WO2015008418A1 - Information provision device for vehicle - Google Patents

Information provision device for vehicle Download PDF

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Publication number
WO2015008418A1
WO2015008418A1 PCT/JP2014/002996 JP2014002996W WO2015008418A1 WO 2015008418 A1 WO2015008418 A1 WO 2015008418A1 JP 2014002996 W JP2014002996 W JP 2014002996W WO 2015008418 A1 WO2015008418 A1 WO 2015008418A1
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WO
WIPO (PCT)
Prior art keywords
traveling state
distribution
unit
driving
traveling
Prior art date
Application number
PCT/JP2014/002996
Other languages
French (fr)
Japanese (ja)
Inventor
近藤　崇之
Original Assignee
日産自動車株式会社
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Filing date
Publication date
Application filed by 日産自動車株式会社 filed Critical 日産自動車株式会社
Priority to JP2015527151A priority Critical patent/JP6008049B2/en
Publication of WO2015008418A1 publication Critical patent/WO2015008418A1/en

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
    • A61B5/18—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state for vehicle drivers or machine operators
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60K—ARRANGEMENT OR MOUNTING OF PROPULSION UNITS OR OF TRANSMISSIONS IN VEHICLES; ARRANGEMENT OR MOUNTING OF PLURAL DIVERSE PRIME-MOVERS IN VEHICLES; AUXILIARY DRIVES FOR VEHICLES; INSTRUMENTATION OR DASHBOARDS FOR VEHICLES; ARRANGEMENTS IN CONNECTION WITH COOLING, AIR INTAKE, GAS EXHAUST OR FUEL SUPPLY OF PROPULSION UNITS IN VEHICLES
    • B60K28/00—Safety devices for propulsion-unit control, specially adapted for, or arranged in, vehicles, e.g. preventing fuel supply or ignition in the event of potentially dangerous conditions
    • B60K28/02—Safety devices for propulsion-unit control, specially adapted for, or arranged in, vehicles, e.g. preventing fuel supply or ignition in the event of potentially dangerous conditions responsive to conditions relating to the driver
    • B60K28/06—Safety devices for propulsion-unit control, specially adapted for, or arranged in, vehicles, e.g. preventing fuel supply or ignition in the event of potentially dangerous conditions responsive to conditions relating to the driver responsive to incapacity of driver
    • B60K28/066—Safety devices for propulsion-unit control, specially adapted for, or arranged in, vehicles, e.g. preventing fuel supply or ignition in the event of potentially dangerous conditions responsive to conditions relating to the driver responsive to incapacity of driver actuating a signalling device
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
    • B60W40/09—Driving style or behaviour
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W50/08—Interaction between the driver and the control system
    • B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
    • A61B5/0816—Measuring devices for examining respiratory frequency
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
    • A61B5/087—Measuring breath flow
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
    • A61B5/091—Measuring volume of inspired or expired gases, e.g. to determine lung capacity
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
    • B60W2040/0818—Inactivity or incapacity of driver
    • B60W2040/0827—Inactivity or incapacity of driver due to sleepiness
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/16—Anti-collision systems
    • G08G1/167—Driving aids for lane monitoring, lane changing, e.g. blind spot detection

Definitions

  • the present invention relates to an information providing apparatus for a vehicle.
  • Patent Document 1 Conventionally, as an information providing apparatus for vehicles, there is a technique described in Patent Document 1, for example.
  • the unstable state of driving can be accurately detected regardless of the difference in traffic environment.
  • the steering characteristics of the driver are statistically learned, and the driving state of the driver is estimated based on the learned steering characteristics. Therefore, for example, if the learning of the steering characteristics, that is, the calculation of the traveling state distribution is not properly performed, there is a possibility that the detection accuracy of the driving state of the driver may be reduced.
  • the present invention focuses on the above-described points, and an object thereof is to improve the estimation accuracy of the driving state.
  • a run state distribution calculation part computes a plurality of run state distributions from which a temporal range differs based on run state data. Subsequently, the driving state of the driver is estimated based on the calculated plurality of traveling state distributions. At that time, the traveling state distribution calculated by the traveling state distribution calculating unit is changed based on the information on the traveling of the vehicle.
  • the traveling state distribution calculated by the traveling state distribution calculating unit is changed based on the information related to the traveling of the vehicle.
  • the traveling state distribution can be calculated more appropriately, and the estimation accuracy of the driving state can be improved.
  • FIG. 6 is a block diagram showing the configuration of a traveling state distribution calculation unit 130. It is a flowchart showing driving instability degree determination processing. It is a figure for demonstrating an example of information presentation ON. It is a flowchart showing the example of calculation of the 1st traveling state distribution and the 2nd traveling state distribution. It is a figure for demonstrating the symbol used for relative entropy RHp calculation. It is a figure for demonstrating the calculation method of 1st driving
  • FIG. 1 is a diagram showing the configuration of a vehicle equipped with the information providing apparatus for vehicles according to the present embodiment.
  • the vehicle includes an accelerator pedal opening degree sensor 1, a brake pedal operation amount sensor 2, a steering angle sensor 3, a wheel speed sensor 4, a turn signal detection sensor 5, and a navigation device 6.
  • the vehicle also includes a G sensor 7, a shift sensor 8, a forward vehicle detection device 9, and a controller 100.
  • the accelerator pedal opening degree sensor 1 detects the opening degree of the accelerator pedal. Then, the accelerator pedal opening degree sensor 1 outputs the detected opening degree to the controller 100.
  • the brake pedal operation amount sensor 2 detects the amount of operation of the brake pedal. Then, the brake pedal operation amount sensor 2 outputs the detected operation amount to the controller 100.
  • the steering angle sensor 3 detects a steering angle of a steering wheel (not shown). Then, the steering angle sensor 3 outputs the detected steering angle to the controller 100.
  • the steering angle sensor 3 for example, an angle sensor that detects the rotation angle of the steering column can be employed.
  • the wheel speed sensor 4 detects the number of revolutions of the wheel (hereinafter also referred to as “wheel speed”). Subsequently, the wheel speed sensor 4 calculates the vehicle speed based on the detected wheel speed. Then, the wheel speed sensor 4 outputs each of the detected wheel speed and the calculated vehicle speed to the controller 100.
  • the winker detection sensor 5 detects an operation state (hereinafter also referred to as “winker operation”) of a winker lever (not shown). As the winker operation, for example, there is the presence or absence of the operation. Then, the blinker detection sensor 5 outputs the detected blinker operation to the controller 100.
  • the shift sensor 8 detects an operation state (hereinafter also referred to as "shift operation") of a shift lever (not shown).
  • shift operation for example, there are positions of shift levers such as P, D, R and the like.
  • the shift sensor 8 outputs the detected shift operation to the controller 100.
  • the information presentation apparatus presents an alarm and other information to the driver in accordance with a control signal (described later) output from the controller 100.
  • a presentation method there are sounds and images.
  • a speaker 10 that provides information to the driver by buzzer sound or voice
  • a display unit that provides information to the driver by displaying an image or text can be adopted.
  • a display unit for example, a display monitor of the navigation device 6 may be diverted.
  • the navigation device 6 includes a GPS (Global Positioning System) receiver, a map database, and a display monitor. Then, the navigation device 6 acquires the current position and road information of the vehicle from the GPS receiver and the map database. Subsequently, the navigation device 6 acquires various types of information such as the type of the road on which the vehicle travels and the road width based on the acquired current position of the vehicle and the road information. Subsequently, the navigation device 6 displays the result of the route search and the result of the route guidance on the display monitor based on the acquired information.
  • the G sensor 7 detects longitudinal acceleration and lateral acceleration generated in the vehicle. Then, the G sensor 7 outputs the detected longitudinal acceleration and lateral acceleration to the controller 100.
  • the forward vehicle detection device 9 detects information (for example, the distance to an obstacle) of another vehicle or other obstacle present ahead of the traveling direction of the vehicle. Then, the forward vehicle detection device 9 outputs the detected information to the controller 100.
  • the front vehicle detection device 9 for example, a laser range finder that emits laser light in the forward direction of travel of the vehicle and detects reflected light can be adopted.
  • the controller 100 includes CPU (central processing unit), and CPU peripheral components such as a read only memory (ROM), a random access memory (RAM), and an analog to digital (A / D) conversion circuit. Then, the controller 100 (CPU, CPU peripheral components) includes the driving support unit 100A that performs driving instability determination processing.
  • the driving support unit 100A determines the operation state of the driving operator that can be operated by the driver based on the detection result output from the accelerator pedal opening amount sensor 1 and the brake pedal operation amount sensor 2 or the like. And traveling condition data including at least one of the vehicle condition and the vehicle condition is acquired. Examples of the driver include a steering wheel, an accelerator pedal, and a brake pedal.
  • As the vehicle state there is inter-vehicle information for a preceding vehicle. In the present embodiment, information on the steering angle output from the steering angle sensor 3 (hereinafter also referred to as “steering angle information”) is adopted as the traveling state data.
  • the driving support unit 100A generates a plurality of traveling state distributions (first traveling state distribution (described later), second traveling state distribution (described later) based on the acquired traveling state data (steering angle information). Calculate). Subsequently, the driving support unit 100A drives the driver based on the difference amount (relative entropy RHp (described later)) among the calculated distributions of the traveling states (the first traveling state distribution and the second traveling state distribution). Estimate the state (the driving instability (described later)).
  • the driving support unit 100A outputs, to the information presenting apparatus, a control signal for causing the driver to present an alarm or other information (hereinafter also referred to as "presentation information") based on the estimated driving state (the driving instability). Do.
  • the driving support unit 100A presents presentation information to the driver and draws attention to the instability of driving (the unstable state of driving).
  • inter-vehicle information (inter-vehicle distance, inter-vehicle time) for the preceding vehicle, acceleration / deceleration information based on the operation of an accelerator pedal or a brake pedal, or the like may be adopted.
  • inter-vehicle information inter-vehicle distance, inter-vehicle time
  • acceleration / deceleration information etc.
  • calculation of the traveling state distribution first traveling state distribution, second traveling state distribution
  • the difference between the distributions (relative entropy RHp)
  • it may be calculated by a known method as described in the publication of International Publication No. WO 2009/013815 (Japanese Patent Application No. 2009-524342).
  • FIG. 2 is a block diagram showing an example of a system configuration of the information providing apparatus for a vehicle according to the present embodiment.
  • a visual information presentation device and an auditory information presentation device are illustrated as the information presentation device.
  • the display monitor of the navigation apparatus 6 and the speaker 10 are illustrated as an auditory information presentation apparatus.
  • FIG. 3 is a block diagram showing the configuration of the driving support unit 100A of the present embodiment.
  • the driving support unit 100A includes a driving condition data acquisition unit 110, a driving condition determination unit 120, a driving condition distribution calculation unit 130, a driving instability determination unit 140, and an information presenting unit 150.
  • the traveling state data acquisition unit 110 acquires the detection result output from the steering angle sensor 3. Then, the traveling state data acquisition unit 110 sets the acquired detection result as traveling state data.
  • the driving condition determination unit 120 detects the current driving condition of the vehicle (disturbing driving condition (described later), normal driving condition (described later) based on the detection results output from the accelerator pedal opening amount sensor 1 and the brake pedal operation amount sensor 2 and the like. )). Specifically, based on the detection results output from the accelerator pedal opening degree sensor 1 and the brake pedal operation amount sensor 2 and the like, the driving condition determination unit 120 operates the operating state and travel of the driving operator that can be operated by the driver. Detect the environment. Subsequently, the driving condition determination unit 120 determines whether the detected operation state of the driving operator is the setting operation state or whether the traveling environment is the set traveling environment.
  • the setting operation state includes, for example, curve operation, lane change operation, acceleration / deceleration of vehicle, operation of accelerator pedal (not shown), operation of brake pedal (not shown), operation of blinker, operation of navigation device 6, and audio device There is an operation (not shown).
  • the curve operation is detected based on the detection result of the steering angle sensor 3, for example. Specifically, it is determined that there is a curve operation when the state of the set steering angle or more continues for the set time or more.
  • the lane change operation is detected based on, for example, a blinker operation, a lane position, a vehicle position, and a traveling direction of the vehicle.
  • the acceleration / deceleration of the vehicle is detected based on the detection result of the G sensor 7, for example.
  • the operation of the accelerator pedal is detected based on the detection result of the accelerator pedal opening amount sensor 1, for example.
  • the operation of the brake pedal is detected based on the detection result of the brake pedal operation amount sensor 2, for example.
  • the blinker operation is detected based on the detection result of the blinker detection sensor 5.
  • the operation of the navigation device 6 is detected based on the signal from the navigation device 6.
  • the operation of the audio device (not shown) is detected based on the signal from the audio device (not shown).
  • the set traveling environment for example, there are irregular road, undulation, tunnel, minute junction, toll plaza, and slope.
  • the irregular road and the undulation are detected based on the detection result of the wheel speed sensor 4, for example.
  • the irregular road calculates the difference between the current value of the wheel speed measured every set time and the previous value, and determines that there is an irregular road when the calculated difference is equal to or greater than the set threshold.
  • the tunnel, the merge and the tollgate are detected based on the signal from the navigation device 6.
  • the slope is detected based on the detection result of the G sensor 7.
  • the driving state determination unit 120 determines that the operation state of the driving operator is the setting operation state, or when it is determined that the traveling environment is the set traveling environment.
  • the driving situation determination unit 120 determines the driving instability.
  • it is determined that the driving condition is a disturbance (hereinafter also referred to as “disturbing driving condition").
  • disisturbing driving condition a disturbance
  • the driving situation determination unit 120 determines that neither the setting operation state nor the setting traveling environment is present, there is no driving situation that causes disturbance with respect to the determination of the driving instability (hereinafter, “normal Also referred to as “operating condition”.
  • the traveling state distribution calculating unit 130 calculates a plurality of traveling state distributions (first traveling state distribution, second traveling state distribution) having different temporal ranges based on the traveling state data (steering angle information) acquired by the traveling state data acquisition unit 110. Calculate). Specifically, based on the traveling state data acquired by the traveling state data acquisition unit 110, the traveling state distribution calculation unit 130 performs the first traveling determined using steering angle information acquired in a relatively long time range set in advance. A state distribution and a second traveling state distribution determined from steering angle information acquired in a temporal range shorter than the first traveling state distribution are calculated.
  • the predetermined relatively long time range is a time range in which the normal steering characteristic can be obtained.
  • a predetermined set time for example, 2160 seconds
  • the trip includes, for example, a period from turning on the ignition switch (not shown) to turning it off, or a period from when the navigation device 6 starts route guidance to when it ends.
  • the set time (2160 seconds) has elapsed from the start of the trip
  • the time range from the present to the set time (2160 seconds) before is set as the time range of the first traveling state distribution.
  • a temporal range (temporal range of the second traveling state distribution) shorter than the first traveling state distribution is a temporal range in which the current steering characteristic (the latest driving characteristic) can be determined. For example, when the set time (2160 seconds) has not elapsed from the start of the trip, the time range up to 120 seconds before the current time is set as the time range of the second traveling state distribution. On the other hand, when the set time (2160 seconds) has elapsed from the start of the trip, the temporal range from the present to 90 seconds before is set as the temporal range of the second traveling state distribution.
  • the first traveling state distribution and the second traveling state distribution are calculated each time the traveling state data acquisition unit 110 acquires traveling state data (steering angle information). Calculation examples of the first traveling state distribution and the second traveling state distribution will be described later.
  • FIG. 4 is a block diagram showing the configuration of the traveling state distribution calculating unit 130 of the present embodiment.
  • the traveling state distribution calculating unit 130 includes a first traveling state distribution calculating unit 130A, a second traveling state distribution calculating unit 130B, a distribution storage unit 130C, a distribution selecting unit 130D, and a distribution setting unit 130E.
  • the first traveling state distribution calculating unit 130A calculates a first traveling state distribution.
  • the second traveling state distribution calculating unit 130B calculates a second traveling state distribution.
  • the distribution storage unit 130C acquires the second traveling state distribution calculated by the second traveling state distribution calculating unit 130B. Then, the distribution storage unit 130C stores the acquired second traveling state distribution.
  • the distribution selection unit 130D changes the traveling state distribution (first traveling state distribution) calculated by the first traveling state distribution calculation unit 130A based on the information related to the traveling of the vehicle. As information on travel of the vehicle, for example, there is an elapsed time since the start of the trip. Specifically, distribution selection unit 130D determines whether or not driving condition determination unit 120 has detected a disturbance driving condition. Then, if the distribution selection unit 130D determines that the driving condition determination unit 120 has detected a disturbance driving condition, whether the elapsed time from the start of the trip is less than a set time (for example, 2160 seconds) Determine As the set time, for example, a temporal range of the first traveling state distribution can be adopted.
  • a set time for example, a temporal range of the first traveling state distribution can be adopted.
  • distribution selection unit 130D determines that the elapsed time from the start of the trip is less than the set time (for example, 2160 seconds), of the second traveling state distribution accumulated in distribution accumulation unit 130C.
  • the second traveling state distribution (the second traveling state distribution in the past) calculated and stored prior to the detection of the disturbance driving state is selected.
  • distribution selection unit 130D determines that the elapsed time from the start of the trip is the set time (for example, 2160 seconds) or more
  • the current second traveling state distribution detected by second traveling state distribution calculation unit 130B choose the distribution setting unit 130E replaces (changes) the first traveling state distribution calculated by the first traveling state distribution calculating unit 130A with the selected second traveling state distribution, as described later.
  • the distribution setting unit 130E determines whether the distribution selection unit 130D has selected the second traveling state distribution.
  • the first traveling state distribution calculation unit 130A calculates the selected second traveling state distribution. State distribution (replaces the first running state distribution).
  • the driving instability determination unit 140 calculates the first traveling state distribution calculated by the traveling state distribution calculating unit 130 (the first traveling state distribution after replacement when replaced by the second traveling state distribution) and the second traveling state
  • the driver's driving condition is estimated based on the distribution.
  • the information presenting unit 150 is a process of presenting presentation information to the driver based on the driver's driving state (driving instability) estimated by the driving instability determination unit 140 (hereinafter also referred to as “information presenting processing” )I do.
  • the information presentation unit 150 outputs, to the information presentation apparatus, a control signal for causing the driver to present presentation information (alarm or other information presented to the driver).
  • FIG. 5 is a flowchart showing the driving instability determination process.
  • the driving support unit 100A (the traveling state data acquisition unit 110, the driving state determination unit 120) acquires vehicle information.
  • the vehicle information includes, for example, traveling state data (steering angle information) and information on the operating state of the driver.
  • the driving support unit 100A (driving condition determination unit 120) acquires traffic environment information.
  • traffic environment information for example, there is information of traveling environment.
  • step S103 the driving support unit 100A (the driving condition determination unit 120) determines the current driving condition of the vehicle based on the vehicle information acquired in step S101 and the traffic environment information acquired in step S102. Determine the disturbance operation condition, normal operation condition). Specifically, based on the vehicle information acquired in step S101 and the traffic environment information acquired in step S102, the driving support unit 100A (driving condition determination unit 120) curves the operation state or traveling environment of the driver.
  • step S104 the process proceeds to step S104, and the driving assistance unit 100A (first traveling state distribution calculating unit 130A, second traveling state distribution calculating unit 130B) calculates the first traveling state distribution and the second traveling state distribution.
  • the driving support unit 100A distributed storage unit 130C
  • the driving assistance unit 100A determines that the current driving condition of the vehicle corresponds to either the normal driving condition or the disturbance driving condition based on the determination result of step S103.
  • the process proceeds to step S106.
  • the distribution selection unit 130D determines whether the elapsed time from the start of the trip is less than a set time (for example, 2160 seconds) judge. Then, when it is determined that the elapsed time from the start of the trip is less than the set time (2160 seconds), the distribution selection unit 130D proceeds to step S107. On the other hand, when it is determined that the elapsed time from the start of the trip is equal to or longer than the set time (2160 seconds), the distribution selection unit 130D proceeds to step S108.
  • step S106 the driving support unit 100A (distribution setting unit 130E) does not replace the first traveling state distribution calculated in step S104 with the second traveling state distribution (without resetting (described later) or restoring (described later)). ), And proceeds to step S109.
  • step S107 after the driving assistance unit 100A (distribution setting unit 130E) replaces the first traveling state distribution calculated in step S104 with the second traveling state distribution calculated in step S104 (hereinafter referred to as "reset") (Also referred to as “)”, and the process proceeds to step S109.
  • step S108 the driving support unit 100A (distribution setting unit 130E) causes the first driving state distribution calculated in step S104 to be the disturbance driving state in the second driving state distribution stored in the distribution storage unit 130C.
  • the process proceeds to step S109.
  • step S109 the driving support unit 100A (the driving instability determination unit 140) uses the steering entropy method to replace the first running state distribution (if it is replaced with the second running state distribution) calculated in step S104.
