Information processing device, vehicle, vehicle control method, and program

Abstract

This information processing device (3) is provided with: a derivation unit (31) that derives the sleep depth of a user using sensor information acquired by a sensor (2) that detects at least one of the state of the user riding in a vehicle (1) and the surroundings of the user; and a control information generation unit (32) that uses the sleep depth derived by the derivation unit (31) to generate control information to be used in travel control of the vehicle (1) by automatic driving.

Description

Technical Field

[0001] This disclosure relates to information processing devices for controlling vehicles, vehicles, vehicle control methods, and procedures. Background Technology

[0002] Sometimes, passengers in a vehicle may fall asleep. Patent Document 1 describes a technique for preventing occupants from sleeping in a series hybrid vehicle. The vehicle described in Patent Document 1 can generate electricity using the engine's power to power an electric motor, and power generation is prohibited before and after the occupants begin to sleep. Therefore, the vehicle described in Patent Document 1 prevents changes in vibration and sound caused by power generation before and after the start of sleep, when occupants' sleep is easily disturbed by vibration and sound.

[0003] Existing technical documents

[0004] Patent documents

[0005] Patent Document 1: Japanese Patent Application Publication No. 2019-131109 Summary of the Invention

[0006] The problem that the invention aims to solve

[0007] In the technology described in Patent Document 1, changes in vibration and sound caused by power generation are prevented before and after the start of sleep for the occupant. However, phenomena that interfere with sleep are not limited to changes in vibration and sound caused by power generation; the movement of the vehicle itself can sometimes affect the user as an occupant. Therefore, it is desirable to implement driving control corresponding to the user's sleep-related state.

[0008] This disclosure was made in view of the above circumstances, and its purpose is to provide an information processing device capable of performing driving control of a vehicle corresponding to a user's sleep-related state.

[0009] Methods for solving problems

[0010] To address the aforementioned issues and achieve the objectives, the information processing apparatus of this disclosure includes: a deduction unit that uses sensor information obtained by sensors that detect at least one of the state of a user riding in a vehicle and the user's surrounding environment to deduce the user's sleep depth; and a control information generation unit that uses the sleep depth deduced by the deduction unit to generate control information used in driving control of the vehicle through autonomous driving.

[0011] Invention Effects

[0012] The information processing device disclosed herein has the following effect: it is capable of performing vehicle driving control corresponding to the user's sleep state. Attached Figure Description

[0013] Figure 1 This is a diagram showing a structural example of the vehicle according to Embodiment 1.

[0014] Figure 2 This is a diagram showing other structural examples of implementation method 1.

[0015] Figure 3 This is a diagram illustrating a structural example of the processing circuit of the information processing device implementing Embodiment 1.

[0016] Figure 4 This is a flowchart illustrating an example of the processing steps in the information processing apparatus of Embodiment 2.

[0017] Figure 5 This is a conceptual diagram illustrating an example of path setting in Implementation Method 2.

[0018] Figure 6 This is a diagram illustrating the structure of a vehicle in Implementation Method 2, where user settings are accepted.

[0019] Figure 7 This is a flowchart illustrating an example of the processing steps in the information processing apparatus of Implementation Method 2, which represents the case where the user's settings are accepted.

[0020] Figure 8 This is a flowchart illustrating an example of the processing steps in the information processing apparatus of Embodiment 3.

[0021] Figure 9 This is a diagram illustrating a structural example of a vehicle according to Embodiment 3, which controls devices within the vehicle that affect the user's surrounding environment.

[0022] Figure 10 This is a flowchart illustrating an example of the processing steps in the information processing apparatus of Embodiment 4.

[0023] Figure 11 This is a diagram illustrating the structure of a vehicle in Implementation Method 4, which uses sleep information to deduce wake-up time.

[0024] Figure 12 This is a flowchart illustrating an example of the processing steps in an information processing device for Implementation 4, which uses sleep information to predict wake-up time.

[0025] Figure 13 This is a diagram illustrating a structural example of the vehicle according to embodiment 5.

[0026] Figure 14 This is a diagram illustrating an example of the structure of the derivation unit in Implementation 5, where machine learning is used for derivation.

[0027] Figure 15This is a schematic diagram illustrating an example of a neural network. Detailed Implementation

[0028] Hereinafter, the information processing apparatus, vehicle, vehicle control method, and program according to the embodiments will be described in detail based on the accompanying drawings.

[0029] Implementation method 1.

[0030] Figure 1 This diagram illustrates a structural example of the vehicle according to Embodiment 1. The vehicle 1 in this embodiment is an autonomous driving vehicle, equipped with sensors 2, an information processing device 3, and a driving mechanism 4. Furthermore, while vehicle 1 is described here as an example of an autonomous driving vehicle, vehicle 1 can also switch between autonomous and manual driving; any vehicle capable of autonomous driving is acceptable.

[0031] Sensor 2 is a detection device that detects at least one of the state of the user riding in vehicle 1 and the user's surrounding environment, and sends the detection results as sensor information to information processing device 3. Sensor information represents at least one of the state of the user riding in vehicle 1 and the user's surrounding environment. Sensor 2 may include, but is not limited to, at least one of the following: a sensor for acquiring the user's vital signs data, a camera for capturing the user's image, a sound sensor, an accelerometer installed on the user, an accelerometer for detecting the acceleration of vehicle 1, and sensors installed on vehicle 1. Vital signs data may include, but is not limited to, at least one of the following: heart rate, body temperature, blood pressure, and respiratory rate. Sensor 2 for acquiring vital signs data may be worn by the user, a non-contact sensor fixed to vehicle 1, or other types of sensors. Furthermore, in Figure 1 The diagram shows one sensor 22, but there can be multiple sensors 2, and the number of sensors 2 is not limited to one. Figure 1 The example shown.

[0032] The information processing device 3 includes a sensor information acquisition unit 30, a deduction unit 31, a control information generation unit 32, a driving control unit 33, and an information storage unit 34. The sensor information acquisition unit 30 acquires sensor information by receiving sensor information from sensor 2 and outputs the acquired sensor information to the deduction unit 31. The deduction unit 31 uses the sensor information received from the sensor information acquisition unit 30 (i.e., the sensor information acquired by sensor 2) to deduce the user's sleep depth. For example, the deduction unit 31 uses the sensor information to determine whether the user is awake or asleep; if the user is asleep, it deduces the user's sleep depth and notifies the control information generation unit 32 of the deduced sleep depth.

[0033] Sleep depth is an indicator of the intensity of sleep. It can be represented by two stages: rapid eye movement (REM) sleep, which is light sleep, and non-rapid eye movement (NREM) sleep, which is deeper than REM sleep. NREM sleep can also be further divided into three or four stages, for a total of four or five stages. Generally, REM sleep and NREM sleep alternate. The cycle of REM sleep and NREM sleep is also called a sleep cycle, which is typically around 90 to 120 minutes. REM sleep is a light sleep stage characterized by rapid eye movements. During REM sleep, breathing, pulse, and blood pressure are higher than during NREM sleep. NREM sleep is deeper sleep compared to REM sleep. As mentioned above, NREM sleep is also classified into three or four stages based on its depth. For example, when classifying non-rapid eye movement (NREM) sleep into four stages, from the lightest stage 1 to the deepest stage 4, generally, during sleep, after falling asleep, the sleep progresses from stage 1 to stage 4, moving towards deeper stages. Rapid eye movement (REM) sleep follows stage 4. Afterward, REM and NREM sleep cycles repeatedly, but in the latter half of sleep, the lighter stages (stages 1 and 2) of NREM sleep increase, as does REM sleep.

[0034] The derivation unit 31 can, for example, deduce which of the four or three stages of REM sleep and non-REM sleep the sleep depth is based on at least one of the sleep parameters, such as vital signs data or indicators representing body movement. Indicators representing body movement include, but are not limited to, the number of times the user turns over or moves a certain number of times in a specific location. These indicators are detected, for example, by a camera capturing the user, an accelerometer installed on the user, or sensors (accelerometer, pressure sensor, etc.) installed on vehicle 1, but are not limited to these. For example, by maintaining a table showing the correspondence between sleep depth and the numerical range of sleep parameters as reference information, the derivation unit 31 uses sensor information and reference information to deduce sleep depth. Alternatively, the relationship between sleep parameters and sleep depth can be learned through machine learning, and the derivation unit 31 uses the learning results and the obtained sleep parameters to deduce sleep depth. In addition, the derivation unit 31 can also deduce sleep depth based on the variation characteristics of each stage of the four or three stages of rapid eye movement (REM) sleep and non-rapid eye movement (NREM) sleep in general sleep (e.g., the standard pattern of repeated stages of the four or three stages of REM sleep and NREM sleep from falling asleep to waking up).

