WO2019097896A1

WO2019097896A1 - Vehicle control device

Info

Publication number: WO2019097896A1
Application number: PCT/JP2018/037434
Authority: WO
Inventors: 悠太郎伊東
Original assignee: 株式会社デンソー
Priority date: 2017-11-17
Filing date: 2018-10-05
Publication date: 2019-05-23

Abstract

This vehicle control device (50) is capable of controlling the travel of the host vehicle (10) so as to cause the host vehicle to follow a preceding vehicle traveling ahead of the host vehicle. This vehicle control device comprises: an environmental prediction unit (33) that predicts whether a change likely to lower the fuel economy of the host vehicle has arisen in the surrounding environment; and an acceleration control unit (32) that, if the environmental prediction unit predicts that a change likely to lower the fuel economy of the host vehicle has arisen in the surrounding environment, performs predictive control capable of restricting the acceleration of the host vehicle.

Description

Vehicle control device

Cross-reference to related applications

This application is based on Japanese Patent Application No. 2017-221734 filed on November 17, 2017, and Japanese Patent Application No. 2018-129289 filed on July 6, 2018, It claims the benefit of its priority, and the entire content of that patent application is incorporated herein by reference.

The present disclosure relates to a vehicle control device.

Conventionally, there is a vehicle control device described in Patent Document 1 below. This vehicle control device sets the minimum inter-vehicle distance according to the speed of the host vehicle, and when the inter-vehicle distance between the preceding vehicle traveling ahead of the host vehicle and the host vehicle becomes smaller than the minimum inter-vehicle distance, The power source such as a motor is stopped to make the vehicle coast. Further, the vehicle control device sets the maximum inter-vehicle distance according to the speed of the host vehicle, and starts driving the power source when the inter-vehicle distance becomes larger than the maximum inter-vehicle distance during coasting.

JP 2007-291919 A

When the preceding vehicle suddenly decelerates or another vehicle breaks in from the adjacent lane, the engine immediately after engine start that occurs due to deceleration control by braking or limitation of acceleration in order to secure an inter-vehicle distance with the preceding vehicle In some cases, stoppages of the As a result, energy loss occurs when deceleration control is performed by braking. In addition, stopping the engine immediately after starting the engine causes deterioration of engine efficiency. Therefore, deceleration control by braking and stopping of the engine immediately after the start of the engine cause deterioration of fuel efficiency.

On the other hand, in order to cope with such problems, it can be considered as a measure that the vehicle distance with the preceding vehicle is constantly increased or the acceleration is limited, or the like. The ability to follow the vehicle is degraded, and the driver feels uncomfortable.
No mention is made of the vehicle control device described in Patent Document 1 with respect to measures against these problems.

An object of the present disclosure is to provide a vehicle control device capable of improving fuel consumption while securing the followability to a preceding vehicle.

A vehicle control device according to an aspect of the present disclosure executes traveling control capable of controlling traveling of the own vehicle in order to cause the preceding vehicle following the own vehicle to follow the own vehicle. The vehicle control device predicts whether a change in the surrounding environment occurs such that the fuel efficiency of the host vehicle is deteriorated, and the change in the surrounding environment causes the fuel efficiency of the host vehicle to be deteriorated by the environment prediction unit. And an acceleration control unit that executes prediction control capable of limiting the acceleration of the host vehicle when it is predicted to occur.

According to this configuration, when a change in the surrounding environment occurs such that the fuel efficiency of the host vehicle is deteriorated, the acceleration of the host vehicle is limited in advance, so that the fuel efficiency of the host vehicle is actually deteriorated. It is possible to avoid. Therefore, the fuel consumption of the host vehicle can be improved.

FIG. 1 is a block diagram showing a schematic configuration of a vehicle according to the first embodiment. FIG. 2 is a graph showing an example of a method of controlling the vehicle by the ACC ECU according to the first embodiment. FIG. 3 is a graph showing an example of a method of controlling a vehicle by the ACC ECU according to the first embodiment. FIG. 4 is a flowchart showing the procedure of processing executed by the ACC ECU and the prediction ECU according to the first embodiment. FIG. 5 is a graph showing an example of a method of calculating the departure amount of the host vehicle with respect to the ideal travel range by the prediction ECU of the first embodiment. FIG. 6 is a graph showing the relationship between the vehicle speed and the probability used by the prediction ECU of the first embodiment. FIGS. 7A to 7C are timing charts showing transitions of the vehicle speed, the driving energy, and the inter-vehicle distance in the vehicle of the first embodiment. FIG. 8 is a block diagram showing a schematic configuration of a vehicle of the second embodiment. FIG. 9 is a flowchart showing the procedure of processing executed by the ACC ECU and the prediction ECU of the second embodiment. FIG. 10 is a map showing the relationship between acceleration and substantial engine efficiency used by the prediction ECU of the second embodiment. FIGS. 11A to 11C are timing charts showing transitions of the vehicle speed, the driving energy, and the engine rotational speed in the vehicle of the second embodiment. FIG. 12 is a time chart showing a switching procedure of a preceding vehicle executed by a prediction ECU according to another embodiment. FIGS. 13A and 13B are timing charts showing an example of the temporal transition of the vehicle speed and the deceleration behavior occurrence probability. FIG. 14 shows the calculated value of the frequency of the decelerating behavior model, the calculated value of the frequency of the passing behavior model, and the decelerated behavior occurrence probability with respect to the difference between the likelihoods of the decelerating behavior model and the passing behavior model of the third embodiment. It is a graph which shows transition of a value. FIG. 15 is a flow chart showing the procedure of processing executed by the ACC ECU and the prediction ECU of the third embodiment. FIG. 16 is a flow chart showing a procedure of behavior occurrence probability calculation processing executed by the prediction ECU of the third embodiment. FIG. 17 is a graph showing an example of a method of measuring the green signal duration according to the third embodiment. FIG. 18 is a graph showing an example of a method of measuring the green signal duration according to the third embodiment. FIG. 19 is a map showing the relationship between the green signal duration γ and the probability p _sig of switching from the green signal to the yellow signal according to the third embodiment.

Hereinafter, embodiments of a vehicle control device will be described with reference to the drawings. In order to facilitate understanding of the description, the same constituent elements in the drawings are denoted by the same reference numerals as much as possible, and redundant description will be omitted.
First Embodiment
First, a schematic configuration of a vehicle on which the vehicle control device of the first embodiment is mounted will be described.

As shown in FIG. 1, vehicle 10 is a so-called electric vehicle that travels based on the motive power of motor generator 20. Vehicle 10 includes inverter device 21, battery 22, and clutch 23 in addition to motor generator 20.
The battery 22 is formed of a secondary battery such as a lithium ion battery capable of charging and discharging. The inverter device 21 converts the DC power charged in the battery 22 into AC power, and supplies the converted AC power to the motor generator 20. The motor generator 20 is driven based on the AC power supplied from the inverter device 21 to rotate the first power transmission shaft 24. The first power transmission shaft 24 is connected to the second power transmission shaft 25 via the clutch 23. The clutch 23 connects the first power transmission shaft 24 and the second power transmission shaft 25 to enable transmission of power therebetween, the first power transmission shaft 24 and the second power transmission shaft. It is possible to transition to the non-connected state in which the transmission of power between them is cut off by releasing the connection with the switch 25. When the clutch 23 is in the connected state, the power transmitted from the motor generator 20 to the first power transmission shaft 24 is transmitted to the wheels 28 of the vehicle 10 through the second power transmission shaft 25, the differential gear 26, and the drive shaft 27. It is transmitted. Thus, the vehicle 10 travels. Thus, in the present embodiment, the motor generator 20 corresponds to a power train.