  • the amount of difference (relative entropy RHp) between the distribution of the first traveling state distribution) and the distribution of the second traveling state distribution is calculated. Note that an example of calculation of the difference between the distributions (relative entropy RHp) will be described later.
  • the driving support unit 100A determines the difference in the current driving operation of the driver compared with the normal driving operation based on the first traveling state distribution and the second traveling state distribution.
  • a difference amount (relative entropy RHp) for determining whether or not the vehicle is in an unstable state as compared to normal driving operation is calculated. That is, the driving support unit 100A (the driving instability determination unit 140) calculates the relative entropy RHp as a value representing the non-smoothness of the driving operation.
  • the driving support unit 100A calculates the relative entropy RHp as a value representing the non-smoothness of the driving operation.
  • step S110 the driving support unit 100A (the driving instability determination unit 140) estimates the driving state of the driver based on the difference amount (relative entropy RHp) between the distributions calculated in step S109. (Determining whether or not the driver's driving condition is unstable). Specifically, the driving support unit 100A (the driving instability determination unit 140) determines whether or not the amount of difference between the distributions (relative entropy RHp) calculated in step S109 is larger than a predetermined determination threshold. . When the driving support unit 100A (the driving instability determination unit 140) determines that the difference between the distributions (relative entropy RHp) is larger than the determination threshold, the driving state of the driver is unstable.
  • the driving support unit 100A determines that the amount of difference between the distributions (relative entropy RHp) is equal to or less than the determination threshold, the driving state of the driver becomes unstable. It is determined that there is not.
  • step S111 the driving support unit 100A (information presenting unit 150) presents presentation information (alarm and other information to be presented to the driver) to the driver based on the driving state estimated in step S110.
  • Perform processing information presentation processing.
  • the driving support unit 100A determines whether or not the state determined to be the unstable state in step S110 continues at least a predetermined instability determination threshold (for example, 5 seconds). .
  • the driving support unit 100A determines that the state determined to be the unstable state continues as the instability determination threshold (for example, 5 seconds) or more, the information presenting process is performed.
  • the information presenting unit 150 determines that the state determined as the unstable state does not continue beyond the instability determination threshold (for example, 5 seconds).
  • the information presenting process is not performed.
  • An example of the information presentation process is shown in FIG. In this example, a warning is displayed and a warning is presented by voice such as "Pee !!
  • FIG. 7 is a flowchart showing an example of calculation of the first traveling state distribution and the second traveling state distribution.
  • the traveling state distribution calculating unit 130 calculates the relative entropy RHp in a traveling scene where it can be calculated. Determine if there is.
  • the traveling state distribution calculating unit 130 calculates the vehicle speed within a predetermined vehicle speed range (for example, 40 to 120 km / h). Determine if there is.
  • a predetermined vehicle speed range for example, 40 to 120 km / h.
  • the traveling state distribution calculating unit 130 determines that the vehicle speed is within the predetermined vehicle speed range (40 km / h to 120 km / h) (Yes)
  • the traveling scene is capable of calculating the relative entropy RHp. It determines with it and transfers to step S202.
  • the traveling state distribution calculating unit 130 determines that the vehicle speed is outside the predetermined vehicle speed range (40 km / h to 120 km / h) If it is determined that the relative entropy RHp can not be calculated (No), it is determined that the traveling scene is not capable of calculating the relative entropy RHp, and the calculation process is ended. As a result, the traveling state distribution calculating unit 130 (the first traveling state distribution calculating unit 130A, the second traveling state distribution calculating unit 130B) detects an extremely slow vehicle speed and an extremely fast vehicle speed (less than 40 km / h or 120 km / h). h) or more) is excluded from the traveling situations where the relative entropy RHp can be calculated.
  • step S202 the traveling state distribution calculating unit 130 (first traveling state distribution calculating unit 130A, second traveling state distribution calculating unit 130B) acquires the steering angle ⁇ output by the steering angle sensor 3. Subsequently, the traveling state distribution calculating unit 130 (first traveling state distribution calculating unit 130A, second traveling state distribution calculating unit 130B) calculates the steering angle prediction error ⁇ e based on the acquired steering angle ⁇ .
  • FIG. 8 shows special symbols used to calculate the relative entropy RHp and names of the special symbols.
  • the steering angle smooth value ⁇ n-tilde is a steering angle ⁇ in which the influence of quantization noise is reduced.
  • the estimated value ⁇ n-hat of the steering angle is a value obtained by estimating the steering angle ⁇ at the sampling time point on the assumption that the steering wheel is operated smoothly.
  • the steering angle estimated value ⁇ n-hat is obtained by performing second-order Taylor expansion on the steering angle smooth value ⁇ n-tilde as shown in the following (Expression 1).
  • tn is a sampling time of the steering angle ⁇ n.
  • the steering angle smooth value ⁇ n -tilde is calculated from the following (Expression 2) as an average value of three adjacent steering angles ⁇ n in order to reduce the influence of quantization noise.
  • Equation (2) l is within 150 milliseconds when the calculation time interval of the steering angle smooth value ⁇ n -tilde is 150 milliseconds, that is, the minimum time interval at which a human can intermittently operate in manual operation.
  • l round (0.15 / Ts) (Equation 3)
  • values of k 1, 2, 3 are taken, and (k * 1) makes the smooth value ⁇ n ⁇ based on the steering angle of 150 millisecond intervals and the total of three steering angles ⁇ n adjacent thereto.
  • the estimated value ⁇ n-hat calculated based on such a smooth value ⁇ n-tilde is calculated by the steering angle ⁇ obtained substantially at an interval of 150 milliseconds.
  • the steering angle prediction error ⁇ e at the sampling time point is the difference between the steering angle estimated value ⁇ n-hat at the sampling time point and the actual steering angle ⁇ n when assuming that the steering wheel is operated smoothly from the following (Equation 4) It can be calculated.
  • the steering angle prediction error ⁇ e is calculated only for the minimum time interval at which a human can intermittently operate in manual operation, that is, the steering angle ⁇ n every 150 milliseconds.
  • the sampling interval Ts of the steering angle ⁇ is, for example, 50 milliseconds.
  • three steering angle smooth values ⁇ n-tilde are calculated from the above (formula 2) using three adjacent steering angles ⁇ n at an interval of 150 milliseconds.
  • the three steering angle smooth values ⁇ n -tilde are expressed by the following (Expression 5).
  • an estimated value ⁇ n-hat of the steering angle is calculated from the above (formula 1) using the calculated three steering angle smooth values ⁇ n-tilde.
  • the estimated value ⁇ n -hat is expressed by the following (Expression 6).
  • the steering angle prediction error ⁇ e is calculated from the above (Equation 4). Subsequently, the process proceeds to step S203, and the traveling state distribution calculating unit 130 (the first traveling state distribution calculating unit 130A, the second traveling state distribution calculating unit 130B) calculates up to the present time and stores it in the memory of the controller 100.
  • the steering angle prediction error ⁇ e during the set time T seconds (for example, 2160 seconds) is updated by adding the current value of the steering angle prediction error ⁇ e calculated in step S202.
  • the traveling state distribution calculating unit 130 calculates the oldest T seconds of the steering angle prediction error ⁇ e stored in the memory of the controller 100. Instead, the current value of the steering angle prediction error ⁇ e calculated in step S202 is accumulated as the latest steering angle prediction error ⁇ e.
  • the driving state distribution calculating unit 130 stores the steering angle from the current value to T seconds (2160 seconds before) in the memory of the controller 100. The prediction error ⁇ e is accumulated.
  • FIGS. 9 and 10 are diagrams for explaining a method of calculating the first traveling state distribution and the second traveling state distribution.
  • FIG. 11 is a diagram illustrating the range of the prediction error class bi.
  • the traveling state distribution calculating unit 130 (first traveling state distribution calculating unit 130A) calculates the first traveling state distribution based on the steering angle prediction error ⁇ e accumulated in the memory of the controller 100. calculate.
  • the traveling state distribution calculating unit 130 (first traveling state distribution calculating unit 130A) calculates the steering angle prediction error ⁇ e accumulated in the memory of the controller 100.
  • the range of the prediction error class bi is set based on the ⁇ value used to calculate the steering entropy.
  • the ⁇ value for example, the steering angle prediction error ⁇ e within a fixed time based on time series data of the steering angle ⁇ , that is, the steering angle estimated value ⁇ n-hat when assuming that the steering wheel is operated smoothly
  • the 90% tile value (the range of the distribution including 90% of the steering angle prediction error ⁇ e) is calculated by obtaining the difference with the steering angle ⁇ n and measuring the distribution (variation) of the steering angle prediction error ⁇ e. That is, the ⁇ value is set such that 90% of the steering angle prediction error ⁇ e is included in the section [ ⁇ , ⁇ ].
  • the prediction error class b1 is less than 5 ⁇
  • the prediction error class b2 is -5 ⁇ or more and less than -2.5 ⁇
  • the prediction error class b3 is -2.5 ⁇ or more and less than - ⁇ .
  • the segment b4 is greater than or equal to - ⁇ and less than -0.5 ⁇
  • the prediction error segment b5 is greater than or equal to -0.5 ⁇ and less than 0.5 ⁇ .
  • the prediction error class b6 is 0.5 ⁇ or more and less than ⁇
  • the prediction error class b7 is ⁇ or more and less than 2.5 ⁇
  • the prediction error class b8 is 2.5 ⁇ or more and less than 5 ⁇ .
  • b9 is 5 ⁇ or more.
  • the traveling state distribution calculating unit 130 calculates the second traveling state distribution based on the steering angle prediction error ⁇ e accumulated in the memory of the controller 100.
  • qi q1, q2, q3, etc.
  • q8 q9
  • the relative entropy RHp is calculated from the following (Equation 7) based on the probability pi calculated in step S204 and the probability qi calculated in step S205.
  • the relative entropy RHp becomes a larger value as the deviation becomes larger.
  • the steering angle information acquired by the driving assistance unit 100A (the first traveling state distribution calculating unit 130A, the second traveling state distribution calculating unit 130B) in the first traveling state distribution (predetermined relatively long time range)
  • the traveling state distribution obtained in step d) and the second traveling state distribution (the traveling state distribution acquired in a temporal range shorter than the first traveling state distribution) are calculated (step S104 in FIG. 5).
  • the driving support unit 100A (first traveling state distribution calculating unit 130A, second traveling state distribution calculating unit 130B) stores the calculated second traveling state distribution in the distribution storage unit 130C (step S104 in FIG. 5).
  • the driving support unit 100A determines that the state determined to be unstable in step S110 does not continue beyond the instability determination threshold (for example, 5 seconds), and performs the information presenting process. No (step S110 in FIG. 5).
  • the driving support unit 100A calculates the first traveling state distribution and the second traveling state distribution based on the steering angle information (traveling state data) (step S104 in FIG. 5). Therefore, the driving support unit 100A wants to obtain only steering angle information (running state data) for determining the degree of driving instability (measuring the driving instability).
  • the driving support unit 100A uses the traveling state distribution (first traveling state distribution) using the steering angle information including the steering angle due to the disturbance of the operation of the steering wheel, the detection accuracy of the driving unstable state is It can get worse.
  • the driving support unit 100A when the driving support unit 100A according to the present embodiment detects a driving situation (disturbing driving situation) that causes disturbance with respect to the unstable state of driving, an elapsed time from the start of the trip is a set time (for example, If it is less than 2160 seconds, the first traveling state distribution based on the traveling state data when the disturbance driving state is detected is replaced with the second traveling state distribution (steps S105 and S107 in FIG. 5).
  • the driving support unit 100A of the present embodiment can prevent the erroneous detection of being in the unstable state in the determination of the driving instability (the measurement of the driving unstable state).
  • an elapsed time from the start of the trip becomes equal to or longer than a set time (for example, 2160 seconds).
  • the driving support unit 100A distributes that the elapsed time from the start of the trip is equal to or longer than a set time (for example, 2160 seconds) (step S105 in FIG. 5).
  • the driving support unit 100A detects the disturbance driving condition from the second driving condition distribution stored in the distribution storage unit 130C, the first driving condition distribution calculated in step S104.
  • the second driving state distribution is replaced (restored) by calculation / accumulation before time (step S108 in FIG. 3).
  • the driving support unit 100A calculates the difference amount (relative entropy RHp) between the replaced first traveling state distribution and the distribution of the second traveling state distribution (FIG. 5 Step S109). Subsequently, the driving support unit 100A (the driving instability determination unit 140) estimates the driving state (determines whether the driving state is the unstable state or not) based on the relative entropy RHp (step S110 in FIG. 5).
  • the driving support unit 100A determines whether or not the state determined to be unstable in step S110 continues more than the instability determination threshold (for example, 5 seconds), and the determination result is The information presentation process is determined based on the information (step S110 in FIG. 5).
  • the driving support unit 100A detects a driving situation (disturbing driving situation) that causes disturbance with respect to the unstable state of driving, an elapsed time from the start of the trip is a set time (for example, 2160).
  • the driving support unit 100A of the present embodiment can prevent the erroneous detection of being in the unstable state in the determination of the driving instability (the measurement of the driving unstable state).
  • the traveling state data acquisition unit 110 of FIG. 3 and step S101 of FIG. 5 constitute a traveling state data acquisition unit.
  • the traveling state distribution calculating unit 130 in FIG. 3, the first traveling state distribution calculating unit 130A in FIG. 4, the second traveling state distribution calculating unit 130B, and step S104 in FIG. 5 constitute a traveling state distribution calculating unit.
  • the traveling state distribution calculating unit 130 of FIG. 3, the distribution selecting unit 130D of FIG. 4, the distribution setting unit 130E, and steps S105, S107, and S108 of FIG. 5 constitute a setting changing unit.
  • the driving instability determination unit 140 of FIG. 3 and step S110 of FIG. 5 constitute a driving condition estimation unit.
  • the information presentation unit 150 of FIG. 3 and step S111 of FIG. 5 constitute an information presentation unit.
  • the driving condition determination unit 120 of FIG. 3 and step S103 of FIG. 5 constitute a driving condition determination unit.
  • the present embodiment has the following effects.
  • the driving support unit 100A calculates a plurality of traveling state distributions (for example, the first traveling state distribution, the second traveling state distribution) having different temporal ranges based on the traveling state data (for example, the steering angle information). .
  • the driving support unit 100A estimates the driving state of the driver based on the calculated plurality of traveling state distributions (for example, the first traveling state distribution, the second traveling state distribution) (for example, in an unstable state) To determine whether or not At that time, the driving support unit 100A changes the traveling state distribution (for example, the first traveling state distribution) calculated by the driving support unit 100A based on the information (for example, the elapsed time since the start of the trip) regarding the traveling of the vehicle. Do. With such a configuration, the traveling state distribution (for example, the first traveling state distribution) calculated by the driving support unit 100A is changed based on the information on the traveling of the vehicle (for example, the elapsed time since the start of the trip). As a result, the traveling state distribution (for example, the first traveling state distribution) can be calculated more appropriately, and the estimation accuracy of the driving state (for example, whether or not it is in the unstable state) can be improved.
  • the traveling state distribution for example, the first traveling state distribution
  • the driving support unit 100A changes the traveling state distribution (for example, the first traveling state distribution) calculated by the driving support unit 100A based on the elapsed time from the start of the trip.
  • the traveling state distribution for example, the first traveling state distribution
  • the driving support unit 100A detects a driving situation (for example, a disturbance driving situation) which is a disturbance for the estimation of the driving state (for example, measurement of the unstable state of the driving), the process from the start of the trip
  • a driving situation for example, a disturbance driving situation
  • a disturbance driving situation that is a disturbance with respect to the estimation of the driving state (for example, measurement of the unstable state of driving)
  • the time is less than a set time (for example, 2160 seconds)
  • the first traveling state distribution is replaced by the second traveling state distribution.
  • Such a configuration can more appropriately improve the estimation accuracy of the driving state.
  • the driving support unit 100A determines that the driving situation is the disturbance with respect to the determination of the driving instability (the measurement of the driving instability).
  • the ratio T A / T B between the cumulative value T A of time and the cumulative value T B of the elapsed time from the start of the trip is calculated, and the driving support unit 100A calculates based on the calculated ratio T A / T B
  • the point which changes driving state distribution (the 1st driving state distribution, the 2nd driving state distribution) differs from a 1st embodiment.
  • the present embodiment is different from the first embodiment in the contents of step S109 in FIG.
  • step S109 the driving support unit 100A (the driving instability determination unit 140) uses the steering entropy method to replace the first running state distribution (if it is replaced with the second running state distribution) calculated in step S104.
  • the amount of difference (relative entropy RHp) between the distribution of the first traveling state distribution) and the distribution of the second traveling state distribution is calculated.
  • the driving support unit 100A determines that the driving situation is a disturbance with respect to the determination of the driving instability (the measurement of the driving instability).
  • a ratio T A / T B (hereinafter also referred to as “mask ratio”) between the cumulative value T A and the cumulative value T B of the elapsed time from the start of the trip is calculated.
  • the driving support unit 100A (the driving instability determination unit 140) reads an ⁇ value corresponding to the calculated mask ratio T A / T B from the control map M. Thereby, the driving support unit 100A (the driving instability determination unit 140) calculates the driving support unit 100A (the first traveling state distribution calculating unit 130A, the second traveling state distribution calculating unit) based on the calculated ratio T A / T B. The traveling state distribution (first traveling state distribution, second traveling state distribution) calculated by 130B) is changed.
  • FIG. 12 is a graph showing the control map M.
  • the mask ratio T A / T B is a first set value T A / T B 1 (e.g., 0.2) or more and a second set value T A / T B 2 (> T a / T B 1. for example, 0.8) in the range below, regardless of the size of the mask ratio T a / T B, set to a predetermined set value ⁇ value.
  • control map M is in the range mask ratio T A / T B is first less than the set value T A / T B 1, linearly from the set value ⁇ value in accordance with a decrease of the mask ratio T A / T B Reduce to Further, in the control map M, when the mask ratio T A / T B is in the range of the second set value T A / T B 2 or more, the ⁇ value is linear from the set value according to the increase of the mask ratio T A / T B To increase.
  • FIG. 13 is a graph showing the sensitivity of the alarm. Accordingly, when the mask ratio T A / T B is less than the first set value T A / T B 1, the operation instability determination unit 140 determines that the ⁇ value is smaller as the mask ratio T A / T B becomes smaller. It becomes smaller. Therefore, as shown in FIG. 13, as the mask ratio T A / T B becomes smaller, the driving instability determination unit 140 increases the sensitivity of the alarm (information presenting process) (an alarm (information presenting process) is executed). Become easier). In addition, when the mask ratio T A / T B is equal to or greater than the second set value T A / T B 2, the operation instability determination unit 140 increases the ⁇ value as the mask ratio T A / T B increases. Become. Therefore, as the mask ratio T A / T B increases, the sensitivity of the alarm (information presentation process) decreases in the driving instability determination unit 140 (the alarm (information presentation process) becomes more difficult to execute).
  • the driving support unit 100A detects a driving situation (for example, a disturbance driving situation, a normal driving situation) of the vehicle. Subsequently, the driving support unit 100A calculates the traveling state distribution (for example, the first traveling state distribution, the second traveling state) calculated by the driving support unit 100A based on the detected driving situation (for example, disturbance driving situation, normal driving situation). Change the distribution). According to such a configuration, it is possible to calculate, for example, the traveling state distribution (for example, the first traveling state distribution, the second traveling state distribution) according to the driving situation of the vehicle.
  • a driving situation for example, a disturbance driving situation, a normal driving situation
  • the traveling state distribution for example, the first traveling state distribution, the second traveling state
  • the driving support unit 100A determines the accumulated value T A of the time when it is determined that the driving situation (for example, the disturbance driving situation) causes disturbance with respect to the estimation of the driving state (for example, determination of the driving instability).
  • State distribution (for example, first state distribution, second state distribution) calculated by the driving support unit 100A based on the ratio T A / T B to the cumulative value T B of the elapsed time from the start of the trip and the trip Change
  • the traveling state distribution (the second state) corresponding to the occurrence of the driving condition (for example, the disturbance driving condition) which is a disturbance with respect to the estimation of the driving condition (for example, determination of the driving instability)
  • Running condition distribution, second running condition distribution can be calculated.
  • the driving support unit 100A determines the driving situation (for example, the disturbance driving situation, the normal driving situation) based on at least one of the curve operation, the lane change, and the acceleration / deceleration of the vehicle. According to such a configuration, it is possible to relatively easily determine an operating condition (for example, a disturbance operating condition) which is a disturbance to the estimation of the operating condition.
  • the driving support unit 100A determines the driving situation (disturbing driving situation) based on at least one of the irregular road and the undulation. According to such a configuration, it is possible to relatively easily determine an operating condition (for example, a disturbance operating condition) which is a disturbance to the estimation of the operating condition.
  • FIG. 14 is a diagram showing the configuration of a vehicle equipped with the information providing apparatus for vehicles of the present embodiment.
