JP2009001096A - Operation support device, operation controller, and program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To perform proper operation support under the consideration of the characteristics of the accelerating operation of a driver. <P>SOLUTION: A featured value calculation part 22 calculates featured values showing acceleration property of a driver to be decided on the basis of a time required from the start of acceleration when a vehicle travels at a fixed acceleration so that a target speed can be obtained at the point of time of the end of acceleration when the driver of the vehicle performs an accelerating operation and the time constants of the vehicle speed when the driver of the vehicle performs the accelerating operation on the basis of the time-sequential data of the vehicle speed when the vehicle travels. Then, operation support is performed on the basis of the featured values calculated by the calculation means. Thus, it is possible to achieve proper operation support under the consideration of the property of the accelerating operation of the driver by calculating the featured values showing the acceleration property to perform the operation support. <P>COPYRIGHT: (C)2009,JPO&INPIT

Description

  The present invention relates to a driving support device, a driving control device, and a program, and more particularly, to a driving support device, a driving control device, and a program that perform driving support based on characteristics of acceleration operation of a driver.

2. Description of the Related Art Conventionally, an apparatus that learns a driver's travel and calculates a target acceleration / deceleration is known. For example, an automatic vehicle speed control device that learns a driving mode from a driving characteristic of a driver and automatically selects an acceleration / deceleration mode is known (Patent Document 1).
JP2003-211996

  However, conventional driving support devices and driving control devices use constant acceleration, maximum acceleration, and maximum jerk as evaluation indexes for acceleration operation characteristics, and the acceleration and jerk time series from the start to the end of acceleration Since the change is not taken into consideration, there is a problem that driving support cannot be performed so as to optimally change the acceleration from the start to the end of acceleration.

  In addition, acceleration and jerk generally have large fluctuations, and there is a problem that if a value at a certain time such as maximum acceleration is used as an evaluation index, it becomes vulnerable to noise.

  The present invention has been made to solve the above-described problems, and provides a driving support device, a driving control device, and a program that can perform appropriate driving support in consideration of the characteristics of the acceleration operation of the driver. The purpose is to provide.

  In order to achieve the above object, a driving support device according to a first aspect of the present invention is a storage means for storing time-series data of vehicle speed when a vehicle travels, and a vehicle driver performs an acceleration operation based on the time-series data. And a driving support means for performing driving support based on the feature amount calculated by the calculating means.

  According to a second aspect of the present invention, there is provided a program for a vehicle speed when a driver of a vehicle performs an acceleration operation based on the time-series data from a storage means that stores time-series data of vehicle speed when the vehicle is traveling. A program for functioning as a driving support unit that performs driving support based on a calculation unit that calculates a feature amount of a series change and the feature amount calculated by the calculation unit.

  According to the first and second inventions, the calculating means calculates the feature quantity of the time-series change of the vehicle speed when the driver of the vehicle performs the acceleration operation based on the time-series data of the vehicle speed when the vehicle travels. The driving support is performed by the driving support means based on the feature amount calculated by the calculating means.

  Thus, by calculating the feature amount of the time-series change of the vehicle speed when performing the acceleration operation and performing the driving support, it is possible to perform the appropriate driving support considering the characteristics of the acceleration operation of the driver.

  The feature amount according to the first aspect of the invention includes the vehicle speed at each of the acceleration start point and the acceleration end point when the vehicle driver performs an acceleration operation, the position of each of the acceleration start point and the acceleration end point, and acceleration. The feature amount can be determined based on the time constant of the vehicle speed when the operation is performed. As a result, it is possible to calculate a feature value considering the characteristics of the driver's acceleration operation using the time constant of the vehicle speed when the acceleration operation is performed.

  The feature amount according to the first aspect of the invention is the time required from the start of acceleration when traveling at a constant acceleration so that the target vehicle speed is obtained at the acceleration end point when the driver of the vehicle performs the acceleration operation, The feature amount can be determined based on the time constant of the vehicle speed when the acceleration operation is performed. As a result, it is possible to calculate a feature amount that takes into account the characteristics of the driver's acceleration operation using the time required for acceleration when traveling at a constant acceleration and the time constant of the vehicle speed when the acceleration operation is performed.

  The driving support means according to the first invention can provide driving assistance by presenting the feature amount to the driver. Thus, the driver can use the presented feature amount for the acceleration operation.

  The driving support device according to the first aspect of the present invention further includes detection means for continuously detecting the vehicle speed in time series, and vehicle speed prediction means for predicting a target vehicle speed of the acceleration operation by the driver. When a person performs an acceleration operation, a time-series change in vehicle speed is predicted based on the feature amount calculated by the calculation unit, the vehicle speed detected by the detection unit, and the target vehicle speed predicted by the prediction unit. Based on the time-series change in the vehicle speed and the time-series change in the vehicle speed detected by the detection means, it is possible to present warning information or perform vehicle speed control as driving assistance. As a result, warning information is presented or vehicle speed control is performed using time-series changes in the vehicle speed predicted based on the feature amount, the vehicle speed, and the target vehicle speed. Can provide support.

  The driving support apparatus according to the first aspect of the present invention further includes detection means for detecting the vehicle speed and vehicle speed prediction means for predicting a target vehicle speed of the acceleration operation by the driver, the driving support means performing the acceleration operation by the driver. Sometimes, based on the feature amount calculated by the calculating means, the vehicle speed detected by the detecting means, and the target vehicle speed predicted by the predicting means, a time-series change in the vehicle speed when the driver performs an acceleration operation is calculated. The vehicle speed can be controlled based on the time-series change of the calculated vehicle speed. Accordingly, since the vehicle speed is controlled using the time-series change of the vehicle speed calculated based on the feature amount, the vehicle speed, and the target vehicle speed, it is possible to perform the driving control considering the characteristics of the acceleration operation of the driver.

  Said vehicle speed prediction means can acquire the speed limit or average vehicle speed of the traveling path where the vehicle is traveling, and can predict the acquired speed limit or average vehicle speed as the target vehicle speed.

  The driving support apparatus further includes acquisition means for acquiring information indicating whether the vehicle is traveling straight ahead, right turn, or left turn, and the calculation means includes straight travel, right turn, and left turn. The feature amount is calculated for each acceleration operation, and the driving support means is a feature according to information indicating whether the traveling path on which the vehicle traveled acquired by the acquiring means is straight, right turn, or left turn Driving assistance can be performed using the quantity. As a result, it is possible to perform appropriate driving support in consideration of the characteristics of the driver's acceleration operation according to straight travel, right turn, and left turn.

