Information processing equipment, vehicles, and programs

Abstract

To provide an information processing device, a vehicle and a program that are able to analyze, during travel, influence of factors such as air resistance and / or friction that occur when an autonomous vehicle travels on a road.SOLUTION: An information processing device 1 of the present disclosure comprises: an information acquisition unit 10 that can acquire a plurality of information items relating to a vehicle; an inference unit 20 that uses deep learning to infer a plurality of index values from the plurality of information items acquired by the information acquisition unit; and an operation control unit 30 that performs operation control for the vehicle on the basis of the plurality of index values.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] The present disclosure relates to an information processing apparatus, a vehicle, and a program that include multivariate analysis using deep learning. Background Art

[0002] Patent Document 1 describes a vehicle having an automatic driving function. Prior Art Documents Patent Documents

[0003] Patent Document 1 Japanese Unexamined Patent Publication No. 2022-035198 Summary of the Invention Problems to be Solved by the Invention

[0004] In conventional autonomous driving vehicles, it has been difficult to analyze the influence of factors such as air resistance and friction generated when traveling on a road while the vehicle is traveling.

[0005] The technology of the present disclosure provides an information processing apparatus, a vehicle, and a program that can analyze the influence of factors such as air resistance and / or friction generated when an autonomous driving vehicle travels on a road while the vehicle is traveling.

[0006] In addition, the technology of the present disclosure provides an information processing apparatus, a vehicle, and a program that can realize high-precision automatic driving in consideration of the actual environment until the vehicle travels and reaches the destination. Means for Solving the Problems

[0007] According to one embodiment of the present disclosure, an information processing device is provided that includes an information acquisition unit capable of acquiring a plurality of pieces of information related to a vehicle, an inference unit that uses deep learning to infer a plurality of index values ​​from the plurality of pieces of information acquired by the information acquisition unit, and a driving control unit that performs driving control of the vehicle based on the plurality of index values.

[0008] According to one embodiment of the present disclosure, in the information processing device described above, the inference unit infers the plurality of index values ​​from the plurality of information by multivariate analysis using the integration method with deep learning.

[0009] According to one embodiment of the present disclosure, in the information processing apparatus, the information acquisition unit acquires the plurality of pieces of information in units of one billionth of a second, and the inference unit and the driving control unit use the plurality of pieces of information acquired in units of one billionth of a second to perform inference of the plurality of index values ​​and driving control of the vehicle in units of one billionth of a second.

[0010] According to one embodiment of the present disclosure, the information processing device further includes a strategy setting unit for setting a driving strategy for the vehicle until it reaches a destination, the driving strategy including at least one theoretical value of the optimal route to the destination, driving speed, tilt, and braking, and the driving control unit includes a strategy updating unit for updating the driving strategy based on the difference between the plurality of index values ​​and the theoretical values.

[0011] According to one embodiment of the present disclosure, in the information processing device, the information acquisition unit includes a sensor provided on the underside of the vehicle that can detect the temperature, material, and tilt of the ground under which the vehicle is traveling.

[0012] According to one embodiment of the present disclosure, an information processing device is provided that is applied to each of a plurality of vehicles, comprising: an information acquisition unit capable of acquiring a plurality of pieces of information related to the vehicle; an inference unit that infers a plurality of index values ​​from the plurality of pieces of information acquired by the information acquisition unit using a deep learning model; a strategy setting unit that sets a driving strategy for the vehicle until it reaches a destination; a strategy update unit that updates the driving strategy based on the driving strategy set by the strategy setting unit and the plurality of index values; and a driving control unit that performs driving control of the vehicle in accordance with the driving strategy updated by the strategy update unit, wherein the strategy update unit updates the driving strategy set by the strategy setting unit based on the plurality of index values ​​inferred by the inference unit of a second vehicle which is ahead of the first vehicle in the plurality of vehicles in terms of the destination.

[0013] According to one embodiment of the present disclosure, in the information processing device, the strategy update unit acquires the plurality of index values ​​used for updating the driving strategy before the first vehicle reaches the destination and while the first vehicle is driving.

[0014] According to one embodiment of the present disclosure, in the information processing device, the strategy update unit acquires the plurality of index values ​​used for updating the driving strategy before the second vehicle reaches the destination.

[0015] According to one embodiment of the present disclosure, in the information processing device, the driving strategy includes a plurality of theoretical values, the plurality of theoretical values ​​including a theoretical value of the route the vehicle takes to reach the destination, a theoretical value of the vehicle's speed to reach the destination, a theoretical value of the slope of the ground to reach the destination, and / or a theoretical value of the brake control value to reach the destination, and the strategy update unit updates the driving strategy by updating the plurality of theoretical values ​​based on the result of comparing the plurality of theoretical values ​​with the plurality of index values.

[0016] According to an embodiment of the present disclosure, the information processing apparatus described above includes a notification unit that issues a notification when a degree of difference between the theoretical value and the index value exceeds a threshold value.

[0017] According to an embodiment of the present disclosure, in the information processing apparatus described above, the strategy updating unit acquires the plurality of index values from a data management apparatus that collects and manages the plurality of index values for at least one vehicle among the plurality of vehicles, and updates the traveling strategy based on the plurality of index values acquired from the data management apparatus.

[0018] According to an embodiment of the present disclosure, in the information processing apparatus described above, the inference unit infers the plurality of index values from the plurality of pieces of information by multivariate analysis based on an integration method using the deep learning model.

[0019] According to an embodiment of the present disclosure, in the information processing apparatus described above, the information acquisition unit includes: a temperature sensor capable of detecting a temperature of the ground on which the vehicle travels; a material sensor capable of detecting a material of the ground; and a slope sensor capable of detecting a slope of the ground.

[0020] According to an embodiment of the present disclosure, there is provided a vehicle including the information processing apparatus described above.

[0021] According to an embodiment of the present disclosure, there is provided a program for causing a computer to function as the information processing apparatus described above.

[0022] Note that the above summary of the invention does not list all of the necessary features of the present invention, and subcombinations of these feature groups may also constitute the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] [Figure 1] It is a schematic diagram illustrating an example of a vehicle equipped with a Central Brain. [Figure 2]It is a block diagram showing an example of an information processing apparatus according to the first embodiment of the present disclosure. [Figure 3] It is a block diagram showing an example of an information processing apparatus according to the second embodiment of the present disclosure. [Figure 4] It is a conceptual diagram showing an example of the configuration of an inference unit included in an information processing apparatus according to the third embodiment of the present disclosure. [Figure 5] It is a conceptual diagram showing an example of processing contents of an information acquisition unit, an inference unit, and a driving control unit included in an information processing apparatus according to the third embodiment of the present disclosure. [Figure 6] It is a flowchart showing an example of the flow of travel strategy update processing according to the third embodiment of the present disclosure. [Figure 7] It is a conceptual diagram showing a modified example of the configuration of an information processing apparatus according to the third embodiment of the present disclosure. [Figure 8] It is a block diagram schematically showing an example of the hardware configuration of a computer that functions as an information processing apparatus. DETAILED DESCRIPTION OF EMBODIMENTS

[0024] Hereinafter, the present invention will be described through embodiments of the invention, but the following embodiments do not limit the invention according to the claims. Furthermore, not all combinations of features described in the embodiments are necessarily essential to the solution of the invention.

