JP6458634B2 - Event detection device - Google Patents

Description

  The present invention relates to an event detection device that detects, based on biological information of a driver, that an event requiring attention has occurred in driving a vehicle.

  Conventionally, when a driver's biometric information such as heart rate and sweating amount is acquired as a numerical value and the numerical value shows an abnormal value, a near-miss (that is, even if an accident does not occur, attention must be paid in driving the vehicle) A technique for determining that an event has occurred has been proposed. Further, in that case, it is determined that a near-miss has occurred when the numerical value of the biological information indicates an abnormal value and there is an abnormal vehicle operation such as a sharp handle that is not performed in normal driving. Has also been proposed (see, for example, Patent Document 1).

JP 2007-47914 A

  However, in the configuration described in Patent Document 1, the driving scene at that time is not reflected in the determination as to whether or not the numerical value of the biological information is an abnormal value. For this reason, for example, when the driver's feeling of tension increases in a driving scene that the driver is not good at, for example, when parked by reversing or when driving on a curve, it may be erroneously determined as a near-miss.

  In the configuration described in Patent Document 1, it is determined that a near-miss has occurred when an abnormal vehicle operation is performed. For this reason, when the driver is dismissed due to jumping out on a straight road while cruising, the event cannot be determined as a near-miss event if the driver obtains nothing without operating the brakes and steering wheel.

  The present invention has been made in view of these problems, and an object of the present invention is to provide an event detection apparatus that can accurately detect the occurrence of a near-miss by referring to a driving scene.

  The event detection apparatus according to the present invention includes biometric information acquisition means, scene acquisition means, threshold setting means, and determination means. The biometric information acquisition unit acquires the biometric information of the driver as a numerical value. The scene acquisition means acquires a driving scene of a vehicle driven by the driver. The threshold setting means sets a threshold for determining whether or not the numerical value of the biological information is within a normal range according to the driving scene acquired by the scene acquisition means. The determination means determines that an event requiring attention in driving the vehicle (that is, a near-miss) has occurred when the numerical value of the biological information acquired by the biological information acquisition means exceeds a threshold set by the threshold setting means. To do.

  According to such a configuration, the driving scene of the vehicle is reflected in the threshold set by the threshold setting means, and when the numerical value of the biological information acquired by the biological information acquisition means exceeds the threshold, a near-miss occurred. It is judged. Therefore, in the present invention, it is possible to accurately detect the occurrence of a near-miss by referring to the driving scene.

It is a block diagram showing the structure of the event detection apparatus to which this invention was applied. It is a flowchart showing the event detection process in the event detection apparatus. It is explanatory drawing showing an example of the travel location identification method in the event detection process. It is explanatory drawing showing the other example of the traveling place identification method. It is a flowchart showing the driving scene discrimination | determination process of the said event detection process. It is explanatory drawing showing an example of the discrimination method in the driving scene discrimination | determination process. It is explanatory drawing showing the other example of the determination method. It is explanatory drawing showing the further another example of the determination method. It is explanatory drawing showing the further another example of the determination method. It is explanatory drawing showing the further another example of the determination method. It is explanatory drawing which represents the effect of the said event detection process typically.

Embodiments to which the present invention is applied will be described below with reference to the drawings.
[1. First Embodiment]
[1-1. Constitution]
The event detection apparatus shown in FIG. 1 is mainly configured by a terminal 1 mounted on a vehicle. The terminal 1 includes a control unit 10, a memory 20, and a map information storage unit 30. The control unit 10 is configured as a microcomputer including a CPU, a ROM, and a RAM. The memory 20 stores threshold data 21 and a biological behavior history 22. The terminal 1 may be incorporated in a car navigation device or the like, or may be incorporated in another part.

  As shown in FIG. 1, the threshold data 21 includes a plurality of data sets (that is, a plurality of sets of data) in which a travel location, a driving scene, and a biological behavior threshold are associated with each other. As the biological behavior history 22, numerical values of biological information acquired through a biological behavior sensor 58 described later are stored as a series of behaviors (hereinafter also referred to as biological behaviors) in association with the acquired time. . The map information storage unit 30 stores the same map information as that provided in a general car navigation device.