[0035] Generally, stages 3 and 4 of non-rapid eye movement (NREM) sleep are deep sleep. During deep sleep, it is difficult to be affected by external stimuli such as sound, vibration, and light. Even if external stimuli occur, the likelihood of sleep disruption is low, and their impact on sleep rhythm is minimal. On the other hand, stages 1 and 2 of NREM sleep are light sleep. Compared to deep sleep, they are more susceptible to external stimuli, making it easier for external stimuli to disrupt sleep or disrupt sleep rhythm. While REM sleep can also be described as light sleep that is easily awakened, because it is difficult to be affected by external stimuli, it can be appropriately categorized as light or deep sleep depending on the intended use of sleep depth. Furthermore, the definitions of deep and light sleep are not limited to this example; they can be appropriately set according to the intended use of sleep depth.

[0036] Furthermore, sensor information representing the user's surrounding environment does not directly indicate the user's state, but can sometimes be used to determine whether the surrounding environment is suitable for sleep. For example, the derivation unit 31 can use sensor information representing the user's surrounding environment to determine whether the environment is unsuitable for sleep, and use the determination result to deduce sleep depth. For example, as described later, suppose a sleep-related driving mode is set, such as a sleep-promoting mode or a user's awake mode, and acceleration is limited in the sleep-promoting mode. In this case, the derivation unit 31 can use sensor information obtained by the acceleration sensor, i.e., sensor 2, which detects the acceleration of vehicle 1, to deduce whether it is a sleep-promoting mode, thereby indirectly grasping the user's sleep-related state.

[0037] The information storage unit 34 stores map information and route information. The map information can be two-dimensional, three-dimensional, or a dynamic map that also includes dynamic information. Regarding the dynamic information of the dynamic map, for example, when received by a communication unit (without illustrations), the received dynamic information is used to update the dynamic information stored in the information storage unit 34. The route information represents the route taken by vehicle 1, and may also include the required time to reach the destination (or the scheduled arrival time), the scheduled arrival time at transit points, etc.

[0038] The control information generation unit 32 uses the sleep depth derived by the derivation unit 31 to generate control information used in the driving control of the vehicle 1 through automatic driving, and outputs the generated control information to the driving control unit 33. The driving control of the vehicle 1 refers to the control of the vehicle 1's movement itself, such as setting the movement path, controlling the vehicle 1's speed, controlling the vehicle 1's acceleration, controlling steering, and controlling the vehicle 1's stopping. The control information generation unit 32 may also use the sleep depth derived by the derivation unit 31 to determine whether the user's sleep is deep; if the sleep is not deep, it generates control information in a way that reduces external stimuli.

[0039] Methods to reduce external stimuli include, but are not limited to, at least one of the following: selecting a path (driving route) with less external stimuli, suppressing sudden acceleration and deceleration, suppressing speed, and slow turning. Additionally, for example, the deduction unit 31 may use the derived sleep depth to deduce the sleep depth over a certain period, that is, predict the sleep depth over a certain period and notify the control information generation unit 32 of the deduction result. In this case, the control information generation unit 32 may also use the sleep depth over a certain period to set a path that allows the user to wake up as naturally as possible at the location where they should wake up. The certain period is a future period, such as a period starting from the current time. The location where the user should wake up can be the user's destination or a place to stop along the way.

[0040] Furthermore, the control information generation unit 32 can also use the sleep depth over a certain period to switch the driving mode (hereinafter also referred to as the driving mode). For example, the driving mode can be switched as follows: when the user is awake rather than asleep, the driving mode is set to normal driving without special restrictions (normal driving mode); when the user is in light sleep, the driving mode is set to restricted driving mode with restrictions to achieve smooth driving; and when the user is in deep sleep, the driving mode is set to normal driving mode. Smooth driving is driving with reduced external stimuli for the user. The definition and switching method of the driving mode are not limited to this example. In addition, when the control information generation unit 32 generates path-related control information, the control information generation unit 32 can further use map information stored in the information storage unit 34 to generate control information.

[0041] In addition to control information for driving control, the control information generation unit 32 can also control devices (not shown) within the vehicle 1 by generating control information for controlling such devices. For example, the control information generation unit 32 can control devices within the vehicle 1, such as seats, lighting adjustment devices, devices that generate music or ambient sounds, and massage devices installed in the seats, based on the user's sleep depth. For instance, in cases of light sleep, the seat can be controlled to recline slowly to promote sleep, or the volume of sound-emitting devices can be reduced. Furthermore, if the journey to the destination is long, controls such as activating massage devices or playing relaxing music can be implemented before the user falls asleep to promote sleep. Additionally, to promote sleep, the control information generation unit 32 can also set the aforementioned driving mode to a restricted driving mode.

[0042] Furthermore, the system can be configured to allow the user to set whether or not to implement sleep-inducing controls. Additionally, the control information generation unit 32 can, for example, set the user's desired wake-up time, perform inverse calculations based on the set wake-up time, and determine the optimal time to fall asleep based on whether the wake-up time falls into REM sleep or light non-REM sleep. It can then initiate sleep-inducing controls before the determined time, making it easier for the user to fall asleep at or near the determined time. Alternatively, the control information generation unit 32 can set the wake-up time to the expected arrival time at the destination, determining the optimal time to fall asleep based on the user's likelihood of waking up near the destination, and similarly implementing sleep-inducing controls.

[0043] The driving control unit 33 controls the driving of vehicle 1 through autonomous driving. The driving control unit 33 controls the driving mechanism 4 based on information obtained by sensors (not shown) such as obstacle detection cameras, LiDAR (Light Detection and Ranging), and millimeter-wave sensors, map information stored in the information storage unit 34, and its own position calculated by a self-position derivation device (not shown), thereby controlling the driving of vehicle 1. For example, after a user or other operator sets a destination, the driving control unit 33 uses the destination and map information to set a route to the set destination and controls the driving mechanism 4 to drive along the set route. The driving control unit 33 stores the route information representing the set route in the information storage unit 34. The driving control unit 33 may also calculate the time required to reach the destination (or the predetermined arrival time) and the predetermined arrival time of the transit points, and include these in the route information. The driving control unit 33 may also use dynamic information related to roads and traffic, such as congestion information and obstacle information, to set the route. Furthermore, regarding the driving control of autonomous driving, it can be carried out by any method. Since general methods can be used, detailed explanations are omitted.

[0044] Furthermore, when the driving control unit 33 receives control information from the control information generation unit 32, it controls the driving mechanism 4 based on the received control information. Additionally, while the example described here is the driving control unit 33 setting a path, it is also possible that the control information generation unit 32 sets a path and notifies the driving control unit 33 of the set path as control information, and the driving control unit 33 controls the driving mechanism 4 according to the path based on the control information.

[0045] The driving mechanism 4 is a mechanism for driving the vehicle 1, such as an operating device with an accelerator pedal or other accelerator, a turning device, a brake or other multiple mechanisms for driving the vehicle 1.

[0046] In this embodiment, the information processing device 3 derives the user's sleep depth and generates control information for controlling the movement of the vehicle 1 based on the derived sleep depth. Thus, the vehicle 1 can control its movement according to the user's sleep state. Therefore, the vehicle 1 can set the user's environment—including acceleration, deceleration, steering, and the path of the vehicle—to be suitable for the user's sleep depth. Furthermore, by controlling the vehicle so that the user's wake-up time is either REM sleep or light non-REM sleep, the vehicle 1 can achieve a natural wake-up for the user.

[0047] In the example described above, the information processing device 3 is located inside the vehicle 1, but it can also be located separately from the vehicle. Figure 2 This is a diagram illustrating another structural example of this embodiment. Figure 2 In the example shown, the vehicle system 100 includes an information processing device 3a and a vehicle 1a. For systems with... Figure 1 The examples shown have the same functional component labels and Figure 1 The same labels are used, and repeated descriptions are omitted. The information processing device 3a is installed separately from the vehicle 1a, replacing... Figure 1 The sensor information acquisition unit 30 of the information processing device 3 shown is supplemented with a communication unit 35 capable of communicating with the vehicle 1a. Furthermore, the communication unit 35 also functions as a sensor information acquisition unit that acquires sensor information by receiving sensor information. Figure 1 The driving control unit 33 in the information processing device 3 shown is installed in the vehicle 1a.

