Information processing device, information processing method, and storage medium

Abstract

Provided are an information processing device, an information processing method, and a storage medium that make it possible to obtain an index or the like representing the riding experience quality or the like of a vehicle.　The information processing device is installed in a vehicle, and comprises an acquisition means that acquires an image captured of a non-driver who is not driving, a measurement means that measures the stress condition of the non-driver on the basis of the image, and an output means that outputs the stress condition to a prescribed output destination.

Description

Information Processing Apparatus, Information Processing Method, and Recording Medium

[0001] The present invention relates to an information processing apparatus, an information processing method, and a recording medium.

[0002] Patent Document 1 discloses a driver management support system including a drive recorder mounted on a vehicle that acquires vital information of a driver from an image of the driver driving the vehicle, and a cloud system that predicts a physical or mental disorder of the driver based on the vital information. Further, the same document lists, as vital information acquired by the drive recorder, heart rate, blood pressure, body temperature, blood oxygen concentration, stress level, blood alcohol concentration, and the like.

[0003] Japanese Patent Application Laid-Open No. 2022-67895

[0004] In the driver management support system of Patent Document 1, as the name indicates, the monitoring target is the driver. There is a possibility that the stress level acquired as vital information includes stress caused by driving, and it is considered that it cannot be used as an index indicating the comfort of the vehicle ride or the like.

[0005] An object of the present disclosure is to provide an information processing apparatus, an information processing method, and a recording medium capable of obtaining an index or the like indicating the comfort of a vehicle ride or the like.

[0006] According to a first aspect, there is provided an information processing apparatus including an acquisition unit that acquires an image of a non-driver who is on board a vehicle and not driving, a measurement unit that measures a stress state of the non-driver based on the image, and an output unit that outputs the stress state to a predetermined output destination.

[0007] According to a second aspect, there is provided an information processing method including acquiring an image of a non-driver who is on board a vehicle and not driving, measuring a stress state of the non-driver based on the image, and outputting the stress state to a predetermined output destination.

[0008] From a third perspective, a recording medium is provided which contains a program that causes a computer to perform the following steps: acquiring images of a non-driver who is in a vehicle but not driving; measuring the stress level of the non-driver based on the images; and outputting the stress level to a predetermined output destination.

[0009] This disclosure makes it possible to provide an information processing device, an information processing method, and a recording medium that can obtain indicators such as the ride comfort of a vehicle.

[0010] This is a diagram showing one configuration of the present disclosure. This is a flowchart showing the operation of the present disclosure. This is a diagram for explaining the operation of the present disclosure. This is a diagram showing one configuration of the present disclosure. This is a functional block diagram showing an example configuration of the operation evaluation system of the present disclosure. This is a diagram showing an example of operation evaluation information managed by the operation evaluation system of the present disclosure. This is a diagram showing another example of operation evaluation information managed by the operation evaluation system of the present disclosure. This is a flowchart for explaining the operation of the present disclosure. This is a diagram showing another configuration of the present disclosure. This is a functional block diagram showing another example configuration of the operation evaluation system of the present disclosure. This is a diagram showing another example of operation evaluation information managed by the operation evaluation system of the present disclosure. This is a flowchart for explaining another operation of the present disclosure. This is a diagram showing another configuration of the present disclosure. This is a functional block diagram showing another example configuration of the operation evaluation system of the present disclosure. This is a diagram for explaining the anomaly detection operation by the operation evaluation system of the present disclosure. This is a diagram showing the configuration of the computers that constitute the operation evaluation system of the present disclosure.

[0011] First, an overview of one embodiment of this disclosure will be described with reference to the drawings. In this disclosure, the drawings are associated with one or more embodiments. The reference numerals in the drawings appended to this overview are provided for convenience as examples to aid understanding and are not intended to limit this disclosure to the illustrated embodiments. In addition, the connecting lines between blocks in the drawings and other references referred to in the following description include both bidirectional and unidirectional lines. Unidirectional arrows schematically indicate the flow of the main signal (data) and do not exclude bidirectionality. The program is executed via a computer device, which includes, for example, a processor, a storage device, an input device, a communication interface, and a display device as needed. This computer device is also configured to communicate with devices (including computers) inside or outside the device via the communication interface, whether wired or wireless. In addition, there are ports or interfaces at the input / output connection points of each block in the figures, but these are omitted from the illustration.

