Information processing device, vehicle, and program

Abstract

An information processing apparatus includes an information acquisition unit capable of acquiring a plurality of pieces of information associated with a vehicle, an estimation unit that estimates a plurality of index values from the plurality of pieces of information acquired by the information acquisition unit using deep learning, and a driving control unit that performs driving control of the vehicle based on the plurality of index values.

Description

Technical Field

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

[0002] Japanese Patent Publication No. 2022-035198 describes a vehicle with an autonomous driving function. Summary of the invention

[0003] Problems to be solved by the invention

[0004] In existing autonomous driving vehicles, it is difficult to analyze the effects of factors such as air resistance and friction generated when driving on the road while driving.

[0005] Means for solving problems

[0006] According to one embodiment of the present disclosure, an information processing device is provided, which includes: an information acquisition unit, which is capable of acquiring multiple information associated with a vehicle; an inference unit, which uses deep learning to infer multiple index values ​​based on the multiple information acquired by the information acquisition unit; and a driving control unit, which performs driving control of the vehicle based on the multiple index values.

[0007] According to one embodiment of the present disclosure, in the above-mentioned information processing device, the inference unit infers the plurality of index values ​​from the plurality of information by multivariate analysis based on an integration method using the deep learning.

[0008] According to one embodiment of the present disclosure, in the above-mentioned information processing device, the information acquisition unit acquires the multiple information in units of nanoseconds, and the inference unit and the driving control unit use the multiple information acquired in units of nanoseconds to perform the inference of the multiple index values ​​and the driving control of the vehicle in units of nanoseconds.

[0009] According to one embodiment of the present disclosure, the above-mentioned information processing device also has a strategy setting unit, which sets the driving strategy of the vehicle until it reaches the destination, and the driving strategy includes at least one theoretical value of the optimal route, driving speed, tilt and braking to the destination. The driving control unit has a strategy updating unit, and the strategy updating unit updates the driving strategy based on the difference between the multiple index values ​​and the theoretical value.

[0010] According to one embodiment of the present disclosure, in the information processing device, the information acquisition unit includes a sensor that is provided at a lower portion of the vehicle and that can detect a temperature, a material, and an inclination of a ground surface on which the vehicle travels.

[0011] According to one embodiment of the present disclosure, in the above-mentioned information processing device, the multiple index values ​​include a first index value related to a first distance, the first distance includes a necessary stopping distance between an obstacle in the driving direction of the vehicle and the vehicle, for the vehicle to stop without colliding with the obstacle, and the driving control includes driving speed control, which is the control of the driving speed of the vehicle and is performed based on the first index value.

[0012] According to one embodiment of the present disclosure, in the above-mentioned information processing device, the driving speed control includes controlling the vehicle to travel at a maximum speed at which the vehicle will not collide with the obstacle, and the maximum speed is calculated based on the first index value.

[0013] According to one embodiment of the present disclosure, in the above-mentioned information processing device, the maximum speed is the maximum speed within a range below the legal speed and / or within a range below the speed of the preceding vehicle, and the preceding vehicle speed is the speed of other vehicles travelling in front of the vehicle.

[0014] According to an embodiment of the present disclosure, in the above-mentioned information processing device, the first distance is a distance obtained by adding the necessary stopping distance to a margin distance.

[0015] According to one embodiment of the present disclosure, there is provided an information processing device, which is an information processing device applicable to each of a plurality of vehicles, and comprises: an information acquisition unit, which is capable of acquiring a plurality of information associated with the vehicle; an inference unit, which infers a plurality of index values ​​based on the plurality of information acquired by the information acquisition unit by using a deep learning model; a strategy setting unit, which sets a driving strategy for the vehicle until it reaches a destination; a strategy updating unit, which 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, which performs driving control of the vehicle according to the driving strategy updated by the strategy updating unit, wherein the strategy updating unit updates the driving strategy set by the strategy setting unit before a first vehicle among the plurality of vehicles arrives at the destination, based on the plurality of index values ​​inferred by the inference unit of a second vehicle among the plurality of vehicles, the second vehicle traveling ahead of the first vehicle relative to the destination.

[0016] According to an embodiment of the present disclosure, in the above-mentioned information processing device, the strategy updating unit acquires the plurality of index values ​​for updating the driving strategy before the first vehicle arrives at the destination and during driving of the first vehicle.

[0017] According to an embodiment of the present disclosure, in the above-mentioned information processing device, the strategy updating unit obtains the plurality of index values ​​for updating the driving strategy before the second vehicle arrives at the destination.

[0018] According to one embodiment of the present disclosure, in the above-mentioned information processing device, the driving strategy includes multiple theoretical values, and the multiple theoretical values ​​include: the theoretical value of the route taken by the vehicle until it reaches the destination, the theoretical value of the driving speed of the vehicle until it reaches the destination, the theoretical value of the inclination of the ground until the vehicle reaches the destination, and / or the theoretical value of the braking control value until the vehicle reaches the destination, and the strategy updating unit updates the driving strategy by updating the multiple theoretical values ​​based on the result obtained by comparing the multiple theoretical values ​​with the multiple index values.

[0019] According to one embodiment of the present disclosure, the information processing device includes a notification unit configured to perform notification when a difference between the theoretical value and the index value exceeds a threshold value.

[0020] According to one embodiment of the present disclosure, in the above-mentioned information processing device, the strategy update unit obtains the multiple index values ​​from a data management device, and the data management device collects and manages the multiple index values ​​about at least one of the multiple vehicles, and updates the driving strategy based on the multiple index values ​​obtained from the data management device.

[0021] According to one embodiment of the present disclosure, in the above-mentioned information processing device, the inference unit infers the multiple index values ​​from the multiple information through multivariate analysis based on an integration method using the deep learning model.

[0022] According to one embodiment of the present disclosure, in the above-mentioned information processing device, the information acquisition unit includes: a temperature sensor, which can detect the temperature of the ground on which the vehicle travels; a material sensor, which can detect the material of the ground; and a tilt sensor, which can detect the tilt of the ground.

[0023] According to one embodiment of the present disclosure, a vehicle including the above-mentioned information processing device is provided.

[0024] According to one embodiment of the present disclosure, there is provided a program for causing a computer to function as the information processing apparatus.

[0025] It should be noted that the above disclosed summary does not list all the necessary features of the present disclosure. In addition, sub-combinations of these feature groups may also become the subject of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is a schematic diagram showing an example of a vehicle equipped with a central brain.

[0027] Figure 2 This is a block diagram showing an example of the information processing device according to the first embodiment of the present disclosure.

[0028] Figure 3 This is a block diagram showing an example of an information processing device according to the second embodiment of the present disclosure.

[0029] Figure 4 It is a schematic diagram showing an example of the configuration of an estimating unit included in the information processing device according to the third embodiment of the present disclosure.

[0030] Figure 5 It is a schematic diagram showing an example of processing contents of an information acquisition unit, an estimation unit, and a driving control unit included in the information processing device according to the third embodiment of the present disclosure.

[0031] Figure 6 This is a flowchart showing an example of the flow of the traveling speed control process according to the third embodiment of the present disclosure.

[0032] Figure 7 It is a schematic diagram showing an example of the configuration of an estimating unit included in the information processing device according to the fourth embodiment of the present disclosure.

[0033] Figure 8 It is a schematic diagram showing an example of processing contents of an information acquisition unit, an estimation unit, and a driving control unit included in the information processing device according to the fourth embodiment of the present disclosure.

[0034] Fig. 9 1 is a flowchart showing an example of the flow of the traveling speed control processing strategy update process according to the fourth embodiment of the present disclosure.

[0035] Fig.10 It is a schematic diagram showing a modified example of the structure of the information processing device according to the fourth embodiment of the present disclosure.

[0036] Fig.11 This is a block diagram schematically showing an example of the hardware configuration of a computer functioning as an information processing device. DETAILED DESCRIPTION

[0037] Through the following detailed description, the present disclosure will be more completely understood. The further application scope of the present disclosure will become clear through the following detailed description. However, the detailed description and specific examples are preferred embodiments of the present disclosure and are recorded only for the purpose of illustration. For those skilled in the art, it can be clear that the various changes and modifications implemented according to the detailed description are included in the spirit and scope of the present disclosure.

[0038] The applicant has no intention to dedicate any of the described implementation methods to the public. Among the disclosed modifications and alternatives, even if some of them may not be included in the claims in terms of wording, they are also part of the invention under the doctrine of equivalents.

[0039] Hereinafter, the present disclosure will be described by way of embodiments, but the following embodiments are not intended to limit the objects described in the claims. In addition, all combinations of features described in the embodiments are not necessarily essential for solving the problems of the present disclosure.

