Driving support means

Abstract

To provide a driving support device capable of considering a driver's brain model (driver internal model) to perform appropriate driving support according to the driver's internal state (state of the driver internal model).SOLUTION: A driving support device includes: traveling scene recognition means 31; driving target calculation means 32; driving operation detection means 33; vehicle behavior detection means 34; driver monitoring means 35; driver internal state estimation means 36 for estimating motion system prediction errors 2A and changes in a body system internal model 1B; and driving support control means 37 for setting a mode of driving support on the basis of the estimation result by the driver internal state estimation means 36.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to a driving assistance device that assists a driver in driving a vehicle, and in particular to an improvement that enables appropriate driving assistance according to the driver's state by taking into account a model inside the driver's head (internal driver model). [Background technology]

[0002] In recent years, vehicles such as automobiles have begun to be equipped with driving assistance devices (e.g., advanced driver assistance systems (ADAS)) that assist drivers in driving, and various technologies have been proposed for such driving assistance devices.

[0003] For example, Patent Document 1 (JP 2022-178816 A) proposes a technology that can improve a driver's driving skills by observing the driver's driving (driving performance) in a vehicle control device, dividing it into cognition, judgment, and operation, and providing driving assistance (coaching) at the optimal timing for driving performance that is determined to be insufficient. Patent Document 2 (JP Patent No. 6428748) also proposes a technology in a driving assistance system that guides the vehicle driver toward a target emotional state (e.g., a comfortable state) by providing appropriate sensory stimuli (such as providing visual and auditory stimuli, changing accelerator sensitivity or steering wheel sensitivity, etc.). Patent Document 3 (JP Patent No. 6221776) also proposes a technology in which a driving evaluation device evaluates driving operations and notifies the driver of the evaluation results, allowing the driver to recognize whether the driving operations they performed were appropriate. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Publication No. 2022-178816 [Patent Document 2] Patent No. 6428748 [Patent Document 3] Patent No. 6221776 Summary of the Invention [Problem to be solved by the invention]

[0005] As such, in the case of conventional driving assistance devices, a technology that devises the timing of notifications for assistance (see Patent Document 1 above) and a technology in which a personified character influences the driver's emotions (see Patent Document 2 above) have been proposed. However, the conventional technology has not included the idea of ​​optimizing driving assistance from the perspective of the driver's internal brain model by considering (modeling) the processing in the brain of each individual driver. In other words, even when conventional driving assistance devices notify the driver (such as issuing an alarm) or intervene in driving operations, they are uniform and do not take into account the driver's internal brain state (driver's internal model), and cannot be said to be optimized for improving the driving skills or improving the emotions of the driver who is the target of driving assistance.

[0006] The present invention has been made with a focus on such problems, and aims to provide a driving assistance device that can provide appropriate driving assistance according to the driver's internal state (state of the driver's internal model) by taking into account the driver's internal brain model (driver's internal model). [Means for solving the problem]

[0007] In order to achieve the above object, the present invention adopts the following solution: That is, as set forth in claim 1, a driving assistance device for assisting a driver of a vehicle includes a driving scene recognition means for recognizing a driving scene of the vehicle, a driving detection means for detecting the driving operation of the driver and / or the behavior of the vehicle, a driving target calculation means for calculating a target driving operation and / or a target vehicle behavior that matches the driving scene, a driver monitoring means for detecting physiological information and / or behavior of the driver, a driver internal state estimation means for estimating the brain state of the driver, a driving assistance means for providing driving assistance to the driving of the driver, and a driving assistance control means for controlling the driving assistance means, wherein the driver internal state estimation means is configured to estimate the behavior prediction of the vehicle by the driver and the target vehicle behavior. In a driver internal model having a motor system internal model that functions to reduce a motor system prediction error between actual behavior and the driver's internal behavior, and an internal body model that controls the driver's internal state, the motor system prediction error is estimated as the difference between the detection result by the driving detection means and the calculation result by the driving target calculation means, and if a significant difference occurs between the previous detection result and the current detection result by the driver monitoring means in the same driving scene, it is estimated that there has been a change in the internal body model, and the driving assistance control means changes the mode of driving assistance by the driving assistance means based on the estimation of the motor system prediction error by the driver internal state estimation means and the estimation of the change in the internal body model.

[0008] According to the above-described solution, the mode of driving assistance provided by the driving assistance means (e.g., HMI 26, driving operation intervention means 27) is set based on an estimate of the motor system prediction error and an estimate of a change in the internal model of the body. That is, the mode of driving assistance is set to a more appropriate one using the motor system prediction error and the presence or absence of a change in the internal model of the body as judgment indicators. Therefore, the driving assistance can be made more appropriate and more suited to the internal state of the driver (the state of the driver's internal model), and it is possible to effectively achieve improvement in the driver's driving technique as well as improvement in the driver's emotions.

[0009] A preferred embodiment based on the above-described solution is as set forth in claim 2 and subsequent claims. Specifically, when the motor system prediction error is estimated to be decreasing in the same driving scene and the internal body model is estimated to be changing, the driving assistance control means reduces the level of driving assistance for that driving scene, whereas when the internal body model is estimated to be unchanged, the driving assistance control means maintains the level of driving assistance for that driving scene (corresponding to claim 2). In this case, if the motor system prediction error is decreasing and there is also a change in the internal body model, it is determined that both the internal motor system model and the internal body model have improved and the need for driving assistance has decreased, so the level of driving assistance is reduced to encourage autonomous driving by the driver. On the other hand, if the motor system prediction error is decreasing but there is no change in the internal body model, it is considered that the improvement in the internal motor system model has not affected the internal body model, so the current level of driving assistance, the effect of which has been confirmed, is maintained to encourage positive changes in the internal body model. Therefore, the mode of driving assistance is appropriately set according to the driver's internal state (the state of the driver's internal model), which promotes improvements in the driver's internal motor system model and internal body system model, effectively improving the driver's driving technique as well as improving their emotional state.