[0048] Vehicle 1a from Figure 1The vehicle 1 shown has the information processing unit 3 removed and a communication unit 36 ​​added, capable of communicating with the information processing unit 3a. The communication line between the vehicle 1a and the information processing unit 3a may include, for example, a wireless communication line, but may also be a combination of a wireless communication line and a wired communication line. Alternatively, communication between the vehicle 1a and the information processing unit 3a may also be conducted via other devices.

[0049] The communication unit 36 ​​of vehicle 1a receives sensor information from sensor 2 and sends the received sensor information to information processing device 3a. The communication unit 35 of information processing device 3a receives sensor information from vehicle 1a and outputs the received sensor information to derivation unit 31. The operation of derivation unit 31 and control information generation unit 32 is related to... Figure 1 The example shown is the same, but the control information generation unit 32 outputs the generated control information to the communication unit 35. The communication unit 35 sends the control information received from the control information generation unit 32 to the vehicle 1a. When the communication unit 36 ​​of the vehicle 1a receives control information from the information processing device 3a, it outputs the received control information to the driving control unit 33. The driving control unit 33, the information storage unit 34, and the driving mechanism 4 are... Figure 1 The example shown is the same.

[0050] Furthermore, when the control information generation unit 32 generates path-related control information, the information processing device 3a may also include an information storage unit 34, which uses map information stored in the information storage unit 34 to generate control information.

[0051] Next, the hardware structure of the information processing apparatus 3 of this embodiment will be described. The information processing apparatus 3 of this embodiment functions as the information processing apparatus 3 by executing a program (computer program) describing the processing within the information processing apparatus 3 on a computer system. The computer system, for example, includes processing circuitry. Figure 3 This is a diagram illustrating an example of the structure of the processing circuit of the information processing device 3 implementing this embodiment. For example... Figure 3 As shown, the processing circuit includes a processor 101, a memory 102, and a communication unit 103. The processing circuit can be a single circuit or multiple circuits. They are connected via a system bus. Alternatively, the communication unit 103 can be provided separately from the processing circuit. Furthermore, the computer system implementing the information processing device 3 may also include at least one of an input unit such as a button or keyboard for accepting input, and a display unit such as a display or monitor.

[0052] exist Figure 3In this embodiment, the processor 101 is a control unit such as a CPU (Central Processing Unit) that executes a program describing the processing described in the information processing apparatus 3 of this embodiment. The memory 102 includes various types of memory such as RAM (Random Access Memory) and ROM (Read Only Memory), as well as storage devices such as hard disks, storing the program to be executed by the processor 101, necessary data obtained during processing, etc. Additionally, the memory 102 also serves as a temporary storage area for programs. The communication unit 103 is a receiver and transmitter that performs communication processing.

[0053] Here, an example of the operation of the computer system up to the point where the program of this embodiment can be executed will be described. In a computer system with the above structure, for example, a CD-ROM or DVD-ROM drive (not shown) is used to load the program into the memory 102. Furthermore, when the program is executed, the program read from the memory 102 is stored in the main storage area of ​​the memory 102. In this state, the processor 101 executes the processing of the information processing device 3 of this embodiment according to the program stored in the memory 102.

[0054] Furthermore, in the above description, CD-ROM or DVD-ROM is used as the recording medium to provide a program describing the processing in the information processing device 3, but it is not limited to this. Depending on the structure of the computer system, the capacity of the program to be provided, etc., a program provided via the Internet or other transmission medium through the communication unit 103 may be used.

[0055] The procedure of this embodiment, for example, causes the computer system controlling vehicle 1 to perform the following steps: using sensor information obtained by sensor 2 to deduce the user's sleep depth; and using the deduced sleep depth to generate control information used in driving control of vehicle 1 through autonomous driving.

[0056] Figure 1 The derivation unit 31, control information generation unit 32, and driving control unit 33 shown are connected by... Figure 3 The processor 101 shown executes the data stored in... Figure 3 The program implementation of the memory 102 shown. To implement... Figure 1 The derivation unit 31, control information generation unit 32, and driving control unit 33 shown also use Figure 3 The memory 102 shown. Figure 1 The sensor information acquisition unit 30 shown is composed of Figure 3 The communication unit 103 shown is implemented. Figure 1 The information storage unit 34 shown is Figure 3 A portion of the memory 102 shown.

[0057] Similarly, Figure 2 The information processing device 3a shown also comprises... Figure 3 The computer system implementing the processing circuit shown. Furthermore, in addition to the processing circuit, the computer system implementing the information processing device 3a may also include at least one of an input unit and a display unit. Figure 2 The communication unit 35 shown is composed of Figure 3 The communication unit 103 shown is implemented. The information processing device 3a can also be implemented by multiple computer systems. For example, Figure 2 The information processing device 3a shown can also be implemented by a cloud system.

[0058] Implementation method 2.

[0059] In Embodiment 2, an example of vehicle 1 driving control performed by the information processing device 3 described in Embodiment 1 will be explained. The structure of the vehicle 1 in this embodiment is the same as that in Embodiment 1. Furthermore, examples as in Embodiment 1 will be listed here. Figure 1 The information processing device 3 shown is described using an example of a structure installed inside vehicle 1, but in embodiment 1... Figure 2 The same operation of this embodiment can be applied to the structural example shown. Components having the same function as in Embodiment 1 are labeled with the same reference numerals as in Embodiment 1, and repeated descriptions are omitted.

[0060] In this embodiment, the information processing device 3 sets a route to the destination such that the vehicle 1 arrives at a parking area (SA) or similar location and stops before resuming driving during the user's light sleep period. Therefore, the information processing device 3 can prevent the vehicle 1 from stimulating the user during their light sleep period, thus promoting deeper sleep.

[0061] Figure 4 This is a flowchart illustrating an example of the processing steps in the information processing apparatus 3 of this embodiment. For example... Figure 4 As shown, the information processing device 3 determines whether the user is asleep (step S1). Furthermore, in this embodiment, the sensor information acquisition unit 30 acquires sensor information from the sensor 2 periodically, for example.

[0062] In step S1, specifically, the derivation unit 31 uses the sensor information received from the sensor information acquisition unit 30 to determine whether the user riding in the vehicle 1 is asleep. The determination of whether the user is asleep can be made by using a camera as a sensor 2, using the captured data obtained by photographing the user through the camera. For example, the derivation unit 31 can detect the user's movement based on the captured data as sensor information, and determine whether the user is asleep based on the user's movement; or it can determine whether the user's eyelids are closed based on the captured data, and determine whether the user is asleep based on the result of the eyelid closure determination. Alternatively, an accelerometer worn by the user can be used as a sensor 2, and the derivation unit 31 can determine whether the user is asleep based on the detection result of the accelerometer, i.e., sensor information. Alternatively, a sensor that acquires vital signs data can be used as a sensor 2, and the derivation unit 31 can determine whether the user is asleep based on the sensor information as vital signs data. Alternatively, the derivation unit 31 can combine captured data and vital signs data to determine whether the user is asleep. The method for determining whether the user is asleep is not limited to these examples.

[0063] If the user is not asleep (step S1 is "No"), step S1 is repeated. If the user is asleep (step S1 is "Yes"), the information processing device 3 calculates the sleep depth up to the destination (step S2). Specifically, the calculation unit 31 extracts the path and required time up to the destination from the path information stored in the information storage unit 34, calculates the sleep depth at each time from the current moment until the time of arrival at the destination using the extracted information, and outputs the calculated sleep depth to the control information generation unit 32. In addition, the path and required time up to the destination are calculated by the driving control unit 33, for example, as described in Embodiment 1, and stored as path information in the information storage unit 34. Alternatively, as described in Embodiment 1, the control information generation unit 32 may also set the path using the destination and map information and calculate the required time. In this case, the control information generation unit 32 stores the path information in the information storage unit 34. Furthermore, here we will describe an example of the derivation unit 31 deriving the sleep depth up to the destination, but it is not limited to this. The derivation unit 31 can derive the sleep depth for a certain period of time in the future. The certain period of time can be, as mentioned above, the time required to reach the destination, or a time shorter than the time required to reach the destination, such as half of the time required to reach the destination, or a predetermined time.

