JP2021068344A - Driving index output device - Google Patents

Abstract

To provide a driving index output device capable of outputting a driving index such as an accident risk.SOLUTION: In a driving index output device 1, a surrounding environment detection unit 5 acquires a driving environment of a vehicle; a speed sense detection unit 8 acquires a sense of speed when there is no other vehicle in front of the vehicle; and an inter-vehicle time detection unit 7 acquires an inter-vehicle time of a driver statistically obtained from an inter-vehicle distance when another vehicle is in front and a traveling speed of the driver's own vehicle. Then, a coping distance detection unit 9 calculates a coping distance by multiplying the statistical value of the traveling speed and the statistical value of the inter-vehicle time as driving indexes.SELECTED DRAWING: Figure 1

Description

The present invention relates to a driving index output device that outputs a driving index of a driver.

Conventionally, driving indicators such as driver's accident risk, safe driving evaluation, and driving characteristic evaluation have been performed by detecting sudden braking during driving. However, with that method, it is not detected as an accident risk in a state without sudden braking.

In Patent Document 1, the vehicle speed fluctuation estimation unit 106b of the preceding vehicle estimates the vehicle speed fluctuation of the preceding vehicle, and determines the psychological state of the driver only when it is estimated that the preceding vehicle is traveling stably. In determining the psychological state, the driving scene determination unit c determines whether the driving scene of the own vehicle is an "acceleration scene", a "steady driving scene", or a "deceleration scene", and the psychological state determination unit 106a Is described to execute the process according to the determined driving scene and determine the psychological state of the driver.

In Patent Document 2, the driver psychological determination means 31 describes the psychological state of the driver based on the comparison between the running state of the own vehicle when the preceding vehicle is present and the running state of the own vehicle when the preceding vehicle does not exist. Is described to determine.

In Patent Document 3, in the input system 10, the actual inter-vehicle distance secured between the vehicle and another vehicle in actual driving is input to the determination control system 30 in association with the vehicle speed at that time, and the determination control system 30 inputs. , The basic inter-vehicle distance, which is the standard for psychological judgment, is read from the data management system 20 in correspondence with the vehicle speed acquired by the input system 10, and is read from the actual inter-vehicle distance input from the input system 10 and the data management system 20. It is described that the driver's psychological state is determined based on the difference in the inter-vehicle distance by comparing with the basic inter-vehicle distance issued.

Japanese Unexamined Patent Publication No. 2005-230381 JP-A-2007-133673 Japanese Unexamined Patent Publication No. 2003-51097

Patent Documents 1 to 3 are up to the determination of the psychological state of the driver, and are not up to the output of a driving index such as what happens to the accident risk as a result.

One example of the problem to be solved by the present invention is to provide a driving index output device capable of outputting a driving index such as an accident risk.

In order to solve the above problems, the invention according to claim 1 includes a driving environment acquisition unit that acquires the driving environment of a vehicle and the driving environment statistically obtained when no other vehicle is present in front of the vehicle. It was statistically obtained from the driving speed information acquisition unit that acquires the statistical value of the driving speed of the driver based on the relationship with the speed, the inter-vehicle distance when another vehicle is in front, and the traveling speed of the own vehicle. An inter-vehicle time information acquisition unit that acquires the statistical value of the driver's inter-vehicle time, and an output unit that outputs the driver's driving index based on the statistical value of the sense of speed and the statistical value of the inter-vehicle time. It is characterized by being prepared.

The invention according to claim 6 is a driving index output method executed by a driving index output device that outputs a driver's driving index, in which a driving environment acquisition step for acquiring the driving environment of the vehicle and another driving environment acquisition step in front of the vehicle. The driving speed information acquisition process for acquiring the statistical value of the driving speed of the driver based on the relationship between the driving environment and the speed statistically obtained when the vehicle does not exist, and when another vehicle exists in front of the vehicle. The inter-vehicle time information acquisition process for acquiring the statistical value of the inter-vehicle time of the driver obtained statistically from the inter-vehicle distance and the traveling speed of the own vehicle, and the statistical value of the sense of speed and the statistical value of the inter-vehicle time. It is characterized by including an output process for outputting the driving index of the driver based on the above.

The invention according to claim 7 is characterized in that the operation index output method according to claim 6 is executed by a computer.

The invention according to claim 8 is characterized in that the operation index output program according to claim 7 is stored.