The motor generator 20 performs regenerative power generation when the vehicle 10 is braked. That is, the braking force acting on the wheels 28 at the time of braking of the vehicle 10 is input to the motor generator 20 via the drive shaft 27, the differential gear 26, the second power transmission shaft 25, the clutch 23 and the first power transmission shaft 24. Ru. The motor generator 20 generates electric power based on the power input from the wheel 28. The electric power generated by motor generator 20 is converted from AC power to DC power by inverter device 21 and charged in battery 22.

The vehicle 10 includes an MG (Motor Generator) ECU (Electronic Control Unit) 30, an EV (Electric Vehicle) ECU 31, an ACC (Adaptive Cruise Control) ECU 32, a prediction ECU 33, a periphery monitoring device 34, and a vehicle state quantity sensor 35. And further. Each of the ECUs 30 to 33 is mainly configured of a microcomputer having a storage device such as a CPU, a ROM, and a RAM, and executes various controls by executing a program stored in advance in the storage device.

The vehicle state quantity sensor 35 detects various state quantities of the vehicle 10. The various state quantities detected by the vehicle state quantity sensor 35 include information such as the speed and acceleration of the vehicle 10.
The periphery monitoring device 34 includes a camera, a millimeter wave radar device, a laser radar device, and the like. The surrounding area monitoring device 34 detects surrounding vehicles traveling around the vehicle 10 and calculates various state quantities related to the surrounding vehicles. The surrounding vehicles include a preceding vehicle traveling in front of the vehicle 10 in the lane in which the vehicle 10 is traveling, and an adjacent traveling vehicle traveling in the adjacent lane adjacent to the lane in which the vehicle 10 is traveling. . The state quantities detected by the periphery monitoring device 34 include the relative position, relative distance, relative velocity, relative acceleration, and the like of the surrounding vehicle with respect to the host vehicle 10. The relative distance of the surrounding vehicles corresponds to the distance between the vehicles. The relative position of the surrounding vehicle to the host vehicle 10 is defined as, for example, a position of a biaxial coordinate system using an axis in the left-right direction of the host vehicle 10 and an axis in the front-rear direction of the vehicle 10. In the present embodiment, the periphery monitoring device 34 corresponds to the periphery monitoring unit.

MGECU 30 controls the operation of motor generator 20 by driving inverter device 21 based on a command from EVECU 31. For example, EVECU 31 transmits, to MGECU 30, a power command value which is a command value of output power of motor generator 20. When receiving the power command value transmitted from EVECU 31, MGECU 30 controls the drive of inverter device 21 such that the power corresponding to the power command value is output from motor generator 20. When the vehicle 10 is braked, MGECU 30 drives inverter device 21 such that the electric power generated by the regenerative power generation of motor generator 20 is charged to battery 22.

The EVECU 31 calculates a power command value necessary to realize traveling according to the driver's driving request, and transmits the calculated power command value to the MGECU 30 so that the vehicle responds to the driver's driving request. Achieve 10 runs. Further, the EVECU 31 exchanges information necessary for various controls with the ACCECU 32 and calculates a power command value according to the request of the ACCECU 32. For example, when the EVECU 31 receives an acceleration command value which is a command value of the acceleration of the vehicle 10 from the ACCECU 32, the EVECU 31 calculates a power command value corresponding to the acceleration command value, and transmits the calculated power command value to the MGECU 30. The vehicle 10 is accelerated at an acceleration according to the command value. Further, the EVECU 31 causes the clutch 23 to transition to the connected or disconnected state, for example, in response to a request from the ACC ECU 32. In the present embodiment, the EVECU 31 corresponds to a traveling control unit.

The ACC ECU 32 executes traveling control of the vehicle based on, for example, the operation of an operation unit provided on the vehicle 10 by the occupant. The ACC ECU 32 performs, as traveling control, CC (Cruise Control) control for controlling the traveling of the vehicle 10 so that the vehicle 10 travels at a constant speed, and the preceding vehicle traveling ahead of the host vehicle 10. Execute ACC (Adaptive Cruise Control) control to control traveling. In the present embodiment, ACC control corresponds to speed control for controlling the acceleration and deceleration of the host vehicle 10 in order to cause the host vehicle 10 to follow the preceding vehicle. In the present embodiment, the ACC ECU 32 corresponds to an acceleration control unit.

Specifically, based on the relative velocity and relative distance of the preceding vehicle with respect to the vehicle 10, the ACC ECU 32 calculates an inter-vehicle time THW which is the time until the vehicle 10 catches up with the preceding vehicle. As shown in FIG. 2, when the inter-vehicle time THW is equal to or greater than a predetermined first time threshold Tth1, the ACC ECU 32 has a time margin before the vehicle 10 catches up with the preceding vehicle. Performs CC control. The ACC ECU 32 repeatedly executes acceleration and deceleration of the vehicle 10 as CC control. At that time, the ACC ECU 32 controls the acceleration and the deceleration of the vehicle 10 so that the average velocity of the vehicle 10 becomes the velocity Vset set by the occupant through the operation unit.

Specifically, as shown in FIG. 3, the ACC ECU 32 sets a lower limit velocity VL smaller than the set velocity Vset and an upper limit velocity VH larger than the set velocity, based on the set velocity Vset of the occupant. The ACC ECU 32 executes acceleration control to accelerate the vehicle 10 when the velocity Vc of the vehicle 10 reaches the lower limit velocity VL as the vehicle 10 decelerates. The ACC ECU 32 transmits, to the EVECU 31, an acceleration command value which is a preset positive value as acceleration control. Thus, the EVECU 31 calculates a power command value of a positive value corresponding to the acceleration command value, and transmits the power command value to the MGECU 30, whereby the vehicle 10 accelerates with a predetermined acceleration.

Further, when accelerating the vehicle 10, the ACC ECU 32 performs coasting control to decelerate the vehicle 10 by coasting the vehicle 10 when the velocity Vc of the vehicle reaches the upper limit velocity VH. The ACC ECU 32 transmits an acceleration command value set to zero to the EVECU 31 as a coasting control, and transmits to the EV ECU 31 a command to put the clutch 23 in a disconnected state. Thereby, the EVECU 31 transmits the power command value set to zero to the MGECU 30 and puts the clutch 23 in the non-connected state. As a result, the drive of the motor generator 20 is stopped, and the vehicle 10 is coasting, so the vehicle 10 is naturally decelerated. Thereafter, when the speed Vc of the vehicle 10 reaches the lower limit speed VL, the ACC ECU 32 transmits a command to put the clutch 23 in the connected state to the EVECU 31 and executes the above acceleration control again.

On the other hand, as shown in FIG. 2, when the inter-vehicle time THW is equal to or greater than the second time threshold Th2 and less than the first time threshold Tth1, the ACC ECU 32 executes the ACC control. The ACC ECU 32 executes, as ACC control, so-called burn and coast control in which acceleration and deceleration of the vehicle 10 are repeatedly performed so that the host vehicle 10 travels following the preceding vehicle.

Specifically, if the relative speed Vr of the preceding vehicle is less than the predetermined first speed threshold Vth1, that is, if the host vehicle 10 is rapidly approaching the preceding vehicle, the ACC ECU 32 performs regeneration control. Do. The ACC ECU 32 transmits an acceleration command value set to a negative value to the EVECU 31 as regeneration control. Thus, EVECU 31 calculates a power command value of a negative value corresponding to the acceleration command value, and transmits the power command value to MGECU 30 to cause motor generator 20 to perform regenerative power generation. When the motor generator 20 performs regenerative power generation, a braking force is applied to the wheels 28 of the vehicle 10 by the regenerative energy, so that the vehicle 10 can be decelerated more quickly than when the vehicle 10 is coasting. Thus, the inter-vehicle distance between the vehicle 10 and the preceding vehicle can be increased.