  • FIG. 15 is a flowchart showing the driving instability determination process.
  • steering angle prediction between the removal or attachment of the seat belt at the driver's seat hereinafter also referred to simply as “detachment”
  • T seconds for example, 2160 seconds
  • the vehicle includes a seat belt sensor 11.
  • the seat belt sensor 11 detects an attachment / detachment state (removal or attachment) of a seat belt (not shown) at the driver's seat. Subsequently, the seat belt sensor 11 outputs the detected attachment / detachment state to the controller 100.
  • the driving support unit 100A determines whether the seat belt (not shown) on the driver's seat has been detached (removed or attached) based on the attachment / detachment state output by the seat belt sensor 11.
  • the steering angle for the set time T seconds (for example, 2160 seconds) accumulated in the memory of the controller 100 After the prediction error ⁇ e is initialized, this operation process is ended.
  • the driving support unit 100A determines that the seat belt (not shown) on the driver's seat is not attached and detached (No)
  • the process proceeds to step S203.
  • the seat belt sensor 11 detects the attachment / detachment state of the seat belt at the driver's seat. Subsequently, the driving support unit 100A changes the traveling state distribution (for example, the first traveling state distribution, the second traveling state distribution) calculated by the driving support unit 100A based on the detected attachment / detachment state. According to such a configuration, for example, when the driver takes turns and the seat belt is attached or detached, the running state distribution (first running state distribution, second running state distribution) can be changed.
  • the traveling state distribution for example, the first traveling state distribution, the second traveling state distribution
  • the driving support unit 100A uses distribution data (for example, first traveling state distribution, second traveling state distribution) to calculate a plurality of traveling state distributions based on the traveling state data (for example, steering angle information).
  • the steering angle prediction error ⁇ e) is stored in the memory of the controller 100.
  • the driving support unit 100A estimates the driving state of the driver based on the distribution data (for example, the steering angle prediction error ⁇ e) accumulated in the memory of the controller 100.
  • the driving support unit 100A initializes distribution data (for example, steering angle prediction error ⁇ e) accumulated in the memory of the controller 100.
  • the traveling state distribution (first traveling state distribution, second traveling state distribution) can be initialized.
  • the steering angle for the set time T seconds (for example, 2160 seconds) accumulated in the memory of the controller 100
  • the prediction error ⁇ e is initialized.
  • the steering angle for the set time T seconds (for example, 2160 seconds) stored in the memory of the controller 100
  • the prediction error ⁇ e may be initialized.
  • FIG. 16 is a diagram showing the configuration of a vehicle equipped with the information providing apparatus for vehicles of the present embodiment.
  • the vehicle includes a door open / close sensor 12.
  • the door open / close sensor 12 detects the open / close state (opened or closed) of the door of the driver's seat. Subsequently, the door open / close sensor 12 outputs the detected open / close state to the controller 100.
  • the driving support unit 100A determines whether the door of the driver's seat has been opened or closed based on the open / close state output by the door open / close sensor 12.
  • the steering angle prediction error ⁇ e for the set time T seconds (for example, 2160 seconds) accumulated in the memory of the controller 100 is initially set. After conversion, this arithmetic processing ends. On the other hand, when it is determined that the driving support unit 100A does not open and close the driver's seat door (No), the process proceeds to step S203.
  • the door open / close sensor 12 of FIG. 16 constitutes an open / close state detection unit.
  • the door open / close sensor 12 detects the open / close state of the driver's seat door. Subsequently, the driving support unit 100A changes the traveling state distribution (for example, the first traveling state distribution, the second traveling state distribution) calculated by the driving support unit 100A based on the detected open / close state. According to such a configuration, for example, when the driver takes turns and opens and closes the door, it is possible to change the traveling state distribution (first traveling state distribution, second traveling state distribution).
  • the driving support unit 100A uses distribution data (for example, first traveling state distribution, second traveling state distribution) to calculate a plurality of traveling state distributions based on the traveling state data (for example, steering angle information). A steering angle prediction error ⁇ e) is generated. Subsequently, the driving support unit 100A stores the generated distribution data (for example, the steering angle prediction error ⁇ e) in the memory of the controller 100. Further, the driving support unit 100A estimates the driving state of the driver based on the distribution data (for example, the steering angle prediction error ⁇ e) accumulated in the memory of the controller 100. At this time, when it is determined that the driver's seat door has been opened and closed, the driving support unit 100A initializes distribution data (for example, steering angle prediction error ⁇ e) accumulated in the memory of the controller 100.
  • distribution data for example, first traveling state distribution, second traveling state distribution

Abstract

Provided is an information provision device for a vehicle in which a driving assistance unit calculates a plurality of travel state distributions (for example, a first travel state distribution and a second travel state distribution) having different temporal ranges on the basis of travel state data (for example, steering angle information). Next, the driving assistance unit estimates the driving state of a driver (for example, determination of whether the driver is in an unstable state) on the basis of the calculated plurality of travel state distributions (for example, the first travel state distribution and the second travel state distribution). On this occasion, the driving assistance unit changes a travel state distribution (for example, the first travel state distribution) that is calculated by said driving assistance unit on the basis of information related to travel of the vehicle (for example, the amount of time that has elapsed since a trip started).

Description

車両用情報提供装置Vehicle information provision device
　本発明は、車両用情報提供装置に関する。 The present invention relates to an information providing apparatus for a vehicle.
　従来、車両用情報提供装置としては、例えば、特許文献１に記載の技術がある。
　特許文献１に記載の技術では、ステアリングホイールの操舵角に基づいて、普段の操舵特性に対応する相対的に時間的範囲の長い走行状態分布と、現在の操舵特性に対応する相対的に時間的範囲の短い走行状態分布とを算出する。そして、算出した２つの走行状態分布の分布間の相違量に基づいて運転者の運転状態を推定する。すなわち、走行状態分布、つまり、運転者の操舵特性を統計的に学習し、学習した操舵特性に基づいて運転者の運転状態を推定するようになっていた。これにより、特許文献１に記載の技術では、交通環境の違いによらず、運転の不安定状態を精度よく検出可能となっている。 Conventionally, as an information providing apparatus for vehicles, there is a technique described in Patent Document 1, for example.
In the technology described in Patent Document 1, based on the steering angle of the steering wheel, a relatively long time travel condition distribution corresponding to the normal steering characteristics and a relative temporal response corresponding to the current steering characteristics. Calculate a short traveling state distribution of the range. Then, the driving state of the driver is estimated based on the calculated difference between the two distributions of the traveling state distribution. That is, the driving state distribution, that is, the steering characteristic of the driver is statistically learned, and the driving state of the driver is estimated based on the learned steering characteristic. Thus, with the technology described in Patent Document 1, the unstable state of driving can be accurately detected regardless of the difference in traffic environment.
特開２００９－９４９５号公報JP, 2009-9495, A
　しかしながら、上記特許文献１に記載の技術では、運転者の操舵特性を統計的に学習し、学習した操舵特性に基づいて運転者の運転状態を推定するようになっていた。それゆえ、例えば、操舵特性の学習、つまり、走行状態分布の算出が適切に行われないと、運転者の運転状態の検出精度が低下する可能性があった。
　本発明は、上記のような点に着目したもので、運転状態の推定精度を向上可能とすることを目的とする。 However, in the technology described in Patent Document 1, the steering characteristics of the driver are statistically learned, and the driving state of the driver is estimated based on the learned steering characteristics. Therefore, for example, if the learning of the steering characteristics, that is, the calculation of the traveling state distribution is not properly performed, there is a possibility that the detection accuracy of the driving state of the driver may be reduced.
The present invention focuses on the above-described points, and an object thereof is to improve the estimation accuracy of the driving state.
　上記課題を解決するために、本発明の一態様では、走行状態データに基づいて時間的範囲の異なる複数の走行状態分布を走行状態分布算出部が算出する。続いて、算出した複数の走行状態分布に基づいて運転者の運転状態を推定する。その際、車両の走行に関する情報に基づいて走行状態分布算出部が算出する走行状態分布を変更する。 In order to solve the above-mentioned subject, in one mode of the present invention, a run state distribution calculation part computes a plurality of run state distributions from which a temporal range differs based on run state data. Subsequently, the driving state of the driver is estimated based on the calculated plurality of traveling state distributions. At that time, the traveling state distribution calculated by the traveling state distribution calculating unit is changed based on the information on the traveling of the vehicle.
　本発明の一態様によれば、車両の走行に関する情報に基づいて走行状態分布算出部が算出する走行状態分布を変更する。これによって、走行状態分布をより適切に算出でき、運転状態の推定精度を向上できる。 According to one aspect of the present invention, the traveling state distribution calculated by the traveling state distribution calculating unit is changed based on the information related to the traveling of the vehicle. As a result, the traveling state distribution can be calculated more appropriately, and the estimation accuracy of the driving state can be improved.
車両用情報提供装置を搭載した車両の構成を表す図である。It is a figure showing the composition of the vehicles carrying the information providing device for vehicles. 車両用情報提供装置のシステム構成例を表すブロック図である。It is a block diagram showing the example of a system configuration of the information providing device for vehicles. 運転支援部１００Ａの構成を表すブロック図である。It is a block diagram showing composition of driving support part 100A. 走行状態分布算出部１３０の構成を表すブロック図である。FIG. 6 is a block diagram showing the configuration of a traveling state distribution calculation unit 130. 運転不安定度判定処理を表すフローチャートである。It is a flowchart showing driving instability degree determination processing. 情報呈示オン例を説明するための図である。It is a figure for demonstrating an example of information presentation ON. 第１走行状態分布、第２走行状態分布の算出例を表すフローチャートである。It is a flowchart showing the example of calculation of the 1st traveling state distribution and the 2nd traveling state distribution. 相対エントロピーＲＨp算出に用いる記号を説明するための図である。It is a figure for demonstrating the symbol used for relative entropy RHp calculation. 第１走行状態分布および第２走行状態分布の算出方法を説明するための図である。It is a figure for demonstrating the calculation method of 1st driving | running state distribution and 2nd driving | running state distribution. 第１走行状態分布および第２走行状態分布の算出方法を説明するための図である。It is a figure for demonstrating the calculation method of 1st driving | running state distribution and 2nd driving | running state distribution. 予測誤差区分ｂｉの範囲を表す図である。It is a figure showing the range of prediction error division bi. 制御マップＭを表すグラフである。It is a graph showing the control map M. 警報の感度を表すグラフである。It is a graph showing the sensitivity of an alarm. 車両用情報提供装置を搭載した車両の構成を表す図である。It is a figure showing the composition of the vehicles carrying the information providing device for vehicles. 運転不安定度判定処理を表すフローチャートである。It is a flowchart showing driving instability degree determination processing. 車両用情報提供装置を搭載した車両の構成を表す図である。It is a figure showing the composition of the vehicles carrying the information providing device for vehicles.
（第１実施形態）
　まず、本発明に係る第１実施形態について図面を参照しつつ説明する。
（構成）
　図１は、本実施形態の車両用情報提供装置を搭載した車両の構成を表す図である。
　図１に示すように、車両は、アクセルペダル開度量センサ１、ブレーキペダル操作量センサ２、操舵角センサ３、車輪速センサ４、ウインカ検出センサ５、およびナビゲーション装置６を備える。また、車両は、Ｇセンサ７、シフトセンサ８、前方車両検出装置９、およびコントローラ１００を備える。 First Embodiment
First, a first embodiment according to the present invention will be described with reference to the drawings.
(Constitution)
FIG. 1 is a diagram showing the configuration of a vehicle equipped with the information providing apparatus for vehicles according to the present embodiment.
As shown in FIG. 1, the vehicle includes an accelerator pedal opening degree sensor 1, a brake pedal operation amount sensor 2, a steering angle sensor 3, a wheel speed sensor 4, a turn signal detection sensor 5, and a navigation device 6. The vehicle also includes a G sensor 7, a shift sensor 8, a forward vehicle detection device 9, and a controller 100.
　アクセルペダル開度量センサ１は、アクセルペダルの開度量を検出する。そして、アクセルペダル開度量センサ１は、検出した開度量をコントローラ１００に出力する。
　ブレーキペダル操作量センサ２は、ブレーキペダルの操作量を検出する。そして、ブレーキペダル操作量センサ２は、検出した操作量をコントローラ１００に出力する。
　操舵角センサ３は、ステアリングホイール（不図示）の操舵角を検出する。そして、操舵角センサ３は、検出した操舵角をコントローラ１００に出力する。操舵角センサ３としては、例えば、ステアリングコラムの回転角を検出する角度センサを採用できる。 The accelerator pedal opening degree sensor 1 detects the opening degree of the accelerator pedal. Then, the accelerator pedal opening degree sensor 1 outputs the detected opening degree to the controller 100.
The brake pedal operation amount sensor 2 detects the amount of operation of the brake pedal. Then, the brake pedal operation amount sensor 2 outputs the detected operation amount to the controller 100.
The steering angle sensor 3 detects a steering angle of a steering wheel (not shown). Then, the steering angle sensor 3 outputs the detected steering angle to the controller 100. As the steering angle sensor 3, for example, an angle sensor that detects the rotation angle of the steering column can be employed.
　車輪速センサ４は、車輪の回転数（以下、「車輪速」とも呼ぶ）を検出する。続いて、車輪速センサ４は、検出した車輪速に基づいて車速を算出する。そして、車輪速センサ４は、検出した車輪速および算出した車速のそれぞれをコントローラ１００に出力する。
　ウインカ検出センサ５は、ウインカレバー（不図示）の操作状態（以下、「ウインカ操作」とも呼ぶ）を検出する。ウインカ操作としては、例えば、操作の有無がある。そして、ウインカ検出センサ５は、検出したウインカ操作をコントローラ１００に出力する。 The wheel speed sensor 4 detects the number of revolutions of the wheel (hereinafter also referred to as "wheel speed"). Subsequently, the wheel speed sensor 4 calculates the vehicle speed based on the detected wheel speed. Then, the wheel speed sensor 4 outputs each of the detected wheel speed and the calculated vehicle speed to the controller 100.
The winker detection sensor 5 detects an operation state (hereinafter also referred to as “winker operation”) of a winker lever (not shown). As the winker operation, for example, there is the presence or absence of the operation. Then, the blinker detection sensor 5 outputs the detected blinker operation to the controller 100.
　シフトセンサ８は、シフトレバー（不図示）の操作状態（以下、「シフト操作」とも呼ぶ）を検出する。シフト操作としては、例えば、Ｐ、Ｄ、Ｒ等のシフトレバーの位置がある。そして、シフトセンサ８は、検出したシフト操作をコントローラ１００に出力する。
　情報呈示装置は、コントローラ１００が出力した制御信号（後述）に従って、警報その他の情報を運転者に呈示する。呈示方法としては、音声や画像がある。情報呈示装置としては、例えば、ブザー音や音声により運転者への情報提供を行うスピーカ１０、および画像やテキストの表示により運転者への情報提供を行う表示ユニットを採用できる。表示ユニットとしては、例えば、ナビゲーション装置６の表示モニタを流用してもよい。 The shift sensor 8 detects an operation state (hereinafter also referred to as "shift operation") of a shift lever (not shown). As the shift operation, for example, there are positions of shift levers such as P, D, R and the like. Then, the shift sensor 8 outputs the detected shift operation to the controller 100.
The information presentation apparatus presents an alarm and other information to the driver in accordance with a control signal (described later) output from the controller 100. As a presentation method, there are sounds and images. As the information presentation apparatus, for example, a speaker 10 that provides information to the driver by buzzer sound or voice, and a display unit that provides information to the driver by displaying an image or text can be adopted. As a display unit, for example, a display monitor of the navigation device 6 may be diverted.
　ナビゲーション装置６は、ＧＰＳ(Global Positioning System)受信機、地図データベース、および表示モニタを備える。そして、ナビゲーション装置６は、ＧＰＳ受信機および地図データベースから車両の現在位置および道路情報を取得する。続いて、ナビゲーション装置６は、取得した車両の現在位置および道路情報に基づいて車両が走行する道路の種別や道路幅員等の各種情報を取得する。続いて、ナビゲーション装置６は、取得した情報に基づいて経路探索の結果および経路案内の結果等を表示モニタに表示する。
　Ｇセンサ７は、車両に発生した前後加速度および横加速度を検出する。そして、Ｇセンサ７は、検出した前後加速度および横加速度をコントローラ１００に出力する。
　前方車両検出装置９は、車両の進行方向前方に存在する他の車両その他の障害物の情報（例えば、障害物までの距離）を検出する。そして、前方車両検出装置９は、検出した情報をコントローラ１００に出力する。前方車両検出装置９としては、例えば、車両の進行方向前方にレーザー光を出射して反射光を検出するレーザ距離計を採用できる。 The navigation device 6 includes a GPS (Global Positioning System) receiver, a map database, and a display monitor. Then, the navigation device 6 acquires the current position and road information of the vehicle from the GPS receiver and the map database. Subsequently, the navigation device 6 acquires various types of information such as the type of the road on which the vehicle travels and the road width based on the acquired current position of the vehicle and the road information. Subsequently, the navigation device 6 displays the result of the route search and the result of the route guidance on the display monitor based on the acquired information.
The G sensor 7 detects longitudinal acceleration and lateral acceleration generated in the vehicle. Then, the G sensor 7 outputs the detected longitudinal acceleration and lateral acceleration to the controller 100.
The forward vehicle detection device 9 detects information (for example, the distance to an obstacle) of another vehicle or other obstacle present ahead of the traveling direction of the vehicle. Then, the forward vehicle detection device 9 outputs the detected information to the controller 100. As the front vehicle detection device 9, for example, a laser range finder that emits laser light in the forward direction of travel of the vehicle and detects reflected light can be adopted.
　コントローラ１００は、ＣＰＵ(Central Processing Unit)、並びにＲＯＭ(Read Only Memory)、ＲＡＭ(Random Access Memory)およびＡ/Ｄ(Analog to Digital)変換回路等のＣＰＵ周辺部品を備える。そして、コントローラ１００（ＣＰＵ、ＣＰＵ周辺部品）は、運転不安定度判定処理を行う運転支援部１００Ａを備える。運転不安定度判定処理では、運転支援部１００Ａは、アクセルペダル開度量センサ１およびブレーキペダル操作量センサ２等が出力した検出結果に基づいて、運転者が操作可能な運転操作子の操作状態、および車両状態の少なくとも一方を含む走行状態データを取得する。運転操作子としては、例えば、ステアリングホイール、アクセルペダル、およびブレーキペダルがある。車両状態としては、前方車両に対する車間情報がある。本実施形態では、走行状態データとして、操舵角センサ３が出力した操舵角の情報（以下、「操舵角情報」とも呼ぶ）を採用する。 The controller 100 includes CPU (central processing unit), and CPU peripheral components such as a read only memory (ROM), a random access memory (RAM), and an analog to digital (A / D) conversion circuit. Then, the controller 100 (CPU, CPU peripheral components) includes the driving support unit 100A that performs driving instability determination processing. In the driving instability determination processing, the driving support unit 100A determines the operation state of the driving operator that can be operated by the driver based on the detection result output from the accelerator pedal opening amount sensor 1 and the brake pedal operation amount sensor 2 or the like. And traveling condition data including at least one of the vehicle condition and the vehicle condition is acquired. Examples of the driver include a steering wheel, an accelerator pedal, and a brake pedal. As the vehicle state, there is inter-vehicle information for a preceding vehicle. In the present embodiment, information on the steering angle output from the steering angle sensor 3 (hereinafter also referred to as “steering angle information”) is adopted as the traveling state data.
　続いて、運転支援部１００Ａは、取得した走行状態データ（操舵角情報）に基づいて時間的範囲の異なる複数の走行状態分布（第１走行状態分布（後述）、第２走行状態分布（後述））を算出する。続いて、運転支援部１００Ａは、算出した複数の走行状態分布（第１走行状態分布、第２走行状態分布）の分布間の相違量（相対エントロピーＲＨp（後述））に基づいて運転者の運転状態（運転の不安定度（後述））を推定する。そして、運転支援部１００Ａは、推定した運転状態（運転の不安定度）に基づいて警報その他の情報（以下、「呈示情報」とも呼ぶ）を運転者に呈示させる制御信号を情報呈示装置に出力する。これにより、運転支援部１００Ａは、運転者に呈示情報を呈示し、運転の不安定度（運転の不安定状態）について注意を喚起する。 Subsequently, the driving support unit 100A generates a plurality of traveling state distributions (first traveling state distribution (described later), second traveling state distribution (described later) based on the acquired traveling state data (steering angle information). Calculate). Subsequently, the driving support unit 100A drives the driver based on the difference amount (relative entropy RHp (described later)) among the calculated distributions of the traveling states (the first traveling state distribution and the second traveling state distribution). Estimate the state (the driving instability (described later)). Then, the driving support unit 100A outputs, to the information presenting apparatus, a control signal for causing the driver to present an alarm or other information (hereinafter also referred to as "presentation information") based on the estimated driving state (the driving instability). Do. Thus, the driving support unit 100A presents presentation information to the driver and draws attention to the instability of driving (the unstable state of driving).