  According to a third aspect of the present invention, there is provided a driving support apparatus based on a predetermined relationship between a saturated traffic flow rate and a feature amount of a time-series change in vehicle speed when the driver of the vehicle performs an acceleration operation. Determining means for determining the feature quantity corresponding to the flow rate; detecting means for continuously detecting the speed of the driver's vehicle in time series; and vehicle speed predicting means for predicting a target vehicle speed of the acceleration operation by the driver; When the driver performs an accelerating operation, the driving is performed based on the feature amount calculated by the determining unit, the vehicle speed detected by the detecting unit, and the target vehicle speed predicted by the vehicle speed predicting unit. Driving control means for calculating a time-series change in the vehicle speed when the person performs the acceleration operation and controlling the vehicle speed based on the calculated time-series change.

  According to a fourth aspect of the present invention, there is provided a program for causing a computer to perform a desired saturation based on a predetermined relationship between a saturated traffic flow rate and a feature amount of a time-series change in vehicle speed when the vehicle driver performs an acceleration operation. Determining means for determining the characteristic amount corresponding to the traffic flow rate, detecting means for continuously detecting the speed of the driver's vehicle in time series, vehicle speed predicting means for predicting a target vehicle speed of the acceleration operation by the driver, and When the driver performs an acceleration operation, based on the feature amount calculated by the determining unit, the vehicle speed detected by the detecting unit, and the target vehicle speed predicted by the vehicle speed predicting unit, the driver Calculates a time-series change in the vehicle speed when the acceleration operation is performed, and functions as an operation control unit that controls the vehicle speed based on the calculated time-series change A.

  According to the third and fourth inventions, the determining means is based on a predetermined relationship between the saturated traffic flow rate and the feature quantity of the time-series change in the vehicle speed when the driver of the vehicle performs the acceleration operation. Thus, a feature amount corresponding to a desired saturated traffic flow rate is determined. Further, the speed of the driver's vehicle is continuously detected in time series by the detection means, and the target vehicle speed of the acceleration operation by the driver is predicted by the vehicle speed prediction means.

  Then, when the driver performs an accelerating operation by the driving control means, the driver is based on the feature amount calculated by the determining means, the vehicle speed detected by the detecting means, and the target vehicle speed predicted by the vehicle speed predicting means. Calculates a time-series change in the vehicle speed when performing an acceleration operation, and controls the vehicle speed based on the calculated time-series change.

  In this way, the vehicle speed is controlled using the time-series change of the vehicle speed calculated based on the feature amount, the vehicle speed, and the target vehicle speed of the vehicle speed corresponding to the desired saturated traffic flow rate. Saturated traffic flow rate can be realized.

  As described above, according to the driving support device and the program of the present invention, by calculating the feature amount of the time-series change of the vehicle speed when performing the acceleration operation and performing the driving support, the acceleration operation of the driver is performed. The effect that appropriate driving support in consideration of characteristics can be performed is obtained.

  According to the operation control apparatus and program of the present invention, using the time-series change of the vehicle speed calculated based on the feature amount of the vehicle speed change corresponding to the desired saturated traffic flow rate, the vehicle speed, and the target vehicle speed, Since the vehicle speed is controlled, it is possible to achieve a desired saturated traffic flow rate.

  Hereinafter, preferred embodiments of the present invention will be described with reference to the drawings. In the present embodiment, a case where the present invention is applied to a driving support device mounted on a vehicle will be described.

  As shown in FIG. 1, the driving support device 10 according to the first embodiment includes a speed sensor 12 that detects a vehicle speed, an acceleration sensor 13 that detects the acceleration of the vehicle, a GPS, and the like. An acceleration characteristic learning processing routine that learns acceleration characteristics based on a travel position measurement device 14 that measures a position, a vehicle speed from the speed sensor 12, an acceleration from the acceleration sensor 13, and a travel position from the travel position measurement device 14; The computer 16 which stored the program for implement | achieving and the display apparatus 18 for displaying the learning result in the computer 16 are provided.

  The computer 16 includes a CPU, a ROM that stores a program for an acceleration characteristic learning process routine that will be described later, a RAM that stores data, and a bus that connects these. When the computer 16 is described with function blocks divided for each function realizing means determined based on hardware and software, the vehicle speed output from the speed sensor 12 and the acceleration sensor 13 are output as shown in FIG. A driving data recording unit 20 that continuously records the acceleration and the driving position output from the driving position measuring device 14 in time series, and an acceleration operation by the driver based on the driving data recorded in the driving data recording unit 20 A feature amount calculation unit 22 that calculates an acceleration characteristic that is an acceleration characteristic that is a time-series change characteristic of the vehicle speed, and a display control unit 24 that causes the display device 18 to display the feature amount calculated by the feature amount calculation unit 22; And a feature amount storage unit 26 for storing the calculated feature amount.

  The vehicle speed recorded continuously in time series by the travel data recording unit 20 changes during the acceleration operation period, for example, as shown in FIG. In FIG. 2, the time-series changes in the vehicle speed from the state where the vehicle is stopped until the acceleration is finished, which are detected for each of the plurality of acceleration operations, are displayed in an overlapping manner. Both the vertical and horizontal axes are normalized. As shown in FIG. 2, there is a difference in the method of acceleration, and there is a case where acceleration is accelerated at the start of acceleration, and then the vehicle is accelerated so as to converge slowly to the target vehicle speed, and in a case where acceleration is performed at a substantially constant tone from start to finish. I understand that there is.

  Many conventional driving support systems detect abnormal behavior or perform vehicle control mainly using a constant acceleration as an index. However, as shown in FIG. 2, the acceleration of the vehicle is not constant and changes from the start of acceleration to the end of acceleration. Furthermore, the method of changing the acceleration is not uniform and there are various patterns. Therefore, in the present invention, the characteristic of the vehicle speed change from the start to the end of the acceleration is represented by a numerical index, and optimal driving support is provided for each driver based on the numerical index representing the characteristic of the vehicle speed change. .

