JP2013114365A - Safe driving support information distribution system - Google Patents

Abstract

PROBLEM TO BE SOLVED: To allow distribution of support information about more points.SOLUTION: Danger occurrence points where danger has occurred and their danger occurrence forms are collected by, for example, personal inspection of a probe car V2, and collected data is stored in a danger occurrence point and occurrence form database 7. Potentially dangerous points having the same features as features of road environments at the collected danger occurrence points are estimated together with their danger occurrence forms, and they are stored in a potentially dangerous point and danger occurrence form database 6. The danger occurrence points and the danger occurrence forms stored in the database 7 and the potentially dangerous points and the danger occurrence forms stored in the database 6 are distributed to a target vehicle V to be supported.

Description

  The present invention relates to a safe driving support information distribution system.

  In order to support safe driving, it is preferable to inform the driver of dangerous points on the road. In Patent Document 1, a point where a dangerous case actually occurs (a point where an accident occurred, a near-missed dangerous case occurred) is made into a database as a dangerous point, and approaches the dangerous point registered in the database. Has been disclosed to inform the vehicle (the driver) that the vehicle is approaching a danger point. Japanese Patent Application Laid-Open No. 2004-151561 discloses a technique for notifying a vehicle of information related to the occurrence of an accident when the vehicle performs an operation that may cause an accident similar to an accident that has occurred in the past. In the following description, “approached or hated” is expressed as “missed hat”.

JP 2010-97345 A JP-A-4-309810

  In order to support safe driving, it is important to increase the number of dangerous points to which information is distributed. On the other hand, it takes a great deal of time and difficulty to collect actual accidents and near-miss incidents through field inspections. In other words, even if there are dangerous points where accidents and near-misses may occur, it is virtually impossible to specify all of them by field inspection.

  The present invention has been made in view of the above circumstances, and the purpose of the present invention is to enable safe driving that enables distribution of information on a number of potentially dangerous points in addition to the dangerous points collected by on-site inspection. It is to provide a support information distribution system.

In order to achieve the above object, the following solution is adopted in the present invention. That is, as described in claim 1,
A first storage means for storing a point where the dangerous situation collected by the on-site inspection has occurred or is likely to occur as a risk manifesting point, and storing the road environment factor;
An estimation means for estimating a point having a road environment similar to the danger manifesting point as a risk potential point based on road environment information and a road environmental factor stored in the first storage unit;
Second storage means for storing the danger potential point estimated by the estimation means;
Support information distribution means for distributing to the vehicle support information indicating that the danger exposure point stored in the first storage means and the danger exposure point stored in the second storage means are dangerous;
With
It is like that. According to the above-described solution technique, it is possible to distribute information on a large number of risk potential points that are not obtained by the on-site inspection, which is extremely preferable for supporting safe driving.

A preferred mode based on the above solution is as described in claim 2 and the following claims. That is,
The first storage means is at least one piece of information selected from the road environment at the danger occurrence point, the danger occurrence time as the danger occurrence mode, the behavior of the danger target, the moving direction of the own vehicle, and the speed of the own vehicle. Categorized and remembered,
The estimation means estimates a feature of a point where a risk occurrence is predicted based on the categorized information, and estimates a point having the estimated feature as the risk potential point.
(Corresponding to claim 2). In this case, it is preferable in terms of specifying the risk potential point more accurately.

The first storage means stores, in addition to the danger manifesting point, a danger occurrence form collected by an actual inspection,
The estimation means estimates a risk occurrence mode that may occur at the risk potential point based on the stored content stored in the first storage means in addition to the risk potential point,
The second storage means also stores a danger occurrence form corresponding to the danger potential point,
The support information distribution means also distributes the risk occurrence form stored in the first storage means and the second storage means to the vehicle as support information.
(Corresponding to claim 3). In this case, it is possible to distribute information about the danger occurrence mode.

The stored content of the first storage means is updated by a new on-site inspection,
The storage content of the second storage means is updated in response to the update of the first storage means.
(Corresponding to claim 4). In this case, it is preferable for performing appropriate information distribution by performing an update based on the on-site inspection, and also preferable for increasing the number of risk exposure points and risk potential points.

