JP2011194980A - Vehicle controller - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a vehicle controller which can suppress the amount of data and thereby having improved feasibility.SOLUTION: In the vehicle controller 1, a vehicle speed Vwhen a vehicle arrives at the entrance of a curve and the maximum vehicle speed Vwhen the vehicle enters the curve are calculated based on a deceleration αof the normal brake of a driver, and when the vehicle Vis larger than the maximum vehicle speed V, driving support control is executed. Thus, it is possible to perform appropriate driving support only by storing the deceleration αof the normal brake of the driver, so that it is not necessary to store any parameter such as the curve for each driver. Consequently, it is possible to suppress the amount of data to be stored in a data storage capacity, and to improve feasibility.

Description

  The present invention relates to a vehicle control device.

  Conventionally, a technique for performing vehicle deceleration control when traveling on a curve is known. For example, in the vehicle control device described in Patent Document 1, the vehicle speed, the throttle operation pattern, and the brake operation pattern at a point before entering the curve are set to the curve curvature and the road surface inclination angle of the road connected to the curve for each driver. When it is determined that there is a curve ahead of the course of the vehicle, the curvature of the curve and the road surface inclination angle of the connecting road are searched, and the vehicle is safely placed on the curve by the curve curvature and the road surface inclination angle. The limit speed that can be entered is being sought. In this vehicle control device, the speed at the time of curve entry of the vehicle at the curve curvature searched based on the throttle operation pattern and the brake operation pattern corresponding to the searched curve curvature is estimated, and this speed exceeds the limit speed. When it is determined that the speed change control means, the speed reduction control is performed.

JP-A-9-142175

  However, in the control device described in Patent Document 1, it is necessary to hold data for each driver and the curvature of the curve, and the learning method for performing the deceleration control is complicated. Data storage capacity is required. For this reason, the conventional vehicle control device has poor feasibility.

  The present invention has been made to solve the above-described problems, and an object of the present invention is to provide a vehicle control device that can reduce the amount of data and improve the feasibility.

  In order to solve the above-described problem, a vehicle control device according to the present invention includes a turning operation characteristic calculating unit that calculates a turning operation characteristic of the host vehicle during curve driving based on a driver's deceleration tendency, and a turning operation characteristic. Based on the turning operation characteristic calculated by the calculating means, the arrival vehicle speed estimation means for estimating the arrival vehicle speed when reaching the entrance of the curve, and the turning operation characteristic calculated by the turning operation characteristic calculation means, to the curve A maximum vehicle speed calculating means for calculating the maximum vehicle speed at the time of entry of the vehicle, and control so that driving assistance is performed when the reaching vehicle speed estimated by the reaching vehicle speed estimating means is larger than the maximum vehicle speed calculated by the maximum vehicle speed calculating means Driving assistance control means for performing the above-described operation.

  In this vehicle control device, the turning operation characteristic is calculated based on the driver's deceleration tendency, the arrival vehicle speed when reaching the entrance of the curve is estimated from the turning operation characteristic, and the maximum vehicle speed at the time of entering the curve is estimated. Is calculated. Then, when the reaching vehicle speed is higher than the maximum vehicle speed, control is performed so that driving assistance is performed. In this way, the arrival vehicle speed and the maximum vehicle speed are calculated based on the driver's deceleration tendency, and the driving assistance is performed by comparing the size of the arrival vehicle speed and the maximum vehicle speed. You only have to memorize the data. Therefore, since it is not necessary to store various parameters such as curves for each driver, the amount of data can be reduced. As a result, since an enormous data storage capacity is not required, the feasibility can be improved.

  Further, the vehicle is provided with deceleration learning means for acquiring deceleration during normal driving of the driver and learning about deceleration, and the turning operation characteristic calculating means is preset with the deceleration learned by the deceleration learning means. Applied to the regression equation, the deceleration at the time of entering the curve and the lateral acceleration of the host vehicle traveling on the curve are calculated as turning operation characteristics, and the arrival vehicle speed estimating means calculates the curve based on the deceleration at the time of entering the curve. It is preferable that the arrival vehicle speed when reaching the entrance of the vehicle is estimated, and the maximum vehicle speed calculation means calculates the maximum vehicle speed when entering the curve based on the lateral acceleration of the vehicle traveling on the curve. As described above, since the deceleration at the time of entering the curve and the lateral acceleration during the curve running which are turning operation characteristics can be calculated by applying the deceleration (deceleration tendency) learned in the preset regression equation, it is complicated. There is no need to learn. Therefore, the calculation load can be reduced.