[0064] Next, the information processing device 3 sets a route to the parking location during a period of light sleep (step S3). Specifically, the control information generation unit 32 uses the sleep depth derived by the derivation unit 31 up to the destination to determine the predicted period of light sleep for the user, and determines the route for the vehicle 1 to reach the parking location within the determined period, generating control information representing the determined route. This control information can be output to the driving control unit 33 or stored as route information in the information storage unit 34.

[0065] For example, the control information generation unit 32 first uses the route to the destination and the sleep depth to the destination received from the derivation unit 31 to calculate the position of vehicle 1 during the shallow sleep period. If there is a parking lot around the calculated position, the control information generation unit 32 determines the route so that the vehicle arrives at the parking lot during that time period and parks there. The parking lot may include at least one of SA and PA (parking areas), but may also include rest areas such as road stations, or parking lots that allow parking. The parking lot may also be a parking lot of a shop, park, etc. Furthermore, if there is a parking lot on the initially set route, the driving speed may also be determined so that the vehicle arrives at the parking lot during the shallow sleep period, and the determined driving speed is also output as control information to the driving control unit 33. If there is no parking lot on the initially set route, the control information generation unit 32 selects a parking lot around the route that the vehicle arrives at during the shallow sleep period and sets the route via that parking lot. That is, the control information generation unit 32 updates the route information stored in the information storage unit 34 to route information (control information) indicating the route via that parking lot. Alternatively, the control information generation unit 32 may output path information representing the path through the parking lot as control information to the driving control unit 33. Furthermore, the control information generation unit 32 generates control information to stop the vehicle 1 at the parking lot and outputs the generated control information to the driving control unit 33. Additionally, the path information may include an instruction to stop the vehicle 1 at the parking lot. Thus, the driving control unit 33 controls the movement of the vehicle 1 based on the updated path information, and upon arrival at the parking lot, controls the driving mechanism 4 to stop the vehicle 1 at the parking lot.

[0066] Alternatively, it can be assumed that if the period of light sleep occurs more than a certain number of times during the time until reaching the destination, the vehicle can be driven again after parking in a parking lot for a certain period of time. Then, for each of the two or more instances of light sleep, a corresponding parking lot can be determined, and a route can be set through two or more determined parking lots. For example, if the time required to reach the destination is more than 7 hours, there are 4 to 5 sleep cycles, but the latter half of the sleep cycle becomes preparation for wakefulness. Therefore, the control information generation unit 32 can also set a route through two parking lots to promote deep sleep during the sleep cycle up to the second cycle of the first half.

[0067] Next, the information processing device 3 determines whether the vehicle 1 has arrived at the parking lot (step S4). In detail, the control information generation unit 32 can determine that the vehicle 1 has arrived at the parking lot by receiving a notification from the driving control unit 33 to stop the vehicle 1 at the parking lot, or it can obtain the current position of the vehicle 1 from the driving control unit 33 and determine that the vehicle 1 has arrived at the parking lot if the vehicle 1 is already in the parking lot.

[0068] If the parking location has not been reached (step S4 is "No"), step S4 is repeated. If the parking location has been reached (step S4 is "Yes"), the information processing device 3 calculates the sleep depth (step S5). Specifically, the control information generation unit 32 instructs the calculation unit 31 to calculate the sleep depth. The calculation unit 31 uses sensor information to calculate the current user's sleep depth and outputs the calculation result to the control information generation unit 32.

[0069] The information processing device 3 determines whether the sleep is deep (step S6). Specifically, the control information generation unit 32 determines whether the user's sleep is deep based on the derivation result received from the derivation unit 31.

[0070] If the user is not in a deep sleep (step S6 is "No"), the processing from step S5 onwards is repeated. If the user is in a deep sleep (step S6 is "Yes"), the information processing device 3 restarts driving (step S7). Specifically, the control information generation unit 32 generates control information instructing the user to restart driving and outputs the generated control information to the driving control unit 33. Based on the control information received from the control information generation unit 32, the driving control unit 33 controls the driving mechanism 4 to restart the driving of the vehicle 1. Thus, after the vehicle 1 arrives at the parking lot, the control information generation unit 32, using the deduction result of the sleep depth derived by the deduction unit 31 based on sensor information, determines that the user is in a deep sleep and generates control information indicating that the vehicle 1 should depart from the parking lot and restart driving.

[0071] After resuming driving, the information processing device 3 determines whether stopping is unnecessary (step S8). Specifically, the control information generation unit 32 determines that stopping is unnecessary if the vehicle has already stopped at all parking locations along the path indicated by the path information. Furthermore, the control information generation unit 32 may also determine that stopping is unnecessary in step S8 if there are parking locations along the path indicated by the path information where the vehicle has not yet stopped, or if there are parking locations where the estimated travel time compared to the initial plan is delayed by a predetermined time or more.

[0072] If stopping is required (step S8 is "No"), the processing from step S4 onwards is repeated. If stopping is not required (step S8 is "Yes"), the information processing device 3 drives the vehicle 1 to its destination (step S9). Specifically, in step S9, if the vehicle has already stopped at all the parking locations it has passed, the driving control unit 33 controls the driving mechanism 4 based on the route information to drive the vehicle 1 to its destination. If there are parking locations that have not yet been visited but it was determined in step S8 that stopping is not required, in step S9, the control information generation unit 32 sets a route that does not pass through the remaining parking locations (parking locations that have not yet been reached). That is, the control information generation unit 32 updates the route information stored in the information storage unit 34 to route information indicating a route that does not pass through the remaining parking locations. Thus, the driving control unit 33 controls the driving of the vehicle 1 based on the updated route information, driving the vehicle 1 to its destination.

[0073] In addition, Figure 4 In the example shown, the information processing device 3 restarts driving after the vehicle 1 has stopped in the parking space and the user has fallen into a deep sleep. However, it is not limited to this; it can also restart driving after a predetermined time has elapsed in the parking space. Alternatively, the information processing device 3 can restart driving if at least one of the following conditions is met: the user has fallen into a deep sleep, or the user has been in the parking space for a predetermined time.

[0074] Figure 5 This is a conceptual diagram illustrating an example of path setting in this embodiment. Figure 5 In the example shown, a path 201 is defined from the starting point to the destination. Assume that after vehicle 1 departs from the starting point, the user falls asleep at location 202 on path 201. Figure 5In the example shown, the information processing device 3 predicts the user's sleep depth and determines that the user's sleep is light during the time (period) of travel in interval 203, thus setting the path 201 via SA301 and SA302. Interval 204 is the interval corresponding to the time (period) of deep sleep for the user. In this way, since vehicle 1 parks in parking areas such as SA301 and 302 during the user's light sleep time, it is possible to prevent the movement of vehicle 1 from stimulating the user and promote deep sleep. Furthermore, Figure 5 This is just an example; the number of routes and parking locations set is not limited to... Figure 5 The example shown is not limited to SA; as mentioned above, it can be PA, or parking lots for shops, parks, etc.

[0075] In the example described above, parking in a parking lot promotes deep sleep for the user. However, it's also possible to consider situations where the user might want to use the restroom, rest, shop, or get off at the parking lot. Therefore, it's also possible to allow the user to choose whether parking in a parking lot promotes deep sleep or keeps them awake.

[0076] Figure 6 This diagram illustrates a structural example of a vehicle in this embodiment when the user's settings are accepted. Figure 6 The vehicle 1b shown, in addition to having an added wakefulness-enhancing device 5, and replacing the information processing device 3 with an information processing device 3b, is similar to... Figure 1 The vehicle shown is the same as vehicle 1. For those with the same... Figure 1 The examples shown have the same functional component labels and Figure 1 The same labels are used, and repeated descriptions are omitted.

[0077] like Figure 6 As shown, the information processing device 3b in Figure 1An input receiving unit 37 is added to the information processing device 3 shown. The input receiving unit 37 receives input from the user indicating whether they will get off at the parking lot or not. The input receiving unit 37 outputs the received selection result to the control information generation unit 32. If the selection result indicates getting off at the parking lot, the control information generation unit 32 sets the parking lot operation mode to wake-up promotion. When the parking lot operation mode is set to wake-up promotion, the control information generation unit 32 generates control information instructing the user to wake up upon arrival at the parking lot and outputs the generated control information to the wake-up promotion device 5. Upon receiving the control information from the control information generation unit 32, the wake-up promotion device 5 performs actions to promote the user's wake-up. These actions may include generating sounds indicating arrival at the parking lot, alarm sounds, music, etc., vibrating the seat, or other actions. If the selection result indicates not getting off at the parking lot, the control information generation unit 32 sets the parking lot operation mode to sleep promotion. Figure 4 Similarly, the example shown does not implement any controls to alert the user even upon arrival at the parking area.