  Connected to the terminal 1 are a GPS receiver 51, a vehicle speed sensor 52, an acceleration sensor 53, a blinker 54, a brake lamp 55, a reverse lamp 56, a steering angle sensor 57, and a biological behavior sensor 58 mounted on the vehicle. . The GPS receiver 51 is a well-known device that receives radio waves from a satellite and detects the position of the vehicle (that is, latitude and longitude). Information on the detected position is input to the control unit 10. The GPS receiver 51 may be replaced with a GNSS (global navigation satellite system) other than the GPS (global positioning system) standard, or may be replaced with another position detection device.

  The vehicle speed sensor 52 is a well-known sensor that detects the vehicle speed and inputs the detection result to a vehicle speedometer or the like, and the detected vehicle speed is also input to the control unit 10. The acceleration sensor 53 detects at least the longitudinal acceleration of the vehicle, and the detected acceleration is input to the control unit 10. The blinker 54 is a known one that lights up in response to a lever operation by a driver, and the lighting state is input to the control unit 10. The brake lamp 55 is a well-known lamp that lights up when the brake pedal is depressed, and the lighting state is input to the control unit 10. The reverse lamp 56 is a known lamp that lights up when the shift position is set to R, and the lighting state is input to the control unit 10. The steering angle sensor 57 is a well-known sensor that detects an operation angle (that is, a steering angle) of the steering wheel, and the detected operation angle is input to the control unit 10.

The biological behavior sensor 58 detects biological information such as a body temperature, a pulse, and a sweat amount. As the biological behavior sensor 58, for example, a sensor that detects a pulse via a seat belt as described in Japanese Patent Application Laid-Open No. 2011-182827 can be used. Moreover, the structure which measures the body temperature of a driver | operator's palm with the thermometer attached to the stearin wheel etc. can also be utilized. Note that when the biological behavior sensor 58 can detect a plurality of types of biological information such as body temperature, pulse, sweating amount, etc., the higher the value the more the mental state becomes unstable by applying the various types of biological information to a predetermined mathematical formula. Biometric information that is a single numerical value indicating the number may be input to the control unit 10. Further, when the biological behavior sensor 58 can detect one of the biological information, the numerical value of the biological information may be input to the control unit 10 as it is.

  When the biological behavior sensor 58 can detect a plurality of types of biological information, the numerical value of each biological information is held for a certain period of time, and may be overwritten from the oldest one each time a new numerical value is acquired. In this case also, as will be described later, the standard deviation and change amount of the numerical value held for a certain period of time are obtained, and the numerical value obtained by adding 1.96σ (σ is the standard deviation) to the average value is used as the biological behavior threshold value. May be.

[1-2. processing]
Next, event detection processing executed by the control unit 10 will be described with reference to the flowchart of FIG. This process is repeatedly executed at predetermined time intervals (for example, every second) by the CPU of the control unit 10 based on a program stored in the ROM of the control unit 10.

  As shown in FIG. 2, in this process, first, in S1 (S represents a step: the same applies hereinafter), the travel location of the vehicle is determined. In this processing, the position of the vehicle (ie, latitude and longitude) detected via the GPS receiver 51 is collated with the map information stored in the map information storage unit 30, so that the position of the vehicle on the map is determined. To be acquired.

  More specifically, in the map information, as is well known, a section ID (also called a link ID) is assigned to each road section. In addition, it is desirable that the road section is a section of the road between the intersections, but the road is divided into a plurality of sections at appropriate positions in consideration of convenience in displaying traffic information. It may be a thing. Then, as schematically shown in FIG. 3, when a vehicle is present on the road section L1 whose section ID is “000000000001”, it is determined in S1 that the travel location of the vehicle is on the “road 000000000001”. Further, when a vehicle is present at the intersection C1 between the road section L1 and the road section L2 having the section ID “000000000002”, it is determined in S1 that the vehicle travels at “intersection 000000000001−000000000002”. The

  In the example of FIG. 3, the road section L3, L4 to which another section ID is assigned intersects the intersection C1, but is defined using the section ID of the road section that is the priority road. It is desirable to set a rule so that the definition of the intersection ID is uniform. Further, when the vehicle is within a radius of 20 m from the joint of the road section at the intersection C1, it is determined that the travel location of the vehicle is at the intersection C1.