[0010] When it is estimated that the motor system prediction error has not changed in the same driving scene and the internal body model has changed, the assistance mode setting means maintains the level of driving assistance for that driving scene, and when it is estimated that the internal body model has not changed, it increases the level of driving assistance for that driving scene (corresponding to claim 3). In this case, if there is no change in the motor system prediction error and no change in the internal body model, it is estimated that the current level of driving assistance is not effective, so the level of driving assistance is increased to increase the effectiveness of the driving assistance. On the other hand, if there is no change in the motor system prediction error but there is a change in the internal body model, it is possible that the driving assistance is having some effect on the driver, so the current level of driving assistance is maintained and the driving assistance is waited for to lead to improvement in the driver's driving skill. Therefore, the mode of driving assistance is appropriately set according to the driver's internal state (state of the driver's internal model), which promotes improvement of the driver's internal motor system model and internal body model, effectively achieving improvement in the driver's driving skill and improvement in emotions.

[0011] When the motor system prediction error is estimated to be increasing in the same driving scene and the internal body model is estimated to be changing, the assistance mode setting means stops the current driving assistance for that driving scene, and when the internal body model is estimated to be unchanged, the assistance mode setting means maintains the level of driving assistance for that driving scene (corresponding to claim 4). In this case, if the motor system prediction error is increasing and the internal body model is changing, it is considered that driving assistance is counterproductive and that the adverse effects are extending to the internal body model, so the current driving assistance is stopped. On the other hand, if the motor system prediction error is increasing but the internal body model is not changing, it is possible that the driver is in the process of improving his driving skill (is in a trial and error state), so the current driving assistance is maintained and the driver's skill improves. Therefore, the mode of driving assistance is appropriately set according to the driver's internal state (state of the driver's internal model), which promotes improvement of the driver's internal motor system model and internal body model, effectively achieving improvement of the driver's driving skill and improvement of his emotions.

[0012] The driving assistance means includes an interface means for notifying the driver of driving-related information (corresponding to claim 5). In this case, the interface means (e.g., HMI 26) can provide appropriate driving assistance that takes into account the state of the driver's internal model.

[0013] The driving assistance means includes a driving operation intervention means for intervening in the driving operation of the driver (claim 6). In this case, the driving operation intervention means can provide appropriate driving assistance taking into account the state of the driver's internal model.

[0014] The physiological information is at least one of the driver's heart rate, breathing rate, sweat rate, and heart rhythm disturbance (corresponding to claim 7). In this case, the change in the internal model of the body system can be accurately estimated based on the fluctuation of the physiological information. [Effects of the Invention]

[0015] According to the present invention, the mode of driving assistance in the driving assistance device is set based not only on the motor system prediction error but also on the presence or absence of a change in the internal model of the body (i.e., using the change in the internal model of the body as well as the motor system prediction error as a judgment index). Therefore, the mode of driving assistance becomes appropriate and suited to the internal state of the driver (the state of the driver's internal model), and improvement of the internal model of the body as well as the internal model of the motor system is promoted, thereby effectively achieving improvement in the driver's driving technique as well as improvement in the driver's emotions. [Brief explanation of the drawings]

[0016] [Figure 1] FIG. 2 is a diagram showing a driver internal model in the present invention. [Figure 2] FIG. 2 is a block diagram showing an example of a control system of the driving assistance device of the present invention. [Figure 3] FIG. 10 is a diagram showing an example of a screen display on an HMI display at a merging point. [Figure 4] FIG. 10 is a diagram showing an example of a screen display on an HMI display at a merging point. [Figure 5] 10 is a diagram showing changes in the mode of driving assistance in the present invention. [Figure 6] 10 is a flowchart illustrating an example of control according to the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0017] Hereinafter, an embodiment of the present invention will be described with reference to the accompanying drawings. FIG. 1 shows an internal driver model 1 that models the internal brain state of a driver while driving a vehicle. This internal driver model 1 was constructed by the inventor of the present invention by applying the idea of ​​the free energy principle related to human brain function to the internal brain processing of a driver, particularly while driving a vehicle. The present invention configures a driving assistance device based on this internal driver model 1. First, the contents of the internal driver model 1 will be described in detail.

[0018] The free energy principle is a theory proposed by Friston that provides a unified explanation of the various functions of the human brain as a system that attempts to minimize free energy. According to the free energy principle, the internal model of the human brain is updated (modified) so as to minimize prediction error.

[0019] As shown in Fig. 1, the driver internal model 1 includes a motor system internal model 1A and a body system internal model 1B. Here, the motor system internal model 1A models the function in the brain of the driver while driving, which makes inference about the motor system (i.e., predicts the behavior of the vehicle in response to driving operations) and controls the body movement (driving operations) based on this inference. On the other hand, the body system internal model 1B models the function in the brain of the driver while driving, which makes inference about the body system (i.e., predicts the body states of the internal organs, blood vessels, etc. while driving a vehicle), and controls the body states (states of the internal organs, blood vessels, etc.) based on this inference.