　なお、走行状態データとしては、前方車両に対する車間情報（車間距離、車間時間）や、アクセルペダルやブレーキペダルの操作に基づく加減速情報等を採用してもよい。車間情報（車間距離、車間時間）や、加減速情報等を採用した場合、走行状態分布（第１走行状態分布、第２走行状態分布）および分布間の相違量（相対エントロピーＲＨp）の算出は、例えば、国際公開番号ＷＯ２００９／０１３８１５（特願２００９－５２４３４２号）の公報等に記載しているような公知の方法によって算出すればよい。 As the traveling state data, inter-vehicle information (inter-vehicle distance, inter-vehicle time) for the preceding vehicle, acceleration / deceleration information based on the operation of an accelerator pedal or a brake pedal, or the like may be adopted. When adopting inter-vehicle information (inter-vehicle distance, inter-vehicle time), acceleration / deceleration information, etc., calculation of the traveling state distribution (first traveling state distribution, second traveling state distribution) and the difference between the distributions (relative entropy RHp) For example, it may be calculated by a known method as described in the publication of International Publication No. WO 2009/013815 (Japanese Patent Application No. 2009-524342).
　図２は、本実施形態の車両用情報提供装置のシステム構成例を表すブロック図である。
　図２に示すように、本実施形態では、情報呈示装置として、視覚情報呈示装置、および聴覚情報呈示装置を例示している。また、視覚情報呈示装置としては、ナビゲーション装置６の表示モニタ、聴覚情報呈示装置としては、スピーカ１０を例示している。
　図３は、本実施形態の運転支援部１００Ａの構成を表すブロック図である。
　図３に示すように、運転支援部１００Ａは、走行状態データ取得部１１０、運転状況判定部１２０、走行状態分布算出部１３０、運転不安定度判定部１４０、および情報呈示部１５０を備える。
　走行状態データ取得部１１０は、操舵角センサ３が出力した検出結果を取得する。そして、走行状態データ取得部１１０は、取得した検出結果を走行状態データとする。 FIG. 2 is a block diagram showing an example of a system configuration of the information providing apparatus for a vehicle according to the present embodiment.
As shown in FIG. 2, in the present embodiment, a visual information presentation device and an auditory information presentation device are illustrated as the information presentation device. Moreover, as a visual information presentation apparatus, the display monitor of the navigation apparatus 6 and the speaker 10 are illustrated as an auditory information presentation apparatus.
FIG. 3 is a block diagram showing the configuration of the driving support unit 100A of the present embodiment.
As shown in FIG. 3, the driving support unit 100A includes a driving condition data acquisition unit 110, a driving condition determination unit 120, a driving condition distribution calculation unit 130, a driving instability determination unit 140, and an information presenting unit 150.
The traveling state data acquisition unit 110 acquires the detection result output from the steering angle sensor 3. Then, the traveling state data acquisition unit 110 sets the acquired detection result as traveling state data.
　運転状況判定部１２０は、アクセルペダル開度量センサ１、およびブレーキペダル操作量センサ２等が出力した検出結果に基づいて、現在の車両の運転状況（外乱運転状況（後述）、通常運転状況（後述））を判定する。具体的には、運転状況判定部１２０は、アクセルペダル開度量センサ１、およびブレーキペダル操作量センサ２等が出力した検出結果に基づいて、運転者が操作可能な運転操作子の操作状態および走行環境を検出する。続いて、運転状況判定部１２０は、検出した運転操作子の操作状態が設定操作状態であるか、または走行環境が設定走行環境であるかを判定する。 The driving condition determination unit 120 detects the current driving condition of the vehicle (disturbing driving condition (described later), normal driving condition (described later) based on the detection results output from the accelerator pedal opening amount sensor 1 and the brake pedal operation amount sensor 2 and the like. )). Specifically, based on the detection results output from the accelerator pedal opening degree sensor 1 and the brake pedal operation amount sensor 2 and the like, the driving condition determination unit 120 operates the operating state and travel of the driving operator that can be operated by the driver. Detect the environment. Subsequently, the driving condition determination unit 120 determines whether the detected operation state of the driving operator is the setting operation state or whether the traveling environment is the set traveling environment.
　設定操作状態としては、例えば、カーブ動作、車線変更操作、車両の加減速、アクセルペダル（不図示）の操作、ブレーキペダル（不図示）の操作、ウインカ操作、ナビゲーション装置６の操作、およびオーディオ装置（不図示）の操作がある。カーブ動作は、例えば、操舵角センサ３の検出結果に基づき検出する。具体的には、カーブ動作は、設定操舵角以上の状態が設定時間以上継続した場合にカーブ動作があると判定する。車線変更操作は、例えば、ウインカ操作、車線位置、自車位置、および自車の進行方向等に基づき検出する。車両の加減速は、例えば、Ｇセンサ７の検出結果に基づき検出する。アクセルペダルの操作は、例えば、アクセルペダル開度量センサ１の検出結果に基づき検出する。ブレーキペダルの操作は、例えば、ブレーキペダル操作量センサ２の検出結果に基づき検出する。ウインカ操作は、ウインカ検出センサ５の検出結果に基づき検出する。ナビゲーション装置６の操作は、ナビゲーション装置６からの信号に基づき検出する。オーディオ装置（不図示）の操作は、オーディオ装置（不図示）からの信号に基づき検出する。 The setting operation state includes, for example, curve operation, lane change operation, acceleration / deceleration of vehicle, operation of accelerator pedal (not shown), operation of brake pedal (not shown), operation of blinker, operation of navigation device 6, and audio device There is an operation (not shown). The curve operation is detected based on the detection result of the steering angle sensor 3, for example. Specifically, it is determined that there is a curve operation when the state of the set steering angle or more continues for the set time or more. The lane change operation is detected based on, for example, a blinker operation, a lane position, a vehicle position, and a traveling direction of the vehicle. The acceleration / deceleration of the vehicle is detected based on the detection result of the G sensor 7, for example. The operation of the accelerator pedal is detected based on the detection result of the accelerator pedal opening amount sensor 1, for example. The operation of the brake pedal is detected based on the detection result of the brake pedal operation amount sensor 2, for example. The blinker operation is detected based on the detection result of the blinker detection sensor 5. The operation of the navigation device 6 is detected based on the signal from the navigation device 6. The operation of the audio device (not shown) is detected based on the signal from the audio device (not shown).
　また、設定走行環境としては、例えば、不整路、うねり、トンネル、分合流、料金所、および坂道がある。不整路およびうねりは、例えば、車輪速センサ４の検出結果に基づき検出する。具体的には、不整路は、設定時間毎に計測した車輪速の今回値と前回値との差異を算出し、算出した差異が設定閾値以上の場合に不整路があると判定する。トンネル、分合流および料金所は、ナビゲーション装置６からの信号に基づき検出する。坂道は、Ｇセンサ７の検出結果に基づき検出する。また、運転状況判定部１２０は、運転操作子の操作状態が設定操作状態であると判定した場合、または走行環境が設定走行環境であると判定した場合には、運転の不安定度の判定に対して、外乱となる運転状況（以下、「外乱運転状況」とも呼ぶ）にあると判定する。一方、運転状況判定部１２０は、設定操作状態および設定走行環境のいずれでもないと判定した場合には、運転の不安定度の判定に対して、外乱となる運転状況にない（以下、「通常運転状況」とも呼ぶ）と判定する。 In addition, as the set traveling environment, for example, there are irregular road, undulation, tunnel, minute junction, toll plaza, and slope. The irregular road and the undulation are detected based on the detection result of the wheel speed sensor 4, for example. Specifically, the irregular road calculates the difference between the current value of the wheel speed measured every set time and the previous value, and determines that there is an irregular road when the calculated difference is equal to or greater than the set threshold. The tunnel, the merge and the tollgate are detected based on the signal from the navigation device 6. The slope is detected based on the detection result of the G sensor 7. In addition, when the driving state determination unit 120 determines that the operation state of the driving operator is the setting operation state, or when it is determined that the traveling environment is the set traveling environment, the driving situation determination unit 120 determines the driving instability. On the other hand, it is determined that the driving condition is a disturbance (hereinafter also referred to as "disturbing driving condition"). On the other hand, when the driving situation determination unit 120 determines that neither the setting operation state nor the setting traveling environment is present, there is no driving situation that causes disturbance with respect to the determination of the driving instability (hereinafter, “normal Also referred to as “operating condition”.
　走行状態分布算出部１３０は、走行状態データ取得部１１０が取得した走行状態データ（操舵角情報）に基づいて時間的範囲の異なる複数の走行状態分布（第１走行状態分布、第２走行状態分布）を算出する。具体的には、走行状態分布算出部１３０は、走行状態データ取得部１１０が取得した走行状態データに基づき、予め定めた相対的に長い時間的範囲で取得した操舵角情報で求めた第１走行状態分布と、第１走行状態分布よりも短い時間的範囲で取得した操舵角情報で求めた第２走行状態分布とを算出する。 The traveling state distribution calculating unit 130 calculates a plurality of traveling state distributions (first traveling state distribution, second traveling state distribution) having different temporal ranges based on the traveling state data (steering angle information) acquired by the traveling state data acquisition unit 110. Calculate). Specifically, based on the traveling state data acquired by the traveling state data acquisition unit 110, the traveling state distribution calculation unit 130 performs the first traveling determined using steering angle information acquired in a relatively long time range set in advance. A state distribution and a second traveling state distribution determined from steering angle information acquired in a temporal range shorter than the first traveling state distribution are calculated.
　ここで、予め定めた相対的に長い時間的範囲（第１走行状態分布の時間的範囲）は、普段の操舵特性を取得可能な時間的範囲である。例えば、トリップの開始時から予め定めた設定時間（例えば、２１６０秒）が経過していない場合には、現在から１８０秒前までの時間範囲を第１走行状態分布の時間的範囲とする。トリップとしては、例えば、イグニッションスイッチ（不図示）をオン状態としてからオフ状態とするまでの期間、或いはナビゲーション装置６が経路案内を開始してから終了するまでの期間がある。一方、トリップの開始時から設定時間（２１６０秒）が経過している場合には、現在から設定時間（２１６０秒）前までの時間範囲を第１走行状態分布の時間的範囲とする。 Here, the predetermined relatively long time range (the time range of the first traveling state distribution) is a time range in which the normal steering characteristic can be obtained. For example, when a predetermined set time (for example, 2160 seconds) has not elapsed from the start of the trip, the time range up to 180 seconds before the current time is set as the time range of the first traveling state distribution. The trip includes, for example, a period from turning on the ignition switch (not shown) to turning it off, or a period from when the navigation device 6 starts route guidance to when it ends. On the other hand, when the set time (2160 seconds) has elapsed from the start of the trip, the time range from the present to the set time (2160 seconds) before is set as the time range of the first traveling state distribution.
　また、第１走行状態分布よりも短い時間的範囲（第２走行状態分布の時間的範囲）は、現在の操舵特性（直近の運転特性）を判定可能な時間的範囲である。例えば、トリップの開始時から設定時間（２１６０秒）が経過していない場合には、現在から１２０秒前までの時間範囲を第２走行状態分布の時間的範囲とする。一方、トリップの開始時から設定時間（２１６０秒）が経過している場合には、現在から９０秒前までの時間的範囲を第２走行状態分布の時間的範囲とする。第１走行状態分布および第２走行状態分布は、走行状態データ取得部１１０が走行状態データ（操舵角情報）を取得するたびに算出する。第１走行状態分布および第２走行状態分布の算出例については、後述する。 Further, a temporal range (temporal range of the second traveling state distribution) shorter than the first traveling state distribution is a temporal range in which the current steering characteristic (the latest driving characteristic) can be determined. For example, when the set time (2160 seconds) has not elapsed from the start of the trip, the time range up to 120 seconds before the current time is set as the time range of the second traveling state distribution. On the other hand, when the set time (2160 seconds) has elapsed from the start of the trip, the temporal range from the present to 90 seconds before is set as the temporal range of the second traveling state distribution. The first traveling state distribution and the second traveling state distribution are calculated each time the traveling state data acquisition unit 110 acquires traveling state data (steering angle information). Calculation examples of the first traveling state distribution and the second traveling state distribution will be described later.
　図４は、本実施形態の走行状態分布算出部１３０の構成を表すブロック図である。
　図４に示すように、走行状態分布算出部１３０は、第１走行状態分布算出部１３０Ａ、第２走行状態分布算出部１３０Ｂ、分布蓄積部１３０Ｃ、分布選択部１３０Ｄ、および分布設定部１３０Ｅを備える。
　第１走行状態分布算出部１３０Ａは、第１走行状態分布を算出する。
　第２走行状態分布算出部１３０Ｂは、第２走行状態分布を算出する。
　分布蓄積部１３０Ｃは、第２走行状態分布算出部１３０Ｂが算出した第２走行状態分布を取得する。そして、分布蓄積部１３０Ｃは、取得した第２走行状態分布を蓄積する。 FIG. 4 is a block diagram showing the configuration of the traveling state distribution calculating unit 130 of the present embodiment.
As shown in FIG. 4, the traveling state distribution calculating unit 130 includes a first traveling state distribution calculating unit 130A, a second traveling state distribution calculating unit 130B, a distribution storage unit 130C, a distribution selecting unit 130D, and a distribution setting unit 130E. .
The first traveling state distribution calculating unit 130A calculates a first traveling state distribution.
The second traveling state distribution calculating unit 130B calculates a second traveling state distribution.
The distribution storage unit 130C acquires the second traveling state distribution calculated by the second traveling state distribution calculating unit 130B. Then, the distribution storage unit 130C stores the acquired second traveling state distribution.
　分布選択部１３０Ｄは、車両の走行に関する情報に基づいて第１走行状態分布算出部１３０Ａが算出する走行状態分布（第１走行状態分布）を変更する。車両の走行に関する情報としては、例えば、トリップの開始時からの経過時間がある。具体的には、分布選択部１３０Ｄは、運転状況判定部１２０が外乱運転状況を検出したか否かを判定する。そして、分布選択部１３０Ｄは、運転状況判定部１２０が外乱運転状況を検出したと判定した場合には、トリップの開始時からの経過時間が設定時間（例えば、２１６０秒）未満であるか否かを判定する。設定時間としては、例えば、第１走行状態分布の時間的範囲を採用できる。そして、分布選択部１３０Ｄは、トリップの開始時からの経過時間が設定時間（例えば、２１６０秒）未満であると判定すると、分布蓄積部１３０Ｃに蓄積している第２走行状態分布のうちから、外乱運転状況の検出時よりも前に算出・蓄積した第２走行状態分布（過去の第２走行状態分布）を選択する。
　一方、分布選択部１３０Ｄは、トリップの開始時からの経過時間が設定時間（例えば、２１６０秒）以上であると判定すると、第２走行状態分布算出部１３０Ｂが検出した現在の第２走行状態分布を選択する。これにより、分布設定部１３０Ｅは、後述するように、選択した第２走行状態分布で第１走行状態分布算出部１３０Ａが算出した第１走行状態分布を置き換える（変更する）。 The distribution selection unit 130D changes the traveling state distribution (first traveling state distribution) calculated by the first traveling state distribution calculation unit 130A based on the information related to the traveling of the vehicle. As information on travel of the vehicle, for example, there is an elapsed time since the start of the trip. Specifically, distribution selection unit 130D determines whether or not driving condition determination unit 120 has detected a disturbance driving condition. Then, if the distribution selection unit 130D determines that the driving condition determination unit 120 has detected a disturbance driving condition, whether the elapsed time from the start of the trip is less than a set time (for example, 2160 seconds) Determine As the set time, for example, a temporal range of the first traveling state distribution can be adopted. Then, when distribution selection unit 130D determines that the elapsed time from the start of the trip is less than the set time (for example, 2160 seconds), of the second traveling state distribution accumulated in distribution accumulation unit 130C, The second traveling state distribution (the second traveling state distribution in the past) calculated and stored prior to the detection of the disturbance driving state is selected.
On the other hand, when distribution selection unit 130D determines that the elapsed time from the start of the trip is the set time (for example, 2160 seconds) or more, the current second traveling state distribution detected by second traveling state distribution calculation unit 130B Choose Thereby, the distribution setting unit 130E replaces (changes) the first traveling state distribution calculated by the first traveling state distribution calculating unit 130A with the selected second traveling state distribution, as described later.
　分布設定部１３０Ｅは、分布選択部１３０Ｄが第２走行状態分布を選択したか否かを判定する。そして、分布設定部１３０Ｅは、分布選択部１３０Ｄが第２走行状態分布を選択したと判定した場合には、選択した第２走行状態分布を第１走行状態分布算出部１３０Ａが算出した第１走行状態分布とする（第１走行状態分布を置き換える）。
　運転不安定度判定部１４０は、走行状態分布算出部１３０が算出した第１走行状態分布（第２走行状態分布で置き換えた場合には、置き換え後の第１走行状態分布）および第２走行状態分布に基づいて運転者の運転状態（運転の不安定度）を推定する。
　情報呈示部１５０は、運転不安定度判定部１４０が推定した運転者の運転状態（運転の不安定度）に基づいて運転者に呈示情報を呈示する処理（以下、「情報呈示処理」とも呼ぶ）を行う。情報呈示処理では、情報呈示部１５０は、呈示情報（運転者に呈示する警報その他の情報）を運転者に呈示させる制御信号を情報呈示装置に出力する。 The distribution setting unit 130E determines whether the distribution selection unit 130D has selected the second traveling state distribution. When the distribution setting unit 130E determines that the distribution selection unit 130D has selected the second traveling state distribution, the first traveling state distribution calculation unit 130A calculates the selected second traveling state distribution. State distribution (replaces the first running state distribution).
The driving instability determination unit 140 calculates the first traveling state distribution calculated by the traveling state distribution calculating unit 130 (the first traveling state distribution after replacement when replaced by the second traveling state distribution) and the second traveling state The driver's driving condition (driving instability) is estimated based on the distribution.
The information presenting unit 150 is a process of presenting presentation information to the driver based on the driver's driving state (driving instability) estimated by the driving instability determination unit 140 (hereinafter also referred to as “information presenting processing” )I do. In the information presentation process, the information presentation unit 150 outputs, to the information presentation apparatus, a control signal for causing the driver to present presentation information (alarm or other information presented to the driver).
（運転不安定度判定処理）
　次に、運転支援部１００Ａが実行する運転不安定度判定処理について説明する。運転不安定度判定処理は、予め設定した制御周期（例えば、１００ミリ秒毎）で実施する。
　図５は、運転不安定度判定処理を表すフローチャートである。
　図５に示すように、まず、ステップＳ１０１では、運転支援部１００Ａ（走行状態データ取得部１１０、運転状況判定部１２０）は、車両情報を取得する。車両情報としては、例えば、走行状態データ（操舵角情報）、および運転操作子の操作状態の情報がある。
　続いてステップＳ１０２に移行して、運転支援部１００Ａ（運転状況判定部１２０）は、交通環境情報を取得する。交通環境情報としては、例えば、走行環境の情報がある。 (Operational instability judgment processing)
Next, the driving instability determination processing executed by the driving support unit 100A will be described. The driving instability determination process is performed at a control cycle (for example, every 100 milliseconds) set in advance.
FIG. 5 is a flowchart showing the driving instability determination process.
As shown in FIG. 5, first, in step S101, the driving support unit 100A (the traveling state data acquisition unit 110, the driving state determination unit 120) acquires vehicle information. The vehicle information includes, for example, traveling state data (steering angle information) and information on the operating state of the driver.
Subsequently, the process proceeds to step S102, and the driving support unit 100A (driving condition determination unit 120) acquires traffic environment information. As traffic environment information, for example, there is information of traveling environment.