  Further, there is a case where a section ID is not given to the narrow street L6 as schematically shown in FIG. When there is a vehicle on such a narrow street L6, in S1, the travel location of the vehicle is determined to be “narrow street 000000000021-000000000031” using the section IDs of the roads L7 and L8 surrounding the narrow street L6. The That is, in this case, the narrow street L6 is defined using the section IDs of the roads L7 and L8 as so-called section roads to which the section ID is given. In this case, it is not considered whether the position of the vehicle is an intersection of narrow streets or a part belonging to a single narrow street, but such a situation may be defined so that it can be grasped.

  Returning to FIG. 2, in S3 following S1, the driving scene is determined. In this process, as shown in detail in FIG. 5, first, the traveling state of the vehicle is determined (S31), and then the driving scene according to the traveling state and the traveling location of the vehicle determined in S1 is determined. (S33).

  That is, in S31, based on inputs from the vehicle speed sensor 52, the acceleration sensor 53, the blinker 54, the brake lamp 55, the reverse lamp 56, the steering angle sensor 57, etc., the vehicle is turning left or right, or the vehicle speed is 30 km. It is determined whether the vehicle is traveling more than / h and the accelerator is depressed. In subsequent S33, the driving scene is discriminated as follows, for example, by collating the traveling place and the traveling state of the vehicle.

  For example, when the travel location of the vehicle is an intersection, the driving scene is determined as schematically shown in FIG. That is, if it is determined in S31 that the vehicle is running left, the driving scene is determined to be an intersection left turn in S33. If the vehicle traveling state is determined to be a right turn in S31, it is determined in S33 that the driving scene is an intersection right turn. If it is determined in S31 that the vehicle is running at a speed of 30 km / h or more and the accelerator is stepped on, it is determined in S33 that the driving scene is cruise. If it is determined in S31 that the running state of the vehicle is being braked, it is determined in S33 that the driving scene is decelerating. If it is determined in S31 that the vehicle traveling state is accelerating from 0 km / h and the vehicle speed is 30 km / h or less, it is determined in S33 that the driving scene is a start. If it is determined in S31 that the driving state of the vehicle is that the turn signal 54 is lit and there is no change in the driving direction of the vehicle, it is determined in S33 that the driving scene is a lane change.

  In these cases, the operation state such as normal brake operation or sudden brake, normal handle operation or sudden handle is not considered, but such differences in operation state are not considered. It may be seized.

  Further, when the travel location of the vehicle is a section road (however, excluding the main road and the highway), the driving scene is determined as schematically shown in FIG. That is, if it is determined in S31 that the vehicle is traveling right or left, it is determined in S33 that the driving scene is turning. If it is determined in S31 that the vehicle is running at a speed of 30 km / h or more and the accelerator is stepped on, it is determined in S33 that the driving scene is cruise. If it is determined in S31 that the running state of the vehicle is being braked, it is determined in S33 that the driving scene is decelerating. If it is determined in S31 that the vehicle traveling state is accelerating from 0 km / h and the vehicle speed is 30 km / h or less, it is determined in S33 that the driving scene is a start. If it is determined in S31 that the driving state of the vehicle is that the turn signal 54 is lit and there is no change in the driving direction of the vehicle, it is determined in S33 that the driving scene is a lane change.

  Further, when the travel location of the vehicle is a narrow street, the driving scene is determined as schematically shown in FIG. That is, if it is determined in S31 that the vehicle is traveling right or left, it is determined in S33 that the driving scene is turning. If it is determined in S31 that the vehicle is running at a speed of 30 km / h or more and the accelerator is stepped on, it is determined in S33 that the driving scene is cruise. If it is determined in S31 that the running state of the vehicle is being braked, it is determined in S33 that the driving scene is decelerating. If it is determined in S31 that the vehicle traveling state is accelerating from 0 km / h and the vehicle speed is 30 km / h or less, it is determined in S33 that the driving scene is a start.