  The regression equation is composed of a first regression equation for calculating the deceleration at the time of entering the curve and a second regression equation for calculating the lateral acceleration during the curve running. The first regression equation includes a plurality of regression equations. The second regression equation is obtained based on the relationship distribution map of deceleration during normal driving and deceleration during curve entry obtained from the driver. The second regression equation represents the deceleration during normal driving obtained from multiple drivers. It is preferable to be obtained based on a relationship distribution diagram of lateral acceleration of a vehicle traveling on a speed and a curve. As described above, since the first and second regression equations are set based on data acquired from a plurality of drivers, the turning operation characteristics can be calculated without specifying the driver. Therefore, the configuration of an authentication system, a switch and the like necessary for holding data for each driver is unnecessary, and the device can be simplified.

  According to the present invention, it is possible to reduce parameters necessary for driving support control in curve driving, and to suppress the amount of data stored in the data storage capacity. Thereby, improvement of feasibility can be aimed at.

It is a figure which shows the structure of the vehicle control apparatus which concerns on one Embodiment of this invention. It is a figure explaining the setting of the deceleration in a normal brake. FIG. 6 is a relationship distribution diagram showing a correlation between deceleration of a normal brake and deceleration when entering a curve. It is a figure which shows a 1st regression type. FIG. 6 is a relationship distribution diagram showing a correlation between deceleration of a normal brake and lateral G in a curve. It is a figure which shows a 2nd regression type. It is a flowchart which shows operation | movement of a vehicle control apparatus. It is a figure which shows typically operation | movement of a vehicle control apparatus.

  Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the drawings.

  FIG. 1 is a diagram illustrating a configuration of a vehicle control device according to an embodiment of the present invention. When the host vehicle enters the curve, the vehicle control device 1 issues an alarm when the host vehicle is in an overspeed state with respect to the curve ahead of travel, and further assists the driver when the driver's operation is insufficient. It is a device that implements.

  As shown in FIG. 1, the vehicle control device 1 includes a deceleration acquisition unit 11, a deceleration storage unit 12, a curve determination unit 13, a distance acquisition unit 14, a curvature acquisition unit 15, and a deceleration learning unit ( Deceleration learning means) 16, turning operation characteristic calculation section (turning operation characteristic calculation means) 17, arrival vehicle speed estimation section (arrival vehicle speed estimation means) 18, maximum vehicle speed calculation section (maximum vehicle speed calculation means) 19, and vehicle speed A determination unit 20 and a driving support control unit (driving support control means) 21 are provided. The vehicle control device 1 includes a CPU (Central Processing Unit) that performs calculations, a ROM (Read Only Memory) that stores programs for causing the CPU to execute each process, and a RAM (Random) that stores various data such as calculation results. It is an electronic control unit (ECU: Electrical Control Unit) composed of an Access Memory).

  The deceleration acquisition unit 11 is a part that acquires a deceleration during braking of the vehicle. The deceleration acquisition unit 11 acquires a deceleration due to a brake during normal driving of the driver (hereinafter also referred to as a normal brake). The brake during normal travel is a brake when approaching a traffic light or a preceding vehicle, and includes an engine brake and a downshift. The deceleration acquisition unit 11 outputs deceleration information indicating the acquired deceleration to the deceleration storage unit 12. In addition, the deceleration acquisition part 11 acquires deceleration from the front-back G sensor (not shown), for example.

  The deceleration storage unit 12 is a part that stores deceleration information. Upon receiving the deceleration information output from the deceleration acquisition unit 11, the deceleration storage unit 12 stores and stores this deceleration information.

  The curve determination unit 13 is a part that determines whether a curve exists ahead of the traveling direction of the host vehicle. The curve determination unit 13 includes a car navigation system using GPS (Global Positioning System), information output from an infrastructure device such as RSE (Road Side Equipment), and information from a vehicle traveling ahead (steering, behavior, etc.) Alternatively, it is determined by a sensor or the like mounted on the host vehicle whether a curve exists ahead of the host vehicle in the traveling direction. When it is determined that there is a curve ahead of the traveling direction of the host vehicle, the curve determination unit 13 outputs curve information indicating the fact to the distance acquisition unit 14 and the curvature acquisition unit 15.

