Attention reminding system and attention reminding method

Abstract

The present application provides an attention reminding system and an attention reminding method to notify a driver of a vehicle of a risk of lane departure, which is one of dangerous driving, in an easily acceptable manner. The attention reminding system includes a notification unit that notifies a driver of a predetermined level of prominence through an HMI device when a risk index indicating a risk of the vehicle departing from a lane enters a predetermined value range; a decision unit that decides a notification parameter set including parameters of a notification timing and a level of prominence; a behavior recording unit that records whether or not the driver has a reaction behavior to a notification using the notification parameter set; and a reaction information recording unit that stores, in a storage device, reaction information associated with a ratio of a number of notifications with the reaction behavior to a number of notifications, i.e., a reaction ratio, for each of different notification parameter sets. The decision unit calculates and decides an optimal parameter set that is estimated to have the largest reaction ratio based on the reaction information.

Description

Technical Field

[0001] This invention relates to a attention reminder system and method for reminding a vehicle driver to pay attention. Background Technology

[0002] In recent years, research and development has become active in providing sustainable transportation systems that take into account the disadvantaged among transportation participants. To achieve this, research and development related to preventative safety technologies is being conducted to further improve the safety and convenience of transportation.

[0003] Patent document 1 describes the following: using a camera to detect the position of the driver's hands holding the steering wheel, and correcting the steering reaction force of the steering wheel based on the detected hand position, thereby reducing the load on the driver's hands.

[0004] Patent document 2 describes the following: the vehicle's swaying driving state is detected based on the change in steering angle, and whether the temperature inside the vehicle is uncomfortable for the driver is detected. Based on these detection results, the main cause of the vehicle swaying is inferred, and the driver is notified accordingly based on the inferred result.

[0005] [Existing Technical Documents]

[0006] [Patent Literature]

[0007] Patent Document 1: Japanese Patent Application Publication No. 2007-253640

[0008] Patent Document 2: Japanese Patent Application Publication No. 2012-252498 Summary of the Invention

[0009] [The problem the invention aims to solve]

[0010] However, in preventive safety technology, there is a challenge: to communicate the signs of dangerous driving, such as lane departure caused by swerving (vehicle swaying while driving), to the driver as early as possible so that they are reflected in their driving behavior.

[0011] When warning drivers of dangerous driving signs in advance, and when the risk of dangerous driving is not too high, it is important that the stimuli (visual, auditory, and / or tactile) given to the driver for the purpose of giving such a warning suppress any annoyance to the driver, and that the driver can easily recognize and accept the warning.

[0012] To address this trade-off, one could consider altering the presentation of stimuli in light of the driver's attentional state and driving behavior. However, individual perceptions of stimuli (e.g., ease of acceptance, degree of discomfort) vary, and it cannot be argued that considering only the driver's attentional state and driving behavior adequately addresses the acceptability of notifications to the driver.

[0013] To address the aforementioned issues, the purpose of this application is to inform drivers of the risks of lane departure, a form of dangerous driving, in a manner easily acceptable to them. Furthermore, this contributes to the development of a sustainable transportation system.

[0014] [Methods used to solve problems]

[0015] One aspect of the present invention is a attention reminder system comprising: a risk calculation unit that calculates a risk index representing the risk of a vehicle leaving a lane; and a notification unit that, when the calculated risk index value enters a predetermined range, notifies the driver of the vehicle via an HMI device of a predetermined salience level. The attention reminder system further comprises: a decision unit that determines a notification parameter set, the notification parameter set including parameters specifying the timing of the notification and parameters specifying the salience level of the notification issued by the HMI device; and a behavior recording unit that records the notification based on the timing indicated by the determined notification parameter set. When the notification is issued at the salience level, the behavior recording unit records whether there is a reaction behavior in response to the notification in the driver's driving behavior; and the reaction information recording unit calculates the reaction ratio for different notification parameter sets and stores the reaction information formed by associating the notification parameter sets with the reaction ratio in a storage device, wherein the reaction ratio is the ratio of the number of times the driver reacts to the notification to the number of times the notification is issued, and the decision unit calculates and determines the optimal parameter set based on the relationship between the notification parameter set represented by the reaction information and the reaction ratio, wherein the optimal parameter set is the notification parameter set that is presumed to have the largest reaction ratio.

[0016] According to another aspect of the invention, the decision unit further determines an update period and at least one set of data collection parameters, the update period being the period up to the optimal set of parameters determined in the next update, the at least one set of data collection parameters being the notification parameter set specifically used for data collection, and during the update period, the decision unit, in cooperation with the notification unit, the behavior recording unit, and the reaction information recording unit, selectively notifies the use of the determined optimal set of parameters and the set of data collection parameters, and obtains the reaction information regarding the optimal set of parameters and the set of data collection parameters used.

[0017] According to another aspect of the invention, the update period is determined to be a period of predetermined length or a period during which the notification is made a predetermined number of times by the notification unit.

[0018] According to another aspect of the invention, the decision unit generates execution order information that specifies the order in which the notification unit executes the notification using the optimal parameter set and the notification using the data collection parameter set among multiple notifications during the update period, and the notification unit selects and uses the optimal parameter set and the data collection parameter set to perform the notification according to the execution order information.

[0019] According to another aspect of the invention, the attention alert system has a learning unit that, based on the reaction information, enables a driver reaction model to learn the relationship between the notification parameter set and the reaction ratio through machine learning, and the decision unit uses the driver reaction model to determine the optimal parameter set presumed to maximize the reaction ratio.

[0020] According to another aspect of the invention, the risk indicator is the distance between the vehicle and the lane boundary of the road on which the vehicle travels.

[0021] According to another aspect of the invention, the interval distance is calculated based on the position of lane markings in road images captured by cameras equipped with the vehicle, and / or the current position of the vehicle obtained from GPS information and the position information of lane boundaries of the road the vehicle is traveling on obtained from map information.

[0022] According to another aspect of the invention, the presence or absence of the reactive behavior is determined based on the increase in the driver's grip force or grip area on the steering wheel of the vehicle, and / or the presence or absence of a change in the steering angle achieved by the steering wheel by a certain angle or more.

[0023] According to another aspect of the invention, the notification unit notifies the driver of the vehicle via an HMI device when the value of the risk indicator indicating the escape from risk enters a predetermined value range, provided that the vehicle's direction indicator is not activated.

[0024] According to other aspects of the invention, the HMI device includes a tactile HMI device, an auditory HMI device, and / or a visual HMI device.

[0025] According to another aspect of the invention, a server device is included that is communicatively connected to the vehicle, the server device comprising the decision unit and the reaction information recording unit, and the vehicle comprising the risk calculation unit and the notification unit.

[0026] According to another aspect of the present invention, the decision unit, the reaction information recording unit, the risk calculation unit, and the notification unit are disposed in the vehicle.

