JPWO2009128398A1 - Mobile object warning device, mobile object warning method, and mobile object warning program - Google Patents

Abstract

By using a near-miss map that describes points of caution regarding driving for each location, warnings can be issued with higher accuracy in terms of both precision and recall. In order to issue a warning to a user in association with user status input means for inputting information indicating the status of the client, the user and the surrounding environment, and information indicating the status of the client, the user and the surrounding environment. A situation-dependent near-miss map that stores the near-miss information of the incident, a situation-dependent near-miss registration means that obtains information indicating the situation when the near-miss information is input from the user situation input means, and registers the information in the situation-dependent near-miss map, Situation matching means for searching and extracting near-miss information corresponding to the situation of the client, user and surrounding environment from the situation-dependent near-miss map, and warning means for notifying the user of the near-miss information extracted by the situation matching means, Prepare.

Description

[Description of related applications]
The present invention is based on the priority claim of Japanese patent application: Japanese Patent Application No. 2008-105323 (filed on April 15, 2008), the entire contents of which are incorporated herein by reference. Shall.
The present invention relates to a mobile warning device that warns of a danger before a dangerous situation occurs while driving or walking a vehicle, a client and a server constituting the device, a mobile warning method, and a mobile warning program. The present invention relates to a mobile object warning device using a near-miss map in which notes to be warned are described for each place.

  The following technology is disclosed as a moving body warning device using a near-miss map. Here, Hiyari-Hatto is a term related to accident prevention and safety measures, and refers to mistakes and errors that may lead to accidents and disasters, or dangerous situations or emergencies caused by these, incidents ( also referred to as incident) information. Also, “to near-miss” means recognizing such a mistake or error. The “miss” of the near-miss is derived from the adverb “hiyari” when feeling a sense of fear, and the “hat” is derived from the adverb “hat” representing the surprise.

  Patent Document 1 describes a danger reaction point recording system capable of more specifically recognizing a dangerous situation according to the surrounding situation of a place where a vehicle actually travels on a road. The danger reaction point recording system described in Patent Document 1 generates a near-miss map. A near-miss map is generally constructed manually based on accident cases. For this reason, unless an accident occurs once, it is not entered in the near-miss map and there is a problem that the number of cases is small. On the other hand, in the system described in Patent Document 1, an accident occurs due to the system automatically recognizing a place where the driver near-misses using a biological information acquisition unit such as a sensor for measuring a heart rate. Data can be entered into the near-miss map at a dangerous place even in a non-existing place.

Patent Document 2 describes a danger location information display device that can specifically notify a driver of what danger may exist when a vehicle travels in a dangerous area.
The danger location information display device described in Patent Literature 2 displays a warning to the driver on the assumption that a near-miss map already exists. Specifically, the position of the vehicle is detected by the position detection unit, the detection result is matched with the data storage unit storing the near-miss map, and the warning is displayed on the display device.

  Further, Patent Literature 3 describes a search system that displays a search range to a user and accurately displays the similarity between a search keyword and a search result document.

JP 2007-47914 A JP 2007-51973 A JP 2004-102407 A

Note that the entire disclosure of Patent Documents 1, 2, and 3 is incorporated herein by reference. The following analysis is given by the present invention.
Patent Document 1, Patent Document 2, and combinations thereof have the following problems.

  First, there is a problem that the accuracy rate of warnings is low, that is, there are many false alarms. The near-miss map is a table for associating caution points with places. So, if anyone goes to that place anytime, will he do a near-miss? There is a doubt. In extreme cases, most warnings can be false alarms. If the warning is too high, the warning effect may be lowered, which is a problem. It can be said that a highly accurate system with few false alarms is necessary.

  In addition, there is a problem that the reproducibility of the warning is low, that is, the warning is often not issued. When creating a near-miss map, information on the location where the accident actually occurred or the location where the driver actually missed is collected, and the frequently occurring portion is added to the near-miss map entry. Generally, abnormal cases such as accident cases and near-miss cases are infrequent and a sufficient number is not collected. For this reason, there is no warning, but there are many incidents of near-miss, which is a problem.

These problems are improved to some extent by the system described in Patent Document 1.
In the system described in Patent Document 1, since there is no incident by the ecological information acquisition means, it is added to the case only by near-miss, so there are many cases of near-miss. However, in reality, heart rate may increase due to various factors while driving, so many people can not enter unless they are in common at the place, and it is difficult to increase the number of cases in practice. .

  In the first place, not only “place” but also various “situations” such as relationship with other vehicles, environment such as weather, driver skill, and car speed are the reason for the near-miss. For this reason, the methods described in Patent Documents 1 and 2 cannot sufficiently cope with the problem that the precision of warning is low and the problem that the warning reproduction rate is low. It is necessary to increase the coverage of the near-miss map.

  In the method described in Patent Document 3, a general fuzzy search for a sentence can be performed. However, if a general fuzzy search is performed on data related to an accident, there is a possibility of erroneous data expansion.

  Therefore, the present invention uses a near-miss map in which precautions relating to driving are described for each place, and can provide a warning with higher accuracy in terms of both precision and recall, a mobile object warning device, a client, and a server An object of the present invention is to provide a moving object warning method and a moving object warning program.

  According to a first aspect of the present invention, user status input means for inputting information indicating the status of the client, the user, and the surrounding environment, near-miss input means for inputting information indicating that the user has near-miss, and client A situation-dependent near-miss map for storing near-miss information for issuing a warning to the user in association with information indicating the status of the user and the surrounding environment, and information indicating the situation when the near-miss information is input to the user Situation matching that is obtained from the situation input means and is registered in the situation dependent near miss map, and situation matching that searches for and extracts the near hat information corresponding to the situation of the client, the user, and the surrounding environment from the situation dependent near hat map. Means and the situation matching means And warning means for notifying the near-miss information to the user, the mobile warning device equipped with is provided.

  According to the second aspect of the present invention, when the information indicating the status of the client, the user, and the surrounding environment is input, and when the information indicating that the user is accidentally shut is input, the client, the user, and Corresponding to the information indicating the status of the surrounding environment, as a near-miss information for issuing a warning to the user, storing in the situation-dependent near-miss map, and the near-hat information corresponding to the situation of the client, the user, and the surrounding environment A mobile object warning method comprising: searching and extracting from a situation dependent near miss hat map and notifying a user.

  According to a third aspect of the present invention, when a process for inputting information indicating the status of the predetermined client, the user, and the surrounding environment is input to the computer, and information indicating that the user is accidentally shut down, Corresponding to information indicating the situation of the client, the user and the surrounding environment, corresponding to the situation of the client, the user, and the surrounding environment as a near-miss information for issuing a warning to the user and storing in the situation-dependent incident map There is provided a program for asynchronously executing the process of retrieving and extracting near-miss information from the situation-dependent near-miss map and notifying the user.

According to a fourth aspect of the present invention, user status input means for inputting information indicating the status of the device, the user, and the surrounding environment, and near-miss input means for inputting information indicating that the user has near-missed, Information indicating the situation when the near-miss information is input is obtained from the user-status input means and registered in a situation-dependent near-miss map stored in the server, and the own device, the user, and the surrounding environment There is provided a client comprising situation matching means for searching and extracting near-miss information corresponding to the situation from the situation-dependent near-miss map, and warning means for notifying the user of the near-miss information extracted by the situation matching means. .
According to a fifth aspect of the present invention, there is provided a situation-dependent near-miss map for storing near-miss information for issuing a warning to the user in association with information indicating the status of the client, the user, and the surrounding environment, and the client described above. In response to registration of near-miss information, a server for providing near-miss information is provided.

  According to the present invention, there is an effect that warning can be performed with higher accuracy from the viewpoint of both precision and recall by using a near-miss map in which precautions regarding driving are described for each place.

It is a block diagram which shows the structural example of the mobile body warning apparatus (system) of 1st Embodiment. It is a block diagram which shows the physical structural example of the mobile body warning system of 1st Embodiment. It is explanatory drawing which shows the example of registration of the situation dependence near-miss map in 1st Embodiment. It is explanatory drawing which shows the example of registration of the table which the user condition history memory | storage means in 1st Embodiment memorize | stores. It is a block diagram which shows the structural example of the situation dependence near-miss map expansion means in 1st Embodiment. It is explanatory drawing which shows the example of the map which displays the position which near-miss data shows. It is explanatory drawing which shows the example of the near-miss data grouped. It is a sequence diagram which shows the example of operation | movement of the moving body warning system of 1st Embodiment. It is a flowchart which shows the example of operation | movement of near-miss information registration step S1. It is a flowchart which shows the example of operation | movement of near miss information expansion step S2. It is a flowchart which shows the example of operation | movement of near-miss warning step S3. It is a block diagram which shows the structural example of the situation dependence near-miss map expansion means in 2nd Embodiment. It is explanatory drawing which shows the example of registration of road attribute information DB. It is explanatory drawing which shows the example of registration of a situation dependence near-miss map. It is a flowchart which shows the example of operation | movement of near miss information expansion step S2 in 2nd Embodiment. It is a block diagram which shows the structural example of the mobile body warning system of 3rd Embodiment. It is a block diagram which shows the physical structural example of the mobile body warning system of 3rd Embodiment. It is explanatory drawing which shows the example of registration of traffic law DB. FIG. It is explanatory drawing which shows the example of registration of the situation dependence near-miss map in 3rd Embodiment. It is explanatory drawing which shows the example of registration of the table which the user condition history memory | storage means in 3rd Embodiment memorize | stores. It is a flowchart which shows the example of operation | movement of near-miss warning step S3 in 3rd Embodiment. It is a block diagram which shows the structural example of the mobile body warning system of 4th Embodiment. It is a block diagram which shows the physical structural example of the mobile body warning system of 4th Embodiment. It is explanatory drawing which shows the example of registration of a user model. It is explanatory drawing which shows the example of registration of the situation dependence near-miss map in 4th Embodiment. It is explanatory drawing which shows the example of registration of the table which the user condition history memory | storage means in 4th Embodiment memorize | stores. It is a sequence diagram which shows the example of operation | movement of the mobile body warning system in 4th Embodiment. It is a flowchart which shows the example of operation | movement of near-miss warning step S3 in 4th Embodiment. It is a block diagram which shows the structural example of the mobile body warning system of 5th Embodiment. It is a block diagram which shows the physical structural example of the mobile body warning system of 5th Embodiment. It is explanatory drawing which shows the example of registration of user life zone DB. It is explanatory drawing which shows the example of registration of the situation dependence near-miss map in 5th Embodiment. It is explanatory drawing which shows the example of registration of the table which the user condition history memory | storage means in 5th Embodiment memorize | stores. It is a sequence diagram which shows the example of operation | movement of the mobile body warning system in 5th Embodiment. It is a flowchart which shows the example of operation | movement of near-miss warning step S3 in 5th Embodiment. It is a block diagram which shows the structural example of the mobile body warning system of 6th Embodiment. It is a block diagram which shows the physical structural example of the mobile body warning system of 6th Embodiment. It is explanatory drawing which shows the example of registration of dangerous driving DB. It is a flowchart which shows the example of operation | movement of the dangerous driver distribution production | generation means in 6th Embodiment. It is explanatory drawing which shows the example of registration of the situation dependence near-miss map in 6th Embodiment. It is explanatory drawing which shows the example of registration of the table which the user condition history memory | storage means in 6th Embodiment memorize | stores. It is a sequence diagram which shows the example of operation | movement of the mobile body warning system in 6th Embodiment. It is a flowchart which shows the example of operation | movement of near miss warning step S3 in 6th Embodiment.