  The distance acquisition unit 14 is a part that acquires the distance from the current position of the host vehicle to the entrance of the curve. When the distance acquisition unit 14 receives the curve information output from the curve determination unit 13, the distance acquisition unit 14 acquires the distance from the position of the host vehicle to the curve indicated by the curve information. Specifically, the distance acquisition unit 14 searches for the position of the host vehicle and the position of the curve from road information stored in a car navigation system using GPS, for example, and from the position of the host vehicle to the entrance of the curve. Get the distance. The distance acquisition unit 14 outputs distance information indicating the acquired distance to the arrival vehicle speed estimation unit 18.

  The curvature acquisition unit 15 is a part that acquires the curvature of the target curve. When the curvature acquisition unit 15 receives the curve information output from the curve determination unit 13, the curvature acquisition unit 15 acquires the curvature of the target curve indicated by the curve information. Specifically, for example, the curvature acquisition unit 15 searches the road information stored in the car navigation system for a curve indicated by the curve information, and acquires the curvature of the target curve from the road information. The curvature acquisition unit 15 outputs curvature information indicating the curvature of the acquired curve to the arrival vehicle speed estimation unit 18 and the maximum vehicle speed calculation unit 19. The curvature acquisition unit 15 acquires the curvature of the curve using information output from an infrastructure device such as RSE, information from a vehicle traveling ahead (steering, behavior, etc.), or a sensor mounted on the host vehicle. May be.

The deceleration learning unit 16 is a part that learns about the driver's deceleration. As shown in FIG. 2, the deceleration learning unit 16 samples the deceleration by each brake on the basis of the deceleration during normal driving of the driver stored in the deceleration storage unit 12, and calculates the average. Thus, learning of deceleration α _normal during normal driving, that is, learning of the driver's deceleration tendency is performed. The deceleration learning unit 16 outputs deceleration information indicating the learned deceleration α _normal to the turning operation characteristic calculation unit 17. The deceleration learning unit 16 is often intended to adjust the inter-vehicle distance from the preceding vehicle when the deceleration due to one brake is equal to or less than a predetermined value (ΔV _l ). A value corresponding to the predetermined value is omitted. Thereby, the learning accuracy can be improved.

The turning operation characteristic calculation unit 17 is a part that calculates the turning operation characteristic of the host vehicle during curve driving from the driver's deceleration tendency. The turning operation characteristic calculation unit 17 calculates a deceleration at the time of entering a curve and a lateral acceleration (hereinafter referred to as a lateral G) while traveling along the curve as the turning operation characteristics. Specifically, when the turning operation characteristic calculation unit 17 receives the deceleration information output from the deceleration learning unit 16, the first and second regressions in which the deceleration α _normal indicated by the deceleration information is preset. Apply to the equation to calculate the deceleration at the time of entering the curve and the lateral G while driving the curve.

Here, the first and second regression equations will be described in detail with reference to FIGS. FIG. 3 is a relationship distribution diagram showing a correlation between deceleration of the normal brake and deceleration at the time of entering the curve. In the figure, the horizontal axis represents the normal braking deceleration rate α _normal , and the vertical axis represents the deceleration rate α _ToCurve when entering the curve. FIG. 4 is a diagram showing the first regression equation. As shown in FIGS. 3 and 4, there is a correlation between normal braking and deceleration when decelerating when entering a curve. Therefore, the first regression equation R1 is set from the distribution of samples obtained by collecting samples of deceleration of normal brakes and deceleration at the time of entering a curve of a plurality of general drivers. As shown in FIG. 4, the turning operation characteristic calculation unit 17 applies the deceleration α _normal of the normal brake to the first regression equation R1, and calculates the deceleration α _ToCurve when entering the curve.

FIG. 5 is a relationship distribution diagram showing the correlation between the deceleration of the normal brake and the lateral G in the curve. In the figure, the horizontal axis is the deceleration α _normal of the normal brake, and the vertical axis is the horizontal Gα _{curve in the curve} . FIG. 6 is a diagram showing the second regression equation. As shown in FIGS. 5 and 6, there is a correlation between the deceleration of the normal brake (the driver's normal region with respect to the longitudinal acceleration) and the lateral G in the curve (the driver's normal region with respect to the lateral acceleration). For this reason, the second regression equation R2 is set from the distribution of this sample by collecting the samples of the normal driver's normal brake deceleration and the lateral G in the curve. As shown in FIG. 6, the turning operation characteristic calculation unit 17 applies the _normal braking deceleration rate α _normal to the second regression equation R2 to calculate the lateral Gα _{curve in the curve} . The turning operation characteristic calculation unit 17 outputs curve deceleration information indicating the calculated deceleration α _ToCurve at the time of entering the curve to the arrival vehicle speed estimation unit 18 and maximizes the lateral G information indicating the lateral Gα _curve in the calculated _curve. It outputs to the vehicle speed calculation part 19.