It is a functional block diagram of the operation index output device which concerns on 1st Embodiment of this invention. It is a flowchart of the data collection and learning operation which concerns on the statistical data and learning data of the operation index output device shown in FIG. It is a flowchart of the risk detection operation of the operation index output device shown in FIG. It is a graph which shows the result of the virtual running experiment performed by the present inventors. It is the result of further analysis in the group (a) shown in FIG. It is the result of further analysis in the group (b) shown in FIG. It is a flowchart of the risk detection operation of the operation index output device which concerns on 2nd Embodiment of this invention. It is a functional block diagram of the operation index output device which concerns on 3rd Example of this invention. It is a graph explaining the psychological method of the driver in the driving index output device shown in FIG. It is a graph explaining the psychological method of the driver in the driving index output device shown in FIG.

Hereinafter, the operation index output device according to the embodiment of the present invention will be described. In the driving index output device according to the embodiment of the present invention, the driving environment acquisition unit acquires the driving environment of the vehicle, and the traveling speed information acquisition unit obtains the driving statistically when there is no other vehicle in front of the vehicle. The driver's running speed statistical value is acquired based on the relationship between the environment and the speed, and the inter-vehicle time information acquisition unit statistically obtains the inter-vehicle distance when another vehicle is in front and the running speed of the own vehicle. Obtain the obtained statistical value of the driver's inter-vehicle time. Then, the output unit outputs the driving index of the driver based on the statistical value of the traveling speed and the statistical value of the inter-vehicle time. By doing so, the driving index can be output based on the acquired statistical value. In addition, it is not necessary to determine the psychological state of the driver when outputting the driving index.

Further, the output unit may output the coping distance calculated by multiplying the statistical value of the traveling speed and the statistical value of the inter-vehicle time as a driving index. By doing so, the value calculated based on these statistical values can be output as an operation index.

Further, the output unit may determine and output the driver's accident risk as a driving index based on the manageable distance. By doing so, it is possible to output as specific information such as accident risk.

Further, the output unit may determine that there is an accident risk when the coping distance is equal to or less than the first reference value. By doing so, it is possible to determine the presence or absence of an accident risk according to the manageable distance.

Further, the output unit may output information related to the psychological state of the driver as a driving index. By doing so, it is possible to detect a change in the driver's psychological state and give a warning to the driver.

In addition, a sudden braking detection unit that detects sudden braking of the vehicle is further provided, and the output unit determines the statistical value of the sense of speed and the inter-vehicle time when the number of sudden braking detected by the sudden braking detection is less than or equal to the second reference value. The driving index of the driver may be output based on the statistical value. By doing so, it is possible to output a driving index when sudden braking is not detected, and it is possible to reliably determine the accident risk when sudden braking is not detected.

Further, the driving index output method according to the embodiment of the present invention is statistically obtained when the driving environment of the vehicle is acquired in the driving environment acquisition process and no other vehicle exists in front in the driving speed information acquisition process. Obtains statistical values of the driver's driving speed based on the relationship between the driving environment and speed, and statistics from the inter-vehicle distance and the traveling speed of the own vehicle when another vehicle is in front in the inter-vehicle time information acquisition process. Obtain the statistical value of the driver's inter-vehicle time obtained. Then, in the output process, the driving index of the driver is output based on the statistical value of the traveling speed and the statistical value of the inter-vehicle time. By doing so, the driving index can be output based on the acquired statistical value. In addition, it is not necessary to determine the psychological state of the driver when outputting the driving index.

Further, the above-mentioned operation index output method is executed by a computer. By doing so, it is possible to output the driving index based on the acquired statistical value by using a computer.

Further, the above-mentioned operation index output program may be stored in a computer-readable storage medium. By doing so, the program can be distributed as a single unit in addition to being incorporated in the device, and version upgrades and the like can be easily performed.

The operation index output device according to the first embodiment of the present invention will be described with reference to FIGS. 1 to 6. As shown in FIG. 1, the driving index output device 1 according to this embodiment includes a sudden braking detection unit 2, an inter-vehicle distance detection unit 3, a traveling speed detection unit 4, a surrounding environment detection unit 5, and an object. Approach presence / absence detection unit 6, inter-vehicle time detection unit 7, speed feeling detection unit 8, coping distance detection unit 9, accident risk detection unit 10, statistical data D1, statistical data D2, map data D3 , Learning data D4 and accident risk statistical data D5. The configuration shown in FIG. 1 may be configured as hardware or as a computer program (operation index output program). When configured as a computer program, the program may be stored in a storage medium such as a hard disk or a USB memory.

The sudden braking detection unit 2 detects sudden braking based on a signal indicating the acceleration of the vehicle being driven by the target driver detected by the acceleration sensor 21. The sudden braking is detected as sudden braking when, for example, the detected acceleration is equal to or higher than a predetermined threshold value. The detected sudden braking is output for aggregation in the statistical data D1.