　続いてステップＳ１０３に移行して、運転支援部１００Ａ（運転状況判定部１２０）は、ステップＳ１０１で取得した車両情報、およびステップＳ１０２で取得した交通環境情報に基づいて、現在の車両の運転状況（外乱運転状況、通常運転状況）を判定する。具体的には、運転支援部１００Ａ（運転状況判定部１２０）は、ステップＳ１０１で取得した車両情報、およびステップＳ１０２で取得した交通環境情報に基づいて、運転操作子の操作状態または走行環境がカーブ動作、車線変更操作、車両の加減速、アクセルペダル（不図示）の操作、ブレーキペダル（不図示）の操作、ウインカ操作、ナビゲーション装置６の操作、オーディオ装置（不図示）の操作、不整路、うねり、トンネル、分合流、料金所、および坂道の少なくともいずれかに該当するか否かを判定する。そして、運転支援部１００Ａ（運転状況判定部１２０）は、少なくともいずれかに該当すると判定した場合には、現在の車両の運転状況が外乱運転状況にあると判定する。一方、運転支援部１００Ａ（運転状況判定部１２０）は、いずれにも該当しないと判定した場合には、現在の車両の運転状況が通常運転状況にあると判定する。 Subsequently, the process proceeds to step S103, and the driving support unit 100A (the driving condition determination unit 120) determines the current driving condition of the vehicle based on the vehicle information acquired in step S101 and the traffic environment information acquired in step S102. Determine the disturbance operation condition, normal operation condition). Specifically, based on the vehicle information acquired in step S101 and the traffic environment information acquired in step S102, the driving support unit 100A (driving condition determination unit 120) curves the operation state or traveling environment of the driver. Operation, lane change operation, acceleration / deceleration of vehicle, operation of accelerator pedal (not shown), operation of brake pedal (not shown), operation of blinker, operation of navigation device 6, operation of audio device (not shown), irregular road, It is determined whether it corresponds to a swell, a tunnel, a junction, a toll station, and / or a slope. Then, when it is determined that the driving support unit 100A (the driving condition determination unit 120) corresponds to at least one of them, it is determined that the current driving condition of the vehicle is in the disturbance driving condition. On the other hand, when it is determined that the driving support unit 100A (the driving condition determination unit 120) does not correspond to any of them, the driving support unit 100A determines that the current driving condition of the vehicle is in the normal driving condition.
　次いてステップＳ１０４に移行して、運転支援部１００Ａ（第１走行状態分布算出部１３０Ａ、第２走行状態分布算出部１３０Ｂ）は、第１走行状態分布および第２走行状態分布を算出する。続いて、運転支援部１００Ａ（分布蓄積部１３０Ｃ）は、算出した第２走行状態分布を分布蓄積部１３０Ｃに蓄積する（記憶する）。
　続いてステップＳ１０５に移行して、運転支援部１００Ａ（分布選択部１３０Ｄ）は、ステップＳ１０３の判定結果に基づいて、現在の車両の運転状況が通常運転状況、および外乱運転状況のいずれに該当するのかを判定する。そして、運転支援部１００Ａ（分布選択部１３０Ｄ）は、通常運転状況に該当すると判定した場合には、ステップＳ１０６に移行する。一方、運転支援部１００Ａ（分布選択部１３０Ｄ）は、外乱運転状況に該当すると判定した場合には、トリップの開始時からの経過時間が設定時間（例えば、２１６０秒）未満であるか否かを判定する。そして、分布選択部１３０Ｄは、トリップの開始時からの経過時間が設定時間（２１６０秒）未満であると判定した場合には、ステップＳ１０７に移行する。一方、分布選択部１３０Ｄは、トリップの開始時からの経過時間が設定時間（２１６０秒）以上であると判定した場合には、ステップＳ１０８に移行する。 Next, the process proceeds to step S104, and the driving assistance unit 100A (first traveling state distribution calculating unit 130A, second traveling state distribution calculating unit 130B) calculates the first traveling state distribution and the second traveling state distribution. Subsequently, the driving support unit 100A (distribution storage unit 130C) stores (stores) the calculated second traveling state distribution in the distribution storage unit 130C.
Subsequently, the process proceeds to step S105, and the driving assistance unit 100A (distribution selecting unit 130D) determines that the current driving condition of the vehicle corresponds to either the normal driving condition or the disturbance driving condition based on the determination result of step S103. Determine the Then, when it is determined that the driving support unit 100A (distribution selection unit 130D) corresponds to the normal driving situation, the process proceeds to step S106. On the other hand, when it is determined that the driving support unit 100A (distribution selection unit 130D) corresponds to the disturbance driving condition, whether the elapsed time from the start of the trip is less than a set time (for example, 2160 seconds) judge. Then, when it is determined that the elapsed time from the start of the trip is less than the set time (2160 seconds), the distribution selection unit 130D proceeds to step S107. On the other hand, when it is determined that the elapsed time from the start of the trip is equal to or longer than the set time (2160 seconds), the distribution selection unit 130D proceeds to step S108.
　ステップＳ１０６では、運転支援部１００Ａ（分布設定部１３０Ｅ）は、ステップＳ１０４で算出した第１走行状態分布を第２走行状態分布で置き換えることなく（リセット（後述）、リストア（後述）を行うことなく）、ステップＳ１０９に移行する。
　一方、ステップＳ１０７では、運転支援部１００Ａ（分布設定部１３０Ｅ）は、ステップＳ１０４で算出した第１走行状態分布を、ステップＳ１０４で算出した第２走行状態分布で置き換えた後（以下、「リセット」とも呼ぶ）、ステップＳ１０９に移行する。
　一方、ステップＳ１０８では、運転支援部１００Ａ（分布設定部１３０Ｅ）は、ステップＳ１０４で算出した第１走行状態分布を、分布蓄積部１３０Ｃが蓄積している第２走行状態分布のうち、外乱運転状況の検出時よりも前に算出・蓄積した第２走行状態分布で置き換えた後（以下、「リストア」とも呼ぶ）、ステップＳ１０９に移行する。 In step S106, the driving support unit 100A (distribution setting unit 130E) does not replace the first traveling state distribution calculated in step S104 with the second traveling state distribution (without resetting (described later) or restoring (described later)). ), And proceeds to step S109.
On the other hand, in step S107, after the driving assistance unit 100A (distribution setting unit 130E) replaces the first traveling state distribution calculated in step S104 with the second traveling state distribution calculated in step S104 (hereinafter referred to as "reset") (Also referred to as “)”, and the process proceeds to step S109.
On the other hand, in step S108, the driving support unit 100A (distribution setting unit 130E) causes the first driving state distribution calculated in step S104 to be the disturbance driving state in the second driving state distribution stored in the distribution storage unit 130C. After replacing with the second traveling state distribution calculated and stored before the detection of (in the following, also referred to as "restoring"), the process proceeds to step S109.
　ステップＳ１０９では、運転支援部１００Ａ（運転不安定度判定部１４０）は、ステアリングエントロピー法によって、ステップＳ１０４で算出した第１走行状態分布（第２走行状態分布で置き換えた場合には、置き換え後の第１走行状態分布）および第２走行状態分布の分布間の相違量（相対エントロピーＲＨp）を算出する。なお、分布間の相違量（相対エントロピーＲＨp）の算出例については、後述する。これにより、運転支援部１００Ａ（運転不安定度判定部１４０）は、第１走行状態分布および第２走行状態分布に基づいて、運転者の現在の運転操作が普段の運転操作と比べてどう違うか、つまり、普段の運転操作と比べて不安定な状態であるか否かを判定するための相違量（相対エントロピーＲＨp）を算出する。すなわち、運転支援部１００Ａ（運転不安定度判定部１４０）は、運転操作の滑らかでない乱雑さを表す値として、相対エントロピーＲＨpを算出する。一般的に、運転者の注意が運転に集中していない状態では、操舵が行われない時間が運転に集中した正常運転時よりも長くなり、大きな操舵角の誤差が蓄積する。したがって、運転者の注意が運転に戻ったときの修正操舵量が大きくなるという傾向がある。 In step S109, the driving support unit 100A (the driving instability determination unit 140) uses the steering entropy method to replace the first running state distribution (if it is replaced with the second running state distribution) calculated in step S104. The amount of difference (relative entropy RHp) between the distribution of the first traveling state distribution) and the distribution of the second traveling state distribution is calculated. Note that an example of calculation of the difference between the distributions (relative entropy RHp) will be described later. Thus, the driving support unit 100A (the driving instability determination unit 140) determines the difference in the current driving operation of the driver compared with the normal driving operation based on the first traveling state distribution and the second traveling state distribution. That is, a difference amount (relative entropy RHp) for determining whether or not the vehicle is in an unstable state as compared to normal driving operation is calculated. That is, the driving support unit 100A (the driving instability determination unit 140) calculates the relative entropy RHp as a value representing the non-smoothness of the driving operation. In general, when the driver's attention is not concentrated on driving, the time when the steering is not performed is longer than that in normal driving where driving is concentrated, and a large steering angle error is accumulated. Therefore, there is a tendency that the correction steering amount when the driver's attention returns to the driving becomes large.
　続いてステップＳ１１０に移行して、運転支援部１００Ａ（運転不安定度判定部１４０）は、ステップＳ１０９で算出した分布間の相違量（相対エントロピーＲＨp）に基づいて運転者の運転状態を推定する（運転者の運転状態が不安定状態にあるか否かを判定する）。具体的には、運転支援部１００Ａ（運転不安定度判定部１４０）は、ステップＳ１０９で算出した分布間の相違量（相対エントロピーＲＨp）が予め定めた判定閾値よりも大きいか否かを判定する。そして、運転支援部１００Ａ（運転不安定度判定部１４０）は、分布間の相違量（相対エントロピーＲＨp）が判定閾値よりも大きいと判定した場合には、運転者の運転状態が不安定状態にあると判定する。一方、運転支援部１００Ａ（運転不安定度判定部１４０）は、分布間の相違量（相対エントロピーＲＨp）が判定閾値以下であると判定した場合には、運転者の運転状態が不安定状態にないと判定する。 Subsequently, the process proceeds to step S110, where the driving support unit 100A (the driving instability determination unit 140) estimates the driving state of the driver based on the difference amount (relative entropy RHp) between the distributions calculated in step S109. (Determining whether or not the driver's driving condition is unstable). Specifically, the driving support unit 100A (the driving instability determination unit 140) determines whether or not the amount of difference between the distributions (relative entropy RHp) calculated in step S109 is larger than a predetermined determination threshold. . When the driving support unit 100A (the driving instability determination unit 140) determines that the difference between the distributions (relative entropy RHp) is larger than the determination threshold, the driving state of the driver is unstable. Determine that there is. On the other hand, when the driving support unit 100A (the driving instability determination unit 140) determines that the amount of difference between the distributions (relative entropy RHp) is equal to or less than the determination threshold, the driving state of the driver becomes unstable. It is determined that there is not.
　続いてステップＳ１１１に移行して、運転支援部１００Ａ（情報呈示部１５０）は、ステップＳ１１０で推定した運転状態に基づいて運転者に呈示情報（運転者に呈示する警報その他の情報）を呈示する処理（情報呈示処理）を行う。具体的には、運転支援部１００Ａ（情報呈示部１５０）は、ステップＳ１１０で不安定状態と判定した状態が予め定めた不安定判定閾値（例えば、５秒）以上継続したか否かを判定する。そして、運転支援部１００Ａ（情報呈示部１５０）は、不安定状態と判定した状態が不安定判定閾値（例えば、５秒）以上継続したと判定した場合には、情報呈示処理を行う。一方、運転支援部１００Ａ（情報呈示部１５０）は、不安定状態と判定した状態が不安定判定閾値（例えば、５秒）以上継続していないと判定した場合には、情報呈示処理を行わない。
　情報呈示処理の例を、図６に示す。この例では、警告表示を行うとともに、「ピー！！そろそろ休憩しませんか。」等と音声で警告の呈示を行う。 Subsequently, the process proceeds to step S111, where the driving support unit 100A (information presenting unit 150) presents presentation information (alarm and other information to be presented to the driver) to the driver based on the driving state estimated in step S110. Perform processing (information presentation processing). Specifically, the driving support unit 100A (the information presenting unit 150) determines whether or not the state determined to be the unstable state in step S110 continues at least a predetermined instability determination threshold (for example, 5 seconds). . Then, when the driving support unit 100A (the information presenting unit 150) determines that the state determined to be the unstable state continues as the instability determination threshold (for example, 5 seconds) or more, the information presenting process is performed. On the other hand, when the driving support unit 100A (the information presenting unit 150) determines that the state determined as the unstable state does not continue beyond the instability determination threshold (for example, 5 seconds), the information presenting process is not performed. .
An example of the information presentation process is shown in FIG. In this example, a warning is displayed and a warning is presented by voice such as "Pee !!
（第１走行状態分布、第２走行状態分布の算出例）
　次に、第１走行状態分布、第２走行状態分布の算出例について説明する。
　図７は、第１走行状態分布、第２走行状態分布の算出例を表すフローチャートである。
　図７に示すように、まず、ステップＳ２０１では、走行状態分布算出部１３０（第１走行状態分布算出部１３０Ａ、第２走行状態分布算出部１３０Ｂ）は、相対エントロピーＲＨpを算出可能な走行場面であるか否かを判定する。具体的には、走行状態分布算出部１３０（第１走行状態分布算出部１３０Ａ、第２走行状態分布算出部１３０Ｂ）は、車速が予め定めた車速範囲（例えば、４０～１２０km/h）内にあるか否かを判定する。そして、走行状態分布算出部１３０は、車速が予め定めた車速範囲（４０km/h～１２０km/h）内にあると判定した場合には（Ｙｅｓ）、相対エントロピーＲＨpを算出可能な走行場面であると判定し、ステップＳ２０２に移行する。 (Example of calculation of first traveling state distribution and second traveling state distribution)
Next, calculation examples of the first traveling state distribution and the second traveling state distribution will be described.
FIG. 7 is a flowchart showing an example of calculation of the first traveling state distribution and the second traveling state distribution.
As shown in FIG. 7, first, in step S201, the traveling state distribution calculating unit 130 (the first traveling state distribution calculating unit 130A and the second traveling state distribution calculating unit 130B) calculates the relative entropy RHp in a traveling scene where it can be calculated. Determine if there is. Specifically, the traveling state distribution calculating unit 130 (the first traveling state distribution calculating unit 130A and the second traveling state distribution calculating unit 130B) calculates the vehicle speed within a predetermined vehicle speed range (for example, 40 to 120 km / h). Determine if there is. When the traveling state distribution calculating unit 130 determines that the vehicle speed is within the predetermined vehicle speed range (40 km / h to 120 km / h) (Yes), the traveling scene is capable of calculating the relative entropy RHp. It determines with it and transfers to step S202.
　一方、走行状態分布算出部１３０（第１走行状態分布算出部１３０Ａ、第２走行状態分布算出部１３０Ｂ）は、車速が予め定めた車速範囲（４０km/h～１２０km/h）外にあると判定した場合には（Ｎｏ）、相対エントロピーＲＨpを算出可能な走行場面ではないと判定し、この演算処理を終了する。これにより、走行状態分布算出部１３０（第１走行状態分布算出部１３０Ａ、第２走行状態分布算出部１３０Ｂ）は、車速が極端に遅い場合および極端に速い場合（４０km/h未満、または１２０km/h以上）を、相対エントロピーＲＨpを算出可能な走行場面から除外する。 On the other hand, the traveling state distribution calculating unit 130 (the first traveling state distribution calculating unit 130A, the second traveling state distribution calculating unit 130B) determines that the vehicle speed is outside the predetermined vehicle speed range (40 km / h to 120 km / h) If it is determined that the relative entropy RHp can not be calculated (No), it is determined that the traveling scene is not capable of calculating the relative entropy RHp, and the calculation process is ended. As a result, the traveling state distribution calculating unit 130 (the first traveling state distribution calculating unit 130A, the second traveling state distribution calculating unit 130B) detects an extremely slow vehicle speed and an extremely fast vehicle speed (less than 40 km / h or 120 km / h). h) or more) is excluded from the traveling situations where the relative entropy RHp can be calculated.
　ステップＳ２０２では、走行状態分布算出部１３０（第１走行状態分布算出部１３０Ａ、第２走行状態分布算出部１３０Ｂ）は、操舵角センサ３が出力した操舵角θを取得する。続いて、走行状態分布算出部１３０（第１走行状態分布算出部１３０Ａ、第２走行状態分布算出部１３０Ｂ）は、取得した操舵角θに基づいて操舵角予測誤差θeを算出する。ここで、図８に、相対エントロピーＲＨpを算出するために用いる特殊記号および該特殊記号の名称を示す。操舵角円滑値θn-tildeは、量子化ノイズの影響を低減した操舵角θである。また、操舵角の推定値θn-hatは、ステアリングホイールを滑らかに操作したと仮定してサンプリング時点における操舵角θを推定した値である。操舵角推定値θn-hatは、以下の（式１）に示すように、操舵角円滑値θn-tildeに対して二次のテイラー展開を施して得る。 In step S202, the traveling state distribution calculating unit 130 (first traveling state distribution calculating unit 130A, second traveling state distribution calculating unit 130B) acquires the steering angle θ output by the steering angle sensor 3. Subsequently, the traveling state distribution calculating unit 130 (first traveling state distribution calculating unit 130A, second traveling state distribution calculating unit 130B) calculates the steering angle prediction error θe based on the acquired steering angle θ. Here, FIG. 8 shows special symbols used to calculate the relative entropy RHp and names of the special symbols. The steering angle smooth value θn-tilde is a steering angle θ in which the influence of quantization noise is reduced. Further, the estimated value θn-hat of the steering angle is a value obtained by estimating the steering angle θ at the sampling time point on the assumption that the steering wheel is operated smoothly. The steering angle estimated value θn-hat is obtained by performing second-order Taylor expansion on the steering angle smooth value θn-tilde as shown in the following (Expression 1).
Figure JPOXMLDOC01-appb-M000001
Figure JPOXMLDOC01-appb-M000001
　（式１）において、ｔnは操舵角θnのサンプリング時刻である。
　操舵角円滑値θn-tildeは、量子化ノイズの影響を低減するために、３個の隣接した操舵角θnの平均値として以下の（式２）から算出する。 In equation (1), tn is a sampling time of the steering angle θn.
The steering angle smooth value θ n -tilde is calculated from the following (Expression 2) as an average value of three adjacent steering angles θ n in order to reduce the influence of quantization noise.
Figure JPOXMLDOC01-appb-M000002
Figure JPOXMLDOC01-appb-M000002
　（式２）において、ｌは、操舵角円滑値θn-tildeの算出時間間隔を１５０ミリ秒、すなわち、手動操作において人間が断続的に操作可能な最小時間間隔とした場合に、１５０ミリ秒内に含まれる操舵角θnのサンプル数を表す。
　操舵角θnのサンプリング間隔をＴsとすると、サンプル数ｌは、以下の（式３）で表しる。
　　　ｌ＝round（０．１５／Ｔs）　・・・（式３）
　（式３）において、ｋ＝１、２、３の値をとり、（ｋ＊１）により１５０ミリ秒間隔の操舵角とそれに隣接する合計３個の操舵角θnに基づいて、円滑値θn-tildeを求めることができる。したがって、このような円滑値θn-tildeに基づいて算出する推定値θn-hatは、実質的に１５０ミリ秒間隔で得た操舵角θにより算出したことになる。
　サンプリング時点における操舵角予測誤差θeは、ステアリングホイールが滑らかに操作したと仮定した場合のサンプリング時点における操舵角推定値θn-hatと実際の操舵角θnとの差として、以下の（式４）から算出できる。 In equation (2), l is within 150 milliseconds when the calculation time interval of the steering angle smooth value θ n -tilde is 150 milliseconds, that is, the minimum time interval at which a human can intermittently operate in manual operation. Represents the number of samples of the steering angle θ n included in
Assuming that the sampling interval of the steering angle θn is Ts, the number of samples l is expressed by the following (Equation 3).
l = round (0.15 / Ts) (Equation 3)
In equation (3), values of k = 1, 2, 3 are taken, and (k * 1) makes the smooth value θ n − based on the steering angle of 150 millisecond intervals and the total of three steering angles θ n adjacent thereto. You can ask for tilde. Therefore, the estimated value θn-hat calculated based on such a smooth value θn-tilde is calculated by the steering angle θ obtained substantially at an interval of 150 milliseconds.
The steering angle prediction error θe at the sampling time point is the difference between the steering angle estimated value θn-hat at the sampling time point and the actual steering angle θn when assuming that the steering wheel is operated smoothly from the following (Equation 4) It can be calculated.
Figure JPOXMLDOC01-appb-M000003
Figure JPOXMLDOC01-appb-M000003
　ただし、操舵角予測誤差θeは、手動操作において人間が断続的に操作可能な最小時間間隔、すなわち、１５０ミリ秒毎の操舵角θnに対してのみ算出するものとする。
　以下に、操舵角予測誤差θeの具体的な算出方法を説明する。なお、操舵角θのサンプリング間隔Ｔsは、例えば、５０ミリ秒とする。まず、１５０ミリ秒間隔の隣接する３個の操舵角θnを用いて、上記（式２）から３個の操舵角円滑値θn-tildeを算出する。３個の操舵角円滑値θn-tildeは、以下の（式５）で表す。 However, the steering angle prediction error θe is calculated only for the minimum time interval at which a human can intermittently operate in manual operation, that is, the steering angle θn every 150 milliseconds.
A specific method of calculating the steering angle prediction error θe will be described below. The sampling interval Ts of the steering angle θ is, for example, 50 milliseconds. First, three steering angle smooth values θn-tilde are calculated from the above (formula 2) using three adjacent steering angles θn at an interval of 150 milliseconds. The three steering angle smooth values θ n -tilde are expressed by the following (Expression 5).