Next, a description will be given of a method for calculating the feature amount representing the acceleration characteristics of the driver in the feature amount calculation unit 22. First, the time constant T of the vehicle speed during the acceleration operation period is calculated based on the acceleration behavior model that models the acceleration behavior of the vehicle by the driver's acceleration operation as shown in FIG. The acceleration behavior model of FIG 3, the difference between the target vehicle speed v _opt and the vehicle speed v of the acceleration by multiplying the reciprocal of the time constant T, the acceleration a of the vehicle is obtained by integrating the acceleration a, the acceleration It is shown that the obtained vehicle speed v can be obtained. Then, the acceleration a of the vehicle is obtained by multiplying the difference between the target vehicle speed v _{opt for} acceleration and the obtained vehicle speed v by the reciprocal of the time constant T again.

  Here, T is determined so that the vehicle speed v and acceleration a calculated from the acceleration behavior model match the actual vehicle speed obtained by the speed sensor 12 and the actual acceleration obtained by the acceleration sensor 13 with the highest accuracy. It is a parameter.

The time constant T is determined as follows. First, the acceleration start time is t _s , the acceleration end time is t _e , the actual vehicle position at time t is d (t), and the predicted position of the vehicle calculated from the acceleration behavior model is d _sim (t, T). At this time, the deviation J between the acceleration traveling model and the actual vehicle position in the entire acceleration period is calculated as a function of T by the following equation (1).

  Then, the optimum time constant T is determined by searching for the value of T that minimizes J (T) in equation (1).

Further, when accelerating at a constant acceleration, the time required from the start of acceleration to the end of acceleration is defined as t _cst , and the constant acceleration time t _cst is calculated by the following equation (2).

However, v _opt represents the target vehicle speed for acceleration, d _opt represents the target position, v ₀ represents the vehicle speed at the acceleration start point, and d ₀ represents the position at the acceleration start point.

Then, based on the time constant T calculated based on the acceleration behavior model and the constant acceleration time t _cst calculated based on the above equation (2), the feature quantity K representing the acceleration characteristic is expressed by the following equation (3). calculate.

  However, α and β are constants.

  As described above, when the feature value K representing the driver's acceleration characteristic is defined by the above equations (2) and (3) and the value of K is calculated from the actual travel data, as shown in FIG. The characteristic amount K is an index representing the convergence tendency of the characteristics of the time-series change of the vehicle speed from the start to the end of acceleration, that is, whether to accelerate at a constant acceleration or to accelerate while changing the acceleration with time. That is, when the value of the feature value K is large, the acceleration is first strongly accelerated, and thereafter, the acceleration is gradually reduced to express a sharp acceleration characteristic that converges to a desired vehicle speed, and the feature value K is small. Represents acceleration characteristics without such sharpness. Further, as shown in FIG. 4, the feature value K representing the acceleration characteristic varies depending on the driver, and the feature value K for the same driver varies within a certain range.

The feature amount calculation unit 22 extracts travel data during the acceleration operation period from the travel data recorded in the travel data recording unit 20, and the vehicle speed and acceleration of the extracted travel data during the acceleration operation period are extracted. A time constant T is calculated from the series data using the acceleration behavior model described above. Then, the vehicle speed v _{0 at} the acceleration start point, the position d ₀ at the acceleration start point, the target vehicle speed v _{opt at} the acceleration end point, and the target position d _opt at the acceleration end point are acquired and acquired from the travel data during the acceleration operation period. Based on each value and the calculated time constant T, a feature value K representing acceleration characteristics is calculated by the above equations (2) and (3).

  The period during which the acceleration operation is performed is, for example, a period from when the driver releases his / her foot from the brake pedal to acceleration of the vehicle until the vehicle speed accelerated by depressing the accelerator pedal converges. Say.

  The display control unit 24 displays the feature value K calculated by the feature value calculation unit 22 on the display device 18 such as “Your acceleration break is 3.4”, and operates the feature value K. For driving diagnosis and education.

  Next, the operation of the driving support apparatus 10 according to the first embodiment will be described. When the driver instructs the start of the acceleration characteristic diagnosis process while driving the vehicle or before the start of driving, an acceleration characteristic learning process routine shown in FIG. 5 is executed in the computer 16.

  First, in step 100, the vehicle speed output from the speed sensor 12, the acceleration output from the acceleration sensor 13, and the travel position output from the travel position measuring device 14 are continuously recorded as travel data in time series, and travel Records time-series data for a predetermined period of data.

  In step 101, time series data of the vehicle speed and acceleration during the acceleration operation period is acquired from the travel data recorded in step 100, and a time constant T is calculated based on the acceleration behavior model. In the above-described step 101, when travel data for a plurality of acceleration operation periods is extracted from the travel data, a time constant T is calculated for each acceleration operation period.

  In the next step 102, the vehicle speed at the acceleration start point, the position of the acceleration start point, the target vehicle speed at the acceleration end point, and the target position of the acceleration end point are determined for each acceleration operation period from the travel data recorded in step 100 above. Get each value.

  In the next step 104, feature amounts representing acceleration characteristics are calculated based on the values of the acceleration operation periods acquired in step 102. In step 106, the feature amounts calculated in step 104 are calculated. It is stored in the feature amount storage unit 26.

  In the next step 108, the calculated feature amount is displayed on the display device 18, and the acceleration characteristic learning processing routine is terminated. Note that the feature quantity to be displayed may be an average value of a plurality of calculated feature quantities, or a range of calculated feature quantities may be displayed.

  Then, the driver who is the subject of diagnosis can know the trap of the acceleration operation from the feature amount displayed on the display device 18, and can make the feature amount representing the acceleration characteristic useful for the drive operation.

  As described above, according to the driving support apparatus according to the first embodiment, the feature amount representing the characteristics of the time-series change in the vehicle speed when performing the acceleration operation is calculated, and the calculated feature amount is transmitted to the driver. By providing driving assistance so as to be presented and used for acceleration operation, it is possible to perform appropriate driving assistance tailored to the individual in consideration of the characteristics of the acceleration operation of the driver.

  Also, the acceleration characteristics of the driver are digitized and presented to the driver, which can be used for driving improvement education.

  Next, a driving support apparatus according to a second embodiment will be described. In addition, about the part which becomes the structure similar to 1st Embodiment, the same code | symbol is attached | subjected and description is abbreviate | omitted.