[0020] In the brain of a driver while driving a vehicle, two processing loops are carried out: a motor system processing loop 3A (in the figure, a processing loop including motor system internal model 1A → result prediction 11 → driving operation 12 → vehicle behavior 13 → perception and cognition 14 → motor system prediction error 2A) for reducing motor system prediction error 2A centered on motor system internal model 1A, and an internal system processing loop 3B (in the figure, a processing loop including internal system model 1B → internal state prediction 15 → internal state 16 → internal system prediction error 2B) for reducing internal system prediction error 2B centered on internal system internal model 1B.

[0021] Here, the motor system prediction error 2A is recognized by the motor system internal model 1A as the difference between the vehicle behavior predicted by the driver (predicted result 11) and the actual vehicle behavior 13 (realized result). On the other hand, the internal body system prediction error 2B is recognized by the internal body system model 1B as the difference between the prediction 15 of the driver's internal body state and the actual internal body state 18. In the driver internal model 1, the motor system internal model 1A and the internal body system model 1B are updated in the process of reducing the motor system prediction error 2A and the internal body system prediction error 2B in the motor system processing loop 3A and the internal body system processing loop 3B.

[0022] To explain in more detail below, while driving a vehicle, information about the vehicle's driving situation (for example, the driving scene in which the vehicle is driving and various accompanying information) is perceived by the driver (perceptual perception 10). Based on this perceptual perception 10, the motor system internal model 1A infers the vehicle's behavior that should be executed in that situation (driving scene), and this is used as a result prediction 11 (predicted vehicle behavior).

[0023] Furthermore, the motor system internal model 1A infers a driving operation 12 required to obtain a result prediction 11 (predicted vehicle behavior). That is, the motor system internal model 1A has a hypothesis (belief) for inferring (predicting) what driving operation should be performed to obtain the predicted vehicle behavior, and based on this hypothesis, it infers the driving operation 12 to be performed. The driver will perform the driving operation 12 in accordance with this inference (judgment).

[0024] For example, in a driving scene where a lane change is required, the vehicle behavior required for an appropriate lane change (e.g., a driving path for an appropriate lane change) is predicted (result prediction 11) based on perceptual recognition of the presence of vehicles between the destination vehicles and the presence of vehicles behind, and specific driving operations such as steering to change lanes and depressing the pedal to adjust the vehicle speed are inferred and executed as driving operations 12 required to achieve the predicted result.

[0025] The driving operation 12 thus executed results in an actual vehicle behavior 13, and this actual vehicle behavior 13 (realized result) is perceived by the driver (perceptual recognition 14). Specifically, the vehicle behavior 13 is perceived by confirming the vehicle behavior visually (optical flow) or aurally, or by bodily experiencing the sense of acceleration (G value).

[0026] The vehicle behavior 13 thus perceived is compared with the result prediction 11, and a motion system prediction error 2A is recognized as the difference between the result prediction 11 (predicted vehicle behavior) and the actual vehicle behavior 13 (realized result). For example, if the timing (e.g., steering timing) of a driving operation 12 determined to obtain the result prediction 11 (predicted vehicle behavior) is actually too late to obtain the result prediction 11, the delay in the vehicle behavior 13 relative to the result prediction 11 is recognized as a motion system prediction error 2A.

[0027] When driving a vehicle, driving operation 12 is adjusted to reduce motor system prediction error 2A. Then, in motor system internal model 1A, the inference content (hypothesis for inference) regarding driving operation is changed so as to reduce motor system prediction error 2A, thereby updating motor system internal model 1A. Such positive updating of motor system internal model 1A (updating that reduces motor system prediction error 2A) enables the driver to perform appropriate driving operations, and the driver's driving skills improve.

[0028] For example, if the timing of steering when changing lanes was delayed, preventing smooth lane changes, then if the internal model of the motor system 1A is updated so that the prediction error of the motor system 2A is reduced (a new hypothesis is adopted that steering must be performed at an earlier timing in order to change lanes), the timing of steering when changing lanes will be appropriately advanced, allowing steering to be performed at the appropriate time.

[0029] Meanwhile, in the internal body system processing loop 3B, the internal body system model 1B predicts (internal body state prediction 15) the internal body state (state of internal organs, blood vessels, etc.) based on the perceptual cognition 10. In other words, the internal body system model 1B has a hypothesis (belief) for inferring (predicting) what the internal body state will be in relation to the state recognized by the perceptual cognition 10, and the internal body state is predicted based on this hypothesis.

[0030] This internal body state prediction 15 is compared with the actual internal body state 16, and the difference between the internal body state prediction 15 and the actual internal body state 16 is the internal body system prediction error 2B. The internal body system model 1B is updated to reduce the internal body system prediction error 2B, and as a result, the internal body system (autonomic nerves, etc.) is adjusted so that the internal body state prediction 15 and the actual internal body state 16 match.

[0031] In the brain processing described above, the internal motor system model 1A and the internal body system model 1B interact with each other, and if the internal motor system model 1A changes, this change will affect the internal body system model. Furthermore, the inference in the internal body system model 1B and the internal body system prediction error 2B are the cause of the driver's emotions 17.

[0032] For example, if the vehicle is in a difficult driving situation (such as attempting to merge onto a difficult main road), the internal model 1B of the body will predict that driving will not go well in that driving situation and that the heart rate will rise, and the actual heart rate will also rise. Furthermore, such negative inference can also cause negative emotions such as anxiety (fear) about driving and a lack of enjoyment from driving.

[0033] In contrast, if the internal motor model 1A is improved (positively updated) and driving skills are improved, resulting in smoother driving operations, this improvement in the internal motor model 1A will have a positive effect on the internal circumstantial model 1B. In other words, the internal circumstantial model 1B will be positively updated to not predict an increase in heart rate, reflecting confidence in driving in that driving situation, and the actual increase in heart rate will also disappear. Furthermore, positive inferences about that driving situation (predictions that driving operations will be performed well) will also induce positive emotions such as confidence and a sense of security (calmness) and joy in driving a vehicle (happiness) when faced with that driving situation.