The danger occurrence mode includes a behavior of the vehicle when entering the danger manifestation point or the danger potential point,
Among the vehicles, when the behavior of the vehicle that enters the danger manifesting point or the danger potential point matches the behavior of the vehicle at the time of entry stored in the first storage means or the second storage means, Support information is distributed from the support information distribution means.
(Corresponding to claim 5). In this case, it is preferable for appropriately distributing the support information to a vehicle entering in a form that may cause danger.

The estimation means stores the degree of coincidence between the risk factor for the risk manifesting point stored in the first storage means and the risk factor for the risk potential point stored in the second storage means. And
The support information distribution means makes the support content distributed according to the difference in the degree of coincidence,
(Corresponding to claim 6). In this case, it is preferable to obtain appropriate support contents according to the high degree of coincidence, that is, the degree of occurrence of danger.

  The danger information distribution means is configured to make the contents of support distributed at the danger manifestation point and the danger potential point different (corresponding to claim 7). In this case, it is preferable to obtain appropriate support contents according to the danger manifesting point and the danger potential point.

  According to the present invention, it is possible to distribute information on a large number of danger points.

1 is an overall system diagram showing an embodiment of the present invention. The figure which shows an example of a danger occurrence form master simply. The figure which shows an example of a dangerous road environmental factor master simply. The figure which shows simply an example of a danger generation | occurrence | production form and dangerous road environmental factor corresponding | compatible master. The figure which shows an example of a danger exposure point and a danger potential point. The flowchart which shows the example of control of an information delivery center. The flowchart which shows the example of control of an information delivery center. The flowchart which shows the example of control of an information delivery center. The flowchart which shows the example of control of an information delivery center. The flowchart which shows the example of control of an information delivery center. The flowchart which shows the example of control of an information delivery center. The flowchart which shows the example of control of an information delivery center. The flowchart which shows the example of control of an information delivery center. The flowchart which shows the example of control of an information delivery center. The flowchart which shows the example of control of an information delivery center.

  In FIG. 1, CT is an information distribution center that distributes driving support information for safety to a vehicle V that receives driving support. Various information obtained by a large number of probe cars V1 and V2 that collect information by on-site inspection is input to the information distribution center CT. The probe car V1 is for collecting vehicle dynamics data. For example, the probe car V1 can collect the position and state (dynamics) of the vehicle at the start / stop of the vehicle, at a fixed time, or at a fixed distance. The number of units is extremely large because simple data collection is sufficient. The probe car V2 is used for collecting high-performance data. In addition to the above-described dynamic data, the probe car V2 collects detailed vehicle behavior before and after the occurrence of a near-miss, driver operations, moving images, and the like. . The data obtained by this probe car V2 identifies the dangerous point where the accident or near-miss actually occurred, and information such as the form of danger occurrence and the road environment at that location is also obtained. Since the probe car V2 has a high function, the number of traveling vehicles is smaller than that of the probe car V1.

  The outline of the function of the information distribution center CT will be described as follows. First, the information on the danger point (information on the danger manifestation point) obtained by the probe car V2 is distributed to the vehicle V that receives support together with the danger occurrence form. However, since the information on the dangerous point obtained by the probe car V2 is limited, the information distribution of the dangerous point to the vehicle V must be limited.

  On the other hand, the road information obtained by each probe car V1, V2 is abundant. For this reason, as will be described later, the information distribution center CT identifies (extracts) the characteristics of the road at the dangerous point obtained by the probe car V2, and the road distribution at each point obtained by each of the probe cars V1 and V2 Of these, a point (road) having the specified characteristics (matching or similar) is estimated as a potential danger point. Then, the information about the danger potential point is distributed to the vehicle V together with the danger form that may occur at the danger potential point. As a result, the danger points distributed to the vehicle V are enriched as both the danger exposure points and the danger potential points. The information distribution center CT exchanges information with the vehicle V and each of the probe cars V1 and V2 by wireless communication, and the vehicle V uses the information distributed from the information distribution center CT to the driver. It has a notification function (for example, an alarm using an image and / or sound using a display or a speaker of a navigation device).

