JP2023048359A - Driving assistance device and computer program - Google Patents

Abstract

To provide a driving assistance device and a computer program with which it is possible to detect a danger of malicious tailgating from other vehicles at early stages, reflecting malicious tailgating characteristics of drivers of other vehicles.SOLUTION: A driving assistance device for determining a danger of malicious tailgating to a host vehicle by other vehicles traveling around the host vehicle comprises one or a plurality of processors and one or a plurality of memories which is communicably connected to the one or plurality of processors. The processors execute the processes including: acquiring information regarding a travel state of the host vehicle, information regarding the travel states of other vehicles, information regarding relative positions of the host vehicle and other vehicles, and information regarding malicious tailgating characteristics of drivers of other vehicles; and determining, on the basis of the acquired information, whether the host vehicle is in the state of suffering malicious tailgating from the other vehicles.SELECTED DRAWING: Figure 3

Description

The present disclosure relates to a driving support device and a computer program that support driving of a vehicle by a driver.

Inciting driving is known as nuisance driving of a vehicle. Patent Literature 1 discloses a driving support device for detecting an act of fanning and preventing an accident or trouble with a following vehicle. Specifically, the driving support device of Patent Document 1 determines the surrounding situation from the outputs of the navigation device, the road information collection device, and the camera, and automatically determines the situation from the outputs of the speed sensor, the acceleration sensor, the camera, the lighting system, and the vehicle control system. Determining the state of the vehicle, determining the state of other vehicles from the output of the camera, inter-vehicle communication device and radar, calculating the degree of risk of swaying from the surrounding conditions, own vehicle state and other vehicle state, and notifying based on the calculation result Perform control and motion control.

Further, Patent Literature 2 discloses a driving support device for suppressing lean driving. Specifically, the driving support device of Patent Document 2 acquires driving information related to the driving of the own vehicle, and based on the driving information, determines whether the own vehicle is driving under the influence of other vehicles. Then, notification processing and travel control are executed according to the determination result.

JP-A-2006-205773 Japanese Unexamined Patent Application Publication No. 2021-33529

However, it is conceivable that whether or not the driver of the other vehicle will drive with a bias or the timing at which the driver will drive with a bias will differ depending on the character and way of thinking about driving of each driver. The driving support devices disclosed in Patent Literatures 1 and 2 are designed to determine whether the driver is driving under pressure or being under pressure based on information about the driving conditions of the vehicle and other vehicles, and take into consideration the characteristics of each driver and their way of thinking about driving. It has not been. For this reason, for example, if the driver of the own vehicle is driving inappropriately, but the driver of the vehicle behind has a mild personality and does not take driving behavior that leads to swaying driving or swaying driving, swaying driving or swaying driving Therefore, the driver cannot be aware that the driver is driving inappropriately. In addition, when the driver of another vehicle has a personal characteristic that makes it easy to drive with reckless driving, the reckless driving occurs before the timing at which the driving support device determines that the driver is driving with reckless driving or being driven by reckless driving. , there is a risk that trouble cannot be prevented.

The present disclosure has been made in view of the above problems, and the purpose of the present disclosure is to reflect the driving characteristics of the driver of the other vehicle regarding the backlash driving (reversal driving characteristics) so that the backlash driving from the other vehicle is It is an object of the present invention to provide a driving support device and a computer program capable of improving the detection accuracy of the danger caused by

In order to solve the above problems, according to one aspect of the present disclosure, there is provided a driving assistance device that determines the risk of the host vehicle being harassed by other vehicles traveling around the host vehicle, comprising one or more A processor, and one or more memories communicably connected to the one or more processors, the processor storing information on the running state of the host vehicle, information on the running state of the other vehicle, and the host vehicle. and other vehicle's relative position information, and information on the driver's biased driving characteristics of the other vehicle, and based on the obtained information, determine whether or not the subject vehicle is subject to biased driving from the other vehicle. A driving assistance device is provided that executes a process including:

Further, in order to solve the above problems, according to another aspect of the present disclosure, there is provided a driving support device that determines the risk of the subject vehicle being harassed by other vehicles traveling around the subject vehicle, comprising: an acquisition unit that acquires information on the running state of the vehicle, information on the running state of the other vehicle, information on the relative position between the own vehicle and the other vehicle, and information on the driver's rushing driving characteristics of the other vehicle, and based on the acquired information and a driving support device that determines whether or not the host vehicle is in a situation where the host vehicle is subject to the aggressive driving from another vehicle.

Further, in order to solve the above problems, according to another aspect of the present disclosure, there is provided a computer program applied to a driving assistance device that determines the risk of slamming the own vehicle by other vehicles traveling around the own vehicle. One or a plurality of processors are provided with information on the running state of the own vehicle, information on the running state of other vehicles, information on the relative position between the own vehicle and the other vehicle, and information on the driver's rushing driving characteristics of the other vehicle. and determining, based on the acquired information, whether or not the host vehicle is subject to a rushing drive from another vehicle.

As described above, according to the present disclosure, it is possible to improve the detection accuracy of the danger due to the leaning driving from the other vehicle by reflecting the leaning driving characteristic of the driver of the other vehicle.

1 is a schematic diagram showing a configuration example of a vehicle provided with a driving assistance device according to an embodiment of the present disclosure; FIG. It is a block diagram which shows the structural example of the driving assistance device which concerns on the same embodiment. It is a flow chart which shows the leaning driving judgment processing by the driving assistance device concerning the embodiment. FIG. 11 is an explanatory diagram showing an example of image display of questionnaire results; It is a flowchart which shows the main routine of the processing operation performed by the driving assistance device which concerns on the same embodiment. It is a flowchart which shows a process during driving|running by the driving assistance device which concerns on the same embodiment. It is a flowchart which shows the driving action correction process by the driving assistance device which concerns on the same embodiment. FIG. 10 is a flowchart showing a first example of a tilting driving factor determination process according to the same embodiment; FIG. 9 is a flowchart showing a second example of the leaning driving factor determination process according to the same embodiment. FIG. 5 is an explanatory diagram showing an example of comparison results of driving behavior; It is a flowchart which shows the post-driving end processing by the driving assistance device which concerns on the same embodiment. FIG. 10 is an explanatory diagram showing an example layout of a feedback screen; FIG. 4 is an explanatory diagram showing an example of a reproduced moving image pattern; FIG. 4 is an explanatory diagram showing an example of a reproduced moving image pattern; FIG. 4 is an explanatory diagram showing an example of a reproduced moving image pattern; It is a flowchart which shows the post-driving end processing by the driving assistance device which concerns on the same embodiment. FIG. 4 is an explanatory diagram showing data on the frequency of occurrence of dangerous driving in a travel area; FIG. 4 is an explanatory diagram showing average cruising speed data in a travel area;

Preferred embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. In the present specification and drawings, constituent elements having substantially the same functional configuration are denoted by the same reference numerals, thereby omitting redundant description.

<1. Overall configuration of vehicle>
First, an example of the overall configuration of a vehicle to which the driving assistance device according to the embodiment of the present disclosure can be applied will be described.

FIG. 1 is a schematic diagram showing a configuration example of a vehicle 1 provided with a driving support device 50 according to this embodiment. A vehicle 1 shown in FIG. When not needed, it is configured as a four-wheel drive vehicle that transmits power to the "wheels 3"). The driving force source 9 may be an internal combustion engine such as a gasoline engine or a diesel engine, or may be a driving motor, or may include both an internal combustion engine and a driving motor.

The vehicle 1 may be, for example, an electric vehicle equipped with two drive motors, a front wheel drive motor and a rear wheel drive motor, or an electric vehicle equipped with drive motors corresponding to the respective wheels 3. There may be. When the vehicle 1 is an electric vehicle or a hybrid electric vehicle, the vehicle 1 includes a secondary battery for accumulating electric power supplied to the drive motor, a motor for generating electric power for charging the battery, a fuel cell, and the like. A generator is installed.

The vehicle 1 includes a driving force source 9 , an electric steering device 15 and a brake fluid pressure control unit 20 as devices used for operation control of the vehicle 1 . The drive force source 9 outputs drive torque that is transmitted to the front wheel drive shaft 5F and the rear wheel drive shaft 5R via a transmission (not shown), the front wheel differential mechanism 7F and the rear wheel differential mechanism 7R. Driving of the driving force source 9 and the transmission is controlled by a vehicle control device 41 including one or more electronic control units (ECUs).

An electric steering device 15 is provided on the front wheel drive shaft 5F. The electric steering device 15 includes an electric motor and a gear mechanism (not shown), and is controlled by the vehicle control device 41 to adjust the steering angles of the front left wheel 3LF and the front right wheel 3RF. The vehicle control device 41 controls the electric steering device 15 based on the steering angle of the steering wheel 13 by the driver during manual driving. Further, the vehicle control device 41 controls the electric steering device 15 based on the target steering angle set by the driving support device 50 during automatic driving.

A brake system of the vehicle 1 is configured as a hydraulic brake system. The brake fluid pressure control unit 20 includes brake calipers 17LF, 17RF, 17LR, and 17RR (hereinafter referred to as "brake caliper 17" when no particular distinction is required) provided on the front, rear, left, and right drive wheels 3LF, 3RF, 3LR, and 3RR, respectively. ) to generate braking force. Driving of the brake fluid pressure control unit 20 is controlled by the vehicle control device 41 . When the vehicle 1 is an electric vehicle or a hybrid electric vehicle, the brake fluid pressure control unit 20 is used together with regenerative braking by a drive motor.

The vehicle control device 41 includes a driving force source 9 that outputs the driving torque of the vehicle 1, an electric steering device 15 that controls the steering angle of the steering wheel 13 or steered wheels, and a brake fluid pressure control unit 20 that controls the braking force of the vehicle 1. includes one or more electronic controllers that control the drive of the The vehicle control device 41 may have a function of controlling the driving of a transmission that changes the speed of the output output from the driving force source 9 and transmits the changed speed to the wheels 3 . The vehicle control device 41 is configured to be able to acquire information transmitted from the driving support device 50 and is configured to be able to execute automatic driving control of the vehicle 1 . In addition, when the vehicle 1 is manually driven, the vehicle control device 41 acquires information on the amount of operation by the driver, and the driving force source 9 that outputs the driving torque of the vehicle 1, the steering wheel 13, or the steering wheels are steered. It controls the driving of the electric steering device 15 that controls the steering angle and the brake fluid pressure control unit 20 that controls the braking force of the vehicle 1 .

In addition, the vehicle 1 includes front imaging cameras 31LF and 31RF, a rear imaging camera 31R, a LiDAR (Light Detection And Ranging) 31S, an in-vehicle imaging camera 33, a biosensor 34, a vehicle state sensor 35, a GPS (Global Positioning System) sensor 37, A vehicle-to-vehicle communication unit 39 , a navigation system 40 and an HMI (Human Machine Interface) 43 are provided.