The arrival vehicle speed estimation unit 18 is a part that estimates the arrival vehicle speed of the host vehicle when it reaches the entrance of the curve. When the arrival vehicle speed estimation unit 18 receives the distance information output from the distance acquisition unit 14, the curvature information output from the curvature acquisition unit 15, and the curve deceleration information output from the turning operation characteristic calculation unit 17, The reaching vehicle speed V _curve when reaching the entrance of the _curve is estimated by the following equation (1). Specifically, the arrival vehicle speed estimation unit 18 obtains the _current vehicle speed V _current of the _host vehicle from a vehicle speed sensor (not shown), and the distance indicated by the distance information, the curvature of the target curve indicated by the curvature information, and the deceleration α. _Based on _ToCurve , the time required to reach the curve entrance from the current position of the _host vehicle is obtained.

なお、Ｄ及びｔ_ToCurveは、以下の式（２）、（３）で表される。

Then, the arrival vehicle speed estimation unit 18 estimates the arrival vehicle speed V _curve by applying the above parameter to the following equation (1). In equation (1), V _current is the _current vehicle speed of the _host vehicle, D is the distance from the current point of the _host vehicle to the entrance of the curve, t _{ToCurve is} the time _during which the _vehicle travels at the deceleration α _ToCurve , and RT is the alarm. Reaction time.

Note that D and t _ToCurve are _expressed by the following equations (2) and (3).

Here, as the reaction time RT for the alarm, a general fixed value (0.8 seconds: JARI test) may be used, or the driver may be selected as a variable value. Further, the reaction time of the driver when the alarm is sounded may be actually measured, and that time may be used as the reaction time RT. The arrival vehicle speed estimation unit 18 outputs vehicle speed information indicating the estimated arrival vehicle speed V _curve to the vehicle speed determination unit 20.

最大車速算出部１９は、上記式（４）により算出した最大車速Ｖ_threshを示す最大車速情報を車速判定部２０に出力する。 The maximum vehicle speed calculation unit 19 is a part that calculates the maximum vehicle speed when entering the curve. The maximum vehicle speed calculation unit 19 receives the curvature information output from the curvature acquisition unit 15 and also receives the lateral G information output from the turning operation characteristic calculation unit 17, and the curvature R and the lateral G information indicated by the curvature information indicate. The maximum vehicle speed V _thresh is calculated based on the lateral Gα _{curve in the curve} . The maximum vehicle speed calculation unit 19 calculates the maximum vehicle speed V _thresh by the following equation (4). In equation (4), α _curve : lateral G (lateral acceleration), R: curvature of the curve.

The maximum vehicle speed calculation unit 19 outputs maximum vehicle speed information indicating the maximum vehicle speed V _thresh calculated by the above formula (4) to the vehicle speed determination unit 20.

The vehicle speed determination unit 20 is a part that compares and determines the magnitude of the vehicle speed V _{curve of the host} vehicle when the vehicle reaches the entrance of the _curve and the maximum vehicle speed V _thresh when entering the curve. When the vehicle speed determination unit 20 receives the vehicle speed information output from the arrival vehicle speed estimation unit 18 and receives the maximum vehicle speed information output from the maximum vehicle speed calculation unit 19, the arrival vehicle speed V _curve indicated by the vehicle speed information is the maximum vehicle speed information. It is determined whether or not the vehicle speed is greater than the maximum vehicle speed V _thresh indicated by. The vehicle speed determination unit 20 outputs control information to the driving support control unit 21 when determining that the reaching vehicle speed V _curve is larger than the maximum vehicle speed V _thresh .

  The driving support control unit 21 is a part that performs driving support control according to the determination result of the vehicle speed determination unit 20. When the driving support control unit 21 receives the control information output from the vehicle speed determination unit 20, for example, the driving support control unit 21 performs control so as to sound an alarm from a buzzer, or controls the brake, throttle actuator, transmission, etc. to perform auxiliary braking. . Specifically, the driving support control unit 21 outputs a ringing start signal to the buzzer or outputs a signal instructing the throttle actuator to control to throttle the accelerator slot. As a result, driving assistance such as auxiliary braking is performed in the host vehicle. Note that, as described above, the driving assistance referred to here is to alert the driver with a warning sound or to perform auxiliary braking.