[0027] Another aspect of the present invention is a attention reminder method executed by a computer of an attention reminder system that notifies the driver of a vehicle via an HMI device. This attention reminder method includes: a risk calculation step, calculating a risk index representing the risk of exiting a lane, the risk being the risk of the vehicle leaving the lane; and a notification step, notifying the driver of the vehicle via the HMI device when the calculated risk index value enters a predetermined value range. The attention reminder method further includes: a decision step, determining a notification parameter set, the notification parameter set including parameters specifying the timing of the notification and parameters specifying the salience level of the notification issued by the HMI device; and a behavior recording step, recording the behavior based on the determined... When the notification is issued in the notification step according to the timing and significance level represented by the notification parameter set, it is recorded whether there is a reaction behavior in the driver's driving behavior in response to the notification; and the reaction information recording step calculates the reaction ratio for different notification parameter sets, and stores the reaction information formed by associating the notification parameter set with the reaction ratio in a storage device, wherein the reaction ratio is the ratio of the number of times the driver reacts to the notification to the number of times the notification is issued; and in the decision step, the optimal parameter set is calculated and determined according to the relationship between various notification parameter sets and the reaction ratio, wherein the optimal parameter set is the notification parameter set that is presumed to have the largest reaction ratio.

[0028] [Invention Effects]

[0029] According to the present invention, the risk of the vehicle leaving its lane can be communicated in a manner that is easily acceptable to the driver of the vehicle. Attached Figure Description

[0030] Figure 1 This is a diagram illustrating the structure of a attention reminder system according to one embodiment of the present invention.

[0031] Figure 2 This is a diagram showing the structure of a vehicle equipped with a attention reminder device that constitutes an attention reminder system.

[0032] Figure 3 This diagram illustrates the structure of the interior of a vehicle equipped with a attention alert device.

[0033] Figure 4 This is an example diagram illustrating a scenario where an attention reminder system performs an action.

[0034] Figure 5 This is a diagram showing the structure of the attention reminder device that constitutes the attention reminder system.

[0035] Figure 6 This is a diagram showing the structure of the optimized server that constitutes the attention reminder system.

[0036] Figure 7 This is an explanatory diagram used to illustrate the actions of an attention reminder system.

[0037] Figure 8 This is a flowchart illustrating the steps of the attention reminder method performed by the attention reminder system.

[0038] Explanation of reference numerals in the attached figures

[0039] 1…Attention alert system, 2…Vehicle, 3…Attention alert device, 4…Optimization server, 5…Communication network, 6…HMI device, 6a…Haptic HMI device, 6b…Audioscopic HMI device, 6c…Visual HMI device, 7…Object detection device, 8…Exterior camera, 8a…Front-facing camera, 8b…Left-side camera, 8c…Right-side camera, 8d…Rear camera, 9…GPS device, 10…Vehicle control device, 11…Interior camera, 12…Interior mirror, 13a, 13b…Door mirrors, 14…Road, 15… …lane boundary, 20…driver's seat, 21…seat belt, 22…steering wheel, 23…instrument panel, 24…display device, 25…windshield, 30…processor, 31…memory, 32…first communicator, 33…risk calculation unit, 34…notification unit, 35…judgment unit, 36…behavior recording unit, 37…reaction log, 38, 48…program, 40…processor, 41…memory, 42…second communicator, 43…reaction information recording unit, 44…learning unit, 45…decision unit, 46…reaction information, 47…driver reaction model, 60…line. Detailed Implementation

[0040] The embodiments of the present invention will now be described with reference to the accompanying drawings.

[0041] Figure 1 This is a diagram illustrating the structure of a attention reminder system 1 according to an embodiment of the present invention. Additionally... Figure 2 This is a diagram showing the structure of a vehicle 2 equipped with a attention alert system 1. Figure 3 This is a diagram showing the structure of the interior of vehicle 2.

[0042] [Note the structure of the reminder system]

[0043] Attention alert system 1 includes attention alert device 3 installed on vehicle 2 and optimization server 4, which is a server device located outside vehicle 2. Attention alert device 3 and optimization server 4 are connected to each other in a manner that enables communication, for example, via a communication network 5 such as the Internet.

[0044] [Vehicle Structure]

[0045] Vehicle 2 is equipped with an HMI device 6 ( Figure 5 In this embodiment, the HMI device 6 includes a tactile HMI device 6a that provides tactile stimulation to the driver, an auditory HMI device 6b that outputs sound as auditory stimulation to the driver, and a visual HMI device 6c that conveys visual information to the driver. However, this is just an example; the HMI device 6 can be any one of the tactile, auditory, and visual HMI devices, or it can include any two of them.

[0046] The tactile HMI device 6a, for example, is installed in the driver's seat 20 of vehicle 2, and is an electrically powered seatbelt that applies tactile stimulation to the driver by changing the tension of the seatbelt 21 worn by the driver. However, the electrically powered seatbelt is one example, and the tactile HMI device 6a can be any device capable of applying tactile stimulation to the driver. For example, the tactile HMI device 6a could also be a vibration actuator that applies vibration to the driver's hands via the steering wheel 22, or a massage chair installed in the driver's seat 20, which applies stimulation such as tapping, rubbing, pressing, and vibration to the driver's back and other body parts.

[0047] In this embodiment, the auditory HMI device 6b is a single loudspeaker. Alternatively, the auditory HMI device 6b may also be a loudspeaker system consisting of multiple loudspeakers.

[0048] In this embodiment, the visual HMI device 6c is an instrument display device installed on the instrument panel 23 of the vehicle 2. However, the instrument display device is one example, and the visual HMI device 6c could also be a general display device 24 that displays content other than the instrument panel, or a head-up display (not shown) that displays images on the windshield 25, etc.

[0049] Vehicle 2 also has an object detection device 7 for detecting objects in the surrounding environment of vehicle 2, and an external camera 8 for acquiring images of the surrounding environment. Figure 5The vehicle 2 also includes a GPS device 9. The object detection device 7 can be, for example, a radar, lidar, and / or sonar located at the front of the vehicle 2 to detect objects (hereinafter also referred to as "front objects") in the environment in front of the vehicle 2. Alternatively, the object detection device 7 may also include multiple cameras, radar, lidar, and / or sonar distributed around the vehicle 2 to detect objects in the overall surrounding environment, including the left and right sides and rear of the vehicle 2.

[0050] The external camera 8 includes: a front camera 8a, which is located at the front of the vehicle 2 to capture the environment in front of the vehicle 2; a left-side camera 8b and a right-side camera 8c, which are located on the left and right side mirrors 13a and 13b of the vehicle door to capture the environment to the left and right of the vehicle 2; and a rear camera 8d, which is located at the rear of the vehicle to capture the environment behind the vehicle 2.

[0051] The vehicle 2 also has a vehicle control device 10, which detects the operating status of the accelerator pedal, brake pedal, direction indicator, steering wheel and other driving controls of the vehicle 2, and detects the vehicle speed, acceleration, yaw rate and other motion status of the vehicle 2.

[0052] Vehicle 2 also includes an interior camera 11 for capturing images of the interior, including the driver's, and an interior mirror 12 for the driver's visual confirmation of the rear of vehicle 2. The interior mirror 12 may also be a rearview mirror that displays images captured by a rear camera 8d of the rear of vehicle 2.

[0053] [Note: Summary of the system's actions]

[0054] In the attention alert system 1, the attention alert device 3 and the optimization server 4 cooperate to notify the driver of vehicle 2 of the risk of vehicle 2 leaving the lane. For example, as Figure 4 As shown, when a vehicle 2 traveling on road 14 approaches lane boundary 15 due to swerving (or swaying), the attention alert system 1 notifies the driver of vehicle 2 by outputting tactile stimuli, auditory stimuli (e.g., sound) and / or visual information (e.g., light) from HMI device 6.