The front imaging cameras 31LF and 31RF, the rear imaging camera 31R and the LiDAR 31S constitute a surrounding environment sensor for acquiring information on the surrounding environment of the vehicle 1. FIG. The forward photographing cameras 31LF and 31RF and the rearward photographing camera 31R photograph the front or rear of the vehicle 1 and generate image data. The forward imaging cameras 31LF and 31RF and the rearward imaging camera 31R are provided with imaging elements such as CCD (Charged-Coupled Devices) or CMOS (Complementary Metal-Oxide-Semiconductor), and transmit generated image data to the driving support device 50.

In the vehicle 1 shown in FIG. 1, the front cameras 31LF and 31RF are configured as stereo cameras including a pair of left and right cameras, and the rear camera 31R is configured as a so-called monocular camera. It can be any camera. The vehicle 1 may be provided with, for example, cameras provided on the side mirrors 11L and 11R for photographing left rear or right rear, in addition to the forward photographing cameras 31LF and 31RF and the rearward photographing camera 31R.

The LiDAR 31S transmits an optical wave, receives a reflected wave of the optical wave, and detects an object and the distance to the object based on the time from the transmission of the optical wave to the reception of the reflected wave. The LiDAR 31S transmits the detection data to the driving support device 50. The vehicle 1 includes one or more of a radar sensor such as a millimeter wave radar and an ultrasonic sensor in place of or in combination with the LiDAR 31S as a surrounding environment sensor for acquiring information on the surrounding environment. A sensor may be provided.

The in-vehicle camera 33 is composed of one or more sensors that detect driver information of the vehicle 1 . The in-vehicle camera 33 has an imaging device such as a CCD or CMOS, takes an image of the inside of the vehicle, and generates image data. The in-vehicle camera 33 transmits the generated image data to the driving support device 50 . In this embodiment, the in-vehicle photographing camera 33 is arranged so as to photograph the driver of the vehicle 1 . Only one in-vehicle camera 33 may be installed, or a plurality of cameras may be installed.

The biosensor 34 detects the biometric information of the driver and transmits the detected data to the driving support device 50 . The biosensor 34 may be, for example, a radio Doppler sensor for detecting the driver's heartbeat, or a non-wearable pulse sensor for detecting the driver's pulse. Alternatively, the biosensor 34 may be an electrode set embedded in the steering wheel 13 for measuring the heartbeat or electrocardiogram of the driver. Also, the biosensor 34 may be a pressure measuring instrument embedded in the driver's seat to measure the seat pressure distribution when the driver is seated on the seat. Alternatively, the biosensor 34 may be a displacement sensor that detects changes in seat belt position to measure the heartbeat or respiration of the driver. Also, the biosensor 34 may be a TOF (Time of Flight) sensor for detecting information on the position of the driver. Also, the biosensor 34 may be a thermography for measuring the surface temperature of the driver's skin.

Also, the biosensor 34 may be a wearable sensor that is attached to the driver and detects the biometric information of the driver. The wearable biosensor 34 may be, for example, a wristwatch type, or a wearable device worn on the head or arm. These wearable devices may have a function of detecting biological information such as the driver's heartbeat, pulse, blood pressure, and body temperature. The wearable biosensor 34 may be connected to the driving support device 50 directly or via communication means such as CAN (Controller Area Network) or LIN (Local Internet). Alternatively, the wearable biosensor 34 communicates with the driving support device 50 via wireless communication means such as Bluetooth (registered trademark), NFC (Near Field Communication), wifi (wireless fidelity), or wireless LAN (Local Area Network). It may be configured to be possible.

The vehicle state sensor 35 consists of one or more sensors that detect the operating state and behavior of the vehicle 1 . The vehicle state sensor 35 includes at least one of, for example, a steering angle sensor, an accelerator position sensor, a brake stroke sensor, a brake pressure sensor, or an engine speed sensor, and detects the steering angle of the steering wheel 13 or steered wheels, accelerator opening, The operating state of the vehicle 1, such as the amount of brake operation or the number of engine revolutions, is detected. The vehicle state sensor 35 includes, for example, at least one of a vehicle speed sensor, an acceleration sensor, and an angular velocity sensor, and detects vehicle behavior such as vehicle speed, longitudinal acceleration, lateral acceleration, and yaw rate. Further, the vehicle state sensor 35 includes a sensor that detects the operation of the direction indicator, and detects the operating state of the direction indicator. The vehicle state sensor 35 includes a sensor that detects the tilted state of the vehicle 1, and detects the tilted state of the road. The vehicle state sensor 35 transmits a sensor signal containing the detected information to the driving assistance device 50 .

The vehicle-to-vehicle communication unit 39 is an interface for communicating with vehicles traveling around the vehicle 1 (hereinafter also referred to as "other vehicles").

The navigation system 40 is a known navigation system that sets a travel route to a destination set by a passenger and notifies the driver of the travel route. A GPS sensor 37 is connected to the navigation system 40 , receives satellite signals from GPS satellites via the GPS sensor 37 , and acquires position information of the vehicle 1 on map data. It should be noted that instead of the GPS sensor 37, an antenna that receives satellite signals from other satellite systems that specify the position of the vehicle 1 may be used.

The HMI 43 is driven by the driving support device 50 and presents various information to the driver by means of image display, voice output, and the like. The HMI 43 includes, for example, a display device provided in the instrument panel and a speaker provided in the vehicle. The display device may have the function of the display device of the navigation system 40 . HMI 43 may also include a head-up display that displays an image on the front window of vehicle 1 .

<2. Driving support device>
Next, the driving support device 50 according to this embodiment will be specifically described.

(2-1. Configuration example)
FIG. 2 is a block diagram showing a configuration example of the driving support device 50 according to this embodiment.
An ambient environment sensor 31, an in-vehicle camera 33, a biosensor 34, and a vehicle state sensor 35 are connected to the driving support device 50 via a dedicated line or communication means such as CAN or LIN. Further, the vehicle-to-vehicle communication unit 39, the navigation system 40, the vehicle control device 41, and the HMI 43 are connected to the driving support device 50 via a communication means such as a dedicated line or CAN or LIN. Note that the driving support device 50 is not limited to the electronic control device mounted on the vehicle 1, and may be a terminal device such as a smart phone or wearable device.

The driving assistance device 50 functions as a device that assists the driving of the vehicle 1 by executing a computer program by a processor such as one or more CPUs (Central Processing Units). The computer program is a computer program for causing the processor to execute an operation to be executed by the driving support device 50, which will be described later. The computer program executed by the processor may be recorded in a recording medium functioning as a storage unit (memory) 53 provided in the driving assistance device 50, or may be recorded in a recording medium built in the driving assistance device 50 or in the driving assistance device. It may be recorded on any recording medium that can be externally attached to the device 50 .

Recording media for recording computer programs include magnetic media such as hard disks, floppy disks and magnetic tapes, CD-ROMs (Compact Disk Read Only Memory), DVDs (Digital Versatile Disks), and Blu-ray (registered trademark). Optical recording media, magneto-optical media such as floptical disks, storage devices such as RAM (Random Access Memory) and ROM (Read Only Memory), and flash such as USB (Universal Serial Bus) memory and SSD (Solid State Drive) It may be a memory or other medium capable of storing programs.

The driving assistance device 50 includes a control section 51 and a storage section 53 . The control unit 51 includes one or more processors such as CPUs. A part or the whole of the control unit 51 may be composed of firmware or the like that can be updated, or may be a program module or the like that is executed by a command from a CPU or the like. The storage unit 53 is configured by a recording medium (memory) such as RAM or ROM. However, the number and types of storage units 53 are not particularly limited. The storage unit 53 records information such as computer programs executed by the control unit 51, various parameters used in arithmetic processing, detected data, and arithmetic results.

(2-2. Database)
The driving support device 50 is communicably connected to the driving database 71, the excessive driving example database 73, the normal driving database 75, and the driver database 77. The driving database 71, the bias driving example database 73, the normal driving database 75, and the driver database 77 are each stored in a storage device such as a RAM, or an HDD (Hard Disk Drive), CD (Compact Disk), DVD (Digital Versatile Disk), SSD, or the like. (Solid State Drive), USB flash, storage device, or other renewable recording medium. However, the type of recording medium is not particularly limited.

One or all of the driving database 71, the rushing driving example database 73, the normal driving database 75, and the driver database 77 may be installed in the vehicle 1, respectively, and the driving is performed via wireless communication means such as mobile communication. It may be stored in a server that can communicate with the support device 50 . Also, part or all of each database may be configured as one database.

(operation database)
The driving database 71 is a database that records information on the running state of the vehicle 1 . The driving state information recorded in the driving database 71 includes, for example, vehicle behavior data, driving behavior data, and surrounding environment data. Vehicle behavior data includes, for example, vehicle speed, longitudinal acceleration, lateral acceleration, longitudinal jerk, and lateral jerk. The driving behavior data includes, for example, data on the amount of accelerator operation, the amount of brake operation, and the steering angle by the driver's operation. The vehicle behavior data and the driving behavior data are generated and recorded based on the vehicle behavior and operation state information detected by the vehicle state sensor 35 .

Surrounding environment data includes, for example, the curve shape of the road on which the vehicle is traveling, the slope, the lane, the width of the road, the relative relationship between surrounding obstacles and other vehicles (relative speed, relative position and relative distance), the speed limit, and traffic conditions. It includes at least one data of the traffic environment in which the vehicle 1 is traveling, such as the time of day. The surrounding environment data includes at least one of information on the position where the vehicle 1 travels, information on the section, or information on the area (hereinafter collectively referred to as "travel area information"). The ambient environment data is generated and recorded based on the information detected by the ambient environment sensor 31 and the positional information acquired via the GPS sensor 37 .

The driving state information recorded in the driving database 71 may be all the driving data collected while the vehicle 1 is running. It may be travel data when it is determined that the However, among the information on the driving state recorded in the driving database 71, the driving data when it is determined that the vehicle is in a state of being subject to excessive driving from another vehicle by the excessive driving determination unit 63 includes what kind of driving is being received. Some information is fanned and recorded together with the driving flag.

(Database of case examples of distracted driving)
The fanning driving example database 73 is a database that records information on the driving state when fanning driving occurs. The driving state information recorded in the rush driving example database 73 includes, for example, vehicle behavior data, driving behavior data, and surrounding environment data, like the information recorded in the driving database 71 . The information on driving conditions recorded in the database 73 of the driving example database 73 includes data collected from cases in which drivers of other vehicles felt anxiety, anger, or stress toward a certain vehicle, and data collected from cases in which drivers of other vehicles felt uneasy, angry, or stressed toward a certain vehicle. and data collected in cases in which driving was performed (each case is collectively referred to as a "reckoning driving event").

The data in the event of harassment driving includes information on the driving state of the vehicle that caused the surrounding vehicles to feel anxiety, anger or stress, or the vehicle that was harassed (hereinafter collectively referred to as the ``furious driving vehicle''), and the surrounding vehicle. It also includes information on the running state of a vehicle driven by a driver who feels anxiety, anger, or stress about the vehicle, or a vehicle that has been driven aggressively (hereinafter collectively referred to as a “furiously driven vehicle”). The driving state information recorded in the tilting driving example database 73 includes driving state information of the tilting driving vehicle and the tilting driving vehicle for a certain period of time before and after the tilting driving event occurred.