Further, when the vehicle travels on a main road, a driving scene is determined as schematically shown in FIG. That is, if it is determined in S31 that the vehicle is traveling right or left, it is determined in S33 that the driving scene is turning. If it is determined in S31 that the vehicle is running at a speed of 30 km / h or more and the accelerator is stepped on, it is determined in S33 that the driving scene is cruise. If it is determined in S31 that the running state of the vehicle is being braked, it is determined in S33 that the driving scene is decelerating. If it is determined in S31 that the vehicle traveling state is accelerating from 0 km / h and the vehicle speed is 30 km / h or less, it is determined in S33 that the driving scene is a start. If it is determined in S31 that the driving state of the vehicle is that the turn signal 54 is lit and there is no change in the driving direction of the vehicle, it is determined in S33 that the driving scene is a lane change. If it is determined in S31 that the running state of the vehicle is passing through the highway merge route, it is determined in S33 that the driving scene is a highway merge.

  Further, when the traveling place of the vehicle is an expressway, the driving scene is determined as schematically shown in FIG. That is, if it is determined in S31 that the vehicle is traveling at a speed of 30 km / h or more and the accelerator is stepped on, it is determined in S33 that the driving scene is cruise. If it is determined in S31 that the running state of the vehicle is being braked, it is determined in S33 that the driving scene is decelerating. If it is determined in S31 that the vehicle traveling state is accelerating from 0 km / h and the vehicle speed is 30 km / h or less, it is determined in S33 that the driving scene is a start. If it is determined in S31 that the driving state of the vehicle is that the turn signal 54 is lit and there is no change in the driving direction of the vehicle, it is determined in S33 that the driving scene is a lane change. If it is determined in S31 that the running state of the vehicle is passing the highway merge route, it is determined in S33 that the driving scene is a highway merge. In addition to the above, driving scenes such as back and curve driving may also be determined.

  Returning to FIG. 2, when the driving scene is determined in this manner in S <b> 3, the process proceeds to S <b> 5, and the biological behavior for a predetermined period (for example, 10 seconds) at that time is recorded as the biological behavior history 22. . The biological behavior is linked (ie, associated) with the travel location determined in S1 and the driving scene determined in S3, and is recorded as the biological behavior history 22. For example, the biological behavior detected while decelerating the section road is recorded in association with data such as “Road 000000000001-Decelerate”. In addition, the biological behavior detected during the left turn at the intersection is recorded in association with data such as “intersection 000000000001−000000000002−turn left at the intersection”. The biological behavior detected while turning on the narrow street is recorded in association with data such as “narrow street 000000000021-000000000031-turn”.

  In subsequent S7, it is determined whether or not the driving scene at the travel location determined in S1 and S3 has already learned a threshold for near-miss determination (hereinafter also referred to as a biological behavior threshold). When learning is completed (S7: Y), the process proceeds to S11, and a near-miss determination is made. That is, in S11, the biological behavior threshold value (having three numerical values of standard deviation, maximum variation value, and maximum value) corresponding to the driving scene at the travel location determined in S1 and S3 is read from the threshold data 21. Subsequently, whether the standard deviation, the maximum variation value, or the maximum value related to the numerical value of the biological information input to the control unit 10 as the biological behavior within the predetermined period exceeds the biological behavior threshold value. If it is judged and there is an excess, it is determined that a near-miss has occurred.

  In subsequent S13, it is determined whether or not the determination result in S11 is a determination that a near-miss has occurred. If it is determined that a near-miss has occurred (S13: Y), the process is temporarily terminated as it is. If it is determined that a near-miss has not occurred (S13: N), an abnormal threshold (ie, biological behavior) is determined in S15. After the threshold value is updated, the process is temporarily terminated.