[0034] In this way, if the internal motor system model 1A is updated appropriately, the driving skill in that driving situation will naturally improve, and the driver's emotions 17 will also improve. However, if the internal motor system model 1A is not updated appropriately in a positive manner, the driving skill will not improve and the driver will continue to perform inappropriate driving operations (driving operations with large motor system prediction errors 2A). In addition, negative emotions such as anxiety about driving and a lack of enjoyment from driving will continue.

[0035] The present invention aims to improve the driver's driving skills and emotions by providing appropriate driving assistance for such negative situations. Specifically, when appropriate driving assistance is provided for driving operations (e.g., guidance for appropriate driving operations and evaluation of driving operations using an HMI) while driving a vehicle, the driver can drive appropriately in that situation, and the driver's internal motor system model 1A is rewritten positively (i.e., the motor system prediction error 2A is reduced by the driving assistance-guided actions and attention, resulting in the driver's belief that they can perform driving operations appropriately). When the internal motor system model 1A is rewritten in this way, the interaction 1B between the internal motor system model 1A and the internal physiology model 1B also rewrites the internal physiology model 1B positively, and the driver's emotions (and furthermore, sensibility, including value judgments) improve positively (e.g., anxiety and fear about driving operations due to prediction of a negative outcome are reduced).

[0036] In relation to this, the inventor of the present invention has found that "people cultivate a sense of self-efficacy by accumulating small successful experiences, and eventually this self-efficacy generalizes, making it easier for people to feel happy on a regular basis (happiness level increases)." The positive updating of motor system internal model 1A and physiology internal model 1B according to the present invention is a small successful experience, and is thought to lead to an increase in the driver's happiness level.

[0037] Therefore, by repeatedly performing the appropriate driving assistance of the present invention while driving a vehicle, positive experiences (feelings of happiness) through the driving assistance are accumulated as small successful experiences, and ultimately, a sense of self-efficacy (for example, confidence that one can handle the vehicle) is cultivated, and as this self-efficacy generalizes, a state in which the driver's happiness level (constant sense of happiness) is enhanced can be achieved.

[0038] When providing driving assistance for a vehicle taking into consideration the above-described driver's internal model 1, in order to make the driving assistance effective, it is desirable to consider not only the effective reduction of the driver's motor system prediction error 2A (positive update of the motor system internal model 1A) but also the driver's internal body model 1B. In other words, it can be said that appropriate driving assistance can be performed only when the motor system internal model 1A is improved and the internal body model 1B is also positively updated.

[0039] The present invention has been made from this perspective, and aims to achieve appropriate improvement of the driver's driving skills and improvement of emotions (improvement of happiness) by providing driving assistance that reduces motor system prediction errors and also takes into consideration the estimation of the state of the internal model of the body's system. Specific configuration examples of the driving assistance device will be described in detail below.

[0040] Figure 2 shows a block diagram of an example of a control system in the driving assistance device of the present invention. In the following explanation, a case where the driving assistance device is installed in a driving simulator is illustrated, but the scope of application of the present invention is not limited to this form. The driving assistance device of the present invention can also be installed in vehicles such as automobiles as an advanced driving assistance system (ADAS), for example. As shown in the figure, the control system includes a control unit U, a driving information acquisition means 21, a driving information acquisition means 22, a vehicle information acquisition means 23, a driver state acquisition means 24, a memory means 25, an HMI (human-machine interface) 26, and a driving operation intervention means 27.

[0041] In this embodiment, the driving information acquisition means 21 is a means for acquiring the driving details of the vehicle set in the driving simulator (the external environment of the vehicle, such as roads and other vehicles). When a driving assistance device is installed in a vehicle, the driving information acquisition means 21 becomes a means for acquiring information about the outside of the vehicle (the external environment of the vehicle, such as roads and other vehicles), and is composed of, for example, an exterior camera that captures images of the outside of the vehicle and a sensor that detects the situation outside the vehicle.

[0042] The driving information acquisition means 22 is a means for detecting the driving operations of the driver in the driving simulator, and is provided with sensors for detecting, for example, the driver's steering operation, accelerator operation, brake operation, etc. The vehicle information acquisition means 23 is a means for acquiring the vehicle state (vehicle speed, etc.) in the driving simulator.

[0043] The driver state acquisition means 24 is a means for acquiring information related to the state of the driver of the vehicle (physiological changes and behavior of the driver), and includes, for example, an in-vehicle camera for capturing images of the inside of the vehicle (the driver in the driver's seat), and means for detecting the physiological state of the driver (for example, a heart rate monitor for measuring the heart rate, and a sweat sensor for detecting the amount of sweat). The storage means 25 is a means for storing various data, and is, for example, an external storage device.

[0044] The HMI 26 is a device that provides visual and auditory information to the vehicle driver during driving assistance, and is equipped with notification means such as a display 26A (e.g., an in-vehicle monitor or a head-up display (HDU)) that can display images and text, and a speaker 26B that can output sound.

[0045] The driving operation intervention means 27 is a means for directly intervening (for example, assisting steering) in the driver's driving operations (steering, accelerator operation, braking operation) in driving assistance, and performing part or all of the driving operations on behalf of the driver.