  Next, details of the information distribution center CT will be described. First, the information distribution center CT includes a road environment information database 1, a road environment modification information database 2, a risk occurrence master 3, a dangerous road environment factor master 4, a risk occurrence / road environment factor correspondence master 5, An occurrence pattern database 6 and a dangerous point / occurrence pattern database 7 are provided.

The road environment database 1 is a database obtained by classifying road environment feature information at a certain point, and is always updated to the latest one. The contents stored in the road environment database 1 are, for example, as follows.
(1) Single path part a. Road structure Rough structure, road shape (gradient and curvature), surrounding structures (buildings, landforms), etc. for traffic zones (number of lanes, sidewalks, survey zones, and two-wheeler traffic zones).

b. Signal information Presence / absence of push button signal and signal cycle, presence / absence of warning signal and signal cycle.

c. Regulation information Regulation speed (actual speed), presence / absence of side road (non-priority road), etc.
(2) About the intersection a. Road structure Intersection shape (size, number of connection routes, connection angle of each route, lane information of each route), stop line position, presence / absence and position of pedestrian crossing, right turn waiting position, road shape (passage route) Slope, curvature), surrounding structures (buildings, terrain), etc.

b. Signal information Signal presence / absence, signal control method (fixed cycle, time fluctuation, sensitive type, central control, etc.), each signal pattern (presence / absence of pedestrian signal, pedestrian separation, right separation, time difference type), etc.

c. Restriction information Suspension restriction (distinguishing whether a place where a pause is necessary when there is no signal), travel direction restriction, restriction speed (actual speed)

  The road environment modification information database 2 stores the modification information of the road environment, and stores how and where the change has occurred. Based on the data of the road environment modification information database 2, the stored contents of the road environment information database 1 are updated as needed (updated to the latest information).

  The danger occurrence master 3 is master data that categorizes the situation (situation) when an accident or near-miss occurs (see FIG. 2). For example, the state of the own vehicle (straight, right turn, right start, left turn, etc.), intersection object (four-wheeled vehicle, two-wheeled vehicle, bicycle, pedestrian, etc.), position of the own vehicle and the intersection object The relationship (preceding, facing, from the right, from the left, etc.) is stored.

  The dangerous road environment factor master 4 is master data in which features of the road environment in a place where dangerous events of a specific occurrence form frequently occur are extracted as risk factors (see FIG. 3). The stored contents are, for example, that the single road portion is curved, has a slope, has a high speed, and has no sidewalk. As for the intersection, the intersection angle is 00 degrees or less, it is not a right-right separation signal, it is not a pedestrian separation signal, and the non-priority road is wide.

  The danger occurrence form / road environment factor correspondence master 5 is relation data for associating the danger occurrence form master 3 with the dangerous road environment factor master 4 and is associated with n to m data (see FIG. 4). For example, as a road environment factor (feature) regarding “a place where it is easy to cross with an on-straight vehicle when starting a right turn” as a danger occurrence mode, there is a signal with “two lanes or more on one side, crossing angle on opposite directions of 170 degrees or less” It is set as “intersection of”.

  The risk potential point / occurrence form database 6 uses the danger occurrence form master 3, the dangerous road environment factor master 4, and the road environment database 1, but no data on accidents or near-misses is available, but an accident or near-miss occurs. A place where it is estimated that there is a high possibility is identified, and at the same time, the occurrence form of a dangerous event to be noted at that place is estimated, and a database is created (database related to a risk potential point).

  Hazardous point / occurrence form database 7 is based on data on accidents and near-misses that are actually obtained, and identifies the places where accidents and near-misses are likely to occur and the occurrence forms of dangerous events that should be noted in those places. It is a database (database related to the hazard location).

  FIG. 5 shows an example of the danger manifesting point X, which is an intersection K1. More specifically, the intersection K1 is a five-forked road where a large road D1 and a large road D2 intersect with a small road D3. When the probe car V2 makes a right turn at the intersection K1 from the road D1 and tries to move to the road D2, it is likely that the probe car V2 will cross the vehicle from the road D3 and a near-miss occurs. The feature of the intersection X1 that is a danger manifestation point is that the small road D3 is a small angle θ1 of less than 90 degrees when turning right from a large road and the small road D3 is likely to be a blind spot with respect to the large road D2. .