[0012] In one embodiment, the present disclosure can be realized by an information processing device 10 that functions as a stress evaluation system, as shown in Figure 1, comprising an acquisition means 11, a measurement means 12, and an output means 13. More specifically, the acquisition means 11 acquires images of a non-driver who is in a vehicle but not driving. The measurement means 12 measures the stress state of the non-driver based on the images. The output means 13 outputs the stress state to a predetermined output destination.

[0013] The information processing device 10 configured as described above operates as follows. First, the information processing device 10 acquires images of non-drivers who are in the vehicle but not driving (step S01 in Figure 2). Non-drivers include, for example, people sitting in seats other than the driver's seat. Also, if the vehicle is in an autonomous driving state, the person sitting in the driver's seat is also considered a non-driver. Cameras that make up an in-cabin sensing system can be used as the source for acquiring these images. Of course, cameras from drive recorders or security cameras that can film inside the vehicle can also be used.

[0014] Next, the information processing device 10 measures the stress level of the non-driver based on the image (step S02 in Figure 2). This stress level can be measured, for example, from the facial expression and eye (eyeball) movements of the person in the image. Alternatively, the stress level can be measured based on the behavior of vital sign values, such as pulse rate, estimated from the image, particularly their fluctuations. Generally, it is known that breathing becomes shallower and shorter when stress levels are high. It is also known that heart rate (pulse rate) increases when stressed. Furthermore, it is known that fluctuations in vital sign values ​​decrease when stressed. By utilizing these factors, the stress level of a person can be measured.

[0015] Next, the information processing device 10 outputs the stress state to a predetermined output destination (step S03 in Figure 2). The predetermined output destination may be a display device that can be viewed from the driver's seat, or a terminal of the vehicle operations manager, etc. For example, by presenting the measured stress state to the driver using voice or images, the driver can be informed whether or not their driving is causing stress to passengers. Possible forms of presenting the stress state to the driver include displaying images or icons at a predetermined location on a display device installed in the vehicle, or notification using voice or electronic sound. Also, for example, by presenting the measured stress state to the vehicle operations manager, they can be made aware of whether or not the driver's driving is causing stress to passengers.

[0016] Figure 3 is a diagram illustrating the operation of the present disclosure. As shown in Figure 3, the acquisition means 11 acquires images of non-drivers who are in the vehicle but not driving. In the example in Figure 3, an image of the entire interior of the vehicle, including the driver's seat on the left side of the front seats, is shown. The measurement means 12 measures the stress state of the non-drivers A to C based on such images. The output means 13 then outputs the measured stress state to a predetermined output destination.

[0017] As explained above, this disclosure makes it possible to provide indicators that show the ride comfort of a vehicle, etc.

[0018] [First Embodiment] Next, a first embodiment will be described in which driving is evaluated using an image of a non-driver captured from a vehicle equipped with a camera, etc. Figure 4 is a diagram showing one configuration of the present disclosure. Referring to Figure 4, a configuration is shown that includes a vehicle 200 equipped with a camera C, a driving evaluation system 100 that receives images from the vehicle 200, and a terminal 500 that refers to driving evaluation information created by the driving evaluation system 100.

[0019] Vehicle 200 transmits images captured by camera C to the driving evaluation system 100 at predetermined time intervals. Camera C is a camera capable of capturing images of people sitting in the passenger seat or rear seat of the vehicle. Such a camera could be an in-cabin sensing system camera, for example. Of course, a camera mounted on the user's smartphone or the like may also be used. If the image captured by camera C does not show a non-driver, vehicle 200 may suppress the transmission of the image to the driving evaluation system 100.

[0020] Terminal 500 is the personal computer or mobile terminal of the vehicle 200's operations manager. Alternatively, terminal 500 may be the driver's mobile terminal. Terminal 500 is one of the destinations for the driving evaluation information measured by the driving evaluation system 100.

[0021] Figure 6 is a functional block diagram showing an example configuration of the operation evaluation system 100 of this disclosure. Referring to Figure 6, the configuration includes an acquisition unit 101, a measurement unit 102, an output unit 103, an operation evaluation unit 104, and an operation evaluation information storage unit 105.