  The second embodiment is different from the first embodiment in that the acceleration is continuously measured in time series, and the feature quantity representing the acceleration characteristic is calculated using the measured acceleration. .

  As travel data, time-series data of acceleration detected by the acceleration sensor 13 changes at the time of an acceleration operation, for example, as shown in FIG.

  The feature amount calculation unit of the driving support apparatus according to the second embodiment acquires a time-series change in acceleration during the acceleration operation from the travel data recorded in the travel data recording unit, By integrating the time series change, it is converted into a time series change of the vehicle speed as shown in FIG. Thus, as in the first embodiment, the values of the vehicle speed at the acceleration start point, the position of the acceleration start point, the target vehicle speed at the acceleration end point, and the target position at the acceleration end point are acquired and (2 ) And (3) are used to calculate a feature value K representing acceleration characteristics.

  When the value of the feature quantity K calculated as described above is large, it represents a sharp acceleration characteristic, and when the value of the feature quantity K is small, it represents an acceleration characteristic without a sharpness. Accordingly, the driver to be diagnosed can know the habit of the acceleration operation based on the displayed feature amount.

  Since other configurations and operations are the same as those in the first embodiment, description thereof will be omitted.

  Next, a driving support apparatus according to a third embodiment will be described. In addition, about the part which becomes the structure similar to 1st Embodiment, the same code | symbol is attached | subjected and description is abbreviate | omitted.

  The third embodiment is mainly different from the first embodiment in that a feature value representing acceleration characteristics is calculated for each folding direction and an abnormality in acceleration operation is detected. .

  As shown in FIG. 7, the driving support device 310 according to the third embodiment includes a speed sensor 12, an acceleration sensor 13, a travel position measuring device 14, a vehicle folding direction, and a travel path speed limit. A traffic condition acquisition unit 312 for acquiring a traffic condition, a program for realizing an acceleration characteristic learning processing routine, a vehicle speed from the speed sensor 12, a travel position from the travel position measuring device 14, and a traffic condition acquisition unit 312 A computer 316 storing a program for realizing an abnormality detection processing routine for detecting an abnormality in the acceleration operation based on the traffic situation from the vehicle, and an alarm device for presenting an alarm regarding the abnormality detection in the acceleration operation to the driver 318.

  The traffic condition acquisition unit 312 is based on the road map data stored in the memory (not shown) and the current travel position measured by the travel position measuring device 14 and the speed limit or travel path of the travel path that is being traveled. Get the folding direction (straight, right, left). The traffic condition acquisition unit 312 may acquire the speed limit of the traveling road and the folding direction (straight forward, right turn, left turn) of the traveling road from the communication device set on the road side.

  The computer 316 converts the vehicle speed output from the speed sensor 12, the acceleration output from the acceleration sensor 13, the travel position output from the travel position measurement device 14, and the travel path output from the traffic condition acquisition unit 312. This is a characteristic of time-series changes in vehicle speed during acceleration operation for each folding direction based on travel data recording unit 320 that continuously records directions in time series and travel data recorded in travel data recording unit 20. A feature value calculation unit 322 that calculates a feature value representing acceleration characteristics and a feature value storage unit 326 that stores the calculated feature value for each folding direction are provided.

  In addition, the computer 316 detects the occurrence of acceleration based on the acceleration output from the acceleration sensor 13, and the acceleration sensor 328 detects the occurrence of acceleration from the speed sensor 12. Based on the output vehicle speed, the travel position output from the travel position measuring device 14, the speed limit and folding direction acquired from the traffic condition acquisition unit 312, and the feature amount stored in the feature amount storage unit 326, A range prediction unit 330 that predicts a normal time-series change range, and an acceleration operation based on the time-series change range of the vehicle speed predicted by the range prediction unit 330 and the time-series change of the vehicle speed output from the speed sensor 12 An abnormality detection unit 332 that detects an abnormal state and presents warning information by an alarm device 318.

  The feature amount calculation unit 322 extracts a plurality of travel data during the acceleration operation period from the travel data recorded in the travel data recording unit 320, and the vehicle speed and acceleration of the extracted travel data during each acceleration operation period are extracted. Each time constant T is calculated from the time series data using the acceleration behavior model described above. Then, from the running data for each acceleration operation period, the vehicle speed at the acceleration start point, the position of the acceleration start point, the target vehicle speed at the acceleration end point, and the target position at the acceleration end point are acquired, and acquired for each acceleration operation period. Based on each value and the calculated time constant T, a feature value K representing acceleration characteristics is calculated for each folding direction of the traveling road.

  As shown in FIG. 8, the feature value K changes depending on the folding direction due to the difference in the acceleration characteristics depending on the folding direction. Therefore, a plurality of feature values K are stored in the feature value storage unit 326 for each folding direction.

  When it is determined that acceleration is detected by the acceleration detection unit 328 and the acceleration operation is started, the range prediction unit 330 predicts a normal time-series change range of the vehicle speed as described below. First, a plurality of feature amounts corresponding to the folding direction output from the traffic condition acquisition unit 312 are acquired from the feature amount storage unit 326, and the vehicle speed output from the speed sensor 12 is set as the vehicle speed at the acceleration start point, and the travel position The travel position output from the measuring device 14 is set as the travel position at the acceleration start point.

Further, the speed limit of the travel path output from the traffic condition acquisition unit 312 is set as the target vehicle speed at the predicted acceleration end point, and the target position and target vehicle speed at the acceleration end point as shown in FIG. Based on the determined relationship, the target position d _opt of the acceleration end point with respect to the predicted target vehicle speed v _opt and the acceleration a previously recorded in the memory (not shown) is calculated by the following equation (4).

  The value of the acceleration a recorded in advance in the memory is the absolute value of the average acceleration output from the acceleration sensor 13 during acceleration or deceleration, and this acceleration is measured in advance and recorded in the memory. .

  And for each of the plurality of feature quantities for the folding direction acquired as described above, based on the vehicle speed at the acceleration start point, the travel position at the acceleration start point, the target vehicle speed at the acceleration end point, and the position of the acceleration end point, A normal time-series change of the predicted vehicle speed is calculated using the acceleration behavior model, and the normal time-series change range of the vehicle speed as shown in FIG. 10 is calculated based on the calculated time-series changes of the plurality of vehicle speeds. Predict.