In addition, the data of each aggressive driving event recorded in the aggressive driving case database 73 is classified for each aggressive driving scene set based on the vehicle behavior data, the driving behavior data, and the surrounding environment data. Classification of tempted driving scenes includes, for example, driving scenes involving a large distance between the vehicle and the vehicle in front, and driving behaviors involving a large speed difference with other vehicles traveling in adjacent lanes on a road with two or more lanes on one side. It is associated with factors that induce distracted driving, such as driving scenes.

(normal operation database)
The normal driving database 75 is a database that records information on the driving state in which the own vehicle 1 is not in a state where it is subjected to a rush driving. The driving state information recorded in the normal driving database 75 includes, for example, vehicle behavior data, driving behavior data, and surrounding environment data, like the information recorded in the driving database 71 . The driving state information recorded in the normal driving database 75 may be data collected and recorded by the driving support devices 50 of a plurality of vehicles including the own vehicle, and is created and stored in advance as model driving data. It may be data that contains both of them.

(driver database)
The driver database 77 is a database that records information about drivers. In this embodiment, the driver database 77 records feature amount data extracted from each driver's face image and identification information associated with each feature amount data. The identification information is not particularly limited, and may be data consisting of letters, numbers, and symbols, for example. In addition, the driver database 77 records the data of the rushing driving characteristics of each driver. The data of the rush driving characteristic is recorded in association with the identification information of the driver.

(2-3. Functional configuration of control unit)
The control unit 51 of the driving support device 50 receives information on the running state of the vehicle (self-vehicle) 1, information on the running states of other vehicles running around the self-vehicle 1, and information on the relative positions of the self-vehicle 1 and the other vehicles. Then, information on the driver's bias driving characteristics of the other vehicle is acquired, and based on the acquired information, a process of determining whether or not the host vehicle 1 is subject to bias driving from the other vehicle is executed.

As shown in FIG. 2 , the control unit 51 of the driving support device 50 includes a driver determination unit 61 , an acquisition unit 62 , an aggressive driving determination unit 63 , an aggressive driving factor determination unit 64 and a feedback presentation unit 65 . Each of these units may be a function realized by execution of a computer program by a processor such as a CPU, but may be partly or wholly configured by an analog circuit.

Hereinafter, after briefly describing the function of each part of the control unit 51, the processing operation of the control unit 51 will be specifically described.

(Driver determination unit)
Based on the image data transmitted from the in-vehicle camera 33 , the driver determination unit 61 executes processing for identifying the driver of the vehicle (hereinafter also referred to as “own vehicle”) 1 equipped with the driving support device 50 . Note that the driver determination unit 61 may specify the driver of the vehicle 1 based on information input by the driver or the passenger through an input device such as a touch panel.

(acquisition part)
The acquisition unit 62 executes processing for acquiring various information regarding the own vehicle 1 and other vehicles. Specifically, the acquisition unit 62 obtains information on the running state of the own vehicle 1, information on the running states of other vehicles, information on the running position such as the lane in which the own vehicle 1 runs, relative Information on the position and information on the surrounding environment of the vehicle 1 are acquired. The acquisition unit 62 also acquires information about the detected driver of the other vehicle. Specifically, the acquiring unit 62 acquires information about the emotion of the driver of the other vehicle and information about the aggressive driving characteristic of the driver of the other vehicle. The acquisition unit 62 records the acquired information in the storage unit 53 as time-series data.

The information on the aggressive driving characteristic is information that can estimate the possibility of each driver engaging in aggressive driving. The information on the rush driving characteristics includes, for example, history data of past rush driving or dangerous driving of individual drivers. Further, the information of the leaning driving characteristic may include data of acceleration/deceleration operation, steering operation, passing operation, and high-beam operation performed per certain period of time. These information may be recorded data collected by a controller onboard each vehicle while the respective driver is driving the vehicle. It can be detected by a known determination technique whether or not the driver has been driving in a rush or dangerous driving. In addition, it can be estimated that the higher the frequency of repeating the acceleration/deceleration operation and the steering operation, and the higher the number of passing times or the number of times the high beam is turned on per a certain period of time, the higher the possibility of the vehicle driving.

Further, the information on the driver's rushing driving characteristics may include information on the results of a questionnaire collected in advance from each driver and stored in the control device. The questionnaire result information includes, for example, the frequency of feeling irritated while driving, actions taken when feeling irritated (honking, passing, shouting, etc.), and information such as the cause of feeling irritated.

The acquisition unit 62 may acquire identification information for identifying the driver of the other vehicle, and extract information on the driver's rushing driving characteristic from the data recorded in the driver database 77 . For example, the acquisition unit 62 extracts facial features of the driver of the other vehicle based on the detection data transmitted from the ambient environment sensor 31, and refers to the driver database 77 to identify the driver of the other vehicle. Alternatively, the acquisition unit 62 may recognize the number printed on the license plate of another vehicle based on the image data transmitted from the ambient environment sensor 31, and identify the driver based on the number. The data that associates the number with the driver may be recorded, for example, in the driver database 77, or may be obtained from metadata accessible via mobile communication or the like. Further, the acquisition unit 62 may acquire identification information for identifying the driver of the other vehicle from the other vehicle through inter-vehicle communication.

(Inciting driving determination unit)
The bias driving determination unit 63 is based on the information on the driving state of the own vehicle 1, the information on the driving state of the other vehicle, the information on the relative position between the own vehicle 1 and the other vehicle, and the information on the bias driving characteristic of the driver of the other vehicle. , a process for determining whether or not the own vehicle 1 is in a state of being subjected to a backlash driving from another vehicle is executed. In this embodiment, the judgment formula is based on the information on the running state of the own vehicle 1, the information on the running state of the other vehicle, the information on the relative position between the own vehicle 1 and the other vehicle, and the information on the driver of the other vehicle's driving characteristic. is generated, and it is determined whether or not the own vehicle 1 is in a state of being subjected to a backlash driving from another vehicle using the determination formula.

(Determination unit for the cause of swaying driving)
When it is determined that the host vehicle 1 is in a situation where the host vehicle 1 is subject to the hostile driving by another vehicle, the hostile driving factor determining unit 64 executes a process of determining a factor that induces the hostile vehicle 1 to perform the hostile driving. For example, the rush driving factor determination unit 64 determines the driving state information recorded in the normal driving database 75 in which the driving state information of a plurality of vehicles in the surrounding environment belonging to the same class as the surrounding environment of the own vehicle 1 is accumulated, and the driving state information of the vehicle. By comparing with the information of the running state of the vehicle 1, the factor that induces the backlash driving is determined. The boost driving factor determination unit 64 records information on the determined factors that induce the boost driving in the storage unit 53 .

(Feedback presentation section)
The feedback presenting unit 65 executes a process of presenting feedback information regarding the driving behavior of the driver of the vehicle 1 based on the factor that induces the driving. In the present embodiment, when it is determined that the host vehicle 1 is in a situation where the vehicle 1 is in a state of being subjected to a rushing drive, it is configured to be able to execute a process of presenting an avoidance action. Further, in the present embodiment, the feedback presentation unit 65 is configured to be capable of executing pre-driving start processing for presenting feedback information before the start of driving and post-driving end processing for presenting feedback information after the end of driving.

<3. Operation of Driving Support Device>
Next, an example of the processing operation by the control unit 51 of the driving support device 50 according to this embodiment will be specifically described. In the following description, an example in which the other vehicle is a rear vehicle running behind the own vehicle 1 will be described.

[Deliberation process for rushing driving]
Before describing a series of processing operations by the control unit 51, the boost driving determination processing by the boost driving determination unit 63 of the driving support device 50 according to the present embodiment will be described.

FIG. 3 shows a flowchart of the leaning driving determination process.
The rush driving determination unit 63 recognizes the driving environment of the own vehicle 1 (step S11). Specifically, the rush driving determination unit 63 acquires information about the surrounding environment of the own vehicle 1 based on the detection data transmitted from the surrounding environment sensor 31 and recognizes the driving situation of the own vehicle 1 . The driving situation includes information on the shape of the curve of the road on which the vehicle 1 is traveling, the state of inclination, the lane, the width of the road, the positions of surrounding obstacles, and the number and positions of vehicles behind.

Further, the driving status of the own vehicle 1 may include information on the speed limit of the road on which the vehicle 1 is traveling and information on the travel area. The information on the speed limit and the information on the driving area may be detected based on image data transmitted from the surrounding environment sensor 31, may be acquired by road-to-vehicle communication or beacons, or may be obtained from a map transmitted from the GPS sensor 37. It may be obtained based on the position information on the data. Furthermore, the driving condition of the own vehicle 1 may include information on the current time. In addition, the driving condition of the own vehicle 1 may include information other than the exemplified information.

Next, the fueling driving determination unit 63 generates a determination formula for determining whether or not the host vehicle 1 is subject to fueling driving (step S13). As an example, the fanning driving determination unit 63 generates the following determination formula (1) according to the driving situation.

Driving evaluation value (a x X1 + b x X2 + c x X3 + d x X4) > decision threshold (X0 - X5) (1)
X0: Judgment criteria threshold
X1: Running state evaluation value of host vehicle 1
X2: Driving state evaluation value of the rear vehicle
X3: Relative position evaluation value between host vehicle 1 and the vehicle behind
X4: Emotion evaluation value of the driver of the rear vehicle
X5: Evaluation value of the driving characteristics of the driver of the rear vehicle
a, b, c, d: Coefficients set according to driving conditions

In the determination formula (1), the driving evaluation value calculated based on the running state of the vehicle 1, the running state of the vehicle behind, the relative position between the vehicle 1 and the vehicle behind, and the emotion of the driver of the vehicle behind It is determined that the host vehicle 1 is in a state of being subject to a rushing drive when it exceeds a judgment threshold corrected by the rushing driving characteristic of the driver of the vehicle.

For example, the running state evaluation value X1 of the own vehicle 1 is the speed evaluation value (km/h) of the own vehicle 1, and the running state evaluation value X2 of the rear vehicle is the speed evaluation value (km/h) of the rear vehicle. , and the relative position evaluation value X3 between the vehicle 1 and the vehicle behind is the evaluation value (m) of the inter-vehicle distance between the vehicle 1 and the vehicle behind. The emotion evaluation value X4 of the driver of the rear vehicle is an evaluation value indicating the degree of anxiety, anger, or stress of the driver of the rear vehicle. are evaluated relative to each other.