[0025] The information processing apparatus of the present disclosure may obtain index values required for driving control with high accuracy based on a large amount of information related to vehicle control. Therefore, the information processing apparatus of the present disclosure may be at least partially mounted on a vehicle to realize vehicle control.

[0026] Furthermore, the information processing device disclosed herein can realize Autonomous Driving in real time based on data obtained from Level 6 AI / multivariate analysis / goal seeking / strategy planning / optimal probability solution / optimal speed solution / optimal course management / various sensor inputs at the edge, and can provide a driving system that is adjusted based on the delta optimal solution.

[0027] Figure 1 is a schematic diagram showing an example of a vehicle equipped with a Central Brain. The Central Brain may be an example of the information processing device 1 according to this embodiment. As shown in Figure 1, the Central Brain may have multiple Gateways connected in a communicative manner. The Central Brain according to this embodiment can achieve Level 6 autonomous driving based on multiple pieces of information acquired via the Gateways.

[0028] "Level 6" represents an autonomous driving level, and is even higher than Level 5, which represents fully autonomous driving. Although Level 5 represents fully autonomous driving, it is equivalent to human driving, and there is still a probability of accidents occurring. Level 6 represents a level higher than Level 5, and is equivalent to a level where the probability of accidents occurs is lower than at Level 5.

[0029] The computing power required to achieve Level 6 (i.e., the computing power used to implement Level 6) is approximately 1000 times greater than the computing power required to achieve Level 5 (i.e., the computing power used to implement Level 5). Therefore, high-performance operational control that was not possible at Level 5 is achievable.

[0030] Figure 2 is a block diagram showing an example of an information processing device according to the first embodiment of the present disclosure. The information processing device 1 according to this embodiment includes at least an information acquisition unit 10 capable of acquiring a plurality of pieces of information related to a vehicle, an inference unit 20 that infers a plurality of index values ​​from the plurality of pieces of information acquired by the information acquisition unit 10, and a driving control unit 30 that performs driving control of the vehicle based on the plurality of index values.

[0031] The information acquisition unit 10 is capable of acquiring various information related to the vehicle. This information acquisition unit 10 may include, for example, communication means for acquiring information that can be obtained via a network from sensors attached to various parts of the vehicle or from servers (not shown). Examples of sensors included in the information acquisition unit 10 include radar, LiDAR, high-resolution, telephoto, ultra-wide-angle, 360-degree, and high-performance cameras, vision recognition, micro-sound sensors, ultrasonic sensors, vibration sensors, infrared sensors, ultraviolet sensors, electromagnetic wave sensors, temperature sensors, humidity sensors, spot AI weather forecasting, material sensors, tilt sensors, high-precision multi-channel GPS, and / or low-altitude satellite information. Alternatively, it may include long-tail incident AI data. Long-tail incident AI data refers to trip data from vehicles equipped with Level 5 (i.e., vehicles equipped with devices capable of achieving Level 5 computing power (here, as an example, the information processing device 1)).

[0032] Information that can be acquired by multiple types of sensors includes the temperature and material of the ground (e.g., road), ambient temperature, ground tilt, road freezing conditions and moisture content, the material and wear status of each tire, air pressure, road width, whether overtaking is prohibited, presence or absence of oncoming vehicles, vehicle type information of vehicles in front and behind, cruising status of those vehicles, and / or surrounding conditions (birds, animals, soccer balls, accident vehicles, earthquakes, fires, wind, typhoons, heavy rain, light rain, blizzards, and / or fog). In this embodiment, by utilizing Level 6 computing power, these detections can be performed every one billionth of a second (nanosecond).

[0033] It should be noted that the information acquisition unit 10 described above includes a vehicle underbody sensor located beneath the vehicle that can detect the temperature, material, and tilt of the ground under which the vehicle is traveling. This vehicle underbody sensor can be used to perform independent smart tilt.

[0034] The inference unit 20 may be capable of inferring indexed values ​​related to vehicle control from multiple pieces of information acquired by the information acquisition unit 10 using machine learning, or more specifically, deep learning. In other words, the inference unit 20 can be composed of AI (Artificial Intelligence).

[0035] The inference unit 20 uses the computing power used to achieve Level 6 (hereinafter also referred to as "Level 6 computing power") to perform multivariate analysis using the integral method shown in equation (1) below (see, for example, equation (2)) on nanosecond-by-nanosecond data and long-tail incident AI data collected by the information acquisition unit 10 from a large group of sensors, etc., thereby obtaining accurate index values. More specifically, by calculating the integral values ​​of the delta values ​​of various Ultra High Resolution sensors using Level 6 computing power, the inference unit 20 can obtain the indexed values ​​of each variable at the edge level and in real time, and obtain the results that occur in the next nanosecond (i.e., the indexed values ​​of each variable, which are the indexed values) with the highest probability. To achieve this, for example, the delta value (for example, the change in a small time interval) of a function that can identify each variable (for example, multiple pieces of information acquired by the information acquisition unit 10), such as air resistance, road resistance, road elements (for example, debris), and slip coefficient, is integrated over time and input into the deep learning model of the inference unit 20 (for example, a trained model obtained by performing deep learning on a neural network). The deep learning model of the inference unit 20 outputs an index value (for example, the index value of the highest confidence level (i.e., evaluation value)) corresponding to the input integral value. The output of the index value is done in nanoseconds.

[0036]

number

[0037]

number

[0038] For example, in equation (1), “f(A)” is a simplified representation of a function that shows the behavior of various variables such as air resistance, road resistance, road elements (e.g., debris), and slip coefficient. Also, for example, equation (1) is an equation that shows the time integral v of “f(A)” from time a to time b. In the equation, DL stands for deep learning (for example, a deep learning model optimized by performing deep learning on a neural network), and dA n / dt represents the delta value of f(A,B,C,D,···,N), where A,B,C,D,···,N represent air resistance, road resistance, road elements (e.g., debris), and slip coefficient, and f(A,B,C,D,···,N) represents a function that shows the behavior of A,B,C,D,···,N, V n This indicates the value (i.e., the index value) output from a deep learning model that has been optimized by performing deep learning on a neural network.