Embodiment 1. FIG.
DESCRIPTION OF EXEMPLARY EMBODIMENTS Hereinafter, a first embodiment of the invention will be described with reference to the drawings. FIG. 1 is a block diagram illustrating a configuration example of a moving object warning device (system) according to the first embodiment. 1 includes a user situation input means 1, a situation matching means 2, a warning means 3, a near-miss input means 4, a situation-dependent near-miss registration means 5, a situation-dependent near-miss map 6, and a user. Situation history storage means 7 and situation dependent near-miss map expansion means 8 are provided.

  The first embodiment of the present invention is applied to a moving body warning device that warns a person before a dangerous situation occurs during driving or walking of a car. It is applied to the mobile object warning device using the near-miss map described in 1.

  The user status input means 1 senses (detects) the status of the car, the driver (user), and the surrounding environment, and inputs it to the mobile object warning system as information indicating the status.

  The situation matching means 2 searches and extracts the situation-dependent near-miss map 6 for any near-miss information corresponding to the information indicating the situation input by the user situation input means 1.

  The warning unit 3 notifies the driver of the near-miss information extracted by the situation matching unit 2.

  The near-miss input means 4 notifies (inputs) information indicating that a near-miss occurred to the mobile unit warning system. The near-miss input means 4 inputs, for example, information indicating that the driver has made a near-miss, and outputs the information to the situation-dependent near-miss registration means 5.

  The situation-dependent near-miss registration means 5 obtains information indicating the situation when the information is input (when near-miss) from the user situation input means 1 based on the information input by the near-miss input means 4, and the situation-dependent near-miss map 6 Register with.

  The situation-dependent near-miss map 6 is realized by a table in which near-miss information (near-miss data) indicating a near-miss situation is registered. The near-miss information is registered in the situation-dependent near-miss map 6 based on the notification by the near-miss input means 4 and the information stored in the user situation history storage means 7. Specifically, the situation dependent near miss map 6 is stored in a storage device such as a hard disk device of a server or a client described later.

  The user situation history storage means 7 stores information indicating the situation inputted by the user situation input means 1 to the moving body warning system as history information.

  The situation dependent near-miss map expansion means 8 groups the entries registered in the situation-dependent near-miss map 6 among the information indicating the situation stored in the user situation history storage means 7 based on the information indicating the position. The information indicating the near-miss situation at the grouped position is generalized and registered in the situation-dependent near-miss map 6.

  First, the physical configuration of the first embodiment will be described with reference to FIG. FIG. 2 is a block diagram illustrating a physical configuration example of the moving object warning system according to the first embodiment. The mobile object warning system according to the present invention includes a plurality of clients (for example, automobiles) and a server that accumulates near-miss maps.

  The client operates in accordance with the program and includes user situation input means 1, situation matching means 2, warning means 3, near-miss input means 4, and situation-dependent near-miss registration means 5. FIG. 2 illustrates a case where there is one client.

  The server operates in accordance with the program, and includes a situation-dependent near-miss map 6, user situation history storage means 7, and situation-dependent near-miss map extension means 8.

  Each client is assigned an ID (user ID) for identifying it in advance, and each means constituting the client stores its own (client) user ID in an internal memory or the like. Each client can communicate with the server. As a protocol for communication between the client and the server, a well-known TCP / IP or the like may be used, and the client may store the server address in advance. For data transfer, a wireless LAN may be used.

  The user status input means 1 is a means for inputting information indicating the status of the automobile, the driver, and the surrounding environment. That is, the user status input means 1 is a means for inputting information indicating various situations of the client. The user situation input means 1 is realized by, for example, a plurality of different input means for inputting information indicating these situations. For example, as the user status input means 1, various sensors input information indicating the status (for example, inputting information indicating the position by the GPS function), or in cooperation with the information providing server (for example, traffic jam information, weather information) Etc. from a Web server) or by a person (for example, inputting a vehicle type, sex, age, etc.).

  Information indicating the input status may be expressed as a vector as follows. Hereinafter, information indicating a situation represented by a vector may be referred to as a situation vector. The description of the situation vector is, for example, (34.41N, 135.48E, 56Km / h, 60Km / h) for the format (latitude, longitude, speed, speed limit, traffic jam, weather, gender, age, driving experience). , Congestion, snow, man, 40 years old, 20 years).

  However, this is an example, and items other than latitude and longitude are not essential. For example, the type of car may be added to the element, or the gender may be deleted. The format is not limited to this. Furthermore, each sensor (user situation input means 1) shall update a situation vector in real time.

  The user situation input means 1 transmits the situation vector to the situation matching means 2 and the user situation history storage means 7 at regular intervals, for example, every 10 seconds.

  The situation-dependent near-miss map 6 may be realized by a table including the following fields (items) (see FIG. 3). FIG. 3 is an explanatory diagram illustrating a registration example of the situation-dependent near-miss map 6 according to the first embodiment.

  The status field T0601 is a field in which information indicating a warning situation is registered. The situation field T0601 includes fields of a running situation T060101, an environmental situation T060102, and a user situation T060103.

  The driving situation T060101 is a field in which information indicating the position and speed of a car that changes in real time is registered, and includes the following fields: latitude T06010101, longitude T06010102, and speed (vehicle speed) T06010103.

  Information indicating the latitude of the position of the automobile is registered in the latitude T06010101. Information registered in the latitude T06010101 corresponds to the latitude of the situation vector.

  Information indicating the longitude of the position of the automobile is registered in the longitude T06010102. Information registered in the longitude T06010102 corresponds to the longitude of the situation vector.

  In the speed T06010103, information indicating the speed of the automobile is registered. Information registered in the speed T06010103 corresponds to the speed of the situation vector.

  The environmental situation T060102 is a field in which information indicating the environment around the running car is registered, and includes the following fields: speed limit T06010201, traffic jam situation T06010202, and weather T06010203.

  In the speed limit T06010201, information indicating the speed limit of the position of the automobile is registered. The information registered in the speed limit T06010201 is described in, for example, speed, and corresponds to the speed limit of the situation vector.

  In the traffic situation T06010202, information indicating the traffic situation at the position of the car is registered. The information registered in the traffic jam situation T06010202 is described in, for example, one of traffic jam, congestion, and normal, and corresponds to the traffic jam situation of the situation vector.

In the weather T06010203, information indicating the weather near the position of the car is registered.
Information registered in the weather T06010203 is described in, for example, clear, cloudy, rainy, and snow, and corresponds to the weather of the situation vector.

  The user situation T060103 is a field in which information indicating a user's profile is registered, and includes the following fields: gender T06010301, age T06010302, and driving experience T06010303.

  In the sex T06010301, information indicating whether it is male or female is described. Corresponds to the gender of the situation vector.

  In the age T06010302, information indicating the age is described with numerals. Corresponds to the age of the situation vector.

  In the driving experience T06010303, information indicating the driving experience is described in years. Corresponds to the driving experience of the situation vector.

  The warning field T0602 is a field for storing a warning message for the driver.

  The table in which the near miss data is registered (situation dependent near hat map 6) warns the driver in the situation indicated by the information registered in the situation field T0601 using the value registered in the warning field T0602. It means that. The value of the situation is a representative value, which is warned by matching it vaguely. For example, the speed is plus or minus 5 km (see the description of the situation matching means 2).

  In the first embodiment, * may be registered as a value in the status field T0601 in the meaning of a situation in which no value is specified. Furthermore, * may be registered as a value in the warning field T0602 in the sense that there is no specific message and warning may be made with sound or the like.

  The first record of the example shown in FIG. 3 is “a car running at about 60 km / h around 34.4 degrees latitude and 135.5 degrees longitude, and is congested in an area where the speed limit is 60 km / h. The weather means that in rainy conditions you should be careful about rear-end collisions. "

  The situation matching means 2 searches the situation dependent near miss map 6 every time a situation vector is periodically sent from the user situation input means 1. As a search method, the situation matching unit 2 uses the situation vector elements (latitude, longitude, vehicle speed, speed limit, traffic jam situation, weather, sex, age, driving experience) as latitude T06010101 and longitude T060010102 in the situation-dependent near-miss map 6, respectively. , Speed T06010103, speed limit T06010201, traffic jam situation T06010202, weather T0601020, gender T06010301, age T06010302, and driving experience T06010303.

The situation matching means 2 needs to make the matching somewhat ambiguous.
For example, when the speed of the situation vector is in the range of plus or minus 5 km of the speed T06010103 of the situation dependent near-miss map 6, it is determined that the values match (match).
This is to prevent the warning from being issued at all, since the exact same situation rarely occurs. Further, an item with a value * in the situation-dependent near-miss map 6 means that any value matches.

  For example, the matching when the situation vector is (34.41N, 135.48E, 56 Km / h, 60 Km / h, congestion, snow, male, 40 years old, 20 years) will be described. The first record of the situation-dependent near-miss map 6 illustrated in FIG. 3 does not match the weather item, and therefore does not use the warning registered in the warning field T0602 of the first record as the driver. Next, the matching with the second record is compared. In this case, in order to match, the message “slip attention” is a warning.

  The situation matching unit 2 may succeed in matching with a plurality of records or may fail in matching with all records. If successful, the situation matching unit 2 transmits to the warning unit 3 a warning message registered in the warning field T0602 of the record that has been successfully matched.

  The warning means 3 receives the warning message transmitted by the situation matching means 2 when the situation matching means 2 succeeds in matching, and warns the driver based on the received warning message. As a warning method, the warning means 3 may be displayed on the screen of the car navigation system, displayed on the display area of the vehicle instrument panel, or notified by voice. If the situation vector matches a plurality of records, warnings may be issued in a random order, and information (latitude T06010101, longitude T06010102) indicating a situation that is close to the current location is registered in the registered record. You may be warned in the order they are. If * is registered in the warning field T0602, the warning message is not registered, and it cannot be notified by the display area of the automobile instrument panel or by voice. .

  The near miss input means 4 is means for the driver to notify the mobile unit warning system of information indicating that a near miss occurred. The user may input by operating the car navigation system, or may acquire the biological information such as the heart rate with a sensor and automatically input it if a certain value or more is missed. Moreover, after the user confirms the near-miss detected automatically by the mobile object warning system, it may be registered. The input to the moving object warning system by the near miss input means 4 may be any signal that means that a near miss has occurred, and at least the near missed place and time are input. In addition, when the user inputs information indicating that a near-miss has occurred by operating the car navigation system, the reason for the near-miss may be registered in the mobile navigation system using the keyboard or voice of the car navigation system. I do not care.

  The near miss input means 4 transmits information indicating the location, time, and reason for the near miss to the situation dependent near miss registration means 5. Note that the reason may be transmitted only when input by the driver.

  The user situation history storage means 7 is a means for storing information indicating the situation inputted by the user situation input means 1 to the moving body warning system as a history. The user situation history storage unit 7 is realized by, for example, a database that stores a normal table. FIG. 4 is an explanatory diagram illustrating a registration example of a table stored in the user situation history storage unit 7 according to the first embodiment.

  The user ID field T0701 is a field for identifying which client the user status input means 1 is writing, and the user ID is registered.