[0022] The acquisition unit 101 acquires images taken from the vehicle 200, showing the interior of the vehicle. If the images do not show any non-drivers who are not operating the vehicle, the acquisition unit 101 may discard the acquired images. The acquisition unit 101 transmits images showing non-drivers who are not operating the vehicle to the measurement unit 102. The acquisition unit 101 corresponds to the acquisition means 11 described above.

[0023] The measurement unit 102 analyzes the image of the non-driver in the image, measures their stress level, and outputs it to the driving evaluation unit 104. In the following description, the stress level is evaluated on a five-point scale, with higher values ​​indicating higher stress levels. Conversely, a low stress level indicates that the non-driver is in a relaxed state. If there are multiple non-drivers in the image, the measurement unit 102 may select the non-drivers to be measured according to pre-set rules. For example, the measurement unit 102 may measure the stress level of non-drivers sitting in specific seats (such as the passenger seat, back seat, or rear seats of a bus). This makes it possible to evaluate the stress felt in each seat. Furthermore, by calculating the driving evaluation information described later using the measured stress levels of non-drivers in such specific seats, an evaluation value indicating the stress level of passengers in buses, taxis, etc., can be obtained. In addition, for example, the measurement unit 102 may measure the stress level of non-drivers with specific attributes (such as children, the elderly, or people prone to motion sickness). By using these measurements to calculate the driving evaluation information described later, it is possible to obtain evaluation values ​​tailored to individuals who are prone to driving-related stress (or those who should not experience stress while driving). By communicating these evaluation values ​​to the driver, they can be reflected in their future driving.

[0024] Furthermore, the measurement unit 102 may measure the stress state of multiple non-drivers and output the average value, etc. Also, if there are no non-drivers in the image, the measurement unit 102 may omit subsequent processing. As mentioned above, this stress state can be measured, for example, from the facial expressions of people in the image, eye movements, and vital signs such as pulse rate estimated from the image. In the following description, the stress state will be described as being measured based on the behavior of vital signs such as pulse rate estimated from the image. The measurement unit 102 corresponds to the measurement means 12 described above.

[0025] The driving evaluation unit 104 evaluates the appropriateness of the vehicle or the driver's driving based on the stress state received from the measurement unit 102, and updates the driving evaluation information in the driving evaluation information storage unit 105. The driving evaluation unit 104 functions as an evaluation means that evaluates the appropriateness of the vehicle's driving based on the stress state of the non-driver.

[0026] Figure 6 shows an example of driving evaluation information stored in the driving evaluation information storage unit 105. In the example in Figure 6, driving time and average stress level are managed for vehicles 200A and 200B, respectively. Driving time is, for example, the cumulative driving time for each vehicle, and can be calculated by summing the difference between the start time and the end time of driving. This driving time may also be calculated by counting the elapsed time from the time when the first image was sent from vehicle 200. The average stress level can be determined by the arithmetic mean or moving average of the stress levels for each vehicle, which are measured at regular intervals. In the example in Figure 6, a higher average stress level indicates that the stress level of the non-driver in that vehicle was higher (they were under stress). For example, when comparing vehicle 200A and vehicle 200B, vehicle 200A has a higher average stress level, indicating that the non-driver riding in it was under a higher level of stress. By using this kind of information, it is possible to obtain driving evaluation values ​​from a user experience perspective, which differ from driving evaluations calculated based on the number of sudden braking and sudden accelerations.

[0027] Figure 7 shows another example of driving evaluation information stored in the driving evaluation information storage unit 105. In the example in Figure 7, driving time and average stress level are managed for drivers A and B, respectively. Drivers can be distinguished by inputting driver information identified using biometric authentication or a physical key into the driving evaluation unit 104. Alternatively, the driving evaluation unit 104 may identify the driver by the driver's image in the image acquired by the acquisition unit 101. Driving time is the cumulative driving time for each driver and can be calculated by aggregating the difference between the time the driver started driving and the time the driver ended driving. This driving time may also be calculated by counting the elapsed time from the time the first image was sent from the vehicle 200. The average stress level can be obtained by the arithmetic mean or moving average of the stress levels of each driver obtained by measuring at regular intervals. In the example in Figure 7, a higher average stress level indicates that the stress level of the non-driver passenger was higher (stressed). For example, when comparing driver A and driver B, driver B has a higher average stress level, indicating that the non-driver passenger was subjected to a higher level of stress. By using this kind of information, it is possible to obtain driving evaluation values ​​from a user experience perspective, which differ from driving evaluations calculated based on the number of sudden braking or accelerations.