  The abnormality detection unit 332 compares the time-series change of the vehicle speed output from the speed sensor 12 with the range of the time-series change of the vehicle speed predicted by the range prediction unit 330, as shown in FIG. The case where the time-series change of the vehicle speed thus deviated from the predicted time-series change range of the vehicle speed is detected as an abnormal state of the acceleration operation.

  The alarm device 318 presents warning information to the driver and alerts the driver when the abnormality detection unit 332 detects an abnormal state of the acceleration operation.

  Next, the operation of the driving support apparatus 310 according to the third embodiment will be described.

  When the driver starts driving the vehicle, the acceleration characteristic learning process routine is executed by the computer 316 as in the first embodiment. As a result, feature amounts representing acceleration characteristics are calculated for each folding direction, and a plurality of feature amounts are stored in the feature amount storage unit 326 for each folding direction.

  Further, when the driver is driving the vehicle, the driving support processing routine shown in FIG. 11 is executed by the computer 316. First, in step 350, it is determined whether or not the generation of acceleration is detected. If it is determined that no acceleration is generated based on the acceleration output from the acceleration sensor 13, an acceleration operation is started. Step 350 is repeated, but if it is determined that acceleration has occurred based on the acceleration output from the acceleration sensor 13, it is determined that the acceleration operation has started, and the process proceeds to Step 352. .

  In step 352, the vehicle speed detected by the speed sensor 12 is acquired as the vehicle speed at the acceleration start point, the travel position measured by the travel position measuring device 14 is acquired as the position of the acceleration start point, and in step 354, The travel direction folding direction is acquired from the traffic condition acquisition unit 312 and the speed limit is acquired as the target vehicle speed at the predicted acceleration end point.

  In the next step 356, a plurality of feature amounts with respect to the folding direction acquired in step 354 are acquired from the feature amount storage unit 326. In step 358, the plurality of feature amounts acquired in step 356, the step 352 described above. Based on the vehicle speed and position of the acceleration start point acquired in step S3, and the target vehicle speed of the acceleration end point acquired in step 354, a normal time-series change range of the vehicle speed is predicted.

  In step 360, the vehicle speed detected by the speed sensor 12 is acquired as the vehicle speed during the acceleration operation. In step 362, the time-series change range of the vehicle speed predicted in step 358 and the step 360 are detected. It is determined whether or not the acceleration operation is in an abnormal state by comparing the time-series change of the vehicle speed. When the time-series change in the vehicle speed acquired in step 360 is out of the predicted range, it is determined that the acceleration operation is in an abnormal state. In step 364, the warning information is sent to the driver by the alarm device 318. To return to step 350. On the other hand, when the time-series change in the vehicle speed acquired in step 360 is within the predicted range, it is determined that the acceleration operation is in a normal state, and in step 366, the acceleration output from the acceleration sensor 13 is determined. Based on the above, it is determined whether or not the acceleration operation has been completed. If the acceleration is greater than 0, it is determined that the acceleration operation has not been completed, and the process returns to step 360. On the other hand, the acceleration becomes almost zero. If YES, it is determined that the acceleration operation has been completed, and the process returns to step 350.

  When the driving support processing routine is executed as described above, when the acceleration operation becomes abnormal, warning information is presented and the driver is prompted to perform a safe acceleration operation, so that safe driving is performed. So that driving assistance will be provided.

  As described above, the driving support apparatus according to the third embodiment calculates a feature amount that represents the characteristics of time-series changes in the vehicle speed when performing an acceleration operation, and calculates the calculated feature amount, the detected vehicle speed, And predicting a normal time-series change range of the vehicle speed based on the predicted target vehicle speed, comparing the detected time-series change of the vehicle speed with the normal time-series change range of the vehicle speed, and Since the presentation is performed, it is possible to present an appropriate warning according to the individual in consideration of the acceleration characteristics of the driver.

  Further, since driving assistance is performed using the feature amount calculated for each folding direction, appropriate driving assistance can be performed in consideration of the acceleration characteristics of the driver according to straight travel, right turn, and left turn.

  Moreover, accidents can be reduced by detecting abnormal acceleration behavior of the driver and presenting warning information.

  In addition, since driving assistance is performed based on the feature quantity obtained by quantifying the time series change of the vehicle speed during the acceleration operation period, flexible and optimum driving assistance according to the situation and the individual becomes possible.

  In the above embodiment, the case where the speed limit of the travel path is acquired and the target vehicle speed at the acceleration end point is predicted has been described as an example. However, the present invention is not limited to this, and the average vehicle speed by time zone is described. Alternatively, the current average vehicle speed obtained from a probe car or the like may be acquired to predict the target vehicle speed at the acceleration end point.

  Moreover, the case where the warning information is presented by comparing the detected time-series change of the vehicle speed with the range of the normal time-series change of the predicted vehicle speed has been described as an example, but the time-series of the detected vehicle speed Driving control such as forcibly limiting acceleration may be performed by comparing the change with a normal time-series change range of the predicted vehicle speed. In this case, when the detected time-series change of the vehicle speed is out of the normal time-series change range of the predicted vehicle speed, the acceleration is forcibly limited, and the time-series change of the vehicle speed Should be within the range of normal time series changes.

  Next, a driving assistance apparatus according to a fourth embodiment will be described. In addition, about the part which becomes the structure similar to 1st Embodiment and 3rd Embodiment, the same code | symbol is attached | subjected and description is abbreviate | omitted.

  The fourth embodiment is mainly different from the third embodiment in that automatic speed control is performed using a feature amount representing acceleration characteristics.

  As shown in FIG. 12, the driving assistance apparatus 410 according to the fourth embodiment includes a speed sensor 12, an acceleration sensor 13, a travel position measurement apparatus 14, a traffic condition acquisition unit 312, and an acceleration characteristic learning process routine. And an automatic speed control processing routine for performing automatic speed control based on the vehicle speed from the speed sensor 12, the travel position from the travel position measurement device 14, and the traffic situation from the traffic situation acquisition unit 312. A computer 416 storing a program to be realized and an actuator 418 installed on the accelerator pedal and controlling the amount of depression of the accelerator pedal are provided.

  The computer 416 includes a travel data recording unit 320, a feature amount calculation unit 322, and a feature amount storage unit 326.

