JP4919172B2 - Vehicle guidance device - Google Patents

Description

  The present invention relates to a vehicle guidance device, for example, a device that guides a route by a navigation device.

In recent years, route guidance using navigation devices has been actively performed. In general, these navigation devices perform information presentation, route guidance, and the like at fixed timings.
For example, as in the “navigation device” shown in the following Patent Document 1, when a vehicle deviates from the guide route, this is detected, and a new guide route from the current position of the vehicle to the destination is searched. There is something.
JP 2007-256075 A

In recent years, a technology for sensing a driver's own state and driving behavior and using the data to assist the driver in driving has been developed.
One of such driving support technologies uses a driver model, and there are various types, but as an example using statistical data, the following “driving behavior estimation device, Driving support device and vehicle evaluation system ".
JP 2007-176396 A

This technology models the driver's driving behavior (driving operation) using GMM (mixed Gaussian model). When parameters such as vehicle speed are input, the posterior probability is calculated, the accelerator operation performed by the driver, Brake operation can be predicted.
By using such a driver model, the driver's driving operation with respect to the current driving environment (road environment) is predicted, and appropriate warnings and warnings are given to the driver, the guidance route is changed by navigation, etc. Control can be performed.

  By the way, in the conventional navigation device, the driver simply guides “turn right at OOm right” and so on. There were cases where it was difficult to travel according to the guidance of the navigation device because the signs were overlooked and the sign was overlooked or the lane change was delayed.

  Accordingly, an object of the present invention is to estimate the extent to which a driver deviates from the guidance route based on the driver's tension level, etc., and to perform route guidance according to this.

(1) In order to achieve the above object, according to the first aspect of the present invention, a tension state driver model storage means for storing a tension state driver model in a case where the driver is in a tension state due to a lost route, and the driver is normal. A normal state driver model storage unit that stores a normal state driver model when in a state, a guide route acquisition unit that acquires a guide route, and a parameter acquisition unit that acquires a parameter in front of the branch point of the acquired guide route The acquired parameters are input to the stored tension state driver model and input to the stored normal state driver model, and the degree to which the driver is lost due to the route and is in tension is estimated using the values of both driver models. And a more detailed route guidance method as the estimated degree is larger according to the estimated means and the estimated degree. There is provided a vehicle guidance apparatus comprising: a route guidance method selection unit to be selected; and a route guidance unit that performs route guidance by the selected route guidance method.
( 2 ) In the invention according to claim 2 , the tension driver model is created using data when the driver deviates from the guide route in a tension state at a branch point of the route, and the normal state driver model provides a vehicle guidance device according to claim 1, characterized in that the driver is created using data when deviating from the guidance route intended by the branch point route.
( 3 ) In the invention according to claim 3 , the driver model used by the estimating means uses time-series data of N types of feature amounts detected as the vehicle travels as learning data, and each data in the N-dimensional space exists. The vehicle guidance device according to claim 1 or 2 , wherein the vehicle guidance device is defined by a probability distribution.

(1) In the first aspect of the invention, the driver's tension state is estimated by using the tension state driver model in the case where the driver is in a tension state and is in a tension state, and route guidance according to the degree is performed. be able to.
In addition, more detailed route guidance can be performed as the driver's tension is higher.
Further, by using together the normal state driver model when the driver is in the normal state on the route, the driver's tension state can be estimated more accurately.
( 2 ) In the invention described in claim 2 , the tension state driver model is created using data when the driver deviates from the guide route in a tension state at a branch point of the route, and the normal state driver model is Since the driver is created using data when the driver intentionally deviates from the guide route at the branch point of the route, a more accurate driver model can be used.
( 3 ) In the invention described in claim 3, the driver model used by the estimation means uses time-series data of N types of feature amounts detected as the vehicle travels as learning data, and each data in the N-dimensional space exists. Since it is configured by, for example, EMM defined by the probability distribution, the estimated value can be easily calculated by numerical analysis or the like.

(1) Outline of Embodiment The information processing system 1 (FIG. 1) stores a tension state driver model and a normal state driver model.
The tension driver model is created using data when the driver is lost in the route and falls into the tension state and deviates from the guidance route. The normal state driver model is data when the driver intentionally deviates from the guidance route. It has been created using.
Therefore, in the tension state driver model, the characteristics of the driving operation when the driver is lost in the route and deviates from the guide route are modeled by probability density distribution. In the normal state driver model, the driver intentionally guides the route. The characteristics of driving operations when deviating from are modeled by probability density distribution.

