving control unit 40, and an index value acquisition unit 50.

[0101] The chips 20 (20A, 20B, 20C, 20D) are provided for each piece of information acquired by the plurality of information acquisition units 10 (10A, 10B, 10C, 10D). And, an index value is derived based on predetermined information among the plurality of pieces of information. For example, when the information acquired by the information acquisition unit 10A among the plurality of information acquisition units 10 is air resistance (A), the chip 20A calculates the index value (refer to Equation (2)), when the information acquired by the information acquisition unit 10B is road resistance (B), the chip 20B calculates the index value (refer to Equation (3)), when the information acquired by the information acquisition unit 10C is road element (C), the chip 20C calculates the index value (refer to Equation (4)), and when the information acquired by the information acquisition unit 10D is slip coefficient (D), the chip 20D calculates the index value (refer to Equation (5)). Here, in the present embodiment, the chip 20 can perform these derivations every one billionth of a second (nanosecond) by using L6-level computing power.

[0102] The information processing apparatus 1 of the present embodiment can perform driving control of the vehicle 12 based on the index value derived from "other devices" different from the vehicle 12 as the control object. The "other devices" include, for example, other vehicles, which are vehicles equipped with the same information processing apparatus as the information processing apparatus 1 of the present embodiment. Since the other vehicles are equipped with the same information processing apparatus as the information processing apparatus 1, as the structure of the information processing apparatus, they are equipped with the same information acquisition unit and chip as the information acquisition unit 10 and the chip 20 of the present embodiment.

[0103] In addition, the "other devices" include devices configured to be installed on the road and equipped with the information acquisition unit 10 and the chip 20 of the present embodiment (e.g., traffic lights, various signs and billboards, utility poles, street lights, and law enforcement devices). Furthermore, the "other devices" include various devices, components, and structures configured to be installed outside the road and equipped with the information acquisition unit 10 and the chip 20 of the present embodiment (e.g., median strips, guardrails, roadside buildings (such as buildings), airplanes (manned and unmanned drones), artificial satellites, etc.).

[0104] The index value acquisition unit 50 acquires the index values derived from these "other devices".

[0105] The driving control unit 40 executes the driving control of the vehicle 12 based on the index values aggregated by the anchor chip 30 and the index values acquired by the index value acquisition unit 50. This driving control unit 40 can achieve the autonomous driving control of the vehicle. Specifically, the highest probability value of the result occurring in the next nanosecond can be obtained based on multiple index values, and the driving control of the vehicle considering this probability value can be implemented. Here, in the present embodiment, the driving control unit 40 can implement these driving controls every one billionth of a second (nanosecond) by utilizing the computing power of level L6.

[0106] Next, a third embodiment will be described. In the third embodiment, the same reference numerals as those in the first and second embodiments are given to the same elements, components, etc., and the detailed description thereof is omitted.

[0107] Figure 8 An information processing system 202 according to the third embodiment of the present disclosure is schematically shown. The information processing system 202 targets a plurality of vehicles 12 including the present vehicle 12A and other vehicles 12B.

[0108] The present vehicle 12A is equipped with an information processing device 1A. In the third embodiment, the information processing device 1A includes, for example, an information acquisition unit 10A capable of acquiring information on air resistance and an information acquisition unit 10D capable of acquiring information on the slip coefficient. Moreover, as the chip 20, there are a chip 20A that acquires an index value based on the information on air resistance and a chip 20D that acquires an index value based on the information on the slip coefficient. In addition, as the information processing device 1A of the present vehicle 12A, it may also be like Figure 7 the information processing device 1 of the second embodiment shown, and further include an information acquisition unit 10B capable of acquiring information on road resistance, an information acquisition unit 10C capable of acquiring road element information, and chips 20B and 20C that acquire index values based on this information.

[0109] The other vehicle 12B is equipped with an information processing device 1B. This information processing device 1B is the same as Figure 7The information processing device 1 shown also includes, for example, an information acquisition unit 10A capable of acquiring information on air resistance, an information acquisition unit 10B capable of acquiring information on road resistance, an information acquisition unit 10C capable of acquiring road element information, and an information acquisition unit 10D capable of acquiring information on the slip coefficient. In addition, the information processing device 1B includes chips 20A, 20B, 20C, and 20D that obtain index values based on this information.

[0110] The information processing system 202 of the third embodiment includes the information processing device 1A included in the own vehicle 12A, and the information acquisition units 10B and 10C and the chips 20B and 20C included in another vehicle 12B.