  Further, the computer 416 acquires an acceleration detection unit 328 and a vehicle speed output from the speed sensor 12 when the acceleration detection is detected by the acceleration detection unit 328, a travel position output from the travel position measurement device 14, and a traffic situation acquisition. Based on the speed limit and folding direction acquired from the unit 312 and the feature amount stored in the feature amount storage unit 326, an optimum acceleration calculation unit 430 that calculates an optimal time-series change in the vehicle speed when the acceleration operation is performed. And an operation control unit 432 that controls the operation of the actuator 418 based on the time-series change of the vehicle speed calculated by the optimum acceleration calculation unit 430 and performs vehicle speed control during acceleration traveling.

  When the acceleration detection unit 328 detects the occurrence of acceleration and determines that the acceleration operation is started, the optimum acceleration calculation unit 430 displays the feature amount corresponding to the folding direction output from the traffic condition acquisition unit 312 as the feature amount storage unit. Based on the average value of the plurality of feature values acquired, the vehicle speed at the acceleration start point, the travel position at the acceleration start point, the target vehicle speed at the acceleration end point, and the travel position at the acceleration end point. The optimal time series change of the vehicle speed is calculated using the acceleration behavior model.

  The operation control unit 432 controls the operation of the actuator 418 based on the time-series change of the vehicle speed calculated by the optimum acceleration calculation unit 430, and controls the amount of depression of the accelerator pedal, thereby calculating the time of the calculated vehicle speed. The vehicle speed during acceleration traveling is controlled so that a series change can be obtained.

  Next, the operation of the driving support apparatus 410 according to the fourth embodiment will be described. In addition, about the process similar to 3rd Embodiment, the same code | symbol is attached | subjected and detailed description is abbreviate | omitted.

  When the vehicle is running and the manual acceleration mode is selected, the driver performs an acceleration operation. At this time, the acceleration characteristic learning processing routine is executed by the computer 416 as in the third embodiment. Is done. As a result, feature amounts representing the acceleration characteristics of the driver are calculated for each folding direction, and a plurality of feature amounts are stored in the feature amount storage unit 326 for each folding direction.

  When the automatic acceleration mode is selected, an automatic speed control processing routine shown in FIG.

  First, in step 350, it is determined whether or not the occurrence of acceleration has been detected. If it is determined that the acceleration has occurred, it is determined that the acceleration operation has started, and the process proceeds to step 352 where the speed sensor 12 detects the acceleration. The obtained vehicle speed is acquired as the vehicle speed at the acceleration start point, the travel position measured by the travel position measuring device 14 is acquired as the position of the acceleration start point, and in step 354, the traffic condition acquisition unit 312 While acquiring a folding direction, a speed limit is acquired as the target vehicle speed of the predicted acceleration end point.

  In the next step 356, a plurality of feature amounts for the folding direction acquired in step 354 are acquired from the feature amount storage unit 326, and in step 450, the plurality of feature amounts acquired in step 356, the step 352 is acquired. Based on the vehicle speed and position at the acceleration start point acquired in step S3, and the target vehicle speed at the acceleration end point acquired in step 354, an optimal time-series change in vehicle speed is calculated.

  In step 452, the operation of the actuator 418 is controlled based on the time series change of the vehicle speed calculated in step 450, and the depression amount of the accelerator pedal is controlled, so that the time series change of the calculated vehicle speed is controlled. If the target vehicle speed is obtained by the time-series change of the calculated vehicle speed, the process returns to step 350.

  As described above, according to the driving support apparatus according to the fourth embodiment, the feature amount representing the characteristics of the time-series change in the vehicle speed when the acceleration operation is performed is calculated, and the calculated feature amount is detected. In order to calculate the optimal time series change of the vehicle speed based on the vehicle speed and the predicted target vehicle speed, and to control the vehicle speed based on the optimal time series change of the calculated vehicle speed, the acceleration characteristics of the driver are Considering this, it is possible to perform appropriate vehicle speed control tailored to the individual. In addition, since vehicle speed control suitable for the driver's experience can be performed, the discomfort of the vehicle speed control can be reduced.

  Next, an operation control apparatus according to a fifth embodiment will be described. In addition, about the part which becomes the structure similar to 1st Embodiment, 3rd Embodiment, and 4th Embodiment, the same code | symbol is attached | subjected and description is abbreviate | omitted.

  The fifth embodiment is mainly different from the third embodiment and the fourth embodiment in that operation control is performed so that a desired saturated traffic flow rate is achieved.

  When the characteristic quantity K representing the acceleration characteristic is changed, the characteristic of the time-series change of the vehicle speed is changed, and as a result, the time taken from the start to the end of the acceleration changes. If this is utilized, by controlling the time required for acceleration using the feature value K, the saturated traffic flow rate can be controlled as shown in FIG. 14, and the number of passing vehicles to be judged in the green light period at the intersection can be determined. Can be controlled.

  As shown in FIG. 15, the operation control device 510 according to the fifth embodiment includes a speed sensor 12, an acceleration sensor 13, a travel position measurement device 14, a traffic condition acquisition unit 312, and a desired saturated traffic flow. An input device 512 for setting a rate, a program for realizing an acceleration characteristic learning processing routine, a vehicle speed from the speed sensor 12, a travel position from the travel position measurement device 14, and a traffic from the traffic condition acquisition unit 312 The computer 516 which stored the program for implement | achieving the automatic speed control processing routine which performs automatic speed control based on the condition and the saturated traffic flow rate input from the input device 512, and the actuator 418 are provided.

  The computer 516 includes a travel data recording unit 320, a feature amount calculation unit 322, and a feature amount storage unit 326.

  In addition, the computer 516 acquires the traffic situation when the acceleration detection unit 328, the correspondence relationship storage unit 528 that stores the correspondence relationship between the saturated traffic flow rate and the feature amount, and the acceleration detection unit 328 detect the occurrence of acceleration. A feature that determines an optimum acceleration characteristic based on the folding direction acquired from the unit 312, the feature amount stored in the feature amount storage unit 326, and the saturated traffic flow rate input and set from the input device 512. The amount determination unit 529, the vehicle speed output from the speed sensor 12, the travel position output from the travel position measurement device 14, the speed limit acquired from the traffic condition acquisition unit 312, and the feature determined by the feature amount determination unit 529 An optimal acceleration calculation unit 530 that calculates an optimal time-series change of the vehicle speed when the acceleration operation is performed based on the amount, and an operation control unit 432 are provided.