[0078] Figure 7 This is a flowchart illustrating an example of the processing steps in the information processing apparatus 3b of this embodiment when a user's settings are accepted. Steps S1 to S4 and Figure 4 The example shown is the same. If step S4 is "Yes", the information processing device 3b determines whether it is a setting that promotes wakefulness (step S11). Specifically, the control information generation unit 32 determines whether the parking lot's operating mode promotes wakefulness. If it is not a setting that promotes wakefulness (step S11 is "No"), and... Figure 4 Perform steps S5 to S9 in the same manner.

[0079] When the setting is to promote wakefulness (step S11 is "Yes"), the information processing device 3b promotes the user's wakefulness (step S12). Specifically, the control information generation unit 32 generates control information instructing the user to wake up and outputs the generated control information to the wakefulness promotion device 5. Therefore, when the wakefulness promotion device 5 receives the control information from the control information generation unit 32, it performs an action to promote the user's wakefulness.

[0080] Next, the information processing device 3b determines whether there is a departure instruction, i.e., a departure instruction for vehicle 1b (step S13). If there is a departure instruction (step S13 is "Yes"), the process proceeds to step S7. If there is no departure instruction (step S13 is "No"), the information processing device 3b repeats step S13. Furthermore, for example, the departure instruction for vehicle 1b may be input by a user. After receiving the user's input indicating that vehicle 1b should depart, the input receiving unit 37 outputs information indicating that vehicle 1b has departed to the control information generation unit 32. In step S13, the control information generation unit 32 determines whether there is a departure instruction based on whether it has received information indicating that vehicle 1b has departed from the input receiving unit 37.

[0081] exist Figure 7 In the example shown, when the action mode is set to sleep promotion, the interaction with... Figure 4 The same process is applied when the action mode is set to wakefulness-enhancing, promoting user wakefulness upon arrival at the parking area. When wakefulness enhancement is performed at the parking area, the route is also designed to arrive during a period of light sleep, thus facilitating natural wakefulness. Figure 7 In the example shown, the user can set an action pattern, therefore, the information processing device 3b can perform actions corresponding to the user's expectations. Furthermore, this can also be achieved in Embodiment 1. Figure 2 The vehicle 1a shown is equipped with an additional input receiving unit 37 and a sobriety promotion device 5. The information received by the input receiving unit 37 is sent to the information processing device 3a via the communication unit 36, thereby enabling the information processing device 3a to communicate with... Figure 7 The same process is shown.

[0082] In addition, Figure 6 and Figure 7 In the example shown, vehicle 1b is equipped with a wake-up-promoting device 5, but it is also possible to omit the wake-up-promoting device 5 from vehicle 1b. Since the user arrives at the parking location during a period of light sleep, there is a possibility that the user will sense arrival and wake up naturally. Therefore, information processing device 3b can also wait for the user to disembark for a certain period of time after arriving at the parking location. Even if the user does not wake up after a certain period of time, actions can be performed if the action mode is set to sleep-promoting.

[0083] Figure 6 The information processing device 3b shown, for example, is through... Figure 3 The illustrated processing circuit and the input section (not shown) are implemented, but the input receiving section 37 is implemented by the input section.

[0084] As described above, in this embodiment, the paths of vehicles 1 and 1b are set so that they arrive at the parking lot during periods of light sleep, and vehicles 1 and 1b stop at the parking lot upon arrival. Therefore, vehicles 1 and 1b can avoid being stimulated by their movement during periods of light sleep, thus promoting deep sleep for the user. In other words, driving control corresponding to the user's sleep state is possible.

[0085] Implementation method 3.

[0086] In Embodiment 3, similar to Embodiment 2, an example of vehicle 1 driving control performed by the information processing device 3 described in Embodiment 1 will be explained. The structure of the vehicle 1 in this embodiment is the same as that in Embodiment 1. Furthermore, examples as in Embodiment 1 will be listed here. Figure 1 The information processing device 3 shown is described using an example of a structure installed inside vehicle 1, but in embodiment 1... Figure 2 The same operation of this embodiment can be applied to the structural example shown. Components having the same function as in Embodiment 1 are labeled with the same reference numerals as in Embodiment 1, and repeated descriptions are omitted.

[0087] Figure 8 This is a flowchart illustrating an example of the processing steps of the information processing apparatus 3 in this embodiment. For example... Figure 8 As shown, the information processing device 3 determines whether the user is asleep in the same way as step S1 of embodiment 2. Furthermore, in this embodiment, the sensor information acquisition unit 30 also acquires sensor information from the sensor 2, for example, periodically.

[0088] If the user is not asleep (step S1 is "No"), the information processing device 3 decides to set the vehicle 1 to normal driving (step S21), and repeats step S1. For example, as described in Embodiment 1, the vehicle 1 can be set by the user to two modes: normal driving mode and restricted driving mode. The control information generation unit 32 sets the driving mode to normal driving mode. Furthermore, if the driving mode has already been set to normal driving mode, the control information generation unit 32 can directly set the driving mode to normal driving mode without changing the driving mode.

[0089] When the user is asleep (step S1 is "Yes"), the information processing device 3 calculates the sleep depth (step S22). Specifically, the calculation unit 31 uses sensor information to calculate the user's current sleep depth and outputs the calculation result to the control information generation unit 32. Alternatively, the calculation unit 31 may use the calculation result of the user's current sleep depth to predict the sleep depth at a certain future time and also output the prediction result to the control information generation unit 32.

[0090] Next, the information processing device 3 determines whether the sleep is light (step S23). Specifically, the control information generation unit 32 determines whether the user's sleep is light based on the derivation result received from the derivation unit 31. If the sleep is light (step S23 is "yes"), the information processing device 3 decides to set the vehicle 1's driving to restricted driving (step S24), and repeats the processing from step S22 onwards. Specifically, in step S24, the control information generation unit 32 generates control information to make the vehicle 1 drive smoothly compared to normal driving without restrictions. For example, the control information generation unit 32 sets the driving mode to restricted driving mode. Furthermore, if the driving mode has already been set to restricted driving mode, the driving mode can be changed without modification; the control information generation unit 32 generates control information instructing the driving mode to be directly set to restricted driving mode, and outputs the generated control information to the driving control unit 33. In restricted driving mode, as described in Embodiment 1, driving control is performed to achieve smooth driving.

[0091] Specifically, in the restricted driving mode, restrictions can be imposed either by setting at least one of speed and acceleration below a threshold, or by setting the driving path to a smoother path. A smoother path includes, for example, a path with fewer sharp turns, less uneven road surfaces, and a gentle slope, but is not limited to these. Furthermore, for example, assuming that values ​​representing the curvature of curves, the unevenness of the road surface, and the slope of the road are included in map information, the control information generation unit 32 can determine a curve as a sharp turn if the curvature is above a threshold, determine a path with less unevenness if the value representing the unevenness of the road surface is below a threshold, and determine a path with a gentle slope if the value representing the slope of the road surface is below a threshold. Additionally, as described above, the control information generation unit 32 can also receive a predicted value of sleep depth for a certain future time from the deduction unit 31. In this case, the driving mode during the period of light sleep in the predicted result will be set as the restricted driving mode.

[0092] If the vehicle is not in a light sleep state (step S23 is "No"), the information processing device 3 decides to set the vehicle 1 to normal driving mode (step S25), and repeats the processing from step S22 onwards. Specifically, in step S25, for example, the control information generation unit 32 sets the driving mode to normal driving mode. Furthermore, if the driving mode is already set to normal driving mode, the control information generation unit 32 can directly set the driving mode to normal driving mode without changing the driving mode.