[0055] Specifically, in accordance with the notification parameter set (described later) received from the optimization server 4, when the risk of vehicle 2 leaving the lane reaches a predetermined level, the attention reminder device 3 notifies the driver of vehicle 2 via HMI device 6. The HMI device 6 used for this notification can be any one or a combination of two or more of tactile HMI device 6a, auditory HMI device 6b, and visual HMI device 6c.

[0056] Server 4 optimizes the aforementioned notification parameter set to improve the acceptability of notifications executed by the attention reminder device 3 using the HMI device 6. Here, in this embodiment, the degree of acceptability is the ratio of the number of times the driver reacts to these notifications to the number of times notifications are issued by the HMI device 6, i.e., the reaction ratio.

[0057] Optimize server 4 to perform data collection actions for optimizing the above notification parameter set and to apply the notification using the optimal notification parameter set.

[0058] During the data collection process, optimization server 4 generates a set of notification parameters specifically for data collection, i.e., a set of parameters for data collection. Optimization server 4 generates various sets of parameters for data collection with different values ​​and obtains reaction information representing the relationship between each of these sets of parameters and the reaction ratio.

[0059] Optimization server 4 uses the acquired reaction information to enable the driver reaction model to learn the relationship between the notification parameter set and the reaction ratio through machine learning. For example, whenever reaction information is acquired through data collection actions, optimization server 4 uses the acquired reaction information to further learn the driver reaction model.

[0060] In the application of the action, the optimization server 4 uses a driver reaction model that has been further learned based on the obtained reaction information to determine the set of notification parameters that is presumed to have the highest driver reaction rate, i.e., the optimal parameter set. The optimization server 4 sends the above-determined optimal parameter set to the attention reminder device 3, and the attention reminder device 3 uses the above-determined optimal parameter set to make a notification through the HMI device 6.

[0061] Optimization server 4 also uses reaction information obtained from actions to represent the relationship between the optimal parameter set and the reaction ratio. Optimization server 4 further uses this obtained reaction information to perform additional learning on the driver model.

[0062] Thus, the optimization server 4 can determine the optimal set of parameters in a way that allows it to make notifications with higher response rates (i.e., notifications that are more acceptable to the driver) as the driver's response model is repeatedly learned.

[0063] [Note the structure of the reminder device]

[0064] Figure 5 This is a diagram showing the structure of the attention reminder device 3 provided by vehicle 2.

[0065] The attention reminder device 3 includes a processor 30, a memory 31, and a first communicator 32. The first communicator 32 is a transceiver used for communication between the attention reminder device 3 and the optimization server 4. The memory 31 is, for example, composed of volatile and / or non-volatile semiconductor memory and / or a hard disk device. In the memory 31, a reaction log 37 is stored via a behavior recording unit 36, described later.

[0066] The processor 30 is, for example, a computer equipped with a CPU. The processor 30 may also have a structure that includes a ROM on which programs are written, RAM for temporary data storage, etc. Furthermore, the processor 30 includes a risk calculation unit 33, a notification unit 34, a judgment unit 35, and a behavior recording unit 36 ​​as functional elements or functional units.

[0067] These functional elements of the processor 30 are implemented, for example, by the processor 30, which is a computer, executing a program 38 stored in memory 31. Furthermore, the program 38 can be pre-stored in any computer-readable storage medium. Alternatively, all or part of the aforementioned functional elements of the processor 30 can be constituted by hardware comprising one or more electronic circuit components.

[0068] The risk calculation unit 33 repeatedly calculates, for example, a risk index representing the risk of vehicle 2 leaving the lane, i.e., the risk of leaving the lane, at predetermined time intervals. In this embodiment, the risk index is the distance between vehicle 2 and the lane boundary of the road on which vehicle 2 travels (hereinafter also simply referred to as the distance). The distance can be calculated, for example, based on the position of lane markings in an image of the road on which vehicle 2 travels obtained from the external camera 8. Alternatively, the distance can be calculated based on the current position of vehicle 2 obtained from GPS information from GPS device 9 and the position information of the lane boundary of the road on which vehicle 2 travels obtained from map information. The map information can be stored in memory 31 in advance, for example. The risk calculation unit 33 can set the distance between the lane boundary on the side closer to vehicle 2 on the left and right sides of vehicle 2 and vehicle 2 (i.e., the smaller of the distances between each lane boundary on the left and right sides and vehicle 2) as the distance.

[0069] Furthermore, the lane departure distance is an example of a risk indicator that represents the risk of lane departure. This risk indicator can use any value that is considered to represent the risk of lane departure. For example, the risk indicator representing the risk of lane departure could also be a steering entropy value calculated based on the time change of the steering angle of the steering wheel 22 obtained from the vehicle control device 10.

[0070] The notification unit 34, assuming the vehicle 2's direction indicator is not activated (i.e., when the vehicle 2's direction indicator is not activated), uses the notification parameter set received from the optimization server 4 (i.e., the aforementioned data collection parameter set or optimal parameter set) to notify the driver of vehicle 2 via the HMI device 6 when the value of the risk index calculated by the risk calculation unit 33 falls within a predetermined range. Furthermore, this notification is executed according to the salience level specified by the parameters of the notification parameter set.

[0071] In this embodiment, as shown in equation (1), the notification parameter set includes the values ​​of three parameters: a threshold value a, which is a parameter for specifying the timing of the notification; b, which is a parameter for specifying the significance level of the notification; and c.

[0072] Notification parameter set = (a, b, c)(1)

[0073] The notification unit 34, based on the threshold a included in the notification parameter set, initiates the notification through the HMI device 6 when the risk indicator, i.e., the interval distance value D calculated by the risk calculation unit 33, falls within the range of values ​​less than the threshold a, i.e., when equation (2) is satisfied. This notification uses constants b and c, included in the aforementioned notification parameter set, which serve as parameters defining the significance level of the notification, to execute the notification using the significance level SL given by equation (3).

[0074] D<a (2)

[0075] SL=cb×D (3)

[0076] Here, salience level refers to the degree to which the stimulus output by HMI device 6 attracts or arouses human attention.

[0077] For example, when tactile stimulation is output as the tension of an electric seatbelt, the salience level of the tactile stimulus corresponds to the magnitude of the aforementioned tension. The greater the tension, the higher the salience level of the tactile stimulus.

[0078] Furthermore, the salience level of an auditory stimulus corresponds to the intensity, frequency, repetition period, and / or the period of intensity or frequency variation of the sound. The greater the intensity, the higher the frequency, the shorter the repetition period, and / or the shorter the period of intensity or frequency variation, the higher the salience level of the auditory stimulus.

[0079] Alternatively, when the tactile stimulus is a vibration delivered to the driver's hand from a vibration actuator located on the steering wheel 22 as described above, the salience level of this tactile stimulus corresponds to the intensity, frequency, repetition period, and / or the duration of the intensity or frequency variation period of the vibration. The greater the intensity, the higher the frequency, the shorter the repetition period, and / or the shorter the intensity or frequency variation period, the higher the salience level of the tactile stimulus imparted by the vibration.

[0080] Alternatively, when the visual information is output as graphic elements such as text or graphics displayed on a display device, the salience level of the visual information can be determined based on the brightness, brightness variation period, flicker period, or hue of the displayed graphic elements. For example, the higher the brightness, the shorter the brightness variation or flicker period, or the closer the hue is from cool to warm, the higher the salience level of the visual information.