  The date / time field T0702 is a field indicating the date and time when the user status (information indicating the user status) is added.

  The situation vector field T0703 is a field in which information indicating the situation of the user recognized by the user situation input means 1 is registered, and may be stored for each item of the situation vector. FIG. 4 shows a case where the situation vector is (latitude, longitude, speed, speed limit, traffic jam situation, weather, sex, age, driving experience).

  For example, the user status input means 1 of the client whose user ID is “U001” receives a status vector (34.4N, 135.5E, 60 Km / h, 60 Km / h, normal at 10:12:54 on Oct. 26, 2008, , Sunny, male, 20, 1) is registered, the user situation history storage means 7 stores information such as the first record shown in FIG. 4 as a history.

  When the situation dependent near miss registration means 5 receives notification of information indicating the near missed place, time, and reason from the near miss input means 4, the user situation history storage means 7 uses the notified location, time, and user ID as keys. Is extracted, information indicating the situation at this time is extracted and registered in the situation-dependent near-miss map 6.

  For example, the situation-dependent near-miss registration means 5 receives information indicating the situation of the user whose user ID is “U001” from the near-miss input means 4 as “latitude: 34.4N”, “longitude: 135.5E”, time Suppose that a notification indicating “Jumping out” as a reason is received as “October 26, 2008 10:12:54”.

  The situation-dependent near-miss registration means 5 adds the user ID of the client of the situation-dependent near-miss registration means 5 to the information indicating the notified location and time, and searches the user situation history storage means 7. In the case of the example shown in FIG. 4, the first record in the user situation history storage means 7 is hit. The situation dependent near-miss registration means 5 omits the user ID and date / time fields from the search results and registers them in the situation-dependent near-miss map 6. Each information of latitude, longitude, speed, speed limit, traffic jam situation, weather, sex, age, and driving experience stored in the user situation history storage means 7 corresponds to a field of the same name in the situation-dependent incident map 6. The warning field corresponds to the reason field. The third record shown in FIG. 3 is data obtained by adding this example.

  The situation-dependent near-miss map expansion means 8 includes a near-miss grouping means 801 and a case generalization means 802 (see FIG. 5). FIG. 5 is a configuration example of the situation-dependent near-miss map extension means 8 in the first embodiment. FIG.

  The near-miss grouping means 801 performs grouping in which data having a short distance between positions indicated by each near-miss information among the information registered in the situation-dependent near-miss map 6 is grouped into the same group.

  The case generalization unit 802 analyzes the near-miss data for each group grouped by the near-miss grouping unit 801, thereby generalizing the near-miss data into an expression with a higher level of abstraction.

  Note that the situation-dependent near-miss map expansion means 8 is activated at a timing when a certain amount of update is made in the situation-dependent near-miss map 6. For example, an operation of starting every 7 days may be used. The role of the situation-dependent near-miss map expansion means 8 is to perform maintenance of the situation-dependent near-miss map 6, and may be asynchronous with other means.

  The near-miss grouping unit 801 performs grouping with respect to the entire data of the situation-dependent near-miss map 6 with those that are close to each other. In the grouping method, distances between near-miss data of all combinations are calculated, and near-miss data sets within a certain distance are first grouped. The distance between near-miss data is, for example, the distance between a position calculated based on latitude T06010101 and longitude T06010102 for a certain record (near-hat data) and a position calculated similarly for other records.

  The near-miss data will be described with reference to FIG. 6 displayed as a star on a map. FIG. 6 is an explanatory diagram illustrating an example of a map that displays the position indicated by the near miss data. In the case of the example shown in FIG. 6, the near-miss grouping unit 801 groups the following combinations of near-miss data. For example, the near-miss grouping unit 801 groups the near-miss data into groups P080106 (P080101, P080102), group P080107 (P080102, P080103), and groups P080108 (P080104, P080105) (see FIG. 6A). P080101, P080102, P080103, P080104, and P080105 are examples of positions calculated for each near-miss data.

  Next, the near-miss grouping unit 801 obtains the distance between the groups and integrates the groups having a short distance. The near-miss grouping unit 801 detects the element having the shortest distance from each group, and sets the distance between the detected elements as the distance between the groups. In the example shown in FIG. 6, the group P080106 and the group P080107 are integrated into a group P080109 shown in FIG. However, this grouping method is an example, and a widely known method such as a K-average method may be used.

  The case generalization means 802 generates near-miss data representing each group. The case generalization means 802 compares near field values of near-miss data for each group as near-miss data representing each group, and extracts a field having a common value. However, as in the case of the situation matching means 2, the identity of values is somewhat ambiguous. However, this ambiguity is not necessarily the same between the case generalization means 802 and the situation matching means 2. For example, the case generalization means 802 may be the same if the vehicle speed is a value around 10 km / h, and the situation matching means 2 may be the same if the vehicle speed is a value around 5 km / h. Since the extracted values of the common field are matched fuzzyly, the values vary. Therefore, the case generalization unit 802 extracts near miss data representing these average values. In addition, the case generalization unit 802 sets * as the value of a field whose value differs for each data in the group.

  FIG. 7 is an explanatory diagram illustrating an example of grouped near-miss data. In the example illustrated in FIG. 7, the case generalization unit 802 extracts latitude, longitude, vehicle speed, speed limit, weather, and warning as common fields. Accordingly, the case generalization means 802 extracts the data shown in FIG. 7B as the representative near-miss data of the near-miss data group shown in FIG.

  The case generalization means 802 replaces the near-miss data of the grouped situation-dependent near-miss maps 6 with representative near-miss data. In the example illustrated in FIG. 7, the grouped first to third records are deleted, and representative near miss data is added. The situation-dependent near-miss map 6 is expanded by the processing of the situation-dependent near-miss map extension means 8 described above. For example, in the three cases (data) shown in FIG. 7A, a user's situation vector is (34.42N, 135.52E, 50Km / h, 60Km / h, normal, snow, male, 30, 6). In this case, it does not match any case, but after extension by the case generalization means 802, it matches the representative near-miss data shown in FIG.

  Next, the operation of the first embodiment will be described with reference to the drawings. FIG. 8 is a sequence diagram illustrating an example of the operation of the mobile object warning system according to the first embodiment.

  Referring to FIG. 8, according to the first embodiment of the present invention, a near-miss information registration step S1 for registering a near-miss situation of the driver as an example, and a near-hat situation status registered in the near-miss information registration step S1. By analyzing, the near-miss information expansion step S2 for generalizing the near-miss situation at the position, the near-hat information registered in the near-hat information registration step S1, or the near-miss information generalized in the near-hat information extension step S2, and driving If the situation of the hand is matched and found, the near-miss warning step S3 for warning the driver operates asynchronously.

  In the example shown in FIG. 8, the activation timing is shown in a sequence diagram. Time has passed from the top to the bottom, and the bold line portion indicates that the step is being activated.

  The near-miss information registration step S1 starts at the timing when a near-miss event occurs. Therefore, the activation is repeated irregularly.

  The near-miss information expansion step S2 is started periodically. Therefore, the activation is repeated regularly.

  The near-miss warning step S3 is always activated.

FIG. 9 is a flowchart showing an example of the operation of the near-miss information registration step S1.
Referring to FIG. 9, the near-miss information registration step S <b> 1 includes a near-miss input step S <b> 101 in which the driver notifies the mobile object warning system that a near-miss has occurred, and the car, driver, It includes a user situation acquisition step S102 for sensing the environment situation and inputting it to the mobile warning system, and a situation dependent near miss registration step S103 for registering the user situation obtained in the near miss input step S101 in the database as a near miss map.

  For example, the near hat input step S101 is realized by the near hat input means 4. The user situation acquisition step S102 is realized by the user situation history storage unit 7 being searched by the situation dependent near miss registration unit 5. The situation-dependent near-miss registration step S103 is realized by the situation-dependent near-miss registration means 5.

  FIG. 10 is a flowchart showing an example of the operation of the near-miss information extension step S2. Referring to FIG. 10, the near-miss information expansion step S <b> 2 includes a near-hat grouping step S <b> 201 for performing grouping that makes data close to each other the same for each data registered in the near-miss information database (situation-dependent near-miss map 6). As a result of the near miss grouping step S201, a group check step S202 for branching the process depending on whether a group exists, and when a group exists as a result of the group check step S202 (Yes), near miss information representing each group is generated. Representative near miss data generation step S203, and the situation dependent near miss that registers the representative near miss information of the group generated in the near miss data generation step S203 in the situation dependent near miss map 6 And a door registration step S204. When it is determined in the group check step S202 that the group does not exist (No), the series of processing ends.

  For example, the near-miss grouping step S201 is realized by the near-miss grouping unit 801. The representative near-miss data generation step S203 and the situation-dependent near-miss registration step S204 are realized by the case generalization means 802.

  FIG. 11 is a flowchart showing an example of the operation of the near-miss warning step S3. Referring to FIG. 11, the near-miss warning step S3 is input in a user status acquisition step S301 and a user status acquisition step S301, which periodically sense the status of the environment of the car, the driver, and the surroundings and input it to the mobile object warning system. A user situation history recording step S302 for accumulating the user situation in the database (user situation history storage means 7), and a user situation for matching the user situation input in the user situation acquisition step S301 with the situation dependent near miss map 6 As a result of the near-miss matching step S303 and the user situation near-match matching step S303, if there is a warning as a result of the warning check step S304 and the warning check step S304 that branches depending on whether or not there is a warning to the user (Yes), Warn and a user warning step S305. When it is determined that there is no warning in the warning check step S304 (No), and after the process of the user warning step S305 is completed, the process proceeds to the user status acquisition step S301.

  For example, the user situation acquisition step S301 is realized by the user situation input means 1. The user situation history recording step S302 is realized by the user situation history storage means 7. The user situation near-miss matching step S303 is realized by the situation-dependent near-miss map 6 and the situation matching means 2. The user warning step S305 is realized by the warning means 3.

  As described above, according to the first embodiment, by having a user situation input unit, a situation-dependent near-miss map, and a situation matching unit in the configuration, it is possible to warn of a near-miss according to the user's situation. Since it is possible, there is an effect that warnings are not issued too much. For example, since a warning is given to the user according to the user's situation, a warning with no excess or deficiency can be given.

  In addition, it is possible to set a wider range of conditions by analyzing multiple cases of near-miss incidents and generalizing the chances of a near-miss warning, so that warnings can be performed without excess or deficiency. .

Embodiment 2. FIG.
Hereinafter, a second embodiment of the present invention will be described with reference to the drawings. FIG. 12 is a block diagram illustrating a configuration example of the situation-dependent near-miss map extension unit 8 in the second embodiment.

  Referring to FIG. 12, in the second embodiment of the present invention, instead of the configuration of the situation dependent near-miss map expansion means 8 of the first embodiment shown in FIG. A road attribute information database 803 that stores road attribute information database (DB) 803 that stores road attribute information and roads that are similar to roads corresponding to near-miss cases (near-miss information) stored in the situation-dependent near-miss map 6. The similar position search means 804 for searching from the road attribute information stored in the road, and the similar road search means 804 for the similar road searched by the similar position search means 804 because there is a high possibility of occurrence of a similar near-miss case at a location having a similar road attribute. And a case registration means 805 for newly adding a near-miss case to the situation-dependent near-miss map 6. That.

