Driving support device

Abstract

To provide a driving support device capable of considering a driver's brain model (driver internal model) to perform appropriate driving support optimized for each driver.SOLUTION: A driving support device includes: traveling scene recognition means 31; driving operation detection means 32; driver monitoring means 33; interaction-related operation identification means for identifying an operation element for which the detection result by the driver monitoring means 33 fluctuates beyond a reference fluctuation range while driving in a traveling scene, in an interaction-related operation; and driving support control means 35 for controlling HMI 26 to announce guidance related to the interaction-related operation in a traveling scene including the interaction-related operation.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to a driving assistance device that assists the driver of a vehicle such as an automobile, and in particular to an improvement that enables the provision of assistance optimized for each individual driver by performing driving assistance that takes into account the driver's internal brain model (driver's internal 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 driver of a vehicle 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 conventional driving assistance devices, technologies have been proposed that make it easier for drivers to accept driving assistance through HMI (Human Machine Interface). However, the conventional technology did not have the idea of ​​optimizing driving assistance for each driver by considering (modeling) the processing in the brain of each individual driver. For example, among the series of driving operations included in a single driving scenario (such as merging onto a main lane or backing into a parking space), it is thought that each driver will have different difficulties in certain driving operations. However, there was no technology that provided driving assistance optimized for each driver to help them overcome their difficulties.

[0006] The present invention has been made with a focus on these problems, and aims to provide a driving assistance device that can provide appropriate driving assistance that is optimized for each individual driver by taking into account the driver's brain model (driver 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 described in claim 1, a driving assistance device that assists a driver of a vehicle in driving includes driving scene recognition means that recognizes driving scenes of the vehicle, driver monitoring means that detects physiological information and / or behavior of the driver, driving operation detection means that detects driving operations of the driver, interface means that notify the driver of driving-related information, interaction-related operation identification means that breaks down driving operations required in driving scenes of the vehicle into a plurality of operation elements and identifies an operation element whose detection result detected by the driver monitoring unit during the driver's driving operation has fluctuated beyond a predetermined reference fluctuation range as an interaction-related operation, and driving assistance control means that controls the interface means to notify guidance regarding the interaction-related operation in driving scenes including the interaction-related operation.

[0008] According to the above solution, among the series of operation elements that make up a driving scene, operation elements that the driver has difficulty with are accurately identified as interaction-related operations, and guidance regarding these interaction-related operations can effectively reduce prediction errors in driving (motor system prediction error 2A), thereby improving the driver's driving skills and improving their emotions.

[0009] A preferred embodiment based on the above-described solution is as set forth in claim 2 and subsequent claims. That is, the driving assistance control means controls the interface means to notify the guidance immediately before a driving scene including the interaction-related operation or while the vehicle is driving through a driving scene including the interaction-related operation (corresponding to claim 2). In this case, the notification from the interface means (e.g., HMI 26) is made at an appropriate timing, thereby enabling effective driving assistance.

[0010] The guidance is information regarding the execution timing and / or the operation amount of the interaction-related operation (corresponding to claim 3). In this case, information regarding the execution timing and the operation amount of the interaction-related operation is provided, so that the driver can appropriately execute the interaction-related operation that he or she is not good at.

[0011] The physiological information of the driver is the driver's heart rate and / or the amount of sweating (corresponding to claim 4). In this case, the driver's internal state (emotional state) can be accurately determined from the driver's heart rate and the amount of sweating, so that the operating elements that the driver is weak at can be accurately identified. [Effects of the Invention]

[0012] According to the present invention, among a series of operation elements in a driving scene, operation elements that the driver finds difficult to operate are identified as interaction-related operations, and guidance is provided for these interaction-related operations, thereby effectively reducing the driver's prediction error (motor system prediction error 2A) in that driving scene, thereby achieving an improvement in the driver's driving technique and the accompanying improvement in emotions. [Brief explanation of the drawings]

[0013] [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 for explaining identification of an interaction-related operation in parallel parking. [Figure 4] FIG. 10 is a diagram for explaining identification of an interaction-related operation in back-parking; [Figure 5] 10 is a flowchart illustrating an example of control according to the present invention. [Figure 6] 10 is a flowchart illustrating an example of specific control of an interaction-related operation according to the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0014] 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.

[0015] 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.

[0016] 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.

[0017] 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.

[0018] 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.

[0019] 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).

[0020] 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).

[0021] 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.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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).

[0033] 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.

[0034] 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.