  Next, the operation of the vehicle control device 1 will be described with reference to FIGS. FIG. 7 is a flowchart showing the operation of the vehicle control device, and FIG. 8 is a diagram schematically showing the operation of the vehicle control device.

As shown in FIGS. 7 and 8, first, the deceleration learning unit 16 learns the deceleration (step S01), and the normal braking deceleration α _normal is set. Moreover, as shown in FIG. 7, it is determined in the curve determination part 13 whether a curve exists ahead of the advancing direction of the own vehicle (step S02). If it is determined that there is a curve ahead of the traveling direction of the host vehicle, the process proceeds to step S03. On the other hand, if it is not determined that there is a curve ahead in the traveling direction of the host vehicle, the same processing is repeated.

In step S03, the curvature R of the curve is acquired by the curvature acquisition unit 15. Further, the distance from the current position of the host vehicle to the entrance of the curve is acquired by the distance acquisition unit 14 (step S04). Subsequently, the arrival vehicle speed V _curve when the vehicle reaches the entrance of the _curve is estimated by the arrival vehicle speed estimation unit 18 (step S05). At this time, as shown in FIG. 8, by applying a deceleration alpha _normal normal braking learned in the first regression equation R1 seek deceleration alpha _ToCurve of entering the curve at step S01, the deceleration alpha _ToCurve Based on the distance from the current position of the host vehicle to the curve entrance, the curvature R of the target curve, the vehicle speed V _{current of the host} vehicle, etc., the arrival vehicle speed V _curve (entry speed) when reaching the entrance of the _curve is estimated (set) Is done.

Further, the maximum vehicle speed at the time of entering the curve is calculated by the maximum vehicle speed calculation unit 19 (step S06). At this time, as shown in FIG. 8, obtains a lateral G.alpha _curve in curve by applying a deceleration alpha _normal normal braking learned in step S01 in the second regression equation R2, the curve in the horizontal G.alpha _curve and target Based on the curvature R of the curve, the maximum vehicle speed when entering the curve is calculated (set).

Then, the vehicle speed determination unit 20 determines whether or not the reached vehicle speed V _curve when reaching the entrance of the _curve is larger than the maximum vehicle speed V _thresh when entering the curve (step S07). When it is determined that the arrival vehicle speed V _curve when reaching the entrance of the _curve is larger than the maximum vehicle speed V _thresh at the time of entering the curve, auxiliary control (deceleration control) or the like is performed under the control of the driving support control unit 21. To be implemented. On the other hand, when it is not determined that the arrival vehicle speed V _curve when reaching the entrance of the _curve is larger than the maximum vehicle speed V _thresh at the time of entering the curve, the processing is terminated.

As described above, the vehicle control device 1 obtains the vehicle speed V _curve when reaching the entrance of the curve based on the deceleration α _normal of the driver's normal brake and the maximum vehicle speed V _thresh when entering the curve, When the vehicle speed V _curve is larger than the maximum vehicle speed V _thresh , the driving support control is performed. Thus, appropriate driving assistance can be performed only by storing the deceleration α _normal of the driver's normal brake. Therefore, it is not necessary to store parameters such as curves for each driver. Therefore, the amount of data stored in the data storage capacity can be suppressed, and as a result, the feasibility can be improved.

The vehicle speed V _curve when reaching the entrance of the _curve is obtained by applying the deceleration α _normal to the first regression equation R1, and the maximum vehicle speed V _thresh when entering the curve is the second regression equation R2. _Is obtained by applying a deceleration rate α _normal to. Thus, the deceleration (the deceleration tendency) learned from the first and second regression equations R1 and R2 set in advance for the deceleration α _ToCurve when entering the _curve and the lateral Gα _curve during the curve, which are turning operation characteristics. Since it can be calculated by applying α _normal , there is no need for complicated learning. Therefore, the calculation load can be reduced, and the feasibility can be further improved.