[0081] Furthermore, the details of the tactile stimulation, auditory stimulation, and / or visual information (such as the intensity, frequency, tension, brightness, brightness variation, etc. of the various stimuli or visual information) and the correspondence between the salience level SL can be predetermined and stored in memory 31.

[0082] The notification unit 34 can refer to the information of the above correspondence and notify the driver from the HMI device 6 by providing the above tactile stimulation, auditory stimulation and / or visual information in a manner corresponding to the magnitude of the above-determined salience level SL.

[0083] As can be seen from equation (3) above, during the period when the interval distance value D satisfies equation (2), the salience level of the notification increases as the value D decreases. Therefore, as vehicle 2 approaches the lane boundary, the stimulus output by HMI device 6 may become stronger.

[0084] As described above, in this embodiment, the notification parameter set includes both a data collection parameter set and an optimal parameter set. The notification unit 34, following instructions from the optimization server 4 (e.g., according to the execution order information described later), uses either the data collection parameter set or the optimal parameter set to notify the driver via the HMI device 6.

[0085] When the notification unit 34 uses the HMI device 6 to issue a notification based on the timing and significance level of the notification parameter set determined by the optimization server 4, the judgment unit 35 determines whether the driver's driving behavior in the vehicle 2 reflects any reaction to the notification.

[0086] Specifically, the determination unit 35 obtains changes in the driving behavior of the vehicle 2 from the time the notification unit 34 sends a notification via the HMI device 6 until a predetermined time has elapsed (e.g., 2 seconds from the start of the notification) as the driver's reaction to the notification. In this embodiment, for example, the determination unit 35 makes a determination based on whether there is an increase in the driver's grip force or grip area on the steering wheel 22 of the vehicle 2, and / or whether there is a change in the steering angle achieved by the steering wheel 22 by a certain angle or more. The determination unit 35 determines that the above-mentioned reaction behavior exists when the grip force or grip area increases by more than a predetermined amount, and / or the steering angle changes by more than a predetermined amount.

[0087] The behavior recording unit 36 ​​records whether the driver's driving behavior in response to the notification is as follows when the notification unit 34 uses the HMI device 6 to issue a notification based on the timing and significance level of the notification parameter set determined by the optimization server 4.

[0088] Specifically, the behavior recording unit 36, based on the judgment result of the judgment unit 35 regarding whether there is a reaction behavior, saves the reaction record that associates the notification parameter set used by the notification with the judgment result regarding whether there is a reaction behavior in response to the notification in the reaction log 37 stored in the memory 31.

[0089] Furthermore, at the end of the update period described later, the behavior recording unit 36 ​​sends the reaction records stored in the reaction log 37 to the optimization server 4.

[0090] [Optimize server structure]

[0091] Figure 6 This is a diagram showing the structure of optimized server 4.

[0092] The optimization server 4 includes a processor 40, a memory 41, and a second communicator 42. The second communicator 42 is a transceiver used by the optimization server 4 to communicate with the attention reminder device 3. The memory 41 is, for example, composed of volatile and / or non-volatile semiconductor memory and / or a hard disk device. The memory 41 stores reaction information 46 generated by the reaction information recording unit 43 (described later) and a driver reaction model 47 generated by machine learning through the learning unit 44.

[0093] The processor 40 is, for example, a computer equipped with a CPU. The processor 40 may also have a structure that includes a ROM on which programs are written, RAM for temporary data storage, etc. Furthermore, the processor 40 includes a response information recording unit 43, a learning unit 44, and a decision unit 45 as functional elements or functional units.

[0094] These functional elements of the processor 40 are implemented, for example, by the processor 40, which is a computer, executing a program 48 stored in memory 41. Furthermore, the program 48 can be pre-stored in any computer-readable storage medium. Alternatively, all or part of the aforementioned functional elements of the processor 40 can be constituted by hardware comprising one or more electronic circuit components.

[0095] Based on the reaction records sent from the attention alert device 3 of vehicle 2, the reaction information recording unit 43 calculates the ratio of the number of times the driver reacted to a notification to the number of times a notification using that parameter set was used, i.e., the reaction ratio, for each different notification parameter set. Furthermore, the reaction information recording unit 43 generates reaction information 46 that associates each notification parameter set with the reaction ratio for notifications using that parameter set. Then, the reaction information recording unit 43 stores the generated reaction information 46 in the memory 41.

[0096] The learning unit 44 uses the reaction information 46 stored in the memory 41 by the reaction information recording unit 43 to enable the driver reaction model 47 stored in the memory 41 to learn the relationship between the notification parameter set and the reaction ratio through machine learning. The driver reaction model 47, according to existing technology, can be constructed from a neural network, for example.

[0097] Whenever the reaction information recording unit 43 re-stores the reaction information 46 based on the reaction record received from the attention reminder device 3 of the vehicle 2 (for example, whenever the update period described later ends), the learning unit 44 uses the re-stored reaction information 46 to perform additional learning on the driver reaction model 47. Thus, whenever new reaction information is generated based on new reaction records received from the attention reminder device 3, the driver reaction model 47 stored in the memory 41 is updated to be able to make more accurate predictions.

[0098] The decision unit 45 determines a set of notification parameters for the notification action performed by the notification unit 34 of the attention reminder device 3 equipped in the vehicle 2 through the HMI device 6. As shown in equation (1) above, in this embodiment, the notification parameter set includes a threshold a, which is a parameter for the timing of issuing the notification, and values ​​b and c, which are parameters for the prominence level of the notification.

[0099] In this embodiment, the decision unit 45 determines the optimal set of notification parameters for data collection and the set of notification parameters for application, and uses them as the notification parameter set.

[0100] The data collection parameter set is a set of notification parameters specifically used for data collection operations in the attention alert device 3 equipped in vehicle 2. During the data collection operation, various data collection parameter sets with different values ​​are used, for example, randomly to collect response information.

[0101] For example, the decision unit 45 randomly determines each set of parameters for data collection. That is, the decision unit 45 randomly determines the value of the threshold a and the values ​​of the constants b and c that constitute the notification parameter set from a predetermined value range, and generates a set of parameters for data collection consisting of the threshold a and the constants b and c determined above.

[0102] The optimal parameter set is a set of notification parameters used to enable the attention reminder device 3 of vehicle 2 to perform the following operation: providing notification in a manner easily acceptable to the driver. Specifically, the decision unit 45 calculates the notification parameter set that is presumed to have the highest response rate based on the relationship between various notification parameter sets and response rates, and determines the calculated notification parameter set as the optimal parameter set.

[0103] In this embodiment, a driver response model 47 that has learned the relationship between various notification parameter sets and response ratios is used to calculate the notification parameter set that is presumed to have the largest response ratio, and the calculated notification parameter set is determined as the optimal parameter set.

[0104] The decision unit 45 determines a predetermined period and, in cooperation with the attention reminder device 3, selectively performs data collection operations using the determined data collection parameter set and application operations using the determined optimal parameter set during the predetermined period. In this embodiment, the predetermined period is the update period until the next update of the determined optimal parameter set (i.e., the period until the optimal parameter set is re-determined).