The acquisition unit 62 analyzes the facial expression of the driver of the vehicle behind based on the detection data transmitted from the surrounding environment sensor 31, and estimates the driver's emotion. For example, the acquisition unit 62 can estimate the driver's emotion using an emotion estimation program based on FACS (Facial Action Coding System). Alternatively, if vehicle-to-vehicle communication is possible between the own vehicle 1 and the vehicle behind, the acquisition unit 62 may acquire data of the facial expression of the driver or data of estimation result of emotion from the vehicle behind. Alternatively, the acquisition unit 62 may acquire biometric data such as heart rate and pulse from the vehicle behind, and estimate the emotion of the driver of the vehicle behind based on the biometric data. The rushing driving determination unit 63 relatively evaluates the emotion evaluation value X4 between 0 and 100 based on the estimated emotion of the driver of the vehicle behind.

Coefficients a, b, c, and d of respective evaluation values X1, X2, X3, and X4 are variables that are set according to the driving scene for determining whether or not the vehicle is in a state of subjecting the vehicle to slamming. For example, when the own vehicle 1 is traveling on a highway, it is considered that the slower the vehicle speed of the own vehicle 1 is, the higher the possibility that the vehicle 1 will be subjected to a rushing operation. Therefore, the coefficient a of the running state evaluation value (vehicle speed) X1 of the own vehicle 1 is set so that the smaller the evaluation value X1 of the vehicle speed of the own vehicle 1, the larger the value of "a×X1". Specifically, for example, the evaluation value X1 when the vehicle speed of the own vehicle 1 is 60 km/h is used as a reference (X1=0), and the evaluation value X1 when the own vehicle 1 is traveling at 40 km/h is "- 20", if the value of the coefficient a is a negative value, the slower the vehicle speed of the host vehicle 1, the larger the value of "a×X1". In this way, the coefficient a is set so that the value of “a×X1” increases as the vehicle speed of the own vehicle 1 becomes more likely to be driven by the own vehicle 1 according to the driving scene.

Similarly, the coefficient b increases as the vehicle speed of the rear vehicle becomes more likely to cause the driver of the rear vehicle to feel anxiety, anger, or stress toward the own vehicle 1, depending on the driving scene. set to be large. In addition, the coefficient c is "c×X3", depending on the driving scene, when the inter-vehicle distance is such that the vehicle 1 is likely to be swayed or the driver of the vehicle behind is likely to feel anxiety, anger, or stress toward the vehicle 1. is set so that the value of In addition, the coefficient d is set so that the value of “d×X4” increases as the driving state of the own vehicle 1 affects the emotions of the driver of the vehicle behind the driver in accordance with the driving scene. These coefficients a, b, c, and d are set using, for example, a learning model (deliberation model for judging driving while driving), which will be described later.

The judgment reference threshold value X0 may be set arbitrarily. /h), the inter-vehicle distance (m) between the vehicle 1 and the vehicle behind, and the emotion evaluation value (0 to 100) of the driver of the vehicle behind, the criterion threshold value X0 can be set to 100, for example. Further, the driver of the rear vehicle's distracting driving characteristic evaluation value X5 is an evaluation value indicating the possibility of the driver of the rear vehicle performing the aggressive driving. If the driver of the vehicle behind has a history of aggressive driving in the past, the higher the possibility of aggressive driving, the earlier it is determined that the vehicle 1 is in a situation of aggressive driving. The driving characteristic evaluation value X5 is set to a large value.

For example, the acquisition unit 62 acquires information about the driver's leaning driving characteristics of the driver of the rear vehicle from the rear vehicle through inter-vehicle communication. The information on the driver's aggressive driving characteristics includes, for example, history data of past aggressive driving or dangerous driving of individual drivers. In addition, the information on the driver's rushing driving characteristics may include data on acceleration/deceleration operations, steering operations, number of times of passing, and number of times the high beam is turned on in a certain period of time.

Further, the information on the driver's rushing driving characteristics may include information on the results of a questionnaire collected in advance from each driver and stored in the control device. The questionnaire result information includes, for example, the frequency of feeling irritated while driving, actions taken when feeling irritated (honking, passing, shouting, etc.), and information such as the cause of feeling irritated. Based on the acquired information on the rushing driving characteristic, the rushing driving determination unit 63 relatively evaluates the rushing driving characteristic evaluation value X5 between 0 and 100 using, for example, a preset calculation formula.

The acquisition unit 62 may acquire identification information for identifying the driver of the other vehicle, and extract information on the driver's rushing driving characteristic from the data recorded in the driver database 77 . For example, the acquisition unit 62 extracts facial features of the driver of the other vehicle based on the detection data transmitted from the ambient environment sensor 31, and refers to the driver database 77 to identify the driver of the other vehicle. Alternatively, the acquisition unit 62 may recognize the number printed on the license plate of another vehicle based on the image data transmitted from the ambient environment sensor 31, and identify the driver based on the number. The data that associates the number with the driver may be recorded, for example, in the driver database 77, or may be obtained from metadata accessible via mobile communication or the like.

Note that the running state evaluation value X1 of the host vehicle 1 is not limited to the vehicle speed evaluation value. The running state evaluation value X1 of the own vehicle 1 is, together with the vehicle speed, the amount of change in speed or the amount of change in acceleration/deceleration per predetermined time, the relative speed of the vehicle behind the own vehicle 1, and the speed limit of the road on which the vehicle is traveling. , or the time difference between the lighting timing of the direction indicator and the course change timing. In this case, the calculation formula is set such that the larger the amount of change in speed or the amount of change in acceleration per fixed time in the negative direction, that is, the larger the deceleration state of the vehicle 1, the larger the running state evaluation value X1. . Also, the calculation formula is such that the traveling state evaluation value X1 increases as the relative speed of the vehicle behind the host vehicle 1 increases, as the speed limit increases, and as the time difference between the lighting timing of the direction indicator and the course change timing decreases. is set. Note that the fixed time may be set to any time.

Further, the running state evaluation value X2 of the rear vehicle is not limited to the vehicle speed evaluation value. The running state evaluation value X2 of the rear vehicle may be calculated using either or both of the amount of change in speed or the amount of change in acceleration/deceleration per fixed time together with the vehicle speed. In this case, the calculation formula is set so that the traveling state evaluation value X2 increases as the speed change amount or acceleration change amount per fixed time increases in the positive direction, that is, as the acceleration state of the rear vehicle increases.

Also, the relative position evaluation value X3 between the vehicle 1 and the vehicle behind is not limited to the evaluation value of the inter-vehicle distance between the vehicle 1 and the vehicle behind. The relative position evaluation value X3 between the own vehicle 1 and the following vehicle is, together with the inter-vehicle distance, the amount of deviation between the traveling lane of the own vehicle 1 and the traveling lane of the following vehicle, and the distance between the own vehicle 1 and the following vehicle per fixed time. It may be calculated using any one or all of the amount of change in the inter-vehicle distance or the amount of change in the inter-vehicle distance between the vehicle 1 and the preceding vehicle per fixed time. In this case, the calculation formula is set so that the relative position evaluation value X3 increases as the amount of deviation between the lane of the vehicle 1 and the lane of the vehicle behind is smaller. Also, the greater the amount of change in the inter-vehicle distance between the vehicle 1 and the vehicle behind the vehicle per fixed time in the negative direction, or the more positive the amount of change in the inter-vehicle distance between the own vehicle 1 and the preceding vehicle per fixed time. An arithmetic expression is set such that the larger the value of , the larger the relative position evaluation value X3.

The coefficients a, b, c, and d in the above determination formula (1) are, for example, the driving conditions of the own vehicle 1 when the vehicle behind the own vehicle 1 drives or dangerous driving, and the driver of the own vehicle 1. , and a learning model (reckoning driving determination model) generated by machine learning using the driving behavior and the emotion that the driver of the vehicle behind due to the driving behavior receives as learning data. The leaning driving determination model may be installed in the own vehicle 1 in advance, or may be recorded in a server or the like accessible via mobile communication means. Thus, it is possible to generate a determination formula weighted by information that tends to give anxiety, anger, or stress to the driver of the vehicle behind, according to the driving situation.

The learning data for generating the above-mentioned rushing driving judgment model is, for example, the situation when the driver corresponding to the above-mentioned rear vehicle feels anxiety, anger or stress toward the front vehicle corresponding to the above-mentioned own vehicle 1. can be collected by declaring Each of the coefficients a, b, c, and d, which are set using the leaning driving judgment model, is determined depending on the driving scene, whether the vehicle 1 is likely to be tossed, or whether the driver of the vehicle behind is uneasy about the vehicle 1, The values obtained by multiplying the respective evaluation values X1, X2, X3, and X4 by coefficients a, b, c, and d are set to increase as the person easily feels angry or stressed. A large amount of learning data is required to generate the aggressive driving determination model. An example of the learning data collection method will be described below.

For example, when an unspecified driver feels uneasiness, anger, or stress toward the vehicle ahead while driving the vehicle, information on the driving behavior of the driver of the vehicle (corresponding to the vehicle behind) from the control device mounted on the vehicle. , the recording of information on the driving behavior of the driver of the preceding vehicle, information on the driving conditions of the own vehicle and the preceding vehicle, and information on the emotion of the driver of the own vehicle is started. These pieces of information may be, for example, data detected by the ambient environment sensor 31, the in-vehicle camera 33, the vehicle state sensor 35 mounted on the vehicle, and the biosensor 34, or data calculated based on these detection data. . The recording of various information may be started by the driver's operation, or may be automatically started when the control device detects the driver's anxiety, anger, or stress in the presence of a vehicle ahead.

After that, when the own vehicle stops, the control device displays an image of predetermined questionnaire items set in advance and prompts the driver to answer them. FIG. 4 shows an example of image display of questionnaire results. As exemplified in FIG. 4, for a scene in which the driver feels uneasy, angry or stressed about the vehicle in front, the driver is asked about "feeling", "degree of feeling", "driving situation", and "what would you like me to do". is entered. After the driver inputs the answers, the control device transmits the questionnaire result data to the server or the like via the mobile communication means. The transmission data may include all the recorded detection data of the vehicle state sensor 35 and the biosensor 34 or all the data calculated based on these detection data. information on the driving behavior of the driver (what kind of driving behavior was performed), information on the driving behavior of the driver of the preceding vehicle (what kind of driving behavior was performed), information on the driving situation of the own vehicle and the vehicle ahead (what kind of driving behavior driving situation) and information on the emotion of the driver of the vehicle (what kind of emotion it was).

The sent questionnaire result data is automatically or manually converted into learning data, and the learning data is machine-learned to generate a reckless driving determination model. In order to collect a large amount of learning data, a predetermined incentive may be given according to the transmission of questionnaire result data.

Note that the method of constructing the lean driving judgment model is not particularly limited, and for example, a well-known method such as a support vector machine, a neighborhood method, a neural network such as deep learning, or a Bayesian network can be appropriately adopted.

In step S13, after the determination formula (1) corresponding to the driving situation of the own vehicle 1 is generated, the acquisition unit 62 acquires determination information for determining whether or not the vehicle is in a state of being subject to the backlash driving (step S15). ). The determination information includes information on the running state of the own vehicle 1, information on the surrounding environment of the own vehicle 1, information on the running state of other vehicles, and information on the relative positions of the own vehicle 1 and the other vehicles. The acquisition unit 62 records the acquired various data in the operation database 71 as time-series data.