  On the other hand, the intersection X2 is a five-way intersection where the large road D11 and the large road D12 intersect and the small road D13 intersects. The road D13 has a small angle θ2 with respect to the road D11, and the angle θ2 is substantially the same as the angle θ1. Since the information distribution center CT has the characteristic of the intersection K1 that is the danger manifestation point as the road feature at the intersection K2, the information distribution center CT is regarded as the intersection that becomes the danger manifestation point. Similar to the intersection K1, the possibility of “when turning right from the road D11 and moving to the road D12 crosses with a vehicle from the road D13” is associated. As described above, it is possible to distribute more information regarding the dangerous point to the vehicle V by estimating the dangerous potential point from the dangerous point.

  Here, when information is distributed from the information distribution center CT to the vehicle V, it is possible to make a difference between the case of the danger exposure point and the case of the danger potential point. For example, with respect to the danger potential point, the danger potential point blinks and is displayed on the map of the display, and a possible dangerous event is displayed on the display by a character display. On the other hand, in addition to the display as described above, the danger occurrence point is informed of the danger event by voice, and the level of warning for the danger occurrence point is the warning for the danger potential point. It will be higher than the level.

  A specific example of control in the information distribution center CT as described above will be described with reference to the flowcharts in FIG. In the following description, S indicates a step. First, FIG. 6 shows processing for updating the road environment information database 1. That is, vehicle dynamics data obtained by the probe cars V1 and V2 is input in S1 of FIG. Next, in S2, map matching (determination of accurate vehicle position information) of the vehicles V1 and V2 is performed based on the latest map information (road information). In map matching, the latest road traffic information may be taken into account. Thereafter, in S3, the road environment is analyzed and classified in the travel route of the probe cars V1 and V2. Next, in S4, the road environment is estimated.

  After S4, in S5, the road environment estimated in S4 is compared with the conventional road environment stored in the road environment information database 1. Thereafter, in S6, it is determined whether or not there is road environment information to be newly registered or modified. If the determination in S6 is YES, the stored contents stored in the road environment information database 1 are updated in S7, and the updated information is stored in the road environment modification database 2. If the determination in S6 is NO, the data collected this time is discarded (no update) in S8. In this way, the stored contents stored in the road environment information database 1 are always updated to the latest. Instead of the processes of S1 to S4, the latest map information (road information) is input. In the process of S5, the input latest map information is compared with the conventional road environment information. It may be.

  FIG. 7 shows an outline of processing for updating and distributing the risk possibility database 6. That is, in S11, the dangerous point / occurrence form database 7 is generated. Thereafter, in S12, the danger occurrence form master 3, the road environment factor master 4, and the danger occurrence form / road environment factor correspondence master 5 are generated. Thereafter, in S13, the risk potential point / occurrence form database 6 is generated. Further, in S14, the risk potential point / occurrence form database 6 is updated. Thereafter, in S15, information on the danger potential point and the danger occurrence mode is distributed to the vehicle V based on the latest database updated in S14. Details of S11 to S15 will be described later.

  Details of S11 in FIG. 7 (generation of the dangerous spot / occurrence form database 1) are shown in FIG. That is, in S21 of FIG. 8, information on accidents and near-misses obtained by the probe car V2 is collected (input). Thereafter, in S22, the near-miss occurrence status is analyzed and classified from the data obtained in S21. Thereafter, in S23, the dangerous spot / occurrence form is estimated based on the results of the above analysis and classification. The estimation at S23 is, for example, “When you turn right from xx intersection to route 2 you need to pay attention to the intersection with the oncoming straight vehicle” or “When you enter the point from the northeast, It is necessary to pay attention to the rear-end collision. " In S 23, the estimated danger point and the occurrence form are registered in the danger point / occurrence form database 7.