  The road attribute information DB 803 stores a table including the following fields (see FIG. 13). FIG. 13 is an explanatory diagram illustrating a registration example of the road attribute information DB 803.

  An area T080301 is a field in which information indicating a range of attribute information described later is registered. In the second embodiment, in order to simplify the description, the range of attribute information is described by the center point and its radius, but the outline of the area may be described by a polygon or the like. Area T080301 includes the following fields: center latitude T08030101, center longitude T08030102, and radius T080303.

  The center latitude T08030101 is a field in which information indicating the latitude of the center point is registered. The center longitude T08030102 is a field in which information indicating the longitude of the center point is registered. A radius T080303 is a field in which information indicating the radius of the area is registered.

  An attribute T080302 is a field in which information (attribute information) indicating road attributes is registered. In the second embodiment, in order to simplify the description, it is considered that the attribute T080302 includes the following fields, that is, the road width T08030201, the number of lanes T08030202, the type T080030203, and the line-of-sight T08030204, but is not limited thereto.

  The road width T08030201 is a field in which information indicating the road width is registered. The lane number T08030202 is a field in which information indicating the number of lanes is registered. The type T08030203 is a field in which information indicating the type of road such as a national road, a prefectural road, a living road, and a private road is registered. The prospect T08030204 is a field in which information indicating whether the prospect is good / bad is registered. However, these are examples, and are not limited to these.

  The similar position search unit 804 searches the road attribute information DB 803 based on the positions indicated by all the near-miss data in the situation-dependent near-miss map 6, and extracts attribute information. As a search method, the similar position search means 804 searches the area of the road attribute information DB 803 based on information indicating latitude and longitude included in the near-miss data. When a plurality of data is searched in the area of the road attribute information DB 803, the following processing is repeated, and when not searched, the processing moves to the next data.

  For example, the first record of the near miss data shown in FIG. 3 will be described. Since the position indicated by this near miss data is 34.4N latitude and 135.5E longitude, the second record of the attribute information illustrated in FIG. 13 matches.

Next, the road attribute information DB 803 is searched for similar attribute information in other places. That is, the similar position search unit 804 extracts attribute information similar to the extracted attribute information from the road attribute information DB 803. In this search method, the similar position search unit 804 searches only the attribute field of the attribute information. However, the case where each field has exactly the same value may be defined as similar, and the case where the values are close may be defined as similar.
This search result is called a similar road.

  In the above example, when searching based on “road width = 10, number of lanes = 1, type = life road, outlook = bad” included in the attribute information field of the second record illustrated in FIG. Matches 4 records. Since the second record is itself, the similar position search means 804 extracts the fourth record as a similar road.

  The case registration unit 805 adds near-miss data indicating the position of the similar road extracted by the similar position search unit 804. In the previous example, the same near miss data other than the information indicating the position and the first record of the situation dependent near miss map 6 illustrated in FIG. 3 is used as the position of a similar road, that is, the position of latitude 34.0N and longitude 135.0E. (See the fourth record shown in FIG. 14). FIG. 14 is an explanatory diagram showing a registration example of the situation-dependent near-miss map 6.

  Next, the operation of the second embodiment will be described with reference to the drawings. FIG. 15 is a flowchart illustrating an example of the operation of the near-miss information extension step S2 in the second embodiment.

  Referring to FIG. 15, in the second embodiment according to the present invention, the incident information matching step S401 in which the incident information extension step S2 of the first embodiment searches for the attribute information of the roads at the positions of all incident data, Similar check road S402 that controls the search result repeat processing in attribute information matching step S401, and a similar road that searches for a location that is different in location but similar to the search result in attribute information matching step S401. It changes so that search step S403 and similar near-miss registration step S404 which registers near-miss data with respect to the search result of similar road search step S403 may be included.

  For example, the attribute information matching step S401 is realized by the road attribute information DB 803 and the similar position search unit 804. The similar road search step S403 is realized by the similar position search means 804. The similar near-miss registration step S404 is realized by the case registration unit 805.

  As described above, according to the second embodiment, it is considered that there is a high possibility that a similar near-miss case will occur in a portion having a similar road attribute. By having a case registration means for adding a near-miss case to the configuration, it is possible to add a near-miss element to a place where the situation is different, but the situation is the same.

Embodiment 3. FIG.
Hereinafter, a third embodiment of the present invention will be described with reference to the drawings. FIG. 16 is a block diagram illustrating a configuration example of a mobile object warning system according to the third embodiment.

  Referring to FIG. 16, the third embodiment of the present invention includes a law violation determination means 9 and a traffic law database (DB) 10 that are not provided in the mobile object warning system of the first embodiment.

  The law violation determination means 9 compares the information indicating the user's situation input by the user situation input means 1 with the information indicating the road traffic law stored in the traffic law database 10 so that the user can obtain a law (road traffic law). Determine whether or not

  The traffic law DB 10 stores information indicating the road traffic law by setting (registering) values in information items indicating the maximum speed and the necessity of temporary stop in association with the information indicating the place.

  Further, in the third embodiment, the user situation input means 1, the user situation history storage means 7 and the situation dependent near-miss map 6 in the first embodiment indicate the situation of the user as a result of the law violation judgment means 9 judging. Extend to handle as information.

  Next, a physical configuration of the third embodiment will be described with reference to FIG. FIG. 17 is a block diagram illustrating a physical configuration example of the mobile object warning system according to the third embodiment. Similar to the first embodiment, the mobile warning system according to the third embodiment includes a plurality of clients (for example, automobiles) and a server that stores near-miss maps.

  The law violation determination means 9 is necessary for each client (car) and executes processing for each client (car).

  The traffic law DB 10 may be common to all clients, is executed by a server, and is shared by a plurality of clients.

  The traffic law DB 10 may be realized by a table in which information such as a speed limit, the necessity of temporary stop, parking and stopping is associated with the position. FIG. 18 shows a field example of the table. FIG. 18 is an explanatory diagram illustrating a registration example of the traffic law DB 10.

Area T1001 is a field in which information indicating the effective range of the rule is described.
In the third embodiment, in order to simplify the description, the effective range of the rule is expressed by a single point indicated by a set of latitude T100101 and longitude T100102. However, this is only an example, and the outline of the area may be described by a polygon or the like.

  The rule T1002 is a field in which information indicating the rule is registered. In the third embodiment, the rule T1002 includes the following three fields. However, this is only an example, and information indicating other rules may be freely added or deleted.

  The speed limit T100201 is a field in which information indicating the speed limit at that position is registered. The temporary stop T100202 is a field in which information indicating the necessity of temporary stop is registered. Parking / stopping T100203 is a field in which information indicating whether parking / stopping is possible is registered.

  The law violation judging means 9 is a role of one sensor as seen from the user situation input means 1 and periodically receives a situation vector from the user situation input means 1 and gives information indicating whether the user is in compliance with the law. Return to status input means 1. Upon receiving the situation vector, the law violation determination means 9 searches the traffic law DB 10 using the latitude and longitude as keys. This search result is compared with the situation vector, and if any of the following conditions is satisfied, it is determined that the law is violated.

For example, the law violation determination means 9 indicates that the search result and the situation vector are “the speed of the situation vector> the value of the limit speed T100201 field”, “the value of the temporary stop is necessary, and the speed value of the situation vector. Is determined to be a violation of the law if any of the following conditions is satisfied: “is a value of> 0” or “the value of parking and stopping is impossible and the value of the speed of the situation vector is> 0”.
However, the above condition is an example. For example, it may be operated such that even if the speed limit is exceeded up to 10 km, it is not violated. The law violation determination unit 9 returns the determined result to the user situation input unit 1.

  The user situation input means 1 adds “violation” as an item of the situation vector. That is, the situation vector is (latitude, longitude, vehicle speed, speed limit, traffic jam situation, weather, gender, age, driving experience, violation).

  Before sending the situation vector to the situation matching means 2 and the user situation history storage means 7, the user situation input means 1 sends the situation vector to the law violation determination means 9 and receives a determination as to whether or not the law is violated. The result is sent to the situation matching means 2 and the user situation history storage means 7 in addition to the situation vector.

  As shown in FIG. 19, the situation dependent near miss map 6 includes a field (T06010304) for storing whether or not there is a violation. FIG. 19 is an explanatory diagram illustrating a registration example of the situation-dependent near-miss map 6 according to the third embodiment. The “violation” field T06010304 in the situation-dependent near-miss map 6 corresponds to the item “violation” of the situation vector.

  As shown in FIG. 20, the user situation history storage means 7 includes a field “violation” indicating whether or not there is a violation in the situation vector field (T0703). FIG. 20 is an explanatory diagram illustrating a registration example of a table stored in the user situation history storage unit 7 according to the third embodiment.

  Next, the operation of the third embodiment will be described with reference to the drawings. FIG. 21 is a flowchart illustrating an example of an operation of the near-miss warning step S3 in the third embodiment.

  Referring to FIG. 21, in the third embodiment of the present invention, in the near-miss warning step S3 of the first embodiment, the user status is acquired between the user status acquisition step S301 and the user status history recording step S302. And a law violation determination step S306 for comparing the road traffic law and determining whether it is a violation. Further, in the user situation history recording step S302 and the user situation near-miss matching step S303, modification is made so that the presence or absence of traffic violation is also considered as one element of the user situation. Other processing is the same as the processing in the first embodiment shown in FIG.

  The law violation determination step S306 may be realized by the law violation determination means 9 and the traffic law DB 10.

  As described above, according to the third embodiment, by having the traffic law DB and the law violation determination means in the configuration, a near-miss warning based on the situation of whether or not the law is violated is provided. It becomes possible.

Embodiment 4 FIG.
Hereinafter, a fourth embodiment of the present invention will be described with reference to the drawings. FIG. 22 is a block diagram illustrating a configuration example of the moving object warning system according to the fourth embodiment.

  Referring to FIG. 22, in the fourth embodiment of the present invention, the user model generation means 13, the user model 12, and the driving situation determination which are not provided in the mobile body warning system of the first to third embodiments. Means 11.

  The user model generation means 13 analyzes the user's driving tendency based on the history information indicating the past driving situation stored in the user situation history storage means 7 and generates a user model.

  The user model 12 is realized by a table in which the user model generated by the user model generation unit 13 is registered. Specifically, the user model 12 is stored in a storage device such as a hard disk device of the server.

  The driving status determination unit 11 determines the current driving status of the user by comparing information indicating the current driving status such as hurrying or tired with the user model 12 indicating the past driving status.

  In the fourth embodiment, the user situation input means 1, the user situation history storage means 7 and the situation dependent near-miss map 6 in the first to third embodiments indicate the result of the determination by the driving situation judgment means 11 of the user. It is extended so that it is handled as information indicating the situation.

  Next, the physical configuration of the fourth embodiment will be described with reference to FIG. FIG. 23 is a block diagram illustrating a physical configuration example of the moving object warning system according to the fourth embodiment. Similar to the first to third embodiments, the moving body warning system of the fourth embodiment includes a plurality of clients (for example, automobiles) and a server that accumulates near-miss maps.

  The driving situation determination means 11 is necessary for each client (automobile), and executes processing for each client (automobile).

  The user model 12 and the user model generation means 13 may be common to all clients, are executed by a server, and are shared by a plurality of clients.

  The user model 12 may be registered in a table including the following fields (see FIG. 24). FIG. 24 is an explanatory diagram illustrating a registration example of the user model 12.