[0046] The control unit U is a control device configured by, for example, a microcomputer, and includes a driving scene recognition means 31, a driving target calculation means 32, a driving operation detection means 33, a vehicle behavior detection means 34, a driver monitoring means 35, a driver internal state estimation means 36, and a driving assistance control means 37. These means are provided as control programs in the control unit U.

[0047] The driving scene recognition means 21 is a means for recognizing the driving scene of the vehicle (the situation in which the vehicle is driving) based on the information acquired by the driving information acquisition means 21. The driving scenes are grouped according to their contents (for example, lane change, starting on a slope, etc.), and driving scenes belonging to the same group are treated as the same driving scene.

[0048] The driving target calculation means 32 is a means for calculating, as a target driving operation and a target vehicle behavior, (appropriate) driving operations and vehicle behaviors to be performed in the specific driving scene recognized by the driving scene recognition means 21. The calculation of the target driving operation and the target vehicle behavior is performed based on the content of the specific driving scene recognized, with reference to various information (road information, information on the standard driver model, information on the individual vehicle model) stored in the storage means.

[0049] The driving operation detection means 33 is a means for detecting (calculating) the driving operation of the driver for each driving scene based on the information (detection signal) detected by the driving information acquisition means 22. The vehicle behavior detection means 34 is a means for detecting (calculating) the behavior of the vehicle in each driving scene based on the information acquired by the vehicle information acquisition means 23. In this embodiment, the driving operation detection means 33 and the vehicle behavior detection means 34 correspond to the "driving detection means" in the claims.

[0050] The driver monitoring means 35 is a means for monitoring (monitoring) the driver based on the information acquired by the driver state acquisition means 24. For example, by analyzing an image of the driver taken by an in-vehicle camera, the driver's identity is identified, and physiological information of the driver (e.g., heart rate, respiratory rate, amount of sweating, irregular heartbeat rhythm, etc.) is detected (estimated), and the driver's behavior (e.g., behavior indicating psychological agitation or anxiety, irregular behavior, etc.) is detected. The driver's physiological information may be directly detected by a means for detecting physiological information worn by the driver (e.g., a heart rate monitor or a sweat sensor).

[0051] The driver internal state estimation means 36 is a means for estimating the driver's brain state (the state of the driver internal model 1) based on the detection results by the driving operation detection means 33, the vehicle behavior detection means 34, and the driver monitoring means 35, and the calculation results by the driving target calculation means 32. Specifically, the driver internal state estimation means 36 estimates an increase or decrease in the motor system prediction error 2A in the driver internal model 1, and whether or not the body system internal model 1B has been updated.

[0052] That is, in this embodiment, the driver internal state estimation means 36 compares the target driving operation and / or target vehicle behavior calculated by the driving target calculation means 32 with the driver's driving operation detected by the driving operation detection means 33 and / or the vehicle behavior detected by the vehicle behavior detection means 34. Then, the means 36 calculates the difference between the target driving operation and the driver's driving operation (for example, the difference between the driving operation amount such as the steering amount) and / or the difference between the target vehicle behavior and the vehicle behavior (for example, the difference between the behavior amount such as the driving route), and this calculation result is set as the motion system prediction error 2A.

[0053] Furthermore, the driver internal state estimation means 36 compares the physiological information and / or behavior of the driver detected by the driver monitoring means 35 with the physiological information and / or behavior when the driver previously drove through the same driving scene, determines whether or not there is a significant difference between the detection results (detected measurement values) from the previous time, and if there is a significant difference, estimates that there has been a change in the internal model of the driver's body system 1B in the driver internal model 1. In other words, since it is considered that there is a significant change in the physiological information or behavior along with the change in the internal model of the body system 1B, it estimates whether or not there has been a change in the internal model of the body system based on this significant change.

[0054] To explain in more detail, when making an estimation based on the driver's physiological information, the driver internal state estimation means 36 acquires measured values ​​(e.g., average values, maximum values, etc.) of the driver's physiological information (e.g., physiological quantities such as heart rate, respiratory rate, amount of sweating, irregular heart rhythm, and physiological quantities indicating psychological agitation or anxiety), compares the current measured value with the previous measured value (the measured value the previous time the same driving scene was driven), and if there is a significant difference between the two (e.g., a fluctuation exceeding a judgment reference value), estimates that there has been a change in the internal model 1B of the body's system.

[0055] On the other hand, when making an estimation based on the driver's behavior, the driver internal state estimation means 36 acquires measured values ​​(e.g., average values, maximum values, etc.) of the driver's behavior (e.g., behavior quantities such as the frequency of specific behaviors indicating psychological upset or anxiety, and the magnitude of behavioral disturbances), compares the current measured value with the previous measured value, and if there is a significant difference between the two (e.g., a fluctuation exceeding a judgment reference value), estimates that there has been a change in the internal model 1B of the body's system. Note that the physiological quantities and behavior quantities used for estimation may be used in combination as appropriate.

[0056] The driving assistance control means 37 is a means for determining whether the current driving scene requires driving assistance (whether to provide driving assistance) based on the recognition of the driving scene by the driving scene recognition means 21, and for controlling the driving assistance means (HMI 26 and driving operation intervention means 27) to provide driving assistance if the driving scene requires driving assistance.

[0057] For example, driving assistance is provided by the HMI 26 in the form of pre-assistance (coaching and guidance for appropriate driving in each driving situation) and post-assistance (evaluation of driving in each driving situation).

[0058] In this case, the pre-assistance is performed when it is determined that the driver has started driving in the relevant driving scene, and the post-assistance is performed when it is determined that the driver has finished driving in the relevant driving scene. The start and end timings of the driving operations in the relevant driving scene are set individually for each driving scene based on the driver's history data, data collected about general drivers, the characteristics of the driver, the characteristics of the relevant driving scene, etc.