The inter-vehicle distance detection unit 3 acquires data indicating the distance to an object in front of the vehicle driven by, for example, the target driver, which is positioned by the object positioning sensor 22, and uses the distance as the inter-vehicle distance. To detect.

The traveling speed detection unit 4 acquires the traveling speed of the vehicle driven by the target driver detected by the speed sensor 23.

The surrounding environment detection unit 5 is a point where the vehicle driven by the driver is located based on the image data around the vehicle driven by the driver acquired by the image acquisition sensor 24 and the map data D3. Detect the surrounding environment of. Examples of the surrounding environment include the time zone, weather, the environment related to the road on which the vehicle is traveling (road type, number of lanes, road width, whether it is a curve, etc.), and the surrounding conditions of the road (urban area or suburbs, etc.). Be done. That is, the surrounding environment detection unit 5 functions as a traveling environment acquisition unit that acquires the traveling environment of the vehicle.

The object approaching presence / absence detection unit 6 detects the presence / absence of the approaching object (front vehicle) based on the inter-vehicle distance detected by the inter-vehicle distance detecting unit 3 and the traveling speed detected by the traveling speed detecting unit 4. In this embodiment, for example, when the traveling speed is 10 km / h or more, it is detected that the object is approaching when it is detected at a distance of less than 200 m ahead. In other words, it detects whether or not the vehicle is traveling in front of the own vehicle.

When the object approach detection unit 6 detects the approach of the object, the inter-vehicle time detection unit 7 determines the inter-vehicle time from the inter-vehicle distance detected by the inter-vehicle distance detection unit 3 and the traveling speed detected by the traveling speed detection unit 4. Is calculated and output for aggregation in the statistical data D2. Further, when detecting the accident risk, the inter-vehicle time detection unit 7 acquires the inter-vehicle time from the statistical data D2 and outputs it to the coping distance detection unit 9. That is, the inter-vehicle time detection unit 7 acquires the inter-vehicle time information acquisition unit that acquires the statistical value of the driver's inter-vehicle time statistically obtained from the inter-vehicle distance when another vehicle is in front and the traveling speed of the own vehicle. Functions as.

When the object approach detection unit 6 does not detect the approach of the object, the speed sense detection unit 8 determines the traveling speed detected by the traveling speed detecting unit 4 and the surrounding environment detected by the surrounding environment detecting unit 5. , Output to training data D4 for machine learning. Further, when detecting the accident risk, the speed sense detection unit 8 can deal with the speed sense obtained by outputting the surrounding environment to the learning data D4, that is, the speed sense output by the learning model. Output to.

Here, the sense of speed is a statistical value of the driving speed of the driver based on the statistically obtained relationship between the driving environment (surrounding environment) and the speed, and is obtained by, for example, the following model formula by multiple regression analysis. be able to. In equation (1), V is a sense of speed, E is the surrounding environment such as time zone and road width, and α is a parameter.

That is, the speed sense detection unit 8 acquires the statistical value of the driving speed of the driver based on the relationship between the speed and the surrounding environment (driving environment) statistically obtained when no other vehicle is present in front of the vehicle. It functions as a running speed information acquisition unit.

The coping distance detection unit 9 calculates the coping distance from the inter-vehicle time output from the inter-vehicle time detection unit 7 and the speed feeling output from the speed feeling detection unit 8 and outputs the coping distance to the accident risk detection unit 10. .. The coping distance is calculated by multiplying the inter-vehicle time and the sense of speed.

The accident risk detection unit 10 detects (determines) the accident risk of the target driver based on the coping distance calculated by the coping distance detection unit 9 and the statistical data acquired from the accident risk statistical data D5. Further, when the sudden braking detection unit 2 detects sudden braking more than a predetermined number of times, the accident risk detection unit 10 determines the accident of the target driver based on the sudden braking detection result and the statistical data D1. Detect (determine) risk. Further, the accident risk detection unit 10 outputs the coping distance calculated by the coping distance detection unit 9 to be aggregated in the accident risk statistical data D5.

That is, the coping distance detection unit 9 and the accident risk detection unit 10 function as output units that output the driving index of the driver based on the statistical value of the traveling speed and the statistical value of the inter-vehicle time.

The statistical data D1 aggregates the sudden braking detected by the sudden braking detection unit 2. The statistical data D2 aggregates the inter-vehicle time detected by the inter-vehicle time detection unit 7. That is, the statistical data D2 is a statistical value of the driver's inter-vehicle time statistically obtained from the inter-vehicle distance when another vehicle is in front and the traveling speed of the own vehicle. The map data D3 may be the same data as that mounted on a navigation device, an autonomous vehicle, or the like, and has information such as nodes, links, the number of lanes, and road width.