Further, when the ACCECU 32 has the second speed threshold Vth2 larger than the first speed threshold Vth1 and the relative speed Vr of the leading vehicle is in the range from the first speed threshold Vth1 to the second speed threshold Vth2, Execute the above coasting control. Further, the ACC ECU 32 has a third time threshold Tth3 set to a value between the first time threshold Tth1 and the second time threshold Tth2, and the relative speed Vr of the leading vehicle is equal to or higher than the second speed threshold Vth2. The coasting control described above is executed also when the inter-vehicle time THW is a value in the range from the second time threshold Tth2 to the third time threshold Tth3. This coasting control can increase the inter-vehicle distance between the vehicle 10 and the preceding vehicle.

Furthermore, when the relative speed Vr of the preceding vehicle is equal to or higher than the second speed threshold Vth2 and the inter-vehicle time THW is a value in the range from the third time threshold Tth3 to the first time threshold Tth1, the ACCECU 32 described above Execute acceleration control.
As described above, the ACC ECU 32 causes the host vehicle 10 to follow the preceding vehicle by selectively executing the regeneration control, the coasting control, and the acceleration control according to the inter-vehicle time THW and the relative speed Vr of the preceding vehicle.

By the way, when the preceding vehicle suddenly decelerates in a situation where the ACC ECU 32 executes acceleration control in CC control or ACC control, the inter-vehicle time THW and the relative speed Vr may be rapidly reduced. Thereby, when ACCECU 32 executes regeneration control and causes wheel 28 to generate braking force, part of kinetic energy of vehicle 10 can be recovered as electric energy to battery 22 by regeneration control, but other kinetic energy is When the wheel 28 generates a braking force, it is converted into heat energy and dissipated to the atmosphere, so it can not be recovered. Thus, the loss of energy is inevitable. In addition, when converting kinetic energy of the vehicle 10 into electrical energy, energy loss occurs. Such loss of energy causes the fuel efficiency of the vehicle 10 to deteriorate.

Therefore, in the vehicle 10 of the present embodiment, the prediction ECU 33 predicts whether there is a change in the surrounding environment in which the preceding vehicle rapidly decelerates, that is, a change in the surrounding environment in which the fuel efficiency of the vehicle 10 is deteriorated. In the present embodiment, the prediction ECU 33 corresponds to an environment prediction unit. When the ACCECU 32 predicts that a change in the surrounding environment such as the deterioration of the fuel efficiency of the vehicle 10 is occurring by the prediction ECU 33, the ACCECU 32 performs the vehicle 10 before the regenerative control by the ACC control is performed. Perform predictive control that limits the acceleration of the vehicle in advance.

Further, as shown in FIG. 1, the prediction ECU 33 can wirelessly connect to the network line 40 via the communication unit 36 mounted on the vehicle 10. The prediction ECU 33 performs various communications with the server device 41 via the network line 40. The server device 41 acquires various state quantities from a plurality of vehicles, and makes the state quantities into a database. In addition, the server device 41 creates various traveling models based on the state quantities of the plurality of vehicles made into a database. The prediction ECU 33 can predict the traveling locus of the surrounding vehicle by using the traveling model created by the server device 41. In the present embodiment, a vehicle control device 50 is configured by the ACC ECU 32, the prediction ECU 33, and the communication unit 36.

Since the prediction ECU 33 requires high-speed processing and requires connection with a plurality of ECUs, the prediction ECU 33 is disposed independently of the ECU that controls each component.
Next, with reference to FIG. 4, the processing procedure of the prediction control executed by the ACC ECU 32 and the prediction ECU 33 will be specifically described. The ACC ECU 32 and the prediction ECU 33 repeatedly execute the processing shown in FIG. 4 at a predetermined cycle.

As shown in FIG. 4, the prediction ECU 33 first acquires the current state quantities of the surrounding vehicles from the surrounding area monitoring device 34 as the process of step S10. The information acquired by the prediction ECU 33 from the surroundings monitoring device 34 includes the relative distance, the relative velocity, the relative acceleration, and the like of the surrounding vehicles.

After the process of step S10, the ACC ECU 32 temporarily sets the acceleration command value α transmitted to the EVECU 31 as the process of step S11. Specifically, the ACCECU 32 calculates the inter-vehicle time using the relative speed and the relative distance of the preceding vehicle among the information acquired from the surroundings monitoring device 34 in the process of step S10, and calculates the calculated inter-vehicle time and the relative speed By executing the control shown in FIG. 2 based on the above, the first set value α1 of the acceleration command value α is calculated. Then, the ACC ECU 32 temporarily sets the acceleration command value α to the first set value α1.

The prediction ECU 33 predicts future state quantities of surrounding vehicles including the preceding vehicle and the adjacent traveling vehicle as the process of step S12, subsequent to the process of step S11. The predicted state quantities of the surrounding vehicles include future relative positions, relative distances, relative speeds, relative accelerations, time-series data of the surrounding vehicles, and the like. Specifically, the prediction ECU 33 predicts future state quantities from the present to a predetermined time after the current time and the past values of the state quantities of the surrounding vehicles using a computing equation, a model, and the like. As a result, the prediction ECU 33 can predict the behavior of the surrounding vehicle from the present time until a predetermined time has elapsed.

In addition, you may perform the prediction process of step S12 based not only on the present value and the past value of the state quantity of a surrounding vehicle based on the information regarding the state quantity of another surrounding vehicle. In this prediction, the behavior of surrounding vehicles may be expressed as a predetermined probability model based on past vehicle travel data, and may be predicted as a time-series waveform, or a vehicle that has traveled in the past at a point currently traveling The traveling data of the above may be processed statistically to calculate the deceleration or interruption probability of the vehicle at a certain point. The estimated time is a time that can reach all vehicle speeds that can be considered as traveling vehicle speed by acceleration in normal traveling. For example, the range of acceleration may be set in the range of “−1 [G]” to “1 [G]”, and the total vehicle speed may be a court limited vehicle speed from “0 [km / h]”.

After the process of step S12, the prediction ECU 33 determines whether the vehicle 10 needs to be decelerated based on the behavior of the surrounding vehicle as the process of step S13. Specifically, this determination process is performed by the following method.
When there are N nearby vehicles, when the value i is defined as an integer in the range of "1 ≦ i N N", the vehicle 10 uses the predetermined state quantity b (t I will drive by). The state quantity b (t) is, for example, a function of acceleration with the time t as a variable. Then, when the host vehicle 10 travels with the state quantity b (t), it is assumed that the braking energy generated in the _host vehicle 10 can be expressed by "E _{brk i} (b (t))". “E _{brk i} (b (t))” is a predicted value of braking energy which is predicted to be generated when the host vehicle 10 is decelerated by execution of the ACC control in a period from the current time until the elapse of a predetermined time.

Further, as shown in FIG. 5, the follow-up performance of the host vehicle 10 with respect to the i-th peripheral vehicle is ideal travel within an ideal inter-vehicle distance range when executing ACC control that causes the host vehicle 10 to follow the preceding vehicle. In the case of the range A, evaluation can be made based on the deviation amount y _i of the expected position of the vehicle 10 with respect to the ideal traveling range in the period from the present to the lapse of a predetermined time. The ideal travel range A is set on the basis of the expected travel position of the i-th nearby vehicle indicated by the alternate long and short dash line, and can be obtained from the predicted travel position of the nearby vehicle using an arithmetic expression etc. . The follow-up performance evaluation value C _i (b (t)) of the vehicle 10 can be obtained by the following equation f1 using the deviation amount y _i of the predicted position of the vehicle 10 with respect to the ideal travel range A is there. In addition, "T" of Formula f1 is prediction time.

From the above, the expected value E _brk (b (t)) of the braking energy of the _host vehicle with respect to N neighboring vehicles and the expected value C (b (t)) of the following performance evaluation value are obtained by the following formulas f2 and f3. It can be defined.

Note that “p _i ” in the equations f2 and f3 is the occurrence probability of the behavior of the i-th nearby vehicle. Specifically, in consideration of the fact that the prediction result of the behavior of the i-th peripheral vehicle includes a predetermined uncertainty, in the present embodiment, when the host vehicle 10 travels with the state quantity b (t), The probability p _i is used as a parameter indicating the likelihood that the state quantity of the i-th surrounding vehicle will appear. For example, the vehicle speed of the i-th nearby vehicle at a predetermined time can be expressed as a probability as shown in FIG.