  The user ID (T1201) is a field in which an ID for identifying the user is registered.

  The long-term user model (T1202) is a field in which a long-term user model calculated from the entire driving history so far is registered, and includes the following fields: average speed (T120201), average driving time (T120202) and Includes legal compliance (T120203).

  The average speed (T120201) is a field in which information indicating the average speed of the user's driving so far is registered. The average driving time (T120202) is a field in which information indicating the driving time in units of one day of the user's driving so far is registered. The legal compliance level (T120203) is a field in which information indicating the ratio of the user's previous driving laws is registered.

  The short-term user model (T1203) is a field in which a short-term user model calculated from the entire driving history for one day is registered. The following fields are average speed (T120301) and average driving time (T120302). ) And legal compliance (T120303).

  The average speed (T120301) is a field in which information indicating the average speed of the user's driving for today is registered. The average driving time (T120302) is a field in which information indicating the driving time of the user today is registered. The legal compliance level (T120303) is a field in which information indicating the percentage of users who have been in compliance with the driving law of today is registered.

  The user model generation unit 13 generates a user model based on information stored in the user situation history storage unit 7. The user model generation means 13 generates values for each field of the user model 12 for all users by the following method.

  First, the long-term user model (T1202) will be described.

  The user model generation means 13 may search the user situation history storage means 7 using the user ID as a key, and obtain the average value of the speed field values of the searched situation vector T0703 as the average speed (T120201).

  The user model generation unit 13 searches the user situation history storage unit 7 using the user ID as a key, further groups the search results by the date in the date / time T0702 field, and calculates the operation time for each group as the average operation time (T120202). . For example, the user model generation unit 13 may calculate the operation time of each group by the approximate number of records in the group × the update interval of the user situation history storage unit 7.

  The user model generation unit 13 may search the user situation history storage unit 7 using the user ID as a key, and obtain the ratio of violation field values of the found situation vector T0703 as the legal compliance level (T120203).

  Next, the short-term user model (T1203) will be described.

  The user model generation means 13 calculates the average speed (T120301) using the history for one day today by the same method as the average speed T120201.

  The user model generation means 13 calculates the average driving time (T120302) using the history for one day today by the same method as the average driving time T120202.

  The user model generation means 13 calculates the legal compliance level (T120303) using the history for one day today by the same method as the legal compliance level T120203.

  However, the above calculation method is an example, and the history may be analyzed in detail in order to improve accuracy.

  The timing at which the user model generation unit 13 is activated may be independent of other units. Since it is considered that the user model 12 does not frequently change in value, for example, it may be activated about once a week.

The driving situation determination unit 11 periodically receives a situation vector from the user situation input unit 1, searches the user model 12 based on the user ID, and determines the driving situation from the search result. In the fourth embodiment, the driving situation determination unit 11 determines the following two driving situations.
Hereinafter, the driving status determined by the driving status determination means 11 is shown together with the calculation method.

  The driving situation determination unit 11 determines whether or not the driving situation is “rushing”, that is, whether or not the situation is dull compared to usual. For example, when the average speed T120301 of the short-term user model (T1203) is larger than the average speed T120201 of the long-term user model by a certain amount or more, the driving condition determination unit 11 determines “hurry”.

  The driving state determination means 11 determines whether the driving state is “tired”, that is, whether the driving time is longer than usual. For example, when the average driving time T120302 of the short-term user model (T1203) is longer than the average driving time T120202 of the long-term user model, the driving condition determination unit 11 determines that the user is “tired”.

  However, the above method is an example, and the value of legal compliance may be used as a judgment material.

  The driving situation determination unit 11 writes the determination result in the situation vector and returns it to the user situation input unit 1.

  The user situation input means 1 adds items of “Hurry up” and “I am tired” as items of the situation vector. That is, let the situation vector be (latitude, longitude, vehicle speed, speed limit, traffic jam situation, weather, gender, age, driving experience, violation, hurry, tired).

  Before sending the situation vector to the situation matching means 2 and the user situation history storage means 7, the user situation input means 1 sends the situation vector to the driving situation judgment means 11 and receives the judgment of the current driving situation compared with the past. The result is sent to the situation matching means 2 and the user situation history storage means 7 in addition to the situation vector.

  As shown in FIG. 25, the situation-dependent near-miss map 6 includes a field (T06010305) that stores whether or not you are in a hurry and a field (T06010306) that stores whether or not you are tired. FIG. 25 is an explanatory diagram illustrating a registration example of the situation-dependent near-miss map 6 according to the fourth embodiment. The “Hurry” field (T06010305) corresponds to the situation vector item “I'm hurrying”, and the “Fatigue” field (T06010306) corresponds to the situation vector item “I'm tired”.

  As shown in FIG. 26, the user situation history storage means 7 includes a field “Hurry” for storing whether or not he / she is in a hurry and a field “Fatigue” for storing whether or not he / she is in a situation vector field (T0703). FIG. 26 is an explanatory diagram illustrating a registration example of a table stored in the user situation history storage unit 7 according to the fourth embodiment.

  Next, the operation of the fourth embodiment will be described with reference to the drawings. FIG. 27 is a sequence diagram illustrating an example of operation of the moving object warning system according to the fourth embodiment.

  Referring to FIG. 27, in the fourth embodiment according to the present invention, a user model generation step for analyzing a user's driving tendency from past driving in the operation of the first to third embodiments (see FIG. 8). S4 is added.

  Further, in the fourth embodiment, in the near-miss warning step S3 of the third embodiment, between the law violation determination step S306 and the user situation history recording step S302, the current situation such as rushing or tired A driving situation determination step S307 for judging the current driving situation of the user by comparing the driving with the user model 12 representing the past driving is added. FIG. 28 is a flowchart illustrating an example of an operation of the near-miss warning step S3 in the fourth embodiment.

  In the fourth embodiment, in the user situation history recording step S302 and the user situation near miss matching step S303, the user's current driving situation calculated in the driving situation determining step S307 is also considered as one element of the user situation. Remodel to do.

  The user model generation step S4 may be realized by the user model generation means 13. The driving state determination step S307 may be realized by the driving state determination unit 11.

  As described above, according to the fourth embodiment, the driving situation determination means for judging the driving situation by comparing the user model representing the usual driving situation and the user model representing the current driving situation. By having in the configuration, it is possible to warn of a near-miss by considering whether the road is hurry, tired, or unknown, compared to normal driving.

Embodiment 5. FIG.
Hereinafter, a fifth embodiment of the present invention will be described with reference to the drawings. FIG. 29 is a block diagram illustrating a configuration example of the moving object warning system according to the fifth embodiment.

  Referring to FIG. 29, in the fifth embodiment of the present invention, a user living area adding means 16 and a user living area database (DB) which are not provided in the mobile body warning system of the first to fourth embodiments. 15 and user life zone determination means 14.

  The user life zone adding means 16 counts the number of times the user has run on the road indicated by the history information based on the history information indicating the past driving situation stored in the user situation history storage means 7.

  The user living area DB 15 stores user living area information including information indicating the number of times counted by the user living area determining unit 16.

  Based on the user life area information stored in the user life area DB 15, the user life area determination means 14 determines whether or not the current position indicated by the information indicating the situation input by the user situation input means is the user's life area. judge.

  In the fifth embodiment, the user situation input means 1, the user situation history storage means 7, and the situation dependent near-miss map 6 in the first to fourth embodiments are the user lives determined by the user life zone determination means 14. An element indicating whether or not the vehicle is in a service area, that is, whether or not the vehicle is traveling on a familiar road, is expanded so as to be handled as information indicating the user's situation.

  Next, a physical configuration of the fifth embodiment will be described with reference to FIG. FIG. 30 is a block diagram illustrating a physical configuration example of the moving object warning system according to the fifth embodiment. Similar to the first to fourth embodiments, the mobile object warning system of the fifth embodiment includes a plurality of clients (for example, automobiles) and a server that accumulates near-miss maps.

  The user life zone determination means 14 is necessary for each client (automobile), and executes processing for each client (automobile).

  The user living area DB 15 and the user living area adding means 16 may be common to the entire client, executed by the server, and shared by a plurality of clients.

The user living area DB 15 stores a table including the following fields (see FIG. 31).
). FIG. 31 is an explanatory diagram showing a registration example of the user living area DB 15.

  The user ID (T1501) is a field in which an ID for identifying the user is registered.

  The whereabouts history (T1502) is a field in which information indicating the user's whereabouts history is registered. The whereabouts history (T1502) includes the following fields: latitude (T150201), longitude (T150202), and count (T150203).

  In the latitude (T150201), information indicating the latitude of the user's whereabouts is registered. Information indicating the longitude of the user's whereabouts is registered in the longitude (T150202). In the count (T150203), information indicating the number of times the user has been at the location is registered.

  The user life zone adding unit 16 periodically adds data from the user situation history storage unit 7 to the user life zone DB 15. As an addition method, the user life zone adding unit 16 repeats the following process for each record in the user situation history storage unit 7.

The user life zone adding unit 16 searches the user life zone DB 15 using the user ID, latitude, and longitude values of the record in the user status history storage unit 7 of interest as keys. If a matching record is found, the count (T150203) corresponding to that record is incremented by one.
If not found, a new record is added to the user life zone DB 15 and the user ID, latitude, and longitude values of the record in the user status history storage means 7 of interest are registered in the corresponding fields of the user life zone DB 15. Then, 1 is registered in the count (T150203).

  When receiving the information indicating the latitude and longitude indicating the current position from the user status input unit 1, the user living area determining unit 14 determines whether or not the location indicated by the received information is in the user's living area. The user life zone determining means 14 determines whether or not the location indicated by the received information is in the life zone by one of the following three values: “Frequently used”, “Occasionally used”, and “Not used”. Show.

  The user life zone determination unit 14 determines whether or not the user is in a life zone that is “frequently used”, that is, whether or not the user life zone has been visited a certain number of times (frequent threshold).

  The user life zone determination means 14 determines whether or not the user is in a life zone that is “used occasionally”, that is, whether or not the user life zone has been used at least once but less than a predetermined frequent threshold value.

  The user living area determining means 14 determines whether or not “no use”, that is, whether or not the user has never visited.

  The user life zone determination unit 14 returns the determination result to the situation input unit 1.

  The user situation input means 1 adds an item “living area” as an item of the situation vector. In other words, the situation vector is (latitude, longitude, vehicle speed, speed limit, traffic jam situation, weather, gender, age, driving experience, violation, hurrying, tired, living sphere).

  Before sending the situation vector to the situation matching means 2 and the user situation history storage means 7, the user situation input means 1 sends information indicating the latitude and longitude of the current location to the user life zone determination means 14, and the current location is the living zone. The result is added to the situation vector and sent to the situation matching means 2 and the user situation history storage means 7.

  As shown in FIG. 32, the situation dependent near miss map 6 includes a “living area” field (T06010307) for storing whether or not the user is in the living area. FIG. 32 is an explanatory diagram showing a registration example of the situation-dependent near-miss map 6 in the fifth embodiment. The “living area” field (T06010307) corresponds to the item “living area” of the situation vector.

  As shown in FIG. 33, the user situation history storage unit 7 includes a field “living area” for storing whether or not the user is in the living area in the situation vector field (T0703). FIG. 33 is an explanatory diagram illustrating a registration example of a table stored in the user situation history storage unit 7 according to the fifth embodiment.