The learning data D4 is a learning model in which the speed output from the speed sense detection unit 8 and the surrounding environment are used as teacher data, and learning is performed using the teacher data. That is, the learning data D4 is a statistical value of the driving speed of the driver based on the relationship between the traveling environment and the speed statistically obtained when no other vehicle is present in front of the vehicle. In the accident risk statistical data D5, a threshold value for detecting the accident risk is calculated and set by the accident risk detection unit 10. Further, the statistical data D1, the statistical data D2, the map data D3, the learning data D4, and the accident risk statistical data D5 may be provided in an external server or the like, or may be possessed by the operation index output device 1 itself.

Further, the statistical data D1, the statistical data D2, and the learning data D4 are aggregated and learned for each driver. The threshold value of the accident risk statistical data D5 is calculated based on the collected data of all drivers. Regarding the statistical data D1, the statistical data D2, the map data D3, the learning data D4, and the accident risk statistical data D5, the data storage location is not limited to the device, and some or all of the data is stored on the server or the like. May be good. Further, the statistical data D1, the statistical data D2, and the learning data D4 may be statistically / learned processed for each individual driver, or may be modeled by a driver having a similar driving tendency and statistically / learned processed. ..

The acceleration sensor 21 is a well-known sensor that detects the acceleration of an object (vehicle in this embodiment) in which the sensor is installed, and the type is not limited such as a semiconductor method and an optical method. The object positioning sensor 22 is a sensor that measures the distance to an object (an object in front of the vehicle in this embodiment), and a distance measuring sensor such as a lidar (LiDAR: Light Detection and Ranging) can be used. The speed sensor 23 is a well-known sensor that detects the moving speed of an object (vehicle in this embodiment) in which the sensor is installed, and a vehicle speed pulse or the like provided in the vehicle may be used. The image acquisition sensor 24 is a sensor capable of capturing an image of the surroundings of the vehicle, and an in-vehicle camera can be used.

Next, the operation of the operation index output device 1 having the above-described configuration will be described with reference to FIGS. 2 to 4. FIG. 2 is a flowchart of data collection and machine learning operation related to statistical data D2 and learning data D4. When executing this flowchart, the driving index output device 1 may be mounted on the vehicle, or the detection results of each sensor may be acquired from the vehicle while being installed outside the vehicle via a network or the like. You may try to do it.

In FIG. 2, first, the traveling speed detection unit 4 determines whether or not the vehicle driven by the target driver is traveling at a speed exceeding the slowing speed (step S11), and traveling at a speed equal to or lower than the slowing speed. If this is the case, this step is repeated until the vehicle travels at a speed exceeding the slow speed (step S11: NO). Here, the driving speed is a speed at which the vehicle can stop immediately, and for example, it may be set to 10 km / h or the like.

When the vehicle driven by the target driver is traveling at a speed exceeding the slow-moving speed (step S11: YES), the risk detection flow described later is executed (ON) (step S12) and the target is approached. The presence / absence detection unit 6 determines the presence / absence of a front obstacle based on the distance to the front obstacle detected by the inter-vehicle distance detection unit 3 (step S13). The front obstacle is determined to be present when the object approaching presence / absence detection unit 6 detects the object within a predetermined fixed distance.

When it is determined that there is an obstacle ahead (step S13: Yes), the inter-vehicle time detection unit 7 collects the inter-vehicle time (step S14). This collection is done for each driver. The collected inter-vehicle time is aggregated and analyzed in the statistical data D2. Specifically, for example, the average value of the aggregated inter-vehicle time is calculated and used for calculating the coping distance in the object approaching presence / absence detection unit 6.

On the other hand, when it is determined that there is no front obstacle (step S13: none), the speed sensation detection unit 8 collects statistical data which is the basis of the speed sensation (step S15). This collection is done for each driver. In this step, as described above, the teacher data of the learning data D4 is collected for the traveling speed and the surrounding environment acquired by the speed sense detection unit 8.

Then, it is determined whether or not the running is finished (step S16), and if the running is finished, the flowchart is finished, and if the running is not finished, the process returns to step S11. Whether or not the running is completed may be determined based on, for example, the running speed or the ignition switch being turned off.

In this way, the operation index output device 1 can collect the data in the statistical data D2 and the learning data D4 and perform machine learning.