By using the expected value E _brk (b (t)) of the braking energy of the _host vehicle and the expected value C (b (t)) of the following performance evaluation value, evaluation as represented by the following formula f 4 Function F _E1 can be constructed.

In addition, "k" of Formula f4 is each weighting coefficient of braking energy and a tracking performance evaluation value. The coefficient k is a value set in the range of “0 ≦ k ≦ 1”. In the present embodiment, a predetermined value is used as the weighting factor.

If the state quantity b (t) of the host vehicle 10 is determined so that the value of the evaluation function _FE1 becomes minimum, the state quantity b (t of the host vehicle 10 in which the braking energy is suppressed while ensuring the tracking performance) ) Can be asked. In other words, it is possible to obtain the state quantity b (t) of the vehicle 10 capable of improving the fuel consumption while securing the following performance.

Based on the above method, the prediction ECU 33 executes the determination process of step S13. Specifically, the prediction ECU 33 uses, for example, an arithmetic expression obtained in advance by an experiment or the like as an arithmetic expression of the braking energy E _{brk i} (b (t)).
Further, the prediction ECU 33 calculates the predicted traveling locus of the i-th peripheral vehicle from the traveling model etc. based on the predicted state quantity of the i-th peripheral vehicle among the prediction information acquired in the process of step S12. Further, the prediction ECU 33 obtains an ideal traveling range A based on the calculated predicted traveling locus of the i-th surrounding vehicle, thereby calculating an arithmetic expression of the tracking performance evaluation value C _i (b (t)) of the host vehicle 10. decide.

Further, the prediction ECU 33 acquires the traveling model from the server device 41 via the communication unit 36, and also determines the state quantity of the i-th peripheral vehicle based on the acquired traveling model and the state quantity of the i-th peripheral vehicle. Calculate the occurrence probability p _i of
Thus, the prediction ECU 33 calculates the braking energy E _{brk i} (b (t)) in the above equation f4, the calculation equation of the following performance evaluation value C _i (b (t)), and the occurrence probability p _i Then, the state quantity b (t) of the vehicle 10 is determined such that the value of the evaluation function _FE1 becomes minimum. In the minimization of the evaluation function F _E1 , the behavior of the host vehicle 10 is considered in a plurality of ways, and the values of the evaluation function at those respective times are calculated, and the host vehicle with the smallest value of the evaluation function F _E1 Ten state quantities b (t) may be selected or may be determined using an optimization method. Since the state quantity b (t) is a function of the acceleration of the vehicle 10, the operation of the above, the prediction ECU33 obtains a second set value α2 of the evaluation function F acceleration command value as the value becomes the minimum _E1 alpha be able to.

In addition, when computing prediction setting value alpha 2 of acceleration command value alpha, prediction ECU33 sets the lower limit to 2nd setting value alpha 2, etc., and can carry out the 2nd setting which can carry out coasting control of vehicles 10 The value α2 may be determined. Thus, when the vehicle 10 is decelerated using the second set value α2 as the acceleration command value α, generation of braking energy in the vehicle 10 can be avoided, and the fuel efficiency of the vehicle 10 can be improved.

The prediction ECU 33 determines whether the vehicle 10 needs to be decelerated by comparing the first set value α1 with the second set value α2 in the process of step S13. Specifically, when the first set value α1 is equal to or less than the second set value α2, the prediction ECU 33 determines that deceleration of the vehicle 10 is not necessary. That is, the prediction ECU 33 makes a negative determination in the process of step S13. In this case, the prediction ECU 33 determines that there is no change in the surrounding environment in which the fuel efficiency of the vehicle 10 deteriorates. When the prediction ECU 33 makes a negative determination in the process of step S13, the ACC ECU 32 transmits the acceleration command value α set to the first set value α1 to the EVECU 31 as the process of step S15.

If the second set value α2 is less than the first set value α1 in the process of step S13, the prediction ECU 33 determines that the vehicle 10 needs to be decelerated. That is, the prediction ECU 33 makes an affirmative determination in the process of step S13. In this case, it is determined that there is a change in the surrounding environment in which the fuel efficiency of the vehicle 10 deteriorates. The ACC ECU 32 changes the acceleration command value α from the first set value α1 to the second set value α2 as the process of step S14 when the prediction ECU 33 makes an affirmative determination in the process of step S13. Then, the ACC ECU 32 transmits the acceleration command value α set to the second set value α2 to the EVECU 31 as the process of step S15. Thereby, the second set value α2 smaller than the first set value α1 set by the ACC control is transmitted to the EVECU 31 as the acceleration command value α. As a result, the ACC ECU 32 implements deceleration control to decelerate the host vehicle 10 at a deceleration smaller than the deceleration that can be set by the ACC control.

Next, an operation example of the vehicle control device 50 of the present embodiment will be described.
As shown by a dashed dotted line in FIG. 7A, it is assumed that the speed Vp of the preceding vehicle has dropped sharply from time t11. In such a situation, since the inter-vehicle time and the relative speed between the own vehicle 10 and the preceding vehicle rapidly decrease, when only the ACC control is performed, as shown by a two-dot chain line in FIG. By executing the regeneration control after time t11, the drive energy Ec of the vehicle 10 is rapidly decelerated. The drive energy Ec shown in FIG. 7B indicates the magnitude of the drive energy for traveling of the vehicle 10 generated by the motor generator 20 as a positive value, and the magnitude of the braking energy generated at the time of the regeneration control Is indicated by a negative value. With the execution of such regenerative control, as shown by a two-dot chain line in FIG. 7C, the inter-vehicle distance Lc between the host vehicle 10 and the preceding vehicle is increased after time t11, as shown in FIG. The speed Vb of the vehicle 10 decreases as indicated by the two-dot chain line in FIG. As described above, when braking energy is generated, a part of the energy is converted to heat energy or the like, resulting in energy loss.

In this respect, when the generation of braking energy is predicted after time t11 at time t10 before time t11, the prediction ECU 33 of the present embodiment calculates braking energy by the calculation of the above-mentioned equation f4. While calculating the second set value α2 of the acceleration command value α that can be suppressed, the acceleration command value α is set to the second set value α2. When, for example, the EVECU 31 sets the power command value to zero by transmitting the acceleration command value α from the ACCECU 32 to the EVECU 31, as shown by a solid line in FIG. Ec becomes zero. Thus, as shown by the solid line in FIG. 7 (A), the speed Va of the vehicle 10 decreases after time t10, and the vehicle 10 and the leading vehicle are as shown by the solid line in FIG. 7 (C). Inter-vehicle distance Lc increases. By thus decelerating the vehicle 10, as shown in FIG. 7B, the generation of braking energy can be suppressed, and as a result, the fuel efficiency of the vehicle 10 can be improved.

According to the vehicle control device 50 of the present embodiment described above, the actions and effects shown in the following (1) to (7) can be obtained.
(1) The ACC ECU 32 performs prediction control capable of limiting the acceleration of the vehicle 10 when the prediction ECU 33 predicts that a change in the surrounding environment is occurring such that the fuel efficiency of the vehicle 10 is deteriorated. Run. Thus, when a change in the surrounding environment occurs such that the fuel efficiency of the host vehicle 10 is deteriorated, the acceleration of the host vehicle 10 is limited in advance, so that the fuel efficiency of the host vehicle 10 is actually deteriorated. It is possible to avoid. Thus, the fuel consumption of the host vehicle 10 can be improved.