[0028] The output unit 103 outputs the appropriateness of the operation of the vehicle 200 to a predetermined output destination using the operation evaluation information described above. The output unit 103 corresponds to the output means 13 described above. For example, the output unit 103 transmits operation evaluation information in response to a request from the terminal 500 and displays it on the terminal 500. Alternatively, for example, the output unit 103 outputs operation evaluation information or information processed from operation evaluation information to the display device of the vehicle 200.

[0029] Next, the operation of this embodiment will be described in detail with reference to the drawings. Figure 8 is a flowchart showing the operation of the driving evaluation system 100 of this disclosure. Referring to Figure 8, first, the driving evaluation system 100 acquires images of the interior of the vehicle 200 (step S001).

[0030] Next, the driving evaluation system 100 measures the stress level of the non-driver in the image (step S002).

[0031] Next, the driving evaluation system 100 calculates a stress level value for the vehicle 200 or its driver based on the measured stress state (step S003).

[0032] Finally, the driving evaluation system 100 updates the driving evaluation information of the corresponding driver using the calculated stress level values ​​of the vehicle 200 or its driver (step S004).

[0033] As described above, according to this embodiment, it is possible to obtain driving evaluation values ​​from a user experience perspective that indicate the ride comfort of the vehicle, etc. In the embodiment described above, the driving evaluation was performed on vehicles 200A and 200B and drivers A and B, but similarly, driving evaluation values ​​from a user experience perspective can be obtained even when the driving entity is an automated driving system. In this case, the person sitting in the driver's seat is also a non-driver and can be a subject for measuring stress levels. In the example of Figure 5, the driving evaluation system 100 is configured to include a driving evaluation information storage unit 105, but the driving evaluation information storage unit 105 may be located in a storage device on a network that is accessible from the driving evaluation system 100.

[0034] [Second Embodiment] Next, a second embodiment will be described in which the driving of the vehicle or the driver is evaluated using the vehicle's driving data in addition to the stress level of the non-driver. Figure 9 is a diagram showing one configuration of the present disclosure. The difference from the first embodiment shown in Figure 4 is that the vehicle 200a transmits driving data of the vehicle 200a to the driving evaluation system 100a in addition to images of the interior of the vehicle.

[0035] Figure 10 is a functional block diagram showing another configuration example of the driving evaluation system of this disclosure. The first difference from the first embodiment shown in Figure 5 is that the acquisition unit 101a acquires driving data in addition to images from the vehicle 200a and sends the driving data to the driving evaluation unit 104a. The second difference from the first embodiment is that the driving evaluation unit 104a evaluates the driving of the vehicle or the driver using the vehicle's driving data in addition to the stress state of the non-driver. The third difference from the first embodiment is that the driving evaluation information storage unit 105a is also capable of managing driving data. The other configurations are almost the same as in the first embodiment, so the differences will be explained below.

[0036] The acquisition unit 101a acquires driving data in addition to images of the interior of the vehicle 200a. This driving data includes the operations performed by the driver of the vehicle 200a and data on the vehicle's behavior. In the following description, the acquisition unit 101a will be described as acquiring vehicle behavior data as driving data. The acquisition unit 101a sends the vehicle behavior data received from the vehicle 200a to the driving evaluation unit 104a.

[0037] The driving evaluation unit 104a evaluates the appropriateness of the driver's driving using the stress state received from the measurement unit 102 as well as the driving data (vehicle behavior data) received from the acquisition unit 101a, and updates the driving evaluation information in the driving evaluation information storage unit 105. Specifically, the driving evaluation unit 104a calculates the number of times the vehicle 200a braked suddenly and the number of times it crossed the center line during the driving data measurement period from the driving data (vehicle behavior data).