[0059] For example, the timing of driving assistance when merging from an acceleration lane onto a main lane on a highway or the like is explained below using an example of the screen display (Figures 3 and 4) displayed on the display 26A (e.g., HUD) of the HMI 26.

[0060] As shown in the example screen display in Fig. 3, when a host vehicle 41 traveling on an acceleration lane 42 merges into a space 45 ahead of a parallel vehicle 44 traveling on a main lane 43, for example, the timing at which the host vehicle 41 accelerates and starts to move laterally toward the main lane 43 is the timing at which the driving operation starts, and the timing at which the host vehicle 41 has completely moved into the space 45 is the timing at which the driving operation ends. In this case, on the display 26A, the timing at which the host vehicle 41 should accelerate (increase the accelerator pedal travel) is displayed by an arrow 51 indicating the driving direction, and the timing at which the host vehicle 41 should steer right and start a lane change onto the main lane 43 is displayed by an arrow 52 indicating the steering direction (lane change direction). Such instructions (driving assistance) using arrow displays allow the driver to intuitively grasp the timing of each driving operation.

[0061] 4, when a vehicle 41 traveling in an acceleration lane 42 merges into a space 47 behind a parallel vehicle 46 traveling on a main lane 43, the timing to start a driving operation is, for example, when the vehicle 41 decelerates and starts to move laterally toward the main lane 43, and the timing to end the driving operation is when the vehicle 41 has completely moved into the space 47. In this case, the display 26A displays the timing to decelerate the vehicle 41 (to release the accelerator pedal slightly) with an arrow 53 pointing opposite to the traveling direction, and also displays the timing to steer right and start a lane change onto the main lane 43 with an arrow 54 pointing in the steering direction. This helps the driver intuitively recognize the timing of each operation.

[0062] Specific details of the pre-assistance and post-assistance include notifications by the HMI 26 (screen display on the display 26A and audio from the speaker 26B). For example, in the pre-assistance, the HMI 26 notifies the driver of suggestions regarding the timing and amount of driving operation in the driving scene (for example, suggestions by arrows 51 to 54 in the screen displays of FIGS. 3 and 4), information regarding vehicle behavior and driving conditions, etc. In addition, in the post-assistance, the HMI 26 notifies the driver of an evaluation of the driving in the driving scene, etc.

[0063] The mode of the driving assistance provided by the driving assistance control means 37 is set based on the estimation result by the driver internal state estimation means 36. Specifically, in this embodiment, the level of driving assistance is changed according to a combination of an increase or decrease in the motor system prediction error 2A and the presence or absence of a change in the internal model of the body system 2A.

[0064] Here, since the driving assistance aims to reduce the motor system prediction error 2A, whether the mode of driving assistance is appropriate is basically evaluated based on whether the motor system prediction error 2A has been reduced, and the mode of driving assistance is set according to this evaluation. In contrast, in the driving assistance device of this embodiment, estimation regarding the internal body system model 1B is also used in setting the mode of driving assistance. That is, the driver internal state estimation means 36 estimates whether there is a change in the internal body system model 1B of the driver internal model 1, and by taking this estimation into consideration along with an increase or decrease in the motor system prediction error 2A, the mode of driving assistance is set more appropriately.

[0065] The level of driving assistance can be evaluated based on the amount of information provided by the driving assistance, the frequency of notifications (information provision) by the driving assistance, etc. For example, the greater the amount of information provided by the HMI 26 (for example, increasing the amount of information displayed on the screen or in audio notifications), the higher the level of driving assistance, and the more frequently the notifications by the HMI 26 are made, the higher the level of driving assistance. Furthermore, if driving assistance that simply provided information is changed to driving assistance that provides specific coaching, the level of driving assistance can be said to have increased. Furthermore, in the case of driving assistance by the driving operation intervention means 27, the level of driving assistance increases with increasing intervention level.

[0066] Next, an example of setting the mode of driving assistance by the driving assistance control means 37 will be described in detail with reference to Fig. 5. As shown in Fig. 5, in this embodiment, the setting related to the level of driving assistance (reduced assistance, increased assistance, continued assistance, stopped assistance) is determined based on a combination of an increase or decrease (decrease, no significant change, increase) in the motor system prediction error 2A and whether or not there is a change in the internal model of the body system.

[0067] More specifically, when the motor system prediction error 2A is decreasing and the internal body model 1B is changing, the level of driving assistance is set to be reduced (reduced assistance). On the other hand, when the motor system prediction error 2A is decreasing and the internal body model 1B is not changing, the level of driving assistance is set to be maintained (continued assistance).

[0068] That is, since the driving assistance is basically aimed at reducing the motor system prediction error 2A (improving the driver's driving technique), if the motor system prediction error 2A is reduced, it can be determined that the driving assistance is successful. In this case, if the internal model 1B of the motor system also changes, this change in the internal model 1B can be considered to be a positive change influenced by the improvement of the internal model 1A of the motor system.

[0069] For example, when driving in a difficult driving situation (e.g., merging onto a main lane as shown in Figures 3 and 4), if the heart rate had previously risen due to a prediction of failure (e.g., a prediction that smooth movement onto the main lane would not be possible), if the motor system prediction error 2A decreases this time (smooth movement is possible) and the heart rate also decreases, it is considered that the improvement in motor system internal model 1A has had a positive effect on internal body system model 1B, and internal body system model 1B has also been improved. Therefore, it can be determined that the driver's need for driving assistance for driving in that driving situation is becoming less, so the level of driving assistance is reduced (e.g., the amount of information in advance assistance is reduced) to prevent the driver from feeling annoyed and to promote more autonomous driving by the driver.