(2) When the ACC ECU 32 predicts a change in the surrounding environment in which the host vehicle 10 needs to be decelerated, the ACC ECU 32 predicts that a change in the surrounding environment has occurred such that the fuel efficiency of the host vehicle 10 is degraded. The ACC ECU 32 uses the second set value α2 smaller than the first set value α1 of the acceleration command value α set by the ACC control when predicting a change in the surrounding environment where the host vehicle 10 needs to be decelerated. Acceleration control for actually limiting the acceleration of the vehicle 10 is executed. As a result, the ACC ECU 32 executes, as predictive control, deceleration control to decelerate the host vehicle at a deceleration smaller than the deceleration that can be set by the ACC control. According to such a configuration, it is possible to reduce the loss of energy generated at the time of deceleration for securing the inter-vehicle distance.

(3) The prediction ECU 33 predicts the presence or absence of a change in the surrounding environment in which the host vehicle 10 needs to be decelerated, based on the index related to the fuel efficiency of the host vehicle 10 and the index related to the follow-up performance of the host vehicle with respect to the preceding vehicle. Specifically, the prediction ECU 33 predicts braking energy that is predicted to occur when the host vehicle 10 is decelerated by execution of the ACC control in a period from the present to the end of a predetermined time period as an index related to fuel consumption of the host vehicle 10 Use the value. Further, the prediction ECU 33 uses the deviation y _i of the position of the host vehicle with respect to the ideal value of the ACC control in the period from the present to the end of the predetermined time as an index related to the follow-up performance of the host vehicle with respect to the preceding vehicle. Thus, it is possible to reliably determine the deceleration of the vehicle 10 to obtain the effects of the target fuel efficiency improvement and the suppression of the deterioration of the follow-up performance.

(4) The prediction ECU 33 represents the index related to the fuel efficiency of the host vehicle 10 and the index related to the following performance of the host vehicle with respect to the preceding vehicle as probability information as represented by the above formulas f2 and f3. And prediction ECU33 uses a function which can be expressed by the above-mentioned formula 4 as an evaluation function which consists of an expected value based on an index about fuel consumption of self-vehicles 10, and an index based on an index about follow-up performance of self-vehicle 10 over preceding vehicles At the same time, a change in the surrounding environment in which the vehicle 10 needs to be decelerated is predicted based on the calculated value of the equation f4. This makes it possible to reliably determine the deceleration of the vehicle 10 to obtain the effects of fuel efficiency improvement and suppression of the deterioration of the following performance even when uncertainty is included in the predicted information regarding the change in the surrounding environment. .

(5) The prediction ECU 33 calculates a second set value α2 of the acceleration command value capable of performing coasting control of the vehicle 10. As a result, the ACC ECU 32 performs coasting control that causes the vehicle 10 to coast while the output from the motor generator 20 is not transmitted to the wheels of the vehicle 10. According to such a configuration, when decelerating the vehicle 10 using the prediction information, the vehicle 10 can be decelerated with higher fuel efficiency.

(6) The ACC ECU 32 repeatedly executes acceleration and deceleration of the host vehicle 10 to execute burn-and-coast control that causes the host vehicle 10 to follow the preceding vehicle. Thereby, the vehicle 10 can travel generally by a travel method with high fuel efficiency.
(7) The prediction ECU 33 predicts the deceleration of the preceding vehicle as a change in the surrounding environment such that the fuel efficiency of the host vehicle is deteriorated. This makes it possible to improve the fuel consumption with respect to changes in the surrounding environment that have a large impact on the fuel consumption.

(Modification)
Next, a modification of the vehicle control device 50 of the first embodiment will be described.
As shown by a broken line in FIG. 1, the vehicle control device 50 of the present modification further includes an HMI (human machine interface) ECU 37. The HMI ECU 37 is a part that controls the notification device 38 mounted on the vehicle 10 to notify the occupants of the vehicle 10 in various ways. A speaker, a display or the like can be used as the notification device 38.

The ACC ECU 32 transmits the acceleration command value α to the HMIECU 37 in the process of step S15 shown in FIG. 4. The HMIECU 37 executes instruction control for instructing the occupant of the host vehicle 10 of the driving method so that the acceleration of the host vehicle 10 is limited based on the acceleration command value α transmitted from the ACC ECU 32. For example, the HMI ECU 37 causes the passenger to drive the vehicle by causing the occupant to recognize the acceleration or velocity corresponding to the acceleration command value α by means of a speaker by voice or displaying the acceleration or velocity corresponding to the acceleration command value α on the display. To direct.

The HMI ECU 37 may instruct the driver on the driving method by adjusting the depression amount of the accelerator pedal based on the acceleration command value α or adjusting the depression amount of the brake pedal.
Even with such a method, the vehicle 10 can be decelerated.

Second Embodiment
Next, a second embodiment of the vehicle control device 50 will be described. Hereinafter, differences from the vehicle control device 50 of the first embodiment will be mainly described. First, a schematic configuration of a vehicle 10 on which the vehicle control device 50 of the second embodiment is mounted will be described.

As shown in FIG. 8, the vehicle 10 according to the present embodiment is a so-called hybrid vehicle that uses not only the motor generator 20 but also the engine 60 as a power source. The engine 60 rotates the first power transmission shaft 29a by its drive. The first power transmission shaft 29 a is connected to the second power transmission shaft 29 b via the clutch 23. The clutch 23 connects the first power transmission shaft 29a and the second power transmission shaft 29b to enable transmission of power therebetween, the first power transmission shaft 29a and the second power transmission shaft. It is possible to transition to a non-connected state in which the transmission of power between them is cut off by releasing the connection with 29b.

The motor generator 20 applies power to the second power transmission shaft 29b based on energization. Therefore, when the clutch 23 is in the connected state, power is applied to the second power transmission shaft 29 b from at least one of the engine 60 and the motor generator 20. The power applied to the second power transmission shaft 29 b is input to the transmission 62. The transmission 62 accelerates or decelerates the total power of the engine 60 and the motor generator 20 input from the second power transmission shaft 29 b or the power obtained by subtracting the power converted from the engine 60 into electric power by the motor generator 20. It transmits to the 3rd power transmission shaft 29c. The power transmitted to the third power transmission shaft 29 c is transmitted to the wheels 28 of the vehicle 10 via the differential gear 26 and the drive shaft 27. Thus, the vehicle 10 travels. Thus, in the present embodiment, the motor generator 20 and the engine 60 correspond to a power train.

The vehicle 10 is mounted with an engine ECU 63 that controls the driving of the engine 60 in an integrated manner. The engine ECU 63 also controls the drive of the clutch 23.
In the vehicle 10, an HV (Hybrid Vehicle) ECU 39 is mounted in place of the EVECU 31. The HVECU 39 performs integrated arbitration control of the engine 60, the motor generator 20, and the battery 22 by exchanging information necessary for control with the MGECU 30 and the engine ECU 63. Specifically, the HVECU 39 controls the drive of the motor generator 20 and the engine 60 based on the acceleration command value transmitted from the ACC ECU 32. The HVECU 39 transmits a predetermined power command value to the engine ECU 63 so as to accelerate the vehicle 10, for example, when the engine 60 is in a stopped state and the acceleration command value α is equal to or greater than a predetermined acceleration threshold αth. Causes the engine 60 to restart. Further, when the acceleration command value α is less than the acceleration threshold value αth, the HVECU 39 transmits a command to stop the engine 60 to the engine ECU 63 and transmits a predetermined power command value to the MGECU 30 in order to reduce fuel consumption. Makes the vehicle 10 travel by EV. In the present embodiment, the HVECU 39 corresponds to a traveling control unit that controls driving and stopping of the engine 60 and the motor generator 20 based on the traveling state of the host vehicle 10.

Next, with reference to FIG. 9, the processing procedure of prediction control executed by the ACC ECU 32 and the prediction ECU 33 will be specifically described. The ACC ECU 32 and the prediction ECU 33 repeatedly execute the process shown in FIG. 9 at a predetermined cycle.
As shown in FIG. 9, following the process of step S12, the prediction ECU 33 determines whether it is necessary to limit the acceleration of the vehicle 10 in order to suppress the short-time driving of the engine 60 as the process of step S20. judge. Specifically, this determination process is performed by the following method.