[0038] Figure 11 shows an example of driver-specific driving evaluation information stored in the driving evaluation information storage unit 105a. In the example in Figure 11, driving time, average stress level, number of emergency braking incidents, and number of center line crossing incidents are managed for drivers A and B, respectively. The number of emergency braking incidents and center line crossing incidents are updated by adding the number of emergency braking incidents and center line crossing incidents for each driver, calculated from the driving data. By managing the number of emergency braking incidents and center line crossing incidents over a predetermined period in the past, comparisons between drivers can be made. The driving time and average stress level are the same as in the first embodiment, so their explanation is omitted. In the example in Figure 11, driving time, average stress level, number of emergency braking incidents, and number of center line crossing incidents are used directly as driving evaluation information, but a score may be calculated using these and used as driving evaluation information.

[0039] Next, the operation of this embodiment will be described in detail with reference to the drawings. Figure 12 is a flowchart showing the operation of the driving evaluation system 100a of this disclosure. Referring to Figure 12, first, the driving evaluation system 100a acquires images of the interior of the vehicle and driving data (vehicle behavior data) from the vehicle 200a (step S101).

[0040] Next, the driving evaluation system 100a measures the stress level of the non-driver in the image (step S002). Then, based on the measured stress level, the driving evaluation system 100 calculates the stress level value of the vehicle 200 or its driver (step S003).

[0041] Finally, the driving evaluation system 100a updates the driving evaluation information of the corresponding driver using the calculated driver stress level value and driving data (vehicle behavior data) (step S104).

[0042] As described above, according to the present embodiment, it is possible to obtain a driving evaluation value from the user experience perspective indicating the comfort level of the vehicle or the like, taking into account the actual driving content of the driver. In the above-described embodiment, the driving evaluation system 100a has been described as performing driving evaluations of drivers A and B. However, similar to the first embodiment, the driving evaluation system 100a may perform driving evaluations for each of the vehicles 200A and 200B. In this case, the driving evaluation system 100a receives driving data for each vehicle from the vehicle 200a and performs driving evaluations for each vehicle.

[0043] Also, as another usage method of the driving data (vehicle behavior data), it is possible to obtain a driving evaluation value from the user experience perspective indicating the comfort level of the vehicle or the like for each scene. Using the above-described driving data (vehicle behavior data), it is possible to identify driving scenes such as "during reverse parking operation in shift R", "during rainy-day driving with wiper operation", and "during night-time driving with lights on and time, etc." By using the configuration of the present embodiment, it is possible to obtain a driving evaluation value indicating the comfort level of the vehicle for each of these driving scenes.

[0044] [Third Embodiment] Next, a third embodiment in which an abnormality inside the vehicle can be detected using the stress state of a non-driver will be described. FIG. 13 is a diagram showing one configuration of the present disclosure. The differences from the first embodiment shown in FIG. 4 are that the vehicle 200b is a bus that can accommodate a large number of passengers and that the camera C can capture the interior of this bus. <000​FIG. 14 is a functional block diagram showing another configuration example of the driving evaluation system of the present disclosure. The first difference from the first embodiment shown in FIG. 5 is that an abnormality detection unit 1021 is provided in the measurement unit 102b. The second difference from the first embodiment is that the acquisition unit 101b acquires driving behavior data (driving data) of the vehicle in addition to the image from the vehicle 200b, and sends this driving data to the abnormality detection unit 1021 in the measurement unit 102b. The third difference from the first embodiment is that when an abnormality is detected by the abnormality detection unit 1021, the output unit 103b notifies the driver of the vehicle 200b, the terminal 500 of the operation manager, and the police. Since other configurations are substantially the same as those of the first embodiment, the differences will be mainly described below.

[0046] The acquisition unit 101b acquires driving data in addition to the image inside the vehicle from the vehicle 200b. This driving data is the operation content performed by the driver of the vehicle 200b and the driving behavior data of the vehicle. In the following description, the acquisition unit 101b will be described as acquiring the driving behavior data of the vehicle 200b as the driving data. The acquisition unit 101b sends the driving behavior data of the vehicle received from the vehicle 200b to the measurement unit 102b.

[0047] The measurement unit 102b analyzes the image of the non-driver in the image, measures the stress state of the passenger, and outputs it to the driving evaluation unit 104. Further, the abnormality detection unit 1021 collates the stress state of the passenger measured by the measurement unit 102b with the driving data to detect an abnormality inside the vehicle. When an abnormality inside the vehicle is detected, the abnormality detection unit 1021 requests the output unit 103b to notify. This notification request may include information such as the type of abnormality detected by the abnormality detection unit 1021 and the position information.