[0070] On the other hand, if the motor system prediction error 2A is reduced (i.e., the motor system internal model 1A is improved), but the internal motor system model 1B remains unchanged, it is considered that the improvement in the internal motor system model 1A has not yet reached the internal motor system model 1B. For example, a driver may become proficient in a driving situation that they were previously not good at, but may still be unable to completely eliminate their anxiety, and therefore may not be able to eliminate the negative physiological reaction (e.g., increased heart rate) that occurs when they encounter that driving situation. Therefore, the current driving assistance, the effectiveness of which has been confirmed, is continued (the level of driving assistance is maintained), and the impact of the improvement in the internal motor system model 1A is waited for to reach the internal motor system model 1B.

[0071] Furthermore, if there is no significant change in the motor system prediction error 2A but there is a change in the internal body system model 1B, the level of driving assistance is set to be maintained (continued assistance).On the other hand, if there is no significant change in the motor system prediction error 2A and there is no change in the internal body system model 1B, the level of driving assistance is set to be increased (augmented assistance).

[0072] In other words, the lack of a significant change in the motor system prediction error 2A means that the driving assistance has not resulted in an improvement in the motor system internal model 1A (the driver's driving technique has not improved). In this case, if the internal body model 1B has also not changed, it is expected that the driving assistance has no effect on the driver, and that no effect will be obtained even if the current driving assistance is continued. Therefore, the level of driving assistance is increased so that the effect of the driving assistance can be obtained. For example, the amount of information from the HMI 26 can be increased or coaching can be actively provided to encourage early improvement in driving technique.

[0073] On the other hand, if the internal motor system model 1B is changing, it is considered that the driving assistance is having some effect on the driver, but has not yet led to an improvement in driving skill (improvement of the internal motor system model 1A). Therefore, the current level of driving assistance is maintained, and we wait for the driving assistance to lead to a reduction in the motor system prediction error 2A.

[0074] Furthermore, if the motor system prediction error 2A increases and there is a change in the internal model of the body system, the currently implemented driving assistance itself is stopped (assistance stop).On the other hand, if the motor system prediction error 2A increases but there is no change in the internal model of the body system, the level of driving assistance is maintained (assistance continue).

[0075] That is, if the motor system prediction error 2A is increasing, it is considered that the driver's driving skill has not improved (in fact, it has deteriorated), and the driving assistance is having the opposite effect. In this case, if there is a change in the internal model 1B of the body system, it can be determined that the adverse effect of the motor system prediction error 2A has reached the internal model 1B of the body system. For example, it is considered that the driver is confused by the driving assistance or dislikes the driving assistance. Therefore, since the situation cannot be expected to improve with the currently implemented driving assistance, the driving assistance itself is stopped. Note that after the driving assistance is stopped, another more appropriate driving assistance may be provided (for example, the notification by the HMI 26 may be stopped and assistance by the driving intervention means 27 may be switched to).

[0076] On the other hand, if there is no change in the internal motor system model 1B, it is considered that the adverse effects from the internal motor system model 1A have not yet reached the internal motor system model 1B, so the level of driving assistance is maintained and an improvement in the situation is waited for. In other words, in this case, the driver is in the process of improving his driving technique (performing trial and error that will lead to improved driving technique), and the increase in the motor system prediction error 2A may be temporary, so driving assistance is continued to guide the driver to appropriate driving.

[0077] As described above, according to this embodiment, the degree of driving assistance is set not only by reducing the motor system prediction error 2A (improving the motor system internal model 1A) but also by taking into consideration (as a judgment index) the state of the body system internal model 1B, so that appropriate driving assistance suited to the driver's internal state (the state of the driver internal model 1) can be provided, and improvement of the driver's driving technique and improvement of the driver's emotions can be effectively achieved.

[0078] Next, a control example of the present invention will be described with reference to the flowchart of Fig. 6. Note that this control is executed as part of training in a driving simulator (training to suppress anxiety during driving and increase a sense of well-being by providing appropriate driving assistance during simulated driving), but it can also be applied as is to driving assistance in a vehicle.

[0079] In the control, first, in step S1, a driving scene during driving is recognized. In the following step S2, it is determined whether the recognized driving scene is a target for driving assistance. If it is not a target, the series of processes is ended.

[0080] On the other hand, if the driving scene is a target for driving assistance, the process proceeds to step S3, where data is read to acquire data on the mode (level) of driving assistance to be performed in that driving scene. In the following step S4, driving assistance (for example, pre-assistance and post-assistance by the HMI 26) is performed in a mode based on the data read in step S3.

[0081] In the next step S5, it is determined whether the motor system prediction error has decreased, and if not, the process proceeds to step S8. On the other hand, if the motor system prediction error has decreased, the process proceeds to step S6, where it is determined whether the driver's physiological values ​​have changed significantly compared to the previous same driving scene.

[0082] If it is determined in step S6 that the physiological amount of the driver has not changed, the process proceeds to step S13, where the degree of driving assistance is set to continue, and in step S14, the driver data is updated, and the series of processes ends. That is, since the driving system prediction error 2A has decreased but the internal model of the body system 1B has not yet improved, the degree of driving assistance is maintained and the internal model of the body system 1B is set to wait for improvement.

[0083] On the other hand, if the determination in step 6 indicates that the physiological amount of the driver has changed, the process proceeds to step S7, where the level of driving assistance is reduced, and in step S14, the driver data is updated, and the series of processes ends. That is, this is the case where the driving system prediction error has decreased and the internal model 1B of the body system has also been improved, and it is determined that the need for driving assistance has decreased, so the level of driving assistance is set to be reduced.