[0105] Furthermore, during the aforementioned update period, the decision unit 45, in cooperation with the notification unit 34, the behavior recording unit 36, and the reaction information recording unit 43, selectively uses the optimal parameter set of the aforementioned decision and the aforementioned multiple data collection parameter sets, and obtains reaction information regarding the optimal parameter set and data collection parameter sets used in these notifications.

[0106] Specifically, the decision unit 45 first uses the driver reaction model 47 to determine the optimal parameter set, and then determines the period until the next update of the optimal parameter set, i.e., the update period. Additionally, the decision unit 45 generates multiple data collection parameter sets to be used during the update period. Here, the update period can be defined as a predetermined time period or a period during which the notification unit 34 sends a predetermined number of notifications.

[0107] The decision unit 45 also generates execution order information, which determines the order in which the notification unit 34 of the attention reminder device 3 executes notifications using the optimal parameter set and notifications using any parameter set from the multiple data collection parameter sets during the update period.

[0108] For example, the decision unit 45 determines the number of notifications using the data collection parameter set (hereinafter referred to as data collection notifications) and the number of notifications using the optimal parameter set (hereinafter referred to as application notifications) that should be executed during the aforementioned update period. Then, the decision unit 45 generates execution order information that randomly determines the order of data collection notifications and application notifications. The execution order information can, for example, specify whether the nth notification (n>1) from the beginning of the aforementioned update period is a data collection notification or an application notification, such as the first notification from the beginning of the aforementioned update period being a data collection notification, the second and third notifications being application notifications, the fourth being an application notification, and so on.

[0109] Here, the number of data collection notifications and the number of application notifications determined when generating the aforementioned execution order information can be determined, for example, based on the learning status of the driver reaction model 47 (e.g., the amount of reaction information used in previous learning). More specifically, the number of data collection notifications and the number of application notifications can be determined such that the ratio of the number of data collection notifications to the number of application notifications decreases as the driver reaction model 47 progresses in its learning (e.g., as the amount of reaction information used in previous learning increases).

[0110] The decision unit 45 sends the aforementioned decision or generated optimal parameter set, data collection parameter set, execution order information, and update period information to the attention reminder device 3. Thus, the update period for the aforementioned decision begins, and the notification unit 34 of the attention reminder device 3 executes data collection notifications or application notifications according to the execution order information. Furthermore, the behavior recording unit 36 ​​of the attention reminder device 3 saves a reaction record indicating whether the driver has reacted in the reaction log 37 when data collection notifications or application notifications are issued. Moreover, if the number of notifications occurring during the update period is included in or exceeds the number of notifications in the execution order information, the notification unit 34 can return to the beginning of the execution order information and repeatedly use that execution order information.

[0111] At the end of the aforementioned update period, the behavior recording unit 36 ​​sends the reaction records stored in the reaction log 37 to the optimization server 4. The reaction information recording unit 43 of the optimization server 4 receives the reaction records and generates reaction information. In addition, the learning unit 44 of the optimization server 4 uses the generated reaction information to perform additional learning on the driver reaction model 47.

[0112] Next, the decision unit 45 uses the driver reaction model 47 after the additional learning described above to determine a new optimal parameter set (i.e., update the optimal parameter set), and determines or generates the next update period, the parameter set for data collection, and the execution order information. The decision unit 45 sends these new optimal parameter sets, the parameter set for data collection, the execution order information, and the update period information to the attention reminder device 3, and begins the operation of the new update period.

[0113] Subsequently, similarly as described above, during repeated update periods, data collection notifications and application notifications are executed in combination within each update period (i.e., data collection actions and application actions are repeated within each update period).

[0114] [Note the system's actions]

[0115] Figure 7 This is an explanatory diagram illustrating the operation of the attention reminder system 1 having the above-described structure. Figure 7 In the diagram, the horizontal axis represents time, and the vertical axis represents the distance D between vehicle 2 and the lane boundary. Additionally, line 60 represents the time variation of the aforementioned distance D, calculated by the risk calculation unit 33 of the attention warning device 3. Figure 7 The example shows two update periods, namely update period-1 and update period-2, but in reality, multiple update periods repeat along the time axis.

[0116] At time t1, the decision unit 45 of the optimization server 4 uses the driver reaction model 47 learned from the reaction information obtained up to this point to determine the optimal parameter set (ao1, bo1, co1), and determines the update period until the next new determination of the optimal parameter set as update period -1. For example, update period -1 is determined to be a period with a predetermined time length.

[0117] Furthermore, the decision unit 45 uses random values, for example, to determine three sets of data collection parameters (a1, b1, c1), (a2, b2, c2), and (a3, b3, c3). Moreover, the decision unit 45 generates execution order information -1 that randomly determines the order in which the above three sets of data collection parameters and the optimal set of parameters are used during the update period -1.

[0118] Then, at time t1, the decision unit 45 sends the aforementioned decision or generated optimal parameter set, data collection parameter set, execution order information -1, and update period -1 to the attention reminder device 3. Thus, for example, the update period -1 begins from time t1.

[0119] The notification unit 34 of the attention reminder device 3 sends a notification in the order specified by the execution order information-1 received from the optimization server 4, using any one of the three data collection parameter sets and the optimal parameter set.

[0120] For example, following the execution sequence information-1, the notification unit 34 first uses the data collection parameter set (a1, b1, c1) to issue a notification indicating a risk of lane departure via the HMI device 6 at the moment t2 when the interval distance value D decreases after crossing the threshold a1, using the significance level specified by the values ​​b1 and c1 of the aforementioned data collection parameter set. Additionally, the behavior recording unit 36 ​​of the attention reminder device 3 records and saves the driver's reaction to the notification in the reaction log of the memory 31.

[0121] Next, the notification unit 34, following the execution order information-1, uses the data collection parameter set (a2, b2, c2) to issue a notification indicating the risk of lane departure at the moment t3 when the value of the interval distance D decreases after crossing the threshold a2, with the significance level specified by the values ​​b2 and c2 of the aforementioned data collection parameter set.

[0122] Next, similarly as described above, the notification unit 34 sequentially uses the optimal parameter set (ao 1, bo 1, co 1), the data collection parameter set (a 3, b 3, c 3), and the optimal parameter set (ao 1, bo 1, co 1) to issue notifications at the times t4, t5, and t6 when the value of the interval distance D crosses the thresholds ao 1, a 3, and ao 1 and decreases, respectively, according to the significance level specified by the corresponding notification parameter set. Each time a notification is issued, the behavior recording unit 36 ​​of the attention reminder device 3 saves the driver's reaction record in the reaction log of the memory 31.

[0123] Subsequently, the notification unit 34 also issues notifications according to the execution sequence information -1, and the behavior recording unit 36 ​​records the driver's reactions to these notifications in the reaction log of the memory 31. Then, at time t7, after a period of time designated as update period -1 has elapsed from time t1, update period -1 ends. Correspondingly, the behavior recording unit 36 ​​of the attention reminder device 3 sends the reaction records stored in the reaction log 37 of the memory 31 to the optimization server 4.

[0124] Based on the above reaction records, the reaction information recording unit 43 of the optimization server 4 calculates the reaction ratio in the notifications of the three data collection parameter sets and the optimal parameter set, and generates reaction information 46 that associates the three data collection parameter sets and the optimal parameter set with the calculated reaction ratios.