Figure JPOXMLDOC01-appb-M000004
Figure JPOXMLDOC01-appb-M000004
　次に、算出した３個の操舵角円滑値θn-tildeを用いて、上記（式１）から操舵角の推定値θn-hatを算出する。推定値θn-hatは、以下の（式６）で表す。 Next, an estimated value θn-hat of the steering angle is calculated from the above (formula 1) using the calculated three steering angle smooth values θn-tilde. The estimated value θ n -hat is expressed by the following (Expression 6).
Figure JPOXMLDOC01-appb-M000005
Figure JPOXMLDOC01-appb-M000005
　そして、算出した操舵角推定値θn-hatと実際の操舵角θnとを用いて、上記（式４）から操舵角予測誤差θeを算出する。
　続いてステップＳ２０３に移行して、走行状態分布算出部１３０（第１走行状態分布算出部１３０Ａ、第２走行状態分布算出部１３０Ｂ）は、現時点までに算出し、コントローラ１００のメモリに蓄積している設定時間Ｔ秒（例えば、２１６０秒）間の操舵角予測誤差θeを、ステップＳ２０２で算出した操舵角予測誤差θeの現在値を加えて更新する。すなわち、走行状態分布算出部１３０（第１走行状態分布算出部１３０Ａ、第２走行状態分布算出部１３０Ｂ）は、コントローラ１００のメモリに蓄積している操舵角予測誤差θeのうち最も古いＴ秒（２１６０秒）前のデータを捨てて、代わりに最新の操舵角予測誤差θeとして、ステップＳ２０２で算出した操舵角予測誤差θeの現在値を蓄積する。これにより、走行状態分布算出部１３０（第１走行状態分布算出部１３０Ａ、第２走行状態分布算出部１３０Ｂ）は、コントローラ１００のメモリに現在値からＴ秒（２１６０秒前）前までの操舵角予測誤差θeを蓄積する。 Then, using the calculated steering angle estimated value θn-hat and the actual steering angle θn, the steering angle prediction error θe is calculated from the above (Equation 4).
Subsequently, the process proceeds to step S203, and the traveling state distribution calculating unit 130 (the first traveling state distribution calculating unit 130A, the second traveling state distribution calculating unit 130B) calculates up to the present time and stores it in the memory of the controller 100. The steering angle prediction error θe during the set time T seconds (for example, 2160 seconds) is updated by adding the current value of the steering angle prediction error θe calculated in step S202. That is, the traveling state distribution calculating unit 130 (the first traveling state distribution calculating unit 130A, the second traveling state distribution calculating unit 130B) calculates the oldest T seconds of the steering angle prediction error θe stored in the memory of the controller 100. Instead, the current value of the steering angle prediction error θe calculated in step S202 is accumulated as the latest steering angle prediction error θe. As a result, the driving state distribution calculating unit 130 (the first driving state distribution calculating unit 130A, the second driving state distribution calculating unit 130B) stores the steering angle from the current value to T seconds (2160 seconds before) in the memory of the controller 100. The prediction error θe is accumulated.
　図９、図１０は、第１走行状態分布および第２走行状態分布の算出方法を説明するための図である。図１１は、予測誤差区分ｂｉの範囲を表す図である。
　続いてステップＳ２０４に移行して、走行状態分布算出部１３０（第１走行状態分布算出部１３０Ａ）は、コントローラ１００のメモリに蓄積している操舵角予測誤差θeに基づいて第１走行状態分布を算出する。具体的には、走行状態分布算出部１３０（第１走行状態分布算出部１３０Ａ）は、図９、図１０、図１１に示すように、コントローラ１００のメモリに蓄積している操舵角予測誤差θeのうち、設定時間Ｔ秒（例えば、２１６０秒）前（トリップの開始時から設定時間（２１６０秒）が経過していない場合には、１８０秒前）から現在までの２１６０秒分の操舵角予測誤差θeを９個の予測誤差区分ｂi（＝ｂ１、ｂ２、ｂ３、ｂ４、ｂ５、ｂ６、ｂ７、ｂ８、ｂ９）に分類する。 FIGS. 9 and 10 are diagrams for explaining a method of calculating the first traveling state distribution and the second traveling state distribution. FIG. 11 is a diagram illustrating the range of the prediction error class bi.
Subsequently, the process proceeds to step S204, and the traveling state distribution calculating unit 130 (first traveling state distribution calculating unit 130A) calculates the first traveling state distribution based on the steering angle prediction error θe accumulated in the memory of the controller 100. calculate. Specifically, as shown in FIG. 9, FIG. 10, and FIG. 11, the traveling state distribution calculating unit 130 (first traveling state distribution calculating unit 130A) calculates the steering angle prediction error θe accumulated in the memory of the controller 100. Steering angle prediction for 2160 seconds from the setting time T seconds (for example, 2160 seconds) (180 seconds before when the setting time (2160 seconds) has not elapsed from the start of the trip) The error θe is classified into nine prediction error sections bi (= b1, b2, b3, b4, b5, b6, b7, b8, b9).
　予測誤差区分ｂｉの範囲は、ステアリングエントロピーの算出に用いるα値に基づいて設定する。α値としては、例えば、操舵角θの時系列データに基づいて一定時間内の操舵角予測誤差θe、すなわち、ステアリングホイールを滑らかに操作したと仮定した場合の操舵角推定値θn-hatと実際の操舵角θnとの差を求め操舵角予測誤差θeの分布（ばらつき）を測定して９０パーセントタイル値（操舵角予測誤差θeの９０％が含まれる分布の範囲）を算出したものがある。すなわち、α値は、操舵角予測誤差θeの９０％が区間[－α、α]の中に含まれるように設定する。 The range of the prediction error class bi is set based on the α value used to calculate the steering entropy. As the α value, for example, the steering angle prediction error θe within a fixed time based on time series data of the steering angle θ, that is, the steering angle estimated value θn-hat when assuming that the steering wheel is operated smoothly The 90% tile value (the range of the distribution including 90% of the steering angle prediction error θe) is calculated by obtaining the difference with the steering angle θn and measuring the distribution (variation) of the steering angle prediction error θe. That is, the α value is set such that 90% of the steering angle prediction error θe is included in the section [−α, α].
　具体的には、予測誤差区分ｂ１は５α未満とし、予測誤差区分ｂ２は－５α以上で且つ－２．５α未満とし、予測誤差区分ｂ３は－２．５α以上で且つ－α未満とし、予測誤差区分ｂ４は－α以上で且つ－０．５α未満とし、予測誤差区分ｂ５は－０．５α以上で且つ０．５α未満とする。また、予測誤差区分ｂ６は０．５α以上で且つα未満とし、予測誤差区分ｂ７はα以上で且つ２．５α未満とし、予測誤差区分ｂ８は２．５α以上で且つ５α未満とし、予測誤差区分ｂ９は５α以上とする。予測誤差区分ｂｉ（＝ｂ１～ｂ９）の範囲は、第１走行状態分布および第２走行状態分布について同じものを用いる。続いて、走行状態分布算出部１３０（第１走行状態分布算出部１３０Ａ）は、各予測誤差区分ｂｉに含まれる操舵角予測誤差θeの度数の全度数に対する確率ｐi（＝ｐ１、ｐ２、ｐ３、ｐ４、ｐ５、ｐ６、ｐ７、ｐ８、ｐ９）を求める。 Specifically, the prediction error class b1 is less than 5α, the prediction error class b2 is -5α or more and less than -2.5α, and the prediction error class b3 is -2.5α or more and less than -α. The segment b4 is greater than or equal to -α and less than -0.5α, and the prediction error segment b5 is greater than or equal to -0.5α and less than 0.5α. The prediction error class b6 is 0.5α or more and less than α, the prediction error class b7 is α or more and less than 2.5α, and the prediction error class b8 is 2.5α or more and less than 5α. b9 is 5α or more. The range of the prediction error class bi (= b1 to b9) is the same for the first traveling state distribution and the second traveling state distribution. Subsequently, the traveling state distribution calculating unit 130 (first traveling state distribution calculating unit 130A) calculates the probability pi (= p1, p2, p3, and the like) of the steering angle prediction error θe included in each prediction error class bi with respect to the total frequency. Determine p4, p5, p6, p7, p8, p9).
　続いてステップＳ２０５に移行して、走行状態分布算出部１３０（第２走行状態分布算出部１３０Ｂ）は、コントローラ１００のメモリに蓄積している操舵角予測誤差θeに基づいて第２走行状態分布を算出した後、この演算処理を終了する。具体的には、走行状態分布算出部１３０（第２走行状態分布算出部１３０Ｂ）は、コントローラ１００のメモリに蓄積している操舵角予測誤差θeのうち、現在から直近の９０秒前（トリップの開始時から設定時間（２１６０秒）が経過していない場合には、１２０秒前）までの操舵角予測誤差θeを９個の予測誤差区分ｂi（＝ｂ１～ｂ９）に分類する。続いて、走行状態分布算出部１３０（第２走行状態分布算出部１３０Ｂ）は、各予測誤差区分ｂｉに含まれる操舵角予測誤差θeの度数の全度数に対する確率ｑi（＝ｑ１、ｑ２、ｑ３、ｑ４、ｑ５、ｑ６、ｑ７、ｑ８、ｑ９）を求める。
（相対エントロピーＲＨpの算出例）
　相対エントロピーＲＨpの算出例について説明する。
　相対エントロピーＲＨpは、ステップＳ２０４で算出した確率ｐiおよびステップＳ２０５で算出した確率ｑiに基づき、以下の（式７）から算出する。 Subsequently, the process proceeds to step S205, and the traveling state distribution calculating unit 130 (second traveling state distribution calculating unit 130B) calculates the second traveling state distribution based on the steering angle prediction error θe accumulated in the memory of the controller 100. After calculation, this arithmetic processing ends. Specifically, the traveling state distribution calculating unit 130 (the second traveling state distribution calculating unit 130B) calculates 90 seconds before the current (the time of the trip) of the steering angle prediction errors .theta.e accumulated in the memory of the controller 100. If the set time (2160 seconds) has not elapsed from the start time, the steering angle prediction error θe up to 120 seconds before is classified into nine prediction error classes bi (= b1 to b9). Subsequently, the traveling state distribution calculating unit 130 (second traveling state distribution calculating unit 130B) calculates the probability qi (= q1, q2, q3, etc.) with respect to the total frequency of the steering angle prediction error θe included in each prediction error class bi. Find q4, q5, q6, q7, q8, q9).
(Example of calculation of relative entropy RHp)
An example of calculation of the relative entropy RHp will be described.
The relative entropy RHp is calculated from the following (Equation 7) based on the probability pi calculated in step S204 and the probability qi calculated in step S205.
Figure JPOXMLDOC01-appb-M000006
Figure JPOXMLDOC01-appb-M000006
　上記（式７）より、相対エントロピーＲＨpは、第１走行状態分布の確率ｐi（＝ｐ１～ｐ９）と第２走行状態分布の確率ｑi（＝ｑ１～ｑ９）とが等しい場合には０になる。一方、相対エントロピーＲＨpは、第１走行状態分布の確率ｐiと第２走行状態分布の確率ｑiとがずれている場合にはずれが大きくなるほど大きな値になる。 From the above (Equation 7), the relative entropy RHp is 0 when the probability pi (= p1 to p9) of the first running state distribution and the probability qi (= q1 to q9) of the second running state distribution are equal. . On the other hand, when the probability pi of the first running state distribution and the probability qi of the second running state distribution deviate, the relative entropy RHp becomes a larger value as the deviation becomes larger.
（動作その他）
　次に、本実施形態の車両用情報提供装置を搭載した車両の動作について説明する。
　運転者が、イグニッションスイッチ（不図示）をオン状態とし、トリップを開始したとする。そして、トリップの開始時からの経過時間が設定時間（例えば、２１６０秒）未満であるときに、車両がカーブに進入したとする。すると、運転支援部１００Ａ（運転状況判定部１２０）が、カーブ動作が行われたと判定し、現在の車両の運転状況が外乱運転状況にあると判定する（図５のステップＳ１０３）。続いて、運転支援部１００Ａ（第１走行状態分布算出部１３０Ａ、第２走行状態分布算出部１３０Ｂ）が、第１走行状態分布（予め定めた相対的に長い時間的範囲で取得した操舵角情報で求めた走行状態分布）および第２走行状態分布（第１走行状態分布よりも短い時間的範囲で取得した走行状態分布）を算出する（図５のステップＳ１０４）。続いて、運転支援部１００Ａ（第１走行状態分布算出部１３０Ａ、第２走行状態分布算出部１３０Ｂ）が、算出した第２走行状態分布を分布蓄積部１３０Ｃに蓄積する（図５のステップＳ１０４）。 (Operation other)
Next, the operation of the vehicle equipped with the information providing apparatus for vehicles of the present embodiment will be described.
It is assumed that the driver turns on an ignition switch (not shown) and starts tripping. Then, it is assumed that the vehicle has entered a curve when the elapsed time from the start of the trip is less than a set time (for example, 2160 seconds). Then, the driving support unit 100A (the driving condition determination unit 120) determines that the curve operation is performed, and determines that the current driving condition of the vehicle is in the disturbance driving condition (step S103 in FIG. 5). Subsequently, the steering angle information acquired by the driving assistance unit 100A (the first traveling state distribution calculating unit 130A, the second traveling state distribution calculating unit 130B) in the first traveling state distribution (predetermined relatively long time range) The traveling state distribution obtained in step d) and the second traveling state distribution (the traveling state distribution acquired in a temporal range shorter than the first traveling state distribution) are calculated (step S104 in FIG. 5). Subsequently, the driving support unit 100A (first traveling state distribution calculating unit 130A, second traveling state distribution calculating unit 130B) stores the calculated second traveling state distribution in the distribution storage unit 130C (step S104 in FIG. 5). .
　続いて、運転支援部１００Ａ（分布選択部１３０Ｄ）が、ステップＳ１０３の判定結果に基づき現在の車両の運転状況が外乱運転状況にあると判定する（図５のステップＳ１０５）。続いて、運転支援部１００Ａ（分布選択部１３０Ｄ）が、トリップの開始時からの経過時間が設定時間（２１６０秒）未満であると判定する（図５のステップＳ１０５）。続いて、運転支援部１００Ａ（分布設定部１３０Ｅ）が、ステップＳ１０４で算出した第１走行状態分布をステップＳ１０４で算出した第２走行状態分布で置き換える（リセットする）（図３のステップＳ１０７）。これにより、運転支援部１００Ａが、第１走行状態分布の確率ｐi（＝ｐ１～ｐ９）と第２走行状態分布の確率ｑi（＝ｑ１～ｑ９）とを等しくする。 Subsequently, the driving support unit 100A (distribution selecting unit 130D) determines that the current driving condition of the vehicle is in the disturbance driving condition based on the determination result of step S103 (step S105 of FIG. 5). Subsequently, the driving support unit 100A (distribution selection unit 130D) determines that the elapsed time from the start of the trip is less than the set time (2160 seconds) (step S105 in FIG. 5). Subsequently, the driving support unit 100A (distribution setting unit 130E) replaces (resets) the first traveling state distribution calculated in step S104 with the second traveling state distribution calculated in step S104 (step S107 in FIG. 3). Thus, the driving support unit 100A equalizes the probability pi (= p1 to p9) of the first traveling state distribution and the probability qi (= q1 to q9) of the second traveling state distribution.
　続いて、運転支援部１００Ａ（運転不安定度判定部１４０）が、置き換えた第１走行状態分布、および第２走行状態分布の分布間の相違量（相対エントロピーＲＨp＝０）を算出する（図５のステップＳ１０９）。続いて、運転支援部１００Ａ（運転不安定度判定部１４０）は、相対エントロピーＲＨp（＝０）が判定閾値以下であると判定し、運転状態を推定する（不安定状態にはないと判定する。図５のステップＳ１１０）。続いて、運転支援部１００Ａ（情報呈示部１５０）は、ステップＳ１１０で不安定状態と判定した状態が不安定判定閾値（例えば、５秒）以上継続していないと判定し、情報呈示処理を行わない（図５のステップＳ１１０）。ここで、運転支援部１００Ａは、第１走行状態分布、および第２走行状態分布を、操舵角情報（走行状態データ）に基づき算出している（図５のステップＳ１０４）。それゆえ、運転支援部１００Ａは、運転の不安定度を判定（運転の不安定状態を計測）するための操舵角情報（走行状態データ）だけを取得したい。 Subsequently, the driving support unit 100A (the driving instability determination unit 140) calculates the amount of difference (relative entropy RHp = 0) between the replaced first traveling state distribution and the distribution of the second traveling state distribution (FIG. Step S109 of 5). Subsequently, the driving support unit 100A (the driving instability determination unit 140) determines that the relative entropy RHp (= 0) is equal to or less than the determination threshold, and estimates the driving state (determines that the state is not unstable). Step S110 of FIG. Subsequently, the driving support unit 100A (the information presenting unit 150) determines that the state determined to be unstable in step S110 does not continue beyond the instability determination threshold (for example, 5 seconds), and performs the information presenting process. No (step S110 in FIG. 5). Here, the driving support unit 100A calculates the first traveling state distribution and the second traveling state distribution based on the steering angle information (traveling state data) (step S104 in FIG. 5). Therefore, the driving support unit 100A wants to obtain only steering angle information (running state data) for determining the degree of driving instability (measuring the driving instability).
　しかしながら、運転支援部１００Ａは、運転操作子の操作状態や走行環境に起因する操舵変化等が発生する運転状況（外乱運転状況）でステアリングホイールの操作に乱れが発生する場合がある。それゆえ、運転支援部１００Ａは、ステアリングホイールの操作の乱れによる操舵角を含んだ操舵角情報を使用した走行状態分布（第１走行状態分布）を使用すると、運転の不安定状態の検出精度が悪くなる可能性がある。これに対し、本実施形態の運転支援部１００Ａは、運転の不安定状態に対して外乱となる運転状況（外乱運転状況）を検出すると、トリップの開始時からの経過時間が設定時間（例えば、２１６０秒）未満である場合、外乱運転状況を検出しているときの走行状態データによる第１走行状態分布を第２走行状態分布で置き換える（図５のステップＳ１０５、Ｓ１０７）。これにより、本実施形態の運転支援部１００Ａは、運転の不安定度の判定（運転の不安定状態の計測）において、不安定状態にあると誤検知することを防止できる。 However, in the driving support unit 100A, disturbance may occur in the operation of the steering wheel in a driving condition (disturbing driving condition) in which a steering change or the like due to the operation state of the driving operation element or the traveling environment occurs. Therefore, when the driving support unit 100A uses the traveling state distribution (first traveling state distribution) using the steering angle information including the steering angle due to the disturbance of the operation of the steering wheel, the detection accuracy of the driving unstable state is It can get worse. On the other hand, when the driving support unit 100A according to the present embodiment detects a driving situation (disturbing driving situation) that causes disturbance with respect to the unstable state of driving, an elapsed time from the start of the trip is a set time (for example, If it is less than 2160 seconds, the first traveling state distribution based on the traveling state data when the disturbance driving state is detected is replaced with the second traveling state distribution (steps S105 and S107 in FIG. 5). As a result, the driving support unit 100A of the present embodiment can prevent the erroneous detection of being in the unstable state in the determination of the driving instability (the measurement of the driving unstable state).
　また、上記カーブを走行中に、トリップの開始時からの経過時間が設定時間（例えば、２１６０秒）以上になったとする。すると、運転支援部１００Ａ（分布選択部１３０Ｄ）が、トリップの開始時からの経過時間が設定時間（例えば、２１６０秒）以上であると判定する（図５のステップＳ１０５）。続いて、運転支援部１００Ａ（分布設定部１３０Ｅ）が、ステップＳ１０４で算出した第１走行状態分布を、分布蓄積部１３０Ｃに蓄積している第２走行状態分布のうちから、外乱運転状況の検出時よりも前に算出・蓄積し第２走行状態分布で置き換える（リストアする）（図３のステップＳ１０８）。 Further, it is assumed that while traveling on the curve, an elapsed time from the start of the trip becomes equal to or longer than a set time (for example, 2160 seconds). Then, the driving support unit 100A (distribution selecting unit 130D) determines that the elapsed time from the start of the trip is equal to or longer than a set time (for example, 2160 seconds) (step S105 in FIG. 5). Subsequently, the driving support unit 100A (distribution setting unit 130E) detects the disturbance driving condition from the second driving condition distribution stored in the distribution storage unit 130C, the first driving condition distribution calculated in step S104. The second driving state distribution is replaced (restored) by calculation / accumulation before time (step S108 in FIG. 3).