[0039] Here, we have given an example of a configuration in which the integral value obtained by integrating the delta value of a function over time is input to the deep learning model of the inference unit 20, but this is merely one example. For example, the integral value obtained by integrating the delta value of a function that represents the behavior of each variable such as air resistance, road resistance, road elements, and slip coefficient over time (for example, the result that will occur in the next nanosecond) may be inferred by the deep learning model of the inference unit 20, and as an inference result, the integral value with the highest confidence level (i.e., evaluation value) may be obtained by the inference unit 20 every nanosecond.

[0040] Furthermore, while examples of inputting integral values ​​into a deep learning model and outputting integral values ​​from a deep learning model are given here, these are merely examples, and the technology of this disclosure can be implemented without using integral values. For example, deep learning may be performed on a neural network using training data where values ​​corresponding to A, B, C, D, ..., N are used as example data and values ​​corresponding to at least one index value (for example, the result that occurs in the next nanosecond) are used as ground truth data, so that at least one index value is inferred by an optimized deep learning model.

[0041] The indexed values ​​(i.e., index values) of each variable obtained by the inference unit 20 can be further refined by increasing the number of Deep Learning iterations. For example, more accurate index values ​​can be calculated using vast amounts of data such as tire and motor rotation, steering angle, road material, weather, the effects of debris and quadratic deceleration, slippage, and steering and speed control methods for balance loss and recovery, as well as long-tail incident AI data.

[0042] The driving control unit 30 may perform vehicle driving control based on a plurality of index values ​​identified by the inference unit 20. This driving control unit 30 may be capable of realizing automatic vehicle driving control. Specifically, the result occurring in the next nanosecond can be obtained from the plurality of index values ​​with the highest probability value, and vehicle driving control can be performed taking this probability value into consideration. That is, the driving control unit 30 may obtain the index value with the highest confidence level (i.e., evaluation value) as the result occurring in the next nanosecond from the plurality of index values, and perform vehicle driving control according to the obtained index value.

[0043] According to the information processing device 1 with the configuration described above, it is possible to perform information analysis and inference using Level 6 computing power, which is far greater than Level 5 computing power, thus enabling a level of sophistication incomparable to before. This makes it possible to control vehicles for safe autonomous driving. Furthermore, the multivariate analysis by the above AI can create a value difference of 1000 times compared to the world of Level 5.

[0044] Figure 3 is a block diagram showing an example of an information processing device according to a second embodiment of the present disclosure. The information processing device 1A according to this embodiment differs from the information processing device 1 according to the first embodiment described above in that, in addition to the information processing device 1, it includes a strategy setting unit 40 for setting a driving strategy until the vehicle reaches its destination.

[0045] The strategy setting unit 40 may set a driving strategy from the current location to the destination based on destination information entered by the vehicle occupants, traffic information between the current location and the destination, etc. In doing so, it may take into account the information at the time the strategy setting is calculated, that is, the data currently acquired by the information acquisition unit 10. This is to calculate a more realistic theoretical value by taking into account the surrounding conditions at that moment, rather than just calculating a simple route to the destination. The driving strategy may consist of the optimal route to the destination (strategic route), driving speed, tilt, and at least one theoretical value of braking (i.e., the theoretical value of driving speed, the theoretical value of tilt, and / or the theoretical value of brake control value (in other words, the parameter that controls the braking system)). Preferably, the driving strategy can consist of all of the theoretical values ​​of the optimal route, driving speed, tilt, and braking mentioned above (i.e., the theoretical value of driving speed, the theoretical value of tilt, and the theoretical value of brake control value).

[0046] Multiple theoretical values ​​constituting the driving strategy set in the strategy setting unit 40 can be used for automatic driving control in the driving control unit 30. In addition, it is preferable that the driving control unit 30 includes a strategy update unit 31 that can update the driving strategy based on the difference between multiple index values ​​inferred by the inference unit 20 (for example, an index value indicating driving speed, an index value indicating tilt, and an index value indicating brake control value) and each theoretical value set in the strategy setting unit 40 (for example, a theoretical value for driving speed, a theoretical value for tilt, and a theoretical value for brake control value).

[0047] The index value inferred by the inference unit 20 is based on information acquired while the vehicle is in motion, specifically detected during actual driving. For example, it is inferred based on the coefficient of friction. Therefore, by considering this index value, the strategy update unit 31 can respond to the moment-to-moment changes that occur when traveling along the strategic route. Specifically, the strategy update unit 31 can derive the optimal solution again and revise the strategic route by calculating the difference (delta value) between the theoretical value and the index value included in the driving strategy.

[0048] The first example of an optimal solution is an index value used in place of a theoretical value. The second example of an optimal solution is an adjusted index value used in place of a theoretical value. The third example of an optimal solution is a solution obtained by performing regression analysis using at least one theoretical value and at least one index value. The fourth example of an optimal solution is a statistical value (e.g., median and / or mean) obtained from the theoretical value and the index value. Whether the strategy update unit 31 derives an optimal solution from the first to fourth examples may be determined, for example, according to the magnitude of the difference. Note that the optimal solution may be obtained by any method other than the first to fourth examples.

[0049] In this way, the strategy update unit 31 derives the optimal solution again, making it possible to achieve, for example, automated driving control that is just within the limits of not slipping. That is, if automated driving control is performed according to theoretical values ​​only, the automated driving control will result in the vehicle slipping, and if automated driving control is performed according to index values ​​only, automated driving control will be performed with a safety margin to prevent slipping. In this case, the index value adjusted by subtracting the margin is used as the theoretical value by the driving control unit 30, thereby achieving automated driving control that is just within the limits of not slipping. Furthermore, since the computational power of Level 6 described above can be used even during such update processing, it becomes possible to correct and fine-tune in units of one billionth of a second, enabling more precise driving control.

[0050] In addition, if the information acquisition unit 10 has the aforementioned under-vehicle sensor, this sensor also detects the temperature and material of the ground, making it possible to respond to the moment-to-moment changes when traveling along the strategic route. When calculating the driving course included in the driving strategy, independent smart tilt can also be implemented. Furthermore, even if other information is detected (flying tires, debris, animals, etc.), by responding to the moment-to-moment changes when traveling along the strategic route, the optimal driving course can be recalculated at each moment, enabling optimal course management.

[0051] Figure 4 is a conceptual diagram showing an example of the configuration of the information processing device 1B according to the third embodiment of this disclosure.

[0052] The information processing device 1B is installed in multiple vehicles 54 (see also Figure 5). Similar to the first embodiment described above, the information processing device 1B includes an information acquisition unit 10, an inference unit 20, a driving control unit 30, and a strategy setting unit 40. The driving control unit 30 has a strategy update unit 31. In the example shown in Figure 4, the strategy update unit 31 is shown as part of the driving control unit 30, but this is merely an example, and the strategy update unit 31 may be provided outside the driving control unit 30.