Next, the risk detection operation (driving index output method) in this embodiment will be described with reference to the flowchart of FIG. The flowchart shown in FIG. 3 is an operation in the case of analysis after the actual running is completed. Therefore, during traveling, the results detected by the acceleration sensor 21, the object coronation sensor 22, the speed sensor 23, and the image acquisition sensor 24 are recorded as a log. Then, the operation index output device 1 reads the recorded log and executes the following steps. Therefore, when executing this flowchart, the driving index output device 1 does not have to be mounted on the vehicle.

First, the inter-vehicle time detection unit 7 acquires the inter-vehicle time data from the driver's statistical data D2, which is the target of the accident risk determination (step S21), and the speed sense detection unit 8 is the driving target of the accident risk determination. The data of the sense of speed is acquired from the learning data D4 of the person (step S22). The statistical data of the sense of speed indicates the sense of speed V calculated by the equation (1). The order of step S21 and step S22 may be reversed.

After acquiring the inter-vehicle time and the sense of speed, the accident risk detection unit 10 determines whether or not there is a risk (step S23). In this step, first, the coping distance detection unit 9 calculates the coping distance based on the acquired inter-vehicle time and the sense of speed, and the calculated coping distance and the threshold value (first) acquired from the accident risk statistical data D5. The presence or absence of risk is determined based on the reference value).

The method for determining the presence or absence of risk will be described with reference to FIGS. 4 to 6. 4 to 6 are the results of a virtual driving experiment conducted by the present inventors using a driving simulator.

First, the conditions of the experiment and the like will be described. For the experiment, a virtual experiment environment was created using a cockpit that imitated a vehicle and software for a driving simulator. A road environment of 0.6 km in length and 1.3 km in width was created and used in the virtual space. In order to eliminate the influence of the visual saliency of each building, the building object was generated with reference to the map data so that only the sense of size is actually close. A plurality of sedan-type vehicles were placed on the road, centered on the driving vehicle, and set to be rearranged when the vehicle deviates from a certain range.

We also changed the date and time and the weather to assess the impact of the line-of-sight differences. The date and time was set from 8 o'clock on a winter day, and the time was set to advance in 15 minutes for two days. The weather randomly changed the intensity of rain, precipitation, and visibility (line of sight). In addition, in order to suddenly cause the risk of crossing immediately before a pedestrian imitating an elderly person (walking speed 4.6 km / h), multiple triggers are installed on the road, and there is a 70% chance that the pedestrian will pass. Appears 150m ahead, and when it becomes 50m or less for pedestrians in the same lane and 100m or less for pedestrians in the oncoming lane, there is a 70% chance that it will pop out after 0.6 seconds. It was adjusted. The subjects were 22 males in their 20s to 50s. In addition, the experiment was carried out for a total of 25 minutes, including a break-in run of 5 minutes, a break of 5 minutes, and a main run of 15 minutes.

FIG. 4 shows the results of an experiment conducted under such conditions. In FIG. 4, the vertical axis represents the number of sudden decelerations (0.3 G or more), and the horizontal axis represents the number of contacts (regardless of self-responsibility or other responsibility). The results of FIG. 4 can be divided into two groups, a group (a) with 7 times or more and a group (b) with less than 7 times with respect to the number of sudden decelerations. It turns out that there is a strong correlation for the relationship. It was also found that there was a weak correlation with the number of collisions for group (b).

Then, in group (a), as shown in FIG. 5, it was found that the number of sudden decelerations had a linear relationship with the number of contacts. Further, it was found that when the number of sudden decelerations is less than a certain level in group (b), it is possible to divide the relationship into the relationship shown by logistic regression as shown in FIG.

According to FIG. 6, under the conditions of the above-mentioned experiment, when the coping distance is about 45 m or less, the presence or absence of contact becomes “1”, and it is clear that the risk of accident increases. Therefore, it was found that by calculating the coping distance, it is possible to determine the accident risk even when there is little sudden braking.

Therefore, in this embodiment, the threshold value of the coping distance for determining the presence / absence of accident risk detection when the number of sudden braking detections is small is stored as accident risk statistical data D5 for each surrounding environment or point. The accident risk detection unit 10 reads the threshold value of the coping distance from the accident risk statistical data D5 and determines the accident risk. That is, if the calculated coping distance is less than the threshold value, it is determined that there is an accident risk, and if it is greater than or equal to the threshold value, it is determined that there is no accident risk.