The information processing system 1 uses the current driving data as a parameter for both driver models before the driver actually drives the vehicle and approaches a branching point (including an intersection and the starting point of a right / left turn lane). Then, the posterior probability P1 based on the tension state driver model and the posterior probability P2 based on the normal state driver model are calculated.
The posterior probability P1 represents the probability that the current driver state is in a tension state, and the posterior probability P2 represents the probability that the current driver state is in a normal state.
Then, the information processing system 1 detects and estimates the tension level of the driver by the ratio of the tension state to the total value of the tension state and the normal state, that is, the calculation formula P1 / (P1 + P2).

If the information processing system 1 determines that there is a high possibility that the driver will get lost from the guidance route based on the detection / estimation result, for example, the information processing system 1 may provide guidance at an earlier timing depending on the degree. The guidance method is changed from normal route guidance to detailed route guidance, such as more detailed screen display and voice guidance.
In this way, the information processing system 1 changes the route guidance method in advance when the driver is likely to deviate from the guidance route, so that the driver can travel with confidence by performing detailed route guidance.

(2) Details of Embodiment FIG. 1 is a diagram for explaining an information processing system 1 mounted on a vehicle according to the present embodiment.
The information processing system 1 includes an ECU (Electric Control Unit) 2, a current position detection device 3, an environment information acquisition device 4, a vehicle information acquisition device 5, an input device 6, a storage device 7, a biological measurement device 8, a display device 9, and voice. An output device 10, a vehicle control device 11, and a communication device 12 are provided and function as a vehicle guidance device.

The ECU 2 is, for example, a computer including a CPU, a ROM, a RAM, a storage device, and the like, and controls each part of the vehicle according to a predetermined program and guides the route of the vehicle.
The current position detection device 3 includes, for example, a GPS (Global Positioning Systems) reception device, a beacon reception device, and the like, and detects the current position of the vehicle.

The environment information acquisition device 4 is a device that acquires information related to the current vehicle environment, and includes an inter-vehicle distance / relative speed measurement device, a road traffic condition reception device, a traveling lane determination device, and the like.
The inter-vehicle distance / relative speed measuring device measures the inter-vehicle distance and relative speed with the front vehicle, and the inter-vehicle distance and relative speed with the rear / side vehicle using, for example, a laser or a millimeter wave.
The road traffic condition receiving device receives information on road traffic conditions wirelessly from, for example, a server or a beacon.
The traveling lane determining device determines, for example, a lane in which the vehicle is currently traveling using a road sensor or the like.

The vehicle information acquisition device 5 is a device that connects various sensors by an in-vehicle LAN (Local Area Network, for example, CAN, MOST, FlexRay, etc.) and detects information related to the vehicle.
The vehicle information acquisition device 5 can detect a steering wheel operation, an accelerator operation, a brake operation, an acceleration, a speed, and the like of the driver.

The biological measurement device 8 is a device that acquires the biological information of the driver, and includes, for example, a heartbeat measurement device, a line-of-sight measurement device, and a sweat measurement device.
The heartbeat measuring device detects a driver's heartbeat signal by, for example, mounting a sensor on the driver's body or providing a sensor on a portion such as a handle where the user's body contacts.

The line-of-sight measurement device, for example, captures the driver's face with a camera and extracts the contours of the face and the direction of the pupil by image processing, or attaches a spectacle-shaped sensor to the driver. Detect gaze.
For example, the perspiration measurement device is equipped with a perspiration sensor on the driver's body, or a perspiration sensor is provided on a portion such as a handle where the user's body contacts to measure the driver's skin resistance. Detects sweating.

The storage device 7 is a large-capacity storage device configured by using, for example, a hard disk or a semiconductor memory, and can store various programs and data.
In addition to the usual route guidance function that searches the route from the departure point to the destination and provides route guidance to the driver by voice or display, the navigation program uses the driver model to find the guidance route. The ECU 2 is caused to perform functions such as estimating the degree of disengagement and performing route guidance according to this.