The efficiency at the time of taking energy from the engine 60 is deteriorated due to the intake delay of the engine 60, the energy consumption amount for starting the engine 60, the increase of the fuel consumption amount at the start of the engine 60, and the like. Taking these into consideration, the actual engine efficiency η _eng at the time of engine traveling is expressed as shown in the following equation f5.

In the equation f5, “δ _delay ” indicates a coefficient for the intake delay. “Η _e ” indicates an ideal engine efficiency which is an engine efficiency when the engine 60 is moved in a steady state. “E _out ” indicates the ideal output energy of the engine 60. "E _egon " indicates the starting energy of the engine 60. “E _in ” indicates the input fuel energy of the engine 60. "E _add " indicates the start-up incremental energy. "T _acc " indicates the time required for acceleration.

The substantial engine efficiency η ^* _{eng on} the left side of the equation f5 is used as an index related to the fuel efficiency of the vehicle 10. Further, the value on the right side of the equation f5 represents the ratio of the output energy of the engine to the input energy of the engine.
On the other hand, if the real engine efficiency when the vehicle 10 performs so-called EV travel, which travels with only the power of the motor generator 20, is defined by the system efficiency η _sys based on the past travel results, the real engine efficiency η during EV travel _sys can be expressed as the following equation f6.

In Equation f6, "E _sysout " indicates the output energy of the power train. "E _sysin " represents input fuel energy.
The actual engine efficiency η _sys at the time of EV travel indicates the ratio of the output energy of the powertrain to the input energy of the powertrain of the vehicle 10 in a state where the engine 60 is stopped.

From the above, the future substantial engine efficiency η ^* _eng for the acceleration command value α can be expressed as shown in FIG. That is, when acceleration command value α is less than acceleration threshold value αth, vehicle 10 travels with the motive power of motor generator 20, and thus the actual engine efficiency ^** _{eng in the} future becomes the value of the right side of the above equation f6. . Further, when acceleration command value α is equal to or higher than acceleration threshold value αth and smaller than acceleration command value αbc used at the time of acceleration in burn-and-coast control under ACC control, future engine efficiency η ^* _eng is It can be determined by the right side of the above equation f5. The future real engine efficiency η ^* _eng determined in this way represents the ratio of the output energy of the powertrain to the input energy of the powertrain of the vehicle 10.

As in the case of the deceleration control for suppressing braking energy described in the first embodiment, when the host vehicle 10 travels with a predetermined state quantity b (t) while the i-th peripheral vehicle travels, the host vehicle 10 at that time The expected value ^** _eng (b (t)) of the real engine efficiency ^** _eng of the vehicle 10 can be obtained by the following equation f7.

By using the above expected value η ^* _eng (b (t)), an evaluation function FE ₂ represented by the following formula f 8 can be constructed.

If the state quantity b (t) of the host vehicle 10 is determined such that the evaluation function _FE2 becomes minimum, the host state quantity of the host vehicle 10 in which the short-time driving of the engine 60 is suppressed while securing the following performance. We can find b (t). In other words, it is possible to obtain the state quantity b (t) of the vehicle 10 capable of improving the fuel consumption while securing the following performance.

Based on the above method, the prediction ECU 33 executes the determination process of step S20. Specifically, the prediction ECU 33 has a map showing the relationship between the acceleration command value α and the substantial engine efficiency η ^* _eng as shown in FIG. Note that the prediction ECU 33 stores data of powertrain output energy and input fuel energy up to the present, and based on the stored data, the substantial engine efficiency 時の_sys during EV traveling from the above equation f6 It is calculating sequentially. Then, the prediction ECU 33 uses the calculated substantial engine efficiency _{sys sys} as the substantial engine efficiency ^** _eng when the acceleration command value α is less than the acceleration threshold value α th.

Prediction ECU33 is the evaluation function F _E2 determines the state quantity of the vehicle 10 b (t) so as to minimize. Since the state quantity b (t) is a function of the acceleration of the vehicle 10, the operation of the above, the prediction ECU33 obtains a third set value α3 of the evaluation function F acceleration command value as the value becomes the minimum _E2 alpha be able to.

The prediction ECU 33 is required to limit the acceleration of the vehicle 10 in order to suppress short-time driving of the engine 60 by comparing the first set value α1 with the third set value α3 in the process of step S20. Determine if it is or not. Specifically, when the first set value α1 is equal to or less than the third set value α3, the prediction ECU 33 determines that it is not necessary to limit the acceleration of the vehicle 10. That is, the prediction ECU 33 makes a negative determination in the process of step S20. In this case, the prediction ECU 33 determines that there is no change in the surrounding environment in which the fuel efficiency of the vehicle 10 deteriorates. Then, the ACC ECU 32 and the prediction ECU 33 execute the processing after step S13.

The prediction ECU 33 determines that it is necessary to limit the acceleration of the vehicle 10 when the third set value α3 is less than the first set value α1. That is, the prediction ECU 33 makes an affirmative determination in the process of step S20. In this case, the prediction ECU 33 determines that there is a change in the surrounding environment in which the fuel efficiency of the vehicle 10 deteriorates. The ACC ECU 32 changes the acceleration command value α from the first set value α1 to the third set value α3 as the process of step S21 when the prediction ECU 33 makes an affirmative determination in the process of step S20. After that, the ACC ECU 32 and the prediction ECU 33 execute the processing after step S13.

The prediction ECU 33 determines whether the vehicle 10 needs to be decelerated by comparing the first set value α1, the second set value α2, and the third set value α3 in the process of step S13. . Specifically, when the third set value α3 is less than the first set value α1 and the third set value α3 is less than the second set value α2, the prediction ECU 33 is affirmative in the process of step S13. to decide. On the other hand, when the first set value α1 is equal to or less than the third set value α3 or the second set value α2 is equal to or less than the third set value α3, the prediction ECU 33 makes a negative decision in the process of step S13.

Next, an operation example of the vehicle control device 50 of the present embodiment will be described.
As shown by a dashed dotted line in FIG. 11A, it is assumed that the speed Vp of the preceding vehicle increases sharply and then decreases sharply. When only the ACC control is being performed in such a situation, the ACC ECU 32 starts the engine 60 at time t20 in order to cause the host vehicle 10 to follow the preceding vehicle. When the ACC ECU 32 starts the engine 60 at time t20, the drive energy Ec of the vehicle 10 becomes larger than the energy Es at the time of engine start, as shown by a two-dot chain line in FIG. Further, as indicated by a two-dot chain line in FIG. 11C, the rotational speed Nc of the engine 60 increases after time t20.

Thereafter, when the preceding vehicle suddenly decelerates, the inter-vehicle time and the relative speed between the own vehicle 10 and the preceding vehicle rapidly decrease. As a result, when the regenerative control is executed at time t21, the drive energy Ec of the vehicle 10 is rapidly reduced as shown by the two-dot chain line in FIG. 11 (B). By execution of such regenerative control, as shown by a two-dot chain line in FIG. 11C, after time t21, the rotational speed Nc of the engine 60 rapidly decreases, and the engine 60 is stopped. Thus, if the engine 60 is stopped in a short time after the engine 60 is started, the energy used to start the engine 60 is lost.

In this respect, the prediction ECU 33 according to the present embodiment calculates the third set value α3 of the acceleration command value that can suppress the driving of the engine 60 for a short time by the calculation of the equation f8 described above, and α is set to a third set value α3. Since the acceleration command value α is transmitted from the ACC ECU 32 to the HVECU 39, the actual acceleration of the vehicle 10 does not easily rise to the acceleration threshold value αth for starting the engine 60, so the engine 60 does not start. As a result, as shown in FIG. 11A, the speed Vc of the vehicle 10 decreases, and as shown in FIG. 11B, the drive energy Ec of the vehicle 10 rises to the energy Es at the time of engine start. I will not do. Thus, it is possible to suppress wasteful consumption of the energy Es at the time of engine start, and as a result, the fuel consumption of the vehicle 10 can be improved.