[0125] Furthermore, the learning unit 44 of the optimization server 4 uses the generated reaction information 46 to perform additional learning on the driver reaction model 47. Next, the decision unit 45 of the optimization server uses the driver reaction model 47 to determine the optimal parameter set (ao2, bo2, co2) that is presumed to have the largest reaction ratio, and determines the update period until the next new determination of the optimal parameter set as update period -2. For example, update period -2 is determined to be a period with a predetermined time length.

[0126] Furthermore, the decision unit 45 uses random values, for example, to determine three sets of data collection parameters (a4, b4, c4), (a5, b5, c5), and (a6, b6, c6). Then, the decision unit 45 generates execution order information -2 within the update period -2.

[0127] Then, at time t8, the decision unit 45 sends the aforementioned decision or generated optimal parameter set, data collection parameter set, execution order information -2, and update period -2 to the attention reminder device 3. Thus, for example, the update period -2 begins from time t8.

[0128] During update period-2, similar to the actions in update period-1, the notification unit 34, following the execution order information-2 received from the optimization server 4, uses any one of the three data collection parameter sets (a4, b4, c4), (a5, b5, c5), (a6, b6, c6), and the optimal parameter set (ao2, bo2, co2) at times t9, t10, t11, t12, and t13 when the interval distance D decreases after crossing the thresholds a4, a5, a6, or ao2 of each notification parameter set. The notification is issued via the HMI device 6 at the significance level specified by the corresponding notification parameter set. Additionally, each time a notification is issued, the behavior recording unit 36 ​​of the attention reminder device 3 stores the driver's reaction record in the reaction log of the memory 31.

[0129] Then, at time t14, after a period of time designated as update period -2 from time t8, update period -2 ends.

[0130] Subsequently, optimization server 4 repeats the process of determining the new update period in the same manner as described above, and repeats the same actions as described above.

[0131] [Note the action flow in the reminder system]

[0132] Next, the action steps of the attention reminder system 1 will be explained. Figure 8This is a flowchart illustrating the steps of the attention reminder method executed by the computer of attention reminder system 1 (i.e., the processor 30 of attention reminder device 3 and the processor 40 of optimization server 4). Repeated execution. Figure 8 The processing is shown.

[0133] When processing begins, firstly, the decision unit 45 of the optimization server 4 uses the driver reaction model 47 to calculate and determine the set of notification parameters that is presumed to maximize the driver's reaction ratio, i.e., the optimal parameter set (S100). Additionally, the decision unit 45 determines the update period until the next update of the aforementioned optimal parameter set and the notification parameter set specifically used for data collection during this update period, i.e., multiple data collection parameter sets (S102). The decision unit 45 sends the aforementioned determined or generated optimal parameter set, data collection parameter sets, and update period information to the attention reminder device 3. Thus, the update period begins in the attention reminder device 3. The decision unit 45 may also generate execution order information that determines the usage order of the optimal parameter set and data collection parameter sets during the update period when sending the aforementioned optimal parameter set, and send this information to the attention reminder device 3.

[0134] Next, note that the notification unit 34 of the reminder device 3 executes, for example, arbitrary notifications (S104) that use the data collection parameter set (i.e., data collection notification) and the application notification (i.e., use the optimal parameter set) according to the execution order information described above. According to each notification parameter set, when the value of the interval distance D satisfies the above equation (2), the HMI device 6 sends a notification according to the significance level SL given by equation (3).

[0135] Then, whenever a notification is output from the HMI device 6, the behavior recording unit 36 ​​of the attention reminder device 3 saves and stores the record of whether the driver has reacted, i.e., the reaction record, in the memory 31 as the reaction log 37 (S104) based on the judgment result of the judgment unit 35 regarding whether there is a reaction behavior. Here, whether there is a reaction behavior in response to the notification is determined based on whether the driver has performed a reaction behavior within a first predetermined time period (e.g., 2 seconds) from the start of the notification.

[0136] The notification unit 34 can also repeatedly send notifications while changing the notification parameter set if there is no response within the first predetermined time, until the driver takes a response. For example, the notification unit 34 determines whether the driver has taken a response within a second predetermined time (e.g., 10 seconds) from the date of the notification (S108). Furthermore, if the driver does not take a response within the second predetermined time (S108, no), the notification unit 34 can return to step S104 and repeat the process.

[0137] On the other hand, when the driver reacts within the second specified time (S108, Yes), the notification unit 34 determines whether the update period has ended (S110). If the update period has not ended (S110, No), the notification unit 34 returns to step S104 and repeats the process.

[0138] On the other hand, at the end of the update period (S110, Yes), the behavior recording unit 36 ​​sends the reaction records collected and stored in the reaction log 37 during the update period to the optimization server 4. Based on the received reaction records, the reaction information recording unit 43 of the optimization server 4 generates reaction information 46 and stores it in the memory 41 (S112). The learning unit 44 uses the reaction information 46 stored in the memory 41 to perform additional learning on the driver reaction model 47, and this process ends.

[0139] After this process is completed, remind System 1 to restart the process from step S100 and repeat the above-mentioned actions during data collection and application.

[0140] [Other Implementation Methods]

[0141] In the above implementation, the set of parameters for data collection is determined during each update period, but a portion or all of the set of parameters for data collection can also be used across multiple update periods.

[0142] Furthermore, in the above embodiment, the decision unit 45 randomly determines the execution order of the data collection parameter set and the optimal parameter set to generate execution order information, and the notification unit 34 uses any one of the multiple data collection parameter sets and the optimal parameter set to send a notification according to the execution order information. Alternatively, the decision unit 45 may not generate execution order information, and the notification unit 34 may randomly determine which of the multiple data collection parameter sets and the optimal parameter set to use for notification.

[0143] In addition, in the above-described embodiment, the decision unit 45 determines multiple sets of data collection parameters during each update, but it may also determine at least one set of data collection parameters during each update.

[0144] Furthermore, in the above embodiment, the decision unit 45 of the optimization server 4 selectively executes data collection operations using a data collection parameter set and application operations using an optimal parameter set during the update period. However, the data collection operations and application operations do not necessarily need to be selectively executed by mixing them during the update period; they can also be executed separately in divided periods. The aforementioned period can be set to a predetermined time length or a period during which the notification unit 34 sends a predetermined number of notifications.

[0145] Furthermore, in the above embodiment, the decision unit 45 uses a driver reaction model 47, which is based on reaction information generated by the reaction information recording unit 43 and has undergone machine learning, to determine the optimal parameter set. However, while using the driver reaction model 47 is one method for determining the optimal parameter set, the decision unit 45 can also use any other method that does not use the driver reaction model 47 to determine the optimal parameter set based on the relationship between the notification parameter set represented by the reaction information and the reaction ratio.

[0146] Other methods could include multivariate analysis of the parameters that make up the set of notification parameters (e.g., threshold a and constants b and c) and the response ratio (e.g., analysis of covariance).

[0147] Furthermore, in the above embodiment, the decision unit 45 randomly determines the values ​​of each parameter constituting the notification parameter set to determine the data collection parameter set. However, the method for determining the data collection parameter set is not limited to this method (random value determination). For example, the decision unit 45 may also use a genetic algorithm to perform operations such as selection, crossover, and / or mutation on the value set of each parameter constituting a notification parameter set to generate a new version of the value set, and determine the notification parameter set with the generated version of the value set as the data collection parameter set.