The driver model creation program is a program that causes the ECU 2 to exhibit a function of creating a driver model using vehicle travel data and the like.
As the travel data, data detected by the environment information acquisition device 4 or the vehicle information acquisition device 5 and stored in the storage device 7 is used.
In this embodiment, as an example, the inter-vehicle distance F detected by the environment information acquisition device 4, the vehicle speed V detected by the vehicle information acquisition device 5, the accelerator operation amount G, the brake operation amount B, and the like are used as travel data.

  The driver model DB is a database that stores a driver model created by a driver model creation program, and stores a driver model for each individual driver of the vehicle. The reason why the driver model is created for each individual driver is because the characteristics of driving differ depending on the individual driver.

  The driver model includes a tense state driver model that models features such as driving operations that the driver performs in front of the branch point when the driver travels in a direction different from the guide direction at the branch point, and the driver There is a normal state driver model in which characteristics such as a driving operation that the driver performs in front of the branch point when the driver intentionally travels in a direction different from the guide direction at the branch point.

  Here, the driver model DB stores a tension state driver model storage unit that stores a tension state driver model when the driver is in a tension state and is in a tension state, and a normal state driver model when the driver is in a normal state. It functions as a normal state driver model storage means.

The tension state driver model and the normal state driver model can also be configured to be created for each kind of branch point such as a crossroad or a T-junction.
In addition, the branch point includes not only a location where the road is physically separated but also a location requiring a course change such as a location where the road moves to a right / left turn lane.

The environment data DB is a database that stores environment data detected by the environment information acquisition device 4.
The vehicle data DB is a database that stores vehicle data detected by the vehicle information acquisition device 5.
The biological data DB is a database that stores biological data detected by the biological measurement device 8.

The data stored in these databases can be associated with the detection timing using, for example, date and time as a key.
Thereby, for example, when it is determined that the driver is in a tension state based on biometric data, it is possible to specify environmental data and vehicle data when the biometric data is detected. That is, it is possible to specify environmental data and vehicle data when the driver is in a tension state.

The map DB is a database that stores various data files for route searching and route guidance for vehicles. There are various types such as a map data file, an intersection data file in which intersections are recorded, and a node data file in which routes are represented by links and nodes.
Although not shown, the storage device 7 includes other programs and databases such as a travel data program for generating travel data from vehicle data and environmental data, and a driver registration information DB for specifying a driver. It is remembered.

The display device 9 includes a display device such as a liquid crystal display, for example, and displays a map, displays a guide route on the map, enlarges a map at a branch point, or symbolizes a landmark. Various information for route guidance to the driver is displayed.
The audio output device 10 includes, for example, an audio output device such as a speaker, and can output various audio information for route guidance to the driver.
The vehicle control device 11 controls each part of the vehicle, for example, adjusting the fuel injection amount.
The communication device 12 is, for example, a communication device that performs wireless communication with an access point and thereby communicates with a server device or the like. In addition, a receiving device that receives information transmitted wirelessly from the beacon can be provided.

  As described above, the information processing system 1 uses the in-vehicle LAN data and the data measured by the peripheral sensor, the heart rate measured from the biological measurement device, the line-of-sight data, and the like, when the driver deviates from the guidance route. By learning biometric data and the like, it is possible to construct a tension state driver model that is unintentionally lost and deviates from the guidance route, and a normal state driver model that intentionally deviates from the guidance route.

FIG. 2 is a diagram for explaining a concept relating to creation of a driver model according to the present embodiment and estimation of a driving operation based on the created driver model.
In the present embodiment, it is assumed that the driving operation of the driver is described by a predetermined feature amount.
Specifically, the driver determines the current vehicle speed V, the inter-vehicle distance F, and the primary dynamic feature amounts ΔV and ΔF (first-order differential values according to time) and the secondary dynamic feature amounts ΔΔV and ΔΔF (time). Accelerator pedal operation (accelerator operation amount G and next dynamic feature amount ΔG) and brake pedal operation (brake operation amount B and primary dynamic feature amount ΔB) Assuming that
Here, the reason for modeling in consideration of the primary and secondary quantities is to create a driver model that is smoother in time and more natural.