According to the vehicle control device 50 of the present embodiment described above, in addition to the actions and effects shown in the above (1) to (7), the actions and effects shown in the following (8) to (10) are obtained be able to.
(8) The ACC ECU 32 limits the acceleration of the host vehicle 10 so that the engine ECU 63 can not easily restart the engine 60. As a result, driving of the engine 60 for a short time is suppressed, and energy loss can be reduced. Therefore, the fuel consumption of the vehicle 10 can be improved.

(9) The prediction ECU 33 determines whether to limit the acceleration of the host vehicle 10 based on the index related to the fuel efficiency of the host vehicle 10 and the index related to the follow-up performance of the host vehicle 10 with respect to the preceding vehicle. Specifically, the prediction ECU 33 determines the powertrain for the input energy of the powertrain of the vehicle 10 in a period from the present to the time after the elapse of a predetermined time as shown by the above-mentioned equation f7 as an index related to the fuel efficiency of the vehicle 10. The predicted value of the ratio of the output energy of Further, as an index related to the follow-up performance of the host vehicle 10 with respect to the preceding vehicle, the departure amount _yi of the position of the host vehicle with respect to the ideal value of the ACC control in the period from the present until the elapse of a predetermined time is used. Thus, it is possible to reliably determine the deceleration of the vehicle 10 to obtain the effects of the target fuel efficiency improvement and the suppression of the deterioration of the follow-up performance.

(10) The predicted value of the ratio of the output energy of the powertrain to the input energy of the powertrain of the vehicle 10 includes the predicted value represented by the equation f5 and the predicted value represented by the equation f6. The predicted value represented by the equation f5 is a predicted value of the ratio of the output energy of the engine 60 to the input energy of the engine 60 in a state where the engine 60 is driven. The predicted value represented by the equation f6 is a predicted value of the ratio of the output energy of the powertrain to the input energy of the powertrain of the vehicle 10 in the state where the engine 60 is stopped. As a result, it is possible to make the vehicle 10 travel by determining the method of traveling with high fuel efficiency, including the traveling of the vehicle 10 in the state where the engine 60 is stopped.

Third Embodiment
Next, a third embodiment of the vehicle control device 50 will be described. Hereinafter, differences from the vehicle control device 50 of the first embodiment will be mainly described.
In the present embodiment, an example of a method of calculating the occurrence probabilities p _i of the behavior of the peripheral vehicle used for expression f2, f3 above. In the following, for simplicity, as a probability p _i of the behavior of the peripheral vehicle, near the vehicle it will be described using a deceleration behavior occurrence probability is the probability of decelerating.

First, consider a case where the deceleration behavior of the vehicle is predicted at a point where two patterns of a situation in which the surrounding vehicle decelerates in a predetermined place and a situation in which the surrounding vehicle passes through the predetermined place are assumed. At this time, for example, when information as shown in FIG. 13A is acquired by the surroundings monitoring device 34 as the vehicle speed information of the surroundings, whether or not the surroundings take deceleration behavior at the current time t30 When it comes to prediction, the prediction is performed based on the vehicle speed information of the surrounding vehicles in the past before the current time t30. As shown in FIG. 13A, the speed of the surrounding vehicle is constant before time t30. Therefore, before time t30, as shown in FIG. 13B, the deceleration behavior occurrence probability, which is the probability that the surrounding vehicle takes the deceleration behavior, is calculated as, for example, "0.5", that is, "50%". Can. Therefore, before time t30, the probability that the peripheral vehicle decelerates is "0.5", and the probability that the peripheral vehicle passes is "0.5". Also, after time t30, if the speed of the surrounding vehicle gradually decreases with the passage of time, it is considered that the surrounding vehicle has started to take deceleration behavior, so the value of the deceleration behavior occurrence probability is gradually from "0.5" Will rise to

As described above, in the case of predicting the deceleration behavior of the surrounding vehicle using the traveling data such as the vehicle speed information of the surrounding vehicle in the past, learning the past traveling data to predict the deceleration behavior of the surrounding vehicle It is possible to calculate the deceleration behavior occurrence probability more accurately.
On the other hand, for example, in a situation where the surrounding vehicles pass through a place where a plurality of vehicles tend to be slowing down statistically or the traffic light in front of the surrounding vehicles is switched from green to yellow, It is possible to predict that the surrounding vehicles will take a decelerating behavior before is actually detected. If such a situation is tentatively detected before time t30, if the deceleration behavior occurrence probability is corrected to a value larger than "0.5" at that time, not only the information on the past travel data of the surrounding vehicles but also the surrounding vehicles And, it is possible to calculate the deceleration behavior occurrence probability that also reflects the information of the future prediction behavior of the surrounding vehicles. If travel control of the host vehicle 10 is executed based on the deceleration behavior occurrence probability calculated in this manner, more appropriate travel control of the host vehicle 10 according to the predicted behavior of the surrounding vehicle can be realized. Become.

Therefore, in the present embodiment, the server device 41 constructs a learning model of the vehicle behavior based on past travel data transmitted from a predetermined vehicle. In addition, not only the own vehicle 10 but a vehicle different from the own vehicle 10 may be included in the predetermined vehicle. Further, the number of predetermined vehicles is not limited to one but may be plural. The learning model of the vehicle behavior uses the traveling data of the vehicle as an observation value, and a likelihood function capable of calculating the likelihood consisting of a numerical value representing the likelihood of occurrence of a predetermined behavior of the vehicle with respect to the observation value. It will be The likelihood corresponds to an index that represents the similarity between the travel data of the vehicle and the learning information. The server device 41 creates an arithmetic expression capable of determining the deceleration behavior occurrence probability of the vehicle based on the constructed learning model of the vehicle behavior. This arithmetic expression is created, for example, as follows.

In a case where two patterns of a situation in which the vehicle decelerates in a predetermined place and a situation in which the vehicle passes are assumed, the server device 41 decelerates the behavior model and the passage behavior model based on the traveling data transmitted from the predetermined vehicle And build. The deceleration behavior model and the passage behavior model are learning models of the vehicle behavior. The traveling data includes information on the time series of the vehicle speed.

Further, the server device 41 calculates the likelihood of the deceleration behavior model and the likelihood of the passage behavior model based on traveling data of a predetermined vehicle, and obtains a likelihood difference which is a difference value between them. The server device 41 performs this calculation on all past traveling data to calculate the frequency at which the deceleration behavior occurs and the frequency at which the passage behavior occurs at each likelihood difference. Thus, for example, the relationship between the likelihood difference and the frequency of occurrence of deceleration as shown by the alternate long and short dash line in FIG. 14 and the relationship between the likelihood difference and the frequency of occurrence of passage as shown by the alternate long and two short dashes line in FIG. Can. Based on the information shown in FIG. 14, the server device 41 creates an arithmetic expression of a learning value p _lrn of the deceleration behavior occurrence probability as shown in the following expression f9.

In equation f9, "t" indicates time. “T = 0” indicates the start time of each behavior model, and “t = T _stop ” indicates the end time in each behavior model. Also, “μ _dec ” indicates the average value of the deceleration behavior model, “σ _dec ² ” indicates the dispersion of the deceleration behavior model, “μ _pass ” indicates the average value of the passage behavior model, and “σ _pass ² ” indicates the average value The variance of the passage behavior model is shown. Further, “N _dec ” is a function obtained by normalizing the frequency of the decelerating behavior model shown in FIG. 14, and “N _pass ” is a function obtained by normalizing the frequency of the passing behavior model shown in FIG. 14. Each variable of the functions N _dec and N _pass is the value of the horizontal axis shown in FIG. 14, that is, the likelihood difference of each behavior model. Therefore, the equation f9 is an operational equation capable of _obtaining the learning value p _lrn of the deceleration behavior occurrence probability from the likelihood difference of each model.