Specifically, the acquisition unit 62 acquires information on the running state of the own vehicle 1 based on the detection data transmitted from the vehicle state sensor 35 at predetermined calculation cycles. The information on the running state of the own vehicle 1 includes information on the operation state of the vehicle 1 such as the steering wheel or the steering angle of the steered wheels, the amount of accelerator operation, the amount of brake operation, and the operation state of the direction indicator, as well as vehicle speed, longitudinal acceleration, It includes vehicle behavior information such as lateral acceleration, longitudinal jerk and lateral jerk.

Further, the acquisition unit 62 acquires information on the surrounding environment of the own vehicle 1 based on the detection data transmitted from the surrounding environment sensor 31 at a predetermined calculation cycle. The information about the surrounding environment of the own vehicle 1 includes the shape of the curve of the road on which the vehicle 1 is traveling, the state of inclination, the driving lane, the width of the road, and the information of surrounding obstacles and other vehicles. The information on the surrounding environment of the own vehicle 1 may also include information on the speed limit of the road on which the vehicle 1 is traveling. The speed limit information may be detected based on image data transmitted from the surrounding environment sensor 31, may be obtained by road-to-vehicle communication or beacons, or may be position information on map data transmitted from the GPS sensor 37. can be determined based on

Based on the detection data transmitted from the ambient environment sensor 31, the acquisition unit 62 also acquires information on the running state of the other vehicle and information on the relative position between the own vehicle 1 and the other vehicle at a predetermined calculation cycle. The information on the running state of the other vehicle includes at least information on the behavior of the other vehicle such as the speed, longitudinal acceleration and lateral acceleration of the other vehicle. The information on the relative position between the own vehicle 1 and the other vehicle includes at least information on the relative speed of the other vehicle with respect to the own vehicle 1, the relative position of the other vehicle as seen from the own vehicle 1, and the distance between the own vehicle 1 and the other vehicle. including.

Next, the rushing driving determination unit 63 inputs the acquired determination information into the determination formula (1), and determines whether or not the driving evaluation value exceeds the determination threshold (step S17). If the driving evaluation value does not exceed the determination threshold value (S17/No), it is not determined that the host vehicle 1 is in a state of being subject to a rushing operation, and the routine of the determination processing is terminated as it is. On the other hand, if the driving evaluation value exceeds the determination threshold value (S17/Yes), the fanning driving determination unit 63 determines that the host vehicle 1 is in a state of being subject to fanning from the vehicle behind (step S18).

Next, the fueling driving determination unit 63 sets the fueling driving flag to various data acquired within a certain period of time before and after the time when it is determined that the fueling driving is in effect, and records it in the driving database 71 ( Step S19), the determination processing routine is terminated.

As described above, in the driving support device 50 according to the present embodiment, the rushing driving determination unit 63 includes information on the driving state of the vehicle 1, information on the driving state of the vehicle behind, and information on the relative positions of the vehicle 1 and the vehicle behind. Information and information on the driver's driving characteristics of the driver of the vehicle behind are acquired, and it is determined whether or not the own vehicle 1 is in a situation where the driver of the vehicle behind is in a situation where the vehicle 1 is subject to the influence of driving by the vehicle behind. For example, if the driver of the vehicle behind has a history of driving in the past, the determination threshold becomes small. Therefore, the more likely the driver of the vehicle behind is to drive while driving, the lower the judgment threshold, and the more likely it is to determine that the vehicle 1 is being driven by the vehicle behind.

Further, in the present embodiment, a determination is made that reflects the emotion evaluation value X4 of the driver of the vehicle behind. Therefore, it is determined whether or not the host vehicle 1 is in a state of being subjected to the backing driving from the rear vehicle according to the emotional state of the driver of the back vehicle together with the backing driving characteristics of the driver of the back vehicle. For example, when the driver of the vehicle behind feels angry, the driving evaluation value increases. Therefore, for example, the more angry the driver of the vehicle behind is, the larger the driving evaluation value becomes, making it easier to determine that the vehicle 1 is being driven by the vehicle behind.

Therefore, in the method for determining the tilting driving according to the present embodiment, it is possible to more appropriately determine whether or not the host vehicle 1 is subject to the tilting driving from the vehicle behind.

[Driving support processing operation]
Next, a series of processing operations by the driving assistance device 50 according to this embodiment will be described.
FIG. 5 shows a flowchart showing a main routine of processing operations executed by the control unit 51 .

First, when the in-vehicle system including the driving support device 50 is activated (step S21), the driver determination unit 61 of the control unit 51 executes processing for identifying the driver of the own vehicle 1 (step S23). For example, the driver determination unit 61 executes processing for recognizing the face of the driver sitting in the driver's seat based on the image data transmitted from the in-vehicle camera 33 . The driver determination unit 61 also extracts the feature amount of the recognized driver's face, and determines whether information on the driver matching the extracted feature amount is recorded in the driver database 77 . If the driver database 77 does not record driver information that matches the extracted feature amount, the driver determination unit 61 assigns identification information to each recognized driver, and records it in the driver database 77 together with the feature amount data. Along with this, the identification information is recorded in the storage unit 53 . On the other hand, when driver database 77 records driver information that matches the extracted feature amount, driver determination unit 61 records identification information identifying the detected driver in storage unit 53 .

Next, the feedback presentation unit 65 of the control unit 51 determines whether or not the driving of the own vehicle 1 has started (step S25). For example, after the in-vehicle system is activated, the feedback presenting unit 65 may change the position of the shift lever to the drive (D) range or set the target driving torque of the own vehicle 1 to a positive value exceeding zero. It is determined that driving of the own vehicle 1 has started. However, the method for determining whether or not the vehicle 1 has started to drive is not limited to the above example.

For ease of understanding, the case where the vehicle 1 has started to be driven will be described first, and the case where the vehicle 1 has not started to be driven will be described later.

When it is determined that the driving of the own vehicle 1 has started (S25/Yes), the feedback presentation unit 65 determines whether or not the driving of the own vehicle 1 has ended (step S27). For example, the feedback presentation unit 65 can be used when the position of the shift lever is switched to the parking (P) range, when the switch of the in-vehicle system is switched off, and when the own vehicle 1 reaches the set destination. , it is determined that the driving of the own vehicle 1 has ended. However, the method of determining whether or not the driving of the own vehicle 1 has ended is not limited to the above example.

If it is not determined that the driving of the own vehicle 1 has ended (S27/No), the control unit 51 executes the process during driving (step S29).

[Processing during operation]
FIG. 6 shows a flowchart of processing during operation.
In the process during driving, the fanning driving determination unit 63 executes the fanning driving determination process according to the processing procedure of the flowchart shown in FIG. 3 (step S41). Based on information on the running state of the own vehicle 1, information on the running state of the vehicle behind, information on the relative position between the own vehicle 1 and the vehicle behind, and information on the characteristics of the driver of the vehicle behind, the rushing driving determination unit 63 , it is determined whether or not the host vehicle 1 is in a situation where it is possible for the vehicle 1 to be subjected to a backlash driving.

After executing the fanning driving determination process in step S41, the feedback presenting unit 65 determines whether or not it is determined that the vehicle 1 is subject to fanning driving (step S43). If not determined (S43/No), the process returns to step S41 to repeatedly execute the fanning driving determination process.

On the other hand, when it is determined that the own vehicle 1 is in a state of being subject to a rushing drive (S43/Yes), the feedback presenting unit 65 indicates that the driver of the own vehicle 1 may receive a rushing drive from the vehicle behind. is executed (step S45). Specifically, the feedback presenting unit 65 drives the HMI 43 to notify the vehicle that there is a possibility of being subjected to a backlash driving by means of sound, voice and image display, or any one of them. At this time, it is sufficient to notify the driver of the possibility of the vehicle driving behind the vehicle, but it is also possible to notify the position of the vehicle behind and the current driving situation.

Then, the aggressive driving determination unit 63 determines whether or not the driver of the vehicle 1 has taken a driving action to avoid aggressive driving (step S47). For example, if the emotion evaluation value X4 calculated based on the emotion of the driver of the vehicle behind, which is acquired by the acquisition unit 62, falls below a preset threshold value, the aggressive driving determination unit 63 determines whether the subject vehicle 1 It may be determined that the driver has taken a driving action to avoid aggressive driving. On the other hand, if it is not determined that the driver of the vehicle 1 has performed a driving action to avoid the aggressive driving within a predetermined time set in advance, the aggressive driving determination unit 63 determines that the driver of the vehicle 1 is driving to avoid the aggressive driving. It may be determined that the action is not performed.

When it is determined that the driver of the own vehicle 1 has taken a driving action to avoid the aggressive driving (S47/Yes), the process proceeds to step S51 as it is, while it is judged that the driver of the own vehicle 1 has taken the driving action to avoid the aggressive driving. If not (S47/No), the feedback presenting unit 65 performs a process of correcting the driving behavior of the driver of the own vehicle 1 (step S49).

[Driving behavior correction processing]
FIG. 7 shows a flowchart of driving behavior correction processing.
The fanning driving determination unit 63 executes fanning driving determination processing according to the processing procedure of the flowchart shown in FIG. 3 (step S61). Based on information on the running state of the own vehicle 1, information on the running state of the vehicle behind, information on the relative position between the own vehicle 1 and the vehicle behind, and information on the characteristics of the driver of the vehicle behind, the rushing driving determination unit 63 , it is determined whether or not the host vehicle 1 is in a situation where it is possible for the vehicle 1 to be subjected to a backlash driving.

Next, the feedback presenting unit 65 determines whether or not it has been determined that the host vehicle 1 is in a state of being subjected to the aggressive driving as a result of the aggressive driving determination processing (step S63). If it is determined that the host vehicle 1 continues to be in a state of being subject to fanning driving (S63/Yes), the feedback presenting unit 65 instructs the driver of the host vehicle 1 to take a driving action (hereinafter referred to as " (also called "avoidance action") is issued (step S65). Specifically, the feedback presenting unit 65 sets the avoidance action based on the information on the running state of the own vehicle 1, the information on the running state of the vehicle behind, and the information on the surrounding environment of the own vehicle 1, drives the HMI 43, and The avoidance action is notified to the driver of the vehicle 1 .

For example, the feedback presentation unit 65 notifies the driver to accelerate the vehicle 1 when the vehicle speed of the vehicle 1 is significantly below the legal speed limit. Further, when the vehicle 1 is traveling on a road with two or more lanes in one direction and the adjacent lane is empty, the feedback presentation unit 65 notifies the driver to change the lane of the vehicle 1 . Further, the feedback presentation unit 65 notifies the driver to avoid the vehicle 1 in the space when there is enough space on the road shoulder. However, the content of the avoidance action to be notified is not limited to these examples, and is appropriately set according to the driving conditions of the own vehicle 1 . After the feedback presenting unit 65 issues the notification to instruct the avoidance action, the process returns to step S61, and the aggressive driving determination unit 63 repeatedly executes the aggressive driving determination process.