[0111] In the information processing system 202 of the third embodiment, the index values of the chips 20B and 20C of the information processing device 1B of another vehicle 12B can be transmitted to the index value acquisition unit 50 of the information processing device 1A of the own vehicle 12A. The transmission of these index values can be performed, for example, by direct communication between the other vehicle 12B and the own vehicle 12A, but can also be performed via Figure 1 the cloud 2 or a server capable of data transfer shown.

[0112] In the information processing device 1A of the own vehicle 12A, although it does not have the information acquisition units 10B and 10C and the chips 20B and 20C, the index value derived from the information on road resistance is obtained from the chip 20B of the information processing device 1B. Similarly, the index value derived from the road element information is obtained from the chip 20C of the information processing device 1B. Therefore, even the information processing device 1A that does not have the information acquisition units 10B and 10C and the chips 20B and 20C can aggregate the index values derived from the information on air resistance, slip coefficient, road resistance, and road elements using the anchor chip 30.

[0113] In the information processing device 1A of the own vehicle 12A, in a configuration that does not have the information acquisition units 10B and 10C and the chips 20B and 20C, the structure is simplified. In addition, as described above, the information processing device 1A of the own vehicle 12A can also be Figure 7The second embodiment shown similarly has information acquisition units 10B, 10C and chips 20B, 20C. In this case, when the information acquisition units 10B, 10C and chips 20B, 20C provided in the information processing device 1B of the other vehicle 12B have higher information acquisition capabilities and index value derivation capabilities than the information acquisition units 10B, 10C and chips 20B, 20C provided in the information processing device 1A of the own vehicle 12A, higher-precision index values can be obtained as the index value of the road resistance and the index value of the road element from the chips 20B, 20C provided in the information processing device 1B of the other vehicle 12B.

[0114] Next, a fourth embodiment will be described. In the fourth embodiment, the same symbols as those in the first embodiment and the second embodiment are given to the same elements, components, etc. as in the first embodiment and the second embodiment, and the detailed description thereof is omitted.

[0115] Figure 9 An information processing system 302 as a fourth embodiment of the present disclosure is schematically shown. The information processing system 302 is directed to a structure including the own vehicle 12A and the monitoring device 12C. The monitoring device 12C is a device for monitoring a road and is, for example, installed on the road.

[0116] The monitoring device 12C, for example, includes an information acquisition unit 10B and an information acquisition unit 10C that can respectively acquire information on road resistance and road elements. In addition, the monitoring device 12C includes a chip 20B and a chip 20C that obtain index values based on this information.

[0117] The information processing system 302 of the fourth embodiment includes the information processing device 1A provided in the own vehicle 12A, the information acquisition units 10B, 10C and the chips 20B, 20C provided in the monitoring device 12C.

[0118] In the information processing system 302 of the fourth embodiment, the index values from the chips 20B and 20C of the monitoring device 12C can be sent to the index value acquisition unit 50 of the information processing device 1A of the own vehicle 12A. The transmission of these index values can be performed, for example, by direct communication between the monitoring device 12C and the own vehicle 12A, but can also be performed via Figure 1 the cloud 2 or a server capable of data transfer, etc.

[0119] In the information processing device 1A of the vehicle 12A, although it does not have the information acquisition units 10B, 10C and the chips 20B, 20C, the index value derived from the information on the road resistance is obtained from the chip 20B of the monitoring device 12C. In addition, the index value derived from the road element information is also obtained from the chip 20C of the monitoring device 12C. Therefore, even the information processing device 1A that does not have the information acquisition units 10B, 10B and the chips 20B, 20C can aggregate the index values derived from the information on air resistance, slip coefficient, road resistance and road elements by the anchor chip 30.

[0120] In the information processing device 1A of the vehicle 12A, it does not have the information acquisition units 10C, 10D and the chips 20C, 20D, achieving a simplified structure. In addition, in the fourth embodiment, the information processing device 1A of the vehicle 12A may also have a structure including the information acquisition unit 10B capable of acquiring the information on road resistance, the information acquisition unit 10C capable of acquiring the road element information, and the chips 20B and 20C for obtaining the index value based on these information. In this case, when the information acquisition units 10B, 10C and the chips 20B, 20C of the monitoring device 12C have a higher information acquisition ability and index value derivation ability than those of the information processing device 1A of the vehicle 12A, higher-precision index values can be obtained from the chips 20B, 20C of the monitoring device 12C as the index value of the road resistance and the index value of the road elements.