In S15, the biological behavior threshold update process is performed as follows. In the present embodiment, when 21 pieces of biological behavior data (specifically, the standard deviation and the maximum variation value and the maximum value of the biological behavior within the predetermined period) in which the traveling place and the driving scene match are accumulated. In addition, a biological behavior threshold value is calculated. That is, a numerical value obtained by adding 1.96σ (σ is a standard deviation) to the average value of each of 21 standard deviations, maximum fluctuation values, and maximum values is set as each biological behavior threshold value. The determination that learning is completed in S7 (S7: Y) indicates that 21 pieces of data on the biological behavior have already been accumulated. Therefore, in S15, the biological behavior threshold is updated by recalculating the average value and the standard deviation for the set in which the newly recorded biological behavior data is replaced with the oldest biological behavior data. . The recalculated biological behavior threshold value is associated with the driving location and the driving scene, and is overwritten in the memory 20 as threshold data 21.

  On the other hand, if it is determined in S7 that learning has not been completed (S7: N), the process proceeds to S17. In S17, it is confirmed whether or not the biological behavior threshold of the same driving scene on another road can be used. That is, even in other roads with different section IDs, 21 pieces of data having the same driving location attributes and driving scenes as the intersections, narrow streets, and the like determined in S1 and S3 are already accumulated, It is determined whether or not there is a biological behavior threshold value calculated. In S17, when there are a plurality of driving behavior attributes and driving scenes that are the same as those determined in S1 and S3 this time, 21 pieces of data have already been accumulated, and a biological behavior threshold value has been calculated. Priority is given to roads and intersections with higher similarity in shape.

  In subsequent S19, based on the result of confirmation in S17, it is determined whether the biological behavior threshold of the other road is valid (that is, can be used). When the biological behavior threshold value is valid (S19: Y), in S21, the near-miss determination similar to S11 is performed using the biological behavior threshold value. In S23, it is determined whether or not the determination result in S21 is that a near-miss has occurred. If it is determined that a near-hat has occurred (S23: Y), the process is temporarily terminated.

  On the other hand, when it is determined in S21 that a near-miss has not occurred (S23: N), and in S19, it is determined that the biological behavior threshold of another road is invalid (that is, cannot be used). In this case (S19: N), the process is temporarily terminated after the biological behavior threshold is updated in S25. The processing of S25 is the same as the processing of S15 described above, but until 21 pieces of biological behavior data in which the traveling location including the section ID matches the driving scene are accumulated in the traveling location and the driving scene. The corresponding biological behavior threshold is not calculated. That is, until 21 pieces of the biological behavior data are accumulated, the biological behavior threshold corresponding to the travel location and the driving scene is invalidated. When the terminal 1 is shipped, no biological behavior data is accumulated, so a negative determination is made in S7 and S19 until the data is accumulated in the biological behavior history 22, and the processing of S25 is repeated. When 21 pieces of biological behavior data are accumulated, the biological behavior threshold value is calculated as described above and stored in the memory 20 as the threshold data 21 in S25.

[1-3. effect]
According to the first embodiment described in detail above, the following effects can be obtained.
[1A] The biological behavior threshold set in the present embodiment reflects the driving scene of the vehicle. When the biological behavior data exceeds the biological behavior threshold, it is determined that a near-miss occurred (S13: Y, S23: Y). That is, as illustrated in FIG. 11, even on the same road A, different biological behavior thresholds Aa, Ab, Ac, Ad are set depending on whether the driving scene is start, left turn, stop, or back, The threshold value is compared with biological behavior (that is, behavior of biological information such as body temperature, pulse, sweat rate, etc.). Therefore, in the present embodiment, it is possible to accurately detect the occurrence of a near-miss by reflecting that the driver is not good at each driving scene. For example, even if the data of the biological behavior shows a high value due to tension in a driving scene that is unsatisfactory, it can be suppressed that it is erroneously detected as a near-miss.

[1B] In this embodiment, the position of the vehicle (that is, the travel location) is always acquired (S1), and the biological behavior threshold is set for the combination of the travel location and the driving scene. For this reason, reflecting that the driver is not good at every driving place and every driving scene,
The occurrence of near-miss can be detected more accurately.