When it is not determined that the host vehicle 1 is in a state of being subjected to the fanning driving as a result of the fanning driving determination process (S63/No), the fanning driving determination unit 63 determines that the situation of the host vehicle 1 being subject to the fanning driving has been resolved. Determine (step S67). Next, the boosting driving determination unit 63 determines the driving state of the own vehicle 1 when it is determined that it is subject to the backing driving, information of the driving state of the vehicle behind, and information of the relative positions of the vehicle 1 and the vehicle behind. In association with this, the avoidance action data for which the host vehicle 1 is no longer judged to be in a situation of being subjected to aggressive driving is recorded in the aggressive driving example database 73 as the aggressive driving cancellation data (step S69), and the driving behavior correction processing is terminated. .

If it is determined that the driver of the own vehicle 1 has taken a driving action to avoid leaning (S47/Yes), or if the driving action correction processing in step S39 has been completed, the leaning driving factor determining section 64 of the control section 51 , the host vehicle 1 executes a process of determining the factors that induce the fanning of the vehicle (step S51). In the present embodiment, the fueling driving factor determining unit 64 compares the data regarding the driving state of the own vehicle 1 when it is determined that the vehicle 1 is in a state of fueling with the data of normal driving without the fueling. A process (first example) for determining the factor that induces the driving, data related to the driving behavior of the driver of the own vehicle 1 when it is determined that the driver is in a situation where the driving is driven, and the avoidance action is appropriately taken. and a process (second example) of determining a factor that induces rash driving by comparing the data of the driving behavior at the time of driving. However, either one of the first example and the second example may be configured to be executable.

[Determination process for factors in driving while driving]
FIG. 8 shows a flowchart of a first example of the leaning driving factor determination process.
First, the thrusting driving factor determining unit 64 refers to the driving database 71, and acquires information for determination when the thrusting driving determining unit 63 determines that the host vehicle 1 is in a state of being subject to thrusting driving (step S71). . Next, the tilting driving factor determination unit 64 refers to the normal driving database 75, and data of normal driving in the driving situation that belongs to the same classification as the driving situation when it is determined that the host vehicle 1 is subject to tilting driving. is extracted (step S73). The driving conditions belonging to the same category are, for example, data such as curve shape, inclination state, driving lane, road width, positions of surrounding obstacles, and the number and positions of vehicles behind the vehicle 1 on which the vehicle 1 is traveling. An appropriate number of set data may be common. Further, the driving conditions belonging to the same category may be the driving data of vehicles that have traveled the same section. If it is the data of the same section, the comparison result with the data of the own vehicle 1 can be obtained more correctly.

Further, the condition for determining that the driving situations belong to the same category may include the same driving area or driving time. The data to be extracted is the data to be compared with the data for the determination information, and may be an appropriate number of data selected from the information on the running state of the own vehicle 1. For example, the acceleration/deceleration per fixed time The amount of change, the amount of speed change, the amount of inter-vehicle distance change, etc. are included.

Next, the fanning driving factor determination unit 64 compares the data of the determination information with the extracted data of the normal driving database, and calculates the degree of divergence for each data (step S75). The degree of divergence is obtained, for example, as a ratio of the difference between the normal operation data and the target data of the determination information with respect to the value of the target data (normal operation data) in the normal operation database. Next, the fanning driving factor determination unit 64 stores the comparison result information between the determination information data and the extracted normal driving database data in the operation database 71 (step S79), and ends the fanning driving factor determination process.

FIG. 9 shows a flowchart of a second example of the leaning driving factor determination process.
First, in the same manner as in the process of step S71, the thrust driving factor determination unit 64 refers to the driving database 71, and makes a determination when the thrust driving determination unit 63 determines that the host vehicle 1 is in a state of being subjected to thrust driving. information is acquired (step S81). The determination information includes information that can identify the driving behavior taken by the driver of the vehicle 1 . The information for determination may include information on the line of sight of the driver detected and recorded by the in-vehicle camera 33 .

Next, the bias driving factor determination unit 64 refers to the bias driving example database 73, and the bias driving example in the driving situation that belongs to the same classification as the driving situation when it is determined that the own vehicle 1 is subject to bias driving. In step S83, the data of the avoidance action performed appropriately is extracted. The data of the avoidance action taken appropriately is the data of the driver's driving action taken when the situation in which the driver is forced to drive is quickly resolved, and includes the data recorded in step S69 described above.

Next, the aggressive driving factor determination unit 64 compares the data of the information for determination with the data of the avoidance behavior extracted from the database of instances of aggressive driving, and calculates the degree of divergence for each data (step S85). Next, the aggressive driving factor determination unit 64 stores the information of the comparison result between the data of the driving behavior of the determination information and the data of the avoidance behavior of the extracted aggressive driving example database in the driving database 71 (step S79). Terminate the cause determination process.

FIG. 10 shows an example of a comparison result between the data of the determination information and the extracted data of the normal operation database.
The example shown in FIG. 10 is the result of comparing the judgment information data and the data in the normal driving database in a driving situation in which the speed of the own vehicle 1 is significantly slower than the speed limit and the driver of the vehicle behind feels irritated. indicates In this example, the degree of deviation from the normal operation data of the amount of change in acceleration/deceleration per fixed time is 20%, and the degree of deviation from the normal operation data of the amount of speed change per fixed time is 40%. , the amount of change in the inter-vehicle distance between the own vehicle 1 and the preceding vehicle is 70%. In this case, the order of the degree of influence for inducing jerk driving is that the amount of change in the inter-vehicle distance between the vehicle 1 and the preceding vehicle per certain period of time has the highest degree of influence, followed by the amount of change in speed per certain period of time. Then, the degree of influence of the amount of change in acceleration/deceleration per fixed time is high. Note that the comparison data items shown in FIG. 10 are merely examples, and other data may be compared.

Returning to FIG. 5, while the host vehicle 1 is driving, the control unit 51 does not determine that the in-vehicle system has stopped in step S35, and unless it determines that the driving of the host vehicle 1 has ended in step S27. The above process during operation is repeatedly executed. As a result, it is possible to determine at an appropriate timing that the own vehicle 1 is in a situation where the vehicle 1 is subject to the backing driving from the rear vehicle in consideration of the backing driving characteristic of the driver of the rear vehicle while driving the own vehicle 1. Further, when it is determined that the own vehicle 1 is in a state of being subject to a backlash driving from a vehicle behind, the factors that induce the backlash driving can be determined and recorded.

On the other hand, when it is determined in step S27 that the driving of the own vehicle 1 has ended (S27/Yes), the feedback presenting unit 65 executes post-driving processing (step S31).

[Post-operation processing]
FIG. 11 shows a flowchart of post-operation processing.
First, the feedback presentation unit 65 refers to the operation database 71 (step S91). As described above, driving data associated with each driver is recorded in driving database 71 . The driver type of the driver of the own vehicle 1 is calculated based on the data regarding the driving behavior of the driver of the own vehicle 1 (step S93). The driver type is information on driving characteristics that classifies driving behaviors that the driver of the vehicle 1 is not good at, such as "not good at changing lanes", "not good at maintaining speed", or "not good at grasping an appropriate inter-vehicle distance". are classified into one or more of the items in Specifically, the feedback presenting unit 65 compares the data regarding the driving behavior of the driver of the own vehicle 1 with the driving data recorded in the normal driving database 75, which is not determined as driving that induces a rushing operation. Estimates the driving behavior that drivers are not good at. The feedback presentation unit 65 records the calculated driver type information in the storage unit 53 .

Next, the feedback presentation unit 65 determines whether or not there is a record in the data recorded in the driving database 71 that it is determined that the driver is in a state of being subject to a rushed driving during the period from the start of the current driving to the end of the driving. Determine (step S95). If there is no record indicating that the vehicle is in a situation where the driver is subject to fanning (S95/No), the feedback presenting unit 65 presents feedback information indicating that the situation of being subject to fanning has not occurred (step S99). For example, the feedback presentation unit 65 generates appropriate feedback screen data, drives the HMI 43, and displays the feedback screen as an image. The presentation of the feedback information is not limited to the method of displaying the feedback screen, but may be the method of audio output, or the method of image display and audio output may be used together.

On the other hand, if there is a record that it is determined that the driver is in a state of being subject to slamming driving (S95/Yes), the feedback presenting unit 65 performs a process of creating a driving improvement comment based on the data recorded in the driving database 71. (Step S97). The "driving improvement comment" is a comment for making the driver of the own vehicle 1 aware that he or she is doing driving behavior that induces reckless driving and prompting improvement. The feedback presenting unit 65 uses the factor determined by the aggressive driving factor determining unit 64, the vehicle behavior data recording the situation of the aggressive driving at that time, the driving action data, and the surrounding environment data to generate a "driving improvement comment ” is automatically generated. For example, the feedback presenting unit 65 automatically generates a "driving improvement comment" by extracting an appropriate comment according to the driving situation that caused the situation of being subjected to the temptation from the text data prepared in advance. Specifically, the feedback presenting unit 65 says, "You are taking △△ driving behavior in ○○ situation. If there is a following vehicle, there is a possibility that it will affect □□. Please be careful in the future. Let's try to drive." or the like is generated.

Note that the method of creating the driving improvement comment and the content of the driving improvement comment are not limited to the above examples, and any conventionally known method or content can be applied.

Next, the feedback presentation unit 65 presents feedback information using the created driving improvement comment (step S99). FIG. 12 shows an example of the layout of a feedback screen for notifying advice for avoiding reckless driving. In the example shown in FIG. 12, the feedback screen includes information on the "driver type" of the driver of the host vehicle 1, information on the "driving situation classification" in which the driver was in a situation of being subject to reckless driving, factors that induce reckless driving (" Causes”) and information on “advice (points for improvement)”.

The driver type is information on driving characteristics that classifies driving behaviors that the driver of the vehicle 1 is not good at, which is calculated and recorded in step S93. The information on the classification of the driving situation in which the driver is in a state of being subject to the backlash driving is the classification of the driving situation determined in step S73, and includes the behavior of the own vehicle 1, the driving behavior of the driver of the own vehicle 1, and the driving behavior of the driver of the own vehicle 1. classified based on information about the surrounding environment. The information on the factor that induces the fan driving is the information determined and recorded in step S51 described above. The advice information is the driving improvement comment information created in step S97. By including these pieces of information in the presented feedback information, the driver of the own vehicle 1 can know the driving behavior that the driver is not good at, and the situation and the cause of the fact that he/she is subjected to reckless driving that actually occurred. It is possible to easily understand what kind of driving action should be taken in the situation.

Further, in the example shown in FIG. 12, a "moving image" recording a driving scene judged to be a situation of being subjected to a rush is reproduced. 13 to 15 respectively show examples of reproduced moving image patterns. 13 to 15 are driving scenes corresponding to the example of the comparison result shown in FIG. This is an example of reproducing a moving picture pattern of a scene.