  Next, the operation of the fifth embodiment will be described with reference to the drawings. FIG. 34 is a sequence diagram illustrating an example of the operation of the moving object warning system according to the fifth embodiment.

  Referring to FIG. 34, in the fifth embodiment according to the present invention, in the operation of the fourth embodiment (see FIG. 27), a life zone generation step S5 for counting the number of visits for each location from the past driving is performed. to add.

  In the fifth embodiment, in the near-miss warning step S3 of the fourth embodiment, the number of times the user has visited the current location in the life zone between the driving situation determination step S307 and the user situation history recording step S302 A living area determination step S308 for determining whether or not there is present is added. FIG. 35 is a flowchart illustrating an example of an operation of the near-miss warning step S3 in the fifth embodiment.

  In the fifth embodiment, in the user situation history recording step S302 and the user situation near-miss matching step S303, the life area information of the user's current location calculated in the life area determination step S308 is also an element of the user situation. Modify to take into account.

  The living area generation step S5 may be realized by the user living area adding means 16. The living area determining step S308 may be realized by the user living area determining means 14.

  As described above, according to the fifth embodiment, the user life area determination means for determining the user's life area by counting the road the user has ran from the past driving history, It is possible to give a near-miss warning that distinguishes the driver who is running in the same place (the person who always uses it) from the person who uses it for the first time.

Embodiment 6. FIG.
The sixth embodiment of the present invention will be described below with reference to the drawings. FIG. 36 is a block diagram illustrating a configuration example of the moving object warning system according to the sixth embodiment.

  Referring to FIG. 36, in the sixth embodiment of the present invention, the dangerous driver distribution generation means 19 and the dangerous driving database (DB) that are not provided in the mobile body warning system of the first to fifth embodiments are provided. 18 and a dangerous driving and determination means 17.

  The dangerous driver distribution generation means 19 calculates the ratio of drivers who are different from other people around the user. That is, the dangerous driver distribution generation unit 19 calculates a ratio of drivers who perform dangerous driving around the user.

  The dangerous driving DB 18 stores the result calculated by the dangerous driver distribution generation means 19.

  Based on the value stored in the dangerous driving DB 18, the dangerous driving trapping determination means 17 calculates the possibility that the user will be involved in an accident or the like.

  Further, in the sixth embodiment, the user situation input means 1, the user situation history storage means 7 and the situation dependent near-miss map 6 in the first to fifth embodiments are included in the dangerous driving and the user calculated by the judgment means 17 The possibility of being involved in an accident or the like is expanded to be handled as information indicating the user's situation.

  Next, the physical configuration of the sixth embodiment will be described with reference to FIG. FIG. 37 is a block diagram illustrating a physical configuration example of a mobile object warning system according to the sixth embodiment. Similar to the first to fifth embodiments, the mobile warning system of the sixth embodiment includes a plurality of clients (for example, automobiles) and a server that accumulates near-miss maps.

  The dangerous driving entrainment determination means 17 is necessary for each client (automobile) and executes processing for each client (automobile).

  The dangerous driving DB 18 and the dangerous driver distribution generation means 19 may be common to all clients, are executed by a server, and are shared by a plurality of clients.

The dangerous driving DB 18 stores a table including the following fields (see FIG. 38).
FIG. 38 is an explanatory diagram illustrating an example of registration in the dangerous driving DB 18.

  The latitude (T1801) is a field in which information indicating the latitude of a case where dangerous driving is performed is registered. The longitude (T1802) is a field in which information indicating the longitude of a case where dangerous driving is performed is registered. The user ID (T1803) is a field in which a user ID for identifying a user in a case where dangerous driving is performed is registered.

  The dangerous driver distribution generation means 19 executes the steps shown in FIG. FIG. 39 is a flowchart showing an example of the operation of the dangerous driver distribution generation means 19 in the sixth embodiment.

  In the dangerous driver extraction step S1901, the dangerous driver distribution generation unit 19 searches the user model 12 for a user who performs dangerous driving. The dangerous driver distribution generation means 19 searches for a record in which the legal compliance level (T120203, T120303) of the user model 12 is a certain threshold value or less, and stores the user ID as a driver who performs dangerous driving.

  In case extraction step S1902 during dangerous driving, the dangerous driver distribution generation unit 19 searches the user situation history storage unit 7 using the dangerous driver user ID searched in the dangerous driver extraction step S1901 as a key. The dangerous driver distribution generation means 19 further narrows down the search result to only records close to the current time. By the process of the case extraction step S1902 during the dangerous driving, the driving of the dangerous driver who is currently driving can be specified.

  In the dangerous driving DB registration step S1903, the dangerous driver distribution generation means 19 registers the result of the processing in the case extracting step S1902 during the dangerous driving in the dangerous driving DB 18.

  The dangerous driving trapping determination means 17 periodically receives a situation vector from the user situation input means 1, searches the dangerous driving DB 18 based on information indicating latitude and longitude, and determines whether there is a dangerous driving near the current location. Investigate. However, this search based on latitude and longitude does not search for the exact same value from the dangerous driving DB 18 but searches for a record closer to the straight line distance than a certain threshold. The dangerous driving operation determination unit 17 searches the dangerous driving DB 18 and calculates the degree of risk based on the number of extracted records.

  This method is an example, and an accurate distance may be obtained by performing a route search from the latitude and longitude of the situation vector and the latitude and longitude of each record in the dangerous driving DB 18.

  The dangerous driving operation determination means 17 calculates the risk in three stages from the number of records in the search result as follows. For example, when the number of records is 0, the risky driving detection means 17 sets the risk level to 0, sets the risk level to 1 when the number of records is 1 to 3, and sets the risk level to 3 when the number of records is 4 or more. . However, the risk shown above is an example and is not limited thereto.

  The user situation input means 1 adds a “risk level” item as a situation vector item. That is, the situation vector is (latitude, longitude, vehicle speed, speed limit, traffic jam situation, weather, gender, age, driving experience, violation, hurrying, tired, living area, risk).

  Before sending the situation vector to the situation matching means 2 and the user situation history storage means 7, the user situation input means 1 sends information indicating the latitude and longitude of the current location to the dangerous driving trapping judgment means 17 to determine the danger level of the current location. The calculated result is added to the situation vector and sent to the situation matching means 2 and the user situation history storage means 7.

  As shown in FIG. 40, the situation-dependent near-miss map 6 includes a field (T06010308) for storing the risk level. FIG. 40 is an explanatory diagram illustrating a registration example of the situation-dependent near-miss map 6 according to the sixth embodiment. The “risk level” field (T06010308) in the situation-dependent near-miss map 6 corresponds to the item “risk level” of the situation vector.

  As shown in FIG. 41, the user situation history storage means 7 includes a field indicating the degree of risk in the situation vector field (T0703). FIG. 41 is an explanatory diagram illustrating a registration example of a table stored in the user situation history storage unit 7 according to the sixth embodiment.

  Next, the operation of the third embodiment will be described with reference to the drawings. FIG. 42 is a sequence diagram illustrating an example of operation of the moving object warning system according to the sixth embodiment.

  Referring to FIG. 42, in the sixth embodiment according to the present invention, in the operation of the fifth embodiment (see FIG. 34), the ratio of the driver who is different from other people in the vicinity of the user. A risk DB generation step S6 for calculating whether or not

  In the sixth embodiment, in the near-miss warning step S3 of the fifth embodiment, the possibility of being involved in an accident or the like from the current location is calculated between the living area determination step S308 and the user situation history recording step S302. A risk determination step S309 is added. FIG. 43 is a flowchart illustrating an example of an operation of the near-miss warning step S3 in the sixth embodiment.

  In the sixth embodiment, in the user situation history recording step S302 and the user situation near-miss matching step S303, the risk of the user's current location calculated in the risk determination step S309 is also considered as one element of the user situation. Remodel to do.

  The risk DB generation step S <b> 6 may be realized by the dangerous driver distribution generation means 19. The risk determination step S309 may be realized by the dangerous driving and determination means 17.

  As described above, according to the sixth embodiment, the dangerous driving DB, the dangerous driver distribution generation means, and the dangerous driving trapping determination means are included in the configuration, so A near-miss can be warned while considering the probability of being involved in a risk factor.

  A preferred embodiment of the moving body warning device according to the present invention relates to a moving body warning device that warns before a dangerous situation occurs while a person is moving in a city, such as a car, a motorcycle, and walking. User status input means for sensing the status of automobiles, people, and surrounding environment and inputting them to the system, and user status input means for a mobile warning device using a near-miss map that describes warnings to be warned for each place A situation matching means for searching for near-miss information for the situation entered in the above, a warning means for notifying the driver of the near-miss information, a near-miss input means for the driver notifying the system that the near-miss has occurred, and a near-miss input means. Upon receiving the notification, the situation at this time is obtained from the user situation input means and registered on the situation-dependent near-miss map. Situation-dependent near-miss registration means for storing, situation-dependent near-miss map for storing near-miss situations, user status history storage means for storing statuses input to the system by user status input means, and user status history storage means A situation-dependent near-miss map expansion means for grouping the entries registered in the situation-dependent near-miss map by position, generalizing the situation where the position is near-miss, and registering in the situation-dependent near-miss map Thus, the object of the present invention can be achieved.

  A similar road with respect to the road attribute information DB storing road attribute information such as road width, number of lanes, and visibility, and the incident cases of the situation dependent near-miss map with the configuration of the situation-dependent near-miss map extension means Similar position search means that searches for roads based on road attribute information of situation-dependent near-miss map means, and places that have similar road attributes are likely to have similar near-miss cases The object of the present invention can be achieved by changing to a case registration means for newly adding a near-miss case to a similar road searched by the similar position search means.

  In the configuration described above, comparing the user's situation with the Road Traffic Law, the law violation judgment means for judging whether it is violated, and for the place, values for items such as the maximum speed and the necessity of temporary stop By adding the traffic law DB that stores the road traffic law for each place by setting, the judgment result of the law violation judgment means is treated as the user situation in the user situation input means, user situation history storage means, and situation dependent near miss map The object of the present invention can be achieved by extending to

  In the configuration described above, the user model generation means for analyzing the user's driving tendency from the past driving, the user model for storing the user model calculated by the user model generation means, and the current state of hurrying and tired In the configuration, a driving situation determination means for determining the current driving situation of the user by comparing it with a user model representing driving in the past and a user situation input means, a user situation history storage means, and a situation-dependent near-miss map, The object of the present invention can be achieved by extending the determination result of the driving condition determination means to handle the user's condition.

  In the configuration described above, a user living area adding unit that counts the roads the user has ran from past driving histories, a user living area DB that stores user living area information calculated by the user living area determining unit, and a user living area In addition to the configuration of the user living area determining means for determining the user's living area from the DB, in the user situation input means, the user situation history storing means, and the situation dependent near-miss map, whether or not driving on a familiar road is an element of the user The object of the present invention can be achieved by extending to treat as a situation.

  In the configuration described above, dangerous driver distribution generation means for calculating the proportion of drivers who drive differently from other people around the user, and dangerous driving for storing the calculation results of the dangerous driver distribution generation means In the configuration, a dangerous driving entrapped judgment means for calculating the possibility of being involved in an accident or the like from the DB and the dangerous driving DB value is added to the configuration. In the user situation input means, the user situation history storage means, and the situation dependent near-miss map The object of the present invention can be achieved by extending the possibility of the user being involved in an accident or the like to be handled as the user's situation.