Returning to the description of FIG. If it is determined that there is no risk as a result of determining the presence or absence of risk in step S23 (step S23: none), the accident risk detection unit 10 outputs information indicating that the driving was safe and is not shown. Recording is performed on a recording device or the like (step S24). On the other hand, if it is determined that there is a risk as a result of determining the presence or absence of the risk in step S23 (step S23: yes), the accident risk detection unit 10 outputs information indicating that the operation has a high risk. , Recording on a recording device (not shown) or the like (step S25).

In the flowchart of FIG. 3, the detection of sudden braking is not considered, but it may be executed when the number of detections of sudden braking is small. When the number of sudden braking detections is small, for example, a threshold value (second reference value) may be set for the number of sudden braking detections, and the flowchart may be executed when the number is equal to or less than the threshold value. The threshold value is not limited to 7 times in the above-mentioned experiment and may be changed as appropriate.

Further, in this embodiment, the presence or absence of accident risk is output as a driving index, but the coping distance may be output as a driving index. In this case, the presence or absence of accident risk may be determined by another device or the like.

Further, in the flowchart shown in FIG. 3, the accident risk is determined for one driving, but the coping distance obtained from the log of traveling multiple times on the same route is compared and deteriorated or improved. May be output as an operation index.

According to this embodiment, in the driving index output device 1, the surrounding environment detection unit 5 acquires the driving environment of the vehicle, and the speed sense detection unit 8 acquires the sense of speed when there is no other vehicle in front. The inter-vehicle time detection unit 7 acquires the inter-vehicle time of the driver statistically obtained from the inter-vehicle distance when another vehicle is in front and the traveling speed of the own vehicle. Then, the coping distance detection unit 9 calculates the coping distance by multiplying the sense of speed and the inter-vehicle time as a driving index. By doing so, it is possible to output the coping distance calculated based on machine learning such as the acquired sense of speed and the inter-vehicle time and statistical values as a driving index. In addition, it is not necessary to determine the psychological state of the driver when outputting the driving index.

Further, the accident risk detection unit 10 determines and outputs the driver's accident risk as a driving index based on the manageable distance. By doing so, it is possible to output as specific information such as accident risk.

Further, the accident risk detection unit 10 determines that there is an accident risk when the coping distance is equal to or less than a predetermined threshold value. By doing so, it is possible to determine the presence or absence of an accident risk according to the manageable distance.

In addition, the threshold value of the coping distance for determining the accident risk changes depending on the surrounding environment and the location. By doing so, it is possible to determine the accident risk with an appropriate threshold value for the traveling point.

Further, the driving index output device 1 includes a sudden braking detection unit 2 that detects sudden braking of the vehicle, and the accident risk detection unit 10 indicates that the number of sudden braking detected by the sudden braking detection unit 2 is equal to or less than a predetermined threshold value. In addition, the driver's accident risk is output based on the sense of speed and the inter-vehicle time. By doing so, it is possible to output a driving index such as an accident risk when the sudden braking is small, and it is possible to reliably determine the potential accident risk when the sudden braking is small.

The operation index output device according to the second embodiment of this embodiment will be described with reference to FIG. 7. The same parts as those in the first embodiment described above are designated by the same reference numerals, and the description thereof will be omitted.

In this embodiment, the configuration shown in FIG. 1 is the same. In this embodiment, the risk detection operation is different. In this embodiment, the presence or absence of risk is determined using the inter-vehicle time and running speed acquired (detected) in real time. FIG. 7 shows a flowchart of the risk detection operation according to this embodiment.

First, in step S31, the object approaching presence / absence detection unit 6 determines the presence / absence of a front obstacle based on the distance to the front obstacle (for example, the vehicle in front) detected by the inter-vehicle distance detection unit 3 (step S31). .. This step is basically the same as step S13 described above.

When it is determined that there is an obstacle ahead (step S31: Yes), the inter-vehicle time detection unit 7 determines the inter-vehicle distance detected by the inter-vehicle distance detection unit 3 and the traveling speed detected by the traveling speed detection unit 4. Based on this, the inter-vehicle time is acquired (step S32). Then, the speed feeling detection unit 8 acquires the speed feeling from the learning data D4 according to the surrounding environment (step S33).

Then, the coping distance detection unit 9 calculates the coping distance from the inter-vehicle time acquired in step S32 and the sense of speed acquired in step S33, and obtains from the calculated coping distance and the accident risk statistical data D5. The presence or absence of risk is determined based on the set threshold value (step S36). That is, the presence or absence of risk is determined based on the inter-vehicle time acquired in real time.