[0053] The information acquisition unit 10 acquires environmental information 56 in the same manner as in the first embodiment described above. The environmental information 56 includes information such as the driving route 56A, which is the route the vehicle 54 travels (for example, the route the vehicle 54 is traveling on, defined by coordinates), the driving speed 56B, which is the speed at which the vehicle 54 is traveling, the ground inclination 56C, which corresponds to the tilt mentioned in the first embodiment, the brake control value 56D (in other words, the parameters that control the brakes), the air resistance 56E, the road resistance 56F, and the road elements 56G.

[0054] The inference unit 20 has a deep learning model 20A. The deep learning model 20A is a pre-trained model optimized by performing deep learning on a neural network using multiple training data sets. The inference unit 20 uses the deep learning model 20A to infer multiple index values ​​from the environmental information 56.

[0055] The training data used for deep learning in the deep learning model 20A is a dataset containing example data and ground truth data (i.e., annotations) for the example data. The example data is based on the assumption of environmental information 56, and the ground truth data is based on the assumption of multiple index values ​​58.

[0056] The inference unit 20 inputs the environmental information 56 acquired by the information acquisition unit 10 to the deep learning model 20A. As a result, the deep learning model 20A outputs multiple index values ​​58 corresponding to the environmental information 56 (i.e., multiple control values ​​used for the automatic driving control of the vehicle 54).

[0057] The strategy update unit 31 obtains multiple index values ​​58 output from the deep learning model 20A. The strategy setting unit 40 sets the driving strategy 60 for the vehicle 54 to reach its destination. Here, setting the driving strategy 60 means, for example, the process of assigning the driving strategy 60 to the strategy update unit 31.

[0058] The driving strategy 60 includes a plurality of theoretical values ​​62. The plurality of theoretical values ​​62 include a theoretical value for the route the vehicle 54 takes to reach its destination (for example, a route assumed to be the route the vehicle 54 will take, defined by coordinates), a theoretical value for the driving speed the vehicle 54 will take to reach its destination, a theoretical value for the slope of the ground the vehicle 54 will take to reach its destination, and a theoretical value for the brake control value the vehicle 54 will take to reach its destination.

[0059] The strategy update unit 31 updates the driving strategy 60 based on the driving strategy 60 set by the strategy setting unit 40 and the multiple index values ​​58 output from the inference unit 20. For example, the strategy update unit 31 updates the driving strategy 60 by updating the multiple theoretical values ​​62 based on the difference 64 between the multiple theoretical values ​​62 included in the driving strategy 60 and the multiple index values ​​58.

[0060] Updating the theoretical value 62 means, in other words, deriving the optimal solution. The first example of the optimal solution is the index value 58 used in place of the theoretical value 62. The second example of the optimal solution is the adjusted index value 58 used in place of the theoretical value 62. The third example of the optimal solution is the solution obtained by performing regression analysis using at least one theoretical value 62 and at least one index value 58. The fourth example of the optimal solution is the statistical value (e.g., median and / or mean) obtained from the theoretical value 62 and the index value 58. Whether the strategy update unit 31 derives the optimal solution from the first to fourth examples may be determined, for example, according to the magnitude of the difference 64. Note that the optimal solution may be obtained by other methods, not limited to the first to fourth examples.

[0061] Here, the difference 64 between multiple theoretical values ​​62 and multiple index values ​​58 refers to the difference between the theoretical value 62 and the index value 58 for each of the multiple items (for example, the route the vehicle 54 takes to reach its destination, the speed at which the vehicle 54 travels to reach its destination, the slope of the ground until the vehicle 54 reaches its destination, and the brake control value until the vehicle 54 reaches its destination, etc.).

[0062] Note that difference 64 is an example of "the result of comparing multiple theoretical values ​​and multiple index values" related to the technology disclosed herein. Here, difference 64 is given, but this is merely an example, and it could be, for example, the ratio of one of the theoretical value 62 and the index value 58 to the other, or any value that allows the degree of difference between multiple theoretical values ​​and multiple index values ​​to be identified.

[0063] The driving control unit 30 performs driving control of the vehicle 54 according to the driving strategy 60 (for example, a set of updated theoretical values ​​62) updated by the strategy update unit 31.

[0064] Figure 5 is a conceptual diagram showing an example of how the driving strategy 60 of a first vehicle 54A, one of a plurality of vehicles 54 equipped with the information processing device 1B according to the third embodiment of the present disclosure, is updated based on a plurality of index values ​​58 inferred by the second vehicle 54B.

[0065] Vehicle 1 54A and vehicle 2 54B are traveling toward the same destination, and vehicle 2 54B is ahead of vehicle 1 54A in reaching the destination. In other words, vehicle 2 54B will reach the destination before vehicle 1 54A.

[0066] Data relating to multiple vehicles 54 is collected and managed by a data management device 68. The data management device 68 is wirelessly connected to multiple information processing devices 1B installed in the multiple vehicles 54. An example of a data management device 68 is a server.

[0067] The data management device 68 constructs a database 70 based on various information obtained from the information processing device 1B of each vehicle 54. The database 70 has a vehicle identifier 72, which is an identifier that can identify a vehicle 54, and a plurality of index values ​​58. In the database 70, for each vehicle 54, the vehicle identifier 72 and the plurality of index values ​​58 are associated (i.e., the plurality of index values ​​58 inferred by the inference unit 20 included in the information processing device 1B of the vehicle 54 identified from the corresponding vehicle identifier 72).

[0068] Before the first vehicle 54A reaches its destination and while the first vehicle 54A is in motion, the strategy update unit 31 (hereinafter referred to as "strategy update unit 31 of the first vehicle 54A") included in the information processing device 1B of the first vehicle 54A acquires a plurality of index values ​​58 inferred by the inference unit 20 (hereinafter referred to as "inference unit 20 of the second vehicle 54B") included in the information processing device 1B of the second vehicle 54B from the data management device 68.

[0069] For example, the data management device 68, in response to a request from the strategy update unit 31 of the first vehicle 54A, retrieves multiple index values ​​58 corresponding to the second vehicle 54B from the database 70 and transmits the multiple index values ​​58 retrieved from the database 70 to the strategy update unit 31 of the first vehicle 54A. The strategy update unit 31 of the first vehicle 54A receives the multiple index values ​​58 transmitted from the data management device 68 and updates the driving strategy 60 based on the received multiple index values ​​58.

[0070] For example, updating the driving strategy 60 is achieved by updating multiple theoretical values ​​62 based on the result of comparing multiple theoretical values ​​62 included in the driving strategy 60 with multiple received index values ​​58. Here again, the updating of the theoretical values ​​62 can be performed in the same manner as in the first to fourth examples described above.