[0040] (First Embodiment)

[0041] The information processing device of the present disclosure can obtain the index value required for driving control with high accuracy 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 realize vehicle control.

[0042] In addition, the information processing device disclosed in the present invention can provide the following driving system, which can realize autonomous driving (Autonomous Driving) in real time based on the data obtained by utilizing level L6 (Level 6) AI (Artificial Intelligence) / multivariable analysis / goal seeking / strategy formulation / optimal probability solution / optimal speed solution / optimal route management / input from multiple sensors at the edge, and make adjustments based on the Delta optimal solution.

[0043] 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 an information processing device involved in this embodiment. Figure 1 As shown, the central brain can be connected to multiple gateways (Gate Way) for communication. The central brain involved in this embodiment can realize level L6 autonomous driving based on multiple information obtained through the gateway.

[0044] "Level L6" indicates the level of autonomous driving, which is equivalent to a higher level than Level L5, which indicates fully autonomous driving. Although Level L5 represents fully autonomous driving, it is a level equivalent to the level of human driving, and there is still a probability of accidents. Level L6 indicates a higher level than Level L5, which is equivalent to a level with a lower probability of accidents than Level L5.

[0045] The computing power of level L6 (i.e., the computing power used to implement level L6) is about 1000 times that of level L5 (i.e., the computing power used to implement level L5). Therefore, high-performance driving control that cannot be implemented in level L5 can be implemented.

[0046] Figure 2 1 is a block diagram showing an example of an information processing device according to a first embodiment of the present disclosure. The information processing device 1 according to the present embodiment includes at least: an information acquisition unit 10 capable of acquiring multiple information associated with a vehicle, an inference unit 20 inferring multiple index values ​​based on the multiple information acquired by the information acquisition unit 10, and a driving control unit 30 executing driving control of the vehicle based on the multiple index values.

[0047] The information acquisition unit 10 can acquire various information associated with the vehicle. As the information acquisition unit 10, for example, it may include sensors installed in various parts of the vehicle, and a communication unit for acquiring information that can be obtained via a network from a server not shown in the figure. As an example of the sensor included in the information acquisition unit 10, it can be listed: radar, laser radar (LiDAR), high pixel / telephoto / ultra-wide angle / 360 degree / high performance camera, visual recognition, weak sound sensor, ultrasonic sensor, vibration sensor, infrared sensor, ultraviolet sensor, electromagnetic wave sensor, temperature sensor, humidity sensor, fixed point (Spot) AI weather forecast, material sensor, tilt sensor, high-precision multi-channel global positioning system (Global Positioning System, GPS), and / or low-altitude satellite information, etc. Alternatively, long tail event (Long Tail Incident) AI data, etc. can be listed. Long tail event AI data is the trip data of a car with level L5 installed (i.e., a car with a device capable of achieving level L5 computing power (here as an example, the information processing device 1)).

[0048] The information that can be obtained by various sensors can include: the temperature or material of the ground (such as the road), the temperature of the outside air, the tilt of the ground, the freezing state or moisture content of the road, the material or wear condition of each tire, or the air pressure, the width of the road, whether there is a prohibition on overtaking, whether there are oncoming vehicles, the model information of the front and rear vehicles, the cruising status of these vehicles, and / or the surrounding conditions (birds, animals, football, accident vehicles, earthquakes, fires, wind, typhoons, heavy rain, light rain, blizzards, and / or fog, etc.). In this embodiment, by utilizing the computing power of level L6, these detections can be performed every billionth of a second (nanosecond).

[0049] It is particularly important to note that the information acquisition unit 10 includes a vehicle bottom sensor disposed at the bottom of the vehicle and capable of detecting the temperature, material and inclination of the ground on which the vehicle is traveling. By using the vehicle bottom sensor, an independent smart tilt function can be performed.

[0050] Alternatively, the inference unit 20 may use machine learning, more specifically, deep learning, to infer indexed values ​​related to vehicle control from a plurality of information acquired by the information acquisition unit 10. In other words, the inference unit 20 may be composed of AI (Artificial Intelligence).

[0051] The inference unit 20 uses the computing power used when implementing level L6 (hereinafter also referred to as "computing power of level L6") to perform a multivariate analysis based on the integral method as shown in the following formula (1) on the data of each nanosecond or the long-tail event AI data collected by the information acquisition unit 10 through a large number of sensor groups, etc. (for example, refer to formula (2)), thereby being able to obtain an accurate index value. More specifically, while the integral value of the Delta value of various ultra-high resolutions (Ultra High Resolution) can be obtained at the computing power of level L6, the indexed value of each variable can be obtained in real time at the edge level, and the result occurring in the next nanosecond (that is, the indexed value of each variable, that is, the index value) can be obtained with the highest probability value. To achieve this, for example, an integral value obtained by time-integrating the Delta value (e.g., a change value in a minute time) of a function (in other words, a function representing the change of each variable) that can determine each variable such as air resistance, road resistance, road elements (e.g., garbage), and slip coefficient (e.g., a plurality of information acquired by the information acquisition unit 10) is input to the deep learning model of the inference unit 20 (e.g., a learned model acquired by deep learning of a neural network). The deep learning model of the inference unit 20 outputs an index value (e.g., an index value of the highest confidence (i.e., evaluation value)) corresponding to the input integral value. The output of the index value is performed in nanoseconds.

[0052] [Calculation formula 1]

[0053]

[0054] [Calculation formula 2]

[0055] V n =DL(f(A,B,C,D,…,N)(dA n / dt)) (2)

[0056] It should be noted that, as an example, in formula (1), "f(A)" is a simplified expression of a function representing the change of each variable such as air resistance, road resistance, road elements (such as garbage) and sliding coefficient. In addition, as an example, formula (1) is a formula representing the time integral v of "f(A)" from time a to time b. DL in the formula represents deep learning (for example, a deep learning model optimized by deep learning of a neural network), dAn / dt represents the Delta value of f(A, B, C, D, ..., N), A, B, C, D, ..., N represent air resistance, road resistance, road elements (such as garbage) and sliding coefficient, etc., f(A, B, C, D, ..., N) represents a function showing the change of A, B, C, D, ..., N, and Vn represents the value (i.e., index value) output from the deep learning model optimized by deep learning of a neural network.

[0057] It should be noted that the example form of inputting the integral value obtained by time-integrating the Delta value of the function into the deep learning model of the inference unit 20 is listed here, but this is only an example. For example, the deep learning model of the inference unit 20 can also be used to infer the integral value (for example, the result occurring in the next nanosecond) obtained by time-integrating the Delta value of the function representing the change of each variable such as air resistance, road resistance, road elements and slip coefficient, and as the inference result, the inference unit 20 obtains the integral value with the highest confidence (that is, the evaluation value) every nanosecond.

[0058] In addition, an example form of inputting an integral value to a deep learning model or outputting an integral value from a deep learning model is listed here, but this is only an example, and the technology disclosed in the present invention is also valid even if the integral value is not used. For example, at least one index value can be inferred by using a deep learning model optimized by deep learning of a neural network using supervised data, wherein the values ​​equivalent to A, B, C, D, ..., N in the supervised data are used as example data, and the values ​​equivalent to at least one index value (for example, the result occurring in the next nanosecond) are used as correct answer data.

[0059] The indexed value (i.e., index value) of each variable obtained by the inference unit 20 can be further refined by increasing the number of deep learning. For example, a more accurate index value can be calculated using a large amount of data or long-tail event AI data such as tire or motor rotation, steering angle, road material, weather, garbage, or the impact of quadratic deceleration, slipping, loss of balance, or steering or speed control methods to regain balance.

[0060] The driving control unit 30 can perform driving control of the vehicle based on the multiple index values ​​determined by the inference unit 20. Alternatively, the driving control unit 30 can implement automatic driving control of the vehicle. In detail, it is possible to obtain the result that occurs in the next nanosecond from the multiple index values ​​with the highest probability value, and implement driving control of the vehicle taking into account the probability value. That is, the driving control unit 30 can be configured to obtain the index value with the highest confidence (i.e., evaluation value) from the multiple index values ​​as the result that occurs in the next nanosecond, and perform driving control of the vehicle according to the obtained index value.

[0061] According to the information processing device 1 having the above structure, since the computing power of level L6, which is much greater than the computing power of level L5, can be used to implement information analysis or inference, it is possible to perform a more detailed analysis than before. As a result, vehicle control for safe autonomous driving becomes possible. In addition, through the above-mentioned multivariate analysis using AI, a value difference of 1000 times can be generated compared to the world of level L5.