If it is determined that there is no risk as a result of determining the presence or absence of risk in step S36 (step S36: none), the flowchart ends. Alternatively, the process may return to step S31. On the other hand, if it is determined that there is a risk as a result of determining the presence or absence of the risk in step S36 (step S36: yes), the accident risk detection unit 10 notifies the driver of guidance such as caution / warning (step S37). ). Specifically, the accident risk detection unit 10 generates and outputs information for notifying the driver of guidance such as caution / warning and break. Examples of the output destination include a display means for notifying guidance such as caution / warning and a break, a voice output means, and the like.

On the other hand, when it is determined that there is no front obstacle (step S31: none), the speed feeling detection unit 8 acquires the running speed detected by the running speed detection unit 4 (step S34). Then, the inter-vehicle time detection unit 7 acquires the inter-vehicle time from the statistical data D2 (step S35). Then, the coping distance detection unit 9 calculates the coping distance from the traveling speed acquired in step S34 and the inter-vehicle time acquired in step S35, and obtains from the calculated coping distance and the accident risk statistical data D5. The presence or absence of risk is determined based on the set threshold value (step S36). That is, the presence or absence of risk is determined based on the traveling speed acquired in real time.

According to this embodiment, the presence or absence of an accident risk is determined based on the inter-vehicle time and the traveling speed acquired in real time, so that if the vehicle changes to a state in which the coping distance becomes shorter during driving, it is promptly performed. It is possible to avoid the occurrence of an accident by giving a warning to.

In the second embodiment, the accident risk is determined based on the inter-vehicle time and the traveling speed acquired in real time, but the driving index output device 1 does not necessarily have to be mounted on the vehicle. The driving index output device 1 may be outside the vehicle. For example, the driving index output device 1 may be configured to acquire information detected by each sensor of the vehicle in real time, determine the accident risk in real time, and transmit the information to the vehicle.

Further, in the second embodiment, the accident risk is determined in real time, and the log of the detection result of each sensor is saved so that the accident risk determination after running as in the first embodiment is also performed. It becomes possible.

The operation index output device according to the third embodiment of this embodiment will be described with reference to FIGS. 8 to 10. The same parts as those in the first embodiment described above are designated by the same reference numerals, and the description thereof will be omitted.

In this embodiment, the driver's psychological state is determined from the statistical value of the traveling speed (learning data D4) and the statistical value of the inter-vehicle time (statistical data D2).

The functional configuration of this embodiment is shown in FIG. The configuration shown in FIG. 8 is added to the configuration shown in FIG. 1 in that the inter-vehicle time detection unit 7 and the speed feeling detection unit 8 are directly input to the accident risk detection unit 10, respectively. ing. That is, the inter-vehicle time detection unit 7 inputs the inter-vehicle time data (inter-vehicle time statistical value), and the speed sensation detection unit 8 inputs the speed sensation data (inter-vehicle time statistical value). Then, the accident risk detection unit 10 determines the psychological state of the driver based on these data, and warns of the risk according to the determination result.

The method of determining the psychological state of the driver according to this embodiment will be described with reference to FIGS. 9 and 10. FIG. 9 is a graph obtained by analyzing the data obtained as a result of the experiment described in the first embodiment. In the graph of FIG. 9, the vertical axis is the statistical value of the traveling speed, that is, the reciprocal of the sense of speed, and the horizontal axis is the statistical value of the inter-vehicle time. Further, in the graph of FIG. 9, the points plotted with x marks are the subjects without contact, and the points plotted with ◯ marks are the subjects with contact. Then, what is surrounded by □ is a subject who has contact and the number of sudden braking is less than the threshold value (7 times).

According to FIG. 9, the area surrounded by the line L1 and the line L2 is considered to be a psychological state (calm state) in which many drivers have no contact and can safely drive as the driver's psychology. Further, in a region other than the region surrounded by the line L1 and the line L2, when the value of the reciprocal of the sense of speed is small and the value of the inter-vehicle distance is small, the vehicle tends to travel fast and the inter-vehicle distance is short. It is considered to be in a hurry. Further, when the value of the reciprocal of the sense of speed is not small and the value of the inter-vehicle distance is small, the inter-vehicle distance is short although there is no tendency to travel fast, and it is considered that the psychological state is a decrease in concentration. Further, when the value of the reciprocal of the sense of speed is not small and the value of the inter-vehicle distance is not small, there is no tendency to travel fast and the inter-vehicle distance is not short, so it is considered that the psychological state is uneasy.