[0071] Figure 6 is a flowchart showing an example of the flow of the driving strategy update process performed by the information processing device 1B of the first vehicle 54A.

[0072] In the driving strategy update process shown in Figure 6, in step ST10, the strategy setting unit 40 sets the driving strategy 60 to the strategy update unit 31. After the process in step ST10 is executed, the driving strategy update process proceeds to step ST12.

[0073] In step ST12, the strategy update unit 31 determines whether or not the timing specified in nanoseconds has arrived (for example, whether or not one billionth of a second has elapsed). If the timing specified in nanoseconds has not arrived in step ST12, the determination is denied and the driving strategy update process proceeds to step ST32. If the timing specified in nanoseconds has arrived in step ST12, the determination is affirmed and the driving strategy update process proceeds to step ST14.

[0074] In step ST14, the strategy update unit 31 determines whether or not the timing for acquiring second vehicle information has arrived. The timing for acquiring second vehicle information refers to the timing when the strategy update unit 31 acquires multiple index values ​​58 inferred by the inference unit 20 of the second vehicle 54B. A first example of the timing for acquiring second vehicle information is the timing when the condition that the index value 58 related to the second vehicle 54B, which is stored in the database 70, has been updated is satisfied. A second example of the timing for acquiring second vehicle information is the timing when the condition that a predetermined time (for example, several seconds) has elapsed since the processing in step ST12 has been executed is satisfied.

[0075] In step ST14, if the timing for acquiring the second vehicle information has not yet arrived, the determination is denied, and the driving strategy update process proceeds to step ST20. In step ST14, if the timing for acquiring the second vehicle information has arrived, the determination is affirmed, and the driving strategy update process proceeds to step ST16.

[0076] In step ST16, the strategy update unit 31 obtains multiple index values ​​58 related to the second vehicle 54B from the data management device 68 (i.e., multiple index values ​​58 associated with a vehicle identifier 72 that can identify the second vehicle 54B). After the processing in step ST16 is completed, the driving strategy update process moves on to step ST18.

[0077] In step ST18, the strategy update unit 31 updates the driving strategy 60 based on the multiple index values ​​58 obtained from the data management device 68 in step ST16. That is, the multiple theoretical values ​​62 included in the driving strategy 60 are updated based on the multiple index values ​​58 obtained from the data management device 68. After the processing in step ST18 is completed, the driving strategy update process moves on to step ST20.

[0078] In step ST20, the information acquisition unit 10 acquires environmental information 56. After the processing in step ST12 is executed, the driving strategy update process moves to step ST22.

[0079] In step ST22, the inference unit 20 inputs the environmental information 56 acquired by the information acquisition unit 10 in step ST20 to the deep learning model 20A. In response, the deep learning model 20A outputs an index value 58 corresponding to the input environmental information 56. After the processing in step ST22 is completed, the driving strategy update process moves to step ST24.

[0080] In step ST24, the strategy update unit 31 obtains multiple index values ​​58 output from the deep learning model 20A. After the processing in step ST24 is completed, the driving strategy update process moves on to step ST26.

[0081] In step ST26, the strategy update unit 31 calculates the difference 64 between multiple index values ​​58 and multiple theoretical values ​​62 included in the driving strategy 60. After the processing in step ST26 is completed, the driving strategy update process moves on to step ST28.

[0082] In step ST28, the strategy update unit 31 updates the driving strategy 60 by updating multiple theoretical values ​​62 included in the driving strategy 60 based on the difference 64 calculated in step ST26. After the processing in step ST28 is completed, the driving strategy update process moves on to step ST30.

[0083] In step ST30, the driving control unit 30 performs driving control of the vehicle 54 according to the driving strategy 60 updated in step ST28. After the processing in step ST30 is completed, the driving strategy update process proceeds to step ST32.

[0084] In step ST32, the strategy update unit 31 determines whether the conditions for terminating the driving strategy update process have been met. One example of a condition for terminating the driving strategy update process is that an instruction to terminate the driving strategy update process has been given to the information processing device 1B. If the conditions for terminating the driving strategy update process have not been met in step ST32, the determination is denied, and the driving strategy update process proceeds to step ST12. If the conditions for terminating the driving strategy update process have been met in step ST32, the determination is affirmed, and the driving strategy update process terminates.

[0085] As described above, in this third embodiment, an information processing device 1B is installed on multiple vehicles 54, including the first vehicle 54A and the second vehicle 54B. The information processing device 1B includes the information acquisition unit 10, the inference unit 20, the driving control unit 30, the strategy update unit 31, and the strategy setting unit 40, as described in the first and second embodiments. Before the first vehicle 54A reaches its destination, the strategy update unit 31 updates the driving strategy 60 set by the strategy setting unit 40 based on a plurality of index values ​​58 inferred by the inference unit 20 of the second vehicle 54B, which is ahead of the first vehicle 54A in terms of reaching the destination. As a result, the information processing device 1B of the first vehicle 54A can obtain a driving strategy 60 that is in line with the actual environment until it reaches its destination.

[0086] Furthermore, in this third embodiment, the multiple index values ​​58 used to update the driving strategy 60 (i.e., the multiple index values ​​58 inferred by the inference unit 20 of the second vehicle 54B) are acquired by the strategy update unit 31 of the first vehicle 54A before the first vehicle 54A reaches its destination and while the first vehicle 54A is driving. Therefore, highly accurate automated driving that takes into account the actual environment from the time the first vehicle 54A travels to its destination until it reaches its destination can be realized before the first vehicle 54A travels to its destination and while the first vehicle 54A is driving.

[0087] Furthermore, in this third embodiment, the multiple index values ​​58 used to update the driving strategy 60 (i.e., the multiple index values ​​58 inferred by the inference unit 20 of the second vehicle 54B) are acquired by the strategy update unit 31 of the first vehicle 54A before the second vehicle 54B reaches its destination. Therefore, compared to the case where the multiple index values ​​58 inferred by the inference unit 20 of the second vehicle 54B are acquired by the strategy update unit 31 of the first vehicle 54A after the second vehicle 54B has reached its destination, the strategy update unit 31 of the first vehicle 54A can update the driving strategy 60 earlier.

[0088] Furthermore, in this third embodiment, the strategy update unit 31 updates a plurality of theoretical values ​​62 included in the driving strategy 60 set by the strategy setting unit 40 before the first vehicle 54A reaches its destination, based on a plurality of index values ​​58 inferred by the inference unit 20 of the second vehicle 54B, which is ahead of the first vehicle 54A in terms of its destination. The driving control unit 30 of the first vehicle 54A then performs driving control of the first vehicle 54A according to the plurality of theoretical values ​​62 updated by the strategy update unit 31. As a result, compared to the case where the plurality of theoretical values ​​62 included in the driving strategy 60 are always constant, it is possible to achieve highly accurate automated driving that takes into account the actual environment until the first vehicle 54A travels to its destination.