[0062] (Second Embodiment)

[0063] Figure 3 1 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 is different from the information processing device 1 in that, in addition to the information processing device 1 according to the first embodiment, it also includes a strategy setting unit 40 for setting a driving strategy until the vehicle reaches the destination.

[0064] The strategy setting unit 40 can set a driving strategy from the current position to the destination based on the information of the destination input by the occupants of the vehicle, or the traffic information between the current position and the destination. At this time, the information used in the calculation of the strategy setting can be considered, that is, the data currently acquired by the information acquisition unit 10 can be considered. This is not only to simply calculate the route to the destination, but also to calculate a more realistic theoretical value by considering the surrounding conditions at the moment. The driving strategy can be configured to include at least one theoretical value of the optimal route to the destination (also called the strategy route), driving speed, tilt, and braking. Preferably, the driving strategy can be composed of all the theoretical values ​​of the above-mentioned optimal route, driving speed, tilt, and braking.

[0065] The plurality of theoretical values ​​constituting the driving strategy set by the strategy setting unit 40 can be used for the automatic driving control of the driving control unit 30. In addition, preferably, the driving control unit 30 includes a strategy updating unit 31, which can update the driving strategy based on the difference between the plurality of index values ​​(e.g., index values ​​indicating the driving speed, index values ​​indicating the tilt, and index values ​​indicating the braking control value) inferred by the inference unit 20 and the theoretical values ​​(e.g., theoretical values ​​of the driving speed, theoretical values ​​of the tilt, and theoretical values ​​of the braking control value) set by the strategy setting unit 40.

[0066] The index value inferred by the inference unit 20 is information obtained during vehicle driving, specifically, detected during actual driving. For example, it is a value inferred based on the friction coefficient. Therefore, in the strategy update unit 31, by considering the index value, it is possible to cope with the changes at every moment when traveling on the strategy route. Specifically, in the strategy update unit 31, the difference (Delta value) is calculated based on the theoretical value and the index value contained in the driving strategy, so that the optimal solution can be derived again and the strategy route can be re-formulated. As a first example of the optimal solution, an index value used instead of a theoretical value can be cited. As a second example of the optimal solution, an adjusted index value used instead of a theoretical value can be cited. As a third example of the optimal solution, a solution obtained by performing a regression analysis using at least one theoretical value and at least one index value can be cited. As a fourth example of the optimal solution, a statistical value (for example, a median and / or an average value, etc.) obtained based on the theoretical value and the index value can be cited. Which of the first to fourth examples of the strategy update unit 31 derives the optimal solution can be determined, for example, based on the size of the difference. It should be noted that the optimal solution is not limited to the first to fourth examples, and may also be obtained by other methods.

[0067] In this way, by deriving the optimal solution again by the strategy update unit 31, for example, automatic driving control in a critical state without slipping can also be realized. That is, in the following case, if automatic driving control is performed only according to the theoretical value, automatic driving control in which the vehicle slips will be implemented; if automatic driving control is performed only according to the index value, then automatic driving control in which there is a safety margin and no slipping is implemented, the index value adjusted after deducting the safety margin is used as a theoretical value by the driving control unit 30, thereby realizing automatic driving control in a critical state without slipping of the vehicle. In addition, when performing such an update process, since the computing power of the above-mentioned level L6 can be used, corrections and fine-tuning can be performed in units of nanoseconds, and more detailed driving control can be realized.

[0068] In addition, when the information acquisition unit 10 has the above-mentioned lower vehicle sensor, since the lower vehicle sensor also detects the temperature or material of the ground, etc., it is possible to cope with the changes at every moment when passing on the strategic route. When calculating the driving course included in the driving strategy, independent intelligent tilting can also be performed. Furthermore, even when other information is detected (for example, flying tires, debris, animals, etc.), by coping with the changes at every moment when passing on the strategic route, the optimal driving path can be recalculated in an instant and the optimal path management can be implemented.

[0069] (Third Embodiment)

[0070] In the third embodiment described below, an information processing device or the like 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 traveling will be mainly described.

[0071] Figure 4 It is a schematic diagram showing an example of the configuration of the estimation unit 20 included in the information processing device 1B according to the third embodiment of the present disclosure.

[0072] exist Figure 4 In the example shown, the information processing device 1B is mounted on a vehicle 48. The inference unit 20 included in the information processing device 1B has a deep learning model 20A. The deep learning model 20A is a learning model obtained by optimizing a neural network through deep learning using a plurality of supervisory data 50. The inference unit 20 uses the deep learning model 20A to calculate the environmental information 56 (see FIG. 5 ). Figure 5 ) to infer multiple index values.

[0073] The supervised data 50 is a data set including example question data 52 and correct answer data 54. The example question data 52 includes example question environment information 52A. The example question environment information 52A is assumed to be environment information 56 acquired by the information acquisition unit 10 (see Figure 5 ) information. For example, the example environmental information 52A includes information such as the vehicle-obstacle distance 52A1, the driving speed 52A2, the air resistance 52A3, the road resistance 52A4, the road element 52A5, and the ground inclination 52A6. Here, the air resistance 52A3 is equivalent to the air resistance illustrated in the above-mentioned first embodiment, the road resistance 52A4 is equivalent to the road resistance illustrated in the above-mentioned first embodiment, the road element 52A5 is equivalent to the road element illustrated in the above-mentioned first embodiment, and the ground inclination 52A6 is equivalent to the inclination (tilt) illustrated in the above-mentioned first embodiment. In addition, the vehicle-obstacle distance 52A1 is the distance between the vehicle 48 and the obstacle in the driving direction of the vehicle 48 (in Figure 4In the example shown, it is the distance to the vehicle 53, which is a vehicle in front of the vehicle 48. The travel speed 52A2 is the travel speed of the vehicle 48.

[0074] Correct answer data 54 is correct answer data (ie, comment) for example question data 52. Correct answer data 54 has correct answer distance 54A. Correct answer distance 54A is an example of the "first distance" involved in the technology of the present disclosure.

[0075] Correct distance 54A is the distance obtained by adding necessary stopping distance 54A1 and margin distance 54A2. Necessary stopping distance 54A1 is the distance between vehicle 48 and vehicle 53 required for vehicle 48 to stop without colliding with vehicle 53. Vehicle 53 is an obstacle in the direction of travel of vehicle 48 and is traveling in front of vehicle 48. Figure 4 In the example shown, a vehicle 53 traveling in front of the vehicle 48 is illustrated, but the technology disclosed in the present invention is not limited to this. It may also be a vehicle 53 stopped in front of the vehicle 48, or a pedestrian existing in front of the vehicle 48, as long as it is an obstacle in the traveling direction of the vehicle 48.

[0076] The margin distance 54A2 is a distance (for example, a distance of about several meters) additionally determined in order to increase the certainty of preventing the vehicle 48 from colliding with the vehicle 53 .

[0077] Figure 5 1 is a schematic diagram showing an example of the processing contents of the information acquisition unit 10 , the estimation unit 20 , and the driving control unit 30 included in the information processing device 1B according to the third embodiment.

[0078] The information acquisition unit 10 acquires environmental information 56 in the same manner as in the first embodiment. The environmental information 56 includes information such as vehicle obstacle distance 56A, travel speed 56B, air resistance 56C, road resistance 56D, road element 56E, and ground inclination 56F.

[0079] 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 an index value 58 corresponding to the environmental information 56 (i.e., an index value related to the correct distance 54A corresponding to the environmental information 56). The inference unit 20 acquires the index value 58 output from the deep learning model 20A. The index value 58 is an index value inferred by the deep learning model 20A as a non-collision distance. The non-collision distance refers to, for example, the distance between the vehicle 48 and an obstacle in the direction of travel of the vehicle 48 (in Figure 4In the example shown, it is the distance at which the vehicle 53) will not collide. The index value 58 may also be an index value obtained by performing the multivariate analysis based on the integration method described in the first embodiment. It should be noted that the index value 58 is an example of the "first index value" involved in the technology disclosed in the present invention.

[0080] The driving control unit 30 uses the driving speed calculation formula 60 based on the index value 58 obtained by the inference unit 20 to calculate the maximum speed 62 at which the vehicle 48 will not collide with an obstacle in the driving direction (for example, a vehicle or pedestrian in front of the vehicle 48). The driving speed calculation formula 60 is a calculation formula that uses the index value 58 as an independent variable and the maximum speed 62 as a dependent variable. The maximum speed 62 refers to the maximum speed within a range below the legal speed and below the speed of the vehicle in front. The speed of the vehicle in front is the speed of other vehicles traveling in front of the vehicle 48 (for example, the previous vehicle in the driving direction of the vehicle 48). For example, the speed of the vehicle in front is obtained by the information acquisition unit 10, etc. The driving control unit 30 calculates the maximum speed 62 with reference to the speed of the vehicle in front obtained by the information acquisition unit 10, etc.