The driver model is created as follows.
First, when the vehicle travels, the accelerator operation amount, the brake pedal operation amount, the vehicle speed, and the inter-vehicle distance at time t1, t2,... Are sampled at a predetermined sampling rate (for example, 0.1 second). The first-order differential value and the second-order differential value can be calculated from these.
As a result, travel data 101 in which the driving operation of the driver is recorded in time series is obtained.
The travel data 101 functions as learning data for learning the driving operation of the driver when creating the driver model.

When the EM algorithm is applied to the traveling data collected in this way, a driver model by GMM using a mixed Gaussian distribution is created.
A method for creating (estimating) a GMM by applying an EM algorithm to collected data is described in, for example, Seiichi Nakagawa, “Voice Recognition Using a Stochastic Model” (The Institute of Electronics, Information and Communication Engineers 1988, P51 to P54). Yes.

More specifically, the joint probability density distribution for the travel data 101 is calculated using the EM algorithm, and the parameters of the calculated joint probability density function = {λi, → μi, Σi | i = 1, 2, 3,... M } Is a GMM driver model.
Here, λi represents a weight, → μi represents an average vector group, Σi represents a variance-covariance matrix group, and M represents the number of mixtures. In addition, a symbol that is previously displayed as → μi means a vector.
As described above, the GMM according to the present embodiment uses the full-width covariance matrix in consideration of the correlation between the feature dimensions.

The driver model generation method has been described above. When a driver model is created using the travel data 101 sampled when the driver is in a tension state, a tension driver model is generated, and the travel data 101 sampled in a normal state. When a driver model is created using, a normal state driver model is generated.
Then, the data 103 at the time t is obtained by calculating the measurement and the measured value, and when the data 103 is input as a parameter to both driver models and the posterior probability is calculated, the posterior probability 105 (the posterior probability P1,. P2) is obtained.

  As described above, the driver model (tension state driver model, normal state driver model) used in the present embodiment is generally N-dimensional using N-type feature amount time series data detected as the vehicle travels as learning data. It is defined by the probability distribution that each data in the space exists.

By the way, in order to create a tense state driver model, it is necessary to determine that the driver is in a tense state due to route guidance when collecting data for creating the driver model.
Therefore, in the present embodiment, data when the driver deviates from the guidance route is used to create the tension driver model. This is because when the driver deviates from the guidance route, it is considered that the driver has lost his guidance.
However, since the driver may intentionally deviate from the guidance route due to his / her own intention, in this embodiment, the driver's tension state is measured using biometric data, and the driver is lost in guidance and the guidance route is changed. It was decided whether it deviated or intentionally deviated.

Therefore, a method for determining whether or not the driver is in a tension state using biometric data will be described next.
FIG. 3A shows an example in which the driver's tension state is determined based on fluctuations in the heart rate.
As shown in the figure, it is generally known that heart rate fluctuation is small in a tension state, and fluctuation is large in a normal state.
For this reason, it is possible to determine whether the driver is in a tension state or a normal state by measuring fluctuations in the heart rate of the driver.

FIG. 3B is an example in which the tension state of the driver is determined by analyzing the electrocardiogram.
In general, an analysis called RR interval analysis is performed on the electrocardiogram (FIG. 3 (b) A) (Same B), and when this is Fourier transformed to the frequency domain, peaks appear on the lower and higher frequencies. (LF and HF of the same C, respectively).

In this analysis, as shown in FIG. 3 (c), it is known that HF is detected when the driver is in a normal state, and the strength of HF is reduced when the driver is in a tension state. This makes it possible to determine the tension state of the driver.
FIGS. 3B and 3C are based on the presentation materials of Mr. Hayano in “Current Status and Future of Driver Evaluation Method (July 16, 2001), Automobile Engineering Association”.

  As described above, the determination method using the heartbeat and the electrocardiogram has been described. In addition, it may be configured to detect whether the driver is in a tension state by detecting the state of sweating, the movement state of the line of sight, or the like. it can.

FIG. 4A is a flowchart for explaining data collection processing for collecting data used by the information processing system 1 for creating a driver model.
First, when the driver sets a destination, the information processing system 1 sets a guide route by searching for a route from the current position to the destination.
Then, when the vehicle starts traveling, the information processing system 1 starts route guidance using the navigation function (step 2).

Next, the information processing system 1 acquires various data with the environment information acquisition device 4, the vehicle information acquisition device 5, and the biological measurement device 8 while performing route guidance to the driver, It memorize | stores in vehicle data DB and biometric data DB.
In order to determine later whether the driver has deviated from the guide route, the guide route and the route on which the vehicle actually traveled are also stored (step 4).