[0048] Based on the request from the abnormality detection unit 1021, the output unit 103b notifies the terminal 500 of the operation manager of the vehicle 200b and the police.

[0049] Figure 15 is a diagram illustrating the abnormality detection operation of the driving evaluation system 100b of this disclosure. Referring to Figure 15, the image shows people P1 to P5 (non-drivers). The measurement unit 102b analyzes the images of these people (non-drivers) to measure the stress levels of the passengers. In the example in Figure 15, the stress levels of people P1, P4, and P5 (non-drivers) are low, while the stress levels of people P2 and P3 (non-drivers) are high. The abnormality detection unit 1021 detects whether or not an abnormality has occurred inside the vehicle by referring to this imbalance in stress levels and driving data. For example, if the vehicle 200b's behavior data shows that the vehicle is traveling at a constant speed on a safe, curveless road, the stress levels are unlikely to be as unbalanced as in Figure 15. Thus, the abnormality detection unit 1021 detects an abnormality when an imbalance occurs in the stress levels. In fact, in the example shown in Figure 15, a knife is pointed at individuals P2 and P3, suggesting that individuals P2 and P3 (non-drivers) are in a high-stress state. Using a similar principle, it is possible to detect crimes or medical emergencies inside the vehicle.

[0050] Furthermore, since stress levels vary from person to person, an unbalanced state may occur depending on the driving conditions. Therefore, the driving evaluation system 100b of this disclosure refers to driving data (vehicle behavior) to prevent false detections. However, if the vehicle is a type of vehicle that operates at low speeds, the abnormality detection unit 1021 may detect whether or not an abnormality has occurred inside the vehicle 200b based on the stress level of the non-driver, without referring to driving data.

[0051] Other operations related to the operation evaluation are the same as in the first embodiment, so their explanation will be omitted. Although the above explanation described an example in which an abnormality detection unit 1021 is added to the operation evaluation system of the first embodiment, it is also possible to add an abnormality detection unit 1021 to the operation evaluation system of the second embodiment.

[0052] While the embodiments of this disclosure have been described above, this disclosure is not limited to the embodiments described above, and further modifications, substitutions, and adjustments can be made without departing from the basic technical concept of this disclosure. For example, the network configurations, element configurations, and data representations shown in the drawings are examples to aid in understanding this disclosure and are not limited to the configurations shown in these drawings.

[0053] For example, in the first to third embodiments described above, the driving evaluation systems 100 to 100b were described as performing driving evaluations, but it is also possible to change the configuration so that the driving evaluation systems 100 to 100b output the stress levels of non-drivers. By providing drivers and vehicle operation managers with the stress levels of non-drivers, it becomes possible to understand the quality of the ride comfort of the vehicle.

[0054] Furthermore, although the embodiments described above have explained the application of this disclosure to vehicles and buses, this disclosure can also be applied to trains, ships, monorails, etc. It can also be applied to measuring the ride comfort of rickshaws, horse-drawn carriages, etc.

[0055] (Hardware Configuration) In each embodiment of this disclosure, each component of each device represents a functional unit block. Some or all of each component of each device is realized by any combination of an information processing device 900 and a program, for example, as shown in Figure 16. Figure 16 is a block diagram showing an example of the hardware configuration of the information processing device 900 that realizes each component of each device. The information processing device 900 includes, as an example, the following configuration: ・CPU (Central Processing Unit) 901 ・ROM (Read Only Memory) 902 ・RAM (Random Access Memory) 903 ・Program 904 loaded into RAM 903 ・Storage device 905 that stores the program 904 ・Drive device 907 that reads and writes to the recording medium 906 ・Communication interface 908 that connects to a communication network 909 ・Input / output interface 910 that performs data input and output ・Bus 911 that connects each component

[0056] Each component of each device in each embodiment is realized by the CPU 901 acquiring and executing a program 904 that realizes these functions. That is, the CPU 901 in Figure 16 executes an image acquisition program and a stress state measurement program, and performs update processing of each calculation parameter held in RAM 903, storage device 905, etc. The program 904 that realizes the functions of each component of each device is, for example, stored in advance in storage device 905 or ROM 902, and read by the CPU 901 as needed. The program 904 may be supplied to the CPU 901 via a communication network 909, or it may be stored in advance in a recording medium 906, and the drive device 907 may read the program and supply it to the CPU 901.