  Details of S12 of FIG. 7 (generation of each master 3 to 5) are shown in FIG. That is, in S31, the relationship between the occurrence form at the danger occurrence point and the road environment is analyzed from the danger point / occurrence form database 7 and the road environment information database 1 (the danger occurrence form master 3 and the dangerous road environment factor master 4). And a master as shown in FIGS. 2 and 3). Specifically, for example, an intersection where a number of near-misses that intersect with the oncoming vehicle occur when turning right satisfies, for example, one of the following first to third road environmental factors. That is, firstly, the connection angle between the inflow path and the opposite path is 120 to 170 degrees, or the average slope of the 100 m section of the opposite path with respect to the inflow path is −2% or less. It is assumed that the road width is 10 m or more, and thirdly, when the road is not completely a right separation signal. As another example, a single road portion where a rear-end collision near-miss occurs is when the actual velocity is 60 km / hour, the left curvature radius is 300 m or less, the right curvature radius is 250 m or less, and the 500 m section It is assumed that any one of the conditions when the average slope of the curve is −2% or less is satisfied. After S31, in S32, based on the result of S31, the risk occurrence form and the road environment factor are associated with each other and mastered (the master is generated as shown in FIG. 4).

  Details of S13 in FIG. 7 are shown in FIG. That is, in S41, a point with a dangerous road environment factor is extracted using the road environment information database 1 and the dangerous road environment factor master 4. Thereafter, in S42, the spot extracted in S41 is used as the dangerous spot candidate data. Thereafter, in S43, points not included in the dangerous point / occurrence form database 7 are extracted. Here, when a sudden search is performed from the enormous road environment information database 1 under the conditions of the danger occurrence mode / road environment factor correspondence master 5, the processing load becomes extremely large. For this reason, in the embodiment, the risk candidate points are first narrowed down by a simple search using the dangerous road environment factor master 4.

  After S43, in S44, a risk occurrence mode likely to occur at the point extracted in S43 is estimated. In the process of S44, a combination of dangerous road environment factors possessed by a new danger point candidate and the corresponding risk occurrence form are searched from the risk occurrence form / road environment factor correspondence master 5, and the corresponding risk occurrence form exists. In this case, the point is regarded as a dangerous point, and its occurrence form is a dangerous event that should be alerted. After S44, in S45, the danger potential point and its occurrence form are associated with each other and stored in the danger possibility point / occurrence form database 6.

  Details of S14 in FIG. 7 are shown in FIG. That is, in S51, the road environment modification information at the danger point is extracted. Thereafter, in S52, a change in risk due to the road environment modification is evaluated. Thereafter, in S53, it is determined whether or not there is no possibility of the occurrence of the danger at the danger potential point. When the determination in S53 is YES, in S54, the danger potential point where the danger has ceased is deleted from the danger possibility point / occurrence form database 6 (update). If the determination in S53 is NO, the process ends without passing through S54.

  Details of S15 of FIG. 7 are shown in FIG. That is, danger point information is extracted according to the position of the vehicle V to be supported (the current position, vehicle status, etc. are transmitted from the vehicle V to the information distribution center CT). Thereafter, information regarding the danger point is distributed to the vehicle V in S62.

  FIGS. 13 to 15 show specific examples of dangerous point information generation and distribution in the information distribution center CT. First, the processes of S71 to S74 in FIG. 13 are performed. The process of S71 corresponds to the process of S11 of FIG. 7, and the process of S72 corresponds to the process of S12 of FIG. Details of S73 are shown in FIG. 14, and the processing of FIG. 14 corresponds to FIG. Further, details of S74 are shown in FIG. 15. The processing of FIG. 15 corresponds to the processing of FIG. However, as described above, the alert in S92 in FIG. 15 is an alert at the danger location, so the degree of alert is higher than the alert at the danger potential in S62 in FIG. It will be expensive.

  Although the embodiment has been described above, the present invention is not limited to the embodiment, and can be appropriately changed within the scope described in the scope of claims. For example, the invention includes the following cases. . Information about accidents and near-miss incidents during field inspections can be collected by any appropriate method other than using a probe car. For example, collection based on actual accident data held by the police, It can also be collected based on time observation data. Each step or step group described in the flowchart can be expressed by adding the name of the means to the function. When estimating the risk potential point, the risk point / occurrence form database 7 stores the road environment of the danger manifesting point, the risk occurrence time as the risk occurrence mode, the behavior of the risk target, the moving direction of the host vehicle, and the speed information of the host vehicle. It is preferable to classify and memorize, estimate the characteristics of a point where danger occurrence is predicted based on this categorized information, and estimate a point having this estimated characteristic as a risk potential point It is.