[0081] The driving control unit 30 performs driving control of the vehicle based on the plurality of index values ​​estimated by the estimation unit 20. The driving control includes driving speed control. The driving speed control is control of the driving speed of the vehicle 48, and is performed based on the index value 58. The driving speed control includes control to make the vehicle 48 travel at the maximum speed 62. That is, the driving control unit 30 controls the drive system of the vehicle 48 (e.g., a power source that transmits power to the wheels) so that the vehicle 48 travels at the maximum speed 62 calculated based on the index value 58.

[0082] Figure 6 This is a flowchart showing an example of the flow of the traveling speed control process executed by the information processing device 1B.

[0083] exist Figure 6 In the illustrated driving speed control process, first, in step ST10, the information acquisition unit 10 determines whether the timing specified in nanoseconds has arrived (for example, whether one nanosecond has passed). In step ST10, if the timing specified in nanoseconds has not arrived, the determination result is negative, and the driving speed control process transfers to step ST22. In step ST10, if the timing specified in nanoseconds has arrived, the determination result is positive, and the driving speed control process transfers to step ST12.

[0084] In step ST12, the information acquisition unit 10 acquires the environmental information 56. After the process of step ST12 is executed, the traveling speed control process proceeds to step ST14.

[0085] In step ST14, the inference unit 20 inputs the environmental information 56 acquired by the information acquisition unit 10 in step ST12 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 process of step ST14 is executed, the driving speed control process is transferred to step ST16.

[0086] In step ST16, the inference unit 20 acquires the index value 58 output from the deep learning model 20A. After the process of step ST16 is executed, the traveling speed control process proceeds to step ST18.

[0087] In step ST18, the driving control unit 30 calculates the maximum speed 62 using the traveling speed calculation formula 60 based on the index value 58 acquired by the estimation unit 20 in step ST16. After the process of step ST18 is executed, the traveling speed control process proceeds to step ST20.

[0088] In step ST20, the driving control unit 30 controls the driving system of the vehicle 48 so that the vehicle 48 travels at the maximum speed 62 calculated in step ST18. After the process of step ST20 is executed, the travel speed control process proceeds to step ST22.

[0089] In step ST22, the driving control unit 30 determines whether the end condition of the traveling speed control process is satisfied. As an example of the end condition of the traveling speed control process, there can be cited a condition that an instruction to end the traveling speed control process is given to the information processing device 1B. In step ST22, if the end condition of the traveling speed control process is not satisfied, the determination result is negative, and the traveling speed control process is transferred to step ST10. In step ST22, if the end condition of the traveling speed control process is satisfied, the determination result is positive, and the traveling speed control process is terminated.

[0090] As described above, in the third embodiment, the inference unit 20 infers the index value 58 related to the correct distance 54A, which includes the necessary stopping distance 54A1 required for the vehicle 48 to stop without colliding with the obstacle between the vehicle 48 and the obstacle in the traveling direction of the vehicle 48. And the driving control unit 30 controls the traveling speed of the vehicle 48 based on the index value 58. Therefore, the vehicle 48 can travel to the destination safely and in a short time.

[0091] Furthermore, in the third embodiment, the index value 58 is estimated in nanoseconds, and the maximum speed 62 is calculated based on the index value 58. The maximum speed 62 is the maximum speed at which the vehicle 48 does not collide with an obstacle, and the driving system of the vehicle 48 is controlled by the driving control unit 30 so that the vehicle 48 travels at the maximum speed 62. Therefore, the vehicle 48 can travel to the destination safely and in the shortest time.

[0092] In the third embodiment, the maximum speed 62 is calculated within a range below the legal speed and below the speed of the vehicle ahead. Therefore, the vehicle 48 can travel to the destination safely and in the shortest time while complying with the legal speed.

[0093] In the third embodiment, the distance obtained by adding the necessary stopping distance 54A1 and the margin distance 54A2 is used as the correct distance 54A. Therefore, the possibility of collision between the vehicle 48 and the vehicle 53 can be reduced compared to the case where only the necessary stopping distance 54A1 is used as the correct distance 54A.

[0094] It should be noted that, in the third embodiment described above, as an example of the index value 58, an index value inferred by the deep learning model 20A as the non-collision distance is exemplified, but the technology disclosed in the present invention is not limited thereto. For example, as the index value 58, an index value indicating the maximum speed 62 may be inferred by the deep learning model 20A. In this case, for example, the driving control unit 30 controls the driving system of the vehicle 48 so that the vehicle 48 travels at the maximum speed 62 indicated by the index value 58.

[0095] In the third embodiment, the maximum speed 62 is calculated, but this is only an example, and a speed higher than the designated speed and lower than the maximum speed 62 may be calculated. In addition, the number of rotations of the wheels may be calculated instead of the speed, as long as information related to the speed (i.e., a parameter for controlling the speed of the vehicle 48) is calculated.

[0096] In the third embodiment, the distance obtained by adding the necessary stopping distance 54A1 and the margin distance 54A2 is cited as the correct distance 54A. However, this is merely an example, and only the necessary stopping distance 54A1 may be used as the correct distance 54A.

[0097] (Fourth Embodiment)

[0098] In the fourth embodiment described below, an information processing device and the like that can realize highly accurate autonomous driving taking into account the actual environment in which the vehicle travels until it reaches the destination will be mainly described.

[0099] Figure 7It is a schematic diagram showing an example of the configuration of an information processing device 1C according to the fourth embodiment of the present disclosure.

[0100] The information processing device 1C is mounted on a plurality of vehicles 54 (see also Figure 8 ). The information processing device 1C includes an information acquisition unit 10, an inference unit 20, a driving control unit 30, and a strategy setting unit 40, similarly to the first embodiment. The driving control unit 30 includes a strategy updating unit 31. Figure 7 In the illustrated example, the strategy updating unit 31 is illustrated as a part of the driving control unit 30 , but this is merely an example, and the strategy updating unit 31 may be provided outside the driving control unit 30 .

[0101] The information acquisition unit 10 acquires the environmental information 56 in the same manner as in the first embodiment. The environmental information 56 includes a travel route 56A as a route traveled by the vehicle 54 (for example, a route traveled by the vehicle 54 and defined by coordinates), a travel speed 56B as a speed at which the vehicle 54 travels, a ground inclination 56C equivalent to the tilt listed in the first embodiment, a brake control value 56D (in other words, a parameter for controlling the brakes), air resistance 56E, road resistance 56F, and road elements 56G.

[0102] The inference unit 20 includes a deep learning model 20A. The deep learning model 20A is a learned model optimized by performing deep learning on a neural network using a plurality of supervisory data. The inference unit 20 infers a plurality of index values ​​from the environmental information 56 using the deep learning model 20A.

[0103] The supervised data used in deep learning of the deep learning model 20A is a data set including example data and correct answer data (ie, annotations) for the example data. The example data is data assuming environmental information 56, and the correct answer data is data assuming multiple index values ​​58.

[0104] The inference unit 20 inputs the environment information 56 acquired by the information acquisition unit 10 to the deep learning model 20A. As a result, the deep learning model 20A outputs a plurality of index values ​​58 corresponding to the environment information 56 (ie, a plurality of control values ​​used in the automatic driving control of the vehicle 54).

[0105] The strategy updating unit 31 acquires a plurality of index values ​​58 output from the deep learning model 20A. The strategy setting unit 40 sets a driving strategy 66 until the vehicle 54 reaches the destination. Here, setting of the driving strategy 66 means, for example, a process of providing the driving strategy 66 to the strategy updating unit 31 .

[0106] The driving strategy 66 includes a plurality of theoretical values ​​68. The plurality of theoretical values ​​68 include a theoretical value of a route until the vehicle 54 reaches the destination (for example, a route assumed as the route traveled by the vehicle 54 and defined by coordinates), a theoretical value of a travel speed until the vehicle 54 reaches the destination, a theoretical value of a ground inclination until the vehicle 54 reaches the destination, and a theoretical value of a brake control value until the vehicle 54 reaches the destination.

[0107] The strategy updating unit 31 updates the driving strategy 66 based on the driving strategy 66 set by the strategy setting unit 40 and the plurality of index values ​​58 output from the inference unit 20. For example, the strategy updating unit 31 updates the plurality of theoretical values ​​68 based on the difference 70 between the plurality of theoretical values ​​68 included in the driving strategy 66 and the plurality of index values ​​58, thereby updating the driving strategy 66.