[0057] There are various variations in how each device is implemented. For example, each device may be implemented by any combination of a separate information processing device 900 and a program for each component. Alternatively, multiple components of each device may be implemented by any combination of a single information processing device 900 and a program. In other words, each part (processing means, function) of the above-described information processing device or operation evaluation system can be implemented by a computer program that causes a processor mounted on the device to execute the above-described processes using its hardware.

[0058] Furthermore, some or all of the components of each device are realized by other general-purpose or dedicated circuits, processors, etc., or combinations thereof. These may be made up of a single chip or multiple chips connected via a bus.

[0059] Some or all of the components of each device may be realized by a combination of the circuits and programs described above.

[0060] When some or all of the components of each device are implemented by multiple information processing devices or circuits, these multiple information processing devices or circuits may be centrally located or distributed. For example, the information processing devices or circuits may be implemented in a form in which each is connected via a communication network, such as a client-and-server system or a cloud computing system.

[0061] The embodiments described above are preferred embodiments of this disclosure and do not limit the scope of this disclosure to these embodiments alone. That is, a person skilled in the art can modify or substitute the embodiments described above to construct various modified forms without departing from the gist of this disclosure.

[0062] Some or all of the above embodiments may also be described as follows, but are not limited to these.

[0063] [Note 1] An information processing device comprising: acquisition means for acquiring images of a non-driver who is in a vehicle but not driving; measurement means for measuring the stress state of the non-driver based on the images; and output means for outputting the stress state to a predetermined output destination. [Note 2] The output means of the above-described information processing device may be configured to present the stress state to the driver who is driving the vehicle by voice or display on a display device. [Note 3] The above-described information processing device further comprises evaluation means for evaluating the appropriateness of driving the vehicle based on the stress state of the non-driver, and the output means may be configured to output the appropriateness of driving the vehicle. [Note 4] The evaluation means of the above-described information processing device may be configured to evaluate the appropriateness of driving for each driver, and the output means may be configured to output the appropriateness of driving for each driver. [Note 5] When there are multiple non-drivers, the measurement means of the above-described information processing device may be configured to output the average value of the stress states of the multiple non-drivers. [Note 6] The measurement means of the information processing device described above can be configured to measure the stress state of a non-driver sitting in a specific seat when there are multiple non-drivers. [Note 7] The measurement means of the information processing device described above can be configured to measure the stress state of a non-driver with specific attributes when there are multiple non-drivers. [Note 8] The evaluation means of the information processing device described above can further be configured to include an anomaly detection means that detects whether or not an abnormality has occurred inside the vehicle, based at least on the stress state of the non-driver. [Note 9] The anomaly detection means of the information processing device described above can be configured to acquire driving data for each vehicle or driver, and to detect whether or not an abnormality has occurred inside the vehicle by referring to the driving data for each vehicle or driver. [Note 10] The measurement means of the information processing device described above can be configured to measure the stress state based on fluctuations in the vital sign values ​​of the non-driver captured in the image during a predetermined measurement time.[Note 11] The evaluation means of the information processing device described above can be configured to acquire driving data for each vehicle or driver, and to evaluate the appropriateness of driving for each vehicle or driver based on the stress state of the non-driver and the driving data for each vehicle or driver. [Note 12] The information processing device described above can further be configured to evaluate the appropriateness of driving for each driving scene for each vehicle or driver based on the driving data and the stress state of the non-driver. [Note 13] An information processing method that acquires an image of a non-driver who is in a vehicle but not driving, measures the stress state of the non-driver based on the image, and outputs the stress state to a predetermined output destination. [Note 14] A recording medium that records a program that causes a computer to execute the following: the process of acquiring an image of a non-driver who is in a vehicle but not driving, the process of measuring the stress state of the non-driver based on the image, and the process of outputting the stress state to a predetermined output destination. Furthermore, the forms described in each of the above appendices can be combined with each other after making the necessary modifications. For example, a configuration that combines the contents described in appendice 2 and the contents described in appendice 3, and outputs both stress state and driving evaluation, is also included within the scope of disclosure of this specification. Furthermore, the forms described in appendices 9 to 10 can be expanded into the forms described in appendices 2 to 8, similar to appendice 1.