[0089] Furthermore, in this third embodiment, the data management device 68 collects and manages multiple index values ​​58 from multiple vehicles 54. The strategy update unit 31 of the first vehicle 54A updates the driving strategy 60 based on the multiple index values ​​58 obtained from the data management device 68. Accordingly, the driving control unit 30 can perform automatic driving of the first vehicle 54A according to the driving strategy 60 updated based on the multiple index values ​​58 obtained from a designated vehicle 54 among the multiple vehicles 54 (for example, the second vehicle 54B).

[0090] In the third embodiment described above, an example was given in which the difference 64 is used to update the driving strategy 60, but the use of the difference 64 is not limited to this. For example, as shown in Figure 7, the information processing device 1B may further have a notification unit 74, and the notification unit 74 may provide notification according to the difference 64. For example, the notification unit 74 determines whether the difference 64 exceeds a threshold, and if the difference 64 exceeds the threshold, it notifies that the difference 64 has exceeded the threshold. The threshold may be a fixed value (for example, a default value) or a variable value that is changed according to instructions given to the information processing device 1B by a user or the like. The notification may be realized, for example, by a visible display using a display and / or by an audio output using an audio playback device.

[0091] Alternatively, instead of providing notification each time the difference 64 is calculated, notification may be provided only when the number of times the difference 64 exceeds a threshold (for example, the number of times the difference 64 continuously exceeds the threshold) exceeds a predetermined number (for example, a number determined according to instructions given to the information processing device 1B by a user, etc.), or notification may be provided only when the number of times the difference 64 exceeds the threshold within a predetermined period (for example, a period determined according to instructions given to the information processing device 1B by a user, etc.) exceeds a predetermined number.

[0092] Furthermore, the difference 64 used for comparison with the threshold may be the difference between the theoretical value 62 and the index value 58 for one of several items (for example, the route the vehicle 54 takes to reach the destination, the speed at which the vehicle 54 takes to reach the destination, the slope of the ground until the vehicle 54 takes to reach the destination, and the brake control value until the vehicle 54 takes to reach the destination, etc.), or it may be the difference between the theoretical value 62 and the index value 58 for each of the multiple items.

[0093] In each of the embodiments described above, the difference between the theoretical value 62 and the index value 58 (for example, the difference 64) was used as an example. However, this is merely one example, and any value that can identify the result of comparing the theoretical value 62 and the index value 58 is acceptable. In addition to the difference, other values ​​that can identify the result of comparing the theoretical value 62 and the index value 58 include the ratio (in other words, the proportion) of the other to the other.

[0094] In the third embodiment described above, an example was given in which the strategy update unit 31 of the first vehicle 54A acquires a plurality of index values ​​58 corresponding to the second vehicle 54B before the second vehicle 54B reaches its destination. However, this is merely one example. For example, the strategy update unit 31 of the first vehicle 54A may acquire a plurality of index values ​​58 corresponding to the second vehicle 54B after the second vehicle 54B has reached its destination.

[0095] In the third embodiment described above, an example was given in which the strategy update unit 31 of the first vehicle 54A acquires a plurality of index values ​​58 corresponding to the second vehicle 54B from the data management device 68, but the technology of this disclosure is not limited thereto. For example, the strategy update unit 31 of the first vehicle 54A may acquire a plurality of index values ​​58 inferred by the inference unit 20 from the information processing device 1B of the second vehicle 54B by communicating directly between the information processing device 1B of the first vehicle 54A and the information processing device 1B of the second vehicle 54B. Alternatively, for example, each time an index value 58 is inferred by the inference unit 20 of the second vehicle 54B (for example, each time the index value 58 corresponding to the second vehicle 54B is updated in the data management device 68), the index value 58 inferred by the inference unit 20 of the second vehicle 54B may be acquired by the strategy update unit 31 of the first vehicle 54A and used to update the driving strategy 60.

[0096] In the third embodiment described above, an example was given in which the multiple theoretical values ​​62 include a theoretical value for the route the vehicle 54 takes to reach its destination, a theoretical value for the vehicle's speed to reach its destination, a theoretical value for the slope of the ground to reach its destination, and a theoretical value for the brake control to reach its destination. However, this is merely one example. For example, the multiple theoretical values ​​62 only need to include at least one of the theoretical values ​​for the route the vehicle 54 takes to reach its destination, the vehicle's speed to reach its destination, the slope of the ground to reach its destination, and the theoretical value for the brake control to reach its destination.

[0097] In the third embodiment described above, an example was given in which a plurality of index values ​​58 used to update the driving strategy 60 are acquired by the strategy update unit 31 of the first vehicle 54A while the first vehicle 54A is in motion. However, the technology of this disclosure is not limited thereto. For example, the plurality of index values ​​58 used to update the driving strategy 60 may be acquired by the strategy update unit 31 of the first vehicle 54A before the first vehicle 54A reaches its destination and while the first vehicle 54A is stopped. This makes it possible to achieve highly accurate automated driving that takes into account the actual environment until the first vehicle 54A travels to reach its destination, even if the first vehicle 54A has stopped before reaching its destination.

[0098] In the third embodiment described above, an example was given in which the index value 58 inferred by the inference unit 20 of the second vehicle 54B is used directly to update the driving strategy 60 of the first vehicle 54A. However, this is merely one example. For example, the index value 58 (i.e., the index value 58 inferred by the inference unit 20 of the second vehicle 54B) may be adjusted according to an adjustment value (e.g., a weight) determined according to the confidence level of the index value 58 inferred by the inference unit 20 of the second vehicle 54B, and the theoretical value 62 may be updated based on the adjusted index value 58 in the same manner as in the third embodiment described above.

[0099] For example, the confidence level of the index value 58 inferred by the inference unit 20 of the second vehicle 54B differs depending on the time interval between the time when the index value 58 is inferred by the inference unit 20 of the second vehicle 54B when it passes through the first point (hereinafter referred to as "the first point") and the time when the first vehicle 54A passes through the first point. For example, the longer the time interval between the time when the index value 58 is inferred by the inference unit 20 of the second vehicle 54B when it passes through the first point and the time when the first vehicle 54A passes through the first point, the lower the confidence level of the index value 58 inferred by the inference unit 20 of the second vehicle 54B. Conversely, the shorter the time interval between the time when the inference unit 20 of the second vehicle 54B inferred the index value 58 when it was traveling to the first point and the time when the first vehicle 54A was traveling, the higher the confidence in the index value 58 inferred by the inference unit 20 of the second vehicle 54B.