[0148] Alternatively, the decision unit 45 may, based on the response information obtained so far, use a probabilistic method such as optimal arm identification to determine the set of notification parameters that presumably result in a larger response ratio as a reward as the set of parameters for data collection and / or the optimal set of parameters.

[0149] In addition, in the above embodiment, there is one driver reaction model 47, but in the case where multiple drivers take turns driving the vehicle 2, the driver reaction model 47 can also be made for each driver.

[0150] Furthermore, in the above-described embodiment, the attention reminder system 1 comprises an attention reminder device 3 mounted on the vehicle 2 and an optimization server 4 placed externally on the vehicle 2, but is not limited to this structure. All functional elements of the optimization server 4 and the attention reminder device 3 may also be located within the vehicle 2. For example, the reaction information recording unit 43, learning unit 44, and decision unit 45 of the optimization server 4 may be located within the processor 30 of the attention reminder device 3, while the driver reaction model 47 is stored in the memory 31, thus implementing the attention reminder system 1 solely through the attention reminder device 3.

[0151] Alternatively, all the functional elements of the attention reminder device 3 and the optimization server 4 in the above embodiments can be located on a server device external to the vehicle 2. For example, the risk calculation unit 33, notification unit 34, judgment unit 35, and behavior recording unit 36 ​​of the attention reminder device 3 can be located on the processor 40 of the optimization server 4. In this case, the optimization server 4 can be communicatively connected to the various devices of the vehicle 2 (HMI device 6, object detection device 7, external camera 8, etc.).

[0152] This invention is not limited to the structure of the above embodiments and can be implemented in various ways without departing from its spirit.

[0153] [Structure supported by the above embodiments]

[0154] The above implementation supports the following structure.

[0155] (Structure 1) A attention reminder system comprising: a risk calculation unit that calculates a risk index representing the risk of a vehicle leaving a lane; and a notification unit that, when the calculated risk index value enters a predetermined value range, notifies the driver of the vehicle via an HMI device of a predetermined salience level. The attention reminder system further comprises: a decision unit that determines a notification parameter set, the notification parameter set including parameters specifying the timing of the notification and parameters specifying the salience level of the notification issued by the HMI device; and a behavior recording unit that records the behavior based on the determined notification parameter set and the... When the notification is issued at the salience level, the behavior recording unit records whether there is a reaction behavior in response to the notification in the driver's driving behavior; and the reaction information recording unit calculates the reaction ratio for different notification parameter sets, and stores the reaction information formed by associating the notification parameter set with the reaction ratio in a storage device, wherein the reaction ratio is the ratio of the number of times the driver reacts to the notification to the number of times the notification is issued, and the decision unit calculates and determines the optimal parameter set based on the relationship between the notification parameter set represented by the reaction information and the reaction ratio, wherein the optimal parameter set is the notification parameter set that is presumed to have the largest reaction ratio.

[0156] According to the attention reminder system of Structure 1, the degree of acceptability of the driver to the notification related to the risk of the vehicle leaving the lane is evaluated by setting the driver's reaction rate to the notification, and the notification parameter set for the prescribed notification method is calculated in such a way that the reaction rate is maximized. Therefore, the risk of lane leaving the lane can be notified in a way that is easily acceptable to the driver of the vehicle.

[0157] (Structure 2) According to the attention reminder system of Structure 1, the decision unit further determines an update period and at least one set of data collection parameters. The update period is the period until the optimal set of parameters determined in the next update. The at least one set of data collection parameters is a notification parameter set specifically used for data collection. During the update period, the decision unit, in cooperation with the notification unit, the behavior recording unit, and the reaction information recording unit, selectively uses the determined optimal set of parameters and the set of data collection parameters, and obtains the reaction information regarding the optimal set of parameters and the set of data collection parameters used.

[0158] According to the attention reminder system of Structure 2, data collection and application actions are performed separately during the same update period, thus suppressing the driver's discomfort with notifications of the use of the data collection parameter set.

[0159] (Structure 3) According to the attention reminder system of Structure 2, the update period is determined to be a period of a predetermined length of time or a period during which the notification is made a predetermined number of times by the notification unit.

[0160] According to the attention reminder system of Structure 3, the amount of reaction record data collected during the update period can be controlled by the length of the update period or the number of notifications.

[0161] (Structure 4) According to the reminder system of Structure 2 or 3, wherein the decision unit generates execution order information, which specifies the order in which the notification unit executes the notification using the optimal parameter set and the notification using the data collection parameter set among the multiple notifications during the update period, and the notification unit selects and uses the optimal parameter set and the data collection parameter set to perform the notification according to the execution order information.

[0162] According to the attention reminder system in Structure 4, the notification department can select the notification parameter set according to the execution order information, thus simplifying the process.

[0163] (Structure 5) The attention reminder system according to any one of Structures 1-4, wherein the attention reminder system has a learning unit that, based on the reaction information, enables a driver reaction model to learn the relationship between the notification parameter set and the reaction ratio through machine learning, and the decision unit uses the driver reaction model to determine the optimal parameter set presumed to maximize the reaction ratio.

[0164] Based on the attention reminder system of Structure 5, the optimal set of parameters presumed to maximize the response ratio can be easily determined.

[0165] (Structure 6) A attention reminder system according to any one of Structures 1-5, wherein the risk indicator is the distance between the vehicle and the lane boundary of the road on which the vehicle travels.

[0166] According to the attention alert system of Structure 6, it is possible to easily detect the degree of lane departure risk of the vehicle and notify the driver of the existence of the risk.

[0167] (Structure 7) According to the attention reminder system of Structure 6, the interval distance is calculated based on the position of the lane markings in the road image captured by the camera of the vehicle, and / or the current position of the vehicle obtained from GPS information and the position information of the lane boundary of the road on which the vehicle is traveling obtained from map information.

[0168] According to the attention alert system of structure 7, the distance between the vehicle and the lane boundary can be calculated through various sensors and methods.

[0169] (Structure 8) A attention reminder system according to any one of Structures 1-7, wherein the presence or absence of the reaction behavior is determined based on the increase in the driver's grip force or grip area on the steering wheel of the vehicle, and / or the presence or absence of a change in the steering angle achieved by the steering wheel by a certain angle or more.

[0170] According to the attention alert system in structure 8, it is easy to determine whether the driver has responded to the lane departure risk notification based on whether there are changes in the driver's operation in the vehicle.

[0171] (Structure 9) A attention reminder system according to any one of Structures 1-8, wherein the notification unit notifies the driver of the vehicle via an HMI device when the value of the risk indicator indicating that the vehicle has escaped risk enters a predetermined value range, provided that the vehicle's direction indicator has not been activated.

[0172] According to the attention alert system of structure 9, unnecessary data collection and unnecessary notification to the driver can be prevented when the vehicle approaches the lane due to lane change or preparation for left or right turn.

[0173] (Structure 10) An attention reminder system according to any one of Structures 1-9, wherein the HMI device includes a tactile HMI device, an auditory HMI device, and / or a visual HMI device.

[0174] According to the attention reminder system of structure 10, various HMI devices can be used to notify the driver in various ways.

[0175] (Structure 11) A attention reminder system according to any one of Structures 1-10, wherein a server device is connected to the vehicle in a communicable manner, the server device having the decision unit and the reaction information recording unit, and the vehicle having the risk calculation unit and the notification unit.