[0108] The so-called update of the theoretical value 68, in other words, represents the derivation of the optimal solution. As a first example of the optimal solution, we can cite: the index value 58 used instead of the theoretical value 68. As a second example of the optimal solution, we can cite: the adjusted index value 58 used instead of the theoretical value 68. As a third example of the optimal solution, we can cite: the solution obtained by performing a regression analysis using at least one theoretical value 68 and at least one index value 58. As a fourth example of the optimal solution, we can cite: the statistical value (for example, the median and / or the average, etc.) obtained based on the theoretical value 68 and the index value 58. In which case of the first to fourth examples, the strategy update unit 31 derives the optimal solution can be determined, for example, based on the size of the difference 70. It should be noted that it is not limited to the first to fourth examples, and it can also be an optimal solution obtained by other methods.

[0109] Here, the difference 70 between multiple theoretical values ​​68 and multiple index values ​​58 refers to: the difference between the theoretical value 68 and the index value 58 for each of multiple items (for example, the route taken by the vehicle 54 until it reaches the destination, the driving speed of the vehicle 54 until it reaches the destination, the ground inclination of the vehicle 54 until it reaches the destination, the braking control value of the vehicle 54 until it reaches the destination, etc.).

[0110] It should be noted that the difference value 70 is an example of "the result obtained by comparing multiple theoretical values ​​with multiple index values" involved in the technology disclosed in the present invention. Although the difference value 70 is listed here, it is only an example. For example, it can also be a ratio of one of the theoretical value 68 and the index value 58 to the other, as long as it is a numerical value that can determine the degree of difference between the multiple theoretical values ​​and the multiple index values.

[0111] The driving control unit 30 performs driving control of the vehicle 54 based on the driving strategy 66 (for example, the updated plurality of theoretical values ​​68 ) updated by the strategy updating unit 31 .

[0112] Figure 8 1 is a schematic diagram showing an example of how the driving strategy 66 of the first vehicle 54A among the plurality of vehicles 54 equipped with the information processing device 1C according to the fourth embodiment of the present disclosure is updated based on the plurality of index values ​​58 estimated by the second vehicle 54B.

[0113] The first vehicle 54A and the second vehicle 54B travel toward the same destination, and the second vehicle 54B travels ahead of the first vehicle 54A with respect to the destination. That is, the second vehicle 54B arrives at the destination earlier than the first vehicle 54A.

[0114] Data on the plurality of vehicles 54 are collected and managed by a data management device 72. The data management device 72 is wirelessly connected to a plurality of information processing devices 1C mounted on the plurality of vehicles 54. An example of the data management device 72 is a server.

[0115] The data management device 72 constructs a database 74 based on various information obtained from the information processing device 1C of each vehicle 54. The database 74 has a vehicle identifier 76 and a plurality of index values ​​58. The vehicle identifier 76 is an identifier capable of identifying the vehicle 54. In the database 74, for each vehicle 54, the vehicle identifier 76 is associated with a plurality of index values ​​58 (i.e., a plurality of index values ​​58 inferred by the inference unit 20 included in the information processing device 1C of the vehicle 54 identified from the corresponding vehicle identifier 76).

[0116] Before the first vehicle 54A arrives at the destination and during the driving of the first vehicle 54A, the strategy update unit 31 included in the information processing device 1C of the first vehicle 54A (hereinafter referred to as the "strategy update unit 31 of the first vehicle 54A") obtains from the data management device 72 a plurality of index values ​​58 inferred by the inference unit 20 included in the information processing device 1C of the second vehicle 54B (hereinafter referred to as the "inference unit 20 of the second vehicle 54B").

[0117] For example, the data management device 72 obtains the multiple index values ​​58 corresponding to the second vehicle 54B from the database 74 according to the request from the strategy update unit 31 of the first vehicle 54A, and sends the multiple index values ​​58 obtained from the database 74 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 sent from the data management device 72, and updates the driving strategy 66 based on the received multiple index values ​​58.

[0118] For example, the updating of the driving strategy 66 is achieved by updating the plurality of theoretical values ​​68 based on the result of comparing the plurality of theoretical values ​​68 included in the driving strategy 66 with the plurality of received index values ​​58. Here, the updating of the theoretical values ​​68 can also be performed in the same manner as in the first to fourth examples described above.

[0119] Fig. 9 1 is a flowchart showing an example of the flow of the driving strategy update process executed by the information processing device 1C of the first vehicle 54A.

[0120] exist Fig. 9 In the driving strategy update process shown, in step ST30, the strategy setting unit 40 sets the driving strategy 66 for the strategy update unit 31. After the process of step ST30 is executed, the driving strategy update process proceeds to step ST32.

[0121] In step ST32, the strategy update unit 31 determines whether the timing specified in nanoseconds has arrived (for example, whether one nanosecond has passed). In step ST32, if the timing specified in nanoseconds has not arrived, the determination result is negative, and the driving strategy update process moves to step ST32. In step ST32, if the timing specified in nanoseconds has arrived, the determination result is positive, and the driving strategy update process moves to step ST34.

[0122] In step ST34, the policy update unit 31 determines whether the second vehicle information acquisition timing has arrived. The second vehicle information acquisition timing refers to the timing at which the policy update unit 31 acquires the plurality of index values ​​58 estimated by the estimation unit 20 of the second vehicle 54B. As a first example of the second vehicle information acquisition timing, a timing that satisfies the condition that the index value 58 related to the second vehicle 54B among the index values ​​58 stored in the database 74 has been updated can be cited. In addition, as a second example of the second vehicle information acquisition timing, a timing that satisfies the condition that a predetermined time (for example, several seconds) has passed since the processing of step ST32 was executed can be cited.

[0123] In step ST34, if the second vehicle information acquisition timing has not arrived, the determination result is negative, and the driving strategy update process moves to step ST40. In step ST34, if the second vehicle information acquisition timing has arrived, the determination result is positive, and the driving strategy update process moves to step ST36.

[0124] In step ST36, the strategy update unit 31 obtains a plurality of index values ​​58 related to the second vehicle 54B (i.e., a plurality of index values ​​58 associated with the vehicle identifier 76 that can identify the second vehicle 54B) from the data management device 72. After executing the process of step ST36, the driving strategy update process moves to step ST38.

[0125] In step ST38, the strategy update unit 31 updates the driving strategy 66 based on the plurality of index values ​​58 acquired from the data management device 72 in step ST36. That is, the plurality of theoretical values ​​68 included in the driving strategy 66 are updated based on the plurality of index values ​​58 acquired from the data management device 72. After the process of step ST38 is executed, the driving strategy update process is transferred to step ST40.

[0126] In step ST40, the information acquisition unit 10 acquires the environmental information 56. After the process of step ST32 is executed, the driving strategy update process proceeds to step ST42.

[0127] In step ST42, the inference unit 20 inputs the environmental information 56 acquired by the information acquisition unit 10 in step ST40 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 executing the process of step ST42, the driving strategy update process transfers to step ST44.

[0128] In step ST44, the strategy updating unit 31 acquires a plurality of index values ​​58 output from the deep learning model 20A. After the process of step ST44 is executed, the driving strategy updating process proceeds to step ST46.

[0129] In step ST46, the strategy updating unit 31 calculates the difference 70 between the plurality of index values ​​58 and the plurality of theoretical values ​​68 included in the driving strategy 66. After the process of step ST46 is executed, the driving strategy updating process proceeds to step ST48.

[0130] In step ST48, the strategy update unit 31 updates the plurality of theoretical values ​​68 included in the driving strategy 66 based on the difference 70 calculated in step ST46, thereby updating the driving strategy 66. After the process of step ST48 is executed, the driving strategy update process moves to step ST50.

[0131] In step ST50, the driving control unit 30 executes driving control of the vehicle 54 based on the driving strategy 66 updated in step ST48. After the process of step ST40 is executed, the driving strategy update process proceeds to step ST42.

[0132] In step ST52, the strategy update unit 31 determines whether the end condition of the driving strategy update process is satisfied. As an example of the end condition of the driving strategy update process, a condition in which an instruction to end the driving strategy update process is given to the information processing device 1C can be cited. In step ST52, if the end condition of the driving strategy update process is not satisfied, the determination result is negative, and the driving strategy update process is transferred to step ST32. In step ST52, if the end condition of the driving strategy update process is satisfied, the determination result is positive, and the driving strategy update process is terminated.