Next, the information processing system 1 determines whether or not the vehicle has finished traveling. If the vehicle has not finished traveling (step 6; N), data acquisition and storage are performed in step 4 and the traveling is terminated. If so (step 6; Y), the data collection process is terminated.
Through the above processing, the environment data, the vehicle data, the biological data, and the route traveled by the vehicle are stored in time series at a predetermined sampling rate, and the guide route set by the information processing system 1 is also stored.
The information processing system 1 performs such data collection for a certain period and accumulates data. The more data that is stored, the more appropriate driver models are created.

FIG. 4B is a flowchart for explaining a data extraction process in which the information processing system 1 extracts data for creating a driver model.
First, the information processing system 1 reads data on a guide route stored in the storage device 7 and a route on which the vehicle actually traveled.
Then, the information processing system 1 identifies a branch point of a guide route such as an intersection from the departure point to the destination point, and determines whether the route traveled by the vehicle at the branch point deviates from the guide route (step) 8).

When the travel route of the vehicle does not deviate from the guide route (step 8; N), the information processing system 1 continues the same determination process at step 8 in the next branch point.
On the other hand, when the vehicle deviates from the guidance route (step 8; Y), the information processing system 1 extracts data before and after the branch point from the environmental data DB, the vehicle data DB, and the biological data DB (step). 10) The data is stored in the storage device 7 as driver model creation data.

In this embodiment, in order to perform detailed route guidance according to the driver's condition from a predetermined distance before the branch point, for data before the branch point, data from a point earlier than the predetermined distance is used. It is desirable to extract.
For example, when detailed route guidance is performed from 1 km before the branch point, data from 1 km or more before the branch point (for example, from 1.5 km before the branch point) is extracted.

  After extracting the data, the information processing system 1 checks whether deviations have been determined at all branch points along the route traveled by the vehicle (step 12), and if there are still undecided branch points (step 12; N) Returning to step 8, it is determined whether the vehicle has deviated from the guidance route at the next branch point.

On the other hand, when it is determined whether or not there is a deviation from the guidance route at all branch points (step 12; Y), the data extraction process is terminated.
The information processing system 1 performs the above data extraction processing for all the accumulated data.
With the above processing, it is possible to extract data when the vehicle deviates from the guidance route at the branch point regardless of whether it is lost or intentional.

FIG. 5 is a flowchart for explaining a procedure by which the information processing system 1 creates a driver model.
The information processing system 1 acquires the extracted biometric data (step 20), and determines whether or not the driver is in a tension state (step 22).
When the driver is not in a tension state (step 22; N), it is considered that the driver has intentionally deviated from the guide route. Therefore, the information processing system 1 uses the biological data and the environmental data extracted together with the biological data. The vehicle data is classified into data for creating a normal state driver model (step 24).

On the other hand, when the driver is in a tension state (step 22; Y), it is considered that the driver has lost his or her guidance route. Therefore, the information processing system 1 uses the biological data and the environment extracted together with the biological data. Data and vehicle data are classified into data for creating a tension driver model (step 26).
In the present embodiment, biometric data or the like is not used as a feature amount for creating a driver model. Therefore, in these classifications, data related to travel data is extracted from environmental data and vehicle data, and the travel data is stored. You may comprise as follows.

After classifying the data, the information processing system 1 determines whether all the extracted data has been classified (step 28). If there is data that has not yet been classified (step 28; N), the process returns to step 20. Continue to classify the data.
On the other hand, when all the data are classified (step 28; Y), the information processing system 1 creates a normal state driver model using the data classified for creating the normal state driver model (step 30).
Next, the information processing system 1 creates a tension driver model using the data classified for creating the tension driver model (step 32).

  It is possible to estimate the tension state of the driver using only the tension state driver model, but the driver model when the driver is in the tension state and the driver model when the driver is in the normal state are used. By using this, it is possible to determine which state the psychological state of the current driver is close to, so that a more appropriate determination can be made. Therefore, in this embodiment, the tension driver model and the normal state It was decided to create both driver models.