　続いて、運転支援部１００Ａ（運転不安定度判定部１４０）が、置き換えた第１走行状態分布、および第２走行状態分布の分布間の相違量（相対エントロピーＲＨp）を算出する（図５のステップＳ１０９）。続いて、運転支援部１００Ａ（運転不安定度判定部１４０）は、相対エントロピーＲＨpに基づき、運転状態を推定する（不安定状態であるか否かを判定する。図５のステップＳ１１０）。続いて、運転支援部１００Ａ（情報呈示部１５０）は、ステップＳ１１０で不安定状態と判定した状態が不安定判定閾値（例えば、５秒）以上継続しているか否かを判定し、判定結果に基づき情報呈示処理を判定する（図５のステップＳ１１０）。ここで、本実施形態の運転支援部１００Ａは、運転の不安定状態に対して外乱となる運転状況（外乱運転状況）を検出すると、トリップの開始時からの経過時間が設定時間（例えば、２１６０秒）以上である場合、外乱運転状況を検出しているときの走行状態データによる第１走行状態分布を、外乱運転状況の検出時よりも前に算出・蓄積し第２走行状態分布で置き換える（図５のステップＳ１０５、Ｓ１０８）。これにより、本実施形態の運転支援部１００Ａは、運転の不安定度の判定（運転の不安定状態の計測）において、不安定状態にあると誤検知することを防止できる。 Subsequently, the driving support unit 100A (the driving instability determination unit 140) calculates the difference amount (relative entropy RHp) between the replaced first traveling state distribution and the distribution of the second traveling state distribution (FIG. 5 Step S109). Subsequently, the driving support unit 100A (the driving instability determination unit 140) estimates the driving state (determines whether the driving state is the unstable state or not) based on the relative entropy RHp (step S110 in FIG. 5). Subsequently, the driving support unit 100A (the information presenting unit 150) determines whether or not the state determined to be unstable in step S110 continues more than the instability determination threshold (for example, 5 seconds), and the determination result is The information presentation process is determined based on the information (step S110 in FIG. 5). Here, when the driving support unit 100A according to the present embodiment detects a driving situation (disturbing driving situation) that causes disturbance with respect to the unstable state of driving, an elapsed time from the start of the trip is a set time (for example, 2160). If it is more than one second, the first traveling state distribution based on the traveling state data when the disturbance driving state is detected is calculated and accumulated before the detection of the disturbance driving state, and replaced with the second traveling state distribution ( Steps S105 and S108 in FIG. As a result, the driving support unit 100A of the present embodiment can prevent the erroneous detection of being in the unstable state in the determination of the driving instability (the measurement of the driving unstable state).
　本実施形態では、図３の走行状態データ取得部１１０、図５のステップＳ１０１が走行状態データ取得部を構成する。以下同様に、図３の走行状態分布算出部１３０、図４の第１走行状態分布算出部１３０Ａ、第２走行状態分布算出部１３０Ｂ、図５のステップＳ１０４が走行状態分布算出部を構成する。また、図３の走行状態分布算出部１３０、図４の分布選択部１３０Ｄ、分布設定部１３０Ｅ、図５のステップＳ１０５、Ｓ１０７、Ｓ１０８が設定変更部を構成する。さらに、図３の運転不安定度判定部１４０、図５のステップＳ１１０が運転状態推定部を構成する。また、図３の情報呈示部１５０、図５のステップＳ１１１が情報呈示部を構成する。さらに、図３の運転状況判定部１２０、図５のステップＳ１０３が運転状況判定部を構成する。 In the present embodiment, the traveling state data acquisition unit 110 of FIG. 3 and step S101 of FIG. 5 constitute a traveling state data acquisition unit. Likewise, the traveling state distribution calculating unit 130 in FIG. 3, the first traveling state distribution calculating unit 130A in FIG. 4, the second traveling state distribution calculating unit 130B, and step S104 in FIG. 5 constitute a traveling state distribution calculating unit. Further, the traveling state distribution calculating unit 130 of FIG. 3, the distribution selecting unit 130D of FIG. 4, the distribution setting unit 130E, and steps S105, S107, and S108 of FIG. 5 constitute a setting changing unit. Furthermore, the driving instability determination unit 140 of FIG. 3 and step S110 of FIG. 5 constitute a driving condition estimation unit. Further, the information presentation unit 150 of FIG. 3 and step S111 of FIG. 5 constitute an information presentation unit. Furthermore, the driving condition determination unit 120 of FIG. 3 and step S103 of FIG. 5 constitute a driving condition determination unit.
（本実施形態の効果）
　本実施形態は、次のような効果を奏する。
（１）運転支援部１００Ａは、走行状態データ（例えば、操舵角情報）に基づいて時間的範囲の異なる複数の走行状態分布（例えば、第１走行状態分布、第２走行状態分布）を算出する。続いて、運転支援部１００Ａは、算出した複数の走行状態分布（例えば、第１走行状態分布、第２走行状態分布）に基づいて運転者の運転状態を推定する（例えば、不安定状態にあるか否かを判定する）。その際、運転支援部１００Ａは、車両の走行に関する情報（例えば、トリップの開始時からの経過時間）に基づいて運転支援部１００Ａが算出する走行状態分布（例えば、第１走行状態分布）を変更する。
　このような構成により、車両の走行に関する情報（例えば、トリップの開始時からの経過時間）に基づいて運転支援部１００Ａが算出する走行状態分布（例えば、第１走行状態分布）を変更する。これによって、走行状態分布（例えば、第１走行状態分布）をより適切に算出でき、運転状態（例えば、不安定状態にあるか否か）の推定精度を向上できる。 (Effect of this embodiment)
The present embodiment has the following effects.
(1) The driving support unit 100A calculates a plurality of traveling state distributions (for example, the first traveling state distribution, the second traveling state distribution) having different temporal ranges based on the traveling state data (for example, the steering angle information). . Subsequently, the driving support unit 100A estimates the driving state of the driver based on the calculated plurality of traveling state distributions (for example, the first traveling state distribution, the second traveling state distribution) (for example, in an unstable state) To determine whether or not At that time, the driving support unit 100A changes the traveling state distribution (for example, the first traveling state distribution) calculated by the driving support unit 100A based on the information (for example, the elapsed time since the start of the trip) regarding the traveling of the vehicle. Do.
With such a configuration, the traveling state distribution (for example, the first traveling state distribution) calculated by the driving support unit 100A is changed based on the information on the traveling of the vehicle (for example, the elapsed time since the start of the trip). As a result, the traveling state distribution (for example, the first traveling state distribution) can be calculated more appropriately, and the estimation accuracy of the driving state (for example, whether or not it is in the unstable state) can be improved.
（２）運転支援部１００Ａは、トリップの開始時からの経過時間に基づいて運転支援部１００Ａが算出する走行状態分布（例えば、第１走行状態分布）を変更する。
　このような構成により、例えば、トリップの開始時からの経過時間が短く、取得した走行状態データが少ない場合に、少ない走行状態データに応じた算出方法を設定できる。
（３）運転支援部１００Ａは、運転状態の推定（例えば、運転の不安定状態の計測）に対して外乱となる運転状況（例えば、外乱運転状況）を検出すると、トリップの開始時からの経過時間が設定時間（例えば、２１６０秒）未満である場合に、運転状態の推定（例えば、運転の不安定状態の計測）に対して外乱となる運転状況（例えば、外乱運転状況）を検出したときよりも前に算出した第２走行状態分布を選択し、トリップの開始時からの経過時間が設定時間（例えば、２１６０秒）以上である場合に、現在の第２走行状態分布を選択し、選択した第２走行状態分布で第１走行状態分布を置き換える。
　このような構成により、運転状態の推定精度をより適切に向上できる。 (2) The driving support unit 100A changes the traveling state distribution (for example, the first traveling state distribution) calculated by the driving support unit 100A based on the elapsed time from the start of the trip.
With such a configuration, for example, when the elapsed time from the start of the trip is short and the acquired traveling state data is small, it is possible to set the calculation method according to the small traveling state data.
(3) When the driving support unit 100A detects a driving situation (for example, a disturbance driving situation) which is a disturbance for the estimation of the driving state (for example, measurement of the unstable state of the driving), the process from the start of the trip When a driving situation (for example, a disturbance driving situation) that is a disturbance with respect to the estimation of the driving state (for example, measurement of the unstable state of driving) is detected when the time is less than a set time (for example, 2160 seconds) Select the second travel state distribution calculated earlier and select the current second travel state distribution if the elapsed time from the start of the trip is greater than or equal to the set time (eg, 2160 seconds) The first traveling state distribution is replaced by the second traveling state distribution.
Such a configuration can more appropriately improve the estimation accuracy of the driving state.
（第２実施形態）
　次に、本発明に係る第２実施形態について図面を参照しつつ説明する。
　なお、上記第１実施形態と同様な構成等については同一の符号を使用する。
　本実施形態は、運転支援部１００Ａ（運転不安定度判定部１４０）が、運転の不安定度の判定（運転の不安定状態の計測）に対して、外乱となる運転状況にあると判定した時間の累積値ＴAとトリップの開始時からの経過時間の累積値ＴBとの比率ＴA／ＴBを算出し、算出した比率ＴA／ＴBに基づいて運転支援部１００Ａが算出する走行状態分布（第１走行状態分布、第２走行状態分布）を変更する点が第１実施形態と異なる。具体的には、本実施形態は、第１実施形態とは、図５のステップＳ１０９の内容が異なっている。 Second Embodiment
Next, a second embodiment according to the present invention will be described with reference to the drawings.
The same reference numerals are used for the same configuration as that of the first embodiment.
In the present embodiment, the driving support unit 100A (the driving instability determination unit 140) determines that the driving situation is the disturbance with respect to the determination of the driving instability (the measurement of the driving instability). The ratio T A / T B between the cumulative value T A of time and the cumulative value T B of the elapsed time from the start of the trip is calculated, and the driving support unit 100A calculates based on the calculated ratio T A / T B The point which changes driving state distribution (the 1st driving state distribution, the 2nd driving state distribution) differs from a 1st embodiment. Specifically, the present embodiment is different from the first embodiment in the contents of step S109 in FIG.
　ステップＳ１０９では、運転支援部１００Ａ（運転不安定度判定部１４０）は、ステアリングエントロピー法によって、ステップＳ１０４で算出した第１走行状態分布（第２走行状態分布で置き換えた場合には、置き換え後の第１走行状態分布）および第２走行状態分布の分布間の相違量（相対エントロピーＲＨp）を算出する。その際、運転支援部１００Ａ（運転不安定度判定部１４０）は、運転の不安定度の判定（運転の不安定状態の計測）に対して、外乱となる運転状況にあると判定した時間の累積値ＴAとトリップの開始時からの経過時間の累積値ＴBとの比率ＴA／ＴB（以下、「マスク割合」とも呼ぶ）を算出する。続いて、運転支援部１００Ａ（運転不安定度判定部１４０）は、算出したマスク割合ＴA／ＴBに対応したα値を制御マップＭから読み出す。これにより、運転支援部１００Ａ（運転不安定度判定部１４０）は、算出した比率ＴA／ＴBに基づいて運転支援部１００Ａ（第１走行状態分布算出部１３０Ａ、第２走行状態分布算出部１３０Ｂ）が算出する走行状態分布（第１走行状態分布、第２走行状態分布）を変更する。 In step S109, the driving support unit 100A (the driving instability determination unit 140) uses the steering entropy method to replace the first running state distribution (if it is replaced with the second running state distribution) calculated in step S104. The amount of difference (relative entropy RHp) between the distribution of the first traveling state distribution) and the distribution of the second traveling state distribution is calculated. At that time, the driving support unit 100A (the driving instability determination unit 140) determines that the driving situation is a disturbance with respect to the determination of the driving instability (the measurement of the driving instability). A ratio T A / T B (hereinafter also referred to as “mask ratio”) between the cumulative value T A and the cumulative value T B of the elapsed time from the start of the trip is calculated. Subsequently, the driving support unit 100A (the driving instability determination unit 140) reads an α value corresponding to the calculated mask ratio T A / T B from the control map M. Thereby, the driving support unit 100A (the driving instability determination unit 140) calculates the driving support unit 100A (the first traveling state distribution calculating unit 130A, the second traveling state distribution calculating unit) based on the calculated ratio T A / T B. The traveling state distribution (first traveling state distribution, second traveling state distribution) calculated by 130B) is changed.
　図１２は、制御マップＭを表すグラフである。
　図１２に示すように、制御マップＭは、マスク割合ＴA／ＴBが第１設定値ＴA／ＴB1（例えば、０．２）以上で且つ第２設定値ＴA／ＴB2（＞ＴA／ＴB1。例えば、０．８）未満の範囲では、マスク割合ＴA／ＴBの大きさにかかわらず、α値を予め定めた設定値に設定する。また、制御マップＭは、マスク割合ＴA／ＴBが第１設定値ＴA／ＴB1未満の範囲では、マスク割合ＴA／ＴBの減少に応じてα値を設定値から直線的に減少させる。さらに、制御マップＭは、マスク割合ＴA／ＴBが第２設定値ＴA／ＴB2以上の範囲では、マスク割合ＴA／ＴBの増加に応じてα値を設定値から直線的に増加させる。 FIG. 12 is a graph showing the control map M.
As shown in FIG. 12, in the control map M, the mask ratio T A / T B is a first set value T A / T B 1 (e.g., 0.2) or more and a second set value T A / T B 2 (> T a / T B 1. for example, 0.8) in the range below, regardless of the size of the mask ratio T a / T B, set to a predetermined set value α value. Further, the control map M is in the range mask ratio T A / T B is first less than the set value T A / T B 1, linearly from the set value α value in accordance with a decrease of the mask ratio T A / T B Reduce to Further, in the control map M, when the mask ratio T A / T B is in the range of the second set value T A / T B 2 or more, the α value is linear from the set value according to the increase of the mask ratio T A / T B To increase.
　図１３は、警報の感度を表すグラフである。
　これにより、運転不安定度判定部１４０は、マスク割合ＴA／ＴBが第１設定値ＴA／ＴB1未満である場合には、マスク割合ＴA／ＴBが小さくなるほどα値が小さくなる。それゆえ、運転不安定度判定部１４０は、図１３に示すように、マスク割合ＴA／ＴBが小さくなるほど警報（情報呈示処理）の感度が高くなる（警報（情報呈示処理）が実行しやすくなる）。また、運転不安定度判定部１４０は、マスク割合ＴA／ＴBが第２設定値ＴA／ＴB2以上である場合には、マスク割合ＴA／ＴBが大きくなるほどα値が大きくなる。それゆえ、運転不安定度判定部１４０は、マスク割合ＴA／ＴBが大きくなるほど警報（情報呈示処理）の感度が低くなる（警報（情報呈示処理）が実行し難くなる）。 FIG. 13 is a graph showing the sensitivity of the alarm.
Accordingly, when the mask ratio T A / T B is less than the first set value T A / T B 1, the operation instability determination unit 140 determines that the α value is smaller as the mask ratio T A / T B becomes smaller. It becomes smaller. Therefore, as shown in FIG. 13, as the mask ratio T A / T B becomes smaller, the driving instability determination unit 140 increases the sensitivity of the alarm (information presenting process) (an alarm (information presenting process) is executed). Become easier). In addition, when the mask ratio T A / T B is equal to or greater than the second set value T A / T B 2, the operation instability determination unit 140 increases the α value as the mask ratio T A / T B increases. Become. Therefore, as the mask ratio T A / T B increases, the sensitivity of the alarm (information presentation process) decreases in the driving instability determination unit 140 (the alarm (information presentation process) becomes more difficult to execute).
（本実施形態の効果）
（１）運転支援部１００Ａは、車両の運転状況（例えば、外乱運転状況、通常運転状況）を検出する。続いて、運転支援部１００Ａは、検出した運転状況（例えば、外乱運転状況、通常運転状況）に基づいて運転支援部１００Ａが算出する走行状態分布（例えば、第１走行状態分布、第２走行状態分布）を変更する。
　このような構成によれば、例えば、車両の運転状況に応じた走行状態分布（例えば、第１走行状態分布、第２走行状態分布）を算出できる。 (Effect of this embodiment)
(1) The driving support unit 100A detects a driving situation (for example, a disturbance driving situation, a normal driving situation) of the vehicle. Subsequently, the driving support unit 100A calculates the traveling state distribution (for example, the first traveling state distribution, the second traveling state) calculated by the driving support unit 100A based on the detected driving situation (for example, disturbance driving situation, normal driving situation). Change the distribution).
According to such a configuration, it is possible to calculate, for example, the traveling state distribution (for example, the first traveling state distribution, the second traveling state distribution) according to the driving situation of the vehicle.
（２）運転支援部１００Ａは、運転状態の推定（例えば、運転の不安定度の判定）に対して外乱となる運転状況（例えば、外乱運転状況）にあると判定した時間の累積値ＴAとトリップの開始時からの経過時間の累積値ＴBとの比率ＴA／ＴBに基づいて運転支援部１００Ａが算出する走行状態分布（例えば、第１走行状態分布、第２走行状態分布）を変更する。
　このような構成によれば、例えば、運転状態の推定（例えば、運転の不安定度の判定）に対して外乱となる運転状況（例えば、外乱運転状況）の発生に応じた走行状態分布（第１走行状態分布、第２走行状態分布）を算出できる。 (2) The driving support unit 100A determines the accumulated value T A of the time when it is determined that the driving situation (for example, the disturbance driving situation) causes disturbance with respect to the estimation of the driving state (for example, determination of the driving instability). State distribution (for example, first state distribution, second state distribution) calculated by the driving support unit 100A based on the ratio T A / T B to the cumulative value T B of the elapsed time from the start of the trip and the trip Change
According to such a configuration, for example, the traveling state distribution (the second state) corresponding to the occurrence of the driving condition (for example, the disturbance driving condition) which is a disturbance with respect to the estimation of the driving condition (for example, determination of the driving instability) (1) Running condition distribution, second running condition distribution) can be calculated.
（３）運転支援部１００Ａは、カーブ動作、車線変更、および車両の加減速の少なくとも１つに基づいて運転状況（例えば、外乱運転状況、通常運転状況）を判定する。
　このような構成によれば、運転状態の推定に対し外乱となる運転状況（例えば、外乱運転状況）を比較的容易に判定できる。
（４）運転支援部１００Ａは、不整路、およびうねりの少なくとも１つに基づいて運転状況（外乱運転状況）を判定する。
　このような構成によれば、運転状態の推定に対し外乱となる運転状況（例えば、外乱運転状況）を比較的容易に判定できる。 (3) The driving support unit 100A determines the driving situation (for example, the disturbance driving situation, the normal driving situation) based on at least one of the curve operation, the lane change, and the acceleration / deceleration of the vehicle.
According to such a configuration, it is possible to relatively easily determine an operating condition (for example, a disturbance operating condition) which is a disturbance to the estimation of the operating condition.
(4) The driving support unit 100A determines the driving situation (disturbing driving situation) based on at least one of the irregular road and the undulation.
According to such a configuration, it is possible to relatively easily determine an operating condition (for example, a disturbance operating condition) which is a disturbance to the estimation of the operating condition.
（第３実施形態）
　次に、本発明に係る第３実施形態について図面を参照しつつ説明する。
　なお、上記各実施形態と同様な構成等については同一の符号を使用する。
　図１４は、本実施形態の車両用情報提供装置を搭載した車両の構成を表す図である。図１５は、運転不安定度判定処理を表すフローチャートである。
　本実施形態は、運転席のシートベルトが取り外すまたは取り付ける（以下、「単に着脱」とも呼ぶ）と、コントローラ１００のメモリに蓄積している設定時間Ｔ秒（例えば、２１６０秒）間の操舵角予測誤差θeを初期化する点が第１実施形態と異なる。
　具体的には、図１５に示すように、本実施形態は、第１実施形態とは、図７のステップＳ２０１とステップＳ２０２との間にステップＳ２０６を設けた点が異なっている。 Third Embodiment
Next, a third embodiment according to the present invention will be described with reference to the drawings.
The same reference numerals are used for configurations similar to those of the above-described embodiments.
FIG. 14 is a diagram showing the configuration of a vehicle equipped with the information providing apparatus for vehicles of the present embodiment. FIG. 15 is a flowchart showing the driving instability determination process.
In the present embodiment, steering angle prediction between the removal or attachment of the seat belt at the driver's seat (hereinafter also referred to simply as “detachment”) and the set time T seconds (for example, 2160 seconds) accumulated in the memory of the controller 100 This embodiment differs from the first embodiment in that the error θe is initialized.
Specifically, as shown in FIG. 15, the present embodiment is different from the first embodiment in that step S206 is provided between step S201 and step S202 of FIG.
　また、車両は、図１４に示すように、シートベルトセンサ１１を備える。
　シートベルトセンサ１１は、運転席のシートベルト（不図示）の着脱状態（取り外すまたは取り付ける）を検出する。続いて、シートベルトセンサ１１は、検出した着脱状態をコントローラ１００に出力する。
　ステップＳ２０６では、運転支援部１００Ａは、シートベルトセンサ１１が出力した着脱状態に基づき、運転席のシートベルト（不図示）を着脱（取り外すまたは取り付ける）したかを判定する。そして、運転支援部１００Ａは、運転席のシートベルト（不図示）を着脱したと判定すると（Ｙｅｓ）、コントローラ１００のメモリに蓄積している設定時間Ｔ秒（例えば、２１６０秒）間の操舵角予測誤差θeを初期化した後、この演算処理を終了する。一方、運転支援部１００Ａは、運転席のシートベルト（不図示）を着脱していないと判定すると（Ｎｏ）、ステップＳ２０３に移行する。 Further, as shown in FIG. 14, the vehicle includes a seat belt sensor 11.