  In the embodiment described above, a moving body warning device (system) having a configuration as shown in the following (1) to (18) is shown.

  (1) User situation input means (for example, realized by the user situation input means 1) for detecting the situation of the car, the user who is the driver, and the surrounding environment, and inputting the situation information as information indicating the situation; A situation-dependent near-miss map (for example, realized by the situation-dependent near-miss map 6) for storing the near-miss information to be displayed and near-miss information corresponding to the information indicating the situation input by the user situation input means are retrieved from the situation-dependent near-miss map. Situation matching means (for example, realized by the situation matching means 2), warning means (for example, realized by the warning means 3) for notifying the driver of the near-miss information extracted by the situation matching means, and driving Near-miss input means for inputting information indicating that the hand is near-miss (for example, The situation dependence is realized by the near-miss input means 4), and information indicating the situation when the information is inputted is obtained from the user situation input means based on the information inputted by the near-miss input means and registered in the situation-dependent near-miss map. Near-miss registration means (for example, realized by the situation-dependent near-miss registration means 5) and user situation history storage means (for example, user situation history storage means) that stores information indicating the situation input by the user situation input means as history information. In the information indicating the situation stored in the user situation history storage means, the entries registered in the situation dependent near-miss map are grouped based on the information indicating the position and grouped. Generalize information indicating near-miss situation at location Status registers the flop dependent Hiyari hat map expansion means (for example, implemented in the context-dependent Hiyari hat map expansion means 8) and the mobile alert device provided with.

  (2) Situation-dependent near-miss map expansion means is a near-miss grouping means (for example, near-miss grouping means for grouping data in which the distance between positions indicated by each near-miss information is close to the same group among the near-miss information registered in the situation-dependent near-miss map. And the near-miss grouping means 801) and the case-generalization means for generalizing the near-miss information into a more abstract expression by analyzing the near-miss information for each group grouped by the near-miss grouping means (for example, a case) A mobile warning device having a generalization means 802). Since the moving body warning device configured as described above can set a wider range of conditions, the warning can be reliably performed without excess or deficiency.

  (3) The situation-dependent near-miss map expansion means includes a road attribute information database (for example, realized by the road attribute information database 803) that stores road attribute information indicating the road width, the number of lanes, and the visibility situation, and a situation-dependent near-miss Similar position search means (for example, similar position search means 804) is used to search from road attribute information stored in the road attribute information database whether there is a road similar to the road corresponding to each of the near hat information stored in the map. ) And cases where similar near-miss cases are likely to occur in places with similar road attributes, and new near-miss cases are added to the situation-dependent near-miss map for similar roads searched by similar position search means Mobile body having registration means (for example, realized by case registration means 805) Tell apparatus. The moving body warning device configured as described above can reliably perform warning without excess or deficiency.

  (4) A traffic law database (for example, traffic law) that stores information indicating the road traffic law for each piece of information indicating the location by registering information indicating the maximum speed and the necessity of temporarily stopping the information indicating the place. Whether the user is in violation of the law by comparing the information indicating the user's situation input by the user situation input means with the information indicating the road traffic law stored in the traffic law database. A law violation determination means (for example, realized by the law violation determination means 9) for determining whether or not the user situation input means, the user situation history storage means, and the situation dependent near-miss map are determined by the law violation judgment means. Moving body warning device that treats as information indicating the user's situation. The mobile object warning device configured as described above is capable of warning a near-miss based on the situation of whether or not the law is violated.

  (5) User model generation means (for example, the user model generation means 13) that analyzes the user's driving tendency and generates a user model based on the history information indicating the past driving situation stored in the user situation history storage means. Information that is input by the user model storage means (for example, realized by the user model 12) and the user situation input means that store the user model generated by the user model generation means, and is rushed Driving status determination for determining the current driving status of the user by comparing information indicating the current driving status indicating that the user is tired or tired with a user model indicating the past driving status stored in the user model storage means Means (for example, realized by the driving situation determination means 11), a user situation input means, a user situation history storage means, and a situation dependence. Hiyari hat maps mobile warning device for handling the results is driving condition determining means determines as the information indicating the status of the user. The moving body warning device configured as described above can warn of a near miss in consideration of whether the road is hurrying, tired, or unknown, as compared with normal driving.

  (6) User life area adding means (for example, user life area adding means) for counting the number of times the user ran on the road indicated by the history information based on the history information indicating the past driving situation stored in the user situation history storage means. 16), a user life area database (for example, realized by the user life area database 15) that stores user life area information including information indicating the number of times counted by the user life area adding means, and user life Based on the user living area information stored in the area database, the user living area determining means for determining whether or not the current position indicated by the information indicating the situation input by the user situation input means is the user's living area (for example, User life zone determination means 14), user situation input means, user situation history storage means, and situation dependent near-miss Flop, user living area determination means whether or not the living area of the user is determined, i.e. familiar mobile warning device that handles traveling whether element indicating one of the road as the information indicating the status of the user. The moving body warning device configured as described above enables a near-miss warning that distinguishes a driver (a person who always uses the vehicle) running in the same place from a person who uses the vehicle for the first time.

  (7) Danger driver distribution generation means (for example, realized by the dangerous driver distribution generation means 19) that calculates the ratio of drivers who perform dangerous driving around the user, and dangerous driver distribution generation means A dangerous driving database that stores the result calculated by the dangerous driving database (for example, realized by the dangerous driving database 18) and the dangerous driving that calculates the possibility that the user will be involved in an accident or the like based on the value stored in the dangerous driving database. The user situation input means, the user situation history storage means, and the situation-dependent near-miss map are calculated by the user who has been involved in the dangerous driving and the judgment means. A mobile warning device that handles the possibility of being involved in an accident as information indicating the user's situation. The mobile body warning device configured as described above can warn of a near-miss while taking into consideration the probability of being involved in a surrounding risk factor that is currently running.

  (8) The situation-dependent near-miss map stores information indicating the environment surrounding the running car indicating the weather, traffic jam, and speed limit, and a driving situation field storing information indicating the position and speed of the car changing in real time. A mobile object warning device comprising: an environmental condition field for storing information; a user condition field for storing information indicating a user profile indicating sex, age, and driving experience; and a warning field for storing a warning message for the user.

  (9) The user status field is a field representing driving experience, a field representing the percentage of driving violations, a field representing whether or not you are in a hurry, and whether you are tired. A moving body warning device having a field for representing, a field for representing whether the vehicle is running in a living area or an unknown road, and a field for representing a ratio of a car driving dangerously nearby.

  (10) The road attribute information database is a mobile warning device having an area field that represents a range of road attribute information and an attribute field that stores an attribute value of the area field.

  (11) The attribute field of the road attribute information database includes a field indicating the road width, a field indicating the number of lanes, a field indicating the type of road such as a national road, a prefectural road, a residential road, and a private road, and a field indicating the good visibility. Mobile object warning device.

  (12) The traffic law database has a field that describes the effective range of the rule, a field that represents the speed limit at that position, a field that represents the necessity of stopping, and a field that indicates whether parking is possible. Mobile warning device.

  (13) The user model storage means stores a long-term user model field for storing a long-term user model calculated from the entire driving history so far, and a short-term user model for storing a short-term user model calculated from the recent driving history. And a mobile warning device having a field.

  (14) The mobile warning device having a long-term user model field and a short-term user model field of the user model storage means having a field for storing an average speed, a field for storing driving time, and a field for storing a ratio of complying with laws.

  (15) The moving body warning device, wherein the driving state determination means calculates a value indicating whether or not the vehicle is in a hurry by comparing the average speed of the short-term user model and the average speed of the long-term user model.

  (16) The moving body warning device, wherein the driving state determination means calculates a value indicating whether or not the user is tired by comparing the driving time of the short-term user model with the driving time of the long-term user model.

  (17) The user life zone adding means uses a value indicating whether the user is running in the life zone or an unknown road for each road segment based on the past driving history stored in the user situation history storage means. A moving body warning device that calculates by counting the number of times.

  (18) The dangerous driver distribution generation means specifies the dangerous driver from the value indicating the degree of legal compliance of the user model, and specifies the value indicating the percentage of vehicles that are dangerously driving nearby. A mobile warning device that calculates the relative position of a dangerous driver.

  INDUSTRIAL APPLICABILITY The present invention is suitably applied to a use for warning a person before driving into a dangerous situation while driving a car or walking a person.

  It should be noted that the disclosures of the above patent documents are incorporated herein by reference. Within the scope of the entire disclosure (including claims) of the present invention, the examples and the examples can be changed and adjusted based on the basic technical concept. Various combinations and selections of various disclosed elements are possible within the scope of the claims of the present invention. That is, the present invention of course includes various variations and modifications that could be made by those skilled in the art according to the entire disclosure including the claims and the technical idea.

DESCRIPTION OF SYMBOLS 1 User condition input means 2 Situation matching means 3 Warning means 4 Near miss input means 5 Situation dependent near hat registration means 6 Situation dependent near hat map 7 User situation history storage means 8 Situation dependent near hat map expansion means

Claims (32)