  [1C] The biological behavior threshold is stored in the memory 20 as threshold data 21 in association with a combination of a travel location and a driving scene, as shown in FIG. For this reason, in the near-miss determination (S11, S21), it is only necessary to read out the corresponding biological behavior threshold value from the threshold data 21 and compare it with the biological behavior data, and the processing can be simplified and speeded up.

  [1D] In the embodiment, when the biological behavior threshold corresponding to the travel location and the driving scene determined in S1 and S3 is not stored as the threshold data 21 (S7: N), a similar travel location and the same driving The biological behavior threshold corresponding to the scene is read and used for near-miss determination (S21). For this reason, even when the vehicle travels on a road that has never traveled before, occurrence of a near-miss can be accurately detected.

  [1E] Also, using the newly acquired biological behavior data, the biological behavior threshold corresponding to the travel location and driving scene from which the data has been acquired is updated (S15, S25). For this reason, the biological behavior threshold value is updated to a new value in accordance with changes in the driver's strengths and weaknesses, and changes in the infrastructure maintenance state (for example, a curve mirror) at the same travel location, and the occurrence of near-miss is detected more accurately. It becomes possible.

  [1F] Moreover, since the update of the biological behavior threshold is performed using data when occurrence of a near-miss is not detected (S13: N, S23: N), the biological behavior threshold is updated to a more appropriate value. Thus, the occurrence of a near-miss can be detected even more accurately.

  [1G] In the present embodiment, the details of the processing are disclosed only up to the point where the occurrence of a near-miss is detected, but the near-hat detected in this way can be applied to various applications. For example, the present invention may be applied to control for informing a point requiring attention in driving through a display unit of a car navigation device in advance. As a specific method, the location may be displayed on a map, and a warning may be given when the vehicle reaches the location.

  In the present embodiment, the biological behavior sensor 58 is controlled by the biological information acquisition means, and the vehicle speed sensor 52, the acceleration sensor 53, the blinker 54, the brake lamp 55, the reverse lamp 56, and the steering angle sensor 57 are controlled by the scene acquisition means. The unit 10 corresponds to threshold setting means, determination means, and storage update means, the GPS receiver 51 corresponds to position acquisition means, and the memory 20 corresponds to storage means. Of the processes in the control unit 10, the process of reading out the biological behavior threshold among the processes of S 11 and S 21 is the threshold setting means, and the process of comparing the biological behavior threshold and the biological behavior data in the processes of S 11 and S 21. The processing of S15 and S25 corresponds to the determination unit and the storage update unit, respectively.

[2. Other Embodiments]
As mentioned above, although embodiment of this invention was described, this invention can take a various form, without being limited to the said embodiment.

  [2A] In the above embodiment, the number of data on the biological behavior required for calculating the biological behavior threshold is set to 21, but the present invention is not limited to this. For example, the number of data may be set to a numerical value other than 21.

[2B] In the embodiment, the biological behavior threshold is not set until the necessary number of biological behavior data is accumulated (21 in the above example), but is not limited to this. For example, when the necessary number of pieces of biological behavior data is not accumulated, the biological behavior threshold value related to another driver may be acquired via the Internet or the like, and the biological behavior threshold value may be used for near-miss determination.

  [2C] In the above-described embodiment, the biological behavior threshold corresponding to various combinations of the travel location and the driving scene is stored in the memory 20 as the threshold data 21, but the present invention is not limited to this. For example, the biological behavior threshold corresponding to the travel location may be stored in the memory 20 and the biological behavior threshold may be corrected with a coefficient corresponding to the driving scene. The biological behavior threshold value may be stored in association with only the driving scene regardless of the travel location.

  [2D] In the above-described embodiment, the biological behavior sensor 58 has been described as being mounted on a vehicle. However, this may not necessarily be provided on the vehicle. For example, the biological behavior sensor 58 may be a device carried by a driver such as a smartphone. In this case, a communication unit that is provided in the vehicle and communicates with the smartphone or the like corresponds to the biological information acquisition unit.