[0133] As described above, in the fourth embodiment, the information processing device 1C is mounted on a plurality of vehicles 54 including a first vehicle 54A and a second vehicle 54B, and the information processing device 1C includes the information acquisition unit 10, the estimation unit 20, the driving control unit 30, the strategy updating unit 31, and the strategy setting unit 40 described in the first and second embodiments. Before the first vehicle 54A arrives at the destination, the strategy updating unit 31 updates the driving strategy 66 set by the strategy setting unit 40 based on the plurality of index values ​​58 estimated by the estimation unit 20 of the second vehicle 54B that travels ahead of the first vehicle 54A with respect to the destination. Thus, the information processing device 1C of the first vehicle 54A can obtain the driving strategy 66 that conforms to the actual environment until the vehicle arrives at the destination. As a result, it is possible to realize high-precision automatic driving that takes into account the actual environment until the vehicle 54A travels to the destination.

[0134] In addition, in the fourth embodiment, before the first vehicle 54A reaches the destination and while the first vehicle 54A is traveling, the strategy updating unit 31 of the first vehicle 54A obtains a plurality of index values ​​58 (i.e., a plurality of index values ​​58 estimated by the estimation unit 20 of the second vehicle 54B) for updating the driving strategy 66. Therefore, it is possible to realize high-precision automatic driving that takes into account the actual environment until the first vehicle 54A reaches the destination before the first vehicle 54A reaches the destination and while the first vehicle 54A is traveling.

[0135] In addition, in the fourth embodiment, before the second vehicle 54B arrives at the destination, the strategy updating unit 31 of the first vehicle 54A acquires the plurality of index values ​​58 for updating the driving strategy 66 (i.e., the plurality of index values ​​58 estimated by the estimation unit 20 of the second vehicle 54B). Therefore, compared with the case where the strategy updating unit 31 of the first vehicle 54A acquires the plurality of index values ​​58 estimated by the estimation unit 20 of the second vehicle 54B after the second vehicle 54B arrives at the destination, the strategy updating unit 31 of the first vehicle 54A can update the driving strategy 66 earlier.

[0136] In addition, in the fourth embodiment, the strategy updating unit 31 updates the plurality of theoretical values ​​68 included in the driving strategy 66 set by the strategy setting unit 40 based on the plurality of index values ​​58 estimated by the estimation unit 20 of the second vehicle 54B that is traveling ahead of the first vehicle 54A with respect to the destination before the first vehicle 54A arrives at the destination. Then, the driving control unit 30 of the first vehicle 54A performs driving control of the first vehicle 54A based on the plurality of theoretical values ​​68 updated by the strategy updating unit 31. Thus, compared with the case where the plurality of theoretical values ​​68 included in the driving strategy 66 are always fixed, it is possible to realize high-precision automatic driving that takes into account the actual environment in which the first vehicle 54A travels until it reaches the destination.

[0137] In addition, in the fourth embodiment, the data management device 72 collects and manages a plurality of index values ​​58 from a plurality of vehicles 54. And, the strategy update unit 31 of the first vehicle 54A updates the driving strategy 66 based on the plurality of index values ​​58 acquired from the data management device 72. Therefore, the driving control unit 30 can perform the automatic driving of the first vehicle 54A according to the driving strategy 66 updated based on the plurality of index values ​​58 acquired from a designated vehicle 54 (e.g., the second vehicle 54B) among the plurality of vehicles 54.

[0138] It should be noted that, in the fourth embodiment, an example of the difference 70 being used to update the driving strategy 66 is cited, but the use of the difference 70 is not limited thereto. Fig.10 As shown, the information processing device 1C may also be configured to further include a notification unit 78, and the notification unit 78 performs notification corresponding to the difference 70. For example, the notification unit 78 determines whether the difference 70 exceeds a threshold value, and when the difference 70 exceeds the threshold value, notifies that the difference 70 exceeds the threshold value. The threshold value may be a fixed value (e.g., a default value), or may be a variable value that is changed according to an instruction provided to the information processing device 1C by a user or the like. The notification is implemented, for example, by visual display using a display and / or audio output using an audio playback device.

[0139] In addition, instead of making a notification each time the difference 70 is calculated, a notification may be made when the number of times the difference 70 exceeds a threshold value (for example, the number of times the difference 70 continues to exceed the threshold value) exceeds a predetermined number of times (for example, a number determined based on instructions provided by a user or the like to the information processing device 1C); or a notification may be made when the number of times the difference 70 exceeds the threshold value exceeds a predetermined number of times within a predetermined period (for example, a period determined based on instructions provided by a user or the like to the information processing device 1C).

[0140] In addition, the difference 70 used for comparison with the threshold value can be the difference between the theoretical value 68 and the index value 58 for one item among multiple items (for example, the route taken by the vehicle 54 until it reaches the destination, the driving speed of the vehicle 54 until it reaches the destination, the inclination of the ground when the vehicle 54 reaches the destination, the braking control value when the vehicle 54 reaches the destination, etc.), or it can also be the difference between the theoretical value 68 and the index value 58 for each of the multiple items.

[0141] In the above-mentioned embodiments, the difference between the theoretical value 68 and the index value 58 (for example, the difference 70) is shown as an example, but this is only an example, and any value can be used as long as the result of comparing the theoretical value 68 with the index value 58 can be determined. As a value that can determine the result of comparing the theoretical value 68 with the index value 58, in addition to the difference, the ratio (in other words, ratio) of one of the theoretical value 68 and the index value 58 to the other can be cited.

[0142] In the fourth embodiment, although an example is given in which the policy updating unit 31 of the first vehicle 54A obtains the plurality of index values ​​58 corresponding to the second vehicle 54B before the second vehicle 54B arrives at the destination, this is only an example. For example, the policy updating unit 31 of the first vehicle 54A may obtain the plurality of index values ​​58 corresponding to the second vehicle 54B after the second vehicle 54B arrives at the destination.

[0143] In the fourth embodiment described above, an example is given of a method in which the policy updating 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 72, but the technology of the present disclosure is not limited thereto. For example, the policy updating unit 31 of the first vehicle 54A may acquire a plurality of index values ​​58 estimated by the estimation unit 20 from the information processing device 1C of the second vehicle 54B by directly communicating between the information processing device 1C of the first vehicle 54A and the information processing device 1C of the second vehicle 54B. In addition, for example, whenever the index value 58 is estimated by the estimation unit 20 of the second vehicle 54B (for example, whenever the index value 58 corresponding to the second vehicle 54B is updated in the data management device 72), the index value 58 estimated by the estimation unit 20 of the second vehicle 54B may be acquired by the policy updating unit 31 of the first vehicle 54A and used for updating the driving strategy 66.

[0144] In the fourth embodiment, the plurality of theoretical values ​​68 include the theoretical value of the route until the vehicle 54 reaches the destination, the theoretical value of the travel speed until the vehicle 54 reaches the destination, the theoretical value of the inclination of the ground until the vehicle 54 reaches the destination, and the theoretical value of the braking control value until the vehicle 54 reaches the destination. However, this is only an example. For example, the plurality of theoretical values ​​68 may include at least one of the theoretical value of the route until the vehicle 54 reaches the destination, the theoretical value of the travel speed until the vehicle 54 reaches the destination, the theoretical value of the inclination of the ground until the vehicle 54 reaches the destination, and the theoretical value of the braking control value until the vehicle 54 reaches the destination.

[0145] In the fourth embodiment described above, an example of a method in which the strategy update unit 31 of the first vehicle 54A acquires the multiple index values ​​58 for updating the driving strategy 66 during the driving of the first vehicle 54A is cited, but the technology of the present disclosure is not limited thereto. For example, before the first vehicle 54A reaches the destination and while the first vehicle 54A stops, the strategy update unit 31 of the first vehicle 54A acquires the multiple index values ​​58 for updating the driving strategy 66. Thus, even if the first vehicle 54A stops before reaching the destination, high-precision automatic driving can be achieved that takes into account the actual environment in which the first vehicle 54A travels until it reaches the destination.

[0146] In the fourth embodiment, although an example is given in which the index value 58 estimated by the estimation unit 20 of the second vehicle 54B is directly used to update the driving strategy 66 of the first vehicle 54A, this is only an example. For example, the index value 58 (i.e., the index value 58 estimated by the estimation unit 20 of the second vehicle 54B) may be adjusted according to an adjustment value (e.g., a weight) determined according to the confidence of the index value 58 estimated by the estimation unit 20 of the second vehicle 54B, and the theoretical value 68 may be updated based on the adjusted index value 58 in the same manner as in the fourth embodiment.