FIG. 13 shows an example of reproducing a moving image pattern of a driving scene viewed from the driver's point of view, FIG. 14 shows an example of reproducing a moving image pattern of a driving scene viewed from above, and FIG. This is an example of reproducing a displayed moving picture pattern. In both cases, when the moving image pattern is reproduced, an explanation is displayed indicating that the vehicle speed of the own vehicle 1 and the inter-vehicle distance to the preceding vehicle are increasing and the relative speed is increasing. Therefore, the driver can easily understand that the vehicle 1 is in a situation where the vehicle 1 is being driven while driving, and can obtain a sense of satisfaction with the presented advice.

The feedback presenting unit 65 may create each moving image pattern by animation, and creates from the image data of the front shooting cameras 31LF and 31RF, the rear shooting camera 31R, and cameras (not shown) provided on the left and right side mirrors. You may The displayed moving image pattern may be determined to be any one moving image pattern. For example, depending on the driving scene or the factor that induces reckless driving, the driving scene or the factor that induces reckless driving can be easily understood. may be selected to be

Furthermore, in the example shown in FIG. 12, the feedback screen includes "regional characteristic" information regarding the driving tendency in the travel area where the vehicle 1 has traveled, and "graph" information representing the regional characteristic. Information on regional characteristics and graph information displayed on the feedback screen will be described later in detail.

In this way, the feedback presentation unit 65 executes the post-driving process when the driving of the own vehicle 1 is finished. Returning to FIG. 5, after completion of the post-driving process, the feedback presentation unit 65 determines whether or not the in-vehicle system has stopped (step S45). When the in-vehicle system stops (S45/Yes), the control unit 51 terminates the processing operation. On the other hand, if the in-vehicle system has not stopped (S45/No), the process returns to step S35.

If it is not determined in step S35 that the vehicle 1 has started driving (S35/No), the feedback presenting unit 65 executes a pre-driving process (step S43).

[Pretreatment before starting operation]
FIG. 16 shows a flow chart of pre-start processing.
First, the feedback presentation unit 65 refers to the driving database 71 (step S101), and calculates the driver type of the driver of the vehicle 1 based on the data regarding the driving behavior of the driver of the vehicle 1, as in step S93. (S103).

Next, the feedback presentation unit 65 determines whether or not to execute processing for setting a travel route plan (step S105). For example, the setting information of the destination is acquired from the navigation system 40, and when the destination is set, the feedback presentation unit 65 determines to execute the process of setting the travel route plan. Furthermore, the condition may be that the driver or the like inputs that the processing for setting the travel route plan is to be executed.

If it is determined to execute the process of setting the travel route plan (S105/Yes), the feedback presentation unit 65 acquires travel route setting information from the navigation system 40 (step S107). The travel route information includes information such as the starting point or current position, waypoints, destination, departure time, and the like.

Next, the feedback presenting unit 65 sets a passenger who can be set as a driver who drives the own vehicle 1 (step S109). For example, the feedback presenting unit 65 recognizes the occupants of the own vehicle 1 based on the image data transmitted from the in-vehicle camera 33, and sets the occupants who can be set as drivers. For example, the feedback presenting unit 65 may set all occupants whose travel data have been recorded as drivers in the past as occupants who can be set as drivers, and displays the recognized occupants on the HMI 43 and selects them by the occupants. The passenger may be set as a passenger who can be set as a driver. However, the method of setting passengers who can be set as drivers is not limited to the above example.

Next, the feedback presenting unit 65 refers to the bias driving example database 73 and extracts data of bias driving events related to the travel route (step S111). Specifically, the feedback presenting unit 65 determines the frequency of occurrence of the data on the driving event that occurred in the past on the travel route from the departure point to the destination via the stopover included in the information on the travel route acquired in step S107. Extract the data of the driving scene of the driving event with high swaying. At this time, the current time and the scheduled arrival time at the destination may be taken into consideration to extract the data of the rushing driving event that overlaps the travel time zone.

Next, the feedback presenting unit 65 generates a regional characteristic comment based on the extracted data of the rushing driving event (step S113). The “regional characteristic comment” is information generated for notifying the driver of driving characteristics such as traffic manners and driving habits peculiar to the driving area including the driving route of the own vehicle 1 . Specifically, drivers have different driving characteristics due to cultural backgrounds, racial backgrounds, and the like in each region. Regional characteristics include, for example, many sudden lane changes, a slow average cruising speed, and a wide inter-vehicle distance. Therefore, there is a risk that each driver will be subjected to harassing driving from other vehicles by performing normal driving behavior in an inexperienced area. Therefore, the feedback presenting unit 65 generates a regional characteristic comment based on the extracted data of the rushing driving event.

The regional characteristic comment is created according to the method of creating the "driving improvement comment" described above. For example, the data of the driving behavior that should be taken (or should have been taken) in what kind of driving scene, at what timing, and what kind of driving action should be taken. It is automatically generated by performing a mapping process on the text data of the output comment stored in the storage unit 53 in advance. In addition, in the present embodiment, the feedback presenting unit 65 generates a graph representing data related to the driving characteristics of the travel area together with the regional characteristics comment.

17-18 show examples of graphs each representing data relating to driving characteristics of a driving area. FIG. 17 is data on the frequency of occurrence of dangerous driving in the travel area, and is a graph showing the number of occurrences of dangerous driving for each time of occurrence. FIG. 18 is data of the average cruising speed in the travel area, and is a graph showing the average cruising speed for each passage time zone. However, the data related to the driving characteristics of the travel area are not limited to the above examples.

Next, the feedback presentation unit 65 creates and outputs a drive plan for the destination (step S115). Specifically, depending on the road shape of the travel route and regional characteristics, the possibility that the host vehicle 1 will be subject to slamming operation differs depending on the driver's driving characteristics. For this reason, the feedback presentation unit 65 analyzes the driver type of each passenger who can be set as a driver, and minimizes the risk of being subjected to rush driving according to the road shape and regional characteristics of the travel route from the current location to the destination. Create a drive plan so that More specifically, the feedback presenting unit 65 creates a plan for changing drivers and a plan for rest points for changing drivers so that the risk of being subjected to rush driving is minimized.

For example, the feedback presentation unit 65 outputs the created drive plan to the navigation system 40 . Thereby, in the navigation system 40, a rest point (drop-off point) is set in the middle of the travel route to the destination. However, the created drive plan may not be reflected in the settings of the navigation system 40, and may be displayed on the HMI 43, smart phone, or the like.

Next, the feedback presenting unit 65 performs a process of creating driving improvement comments (step S127). The "driving improvement comment" created here is a comment presented as feedback information together with the regional characteristic comment. The comment may be a comment for advising what kind of driving action should be taken at such timing. The process of creating the driving improvement comment may be the same as the process of creating the driving improvement comment in step S97 described above, except that it is presented together with the regional characteristic comment.

Next, the feedback presentation unit 65 presents feedback information using the created regional characteristic comments and driving improvement comments (step S129). Specifically, the feedback presenting unit 65 may present the feedback information using the feedback screen illustrated in FIG. 12 . In this case, the advice (improvements) information includes the driving improvement comment created in step S127. In addition, the regional characteristic information includes the regional characteristic comment created in step 113 . The regional characteristic information may include map information indicating the corresponding region together with the regional characteristic comment.

Further, as the graph information, for example, the graphs shown in FIGS. 17 and 18 are displayed. By displaying data on the frequency of occurrence of aggressive driving shown in FIG. 17, it is possible to raise awareness to be more cautious when driving the vehicle during times when the frequency of aggressive driving is high. . In addition, it is possible to prompt the driver to set a drive plan that allows him or her to drive in areas where the frequency of occurrence of rush driving is low. In addition, by displaying the average cruising speed data shown in FIG. 18, the driver can know the approximate vehicle speed in the driving area, and can drive in accordance with the flow of traffic. In addition, drivers who are not good at driving at high vehicle speeds can be encouraged to set a drive plan that avoids times when the average cruising speed is high. In addition, by displaying graphs showing various data, it is possible to make it difficult for the driver to drive the vehicle in such a manner as to induce the driver to rush.

On the other hand, if it is not determined in step S105 that the process of setting the travel route plan is to be executed (S105/No), the feedback presenting unit 65 presents feedback information to the driver of the vehicle 1 before starting driving. It is determined whether or not (step S121). For example, when the driver performs an operation input requesting presentation of feedback information, the feedback presentation unit 65 determines to present feedback information. If it is set in advance to present the feedback information when the travel route plan is not set, the determination process of step S121 may be omitted.

If it is determined not to present the feedback information (S121/No), the feedback presentation unit 65 terminates the pre-operation processing. On the other hand, if it is determined to present the feedback information (S121/Yes), the feedback presenting unit 65 selects a learning scene to be used for presenting the feedback information (step S123). The feedback presenting unit 65 presents advice on driving that the driver is not good at, depending on the driver type items such as "not good at changing lanes", "not good at maintaining speed", or "not good at grasping an appropriate inter-vehicle distance". You may choose a learning scene to do. Alternatively, the feedback presenting section 65 may display items of learning scenes and select a number of frames selected by the driver, or may randomly select a learning scene.

Next, the feedback presenting unit 65 refers to the aggressive driving case database 73 and extracts the data of the aggressive driving event corresponding to the selected learning scene (step S125). Specifically, at this time, the feedback presenting unit 65 may extract data of a rushing driving event in which the travel time zones and travel areas overlap in consideration of the current time and current location.

Next, the feedback presenting unit 65 performs a process of creating driving improvement comments (step S127). The "driving improvement comment" created here is used to advise drivers who wish to improve their driving behavior about what kind of driving behavior they should take when they are not good at driving. It can be a comment. The process of creating the driving improvement comment may be the same as the process of creating the driving improvement comment in step S97.

Next, the feedback presentation unit 65 presents feedback information using the created driving improvement comment (step S129). Specifically, the feedback presenting unit 65 may present the feedback information using the feedback screen illustrated in FIG. 12 . In this case, the advice (improvements) information includes the driving improvement comment created in step S127. In addition, information about the current location of the vehicle 1 or information about an arbitrarily selected travel area such as an area where the driver frequently travels may be displayed in the area characteristic information and the graph information.

In this manner, the feedback presentation unit 65 executes the pre-driving process when the own vehicle 1 has not yet started driving. Returning to FIG. 5, after the pre-operation processing is completed, the feedback presentation unit 65 determines whether or not the in-vehicle system has stopped (step S45). When the in-vehicle system stops (S45/Yes), the control unit 51 terminates the processing operation. On the other hand, if the in-vehicle system is not stopped (S45/No), the process returns to step S35 and executes the processing of each step described above.

In this way, the driving assistance device 50 according to the present embodiment classifies the information about the driving state of the vehicle 1 when it is determined that the vehicle 1 is in a state of being subject to the backlash driving in the same classification as the driving state at that time. by comparing accumulated data of driving conditions of a plurality of vehicles in the driving conditions belonging to the above, and determining the factor that induces the backlash driving. Further, the driving support device 50 presents feedback information regarding the driving behavior of the driver of the own vehicle 1 based on the determined factors.