[0093] By conducting Figure 8In the illustrated process, the information processing device 3 can control the vehicle to operate smoothly during the user's light sleep period. That is, it can perform driving control corresponding to the user's sleep state. Furthermore, in the example above, by ensuring smooth driving of the vehicle 1 during the user's light sleep period, the user's sleep is not disturbed. However, it is also possible to further suppress external stimuli to the user during this time, primarily due to factors other than driving. For example, regarding the user's surrounding environment, external stimuli can be suppressed by adjusting at least one of the ambient brightness and ambient sound. For example, in step S24 above, the control information generation unit 32 can set a path to avoid brightly lit areas or a path to avoid noisy areas. A path to avoid brightly lit areas could be, for example, a path to avoid areas with many shops when the vehicle 1 is driving at night, but is not limited to this. A path to avoid noisy areas could be, for example, a path to avoid areas around construction sites or areas with many people or vehicles, but is not limited to this.

[0094] In addition, the control information generation unit 32 can also suppress external stimuli to the user by controlling devices inside the vehicle 1 that affect the user's surrounding environment. Figure 9 This diagram illustrates a structural example of a vehicle according to this embodiment, which controls devices within the vehicle 1c that affect the user's surrounding environment. Figure 9 The vehicle 1c shown is an embodiment of vehicle 1 with the addition of a speaker 11, a noise reduction device 12, and a light reduction device 13.

[0095] The speaker 11 outputs sound data from a sound data output device (not shown) as sound output. The sound data output device may be, for example, a device for playing music inside the vehicle 1c, or a device for acquiring sound data to be broadcast or published. The noise reduction device 12 is a device that reduces the sound inside the vehicle 1c. It may be a combination of a sound-absorbing sheet and a device for controlling the entry and exit of the sound-absorbing sheet, or a noise cancellation device that detects noise inside the vehicle 1c and generates sound with the opposite phase to the detected noise, thereby eliminating the noise through electrical processing. Other devices may also be included. The light reduction device 13 is a device that reduces the light inside the vehicle 1c. For example, it may be a dimming film installed on the rear window of the vehicle 1c that can adjust the light transmittance, or a curtain used to block light when the vehicle 1c is parked. Other devices may also be included.

[0096] exist Figure 9 In the example shown, the control information generation unit 32 is in Figure 8In step S24, control information is further generated to activate the speaker 11, the noise reduction device 12, and the light reduction device 13. This generated control information is then output to the speaker 11, the noise reduction device 12, and the light reduction device 13, thereby suppressing sound and light within the vehicle 1. For example, the control information generation unit 32 generates control information to reduce the volume of the speaker 11 or to turn it off, generates control information to enable the noise reduction device 12 to suppress sound within the vehicle 1, and generates control information to enable the light reduction device 13 to suppress light within the vehicle 1. Thus, for the user, not only can stimulation caused by driving be suppressed, but stimulation caused by the surrounding environment other than driving can also be suppressed. Furthermore, in Figure 9 The example shown illustrates a vehicle 1c equipped with a speaker 11, a noise reduction device 12, and a light reduction device 13. However, the vehicle 1c may also be equipped with one or two of the speaker 11, the noise reduction device 12, and the light reduction device 13.

[0097] In addition, implementation method 1 can also be modified. Figure 2 The vehicle 1a shown is equipped with an additional speaker 11, a noise reduction device 12, and a light reduction device 13. The information processing device 3a generates control information for controlling these devices and sends it to the vehicle 1a via the communication unit 35. In this case, the vehicle 1a may also have one or two of the speaker 11, the noise reduction device 12, and the light reduction device 13.

[0098] Furthermore, the information processing device 3 can also implement both the actions of embodiment 2 and the actions described in this embodiment. Figure 2 The vehicle 1a and information processing device 3a shown can also perform both the actions of Embodiment 2 and the actions described in this embodiment.

[0099] Furthermore, an example of suppressing both stimuli caused by driving and stimuli caused by the user's surrounding environment is given here, but vehicle 1c may also suppress only stimuli caused by the user's surrounding environment when the user is in a light sleep.

[0100] Implementation method 4.

[0101] In Embodiment 4, similar to Embodiments 2 and 3, an example of vehicle 1 driving control performed by the information processing device 3 described in Embodiment 1 will be explained. The structure of the vehicle 1 in this embodiment is the same as that in Embodiment 1. Furthermore, examples as in Embodiment 1 will be listed here. Figure 1 The information processing device 3 shown is described using an example of a structure installed inside vehicle 1, but in embodiment 1... Figure 2 The same operation of this embodiment can be applied to the structural example shown. Components having the same function as in Embodiment 1 are labeled with the same reference numerals as in Embodiment 1, and repeated descriptions are omitted.

[0102] Figure 10 This is a flowchart illustrating an example of the processing steps in the information processing apparatus 3 of this embodiment. Steps S1 and S2 are the same as steps S1 and S2 of Embodiment 2. After step S2, the information processing apparatus 3 predicts the wake-up time (step S31). Specifically, the derivation unit 31 uses the derived sleep depth to predict the user's wake-up time. For example, the derivation unit 31 uses the derivation result of the sleep depth and the standard sleep pattern of a person described in Embodiment 1 to predict the wake-up time, and outputs the prediction result to the control information generation unit 32. Specifically, for example, the derivation unit 31 uses the derivation result of the sleep depth to deduce the sleep induction time and sleep cycle, calculates the time period during which REM sleep occurs within a certain period including the expected arrival time at the destination, and uses the time within the calculated time period as the expected wake-up time. For example, the control information generation unit 32 may also use the midpoint of the time period during which REM sleep occurs as the expected wake-up time.

[0103] Next, the information processing device 3 sets a route to the destination within a certain time from the predicted wake-up time (step S32). Specifically, the control information generation unit 32 determines the route of vehicle 1 to the destination in a manner that enables vehicle 1 to reach the destination within a certain time from the predicted wake-up time in step S31, generates control information, i.e., path information, representing the determined path, and saves the path information in the information storage unit 34. Alternatively, the control information generation unit 32 may output the control information representing the determined path to the driving control unit 33. By driving based on this path information, vehicle 1 can reach the destination within a certain time from the predicted wake-up time. Therefore, the likelihood of the user waking up naturally near the destination can be increased. Thus, in this embodiment, driving control corresponding to the user's sleep state can also be performed.

[0104] Furthermore, in the above example, sleep depth is used to predict the wake-up time, but it is not limited to this. The wake-up time can also be set by the user, as described below, or it can be derived based on sleep information representing at least one of the user's sleep history, the user's wake-up time history, and the user's scheduled wake-up time.

[0105] Figure 11 This is a diagram illustrating a structural example of a vehicle in this embodiment that uses sleep information to deduce wake-up time. (As shown...) Figure 11As shown, vehicle 1d includes an information processing device 3d instead of information processing device 3. The information processing device 3d adds a sleep information acquisition unit 38 to information processing device 3. The sleep information acquisition unit 38 acquires sleep information by receiving sleep information from information providing device 6 that stores user sleep information, and outputs the acquired sleep information to derivation unit 31. Furthermore, in Figure 11 The example shown is an information providing device 6, which is a portable terminal such as a smartphone, tablet, or personal computer carried by a user. The information providing device 6 exists within the vehicle 1d, but it can also exist outside the vehicle 1d. Furthermore, the information providing device 6 is not limited to a portable terminal carried by a user; it could also be a server device that manages the sleep information of multiple users, etc.

[0106] Sleep information can be, for example, information managed by a sleep application (application software) that acquires and manages sleep-related information about the user, but is not limited to this. For example, the information providing device 6 may have an alarm clock function, and the wake-up time set in the alarm clock function may be used as sleep information. In addition, if the information providing device 6 has a schedule management function, and the user registers the wake-up time in the schedule, the registered wake-up time may also be used as sleep information.

[0107] The sleep information managed by the sleep application includes, for example, the user's fall asleep time, wake-up time, and sleep cycle. When using the sleep information managed by the sleep application to predict the wake-up time, the derivation unit 31 can, for example, use the user's historical wake-up times to calculate an average wake-up time and use the calculated average as the predicted wake-up time. Alternatively, the derivation unit 31 can use the historical fall asleep time and wake-up time to calculate an average sleep time, use sensor information to deduce the user's fall asleep time, and use the calculated average time from the deduce fall asleep time as the predicted wake-up time.

[0108] Furthermore, the derivation unit 31 can also use the wake-up time shown in the sleep information as the predicted wake-up time when the sleep information is the wake-up time set in the alarm function. Additionally, the derivation unit 31 can also use the wake-up time shown in the sleep information as the predicted wake-up time when the sleep information is the wake-up time registered in the schedule. Moreover, when the schedule has a target day for which the wake-up time prediction is registered, the wake-up time in the schedule of the target day for which the wake-up time prediction is registered is used as the predicted wake-up time. When the schedule does not have a target day for which the wake-up time prediction is registered, the registered wake-up times of other days of the same week can be used as the predicted wake-up time, or the wake-up time with the most registered wake-up times can be used as the predicted wake-up time.