[0176] The attention reminder system according to structure 11 can be distributed to server devices and vehicle-mounted devices for processing, thus reducing the load on vehicle-mounted devices and lowering vehicle costs, for example.

[0177] (Structure 12) A attention reminder system according to any one of Structures 1 to 10, wherein the decision unit, the reaction information recording unit, the risk calculation unit and the notification unit are disposed in the vehicle.

[0178] According to the attention reminder system of structure 12, the entire attention reminder system can be installed in the vehicle, thus enabling more stable processing that is not affected by the tension of communication lines outside the vehicle.

[0179] (Structure 13) A attention reminder method, executed by a computer of an attention reminder system that notifies a driver of a vehicle via an HMI device, the attention reminder method comprising: a risk calculation step of calculating a risk index representing the risk of exiting a lane, the risk being the risk of a vehicle leaving a lane; and a notification step of notifying the driver of the vehicle via the HMI device when the calculated risk index value enters a predetermined value range, wherein the attention reminder method further comprises: a decision step of determining a notification parameter set, the notification parameter set including parameters specifying the timing of the notification and parameters specifying the salience level of the notification issued by the HMI device; and a behavior recording step of recording behavior based on the determined notification. When the notification is issued in the notification step, based on the timing and significance level represented by the parameter set, the presence or absence of a response to the notification in the driver's driving behavior is recorded; and in the response information recording step, a response ratio is calculated for each of the various notification parameter sets, and the response information, which is associated with the notification parameter set and the response ratio, is stored in a storage device, where the response ratio is the ratio of the number of times the driver responded to the notification to the number of notifications; and in the decision step, an optimal parameter set is calculated and determined based on the relationship between various notification parameter sets and the response ratio, where the optimal parameter set is the notification parameter set presumed to have the largest response ratio.

[0180] According to the attention reminder method of structure 13, the degree of acceptability of the driver to the notification related to the risk of lane departure of the vehicle is set as the driver's reaction rate to the notification for evaluation, and the notification parameter set of the prescribed notification method is calculated in such a way that the reaction rate is maximized. Therefore, the risk of lane departure can be notified in a way that is easily acceptable to the driver of the vehicle.

Claims

1. A attention reminder system, the attention reminder system having: The risk calculation unit calculates a risk index representing the risk of departure, which is the risk of a vehicle leaving the lane. as well as The notification department, when the calculated risk indicator value falls within a predetermined range, notifies the vehicle driver of a predetermined level of significance via the HMI device. The attention reminder system also features: The decision unit determines the notification parameter set, which includes parameters specifying the timing of the notification and parameters specifying the salience level of the notification issued by the HMI device. The behavior recording unit records whether the driver's driving behavior exhibits a reaction to the notification when the notification unit issues the notification based on the timing and salience level indicated by the determined notification parameter set; and The reaction information recording unit calculates a reaction ratio for each of the various notification parameter sets, and stores the reaction information, which is the association between the notification parameter sets and the reaction ratio, in a storage device. The reaction ratio is the ratio of the number of times the driver reacted to the notification to the total number of notifications. The decision unit calculates and determines the optimal parameter set based on the relationship between the notification parameter set represented by the reaction information and the reaction ratio. The optimal parameter set is the notification parameter set that is presumed to have the largest reaction ratio.

2. The attention reminder system according to claim 1, wherein, The decision unit also determines the update period and at least one set of data collection parameters, the update period being the period up to the optimal parameter set determined in the next update, and the at least one set of data collection parameters being the notification parameter set specifically used for data collection. During the update, the decision-making unit, in cooperation with the notification unit, the behavior recording unit, and the reaction information recording unit, selectively notifies the use of the determined optimal parameter set and data collection parameter set, and obtains reaction information regarding the used optimal parameter set and data collection parameter set.

3. The attention reminder system according to claim 2, wherein, The update period is determined to be either a predetermined time period or a period during which the notification department sends a predetermined number of notifications.

4. The attention reminder system according to claim 2, wherein, The decision unit generates execution order information, which specifies the order in which the notification unit executes the notifications using the optimal parameter set and the notifications using the data collection parameter set among multiple notifications during the update period. The notification unit selects and uses the optimal parameter set and the data collection parameter set according to the execution order information to perform the notification.

5. The attention reminder system according to claim 1, wherein, The attention alert system includes a learning unit that, based on the reaction information, enables the driver's reaction model to learn the relationship between the notification parameter set and the reaction ratio through machine learning. The decision unit uses the driver reaction model to determine the optimal set of parameters that is presumed to maximize the reaction ratio.

6. The attention reminder system according to claim 1, wherein, The risk indicator is the distance between the vehicle and the lane boundary of the road on which the vehicle is traveling.

7. The attention reminder system according to claim 6, wherein, The interval distance is calculated based on the position of lane markings in road images captured by the vehicle's camera, and / or the vehicle's current position obtained from GPS information and the position information of the lane boundaries of the road the vehicle is traveling on obtained from map information.

8. The attention reminder system according to claim 1, wherein, The presence or absence of the reaction behavior is determined based on the increase in the driver's grip force or grip area on the vehicle's steering wheel, and / or the presence or absence of a change in the steering angle achieved by the steering wheel at a certain angle or above.

9. The attention reminder system according to claim 1, wherein, The notification unit notifies the driver of the vehicle via the HMI device when the value of the risk indicator indicating that the vehicle has escaped risk enters a predetermined value range, provided that the vehicle's direction indicator has not activated.

10. The attention reminder system according to claim 1, wherein, The HMI device includes a tactile HMI device, an auditory HMI device, and / or a visual HMI device.

11. The attention reminder system according to any one of claims 1 to 10, wherein, The attention alert system includes a server device that is communicatively connected to the vehicle. The server device includes the decision-making unit and the reaction information recording unit. The vehicle is equipped with the risk calculation unit and the notification unit.

12. The attention reminder system according to any one of claims 1 to 10, wherein, The decision-making unit, the reaction information recording unit, the risk calculation unit, and the notification unit are located in the vehicle.

13. A method for reminding attention, the method being executed by a computer of an attention reminder system that notifies the driver of a vehicle via an HMI device, the method comprising: The risk calculation step involves calculating a risk index representing the risk of departure, where the risk of departure is the risk of a vehicle leaving the lane. as well as The notification step involves notifying the vehicle driver via the HMI device when the calculated risk indicator value falls within a predetermined range. This attention reminder method also has the following features: The decision-making process involves determining a set of notification parameters, which includes parameters specifying the timing of the notification and parameters specifying the salience level of the notification issued by the HMI device. The behavior recording step, when the notification is made in the notification step according to the timing and salience level represented by the determined notification parameter set, records whether there is a reaction behavior in the driver's driving behavior in response to the notification; and The reaction information recording step involves calculating a reaction ratio for each of the different notification parameter sets, and storing the reaction information, which is the association between the notification parameter set and the reaction ratio, in a storage device. The reaction ratio is the ratio of the number of times the driver reacted to the notification to the total number of notifications. In the decision step, an optimal set of parameters is calculated and determined based on the relationship between the various sets of notification parameters and the response ratio. This optimal set of parameters is the set of notification parameters presumed to have the largest response ratio.

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