  When estimating the risk potential point, for example, the degree of coincidence with the feature of the road environment at the danger actual point is further estimated, and this degree of coincidence is stored in the risk possibility point / occurrence form database 6 to be stored in the vehicle V. When the support information is distributed to the vehicle, the support content for the vehicle V may be changed according to the difference in the degree of coincidence (for example, the higher the degree of coincidence, the higher the alerting degree). Estimate the degree of coincidence between the situation of the subject vehicle and the danger target and the situation of the vehicle V to be supported and the possibility of crossing the object with respect to this vehicle V. It may be changed. In particular, the behavior of a vehicle entering a hazard manifestation point or a potential hazard point is compared with the approach form of a vehicle that has caused an accident or near-miss, and the degree of coincidence (similarity) is seen. It is preferable to distribute. Of course, the object of the present invention is not limited to what is explicitly stated, but also implicitly includes providing what is substantially preferred or expressed as an advantage.

  The present invention is suitable for improving the safe driving of a vehicle.

V: Vehicle (vehicle to which information is distributed)
V1: Probe car (collection of dynamic information)
V2: Probe car (collection of dynamic information and near-miss)
CT: Information distribution center X: Danger location point Y: Danger potential point 1: Road environment information database 2: Road environment modification information database 3: Danger generation form mass 4: Danger road environment factor master 5: Danger occurrence form / road environment factor Correspondence master 6: Danger point / occurrence form database 7: Danger point / occurrence form database

Claims (7)

A first storage means for storing a point where the dangerous situation collected by the on-site inspection has occurred or is likely to occur as a risk manifesting point, and storing the road environment factor;
An estimation means for estimating a point having a road environment similar to the danger manifesting point as a risk potential point based on road environment information and a road environmental factor stored in the first storage unit;
Second storage means for storing the danger potential point estimated by the estimation means;
Support information distribution means for distributing to the vehicle support information indicating that the danger exposure point stored in the first storage means and the danger exposure point stored in the second storage means are dangerous;
A safe driving support information distribution system characterized by comprising: In claim 1,
The first storage means is at least one piece of information selected from the road environment at the danger occurrence point, the danger occurrence time as the danger occurrence mode, the behavior of the danger target, the moving direction of the own vehicle, and the speed of the own vehicle. Categorized and remembered,
The estimation means estimates a feature of a point where a risk occurrence is predicted based on the categorized information, and estimates a point having the estimated feature as the risk potential point.
A safe driving support information distribution system characterized by that. In claim 1 or claim 2,
The first storage means stores, in addition to the danger manifesting point, a danger occurrence form collected by an actual inspection,
The estimation means estimates a risk occurrence mode that may occur at the risk potential point based on the stored content stored in the first storage means in addition to the risk potential point,
The second storage means also stores a danger occurrence form corresponding to the danger potential point,
The support information distribution means also distributes the risk occurrence form stored in the first storage means and the second storage means to the vehicle as support information.
A safe driving support information distribution system characterized by that. In any one of Claims 1 thru | or 3,
The stored content of the first storage means is updated by a new on-site inspection,
The storage content of the second storage means is updated in response to the update of the first storage means.
A safe driving support information distribution system characterized by that. In claim 3,
The danger occurrence mode includes a behavior of the vehicle when entering the danger manifestation point or the danger potential point,
Among the vehicles, when the behavior of the vehicle that enters the danger manifesting point or the danger potential point matches the behavior of the vehicle at the time of entry stored in the first storage means or the second storage means, Support information is distributed from the support information distribution means.
A safe driving support information distribution system characterized by that. In any one of Claims 1 thru | or 5,
The estimation means stores the degree of coincidence between the risk factor for the risk manifesting point stored in the first storage means and the risk factor for the risk potential point stored in the second storage means. And
The support information distribution means makes the support content distributed according to the difference in the degree of coincidence,
A safe driving support information distribution system characterized by that. In any one of Claims 1 thru | or 6,
The safe driving support information distribution system, wherein the danger information distribution means makes the contents of support distributed at the danger manifesting point and the dangerous potential point different.

Images