User status input means for inputting information indicating the status of the client, the user and the surrounding environment;
Near incident input means for inputting information indicating that the user has incidentally closed,
A situation-dependent near-miss map that stores near-miss information for issuing a warning to the user in association with information indicating the status of the client, the user, and the surrounding environment;
Information indicating the situation when the near-miss information is input from the user-status input means, and the situation-dependent near-miss registration means for registering in the situation-dependent near-miss map;
Situation matching means for retrieving and extracting near-miss information corresponding to the status of the client, user and surrounding environment from the situation-dependent near-miss map;
Warning means for notifying the user of near-miss information extracted by the situation matching means;
A moving body warning device comprising: Furthermore, user status history storage means for storing information indicating the status input by the user status input means as history information;
Among the information indicating the situation stored in the user situation history storage means, the entry registered in the situation dependent near-miss map is grouped based on the information indicating the position, and the situation where the incident is accidentally performed at the grouped position is indicated. The mobile warning device according to claim 1, further comprising: a situation-dependent near-miss map expansion unit that generalizes information to be registered and registers the information in a situation-dependent near-miss map. The situation-dependent near-miss map expansion means
Near-miss grouping means for performing grouping in which the data between the positions indicated by each near-miss information is the same group among the near-miss information registered in the situation-dependent near-miss map,
The mobile object warning device according to claim 1, further comprising: a case generalization unit that generalizes the near-miss information into an expression having a higher level of abstraction by analyzing the near-miss information for each group grouped by the near-miss grouping unit. The situation-dependent near-miss map expansion means
A road attribute information database for storing road attribute information indicating road width, number of lanes, and visibility,
Similar position search means for searching from the road attribute information stored in the road attribute information database whether there is a road similar to the road corresponding to each of the near-miss information stored in the situation dependent near-miss map,
Case registration means for newly adding a near-miss case to the situation-dependent near-miss map for a similar road searched by the similar position search means that there is a high possibility that a similar near-case case will occur at a location having a similar road attribute The moving body warning device according to any one of claims 1 to 3. By registering information indicating the maximum speed and necessity of temporary stop for the information indicating the location, the traffic law database for storing the information indicating the road traffic law for each information indicating the location and the user status input means input Comparing information indicating the user's situation with information indicating the road traffic law stored in the traffic law database, the law violation determination means for determining whether the user is in violation of the law,
5. The user status input unit, the user status history storage unit, and the situation-dependent near-miss map handle a result determined by the law violation determination unit as information indicating a user status. 6. The moving body warning device described in 1. User model generation means for analyzing a user's driving tendency and generating a user model based on history information indicating past driving conditions stored in the user situation history storage means;
User model storage means for storing the user model generated by the user model generation means;
Information input by the user status input means, which indicates the current driving status indicating hurry or tiredness, and a user model indicating the past driving status stored in the user model storage means By comparing, with a driving situation determination means for determining the current driving situation of the user,
The user status input unit, the user status history storage unit, and the situation-dependent near-miss map handle a result determined by the driving status determination unit as information indicating a user status. The moving body warning device described in 1. User life zone adding means for counting the number of times the user ran on the road indicated by the history information based on the history information indicating the past driving situation stored by the user situation history storage means;
A user life area database that stores user life area information including information indicating the number of times the user life area addition means has counted;
Based on user life zone information stored in the user life zone database, user life zone determination means for determining whether or not the current position indicated by the information indicating the status input by the user status input means is the user's life zone And
The user situation input means, the user situation history storage means, and the situation-dependent near-miss map are elements indicating whether or not the user's living area determined by the user living area determining means, that is, whether or not the user is traveling on a familiar road. The moving body warning device according to claim 1, wherein the moving body warning device is handled as information indicating a user's situation. Dangerous driver distribution generation means for calculating a ratio of drivers who perform dangerous driving around the user;
A dangerous driving database for storing results calculated by the dangerous driver distribution generating means;
Based on the value stored in the dangerous driving database, a dangerous driving entrapped judgment means for calculating the possibility that the user will be involved in an accident, etc.
The user situation input means, user situation history storage means, and situation dependent near-miss map handle the possibility that the user is involved in the dangerous driving and is determined to be involved in an accident as information indicating the situation of the user. The moving body warning device according to claim 7. The situation-dependent near-miss map is a driving situation field that stores information indicating the position and speed of a car that changes in real time, and an environmental situation that stores information indicating the environment around the running car indicating weather, traffic congestion, and speed limit. 9. The system according to claim 1, further comprising: a field, a user status field that stores information indicating a user's profile indicating gender, age, and driving experience; and a warning field that stores a warning message to the user. The moving body warning device according to item 1. The user status field is a field that represents driving experience, a field that represents a percentage of driving violations, a field that represents whether you are in a hurry, a field that represents whether you are tired, The mobile body warning device according to claim 9, further comprising: a field that indicates whether the vehicle is running in a living area or an unknown road and a field that indicates a percentage of vehicles that are dangerously driving nearby. The moving body warning device according to claim 4, wherein the road attribute information database includes an area field that represents a range of road attribute information and an attribute field that stores an attribute value of the area field. The attribute field of the road attribute information database includes a field that represents a road width, a field that represents the number of lanes, a field that represents a type of road such as a national road, a prefectural road, a residential road, and a private road, and a field that represents a good prospect. 11. A mobile warning device according to 11. The traffic law database includes a field that describes an effective range of a rule, a field that represents a speed limit at the position, a field that represents a necessity for stopping, and a field that indicates whether parking is possible. The moving body warning device described. The user model storage means includes a long-term user model field for storing a long-term user model calculated from the entire driving history so far, and a short-term user model field for storing a short-term user model calculated from the recent driving history. The moving body warning device according to claim 6. 15. The mobile warning according to claim 14, wherein the long-term user model field and the short-term user model field of the user model storage means include a field for storing an average speed, a field for storing driving time, and a field for storing a ratio of complying with laws. apparatus. The moving body warning device according to claim 6, wherein the driving state determination means calculates a value indicating whether or not the vehicle is in a hurry by comparing an average speed of the short-term user model and an average speed of the long-term user model. The moving body warning device according to claim 6, wherein the driving state determination unit calculates a value indicating whether or not the user is tired by comparing the driving time of the short-term user model with the driving time of the long-term user model. The user life zone adding means counts the number of times of use for each segment of the road based on the past driving history stored in the user status history storage means, indicating whether the user life zone is running on an unknown road. The moving body warning device according to claim 7, wherein the moving body warning device is calculated. The dangerous driver distribution generation means identifies the dangerous driver from the value indicating the degree of legal compliance of the user model, with the value indicating the percentage of vehicles that are dangerously driving nearby nearby. The mobile body warning device according to claim 8, wherein the mobile body warning device is calculated by calculating a relative position with respect to the hand. Entering information indicating the status of the client, user and surrounding environment;
When information indicating that the user has received a near-miss is input, it is associated with information indicating the status of the client, the user, and the surrounding environment, and as a near-miss information for issuing a warning to the user, the situation-dependent near-miss map is displayed. Memorizing step;
Searching and extracting near-miss information corresponding to the situation of the client, the user and the surrounding environment from the situation-dependent near-miss map, and notifying the user;
Mobile object warning method including: A near-miss information registration step for registering information indicating a situation where the driver is near-miss as an example,
A near-miss information expansion step for generalizing near-miss information corresponding to a position in a predetermined range by analyzing near-miss information registered in the near-hat information registration step based on information indicating a position included in the near-hat information. ,
Information indicating the situation of the driver is input, and the near-miss information registered in the near-miss information registration step or the near-miss information generalized in the near-miss information extension step is matched with the information indicating the input driver condition. And a near-miss warning step that warns the driver when matching is successful. The near-miss information registration step
A near-miss input step in which the driver notifies information indicating that a near-miss occurred,
A user situation acquisition step of detecting the situation of the car, the user who is the driver and the surrounding environment at the time when the information is inputted in the near-miss input step, and inputting the situation information as information,
A situation-dependent near-miss registration step for registering near-miss information indicating the user's situation input in the user situation acquisition step in the database as a situation-dependent near-miss map, corresponding to the information entered in the near-miss entry step. And
Near-miss warning step
A user situation history recording step for detecting the situation of a car, a user who is a driver, and the surrounding environment, and storing the situation in the database as information indicating the situation;
A user situation near-miss matching step for matching information indicating the user situation input in the user situation acquisition step with the situation-dependent near-miss map;
The moving body warning method according to claim 21, further comprising: a user warning step of warning a driver based on a warning message included in the near-miss information that has been successfully matched in the user situation near-miss matching step. The near-miss information expansion step is
An attribute information matching step of retrieving road attribute information corresponding to the position indicated by all near-miss information from a road attribute information database storing road attribute information indicating road width, number of lanes, and visibility;
An iterative check step for controlling the iterative processing of search results in the attribute information matching step;
For the search result in the attribute information matching step, a similar road search step for searching for a place where the location is different but other attributes are similar;
The mobile object warning method according to claim 21 or 22, further comprising: a similar near-miss registration step for registering near-miss information on a search result of the similar road search step. Near-miss warning step
Before the user situation history recording step, a law violation determination step for comparing the information indicating the input user situation with the information indicating the road traffic law stored in advance to determine whether the user is in violation of the law Have
24. The user situation history recording step and the user situation near-miss matching step consider the presence or absence of traffic violation determined in the law violation determination step as one element of information indicating the user situation. The moving body warning method according to claim 1. Based on historical information indicating past driving conditions, analyzing a user's driving tendency, and including a user model generation step of generating a user model,
Near-miss warning step
Before the user status history recording step, the current driving status of the user is compared by comparing the information indicating the current driving status indicating rushing or tired with the user model indicating the past driving status. A driving state determination step for determining,
25. The user situation history recording step and the user situation near-miss matching step consider the user's current driving situation calculated in the driving situation determination step as one element of information indicating the user situation. The moving body warning method of any one of them. For each whereabouts indicated by historical information indicating past driving conditions, including a living area generation step for counting the number of visits,
Near-miss warning step
Before the user status history recording step, based on the number of times the current location counted in the life zone generation step has been visited in the past, a life zone determination step for determining whether the current location is a life zone,
The user situation history recording step and the user situation near-miss matching step consider the life area information of the current location of the user calculated in the life area determination step as one element of information indicating the user situation. The moving body warning method of any one of them. Including a risk database generation step of calculating a ratio of drivers performing dangerous driving around the user,
Near-miss warning step
Before the user situation history recording step, based on the ratio calculated in the risk database generation step, has a risk determination step of calculating the possibility of being involved in an accident or the like at the current location,
27. The user situation history recording step and the user situation near-miss matching step consider the risk in the current location of the user calculated in the risk determination step as one element of information indicating the user situation. The moving body warning method of any one of them. On the computer,
A process of inputting information indicating the status of the predetermined client, the user, and the surrounding environment;
When information indicating that the user has received a near-miss is input, it is stored in the situation-dependent near-miss map as near-miss information for issuing a warning to the user in association with information indicating the status of the client, the user, and the surrounding environment. Processing to
Processing for retrieving and extracting near-miss information corresponding to the situation of the client, user and surrounding environment from the situation-dependent near-miss map, and notifying the user;
A program to execute the program asynchronously. On the computer,
A user status input process for detecting the status of a car, a user who is a driver and the surrounding environment, and inputting the status as information indicating the status;
A situation matching process for searching and extracting near-miss information corresponding to information indicating a situation input in the user situation input process from a situation-dependent incident-hat map DB that stores near-miss information indicating a near-miss situation;
A warning process for notifying the driver of the near-miss information extracted in the situation matching process, a near-miss input process for inputting information indicating that the driver has missed,
Based on the information input in the near miss input process, information indicating the situation when the information is input is obtained from the user situation input process, and registered in the situation dependent near hat map,
Among the information indicating the situation stored by the user situation history storage unit that stores the information indicating the situation input in the user situation input process as history information, the entry registered in the situation dependent near miss map is, A mobile object warning program for grouping based on information indicating a position, and performing a situation-dependent near-miss map expansion process for generalizing information indicating a near-miss situation at the grouped position and registering the information in a situation-dependent near-miss map. On the computer,
A process of executing a near-miss information registration step for registering information indicating a situation where the driver is near-miss as an example,
The near-miss information that generalizes near-miss information corresponding to the position in a predetermined range by analyzing the near-miss information registered in the process of executing the near-miss information registration step based on information indicating the position included in the near-hat information. Processing to execute the information expansion step;
Information indicating the driver's situation is input, the near-miss information registered in the process of executing the near-miss information registration step or the near-miss information generalized in the process of executing the near-miss information extension step, and the input driver A moving body warning program that asynchronously executes a process of executing a near-miss warning step that matches information indicating a situation and warns a driver when matching is successful. User status input means for inputting information indicating the status of the device, the user and the surrounding environment;
Near incident input means for inputting information indicating that the user has incidentally closed,
Information indicating the situation when the near-miss information is input from the user-status input means, and the situation-dependent near-miss registration means for registering in the situation-dependent near-miss map stored in the server;
Situation matching means for retrieving and extracting near-miss information corresponding to the status of the own device, the user and the surrounding environment from the situation-dependent near-miss map,
Warning means for notifying the user of near-miss information extracted by the situation matching means;
A client characterized by comprising.   31. A situation-dependent near-miss map for storing near-miss information for issuing a warning to the user in association with information indicating the status of the client, the user, and the surrounding environment, and receiving registration of near-miss information from the client according to claim 30. A server that provides near-miss information.

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