[0035] In the above-described driver internal model 1, a driver who remains poor at driving in a particular driving situation (for example, merging onto a main road or backing into a parking space) can be considered to have a motor system internal model 1A in that driving situation that is in a state of trial and error (a state in which an appropriate opportunity for improvement has not been obtained), and a state in which anxiety has become chronic in the body system internal model 1B. For this reason, it is desirable to provide particularly optimized driving assistance to such drivers.

[0036] In this regard, when driving operations in a driving scene are divided into chronological order, they can be considered as a series of driving operation elements (operational elements) connected in time series, and even a driver who is not good at that driving scene is thought to have some operational elements that they can operate well and some operational elements that they are particularly not good at operating. These operational elements that are not good at not only hinder the construction of a desirable motor system internal model 1A, but are also thought to have a particularly negative impact on the driver's emotions due to the interaction between motor system internal model 1A and body system internal model 1B.

[0037] Therefore, in the present invention, the operation elements that cause negative interactions with the internal model of the body system 1B are identified as interaction-related operation elements, and driving assistance is provided that acts particularly on the interaction-related operation elements in a series of driving operations (for example, notifications that indicate the execution timing and operation amount of the interaction-related operation elements), thereby achieving positive updates to the internal model of the motor system 1A and the internal model of the body system 1B, thereby achieving appropriate improvement of the driver's driving skills and improvement of emotions (improvement of happiness). Specific configuration examples of the driving assistance device will be described in detail below.

[0038] An example of a control system in the driving assistance device of the present invention is shown in a block diagram in Figure 2. The driving assistance device is installed in a vehicle such as an automobile or in a device that simulates vehicle driving (driving simulator), and is, for example, an advanced driving assistance system (ADAS).

[0039] 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 driver information acquisition means 23, a memory means 25, an HMI (human-machine interface) 26, and a driving operation intervention means 27.

[0040] The travel information acquisition means 21 is a means for acquiring information about the outside of the vehicle (external environment of the vehicle, such as roads and other vehicles) related to the travel state of the vehicle, 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. The driving information acquisition means 22 is a means for detecting the state of driving operation, and is equipped with, for example, a steering angle sensor, an accelerator sensor, a brake sensor, etc.

[0041] The driver information acquisition means 23 is a means for acquiring information about the state of the driver of the vehicle, and is composed of, for example, an in-vehicle camera that captures an image of the driver. The storage means 25 is a means for storing various data, and is, for example, an external storage device. The storage device 25 stores data about the driving scene, the driver, etc.

[0042] The HMI 26 is a device that provides visual and auditory information to the driver of the vehicle during driving assistance, and includes, for example, a display 26A (e.g., an in-vehicle monitor or a head-up display (HDU)) that can display images and text, and a notification means such as a speaker 26B that can output sound. In this embodiment, the HMI 26 corresponds to the "interface means" in the claims.

[0043] 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.

[0044] The control unit U is a control device configured by, for example, a microcomputer, and includes a driving scene recognition means 31, a driving operation detection means 32, a driver monitoring means 33, an interaction-related operation identification means 34, and a driving assistance control means 35. These means are provided as control programs in the control unit U.

[0045] The driving scene recognition means 21 is a means for recognizing (detecting) the driving scene of the vehicle (the situation in which the vehicle is driving) based on information outside the vehicle acquired by the driving information acquisition means 21. The driving scenes are grouped according to their content (for example, parallel parking, backing into a parking space, changing lanes, etc.), and driving scenes belonging to the same group are treated as the same driving scene.

[0046] The driving operation detection means 33 is a means for detecting the driving operation of the driver for each driving scene based on the information (detection signal) acquired by the driving information acquisition means 22. The detected driving operation data for each driving scene is stored in the storage means 25 as data for each driver.

[0047] The driver monitoring means 33 is a means for monitoring (monitoring) the driver based on the information acquired by the driver information acquisition means 23. For example, by analyzing an image of the driver taken by an in-vehicle camera, the driver's identity is identified, and the driver's physiological state (heart rate, amount of sweating) is detected (estimated), and the driver's behavior (characteristic gestures, etc.) is detected. Note that the driver's physiological state can also be detected directly by using a means for detecting the physiological state that is directly worn by the driver (for example, a heart rate monitor that measures heart rate or a sweat sensor that detects the amount of sweating) as the driver information acquisition means 23.

[0048] The specific operation element setting means 36 identifies an interaction-related operation from a series of driving operations in a target driving scene based on the detection results by the driving operation detection means 32 and the driver monitoring means 33.