[0064] Furthermore, each disclosure in the above-mentioned patent documents is incorporated into this document by reference and may be used as the basis or part of this disclosure as necessary. Within the framework of this disclosure (including the claims), further modifications and adjustments to the embodiments or examples are possible based on their fundamental technical concept. Also, within the framework of this disclosure, various combinations or selections (including partial deletions) of various disclosure elements (including each element of each claim, each element of each embodiment or example, each element of each drawing, etc.) are possible. In other words, this disclosure naturally includes the entire disclosure, including the claims, and various modifications and alterations that a person skilled in the art could make in accordance with the technical concept. In particular, with respect to the numerical ranges described in this document, any numerical value or sub-range included within that range should be interpreted as being specifically described, even if not otherwise stated. Furthermore, each disclosure item of the above-mentioned cited documents may, as necessary, be used in part or in whole as part of this disclosure, in accordance with the spirit of this disclosure, and this is also considered to be included in the disclosure items of this application.

[0065] 10 Information Processing Device 11 Acquisition Means 12 Measurement Means 13 Output Means 100, 100a, 100b Driving Evaluation System 101, 101a, 101b Acquisition Unit 102 Measurement Unit 103, 103b Output Unit 104, 104a Driving Evaluation Unit 105 Driving Evaluation Information Storage Unit 200, 200b Vehicle 500 Terminal 900 Information Processing Device 901 CPU (Central Processing Unit) 902 ROM (Read Only Memory) 903 RAM (Random Access Memory) 904 Program 905 Storage Device 906 Recording Medium 907 Drive Device 908 Communication Interface 909 Communication Network 910 Input / Output Interface 911 Bus 1021 Anomaly detection unit C Camera P1-P5 Person (non-driver)

Claims

1. An information processing device comprising: an acquisition means for acquiring images of a non-driver who is riding in a vehicle but not driving; a measurement means for measuring the stress state of the non-driver based on the images; and an output means for outputting the stress state to a predetermined output destination.

2. The information processing apparatus according to claim 1, wherein the output means presents the stress state to the driver operating the vehicle by voice or display on a display device.

3. The information processing device according to claim 1 or 2, further comprising an evaluation means for evaluating the appropriateness of driving the vehicle based on the stress state of the non-driver, wherein the output means outputs the appropriateness of driving the vehicle.

4. The information processing apparatus according to claim 3, wherein the evaluation means evaluates the appropriateness of driving for each driver, and the output means outputs the appropriateness of driving for each driver.

5. The information processing device according to any one of claims 1 to 4, wherein the measuring means outputs the average value of the stress state of multiple non-drivers when multiple non-drivers are present.

6. The information processing device according to any one of claims 1 to 4, wherein the measuring means measures the stress state of a non-driver sitting in a specific seat when there are multiple non-drivers.

7. The measurement means measures the stress state of a non-driver with specific attributes when there are multiple non-drivers, an information processing device according to any one of claims 1 to 4.

8. An information processing device according to any one of claims 1 to 7, further comprising an abnormality detection means for detecting whether or not an abnormality has occurred inside the vehicle, based on at least the stress state of the non-driver.

9. The information processing apparatus of claim 8, wherein the abnormality detection means acquires driving data for each vehicle or driver, and detects whether or not an abnormality has occurred inside the vehicle by referring to the driving data for each vehicle or driver.

10. The information processing device according to any one of claims 1 to 9, wherein the measurement means measures the stress state based on fluctuations in the vital sign values ​​of a non-driver captured in the image during a predetermined measurement time.

11. The information processing apparatus according to any one of claims 1 to 10, wherein the evaluation means acquires driving data for each vehicle or driver, and evaluates the appropriateness of driving for each vehicle or driver based on the stress state of the non-driver and the driving data for each vehicle or driver.

12. The information processing device according to claim 11, which evaluates the appropriateness of driving for each driving scene for each vehicle or driver, based on the driving data and the stress state of the non-driver.

13. An information processing method comprising: acquiring images of a non-driver who is in a vehicle but not driving; measuring the stress level of the non-driver based on the images; and outputting the stress level to a predetermined output destination.

14. A recording medium that contains a program that causes a computer to perform the following steps: a process of acquiring images of a non-driver who is in a vehicle but not driving; a process of measuring the stress level of the non-driver based on the images; and a process of outputting the stress level to a predetermined output destination.

Images