In the above, the data is classified into the normal state driver model creation and the tension state driver model creation according to the presence or absence of the tension state, but it is further divided into simple branches, right / left turn lanes, complicated intersections, and landmarks where the landmarks are difficult to understand. Further classification can be performed using the attribute of the branch point.
In this case, for example, the tension driver model creation data is further classified as a simple branch, a right / left turn lane,..., And the tension state driver model in a simple branch, a tension state in a right / left turn lane. It is possible to create a driver model or the like and a driver model that is further segmented according to the situation.

  Further, as described above, in the present embodiment, as an example, among the acquired data, the vehicle speed V, the inter-vehicle distance F, and the primary dynamic feature amounts ΔV and ΔF (first-order differential values according to time). ) Secondary dynamic feature amounts ΔΔV, ΔΔF (second-order differential values with time), accelerator pedal operation (accelerator operation amount G and next dynamic feature amount ΔG), brake pedal operation (brake operation amount B and A driver model is created using travel data such as a primary dynamic feature amount ΔB).

In this way, by creating a driver model excluding biological data, once the driver model is created, the vehicle speed V and accelerator are not used for the driver without using a heart rate sensor, sweat sensor, line-of-sight sensor, or the like. The driver's tension state can be estimated using the operation amount of the pedal / brake pedal.
It is also possible to process biological data such as heartbeats and sweating so that it can be incorporated into the EMM by, for example, averaging, and create a driver model including these as feature quantities.

Furthermore, in this embodiment, as an example, a driver model (tension state driver model and normal state driver model) is created using data in the vicinity of a point where the degree of driver tension is detected.
For example, when calculating the probability of the driver's tension about 1 km before the intersection, a driver model is created using data in the vicinity of 1 km (for example, ± 200 m) from the intersection.
For example, when the driver's tension is further estimated 500 meters before, 200 meters before, and 100 meters before the intersection, a driver model at each point is created using data in the vicinity of these estimated points.

Thus, for example, a tension level is estimated using a driver model created using data near 1 km before the intersection, and a driver model created using data near 500 m before 500 m is used. Thus, a driver model corresponding to the estimated point of estimating the degree of tension can be used.
For example, when the driver is nervous, it is considered that the brake operation and the accelerator operation become more nervous at a point 500 m before the intersection than at 1 km before the intersection.
Therefore, it can be expected that more accurate estimation can be performed by creating driver models according to the distance from the intersection and using these driver models for estimating the degree of tension at the distance.

As another example of the driver model creation method, one driver model (one tension driver model and one normal state driver model) is created using data after passing through the branch point from before the branch point. It can also be configured as follows.
In this case, for example, one driver model is created using data acquired in a section from 1 km before the intersection to 500 m after passing the intersection.
The driver model is used in common at a point where the driver's tension is estimated.

Thus, by creating one driver model using data from before the intersection to after passing, the driver's tension begins to increase before the intersection, and then hesitates or deliberately deviates from the guide route. The features up to can be incorporated into one driver model.
For this reason, the estimated value of the driver model makes it possible to estimate the extent to which the driver strays from the guidance route and deviates intentionally from about 1 km before the intersection.

FIG. 6 is a flowchart for explaining route guidance processing performed by the information processing system 1 before the branch point.
Here, a case where the driver's tension level is estimated 1 km before the branch point and route guidance is performed based on this will be described.

First, the information processing system 1 searches for a route by setting a destination from the driver, and sets a guide route.
As described above, the information processing system 1 includes a guide route acquisition unit that acquires a guide route.
Then, when the vehicle approaches the branch point of the guide route, the information processing system 1 acquires travel data, for example, 1 km before that (step 45).
Here, the travel data corresponds to parameters input to the driver model, and thus the information processing system 1 includes parameter acquisition means for acquiring parameters in front of the branch point of the guide route.

Next, the information processing system 1 inputs the acquired travel data as a parameter to the tension state driver model, and calculates its posterior probability P1 (step 50).
The posterior probability P1 represents the probability that the current driver state is a tension state defined by the tension state driver model.

Next, the information processing system 1 inputs the acquired travel data as a parameter to the normal state driver model, and calculates the posterior probability P2 (step 55).
The posterior probability P2 represents the probability that the current driver state is a normal psychological state defined by the normal state driver model.