[0121] As the monitoring device 12C of the fourth embodiment, for example, it includes a structure configured to be installed on the road and on various road devices (such as traffic lights, various signs, billboards, utility poles, street lights and law enforcement devices) equipped with the information acquisition unit 10 and the chip 20 with the disclosed technology. Furthermore, as the monitoring device 12C, it is not limited to the monitoring device installed on the road, but also includes a structure configured to be installed outside the road and on various devices, components and structures (such as the median strip, guardrail, roadside buildings (buildings, etc.), aircraft (manned aircraft and unmanned drones), artificial satellites, etc.) equipped with the information acquisition unit 10 and the chip 20 with the disclosed technology. In short, as the monitoring device, as long as it is a device for monitoring the road, its installation location and installation form are not limited. If it is a monitoring device installed on the road, there is a high possibility of monitoring the road at a position closer to the road as the monitoring object compared with the monitoring device installed outside the road.

[0122] Next, a fifth embodiment will be described. In the fifth embodiment, the same symbols as those in the first to fourth embodiments are given to the same elements, components, etc. as those in the first to fourth embodiments, and detailed descriptions thereof are omitted.

[0123] Figure 10 An information processing system 402 as a fifth embodiment of the present disclosure is schematically shown. The information processing system 402 of the fifth embodiment targets a structure including the own vehicle 12A, other vehicles 12B, and a monitoring device 12C. The internal structure of the own vehicle 12A is, for example, the same as that in the third and fourth embodiments. The internal structure of the other vehicles 12B is, for example, the same as that in the third embodiment. The internal structure of the monitoring device 12C is, for example, the same as that in the fourth embodiment.

[0124] In the information processing system 402 of the fifth embodiment, in the information processing device 1A of the own vehicle 12A (refer to Figure 8 ), the information acquisition units 10B and 10C and the chips 20B and 20C are not provided. However, the index value derived from the information on the road resistance is obtained from the chip 20B of the information processing device 1B of the other vehicle 12B (refer to Figure 8 ) or the monitoring device 12C (refer to Figure 9 ). In addition, the index value derived from the road element information is similarly obtained from the chip 20C of the information processing device 1B of the other vehicle 12B or the monitoring device 12C. Therefore, even the information processing device 1A that does not have the information acquisition units 10B and 10C and the chips 20B and 20C can use the anchor chip 30 to aggregate the index values derived from the information on air resistance, slip coefficient, road resistance, and road elements.

[0125] In the information processing system 402 of the fifth embodiment, for example, it is possible to switch whether to obtain the index value derived from the information on the road resistance from the chip 20B of either the information processing device 1B of the other vehicle 12B or the monitoring device 12C according to the situation. As an example, it can be configured to obtain from the chip 20B of the other vehicle 12B and the monitoring device 12C that is relatively closer to the own vehicle 12A in terms of relative position. For the index value derived from the road element information, it can also be configured to obtain from the chip 20C of the other vehicle 12B and the monitoring device 12C that is relatively closer to the own vehicle 12A in terms of relative position.

[0126] In addition, in each of the third to fifth embodiments, regarding which chip 20 calculates which index value, it is not limited to one chip 20 calculating one index. For example, in the information processing device 1B, when it is known at the design stage that one chip 20 can calculate both the index value regarding road resistance and the index value regarding road elements, these index values can be calculated by one chip 20 (e.g., chip 20C).

[0127] In addition, in each of the third to fifth embodiments, the driving control unit 40 can also implement driving control every one billionth of a second (nanosecond) by utilizing the computing power of L6 level. For example, in the information processing device 1A, the index values sent from the chips 20B and 20C of the information processing device 1B of the other vehicle 12B are aggregated by the anchor chip 30, and then the driving control unit 40 executes the driving control of the vehicle 12 based on the index values. In this case, the driving control is implemented every one billionth of a second (nanosecond).

[0128] As described above, the present disclosure has been illustrated using the embodiments, but the technical scope of the present disclosure is not limited to the scope described in the above embodiments. It is obvious to those skilled in the art that various changes or improvements can be made to the above embodiments. The modes incorporating such changes or improvements can also be included in the technical scope of the present disclosure, as is obvious from the description of the claims.

[0129] 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 processes in the claims, the specification, and the drawings, even if they are described using "first", "next", etc. for convenience, it does not mean that they must be implemented in that order.

[0130] The disclosures of Japanese Patent Application No. 2022-182132 filed on November 14, 2022, Japanese Patent Application No. 2022-206068 filed on December 22, 2022, Japanese Patent Application No. 2022-206069 filed on December 22, 2022, and Japanese Patent Application No. 2022-206070 filed on December 22, 2022 are incorporated herein by reference in their entirety.