[0084] In step S8, it is determined whether the motor system prediction error is unchanged, and if it is not unchanged (i.e., if it has increased), the process proceeds to step S11. On the other hand, if the motor system prediction error is unchanged, the process proceeds to step S9, where it is determined whether the driver's physiological values ​​have changed significantly compared to the previous same driving scene.

[0085] If it is determined in step S9 that the driver's physiological amount has not changed, the process proceeds to step S13, where the degree of driving assistance is set to continue, and in step S14, the driver data is updated, and the series of processes ends. That is, since the motor system prediction error is unchanged (the driver's driving technique has not improved), but the driving assistance is thought to be having some effect on the driver, the degree of driving assistance is maintained and the setting is made to wait for a decrease in the motor system prediction error.

[0086] On the other hand, if the determination in step 9 indicates a change in the driver's physiological amount, the process proceeds to step S10, where the level of driving assistance is increased, and in step S14, the driver data is updated, and the series of processes ends. In other words, since no effect of driving assistance is observed in either the motor system prediction error or the internal model of the body system, the level of driving assistance is set to be increased.

[0087] In step S11, it is determined whether the driver's physiological values ​​have changed significantly compared to the previous same driving scene, and if they have not changed, the process proceeds to step S13, where the level of driving assistance is set to continue, and in step S14, the driver data is updated and the series of processes ends. That is, since this is a case where the motor system prediction error has increased but there is no change in the internal model of the body system, the level of driving assistance is set to maintain, taking into account the possibility that the driver is in the process of improving his driving technique.

[0088] On the other hand, if the determination in step S11 indicates that the driver's physiological amount has changed, the process proceeds to step S12, where driving assistance is set to be stopped, and in step S14, the driver data is updated, and the series of processes ends. In other words, since both the motor system prediction error and the internal model of the body system have deteriorated, the current driving assistance is stopped.

[0089] Although the embodiments of the present invention have been described above, the present invention is not limited to the above-described embodiments, and appropriate modifications are possible within the scope of the claims. [Industrial Applicability]

[0090] The present invention can be used as a driving assistance device used in driving simulators and various vehicles. [Explanation of symbols]

[0091] 1 Driver internal model 1A Internal model of the motor system 1B Internal model of the body system 2A Motor system prediction error 2B Prediction error of the body system 3A Motor System Processing Loop 3B Internal body processing loop 21 Means of obtaining driving information 22 Driving information acquisition means 23 Vehicle information acquisition means 24 Driver information acquisition method 25 Memory means 26 HMI 26A Display 26B Speaker 27 Driving operation intervention measures 31 Driving scene recognition means 32 Operation target calculation means 33 Driving operation detection means 34 Vehicle behavior detection means 35 Driver Monitoring Measures 36 Driver internal state estimation means 37 Driving assistance control means U Controller

Claims

1. A driving assistance device that assists a driver of a vehicle, a driving scene recognition means for recognizing a driving scene of the vehicle; a driving detection means for detecting a driving operation of the driver and / or a behavior of the vehicle; a driving target calculation means for calculating a target driving operation and / or a target vehicle behavior that is suited to the driving scene; a driver monitoring means for detecting physiological information and / or behavior of the driver; a driver internal state estimation means for estimating a brain state of the driver; a driving assistance means for providing driving assistance to the driver; a driving assistance control means for controlling the driving assistance means; Equipped with the driver internal state estimation means estimates the motor system prediction error as a difference between a detection result by the driving detection means and a calculation result by the driving target calculation means in a driver internal model having a motor system internal model that functions to reduce a motor system prediction error between a behavior prediction of the vehicle by the driver and an actual behavior of the vehicle, and an internal body system model that controls the internal body state of the driver, and when a significant difference occurs between a previous detection result and a current detection result by the driver monitoring means in the same driving scene, it estimates that there has been a change in the internal body system model; The driving assistance control means changes the mode of driving assistance by the driving assistance means based on the estimation of the motor system prediction error by the driver internal state estimation means and the estimation of the change in the internal model of the body system.

2. The driving assistance device according to claim 1, The driving assistance control means is a driving assistance device that, when it is estimated that the motor system prediction error is decreasing in the same driving scene and it is estimated that the internal model of the body system is changing, reduces the level of driving assistance in that driving scene, and when it is estimated that the internal model of the body system is not changing, maintains the level of driving assistance in that driving scene.

3. The driving assistance device according to claim 1, The driving assistance device wherein the assistance mode setting means maintains the level of driving assistance in the same driving scene when it is estimated that there is no change in the motor system prediction error and that the internal model of the body system has changed, and increases the level of driving assistance in the same driving scene when it is estimated that the internal model of the body system has not changed.

4. The driving assistance device according to claim 1, The driving assistance device wherein the assistance mode setting means stops the current driving assistance in the driving scene when it is estimated that the motor system prediction error is increasing in the same driving scene and the internal model of the body system is changing, and maintains the level of driving assistance in the driving scene when it is estimated that the internal model of the body system is not changing.

5. The driving assistance device according to claim 1, The driving assistance means is a driving assistance device including an interface means for notifying the driver of driving-related information.

6. The driving assistance device according to claim 1, The driving assistance means is a driving assistance device including a driving operation intervention means for intervening in the driving operation of the driver.

7. The driving assistance device according to claim 1, The physiological information is at least one of the driver's heart rate, breathing rate, sweat rate, and heart rhythm disturbance.

Images