Data relating to the running state of the own vehicle 1 when it is determined that the vehicle is in a state of being subject to aggressive driving is compared with data of normal driving that is not subject to aggressive driving, and a process of determining a factor that induces aggressive driving is executed. Thus, based on the data of the difference between the driving behavior of the driver of the own vehicle 1 and the driving behavior of the other drivers, it is possible to present a way of driving to avoid being hit by the rear vehicle.

For example, drivers tend to drive at a speed that they think is appropriate even if the distance between the vehicle and the vehicle in front is large, in an area with regional characteristics such as ``high traffic volume and relatively short distance between vehicles''. When driving, the comment "This is an area where there is a high tendency to follow the vehicle ahead" in the "regional characteristics" item, and the advice item is "Drive with an awareness of increasing the vehicle speed more than usual." Or, be conscious of driving in the left lane as much as possible."

Alternatively, drivers who tend to drive at speeds close to other vehicles, including vehicles in adjacent lanes, in areas with regional characteristics that tend to have a large speed difference with adjacent lanes on roads with two or more lanes in each direction. When driving, the item of "regional characteristics" includes the comment "This is an area where the difference between the speed of the adjacent lane and the vehicle tends to be large", and the item of advice is "Please accelerate sufficiently when overtaking". comments are included.

Therefore, even if the driver is traveling in an area different from the area in which he or she usually travels, the feedback information is presented based on factors that induce aggressive driving peculiar to the driving area, and the driver is subject to aggressive driving from other vehicles. You can drive so as not to become

In addition, the driver of the vehicle 1 compares the data on the driving behavior of the driver of the own vehicle 1 when it is determined that he/she is in a situation where he/she is in a situation where he/she is in a situation where he/she is in a situation where he or she is in a situation where he or she is in a situation where he or she is in a situation where he/she is in a situation where he or she is in a situation where he/she is in a situation where he/she is in a situation where he/she is in a situation where he or she is in a situation where he or she is in a situation where he or she is in a situation where he or she is in a situation where he or she is in a situation where he or she is in a situation where he or she is in a situation where he/she is in a situation where he/she is in a situation where he/she is in a situation where he/she is in a situation where he or she is driving. By executing the process of determining factors that cause the driver of the vehicle 1 to avoid or avoid the aggressive driving based on the data of the difference between the driving behavior of the driver of the own vehicle 1 and the appropriately performed avoidance behavior. It is possible to present how to drive to prevent accidents.

For example, in an area with a regional characteristic of "many single-lane roads and few overtaking points", if the appropriate avoidance behavior is "turn on the hazard lights, pull the vehicle over to the side of the road and slow down", then the "regional characteristic" In the item of , there is a comment that "This is an area with few overtaking lanes", and in the item of advice, "A vehicle behind you is approaching. Encourage the vehicle to overtake."

Alternatively, in areas with regional characteristics where there are many lanes and lanes may be changed from adjacent lanes on the left and right, an appropriate avoidance action is to keep the distance between the vehicle and the vehicle in front more than double the normal distance between vehicles. In this case, the comment "This is an area where lane changes occur frequently" is written in the "Regional characteristics" item, and the advice item is "Keep a large distance between you and the vehicle in front so that other vehicles are in your own lane." Let's run so that it is easy to change to ".

Therefore, even when the driver is traveling in an area different from the area in which he or she usually travels, feedback information regarding avoidance behavior specific to that driving area is presented, and if the vehicle is forced to drive, the driver can quickly respond. It is possible to drive so as to avoid the driving situation.

As described above, the driving support device 50 according to the present embodiment provides information about the driving state of the own vehicle 1 when the own vehicle 1 is in a situation where the own vehicle 1 is subjected to the backlash driving by another vehicle, and the driving conditions belonging to the same classification. , to determine the factors that induce the driver to drive the subject vehicle 1, and to present feedback information regarding the driving behavior of the driver of the own vehicle 1. As a result, it is possible to present appropriate advice suited to the driving characteristics of the driver of the own vehicle 1, and to effectively acquire driving skills capable of avoiding rash driving.

In addition, the driving support device 50 according to the present embodiment presents advice on driving behavior for avoiding a situation in which the driver is subject to aggressive driving, and advice on driving behavior for preventing a situation in which the driver is subject to aggressive driving. can be done. As a result, it is possible to present appropriate advice that does not induce aggressive driving in accordance with the driving characteristics of the driver of the own vehicle 1 .

In addition, the driving support device 50 according to the present embodiment presents feedback information together with image data around the time when it is determined that the host vehicle 1 is in a state of being subject to aggressive driving. At this time, the driving support device 50 can switch the moving image pattern according to the factor that induces the aggressive driving and the driving situation, thereby making it possible for the driver to more effectively understand the factor that induces the aggressive driving.

In addition, the driving support device 50 according to the present embodiment presents feedback information when the driver is in a situation where he/she is subject to a rush of driving during the current driving after the driver has finished driving. Therefore, the advice is promptly presented to the driver after the situation in which the driver is forced to drive, so that the driver can be prevented from repeating the driving that induces the aggressive driving.

Further, when the destination is set in the navigation system 40, the driving support device 50 according to the present embodiment creates a drive plan so that the host vehicle 1 does not suffer a rushed drive until reaching the destination. configured to be created. As a result, it is possible to change drivers or create a travel route plan so that each passenger does not drive in a way that they are not good at, thus minimizing the risk of the own vehicle 1 being hit. .

Note that the processing of steps S121 to S129 described above may be executed upon receipt of an instruction signal input from another external device capable of communicating with the driving assistance device 50 via wireless communication means such as mobile communication means. good. As a result, it can be used as a learning device for improving driving skills without getting on the vehicle.

Although the preferred embodiments of the present disclosure have been described in detail above with reference to the accompanying drawings, the present disclosure is not limited to such examples. It is obvious that a person having ordinary knowledge in the technical field to which the present disclosure belongs can conceive of various modifications or modifications within the scope of the technical idea described in the claims. , are also understood to belong to the technical scope of the present disclosure.

For example, in the above embodiment, all the functions of the driving assistance device are installed in the vehicle, but the present disclosure is not limited to such an example. For example, even if some of the functions of the driving support device are provided in a server device that can communicate via mobile communication means, and the driving support device is configured to transmit and receive data to and from the server device. good. Furthermore, the driving support device may be a server device capable of communicating with an in-vehicle vehicle control device, HMI, or head-mounted display.

Further, in the above-described embodiment, the result of determining whether or not the subject vehicle 1 is in a state of being subjected to the distraction driving from the other vehicle reflecting the distraction driving characteristics of the driver of the other vehicle is used to determine the , the feedback information is presented after the end of driving, but the technology of the present disclosure is not limited to this example. For example, it may be used as a determination means for switching to automatic driving when the driving of the own vehicle 1 gives stress to the driver of the other vehicle based on the result of the determination of the driving of the own vehicle 1. Driving conditions that do not stress the driver may be learned and used as setting criteria such as vehicle speed during automatic driving control.

In addition, the following aspects also belong to the technical scope of the present disclosure.
In the driving support device according to the above embodiment, the information on the characteristics of the driver of the other vehicle is the history of the driver of the other vehicle, the history of the driver driving in the past, the result of the questionnaire about the driver's own driving behavior, the acceleration/deceleration operation, and the steering operation. , passing behavior, or high beam behavior.
In the driving support device according to the above-described embodiment, the processor obtains information on the speed of the own vehicle, information on the speed of other vehicles, information on the relative position between the own vehicle and the other vehicle, and information on the emotion or biological information of the other vehicle. A driving evaluation value is calculated based on the information, a judgment threshold is calculated by correcting a preset judgment reference threshold value based on the driving characteristics of the driver of the other vehicle, and if the driving evaluation value exceeds the judgment threshold, the judgment threshold is A driving support device that determines that it is in a situation to receive driving.
In a driving support device that determines the risk of harassing the own vehicle by another vehicle traveling around the own vehicle, information on the driving state of the own vehicle, information on the driving state of the other vehicle, the own vehicle and the other vehicle and an acquisition unit that acquires information on the relative position of the driver of the other vehicle and information on the characteristics of the driver of the other vehicle, and based on the acquired information, determines whether or not the own vehicle is in a situation where the other vehicle is subject to the influence of driving. A driving support device comprising: a rushing driving determination unit;
- A recording medium recording a computer program applied to a driving support device that determines the risk of harassing the own vehicle by other vehicles traveling around the own vehicle, wherein one or more processors store the Acquiring information on the running state of the vehicle, information on the running state of other vehicles, information on the relative position between the own vehicle and the other vehicle, and information on the driver's rushing driving characteristics of the other vehicle, and based on the acquired information and determining whether or not the host vehicle is subject to a rushing drive from another vehicle.

1: vehicle (own vehicle), 31: ambient environment sensor, 33: in-vehicle camera, 34: biological sensor, 35: vehicle state sensor, 37: GPS sensor, 39: vehicle-to-vehicle communication unit, 40: navigation system, 50: Driving support device, 51: control unit, 53: storage unit, 61: driver determination unit, 62: acquisition unit, 63: inflection driving determination unit, 64: inflection driving factor determination unit, 65: feedback presentation unit, 71: driving database , 73: driving example database, 75: normal driving database, 77: driver database

Claims (5)

In a driving support device that determines the danger of harassing the own vehicle by other vehicles traveling around the own vehicle,
one or more processors and one or more memories communicatively connected to the one or more processors,
The processor
Information about the running state of the own vehicle;
Information on the running state of the other vehicle;
Information on the relative positions of the own vehicle and the other vehicle;
Acquiring information on the driver's rushing driving characteristics of the other vehicle,
A driving support device that executes a process including determining whether or not the own vehicle is subject to a driving force from the other vehicle based on the acquired information. The processor
Acquiring identification information for identifying the driver of the other vehicle;
2. The driving support system according to claim 1, which acquires information on the rushing driving characteristic of the driver of the other vehicle corresponding to the acquired identification information. The processor
2. The method according to claim 1, further comprising obtaining information on the emotion or the biometric information of the driver of the other vehicle, and determining whether or not the vehicle is in a state of being subjected to the tempting driving based on the information on the emotion or the biometric information of the driver of the other vehicle. driving assistance device. The processor
Information on the driving behavior of multiple drivers,
information on the driving behavior of the driver of the preceding vehicle traveling in front of the vehicle driven by the driver;
information on the driving conditions of the vehicle driven by the driver and the preceding vehicle; and information on the emotion received by the driver due to the driving behavior of the driver of the preceding vehicle;
2. The driving support device according to claim 1, wherein the acquired information is input to a tilting driving determination model generated by machine learning using learning data containing A computer program applied to a driving support device for determining the danger of harassing the own vehicle by other vehicles traveling around the own vehicle,
to one or more processors,
information on the running state of the own vehicle;
information on the running state of the other vehicle;
Acquiring information on the relative positions of the own vehicle and the other vehicle, and information on the characteristics of the driver of the other vehicle, and
Determining whether or not the own vehicle is subject to the driving of the other vehicle based on the acquired information;
A computer program that causes a process to be performed, including

Images