[0109] Figure 11 The information processing device 3d shown is, for example, composed of Figure 3 The processing circuit shown is implemented as an example. The sleep information acquisition unit 38 is implemented by the communication unit 103.

[0110] Figure 12 This is a flowchart illustrating an example of the processing steps in the information processing device 3d of this embodiment, which uses sleep information to predict wake-up time. Step S1 and Figure 10 The example shown is the same. If step S1 is "Yes", the information processing device 3d uses sleep information to predict the wake-up time (step S41). Specifically, the derivation unit 31 uses the sleep information received from the sleep information acquisition unit 38 to predict the user's wake-up time and outputs the prediction result to the control information generation unit 32. Step S32 is the same as... Figure 10 The example shown is the same.

[0111] In addition, implementation method 1 can also be modified. Figure 2 The vehicle 1a or information processing device 3a shown is equipped with a sleep information acquisition unit 38. The information processing device 3a, in the same manner as in this embodiment, predicts the user's wake-up time based on the sleep information and sets a path to the destination within a certain time from the predicted wake-up time.

[0112] Furthermore, the information processing device 3d can also implement at least one of the actions of embodiment 2 and embodiment 3, as well as the actions described in this embodiment. Figure 2 The vehicle 1a and information processing device 3a shown can also implement at least one of the actions of embodiment 2 and embodiment 3.

[0113] As described above, in this embodiment, the wake-up time is predicted, and a route is set in such a way that the user arrives at the destination within a certain time from the predicted wake-up time. Therefore, it is possible to increase the likelihood that the user will wake up naturally near the destination, thereby improving the user's comfort.

[0114] Implementation method 5.

[0115] Figure 13 This is a diagram illustrating a structural example of the vehicle 1e according to Embodiment 5. The vehicle 1e of this embodiment is identical to the vehicle 1 of Embodiment 1, except that it includes an information processing device 3e instead of the information processing device 3. Components having the same functions as in Embodiment 1 are labeled with the same reference numerals as in Embodiment 1, and repeated descriptions are omitted.

[0116] like Figure 13 As shown, the information processing device 3e adds an identification unit 39 and a characteristic information storage unit 40 to the information processing device 3 of Embodiment 1. Furthermore, examples as in Embodiment 1 are listed here. Figure 1 The following description uses the structure of the information processing device 3e installed inside the vehicle 1e as an example, but it can also be described in embodiment 1. Figure 2 In the structural example shown, an identification unit 39 and a characteristic information storage unit 40 are added to the information processing device 3a to perform the operation of this embodiment.

[0117] In this embodiment, sensor 2 includes, for example, a camera, and sensor information includes captured data obtained by photographing the user through the camera. Sensor information acquisition unit 30 outputs the captured data to recognition unit 39. Recognition unit 39 uses the captured data to identify the user and outputs the recognition result (user recognition result) to derivation unit 31. User recognition can be based on the individual user or on user attributes. Attributes may include, for example, at least one of gender and age, but are not limited to these.

[0118] User identification can be performed, for example, using facial recognition technology. When identifying an individual user, the identification unit 39 pre-registers image data representing the user's face. The identification unit 39 identifies the individual user by comparing the registered image data with the captured data. Furthermore, age may not be age itself; it can be represented by distinctions such as infant, child, adult, etc., or by age groups such as under 10 years old, teens, twenties, etc. The age classification is not limited to these examples. Additionally, in Figure 13 In the example shown, the identification unit 39 identifies the user based on the captured data, but it can also use the life data obtained as sensor information to identify the user, or it can combine the captured data and the life data to identify the user.

[0119] The characteristic information storage unit 40 stores characteristic information representing sleep-related characteristics for each user or each attribute. The characteristic information includes, for example, at least one of the following: characteristics representing vital data such as each sleep cycle, sleep duration, wake-up time, and sleep depth; characteristics of body movement for each sleep depth; characteristics of vital data representing body movement during sleep and wakefulness; and characteristics of body movement during sleep and wakefulness. This information is used for derivation in the derivation unit 31. The characteristic information can be in tabular form, with each user storing data for each item, or it can be a learned model that has been trained through machine learning, as described later. When the characteristic information is in tabular form, it can be calculated, for example, by statistically processing pre-accumulated data for each user or each attribute. Alternatively, the characteristic information can also be calculated based on sensor information obtained by sensor 2 when the user previously rode in vehicle 1e.

[0120] The derivation unit 31 uses the recognition result received from the recognition unit 39 and the characteristic information stored in the characteristic information storage unit 40 to deduce sleep-related indicators. These sleep-related indicators can be sleep depth, wake-up time, the distinction between being asleep and not asleep, or two or more of these. That is, the derivation unit 31 can also... Figure 4 , Figure 7 , Figure 8 , Figure 10 , Figure 12 In the determination of step S1 shown, the identification result received from the identification unit 39 and the characteristic information stored in the characteristic information storage unit 40 are used to deduce whether the user is asleep. In deducing whether the user is asleep, at least one of the characteristic information is used, for example, information such as characteristics of vital data representing body movements during sleep and wakefulness, and characteristics of body movements during sleep and wakefulness.

[0121] In addition, Figure 4 , Figure 7 , Figure 8 and Figure 10 In deriving sleep depth, the derivation unit 31 can use the recognition result received from the recognition unit 39 and the characteristic information stored in the characteristic information storage unit 40 to derive the sleep depth. In deriving the user's sleep depth, at least one of the following characteristic information is used: information representing the user's sleep cycle, characteristics of vital data at each sleep depth, and characteristics of the user's body movement at each sleep depth. Additionally, when deriving the wake-up time described in Embodiment 4, the derivation unit 31 can also use the recognition result received from the recognition unit 39 and the characteristic information stored in the characteristic information storage unit 40 to derive the sleep depth. In deriving the user's wake-up time, at least one of the following characteristic information is used: information representing sleep time, wake-up time, etc. In this case, the characteristic information can also be a statistical value of at least one of the user's wake-up time and sleep time.

[0122] As mentioned above, feature information can also be from the learned model. Figure 14 This diagram illustrates a structural example of the derivation unit 31 in this embodiment when derivation is performed using machine learning. Figure 14 In the example shown, the derivation unit 31 includes a learning unit 311 and an inference unit 312. The learning unit 311, for example, uses multiple datasets consisting of input data as features and corresponding positive solution data related to sleep to generate a learned model through supervised learning, and stores the generated learned model as feature information in the feature information storage unit 40. For example, the features may include information calculated based on sensor information.

[0123] When the learned model is used to infer whether or not the user is asleep, the input data may include at least one of the following: characteristics of vital data representing body movements during sleep and wakefulness, and characteristics of body movements during sleep and wakefulness. When the learned model is used to infer sleep depth, the input data may include at least one of the following: characteristics of the user's sleep cycle, characteristics of vital data for each sleep depth, and characteristics of the user's body movements for each sleep depth. The input data may be data calculated based on data previously acquired by sensor 2, data calculated based on data acquired by a device different from sensor 2, or a mixture of both. Additionally, the input data may be data acquired by a sleep application, etc. Furthermore, regarding corrective data, it may be determined by separately acquiring and using brain waves, by the result of applying stimulation to the user, or by other methods.

[0124] As the supervised learning algorithm used to generate the learning completion model in Learning Department 311, any algorithm can be used, such as a neural network model. The neural network consists of an input layer containing multiple neurons, intermediate layers (hidden layers) containing multiple neurons, and an output layer containing multiple neurons. The intermediate layers can be one or more layers.

[0125] Figure 15 This is a schematic diagram illustrating an example of a neural network. For example, if it is... Figure 15 The 3-layer neural network shown in the diagram, when multiple inputs are input to the input layer (X1-X3), the value is multiplied by weight W1 (w11-w16) and input to the intermediate layer (Y1-Y2). The result is then multiplied by weight W2 (w21-w26) and output from the output layer (Z1-Z3). The output varies depending on the values ​​of weight W1 and weight W2.