Next, the information processing system 1 calculates the probability P = P1 / (P1 + P2), and determines whether this is greater than 70% (step 60).
In this embodiment, the degree of driver tension is defined by the probability P = P1 / (P1 + P2).
It is estimated that the closer the probability P is to 100%, the higher the driver's tension is, and the closer the probability P is to 0%, the lower the driver's tension is.

  As described above, the information processing system 1 includes an estimation unit that inputs the acquired parameter to the tension state driver model and estimates the degree of tension that the driver is lost in the route, and the estimation unit includes Parameters are also input to the normal state driver model, and the degree to which the driver is lost in the route and is nervous is estimated using the values of both driver models.

When the probability P is less than 70% (step 60; N), the information processing system 1 determines that the driver's psychological state is normal, and performs guidance as usual (step 65).
On the other hand, when the probability P is 70% or more (step 60; Y), the information processing system 1 determines that the driver is in a tension state, determines the road condition related to the branch point (step 70), and then the road. Guidance according to the situation and the probability P is performed (step 75).
As described above, the information processing system 1 includes the route guidance method selection unit that selects the route guidance method according to the degree estimated by the estimation unit, and the route guidance unit that performs the route guidance using the selected route guidance method.

The above is an example in which the driver's tension level is determined 1 km before the branch point, and then route guidance according to the tension level is performed. For example, the branch point is 1 km, 500 m, and 200 m. It is also possible to estimate the degree of tension of the driver a plurality of times as approaching and to perform route guidance according to the latest degree of tension.
Thus, more accurate estimation can be performed by estimating the latest degree of tension as it approaches the branch point.

FIG. 7A is a diagram for explaining the guidance method performed in step 75.
The guidance method A is voice guidance at an early timing, and voice guidance is performed earlier at a timing earlier than normal guidance.
For example, in general, when voice guidance is performed at 700 m before the branch point, the guidance is “turn left at the intersection 1 km ahead” 1 km before the branch point. Moreover, you may display the same guidance with the display apparatus 9 with voice guidance.

In the guidance method B, for example, voice guidance is performed by using a landmark such as a gas station or a convenience store as a reference object.
For example, when there is a convenience store at the corner of the branch point, the voice guidance is made such as “turn left at the intersection in front of the convenience store on the left side in the traveling direction”.

In the guidance method C, guidance is performed using the number of traffic lights until turning left and right and lane change.
For example, “5 more traffic lights until left turn”, “4 more traffic lights until left turn”, etc., the number of traffic lights is counted down and voice guidance is provided.
As described above, in the guidance methods A to C, the route guidance is performed by voice guidance, but the guidance content may be displayed on the display device 9 together with the voice guidance.
Moreover, it can also comprise so that it may guide by a display, without performing voice guidance.

FIG. 7B is a diagram for explaining the guidance method using the road situation at step 70 and the probability P at step 75.
For road conditions, simple intersections such as simple intersections and T-junctions, right and left turn lanes with multiple lanes on one side, and complex intersections such as five-way roads, intersections exist continuously at short distances. In the case of continuous intersections, landmarks may be difficult to understand.

The information processing system 1 provides guidance by the guidance method A when the probability P is 70% or more and less than 80% in any road situation, and when the probability P is 80% or more and less than 90%. Guidance is performed by the guidance method A and the guidance method B. When the probability P is 90% or more, guidance is performed by the guidance method A, the guidance method B, and the guidance method C.
As described above, the information processing system 1 performs more detailed route guidance as the degree estimated by the estimation unit increases.

  In the above example, the guidance method is selected with the probability P regardless of the road situation. For example, in the case of an intersection where the road situation is simple, when the probability P is 70% or more, the guidance method A is uniformly adopted. When the route guidance is performed and the landmark is difficult to understand, the guidance method A is used when the probability P is 70% or more and less than 80%, and the probability P is 80% or more. The guidance method can be individually set according to the combination of the road condition and the probability P, such as performing guidance by the guidance method A and the guidance method B uniformly.

FIG. 8 is a diagram for explaining an example of a guidance method at a continuous intersection.
It is assumed that there is a continuous intersection in front of the traveling direction of the vehicle 50 and the vehicle 50 turns left on the route 22.
In the conventional navigation apparatus, voice guidance such as “To the left at the intersection of XX m” is voiced, but in voice guidance A, voice guidance such as “To the left at the intersection after 1 km” is voiced. Voice guidance such as “To the left at the next intersection of the gas station on the left in the direction of travel” or the like is performed. In the guidance method C, voice guidance such as “Left the second traffic light to the left” or the like is performed.