[0131] All the documents, patent applications, and technical standards described in this specification are incorporated herein by reference to the same extent as if each document, patent application, and technical standard were specifically and individually described as being incorporated by reference.

Claims

1. An information processing device, comprising: an information acquisition unit capable of acquiring a plurality of pieces of information related to a vehicle; a plurality of chips provided for each of the plurality of pieces of information, and deriving an index value based on predetermined information among the plurality of pieces of information; and a driving control unit that performs driving control of the vehicle based on the plurality of index values.

2. The information processing device according to claim 1, wherein the chip derives the index value through multivariate analysis using integration method with deep learning.

3. The information processing device according to claim 1, wherein the information processing device includes an anchoring chip that aggregates the plurality of index values derived by the plurality of chips and derives an index value, and the driving control unit performs driving control of the vehicle based on the index value aggregated by the anchoring chip.

4. The information processing device according to claim 3, wherein the information acquisition unit acquires the plurality of pieces of information in units of one billionth of a second, the chip uses the plurality of pieces of information to perform derivation of the index value in units of one billionth of a second, the anchoring chip uses the index value to perform aggregation of the index value in units of one billionth of a second, and the driving control unit uses the aggregated index value to perform driving control of the vehicle in units of one billionth of a second.

5. The information processing device according to claim 1, wherein in response to the load of the plurality of chips, other chips are made to derive index values.

6. The information processing device according to claim 1, wherein in response to the load of the chip, the processing ability of the process of deriving the index value by the chip is changed.

7. The information processing device according to claim 3, wherein the anchoring chip aggregates the plurality of index values derived by the plurality of chips based on weights predetermined for each chip.

8. The information processing device according to claim 7, wherein the weights are obtained by inputting the information into a learned model.

9. The information processing device according to claim 3, wherein the anchoring chip aggregates the plurality of index values derived by the plurality of chips based on weights predetermined for each type of the information for which the chip calculates the index value.

10. The information processing device according to claim 9, wherein the types of the information include information with high priority and information with low priority, and the anchoring chip aggregates the plurality of index values derived by the plurality of chips based on weights predetermined for each priority of the information for which the chip calculates the index value.

11. The information processing device according to claim 5, wherein in a manner that the loads of the plurality of chips become equal, other chips are made to share the process of deriving the index value.

12. The information processing device according to claim 5, wherein according to the heat generation condition of the chip, other chips are made to share the process of deriving the index value.

13. The information processing device according to claim 5, wherein When the remaining power of the battery provided in the vehicle drops below a predetermined threshold, the processing capabilities of the multiple chips are reduced to conserve the battery.

14. The information processing device according to claim 13, wherein the user can select whether to reduce the processing capabilities.

15. An information processing device, comprising: one or more information acquisition units capable of acquiring information related to a vehicle; one or more chips, each provided for each of the one or more pieces of information, and deriving an index value based on predetermined information among the multiple pieces of information; an index value acquisition unit that acquires the index value from the chips provided in other devices different from the vehicle; and a driving control unit that performs driving control of the vehicle based on the multiple index values derived from the vehicle and the other devices.

16. The information processing device according to claim 15, wherein the chip derives the index value through multivariate analysis using integration method with deep learning.

17. The information processing device according to claim 15, wherein the information processing device includes an anchoring chip that aggregates the multiple index values derived by the multiple chips to derive an index value, and the driving control unit performs driving control of the vehicle based on the index value aggregated by the anchoring chip.

18. The information processing device according to claim 15, wherein in response to the load of the multiple chips, other chips are made to derive index values.

19. The information processing device according to claim 15, wherein in response to the load of the chip, the processing capabilities of the process of making the chip derive an index value are changed.

20. A program for causing a computer to function as the information processing device according to any one of claims 1 to 19.

21. An information processing system, comprising: the information processing device according to any one of claims 15 to 19 provided in a vehicle; one or more information acquisition units provided in other devices different from the vehicle, capable of acquiring information related to the vehicle; and one or more chips provided in the other devices for each of the one or more pieces of information, and deriving an index value based on predetermined information among the multiple pieces of information.

22. The information processing system according to claim 21, wherein the other device includes another vehicle, which is a vehicle different from the vehicle.

23. The information processing system according to claim 21, wherein the other device includes a monitoring device for monitoring a road.

24. The information processing system according to claim 23, wherein as the monitoring device, it includes a monitoring device installed on the road.

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