[0147] For example, the confidence level of the index value 58 estimated by the estimation unit 20 of the second vehicle 54B, when the first vehicle 54A and the second vehicle 54B pass through the same place (hereinafter referred to as the "first place"), varies according to the time interval between the time when the index value 58 is estimated by the estimation unit 20 of the second vehicle 54B when traveling at the first place and the time when the first vehicle 54A travels at the first place. For example, the longer the time interval between the time when the index value 58 is estimated by the estimation unit 20 of the second vehicle 54B when traveling at the first place and the time when the first vehicle 54A travels at the first place, the lower the confidence level of the index value 58 estimated by the estimation unit 20 of the second vehicle 54B. On the contrary, the shorter the time interval between the time when the index value 58 is estimated by the estimation unit 20 of the second vehicle 54B when traveling at the first place and the time when the first vehicle 54A travels, the higher the confidence level of the index value 58 estimated by the estimation unit 20 of the second vehicle 54B.

[0148] Therefore, the longer the above-mentioned time interval is, 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 traveling at the first location) on the theoretical value 68 when updating the theoretical value 68, and updates the theoretical value 68 based on the adjusted index value 58. In addition, the shorter the above-mentioned time interval is, 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 traveling at the first location) on the theoretical value 68 when updating the theoretical value 68, and updates the theoretical value 68 based on the adjusted index value 58.

[0149] In addition, here, the time interval between the time when the index value 58 is estimated by the estimation unit 20 of the second vehicle 54B when traveling at the first location and the time when the first vehicle 54A is traveling is illustrated, but this is only an example. For example, the index value 58 (i.e., the index value 58 estimated by the estimation unit 20 of the second vehicle 54B) may be adjusted according to an adjustment value (e.g., weight) determined based on the difference between the external environment when the second vehicle 54B is traveling at the first location and the external environment when the first vehicle 54A is traveling at the first location, and the theoretical value 68 may be updated based on the adjusted index value 58 in the same manner as in the fourth embodiment described above.

[0150] In this case, for example, when the external environment when the second vehicle 54B is traveling at the first location is exactly the same as the external environment when the first vehicle 54A is traveling at the first location, no adjustment value is required (i.e., no adjustment of the index value 58 is required). The greater the difference between the external environment when the second vehicle 54B is traveling at the first location and the external environment when the first vehicle 54A is traveling at the first location, the larger the adjustment value is 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 68 can be updated according to the adjusted index value 58 with the same key points as the fourth embodiment described above.

[0151] It should be noted that the so-called external environment refers to the environment outside the vehicle 54. As an example of the external environment, meteorological conditions can be cited. The so-called meteorological conditions, for example, refer to 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 is traveling at the first location and the external environment when the first vehicle 54A is traveling at the first location can be determined, for example, based on the difference (for example, difference or ratio, etc.) between the meteorological condition associated index value (i.e., index value 58 related to the meteorological condition) inferred by the inference unit 20 of the first vehicle 54A and the meteorological condition associated index value inferred by the inference unit 20 of the second vehicle 54B.

[0152] (Replenish)

[0153] Fig.11 An example of the hardware configuration of a computer 1200 that functions as the above-mentioned information processing device (e.g., central brain) is schematically shown. The program installed in the computer 1200 can make the computer 1200 function as one or more "parts" of the device involved in the above-mentioned embodiments, or make the computer 1200 perform operations associated with the device involved in the above-mentioned embodiments or the one or more "parts", and / or can make the computer 1200 perform the process involved in the above-mentioned embodiments or the stage of the process. Such a program can be executed by the CPU 1212 to make the computer 1200 perform specific operations associated with some or all of the blocks in the flowcharts and block diagrams described in this specification.

[0154] The computer 1200 according to various embodiments may include a CPU 1212, a RAM 1214, and a graphics controller 1216 that may be connected to each other through a host controller 1210. The computer 1200 may further include a communication interface 1222, a storage device 1224, an input / output unit such as a DVD drive and an IC card drive, which may be connected to the host controller 1210 via the 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 further include a ROM 1230 and a conventional input / output unit such as a keyboard, which may be connected to the input / output controller 1220 via an input / output chip 1240.

[0155] The CPU 1212 can control each unit by operating according to the program stored in the ROM 1230 and the RAM 1214. The graphic controller 1216 can obtain image data generated by the CPU 1212 from a frame buffer or the like provided in the RAM 1214 or in itself, and display the image data on the display device 1218.

[0156] 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.

[0157] The ROM 1230 can store therein a boot program or the like executed by the computer 1200 at startup, and / or a program depending on the hardware of the computer 1200. The input / output chip 1240 can also connect various input / output units to the input / output controller 1220 via a USB port, a parallel port, a serial port, a keyboard port, a mouse port, and the like.

[0158] 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, RAM 1214, or ROM 1230, which is also an example of a computer-readable storage medium, and executed by the CPU 1212. The information processing described in these programs can be read by the computer 1200, and the program and the aforementioned various types of hardware resources can be coordinated. The device or method can be configured by realizing the operation or processing of information according to the use of the computer 1200.

[0159] For example, when communication is performed between the computer 1200 and an external device, the CPU 1212 can execute a communication program loaded into the RAM 1214, and 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 provided in the RAM 1214, the storage device 1224, a recording medium such as a DVD-ROM or an IC card, and transmit the read transmission data to the network, or write the reception data received from the network to the reception buffer provided on the recording medium, etc.

[0160] In addition, the CPU 1212 can cause 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. Next, the CPU 1212 can write the processed data back to the external recording medium.

[0161] Various types of information such as various types of programs, data, tables, and databases can be stored in the recording medium to receive information processing. The CPU 1212 can perform various types of processing on the data read from the RAM 1214, and write the results back to the RAM 1214. The various types of processing include various types of operations, information processing, conditional judgments, conditional branches, unconditional branches, information retrieval / replacement, etc. recorded in various places of the present disclosure and specified by the instruction sequence of the program. In addition, the CPU 1212 can retrieve information in files, databases, etc. in the recording medium. For example, in the case where multiple entries each having an attribute value of a first attribute associated with an attribute value of a second attribute are stored in the recording medium, the CPU 1212 can retrieve an entry that is consistent with the condition specifying the attribute value of the first attribute from the multiple entries, and read the attribute value of the second attribute stored in the entry, thereby obtaining the attribute value of the second attribute associated with the first attribute that meets the predetermined condition.

[0162] The program or software module described above may be stored in a computer-readable storage medium on or near the computer 1200. In addition, a recording medium such as a hard disk or RAM provided in a server system connected to a dedicated communication network or the Internet may be used as a computer-readable storage medium, thereby enabling the program to be provided to the computer 1200 via the network.

[0163] The flowcharts and boxes in the block diagrams in this embodiment may represent the stages of the process of performing an operation or the "parts" of the device having the function of performing an operation. Specific stages and "parts" may be implemented by dedicated circuits, programmable circuits supplied together with computer-readable instructions stored on a computer-readable storage medium, and / or processors supplied together with computer-readable instructions stored on a computer-readable storage medium. Dedicated circuits may include digital and / or analog hardware circuits, and may also include integrated circuits (ICs) and / or discrete circuits. Programmable circuits may include reconfigurable hardware circuits such as field programmable gate arrays (FPGAs) and programmable logic arrays (PLAs), which include logical AND, logical OR, logical XOR, logical NAND, logical NOR, and other logical operations, triggers, registers, and storage elements.

[0164] Computer-readable storage media may include any tangible device capable of storing instructions executed by an appropriate device, with the result that a computer-readable storage medium having instructions stored in a tangible device has a product including instructions that can be executed to generate a unit for performing the operations specified in the flowchart or block diagram. Examples of computer-readable storage media 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 may include a floppy disk (registered trademark) disk, a magnetic disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an electrically erasable programmable read-only memory (EEPROM), a static random access memory (SRAM), a compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), a Blu-ray disc (Blu-ray (registered trademark) Disk), a memory stick, an integrated circuit card, and the like.

[0165] Computer readable instructions may include assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state setting data, or any source code or object code described in any combination of one or more programming languages, wherein the one or more programming languages ​​include object-oriented programming languages ​​such as Smalltalk (registered trademark), JAVA (registered trademark), C++, etc. and traditional procedural programming languages ​​such as the "C" programming language or similar programming languages.

[0166] The computer-readable instructions can be provided to a processor or programmable circuit of a general-purpose computer, a special-purpose computer or other programmable data processing device locally or through a local LAN (Local Area Network, LAN), a wide area network (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 the computer-readable instructions to generate a unit for performing the operations specified in the flowchart or block diagram. Examples of processors include computer processors, processing units, microprocessors, digital signal processors, controllers, microcontrollers, etc.