  For example, when guiding by the guidance methods A, B, and C, the information processing system 1 provides voice guidance such as “to the left at the intersection 1 km ahead” 1 km before the intersection, and as the intersection approaches the “third "Left the second traffic light to the left", "Left the second traffic light to the left", "Left at the next intersection of the gas station on the left in the direction of travel", "Next traffic light to the left", etc.

  Further, when the vehicle 50 turns right on the route 23, the voice guidance A gives a voice guidance such as “to the right at the intersection 1 km ahead”, and in the guidance method B “to the right at the intersection in front of the convenience store on the right in the traveling direction”. ”And the like, and in the guidance method C,“ the next traffic light is to the right ”, etc.

FIG. 9 is a diagram for explaining an example of a guidance method at a complicated intersection.
As shown in the figure, it is assumed that there is an intersection formed by a five-way road having a complicated shape in front of the vehicle 50.
When the vehicle 50 travels on the route 31, the voice guidance B provides voice guidance such as “To the left road of the XX convenience store on a five-way road 100 meters ahead”.
Further, when proceeding to the route 32, the voice guidance such as “To the right road of the XX convenience store on a five-way road 100 meters ahead” is given.

As described above, the following effects can be obtained by the present embodiment.
(1) Before reaching the branch point of the guide route, it is possible to estimate the extent to which the driver deviates from the guide route at the branch point.
(2) More detailed route guidance can be performed according to the degree to which the driver deviates from the guidance route.
(3) The driver can receive route guidance according to his own tension and impatience even when passing through an unfamiliar branch point or a complicated branch point.
(4) It is possible to detect a driver state using a driver model and provide an optimum route guidance method based on the detected value.

  In the present embodiment, the driver model is held by the vehicle. However, when the communication device 12 is used to upload and store the driver model to the server, the driver operates. Even if the vehicle is changed, the route guidance service by the information processing system 1 can be received.

It is a figure for demonstrating an information processing system. It is a figure for demonstrating the concept regarding creation of a driver model, and estimation of the driving operation based on the created driver model. It is a figure for demonstrating the method of judging the tension | tensile_strength state of a driver. It is a flowchart for demonstrating a data collection process. It is a flowchart for demonstrating the procedure which produces a driver model. It is a flowchart for demonstrating route guidance processing. It is a figure for demonstrating the guidance method. It is a figure for demonstrating an example of the guidance method in a continuous intersection. It is a figure for demonstrating an example of the guidance method in a complicated intersection.

Explanation of symbols

1 Information processing system 2 ECU
DESCRIPTION OF SYMBOLS 3 Current position detection apparatus 4 Environmental information acquisition apparatus 5 Vehicle information acquisition apparatus 6 Input apparatus 7 Storage apparatus 8 Biometric measurement apparatus 9 Display apparatus 10 Voice output apparatus 11 Vehicle control apparatus 12 Communication apparatus

Claims (3)

A tension state driver model storage means for storing a tension state driver model when the driver is lost in a path and is in a tension state;
Normal state driver model storage means for storing a normal state driver model when the driver is in a normal state;
A guide route acquisition means for acquiring a guide route;
Parameter acquisition means for acquiring parameters in front of the branch point of the acquired guide route; and input the acquired parameters to the stored tension state driver model and the stored normal state driver model; Estimating means for estimating the degree to which the driver is lost and nervous using the value of
According to the estimated degree, route guidance method selection means for selecting a detailed route guidance method as the estimated degree is larger ;
Route guidance means for performing route guidance by the selected route guidance method;
A vehicle guide device comprising:
The tense state driver model is created using data when the driver deviates from the guide route in a tense state at the branch point of the route, and the normal state driver model is the driver's intention at the branch point of the route. The vehicle guidance device according to claim 1, wherein the vehicle guidance device is created using data when the vehicle deviates from the guidance route. The driver model used by the estimation means is defined by a probability distribution in which each type of data in an N-dimensional space exists using time-series data of N types of feature quantities detected as the vehicle travels as learning data. The vehicle guide device according to claim 1 or 2 .

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