[0126] In this embodiment, the relationship between the feature quantity and the positive solution data is learned by adjusting weights W1 and W2 in a manner that makes the output from the output layer when the feature quantity is input close to the sleep-related index, which is the positive solution data. Furthermore, the machine learning algorithm is not limited to neural networks, but can also be other algorithms such as support vector machines. Additionally, the machine learning used in generating the learned model in the learning unit 311 is not limited to supervised learning, but can also be reinforcement learning, etc.

[0127] The inference unit 312 uses the sensor information received from the sensor information acquisition unit 30 to calculate feature quantities, inputs the feature quantities into the learned model stored in the feature information storage unit 40, and thereby infers sleep-related indicators. Thus, sleep-related indicators are derived.

[0128] A learning model can be generated for each user or each attribute. For example, a learning model can also be generated for each era. When the recognition result is an individual user's recognition result, the derivation unit 31 uses the fully learned model corresponding to that individual user to infer sleep-related indicators. Alternatively, when the recognition result is an attribute recognition result for the user, the derivation unit 31 uses the fully learned model corresponding to that attribute to infer sleep-related indicators. Or, instead of generating a fully learned model for each individual user or each attribute, the user's individual recognition information or attributes can also be used as feature inputs to generate the fully learned model, and during inference, the user's individual recognition information or attributes can also be used as feature inputs to the fully learned model.

[0129] In addition, Figure 14 The example shown is that the derivation unit 31 has a learning unit 311, but it is not limited to this. It may also have a learning device that generates a learned model separately from the vehicle 1e. The learning device generates the learned model and the learned model generated by the learning device is stored in the feature information storage unit 40.

[0130] Figure 13 The information processing device 3e shown is, for example, composed of Figure 3 The illustrated processing circuit implementation. Figure 13 The identification unit 39 shown is obtained by means of... Figure 3 The processor 101 shown executes the data stored in... Figure 3 The program implementation of the memory 102 shown. To implement... Figure 13 The identification unit 39 shown also uses Figure 3 The memory 102 shown. Figure 13 The characteristic information storage unit 40 shown is Figure 3 A portion of the memory 102 shown.

[0131] The actions of this embodiment can be applied when sleep-related indicators can be derived in any of the embodiments of embodiments 1 to 4 or in two or more combinations of embodiments 1 to 4.

[0132] As described above, in this embodiment, user attributes are identified, and based on the identification results, sleep-related indicators such as whether the user is asleep, sleep depth, and wake-up time are derived. Thus, the same effect as in Embodiment 1 can be achieved, and the accuracy of the derived sleep-related indicators can be improved.

[0133] The structure shown in the above embodiments is an example that can be combined with other known technologies, and the embodiments can be combined with each other. Furthermore, parts of the structure can be omitted or modified without departing from the spirit of the subject.

[0134] Label Explanation

[0135] 1, 1a, 1b, 1c, 1d, 1e: Vehicle; 2: Sensor; 3, 3a, 3b, 3d, 3e: Information processing device; 4: Driving mechanism; 5: Awake-up enhancement device; 6: Information providing device; 11: Speaker; 12: Noise reduction device; 13: Light reduction device; 30: Sensor information acquisition unit; 31: Deduction unit; 32: Control information generation unit; 33: Driving control unit; 34: Information storage unit; 35, 36: Communication unit; 37: Input receiving unit; 38: Sleep information acquisition unit; 39: Recognition unit; 40: Characteristic information storage unit; 100: Vehicle system; 311: Learning unit; 312: Deduction unit.

Claims

1. An information processing apparatus, characterized by, The information processing apparatus includes: a derivation unit that derives a sleep depth of a user who rides in a vehicle using sensor information acquired by a sensor that detects at least one of a state of the user and a surrounding environment of the user; and a control information generation unit that generates control information used in travel control of the vehicle achieved by automatic driving using the sleep depth derived by the derivation unit.

2. The information processing apparatus according to claim 1, wherein the derivation unit derives the sleep depth for a certain time in the future, the control information generation unit decides a period in which the user is predicted to be in light sleep using the sleep depth derived by the derivation unit for the certain time in the future, decides a route of the vehicle in such a manner that a parking place is reached within the decided period, and generates the control information indicating the decided route.

3. The information processing apparatus according to claim 2, wherein the control information generation unit generates the control information that causes the vehicle to park at the parking place, and after the vehicle reaches the parking place, generates the control information that causes the vehicle to start traveling again from the parking place in a case where it is determined, using a result of derivation of the sleep depth derived by the derivation unit based on the sensor information, that the user is in deep sleep.

4. The information processing apparatus according to claim 2 or 3, wherein the parking place includes at least one of a service area and a parking area.

5. The information processing apparatus according to any one of claims 1 to 4, wherein the control information generation unit generates the control information that causes travel of the vehicle to be smoother than normal travel without restriction in a case where it is determined, using the sleep depth derived by the derivation unit, that the user is in light sleep.

6. The information processing apparatus according to claim 5, wherein the smoother travel includes travel along a route having fewer irregularities in a road surface.

7. The information processing apparatus according to claim 5 or 6, wherein the smoother travel includes travel along a route having fewer sharp turns.

8. The information processing apparatus according to any one of claims 5 to 7, wherein the smoother travel includes travel in which at least one of an acceleration of the vehicle and a speed of the vehicle is below a threshold value.

9. The information processing apparatus according to any one of claims 1 to 8, wherein the control information generation unit causes a sound reduction device that reduces a sound in the vehicle to operate in a case where it is determined, using the sleep depth derived by the derivation unit, that the user is in light sleep.

10. The information processing apparatus according to any one of claims 1 to 9, wherein the control information generation unit causes a light reduction device that reduces a light in the vehicle to operate in a case where it is determined, using the sleep depth derived by the derivation unit, that the user is in light sleep.

11. The information processing apparatus according to any one of claims 1 to 10, wherein the deriving section predicts a wake-up time of the user using the derived sleep depth, the control information generating section decides a route of the vehicle in a manner that a destination is reached within a certain time from the wake-up time predicted by the deriving section, and generates the control information indicating the decided route. The information processing apparatus includes: 12.The information processing apparatus according to claim 11, wherein an identifying section that identifies the user; and a characteristic information obtaining section that obtains characteristic information indicating a characteristic related to sleep of each of the users, the deriving section predicts the wake-up time using a result of identification by the identifying section and the characteristic information.

13. The information processing apparatus according to claim 12, wherein the characteristic information includes a performance value of at least one of a wake-up time and a sleep time of the user. The information processing apparatus includes:

14. The information processing apparatus according to any one of Claims 1 to 13, characterized in that, an identifying section that identifies the user; and a characteristic information obtaining section that obtains characteristic information indicating a characteristic related to sleep of each of the users, the deriving section derives the sleep depth using a result of identification by the identifying section and the characteristic information. The information processing apparatus includes:

15. The information processing apparatus according to any one of Claims 1 to 14, characterized in that, an identifying section that identifies an attribute of the user; and a characteristic information obtaining section that obtains characteristic information indicating a characteristic related to sleep of each of the attributes, the deriving section derives the sleep depth using the attribute identified by the identifying section and the characteristic information.

16. The information processing apparatus according to claim 14 or 15, wherein the characteristic information is a learned model generated by supervised learning using a plurality of data sets each of which is constituted of a feature quantity calculated using the sensor information and a sleep depth as a corresponding correct answer data. The vehicle includes:

17. A vehicle capable of traveling by way of autonomous driving, characterized by a deriving section that derives a sleep depth of a user who rides the vehicle using sensor information obtained by a sensor that detects at least one of a state of the user and a surrounding environment of the user; a control information generating section that generates control information used in travel control of the vehicle using the sleep depth derived by the deriving section; and a travel control section that controls travel of the vehicle realized by the automatic driving using the control information. The vehicle control method includes the steps of:

18. A vehicle control method of controlling a vehicle capable of traveling by automatic driving, the vehicle control method being characterized by comprising: deriving a sleep depth of a user who rides the vehicle using sensor information obtained by a sensor that detects at least one of a state of the user and a surrounding environment of the user; and generating control information used in travel control of the vehicle realized by the automatic driving using the derived sleep depth. The program causes a computer system that controls a vehicle capable of travel realized by automatic driving to execute the steps of: deriving a sleep depth of a user who rides the vehicle using sensor information obtained by a sensor that detects at least one of a state of the user and a surrounding environment of the user; and 19. A program, characterized by ​ ​ ​ Using the derived sleep depth, control information is generated for use in travel control of the vehicle implemented by the automatic driving.

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