Then, in FIG. 9, it was found that there were many subjects who were in contact with each other in the region where the concentration was reduced and the number of sudden braking was less than the threshold value. That is, it was found that it is possible to detect the psychological state of decreased concentration when the number of sudden braking is small. For example, the accident risk detection unit 10 stores the graph shown in FIG. 9 in the accident risk statistical data D5, and acquires the data acquired from the inter-vehicle time detection unit 7 and the speed feeling detection unit 8 from the accident risk statistical data D5. Based on the data corresponding to the graph, the psychological state and the change in the psychological state are judged by the above-mentioned method, and the information indicating the judgment result is output.

In this embodiment, for example, as shown in FIG. 10, when the area showing safety at the start of driving shifts to the area showing a decrease in concentration due to a change in the inter-vehicle time, for example, the concentration is reduced. It can be determined that the vehicle has decreased and the driver can be warned. FIG. 10 shows an example in which what was in the ST position at the start of operation has moved to the SP position due to continuous operation. In this case, it means that the user has moved from the area indicating safety to the area indicating a decrease in concentration, and information indicating a change in psychological state can be output. Examples of the output destination include a display means for giving caution / warning and a voice output means as in the second embodiment.

According to this embodiment, the accident risk detection unit 10 outputs information related to the driver's psychological state as a driving index, so that it detects a change in the driver's psychological state and warns the driver. Can be done. In addition, since the psychological state can be determined, it is possible to give an appropriate warning to the driver by giving a notification or the like according to the psychological state.

Further, in the above-mentioned three embodiments, the accident risk may be determined not only after the end of traveling or during traveling, but also only in a specific section (expressway only, etc.).

Further, the present invention is not limited to the above examples. That is, those skilled in the art can carry out various modifications according to conventionally known knowledge within a range that does not deviate from the gist of the present invention. Even with such a modification, as long as the operation index output device of the present invention is provided, it is, of course, included in the category of the present invention.

1 Driving index output device 2 Sudden braking detection unit 3 Inter-vehicle distance detection unit 4 Driving speed detection unit 5 Surrounding environment detection unit (driving environment acquisition unit)
6 Object approach presence / absence detection unit 7 Inter-vehicle time detection unit (inter-vehicle time information acquisition unit)
8 Speed sense detection unit (running speed information acquisition unit)
9 Dealable distance detection unit (output unit)
10 Accident risk detection unit (output unit)
D1 Statistical data D2 Statistical data D3 Map data D4 Learning data D5 Accident risk statistical data

Claims (9)

The driving environment acquisition department that acquires the driving environment of the vehicle,
A traveling speed information acquisition unit that acquires a statistical value of the driver's traveling speed based on the relationship between the traveling environment and the speed statistically obtained when no other vehicle is present in front of the vehicle.
An inter-vehicle time information acquisition unit that acquires the statistical value of the inter-vehicle time of the driver obtained statistically from the inter-vehicle distance when another vehicle is in front and the traveling speed of the own vehicle.
An output unit that outputs a driving index of the driver based on the statistical value of the traveling speed and the statistical value of the inter-vehicle time.
An operation index output device characterized by being provided with. The driving index output device according to claim 1, wherein the output unit outputs a coping distance calculated by multiplying the statistical value of the traveling speed and the statistical value of the inter-vehicle time as the driving index. .. The driving index output device according to claim 2, wherein the output unit determines and outputs the accident risk of the driver as the driving index based on the coping distance. The driving index output device according to claim 3, wherein the output unit determines that there is an accident risk when the coping distance is equal to or less than the first reference value. The driving index output device according to claim 1, wherein the output unit outputs information related to the psychological state of the driver as the driving index. Further provided with a sudden braking detection unit for detecting sudden braking of the vehicle,
When the number of sudden braking detected by the sudden braking detection is equal to or less than the second reference value, the output unit obtains the driving index of the driver based on the statistical value of the sense of speed and the statistical value of the inter-vehicle time. The operation index output device according to any one of claims 1 to 5, wherein the operation index output device is characterized by outputting. It is a driving index output method executed by a driving index output device that outputs a driver's driving index.
The driving environment acquisition process to acquire the driving environment of the vehicle and
A traveling speed information acquisition process for acquiring a statistical value of a driver's traveling speed based on the relationship between the traveling environment and the speed statistically obtained when no other vehicle is present in front of the vehicle.
The inter-vehicle time information acquisition process for acquiring the statistical value of the inter-vehicle time of the driver obtained statistically from the inter-vehicle distance when another vehicle is in front and the traveling speed of the own vehicle, and
An output process that outputs a driving index of the driver based on the statistical value of the traveling speed and the statistical value of the inter-vehicle time, and
A driving index output method characterized by being provided with. An operation index output program according to claim 7, wherein the operation index output method is executed by a computer. A computer-readable storage medium comprising storing the operation index output program according to claim 8.

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