[0049] Specifically, a series of driving operations in a target driving scene is divided into a plurality of operation elements, the operation of each operation element is detected by the driving operation detection means 32, and the physiological amount (e.g., heart rate, amount of sweat, etc.) and / or behavior (e.g., behavior indicating tension) of the driver while operating each operation element is detected by the driver monitoring means 33. Then, when there is a fluctuation exceeding a predetermined reference fluctuation range in the physiological amount and / or behavior (e.g., behavior amount such as behavior frequency) of the driver while operating the operation element, the operation element is identified as an interaction-related operation.

[0050] In other words, when performing driving operations involving a series of consecutive operating elements, when the driver comes across an operating element that the driver is not good at (an operating element for which the motor system internal model 1A has not been properly constructed), it is thought that this will cause feelings of anxiety, resulting in significant fluctuations in physiological values ​​and behavior from when the driver is calm (when driving an operating element that the driver is not good at).By capturing this fluctuation, the operating element being driven at that time can be identified as an interaction-related element (an operating element that the motor system internal model 1A is thought to be causing a negative interaction with the body system internal model 1B).

[0051] For example, in the case of parallel parking as shown in Fig. 3 (where the vehicle 41 is parked in the space between the vehicle 42 in front and the vehicle 43 in rear), the series of driving operations in the driving scene can be divided into the following operation elements (1) to (7). Note that the method of dividing the driving operations into the operation elements is determined in advance and stored in the storage means 25. (1) Stopping at reverse start position 44 (2) Engaging reverse gear (3) (Start of) further steering (point 45) (4) Start of steering return (point 46) (5) Check the distance between the left rear end of the vehicle and the curb (6) Steering to correct parallelism (7) Adjusting the distance between the vehicle in front 42 and the vehicle in rear 43 The driver monitoring means 33 monitors the driver while operating each of these operation elements (1) to (7) and detects the physiological amount and / or behavior of the driver for each operation element. The interaction-related operation identification means 34 receives the detection result of the driver monitoring means 33, detects an operation element for which the physiological amount and / or behavior of the driver has fluctuated beyond a predetermined reference fluctuation range, and identifies this operation element as an interaction-related operation.

[0052] For example, in the case of parallel parking shown in FIG. 3, if there is a large fluctuation in the driver's heart rate exceeding a predetermined reference fluctuation range (heart rate reference fluctuation range) only in the "steering return" operation of operation element (4) among operation elements (1) to (7), operation element (4) is identified as an interaction-related operation.

[0053] In the case of a driving scene in which vehicle 51 shown in FIG. 4 is parked in reverse into a parking space, a series of driving operations can be divided into the following operation elements (11) to (15). (11) Stopping at the starting position of the reverse (12) Start of backing into parking space (point 52) (13) Check proximity to adjacent vehicles (not shown) (14) Turning operation (point 53) (15) Retract into the parking space The driver is monitored while operating these operation elements (11) to (15), and if, for example, there is a large fluctuation in the driver's heart rate exceeding the standard heart rate fluctuation range only while operating the "turning operation" of the operation element (14), the operation element (14) is identified as an interaction-related operation.

[0054] In the above example, the driver's heart rate is used as the feature (physiological quantity) for identifying the interaction-related operation, but the feature for identifying the interaction-related operation is not limited to the heart rate, and other physiological quantities of the driver (e.g., physiological quantities indicating the driver's anxiety) or the driver's behavior (e.g., specific behavior indicating the driver's anxiety) can also be used. For example, when the driver's sweat amount fluctuates beyond a sweat amount reference fluctuation range or when the driver's specific behavior amount (e.g., frequency) fluctuates beyond a behavior reference fluctuation range, the operation element may be identified as the interaction-related operation.

[0055] In addition, although the above example illustrates a case where there is one interaction-related operation in one driving scene, multiple interaction-related operations may be identified in one driving scene. That is, if there are multiple operation elements in one driving scene where the feature amount (physiological amount, behavior) indicating the driver state fluctuates beyond a predetermined reference fluctuation range, those multiple operation elements may be identified as interaction-related operations.

[0056] The driving assistance control means 35 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 HMI 26 and the driving operation intervention means 27 to provide driving assistance in that driving scene if the driving scene requires driving assistance. In this case, if the driving scene includes an interaction-related operation, driving assistance related to the interaction-related operation is provided instead of or in addition to the normal driving assistance.