The first regression equation R1 is set based on the correlation between the deceleration α _normal of the normal brake acquired from a plurality of drivers and the deceleration α _ToCurve when decelerating to the curve approach (see FIG. 3). The second regression equation R2 is set based on the correlation between the normal braking deceleration α _normal and the lateral Gα _{curve in} the _curve of a plurality of drivers (see FIG. 5). Thus, since the first regression equation R1 and the second regression equation R2 are set based on data acquired from a plurality of drivers, the turning operation characteristics can be calculated without specifying the driver. . Therefore, the configuration of an authentication system, a switch and the like necessary for holding data for each driver is unnecessary, and the device can be simplified.

また、現在速度におけるカーブ入口までの時間は、下記式（６）にて示される。

そして、上記式（６）が下記式（７）で示される警報・制御を開始すべきタイミング（カーブ入口までの時間）を下回ったときに、警報・制御を開始してもよい。

The present invention is not limited to the above embodiment. For example, in addition to the embodiment described above, the timing for starting the alarm or control may be expressed by the distance to the curve entrance or the time. Specifically, even when the alarm / control is started when the distance D to the curve entrance falls below the timing (distance to the curve entrance) at which the alarm / control indicated by the following formula (5) is to be started. Good.

Further, the time to the curve entrance at the current speed is expressed by the following formula (6).

Then, the alarm / control may be started when the above equation (6) falls below the timing (time to the curve entrance) at which the alarm / control indicated by the following equation (7) should be started.

In the above-described embodiment, the deceleration learning unit 16 samples the deceleration by each brake based on the deceleration during normal driving of the driver stored in the deceleration storage unit 12 and takes the average thereof. Thus, the deceleration α _normal during normal driving is learned, but the learning method of the deceleration is not limited to this. For example, a regression equation that approximates the distribution of deceleration due to normal braking of multiple drivers is prepared in advance, the driver's deceleration tendency is obtained from the relationship between this regression expression and the sampled deceleration, and this deceleration trend The deceleration α _normal may be set from the value of.

DESCRIPTION OF SYMBOLS 1 ... Vehicle control apparatus, 16 ... Deceleration learning part (deceleration learning means), 17 ... Turning operation characteristic calculation part (turning operation characteristic calculation means), 18 ... Vehicle speed estimation part (vehicle speed estimation means), 19 ... Maximum vehicle speed calculation part (maximum vehicle speed calculating means, 21 ... driving support control unit (driving support control unit), R1 ... first regression equation, R2 ... second regression equation, V _curve ... reach the vehicle speed, V _ToCurve ... maximum vehicle speed, alpha _normal ... normal Brake deceleration, α _ToCurve … deceleration at the time of entering the _curve , α _curve … lateral G (lateral acceleration).

Claims (3)

Based on the driver's deceleration tendency, a turning operation characteristic calculating means for calculating a turning operation characteristic of the host vehicle during curve driving,
Based on the turning operation characteristic calculated by the turning operation characteristic calculation means, reaching vehicle speed estimation means for estimating a reaching vehicle speed when reaching the entrance of the curve;
Maximum vehicle speed calculating means for calculating a maximum vehicle speed at the time of entering the curve based on the turning operation characteristic calculated by the turning operation characteristic calculating means;
Driving assistance control means for controlling to perform driving assistance when the arrival vehicle speed estimated by the arrival vehicle speed estimation means is larger than the maximum vehicle speed calculated by the maximum vehicle speed calculation means. A vehicle control device. A deceleration learning means for acquiring a deceleration at the time of normal driving of the driver and learning about the deceleration;
The turning operation characteristic calculation unit applies the deceleration learned by the deceleration learning unit to a regression equation set in advance, and the vehicle is traveling along the curve and the deceleration at the time of entering the curve as the turning operation characteristic. The lateral acceleration of the host vehicle is calculated,
The arrival vehicle speed estimation means estimates the arrival vehicle speed when reaching the entrance of the curve based on the deceleration at the time of entering the curve,
2. The vehicle control device according to claim 1, wherein the maximum vehicle speed calculating means calculates a maximum vehicle speed when entering the curve based on a lateral acceleration of a vehicle traveling on the curve. The regression equation includes a first regression equation for calculating a deceleration at the time of entering the curve, and a second regression equation for calculating a lateral acceleration during the curve running,
The first regression equation is obtained based on a relationship distribution map of deceleration during normal driving and deceleration when entering a curve obtained from a plurality of drivers,
The said 2nd regression type is calculated | required based on the relationship distribution map of the lateral acceleration of the vehicle which is driving | running | working the deceleration and curve at the time of normal driving | running | working acquired from these drivers. Vehicle control device.

Images