[0100] Therefore, the strategy update unit 31 of the first vehicle 54A adjusts the index value 58 using an adjustment value that reduces the influence of the index value 58 (for example, the index value 58 inferred by the inference unit 20 of the second vehicle 54B when it traveled to the first point) on the theoretical value 62 when updating the theoretical value 62, as the time interval is longer, and updates the theoretical value 62 based on the adjusted index value 58. Conversely, the strategy update unit 31 of the first vehicle 54A adjusts the index value 58 using an adjustment value that increases the influence of the index value 58 (for example, the index value 58 inferred by the inference unit 20 of the second vehicle 54B when it traveled to the first point) on the theoretical value 62 when updating the theoretical value 62, as the time interval is shorter, and updates the theoretical value 62 based on the adjusted index value 58.

[0101] Furthermore, while the example given here illustrates the time interval between the time when the inference unit 20 of the second vehicle 54B inferred the index value 58 when it traveled to the first point and the time when the first vehicle 54A was traveling, this is merely one example. For instance, the index value 58 (i.e., the index value 58 inferred by the inference unit 20 of the second vehicle 54B) may be adjusted according to an adjustment value (e.g., a weight) determined according to the difference between the external environment when the second vehicle 54B traveled to the first point and the external environment when the first vehicle 54A traveled to the first point, and the theoretical value 62 may be updated based on the adjusted index value 58 in the same manner as in the third embodiment described above.

[0102] In this case, for example, if the external environment when the second vehicle 54B travels to the first point is exactly the same as the external environment when the first vehicle 54A travels to the first point, no adjustment is necessary (i.e., no adjustment of the index value 58 is necessary). The greater the difference between the external environment when the second vehicle 54B travels to the first point and the external environment when the first vehicle 54A travels to the first point, the larger the adjustment value should be used to adjust the index value 58 (i.e., the index value 58 inferred by the inference unit 20 of the second vehicle 54B). The theoretical value 62 should then be updated based on the adjusted index value 58 in the same manner as in the third embodiment described above.

[0103] The external environment refers to the environment outside of vehicle 54. An example of the external environment is weather conditions. Weather conditions refer to, for example, the degree of rainfall, the degree of snowfall, the temperature of the outside air, the humidity of the outside air, the wind speed, and / or the wind direction. The degree of difference between the external environment when the second vehicle 54B travels to point 1 and the external environment when the first vehicle 54A travels to point 1 may be determined, for example, from the degree of difference (e.g., difference or ratio) between the weather condition-related index value (i.e., weather condition index value 58) inferred by the inference unit 20 of the first vehicle 54A and the weather condition-related index value inferred by the inference unit 20 of the second vehicle 54B.

[0104] Figure 8 schematically shows an example of the hardware configuration of a computer 1200 that functions as the information processing device (Central Brain) described above. A program installed on the computer 1200 can cause the computer 1200 to function as one or more "parts" of the device according to each embodiment described above, or to cause the computer 1200 to execute operations associated with the device according to each embodiment or such one or more "parts", and / or to cause the computer 1200 to execute a process or a stage of such process according to each embodiment. Such a program may 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 herein.

[0105] The computer 1200 according to each embodiment may include a CPU 1212, RAM 1214, and a graphics controller 1216, which may be interconnected by a host 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 host controller 1210 via an input / output controller 1220. The DVD drive may be a DVD-ROM drive and a DVD-RAM drive, etc. The storage device 1224 may be a hard disk drive and a solid-state drive, etc. 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.

[0106] The CPU 1212 can control each unit by operating according to programs stored in the ROM 1230 and RAM 1214. The graphics controller 1216 can acquire image data generated by the CPU 1212 and store it in a frame buffer provided in RAM 1214 or within itself, so that the image data can be displayed on the display device 1218.

[0107] The communication interface 1222 can communicate with other electronic devices via a network. The storage device 1224 can store programs and data used by the CPU 1212 in the computer 1200. The DVD drive can read programs or data from a DVD-ROM or the like and provide them to the storage device 1224. The IC card drive can read programs and data from an IC card and / or write programs and data to an IC card.

[0108] The ROM 1230 can store boot programs and / or hardware-dependent programs of the computer 1200, which are executed by the computer 1200 when activated. The input / output chip 1240 may also connect various input / output units to the input / output controller 1220 via USB ports, parallel ports, serial ports, keyboard ports, mouse ports, etc.

[0109] The program may be provided on a computer-readable storage medium such as a DVD-ROM or IC card. This program may be read from the computer-readable storage medium and installed in a storage device 1224, RAM 1214, or ROM 1230, which are examples of computer-readable storage media, and executed by the CPU 1212. The information processing described within these programs may be read by the computer 1200, resulting in coordination between the program and the various types of hardware resources described above. The apparatus or method may be configured to implement information operations or processing in accordance with the use of the computer 1200.

[0110] For example, when communication is performed between a computer 1200 and an external device, the CPU 1212 may execute a communication program loaded into RAM 1214 and, based on the processing described in the communication program, instruct the communication interface 1222 to perform communication processing. 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 RAM 1214, storage device 1224, DVD-ROM, or IC card, transmit the read transmission data to the network, or write received data received from the network to a receive buffer area or the like provided on the recording medium.

[0111] Furthermore, the CPU 1212 may read all or necessary parts of a file or database stored on an external recording medium such as the storage device 1224, a DVD drive (DVD-ROM), or an IC card into the RAM 1214, and perform various types of processing on the data in the RAM 1214. The CPU 1212 may then write the processed data back to the external recording medium.

[0112] Various types of information, such as various types of programs, data, tables, and databases, may be stored on the recording medium and subjected to information processing. The CPU 1212 may perform various types of processing on the data read from RAM 1214, including various types of operations, information processing, conditional judgments, conditional branching, unconditional branching, information retrieval / replacement, etc., as described throughout this disclosure and specified by the program instruction sequence, and may write the results back to RAM 1214. The CPU 1212 may also retrieve information in files, databases, etc., within the recording medium. For example, if multiple entries are stored in the recording medium, each having an attribute value of a first attribute associated with an attribute value of a second attribute, the CPU 1212 may search among the multiple entries for an entry that matches the specified condition for the attribute value of the first attribute, read the attribute value of the second attribute stored in that entry, and thereby obtain the attribute value of the second attribute associated with the first attribute that satisfies the predetermined condition.

[0113] The program or software module described above may be stored on or near the computer 1200 in a computer-readable storage medium. Alternatively, a recording medium such as a hard disk or RAM provided within a server system connected to a dedicated communication network or the Internet can be used as a computer-readable storage medium, thereby providing the program to the computer 1200 via the network.