  [2E] In the above-described embodiment, the operation state such as the normal brake operation or the sudden brake, or the normal handle operation or the sudden handle is not referred to. You may perform near-miss determination with reference to a difference. That may be able to accurately determine the near-miss. However, when the operation state is not referred to as in the embodiment, a near-miss that is overlooked in the configuration of Patent Document 1 can be detected. For example, even if a driver jumps out while cruising on a straight road, and the driver gets a chance without operating the brakes or steering wheel, the biological behavior is compared with the biological behavior threshold value. Can be determined to have occurred.

  [2F] The functions of one component in the embodiment may be distributed as a plurality of components, or the functions of a plurality of components may be integrated into one component. Further, at least a part of the configuration of the embodiment may be replaced with a known configuration having the same function. Moreover, you may abbreviate | omit a part of structure of the said embodiment. In addition, at least a part of the configuration of the embodiment may be added to or replaced with the configuration of the other embodiment. In addition, all the aspects included in the technical idea specified only by the wording described in the claim are embodiment of this invention.

  [2G] In addition to the event detection apparatus described above, various forms such as a system including the event detection apparatus as a constituent element, a program for causing a computer to function as the event detection apparatus, a medium on which the program is recorded, an event detection method, etc. Thus, the present invention can be realized.

DESCRIPTION OF SYMBOLS 1 ... Terminal 10 ... Control part 20 ... Memory 21 ... Threshold data 51 ... GPS receiver 52 ... Vehicle speed sensor 53 ... Acceleration sensor 54 ... Winker 55 ... Brake lamp 56 ... Reverse lamp 57 ... Steering angle sensor 58 ... Living body behavior sensor

Claims (7)

Biometric information acquisition means for acquiring the biometric information of the driver as a numerical value;
Scene acquisition means for acquiring a driving scene determined by collating the attribute of the driving location of the vehicle driven by the driver and the driving state ;
A threshold value for numeric said biometric information to determine whether it is within normal ranges, the threshold value setting means for setting a different threshold depending on whether they are driving scene said scene acquisition means acquires When,
A determination means for determining that an event requiring attention has occurred in driving the vehicle when a numerical value of the biological information acquired by the biological information acquisition means exceeds a threshold set by the threshold setting means;
An event detection apparatus comprising: Position acquisition means for acquiring the position of the vehicle on a map;
In addition,
The event detection apparatus according to claim 1, wherein the threshold setting unit sets the threshold according to a driving scene acquired by the scene acquisition unit and a position acquired by the position acquisition unit. Storage means for storing the threshold value in association with various combinations of the driving scene and the position,
In addition,
The threshold setting means sets the threshold by reading the threshold stored in the storage means in association with the driving scene acquired by the scene acquisition means and the position acquired by the position acquisition means. The event detection device according to claim 2, wherein When the threshold stored in the storage means in association with the driving scene acquired by the scene acquisition means and the position acquired by the position acquisition means does not exist, the threshold setting means is acquired by the scene acquisition means and driving scenes, and the same position the traveling location attribute a position different from the position acquiring means acquires position, by reading the threshold stored in the storage means in association with, the threshold The event detection device according to claim 3, wherein the event detection device is set. For the combination of the driving scene acquired by the scene acquisition means and the position acquired by the position acquisition means, the numerical value of the biological information acquired by the biological information acquisition means at the time when the combination is acquired, A storage update unit that calculates the threshold value based on a stored numerical value and writes the threshold value in association with the combination into the storage unit,
The event detection apparatus according to claim 3, further comprising:   The storage update means stores the numerical value when the numerical value of the biological information acquired by the biological information acquisition means does not exceed the threshold when the combination is acquired, and based on the stored numerical value. The event detection apparatus according to claim 5, wherein the threshold value is calculated and written in the storage unit in association with the combination.   The driving scene includes at least any two of a right turn at an intersection, a left turn at an intersection, cruise, deceleration, start, lane change, merging, or turning, according to any one of claims 1 to 6. The described event detection device.