The seat belt sensor 11 detects an attachment / detachment state (removal or attachment) of a seat belt (not shown) at the driver's seat. Subsequently, the seat belt sensor 11 outputs the detected attachment / detachment state to the controller 100.
In step S206, the driving support unit 100A determines whether the seat belt (not shown) on the driver's seat has been detached (removed or attached) based on the attachment / detachment state output by the seat belt sensor 11. When the driving support unit 100A determines that the seat belt (not shown) on the driver's seat has been attached and detached (Yes), the steering angle for the set time T seconds (for example, 2160 seconds) accumulated in the memory of the controller 100 After the prediction error θe is initialized, this operation process is ended. On the other hand, when the driving support unit 100A determines that the seat belt (not shown) on the driver's seat is not attached and detached (No), the process proceeds to step S203.
（本実施形態の効果）
（１）シートベルトセンサ１１は、運転席のシートベルトの着脱状態を検出する。続いて、運転支援部１００Ａは、検出した着脱状態に基づいて運転支援部１００Ａが算出する走行状態分布（例えば、第１走行状態分布、第２走行状態分布）を変更する。
　このような構成によれば、例えば、運転者が交代し、シートベルトを着脱した場合に、走行状態分布（第１走行状態分布、第２走行状態分布）を変更できる。 (Effect of this embodiment)
(1) The seat belt sensor 11 detects the attachment / detachment state of the seat belt at the driver's seat. Subsequently, the driving support unit 100A changes the traveling state distribution (for example, the first traveling state distribution, the second traveling state distribution) calculated by the driving support unit 100A based on the detected attachment / detachment state.
According to such a configuration, for example, when the driver takes turns and the seat belt is attached or detached, the running state distribution (first running state distribution, second running state distribution) can be changed.
（２）運転支援部１００Ａは、走行状態データ（例えば、操舵角情報）に基づいて複数の走行状態分布（第１走行状態分布、第２走行状態分布）を算出するための分布データ（例えば、操舵角予測誤差θe）をコントローラ１００のメモリに蓄積する。また、運転支援部１００Ａは、コントローラ１００のメモリが蓄積している分布データ（例えば、操舵角予測誤差θe）に基づいて運転者の運転状態を推定する。その際、運転支援部１００Ａは、運転席のシートベルトを着脱したと判定すると、コントローラ１００のメモリが蓄積している分布データ（例えば、操舵角予測誤差θe）を初期化する。
　このような構成によれば、例えば、運転者が交代し、シートベルトを着脱した場合に、走行状態分布（第１走行状態分布、第２走行状態分布）を初期化できる。 (2) The driving support unit 100A uses distribution data (for example, first traveling state distribution, second traveling state distribution) to calculate a plurality of traveling state distributions based on the traveling state data (for example, steering angle information). The steering angle prediction error θe) is stored in the memory of the controller 100. Further, the driving support unit 100A estimates the driving state of the driver based on the distribution data (for example, the steering angle prediction error θe) accumulated in the memory of the controller 100. At that time, when it is determined that the seat belt at the driver's seat has been attached or detached, the driving support unit 100A initializes distribution data (for example, steering angle prediction error θe) accumulated in the memory of the controller 100.
According to such a configuration, for example, when the driver takes turns and the seat belt is attached or detached, the traveling state distribution (first traveling state distribution, second traveling state distribution) can be initialized.
（変形例）
　なお、本実施形態では、シートベルト（不図示）を着脱（取り外すまたは取り付ける）したと判定した場合に、コントローラ１００のメモリに蓄積している設定時間Ｔ秒（例えば、２１６０秒）間の操舵角予測誤差θeを初期化する例を示したが、他の構成を採用することもできる。例えば、運転席のドアを開放または閉止した（以下、「単に開閉」とも呼ぶ）と判定した場合に、コントローラ１００のメモリに蓄積している設定時間Ｔ秒（例えば、２１６０秒）間の操舵角予測誤差θeを初期化してもよい。 (Modification)
In the present embodiment, when it is determined that the seat belt (not shown) is attached and detached (removed or attached), the steering angle for the set time T seconds (for example, 2160 seconds) accumulated in the memory of the controller 100 Although an example in which the prediction error θe is initialized is shown, other configurations can also be adopted. For example, when it is determined that the door of the driver's seat has been opened or closed (hereinafter, also referred to simply as "open and close"), the steering angle for the set time T seconds (for example, 2160 seconds) stored in the memory of the controller 100 The prediction error θe may be initialized.
　図１６は、本実施形態の車両用情報提供装置を搭載した車両の構成を表す図である。
　具体的には、図１６に示すように、車両は、ドア開閉センサ１２を備える。
　ドア開閉センサ１２は、運転席のドアの開閉状態（開放または閉止した）を検出する。続いて、ドア開閉センサ１２は、検出した開閉状態をコントローラ１００に出力する。
　ステップＳ２０６では、運転支援部１００Ａは、ドア開閉センサ１２が出力した開閉状態に基づき、運転席のドアを開閉したか否かを判定する。そして、運転支援部１００Ａは、運転席のドアを開閉したと判定すると（Ｙｅｓ）、コントローラ１００のメモリに蓄積している設定時間Ｔ秒（例えば、２１６０秒）間の操舵角予測誤差θeを初期化した後、この演算処理を終了する。一方、運転支援部１００Ａは、運転席のドアを開閉していないと判定すると（Ｎｏ）、ステップＳ２０３に移行する。
　本実施形態では、図１６のドア開閉センサ１２が開閉状態検出部を構成する。 FIG. 16 is a diagram showing the configuration of a vehicle equipped with the information providing apparatus for vehicles of the present embodiment.
Specifically, as shown in FIG. 16, the vehicle includes a door open / close sensor 12.
The door open / close sensor 12 detects the open / close state (opened or closed) of the door of the driver's seat. Subsequently, the door open / close sensor 12 outputs the detected open / close state to the controller 100.
In step S206, the driving support unit 100A determines whether the door of the driver's seat has been opened or closed based on the open / close state output by the door open / close sensor 12. When the driving support unit 100A determines that the driver's seat door has been opened and closed (Yes), the steering angle prediction error θe for the set time T seconds (for example, 2160 seconds) accumulated in the memory of the controller 100 is initially set. After conversion, this arithmetic processing ends. On the other hand, when it is determined that the driving support unit 100A does not open and close the driver's seat door (No), the process proceeds to step S203.
In the present embodiment, the door open / close sensor 12 of FIG. 16 constitutes an open / close state detection unit.
（本変形例の効果）
（１）ドア開閉センサ１２は、運転席のドアの開閉状態を検出する。続いて、運転支援部１００Ａは、検出した開閉状態に基づいて運転支援部１００Ａが算出する走行状態分布（例えば、第１走行状態分布、第２走行状態分布）を変更する。
　このような構成によれば、例えば、運転者が交代し、ドアを開閉した場合に、走行状態分布（第１走行状態分布、第２走行状態分布）を変更できる。 (Effect of this variation)
(1) The door open / close sensor 12 detects the open / close state of the driver's seat door. Subsequently, the driving support unit 100A changes the traveling state distribution (for example, the first traveling state distribution, the second traveling state distribution) calculated by the driving support unit 100A based on the detected open / close state.
According to such a configuration, for example, when the driver takes turns and opens and closes the door, it is possible to change the traveling state distribution (first traveling state distribution, second traveling state distribution).
（２）運転支援部１００Ａは、走行状態データ（例えば、操舵角情報）に基づいて複数の走行状態分布（第１走行状態分布、第２走行状態分布）を算出するための分布データ（例えば、操舵角予測誤差θe）を生成する。続いて、運転支援部１００Ａは、生成した分布データ（例えば、操舵角予測誤差θe）をコントローラ１００のメモリに蓄積する。また、運転支援部１００Ａは、コントローラ１００のメモリが蓄積している分布データ（例えば、操舵角予測誤差θe）に基づいて運転者の運転状態を推定する。その際、運転支援部１００Ａは、運転席のドアを開閉したと判定すると、コントローラ１００のメモリが蓄積している分布データ（例えば、操舵角予測誤差θe）を初期化する。 (2) The driving support unit 100A uses distribution data (for example, first traveling state distribution, second traveling state distribution) to calculate a plurality of traveling state distributions based on the traveling state data (for example, steering angle information). A steering angle prediction error θe) is generated. Subsequently, the driving support unit 100A stores the generated distribution data (for example, the steering angle prediction error θe) in the memory of the controller 100. Further, the driving support unit 100A estimates the driving state of the driver based on the distribution data (for example, the steering angle prediction error θe) accumulated in the memory of the controller 100. At this time, when it is determined that the driver's seat door has been opened and closed, the driving support unit 100A initializes distribution data (for example, steering angle prediction error θe) accumulated in the memory of the controller 100.
　このような構成によれば、例えば、運転者が交代し、ドアを開閉した場合に、走行状態分布（第１走行状態分布、第２走行状態分布）を初期化できる。
　以上、本願が優先権を主張する日本国特許出願２０１３－１５０７２４（２０１３年７月１９日出願）の全内容は、参照により本開示の一部をなす。
　ここでは、限られた数の実施形態を参照しながら説明したが、権利範囲はそれらに限定されるものではなく、上記の開示に基づく各実施形態の改変は当業者にとって自明なことである。 According to such a configuration, for example, when the driver takes turns and opens and closes the door, it is possible to initialize the traveling state distribution (first traveling state distribution, second traveling state distribution).
As described above, the entire contents of Japanese Patent Application 2013-150724 (filed on July 19, 2013), to which the present application claims priority, form a part of the present disclosure by reference.
Although the description herein has been made with reference to a limited number of embodiments, the scope of rights is not limited to them, and modifications of each embodiment based on the above disclosure are obvious to those skilled in the art.
１１　　　シートベルトセンサ（着脱状態検出部）
１１０　　走行状態データ取得部（走行状態データ取得部）
１２０　　運転状況判定部（運転状況判定部）
１３０　　走行状態分布算出部（走行状態分布算出部、設定変更部）
１３０Ａ　第１走行状態分布算出部（走行状態分布算出部）
１３０Ｂ　第２走行状態分布算出部（走行状態分布算出部）
１３０Ｄ　分布選択部（設定変更部）
１３０Ｅ　分布設定部（設定変更部）
１４０　　運転不安定度判定部（運転状態推定部）
１５０　　情報呈示部（情報呈示部）
ステップＳ１０１（走行状態データ取得部）
ステップＳ１０３（運転状況判定部）
ステップＳ１０５、Ｓ１０７、Ｓ１０８（設定変更部）
ステップＳ１１０（運転状態推定部）
ステップＳ１１１（情報呈示部） 11 Seat Belt Sensor (Detachable State Detection Unit)
110 Driving condition data acquisition unit (driving condition data acquisition unit)
120 driving condition judgment unit (driving condition judgment unit)
130 Running state distribution calculation unit (running state distribution calculation unit, setting change unit)
130A 1st driving state distribution calculating unit (driving state distribution calculating unit)
130 B Second driving state distribution calculating unit (driving state distribution calculating unit)
130D distribution selector (setting changer)
130E Distribution setting unit (setting change unit)
140 Driving instability determination unit (driving condition estimation unit)
150 Information Presentation Unit (Information Presentation Unit)
Step S101 (traveling state data acquisition unit)
Step S103 (Driving condition determination unit)
Steps S105, S107, S108 (setting change unit)
Step S110 (operating condition estimation unit)
Step S111 (information presentation unit)

Claims (11)

  1. 　運転者が操作可能な運転操作子の操作状態および車両状態の少なくとも一方を含む走行状態データを取得する走行状態データ取得部と、
    　前記走行状態データ取得部が取得した前記走行状態データに基づいて時間的範囲の異なる複数の走行状態分布を算出する走行状態分布算出部と、
    　車両の走行に関する情報に基づいて前記走行状態分布算出部が算出する前記走行状態分布を変更する設定変更部と、
    　前記走行状態分布算出部が算出した複数の前記走行状態分布に基づいて前記運転者の運転状態を推定する運転状態推定部と、
    　前記運転状態推定部が推定した前記運転状態に基づいて前記運転者に呈示情報を呈示する情報呈示部と、を備えたことを特徴とする車両用情報提供装置。     A traveling state data acquisition unit that acquires traveling state data including at least one of an operation state of a driver operable by a driver and a vehicle state;
    A traveling state distribution calculating unit that calculates a plurality of traveling state distributions having different temporal ranges based on the traveling state data acquired by the traveling state data acquisition unit;
    A setting change unit configured to change the traveling state distribution calculated by the traveling state distribution calculating unit based on information on traveling of the vehicle;
    A driving state estimation unit that estimates the driving state of the driver based on the plurality of traveling state distributions calculated by the traveling state distribution calculation unit;
    An information providing apparatus for a vehicle, comprising: an information presenting unit that presents presentation information to the driver based on the driving condition estimated by the driving condition estimation unit.
  2. 　前記車両の走行に関する情報として、前記車両の走行の開始時からの経過時間を検出する経過時間検出部を備え、
    　前記設定変更部は、前記経過時間検出部が検出した前記経過時間に基づいて前記走行状態分布算出部が算出する前記走行状態分布を変更することを特徴とする請求項１に記載の車両用情報提供装置。     The information processing apparatus further includes an elapsed time detection unit that detects an elapsed time from the start of traveling of the vehicle as the information related to traveling of the vehicle.
    The information for vehicle according to claim 1, wherein the setting changing unit changes the traveling state distribution calculated by the traveling state distribution calculating unit based on the elapsed time detected by the elapsed time detecting unit. Provision device.
  3. 　前記車両の運転状況を判定する運転状況判定部を備え、
    　前記走行状態分布算出部は、前記走行状態データ取得部が取得した前記走行状態データに基づいて、予め定めた相対的に時間的範囲の長い第１走行状態分布と、前記第１走行状態分布よりも時間的範囲が短い第２走行状態分布とを算出し、
    　前記設定変更部は、前記運転状況判定部が運転状態の推定に対して外乱となる運転状況にあると判定すると、前記経過時間検出部が検出した前記経過時間が設定時間未満である場合に、前記運転状況判定部が運転状態の推定に対して外乱となる運転状況にあると判定したときよりも前に算出した第２走行状態分布を選択し、前記経過時間検出部が検出した前記経過時間が設定時間以上である場合に、前記走行状態分布算出部が算出した現在の前記第２走行状態分布を選択し、選択した前記第２走行状態分布で前記第１走行状態分布を置き換えることを特徴とする請求項２に記載した車両用情報提供装置。     A driving condition determination unit that determines the driving condition of the vehicle;
    The traveling state distribution calculation unit is configured based on the traveling state data acquired by the traveling state data acquisition unit, a first traveling state distribution having a relatively long temporal range and a first traveling state distribution determined in advance. Also calculate the second driving state distribution with a short temporal range,
    If the setting change unit determines that the driving condition determination unit is in a driving condition that causes disturbance with respect to the estimation of the driving condition, the elapsed time detected by the elapsed time detection unit is less than the set time. The second traveling state distribution calculated before the time when the driving state determination unit determines that the driving state is disturbance to the estimation of the driving state is selected, and the elapsed time detected by the elapsed time detection unit And selecting the current second traveling state distribution calculated by the traveling state distribution calculating unit, and replacing the first traveling state distribution with the selected second traveling state distribution. The information providing apparatus for vehicles described in claim 2.
  4. 　前記車両の走行に関する情報として、前記車両の運転状況を判定する運転状況判定部を備え、
    　前記設定変更部は、前記運転状況判定部が判定した前記運転状況に基づいて前記走行状態分布算出部が算出する前記走行状態分布を変更することを特徴とする請求項１から３のいずれか１項に記載の車両用情報提供装置。     The information processing apparatus further includes a driving condition determination unit that determines a driving condition of the vehicle as the information regarding the traveling of the vehicle
    The setting change unit changes the traveling state distribution calculated by the traveling state distribution calculation unit based on the driving condition determined by the driving condition determination unit. The information provision apparatus for vehicles as described in a term.
  5. 　前記設定変更部は、前記運転状況判定部で外乱となる前記運転状況にあると判定した時間の累積値と前記車両の走行の開始時からの経過時間の累積値との比率に基づいて前記走行状態分布算出部が算出する前記走行状態分布を変更することを特徴とする請求項４に記載の車両用情報提供装置。 The setting change unit is configured to perform the traveling based on a ratio of an accumulated value of time determined to be in the driving condition causing disturbance by the driving condition determination unit and an accumulated value of elapsed time from the start of traveling of the vehicle. 5. The information providing apparatus for a vehicle according to claim 4, wherein the traveling state distribution calculated by the state distribution calculating unit is changed.
  6. 　前記運転状況判定部は、カーブ動作、車線変更、および前記車両の加減速の少なくとも１つに基づいて前記運転状況を判定することを特徴とする請求項４または５に記載の車両用情報提供装置。 The information providing device for a vehicle according to claim 4 or 5, wherein the driving condition determination unit determines the driving condition based on at least one of a curve operation, a lane change, and an acceleration / deceleration of the vehicle. .
  7. 　前記運転状況判定部は、不整路、およびうねりの少なくとも１つに基づいて前記運転状況を判定することを特徴とする請求項４または５に記載の車両用情報提供装置。 The information providing device for a vehicle according to claim 4 or 5, wherein the driving condition determination unit determines the driving condition based on at least one of a rough road and a wave.
  8. 　前記車両の走行に関する情報として、運転席のシートベルトの着脱状態を検出する着脱状態検出部を備え、
    　前記設定変更部は、前記着脱状態検出部が検出した前記着脱状態に基づいて前記走行状態分布算出部が算出する前記走行状態分布を変更することを特徴とする請求項１から７のいずれか１項に記載の車両用情報提供装置。     The information processing apparatus further includes an attachment / detachment state detection unit configured to detect an attachment / detachment state of a seat belt at a driver's seat as the information related to traveling of the vehicle.
    The said setting change part changes the said running state distribution which the said running state distribution calculation part calculates based on the said attachment or detachment state which the said attachment or detachment state detection part detected. The information provision apparatus for vehicles as described in a term.
  9. 　前記設定変更部は、前記走行状態データ取得部が取得した前記走行状態データに基づき、複数の前記走行状態分布を算出するための分布データを蓄積し、蓄積している前記分布データに基づいて複数の前記走行状態分布を算出するとともに、前記着脱状態検出部で前記運転席のシートベルトを着脱したと検出した場合に、蓄積している前記分布データを初期化することを特徴とする請求項８に記載の車両用情報提供装置。 The setting change unit accumulates distribution data for calculating a plurality of the traveling state distributions based on the traveling state data acquired by the traveling state data acquisition unit, and a plurality of the distribution data are stored based on the accumulated distribution data. The distribution condition of the driver's seat is detected by the attachment / detachment state detection unit, the distribution state of the driver's seat is initialized, and the accumulated distribution data is initialized. The information provision apparatus for vehicles as described in-.
  10. 　前記車両の走行に関する情報として、運転席のドアの開閉状態を検出する開閉状態検出部を備え、
    　前記設定変更部は、前記開閉状態検出部が検出した前記開閉状態に基づいて前記走行状態分布算出部が算出する前記走行状態分布を変更することを特徴とする請求項１から９のいずれか１項に記載の車両用情報提供装置。     The information processing apparatus further includes an open / close state detection unit that detects an open / close state of a door of a driver's seat as the information related to the traveling of the vehicle.
    The setting change unit changes the traveling state distribution calculated by the traveling state distribution calculation unit based on the open / closed state detected by the open / close state detection unit. The information provision apparatus for vehicles as described in a term.
  11. 　前記設定変更部は、前記走行状態データ取得部が取得した前記走行状態データに基づき、複数の前記走行状態分布を算出するための分布データを蓄積し、蓄積している前記分布データに基づいて複数の前記走行状態分布を算出するとともに、前記開閉状態検出部で前記運転席のシートベルトを開閉したと検出した場合に、蓄積している前記分布データを初期化することを特徴とする請求項１０に記載の車両用情報提供装置。 The setting change unit accumulates distribution data for calculating a plurality of the traveling state distributions based on the traveling state data acquired by the traveling state data acquisition unit, and a plurality of the distribution data are stored based on the accumulated distribution data. The present invention is characterized in that the distribution state of the driver's seat is detected by the open / close state detection unit, and the distribution data stored therein is initialized, while calculating the travel state distribution of the vehicle. The information provision apparatus for vehicles as described in-.
PCT/JP2014/002996 2013-07-19 2014-06-05 Information provision device for vehicle WO2015008418A1 (en)

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