[0114] In this embodiment, blocks in the flowchart and block diagram may represent a stage in a process in which an operation is performed or a "part" of a device that has the role of performing an operation. A particular stage and "part" may be implemented by a dedicated circuit, a programmable circuit supplied with computer-readable instructions stored on a computer-readable storage medium, and / or a processor supplied with computer-readable instructions stored on a computer-readable storage medium. The dedicated circuit may include digital and / or analog hardware circuits, and may include integrated circuits (ICs) and / or discrete circuits. The programmable circuit may include reconfigurable hardware circuits, such as field-programmable gate arrays (FPGAs) and programmable logic arrays (PLAs), which include logical AND, logical OR, exclusive OR, negated AND, negated OR, and other logical operations, flip-flops, registers, and memory elements.

[0115] A computer-readable storage medium may include any tangible device capable of storing instructions to be executed by a suitable device, and as a result, a computer-readable storage medium having instructions stored therein will comprise a product that includes instructions that can be executed to create means for performing operations specified in a flowchart or block diagram. Examples of computer-readable storage media may include electronic storage media, magnetic storage media, optical storage media, electromagnetic storage media, semiconductor storage media, etc. More specific examples of computer-readable storage media may include floppy disks, diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), electrically erasable programmable read-only memory (EEPROM), static random access memory (SRAM), compact disk read-only memory (CD-ROM), digital multipurpose disc (DVD), Blu-ray® disc, memory stick, integrated circuit card, etc.

[0116] Computer-readable instructions may include assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk®, Java®, C++, and traditional procedural programming languages ​​such as the C programming language or similar programming languages.

[0117] Computer-readable instructions may be provided to a general-purpose computer, a special-purpose computer, or a programmable circuit, either locally or via a wide area network (WAN) such as a local area network (LAN) or the internet, so that the computer-readable instructions may be executed by the processor or programmable circuit of a general-purpose computer, a special-purpose computer, or other programmable data processing device, in order to generate means for performing operations specified in a flowchart or block diagram. Examples of processors include computer processors, processing units, microprocessors, digital signal processors, controllers, microcontrollers, and the like.

[0118] Although the present invention has been described above using embodiments, the technical scope of the present invention is not limited to the scope described in the above embodiments. It will be apparent to those skilled in the art that various modifications or improvements can be made to the above embodiments. It will be clear from the claims that such modified or improved forms may also be included in the technical scope of the present invention.

[0119] It should be noted that the execution order of operations, procedures, steps, and stages in the apparatus, systems, programs, and methods shown in the claims, specifications, and drawings is not explicitly stated as "before" or "prior to," and that these can be implemented in any order unless the output of a previous process is used in a later process. Even if the operation flow in the claims, specifications, and drawings is described using phrases such as "first," and "next," for convenience, this does not mean that it is essential to perform the operations in that order.

[0120] In this specification, "α and / or β" is synonymous with "at least one of α and β." That is, "α and / or β" may be α alone, β alone, or a combination of α and β. Furthermore, in this specification, the same concept as "α and / or β" applies when expressing three or more things linked by "and / or." [Explanation of Symbols]

[0121] 10 Information acquisition department 20 Reasoning section 20A Deep Learning Model 30 Operation Control Unit 31. Strategy Update Department 40. Strategy Setting Department 54 vehicles 54A First car 54B Second car 56 Environmental information 56A Route 56B Running speed 56C Ground slope 56D Brake control value 56E Air Resistance 56F road resistance 56G road element 58 Index Values 60 Driving Strategy 62 Theoretical value 64 Difference 68 Data Management Device 70 Databases 72 Vehicle Identifier 74 Hochi Department 1200 Computers 1210 Host Controller 1212 CPU 1214 RAM 1216 Graphics Controller 1218 Display Devices 1220 Input / Output Controller 1222 Communication Interface 1224 Storage device 1230 ROM 1240 Input / Output Chip

Claims

1. An information processing device applied to each of multiple vehicles, An information acquisition unit capable of acquiring multiple pieces of information related to the aforementioned vehicle, An inference unit uses a deep learning model to infer from the multiple pieces of information acquired by the information acquisition unit the route the vehicle will take to reach its destination, the vehicle's speed to reach its destination, the slope of the ground to reach its destination, and multiple index values ​​related to the brake control values ​​to reach its destination. A strategy setting unit sets a driving strategy for the vehicle until it reaches its destination, A strategy update unit updates the driving strategy based on the driving strategy set by the strategy setting unit and the plurality of index values, The vehicle comprises a driving control unit that performs driving control of the vehicle in accordance with the driving strategy updated by the strategy update unit, The strategy update unit updates the driving strategy set by the strategy setting unit before the first vehicle among the plurality of vehicles reaches the destination, based on the multiple index values ​​relating to the route, driving speed, ground slope, and brake control value, which are inferred by the inference unit of the second vehicle that is ahead of the first vehicle among the plurality of vehicles in the direction of the destination. Information processing device.

2. The strategy update unit acquires the plurality of index values ​​used for updating the driving strategy before the first vehicle reaches the destination and while the first vehicle is driving. The information processing apparatus according to claim 1.

3. The strategy update unit acquires the plurality of index values ​​used for updating the driving strategy before the second vehicle reaches the destination. The information processing apparatus according to claim 1.

4. The strategy setting unit sets at least one theoretical value among the theoretical value of the route the vehicle takes to reach the destination, the theoretical value of the vehicle's speed to reach the destination, the theoretical value of the ground incline the vehicle takes to reach the destination, and the theoretical value of the brake control value the vehicle takes to reach the destination as the driving strategy, The system includes a notification unit that provides notification when the difference between the at least one theoretical value and the index value corresponding to the at least one theoretical value among the plurality of index values ​​exceeds a threshold. The information processing apparatus according to claim 1.

5. The aforementioned strategy update unit, The plurality of index values ​​are obtained from a data management device that collects and manages the plurality of index values ​​for at least one of the plurality of vehicles, The driving strategy is updated based on the plurality of index values ​​obtained from the data management device. The information processing apparatus according to claim 1.

6. The inference unit infers the multiple index values ​​from the multiple pieces of information by performing multivariate analysis using an integral method with the deep learning model. The information processing apparatus according to claim 1.

7. The information acquisition unit acquires the plurality of pieces of information in units of one billionth of a second, The inference unit and the driving control unit use the plurality of pieces of information acquired in units of one billionth of a second to perform the inference of the plurality of index values ​​and the driving control of the vehicle in units of one billionth of a second. The information processing apparatus according to claim 1.

8. The aforementioned information acquisition unit, A temperature sensor capable of detecting the temperature of the ground on which the vehicle is traveling, A material sensor capable of detecting the material of the ground, Equipped with a tilt sensor capable of detecting the slope of the ground, The information processing apparatus according to claim 1.

9. The information processing device comprises the information processing device described in any one of claims 1 to 8. vehicle.

10. A program for causing a computer to function as an information processing device according to any one of claims 1 to 8.

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