  In the correspondence relationship storage unit 528, data indicating the correspondence relationship between the saturated traffic flow rate and the feature amount as shown in FIG. 14 is stored for each folding direction.

  The feature amount determination unit 529 determines an optimum feature amount by the method described below. First, when the acceleration detection is detected by the acceleration detection unit 328 and it is determined that the acceleration operation is started, the saturation flow rate corresponding to the folding direction output from the traffic condition acquisition unit 312 is set and the input saturated traffic flow rate is set. Corresponding feature amounts are acquired from the correspondence relationship storage unit 528. Then, a plurality of feature amounts corresponding to the folding direction output from the traffic situation acquisition unit 312 are acquired from the feature amount storage unit 326, the acquired plurality of feature amounts, and the feature amount acquired from the correspondence relationship storage unit 528, Based on the above, a feature amount representing the optimum acceleration characteristic suitable for the driver's acceleration characteristic is realized while realizing the saturated traffic flow rate set as input.

  The optimum acceleration calculation unit 530 accelerates based on the feature amount determined by the feature amount determination unit 529, the vehicle speed at the acceleration start point, the travel position at the acceleration start point, the target vehicle speed at the acceleration end point, and the position at the acceleration end point. Using the behavior model, the optimal time-series change of the vehicle speed is calculated.

  Next, the operation of the operation control apparatus 510 according to the fifth embodiment will be described. In addition, about the process similar to 3rd Embodiment and 4th Embodiment, the same code | symbol is attached | subjected and detailed description is abbreviate | omitted.

  First, a desired saturated traffic flow rate is input and set in advance by the input device 512. When the vehicle is running and the manual acceleration mode is selected, the driver performs an acceleration operation. At this time, as in the third embodiment, the acceleration characteristic learning process routine is executed by the computer 416. Executed by. As a result, a feature quantity representing acceleration characteristics is calculated for each folding direction, and a plurality of feature quantities are stored in the feature quantity storage unit 326 for each folding direction.

  When the automatic acceleration mode is selected, the computer 516 executes an automatic speed control processing routine shown in FIG.

  First, in step 350, it is determined whether or not the occurrence of acceleration has been detected. If it is determined that the acceleration has occurred, it is determined that the acceleration operation has started, and the process proceeds to step 352 where the speed sensor 12 detects the acceleration. The obtained vehicle speed is acquired as the vehicle speed at the acceleration start point, the travel position measured by the travel position measuring device 14 is acquired as the position of the acceleration start point, and in step 354, the traffic condition acquisition unit 312 While acquiring a folding direction, a speed limit is acquired as the target vehicle speed of the predicted acceleration end point.

  In the next step 550, the feature quantity corresponding to the input saturated traffic flow rate is acquired from the correspondence storage unit 528, and in step 356, a plurality of feature quantities for the folding direction obtained in step 354 are obtained. In step 552, an optimal feature amount is determined based on the feature amount acquired in step 550 and the plurality of feature amounts acquired in step 356.

  In the next step 450, the vehicle speed is determined based on the feature amount determined in step 552, the vehicle speed and position of the acceleration start point acquired in step 352, and the target vehicle speed of the acceleration end point acquired in step 354. The optimal time series change of is calculated.

  In step 452, the operation of the actuator 418 is controlled based on the time series change of the vehicle speed calculated in step 450, and the depression amount of the accelerator pedal is controlled, so that the time series change of the calculated vehicle speed is controlled. If the target vehicle speed is obtained by the time-series change of the calculated vehicle speed, the process returns to step 350.

  By executing the above automatic speed control processing routine, the time-series change of the vehicle speed during acceleration traveling is controlled according to the feature amount corresponding to the desired saturated traffic flow rate. Realized.

  As described above, according to the operation control apparatus of the fifth embodiment, the feature amount indicating the characteristics of the time-series change of the vehicle speed corresponding to the desired saturated traffic flow rate, the detected vehicle speed, and the predicted Based on the target vehicle speed, calculate the time series change of the vehicle speed to achieve the desired saturation traffic flow rate, and use the optimal time series change of the calculated vehicle speed to control the vehicle speed. Traffic flow rate can be realized. Moreover, the effect of reducing traffic congestion can be obtained by controlling the vehicle speed to achieve a desired saturated traffic flow rate.

  In the above-described embodiment, the feature amount corresponding to the saturated traffic flow rate and representing the acceleration characteristic of the driver stored by learning is described as an example of determining the optimum feature amount. If the feature amount corresponding to the saturated traffic flow rate and the feature amount stored by learning are greatly different, the feature amount stored by learning is within an allowable range that does not cause discomfort to the driver, and The feature value corresponding to the saturated traffic flow rate may be determined as the optimum feature value.

  Next, an operation control device according to a sixth embodiment will be described. In addition, about the part which becomes the same structure as 4th Embodiment, the same code | symbol is attached | subjected and description is abbreviate | omitted.

  In the driving control apparatus according to the sixth embodiment, a feature amount representing acceleration characteristics that maximizes fuel efficiency is calculated, and a time-series change in vehicle speed during acceleration traveling is calculated using the calculated feature amount. To do. Then, based on the time-series change in the calculated vehicle speed, the operation of the actuator 418 is controlled to control the amount of depression of the accelerator pedal, so that the time-series change in the calculated vehicle speed is achieved. The target vehicle speed is obtained from the time-series change of the calculated vehicle speed.

  As described above, since the fuel consumption of the vehicle can be controlled using the feature amount representing the acceleration characteristic, the energy saving automatic speed control can be performed to obtain the effect of improving the fuel consumption.