[0167] The above embodiments are used to illustrate the technology of the present invention, but the technical scope of the present invention is not limited to the scope described in the above embodiments. It should be clear to those skilled in the art that various changes or improvements can be made to the above embodiments. It can be seen from the description in the claims that the embodiments with such changes or improvements can also be included in the technical scope of the present invention.

[0168] It should be noted that the execution order of each process such as actions, sequences, steps and stages in the devices, systems, programs and methods shown in the claims, specifications and drawings is not specifically indicated as "before", "earlier than", etc., or as long as the output of the previous process is not used in the subsequent process, it can be implemented in any order. Even if the action flow in the claims, specifications and drawings is described using "first", "next", etc. for convenience, it does not mean that it must be implemented in this order.

[0169] As used in this specification, "α and / or β" has the same meaning as "at least one of α and β". That is, "α and / or β" means that it can be only α, only β, or a combination of α and β. In addition, in this specification, when "and / or" is used to connect and express three or more items, the same understanding as "α and / or β" is also applicable.

[0170] This application is based on Special Application No. 2022-170165 filed in Japan on October 24, 2022, Special Application No. 2022-196484 filed on December 8, 2022, and Special Application No. 2022-196485 filed on December 8, 2022, the contents of which are incorporated herein as part of the contents of this application.

[0171] All documents, including publications, patent applications, and patents, cited in this specification are incorporated into this specification by reference to the same extent as if each document was specifically indicated, incorporated by reference, and set forth in its entirety herein.

[0172] The use of nouns used in association with the description of the present disclosure (especially in association with the following claims) and the use of the same indicators, unless otherwise specified in this specification or clearly contradicted by the context, should be interpreted as covering both the singular and the plural. Unless otherwise specified, the terms "have", "have", "include" and "include" are interpreted as open terms (i.e., "including but not limited to"). Unless otherwise specified in this specification, the description of the numerical range in this specification is intended only as a shorthand expression for referring to each corresponding value within the range separately, and each numerical value should be incorporated into the specification as if it has been listed separately in this specification. Unless otherwise specified in this specification or clearly contradicted by the context, all methods described in this specification can be implemented in any suitable order. Unless otherwise specifically stated, any examples or exemplary expressions (e.g., "etc.") used in this specification are only intended to better illustrate the present disclosure, not to set limitations on the scope of the present disclosure. Any statement in the specification should not be interpreted as representing elements not recorded in the claims as essential elements for the implementation of the present disclosure.

[0173] In this specification, preferred embodiments of the present disclosure are described, including preferred modes of implementing the present disclosure known to the inventors. For those skilled in the art, variations of these preferred embodiments will be apparent upon reading the above description. The inventors expect that those skilled in the art will be able to apply such variations as appropriate, and expect that the present disclosure will be implemented in a manner other than that specifically described in this specification. Therefore, as permitted by applicable law, the present disclosure includes all modifications and equivalents to the contents described in the claims attached to this specification. In addition, unless specifically noted in this specification or clearly contradictory to the context, any combination of the above elements in all variations is also included in the present disclosure.

Claims

1. An information processing device, in, The information processing device comprises: an information acquisition unit, the information acquisition unit being capable of acquiring a plurality of information associated with the vehicle; an inference unit, the inference unit using deep learning to infer a plurality of index values ​​according to the plurality of information acquired by the information acquisition unit; as well as A driving control unit is configured to perform driving control of the vehicle based on the plurality of index values.

2. The information processing device according to claim 1, in, The inference unit infers the plurality of index values ​​from the plurality of information by performing a multivariate analysis based on an integration method using the deep learning.

3. The information processing device according to claim 1, in, The information acquisition unit acquires the plurality of information in units of nanoseconds, and the inference unit and the driving control unit perform inference of the plurality of index values ​​and driving control of the vehicle in units of nanoseconds using the plurality of information acquired in units of nanoseconds.

4. The information processing device according to claim 1, in, The information processing device further comprises: a strategy setting unit, the strategy setting unit setting a driving strategy until the vehicle reaches a destination, The driving strategy includes at least one theoretical value of an optimal route to the destination, a driving speed, a tilt, and a brake, The driving control unit includes: A strategy updating unit is configured to update the driving strategy based on differences between the plurality of index values ​​and the theoretical value.

5. The information processing device according to claim 1, in, The information acquisition unit comprises: A sensor is provided at the lower part of the vehicle and is capable of detecting the temperature, material and inclination of the ground on which the vehicle is traveling.

6. The information processing device according to claim 1, in, The plurality of index values ​​include a first index value associated with a first distance, the first distance including a necessary stopping distance between an obstacle in a traveling direction of the vehicle and the vehicle, the vehicle being required to stop without colliding with the obstacle, The driving control includes driving speed control, The driving speed control is control of the driving speed of the vehicle, and is performed based on the first index value.

7. The information processing device according to claim 6, in, The driving speed control includes controlling the vehicle to drive at a maximum speed at which the vehicle will not collide with the obstacle. The information related to the maximum speed is calculated based on the first index value.

8. The information processing device according to claim 7, in, The maximum speed is the maximum speed within a range below the legal speed and / or within a range below the speed of the vehicle ahead, The front vehicle speed is the travel speed of another vehicle traveling in front of the vehicle.

9. The information processing device according to claim 6, in, The first distance is a distance obtained by adding the necessary stopping distance to a margin distance.

10. An information processing device, in, The information processing device is an information processing device applicable to each of a plurality of vehicles, and comprises: an information acquisition unit capable of acquiring a plurality of information associated with the vehicle; an inference unit, the inference unit inferring a plurality of index values ​​according to the plurality of information acquired by the information acquisition unit by using a deep learning model; a strategy setting unit configured to set a driving strategy for the vehicle until the vehicle reaches a destination; a strategy updating unit configured to update the driving strategy based on the driving strategy set by the strategy setting unit and the plurality of index values; as well as a driving control unit configured to execute driving control of the vehicle according to the driving strategy updated by the strategy updating unit, The strategy updating unit updates the driving strategy set by the strategy setting unit based on the multiple index values ​​inferred by the inference unit of a second vehicle among the multiple vehicles before the first vehicle among the multiple vehicles arrives at the destination, and the second vehicle travels ahead of the first vehicle relative to the destination.

11. The information processing device according to claim 10, in, The strategy updating section acquires the plurality of index values ​​for updating the travel strategy before the first vehicle reaches the destination and during travel of the first vehicle.

12. The information processing device according to claim 10, in, The strategy updating unit acquires the plurality of index values ​​for updating the driving strategy before the second vehicle arrives at the destination.

13. The information processing device according to claim 10, in, The driving strategy includes a plurality of theoretical values. The plurality of theoretical values ​​include: a theoretical value of a route taken by the vehicle until it reaches the destination, a theoretical value of a travel speed of the vehicle until it reaches the destination, a theoretical value of a slope of the ground taken by the vehicle until it reaches the destination, and / or a theoretical value of a braking control value taken by the vehicle until it reaches the destination. The strategy updating unit updates the driving strategy by updating the plurality of theoretical values ​​based on a result of comparing the plurality of theoretical values ​​with the plurality of index values.

14. The information processing device according to claim 13, in, The information processing device comprises: A notification unit is configured to perform notification when a difference between the theoretical value and the index value exceeds a threshold value.

15. The information processing device according to claim 10, in, The policy updating unit acquires the plurality of index values ​​from a data management device, the data management device collecting and managing the plurality of index values ​​related to at least one vehicle among the plurality of vehicles, The driving strategy is updated based on the plurality of index values ​​acquired from the data management device.

16. The information processing device according to claim 10, in, The inference unit infers the plurality of index values ​​from the plurality of information by performing a multivariate analysis based on an integration method using the deep learning model.

17. The information processing device according to claim 10, in, The information acquisition unit acquires the plurality of information in units of nanoseconds, The inference unit and the driving control unit perform the inference of the plurality of index values ​​and the driving control of the vehicle in units of nanoseconds using the plurality of information acquired in units of nanoseconds.

18. The information processing device according to claim 10, in, The information acquisition unit comprises: a temperature sensor capable of detecting a temperature of a ground surface on which the vehicle travels; a material sensor capable of detecting the material of the ground; and A tilt sensor is provided, wherein the tilt sensor is capable of detecting the tilt of the ground.

19. A vehicle, in, The vehicle includes the information processing device according to any one of claims 1 to 18.

20. A procedure, in, The program is for causing a computer to function as the information processing device according to any one of claims 1 to 18.

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