[0057] Specifically, in this embodiment, the HMI 26 notifies the driver of the execution timing, operation amount, operation method, etc. of the interaction-related operation during the driving scene and / or immediately before entering the driving scene. For example, guidance regarding the execution timing and the required operation amount is displayed as an image on the display 26A of the HMI 26. In addition, the speaker 26B provides audio instructions in accordance with the execution timing and audio guidance regarding the operation amount (for example, a suggestion as to whether the operation amount is excessive or insufficient).

[0058] In this way, according to this embodiment, the operating element that the driver is most uncomfortable with (is thought to be causing anxiety about) is identified as an interaction-related operation, and appropriate guidance (coaching) is provided pinpoint to this interaction-related operation, thereby effectively reducing the prediction error in the driver's motor system internal model 1A and having a positive effect on the body system internal model 1B, thereby achieving an improvement in the driver's emotions.

[0059] Next, an example of control in the present invention will be described with reference to Figures 5 and 6. Figure 5 is a flowchart showing the overall control of the present invention. In the control, first, in step S1, the driving scene during driving is recognized.

[0060] In the next step S2, it is determined whether the recognized driving scene is a target for driving assistance, and if not, the series of processes ends. On the other hand, if the driving scene is a target for driving assistance, the process proceeds to step S3, where it is determined whether the driving scene includes an interaction-related operation.

[0061] If it is determined in step S3 that an interaction-related operation is included, the process proceeds to step S4, where driving assistance including assistance (guidance) for the interaction-related operation is executed, and the series of processes is terminated. On the other hand, if it is determined that an interaction-related operation is not included, the process proceeds to step S5, where normal driving assistance is executed, and the series of processes is terminated.

[0062] An example of control in identifying an interaction related operation is shown in a flowchart in Figure 6. In identifying an interaction related operation, first, in step S11, a driving scene during driving is recognized.

[0063] In the next step S12, it is determined whether the recognized driving scene is a target for driving assistance, and if it is not a target, the series of processes ends. On the other hand, if the driving scene is a target for driving assistance, the process proceeds to step S13.

[0064] In the subsequent loop of steps S13 to S16, an interaction-related operation in the driving scene is extracted. That is, first, in step S13, the driver's state (physiological amount and / or behavior of the driver) is detected for the operation element during driving.

[0065] In the next step S14, it is determined whether or not there has been a fluctuation in the driver state exceeding a predetermined reference fluctuation range during operation of the operation element, and if there has been no fluctuation, the process proceeds to step S16. On the other hand, if there has been a fluctuation, the process proceeds to step S15, where the operation element is identified as an interaction-related operation, and the process proceeds to step S16.

[0066] In step S16, it is determined whether the driving scene is completed, and if not, the process returns to step S13, and the processing loop of steps S13 to S16 for extracting interaction-related operations is repeated until the driving scene is completed.

[0067] On the other hand, if it is determined in step S16 that the driving scene is completed, the process proceeds to step S17, where the driver data is updated (the identified interaction-related operation is stored), and the series of operations is terminated.

[0068] 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]

[0069] The present invention can be used in a driving assistance device mounted on a vehicle such as an automobile. [Explanation of symbols]

[0070] 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 Driver information acquisition means 25 Memory means 26 HMI 26A Display 26B Speaker 27 Driving operation intervention measures 31 Driving scene recognition means 32 Driving operation detection means 33 Driver Monitoring Methods 36 Interaction-related operation identification 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 driver monitoring means for detecting physiological information and / or behavior of the driver; a driving operation detection means for detecting a driving operation of the driver; an interface means for notifying the driver of driving information; an interaction-related operation identification means for decomposing a driving operation required in a driving scene of the vehicle into a plurality of operation elements, and identifying an operation element whose detection result detected by the driver monitoring unit during the driving operation of the driver fluctuates beyond a predetermined reference fluctuation range as an interaction-related operation; a driving assistance control means for controlling the interface means so as to provide guidance regarding the interaction-related operation in a driving scene including the interaction-related operation; A driving assistance device equipped with the above.

2. The driving assistance device according to claim 1, The driving assistance control means controls the interface means to notify the guidance immediately before a driving scene including the interaction-related operation or while the vehicle is driving through the driving scene including the interaction-related operation.

3. The driving assistance device according to claim 1, The guidance is information regarding the execution timing and / or the operation amount of the interaction-related operation.

4. The driving assistance device according to claim 1, A driving assistance device, wherein the physiological information of the driver is the driver's heart rate and / or sweat rate.

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