It is the schematic which shows the structure of the driving assistance device which concerns on the 1st Embodiment of this invention. It is the graph which showed the time-sequential change of the vehicle speed in the acceleration operation period. It is an image figure which shows an acceleration action model. It is a graph which shows the feature-value showing the acceleration characteristic of each driver | operator. It is a flowchart which shows the content of the acceleration characteristic learning process routine in the driving assistance device which concerns on the 1st Embodiment of this invention. It is the graph which showed the time series change of the acceleration in the acceleration operation period. It is the schematic which shows the structure of the driving assistance device which concerns on the 3rd Embodiment of this invention. It is a graph which shows the feature-value showing the acceleration characteristic with respect to each folding direction. It is a graph which shows the relationship between a target vehicle speed and a target position. It is a graph which shows the time series change range of the normal time series change of the predicted vehicle speed, and the time series change of the vehicle speed outside the range of the normal range. It is a flowchart which shows the content of the driving assistance processing routine in the driving assistance device which concerns on the 3rd Embodiment of this invention. It is the schematic which shows the structure of the driving assistance device which concerns on the 4th Embodiment of this invention. It is a flowchart which shows the content of the automatic speed control processing routine in the driving assistance device which concerns on the 4th Embodiment of this invention. It is a graph which shows the relationship between a feature-value and a saturated traffic flow rate. It is the schematic which shows the structure of the driving assistance device which concerns on the 5th Embodiment of this invention. It is a flowchart which shows the content of the automatic speed control processing routine in the driving assistance device which concerns on the 5th Embodiment of this invention.

Explanation of symbols

10, 310, 410 Driving support device 12 Speed sensor 13 Acceleration sensor 14 Travel position measurement device 16, 316, 416, 516 Computer 18 Display device 20, 320 Travel data recording unit 22, 322 Feature amount calculation unit 26, 326 Feature amount storage Unit 312 Traffic condition acquisition unit 318 Alarm device 330 Range prediction unit 332 Abnormality detection unit 418 Actuator 430, 530 Optimal acceleration calculation unit 432 Operation control unit 510 Operation control device 512 Input device 528 Correspondence relationship storage unit 529 Feature quantity determination unit 530 Optimal acceleration Calculation unit K Feature T Time constant

Claims (11)

Storage means for storing time-series data of vehicle speed during vehicle travel;
Based on the time series data, calculating means for calculating a feature amount of time series change of the vehicle speed when the driver of the vehicle performs an acceleration operation;
Driving support means for providing driving support based on the feature amount calculated by the calculating means;
A driving support device including   The feature amount is determined based on a vehicle speed at each of an acceleration start point and an acceleration end point when the driver of the vehicle performs an acceleration operation, a position of each of the acceleration start point and the acceleration end point, and the acceleration operation. The driving support device according to claim 1, wherein the feature amount is determined based on a time constant of the vehicle speed when the vehicle is operated.   The feature amount is calculated based on the time required from the start of acceleration when traveling at a constant acceleration so that the target vehicle speed is obtained at the acceleration end point when the driver of the vehicle performs the acceleration operation, and the acceleration operation is performed. The driving support device according to claim 1, wherein the feature amount is determined based on a time constant of the vehicle speed.   The driving support device according to claim 1, wherein the driving support means performs driving support by presenting the feature amount to the driver. Detecting means for continuously detecting the vehicle speed in time series;
Vehicle speed prediction means for predicting a target vehicle speed of the acceleration operation by the driver,
The driving support means is based on the characteristic amount calculated by the calculating means, the vehicle speed detected by the detecting means, and the target vehicle speed predicted by the predicting means when the driver performs an acceleration operation. Predicting a time-series change of the vehicle speed, and presenting warning information as the driving assistance based on the predicted time-series change of the vehicle speed and the time-series change of the vehicle speed detected by the detection means. The driving support device according to claim 1, wherein vehicle speed control is performed. Detecting means for detecting the vehicle speed;
Vehicle speed prediction means for predicting a target vehicle speed of the acceleration operation by the driver,
The driving support means is based on the characteristic amount calculated by the calculating means, the vehicle speed detected by the detecting means, and the target vehicle speed predicted by the predicting means when the driver performs an acceleration operation. The vehicle speed is calculated when the driver performs the acceleration operation, and the vehicle speed is controlled based on the calculated time-series change of the vehicle speed. The driving support device according to item.   The driving support device according to claim 5 or 6, wherein the vehicle speed prediction means acquires a speed limit or an average vehicle speed of a travel path on which the vehicle is traveling, and predicts the acquired speed limit or average vehicle speed as the target vehicle speed. . Further comprising an acquisition means for acquiring information indicating whether the vehicle is traveling straight ahead, turning right, or turning left;
The calculation means calculates the feature amount for the acceleration operation in each of straight ahead, right turn, and left turn,
The driving support means performs the driving support using the feature amount according to information indicating whether the traveling path on which the vehicle acquired by the acquiring means is traveling is straight, right turn, or left turn. The driving support device according to any one of claims 5 to 7, which is performed. The feature amount corresponding to a desired saturation traffic flow rate is determined based on a predetermined relationship between a saturated traffic flow rate and a feature amount of a time-series change in vehicle speed when the vehicle driver performs an acceleration operation. A determination means;
Detection means for continuously detecting the speed of the driver's vehicle in time series;
Vehicle speed prediction means for predicting a target vehicle speed of the acceleration operation by the driver;
When the driver performs an acceleration operation, based on the feature amount calculated by the determining unit, the vehicle speed detected by the detecting unit, and the target vehicle speed predicted by the vehicle speed predicting unit, the driver Calculating a time-series change of the vehicle speed when performing the acceleration operation, and based on the calculated time-series change, operation control means for controlling the vehicle speed;
Operation control device including. Computer
Calculation means for calculating a feature quantity of time-series change of the vehicle speed when the driver of the vehicle performs an acceleration operation based on the time-series data from the storage means storing the time-series data of the vehicle speed during vehicle travel; and A program for functioning as driving support means for performing driving support based on the feature amount calculated by the calculating means. Computer
The feature amount corresponding to a desired saturation traffic flow rate is determined based on a predetermined relationship between a saturated traffic flow rate and a feature amount of a time-series change in vehicle speed when the vehicle driver performs an acceleration operation. Decision means,
Detection means for continuously detecting the speed of the driver's vehicle in time series;
Vehicle speed predicting means for predicting a target vehicle speed of an acceleration operation by the driver, and when the driver performs an acceleration operation, the feature amount calculated by the determining means, a vehicle speed detected by the detecting means, and the Based on the target vehicle speed predicted by the vehicle speed prediction means, a time series change of the vehicle speed when the driver performs the acceleration operation is calculated, and the vehicle speed is controlled based on the calculated time series change. Program to function as operation control means.

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