The vehicle control device 50 acquires the deceleration behavior model, the passage behavior model, and the above equation 9 from the server device 41. The vehicle control device 50 calculates the likelihood of the deceleration behavior model and the likelihood of the passage behavior model from the past traveling data of the surrounding vehicle detected by the surroundings monitoring device 34 in a period from the present to a predetermined time ago. The vehicle control device 50 calculates the likelihood difference of each calculated model, and substitutes the calculated likelihood difference of each model into the above-mentioned equation f9 to obtain the learning value p _lrn of the deceleration behavior occurrence probability. calculate.

On the other hand, the vehicle control device 50 of the present embodiment predicts the future deceleration behavior of the surrounding vehicles statistically or based on the information detected by the surroundings monitoring device 34, and the predicted deceleration behavior of the surrounding vehicles Calculate the occurrence probability of The vehicle control device 50 uses the calculated value as the predicted value p _ftr of the deceleration behavior occurrence probability.

Then, the vehicle control device 50, by correcting the learning value p _LRN deceleration behavior probability by the prediction value p _ftr deceleration behavior probability, determine the final deceleration behavior probability p _i. Specifically, the vehicle control device 50 calculates the deceleration behavior probability p _i based on the following equation f10.

However, it is "z = ( _plrn + _plrn2 ) / 2". In the present embodiment, “P _lrn2 ” indicates the probability that a surrounding vehicle passes a predetermined place. For example, in a situation where two patterns of the case where the surrounding vehicle decelerates at a predetermined place and the case where the vehicle passes a predetermined place are assumed, the total value of "P _lrn " and "P _lrn2 " is "1". Become.

When the above equation f10 is used, when the learning value p _lrn of the deceleration behavior occurrence probability is close to “0.5”, the predicted value p _ftr of the deceleration behavior occurrence probability becomes dominant in the behavior occurrence probability p _i . When the learning value p _lrn of the deceleration behavior occurrence probability is close to “0” or “1”, the learning value p _lrn of the deceleration behavior occurrence probability is dominant in the behavior occurrence probability p _i .

Next, a specific method of calculating the deceleration behavior probability p _i. In the following, for convenience, referred to around the vehicle on which an arithmetic operation of the deceleration behavior probability p _i as "specific peripheral vehicle", the peripheral vehicle other than the specific peripheral vehicle is referred to as "other around the vehicle." In addition to the preceding vehicles traveling in front of the own vehicle 10, the surrounding vehicles include vehicles around the own vehicle 10 excluding the preceding vehicles.

As shown in FIG. 15, the prediction ECU 33 of the present embodiment executes a deceleration behavior occurrence probability calculation process as the process of step S <b> 30 following step S <b> 12. A specific procedure of the deceleration behavior occurrence probability calculation process is as shown in FIG.
As shown in FIG. 16, the prediction ECU 33 first determines whether or not a specific surrounding vehicle is present as the process of step S31.

Specifically, when an object is detected around the host vehicle 10, the periphery monitoring device 34 recognizes whether the detected object is a surrounding vehicle. At that time, the recognition accuracy of the specific surrounding vehicle by the periphery monitoring device 34 changes according to the situation. For example, in the surrounding area monitoring device 34, as the distance from the host vehicle 10 to the detected object increases, the object recognition accuracy decreases. Therefore, it is difficult for the periphery monitoring device 34 to accurately detect whether an object present at a location far from the host vehicle 10 is a specific surrounding vehicle. Therefore, when the specific surrounding vehicle is detected, the periphery monitoring device 34 of the present embodiment also calculates the recognition accuracy. For example, upon detecting an object corresponding to a specific surrounding vehicle, the surrounding area monitoring device 34 calculates the recognition accuracy based on the relative distance from the host vehicle 10 to the object using a map, an arithmetic expression, or the like. In the map, the arithmetic expression, etc., the value of the recognition accuracy is set to be smaller as the distance from the host vehicle 10 to the object is longer. The periphery monitoring device 34 transmits the calculated recognition accuracy to the prediction ECU 33. The prediction ECU 33 determines that the specific surrounding vehicle is present based on the detection of the specific surrounding vehicle by the periphery monitoring device 34 and the recognition accuracy of the detected specific surrounding vehicle being equal to or higher than a predetermined threshold.

When it is determined that the specific surrounding vehicle exists, the prediction ECU 33 makes an affirmative determination in the process of step S31, and determines whether or not learning information of traveling data of the specific surrounding vehicle exists as the process of step S32.
Specifically, in order to calculate the learning value p _lrn of the deceleration behavior occurrence probability using the above equation _{f 9} , the deceleration behavior model and the passage behavior model at the traveling point of the own vehicle 10 are constructed by the server device 41 Need to be. In addition, when using the above equation f9, since the likelihood of each model is required, the traveling data of a specific surrounding vehicle needs to be accumulated to such an extent that the likelihood of each model can be calculated. There is. Therefore, the prediction ECU 33 can acquire the deceleration behavior model and the passage behavior model at the traveling point of the own vehicle 10 from the server device 41, and accumulate the traveling data of the specific surrounding vehicles to such an extent that the likelihood of each model can be calculated. If it has been done, an affirmative decision is made in the process of step S32.

In addition, regarding accumulation of past travel data of the surrounding vehicle, travel data such as vehicle speed information is transmitted from the communication unit 36 to the server device 41 to accumulate past travel data of the surrounding vehicle on the server device 41 It is also good. Alternatively, past travel data of the surrounding vehicle may be accumulated by collecting the traveling history of the surrounding vehicle in the own vehicle 10.

When the prediction ECU 33 makes an affirmative determination in the process of step S32, it calculates the learning value p _lrn of the deceleration behavior occurrence probability based on the past traveling data of the specific surrounding vehicle as the process of step S33. Specifically, the prediction ECU 33 calculates the likelihood of each of the deceleration behavior model and the passage behavior model based on the past traveling data of the specific surrounding vehicle, and the above-mentioned equation f9 from the likelihood difference of each model calculated The learning value p _lrn of the deceleration behavior occurrence probability is calculated based on

On the other hand, if the prediction ECU 33 can not acquire the deceleration behavior model and the passage behavior model at the traveling point of the host vehicle 10 from the server device 41, or if the traveling data of the specific surrounding vehicle is sufficient to calculate the likelihood of each model. If not stored, the process of step S32 shown in FIG. 14 makes a negative determination. In this case, the prediction ECU ₃₃ calculates the learning value p _lrn of the deceleration behavior occurrence probability based on the static information of the road detected by the surrounding area monitoring device 34 as the process of step S34. The static information of the road includes the presence or absence of a traffic signal, the travel rule of the road, the speed limit, the slope, the curved road, the presence or absence of an intersection, and the like. For example, when a camera is used as the periphery monitoring device 34, it is possible to detect a road sign, a road state, and the like based on image data of the vehicle periphery captured by the camera. The prediction ECU 33 acquires static information of the road based on the road sign detected by the surrounding area monitoring device 34, the road condition, and the like. The prediction ECU 33 also has in advance a map in which the deceleration behavior occurrence probability is defined for each item of static information of the road. The prediction ECU 33 calculates the deceleration behavior occurrence probability for each item of the acquired static information of the road from the map, and learns the deceleration behavior occurrence probability using an arithmetic expression or the like from the deceleration behavior occurrence probability for each computed item. Calculate the value p _lrn .

After executing the process of step S33 or step S34, the prediction ECU 33 determines whether or not there is a traffic signal as a factor for the specific surrounding vehicle to take deceleration behavior in the future as the process of step S35. For example, the prediction ECU 33 may determine, based on the past travel history detected by the surroundings monitoring device 34, whether or not there is a traffic signal as a factor for the specific surrounding vehicle to take deceleration behavior in the future. . Alternatively, when the prediction ECU 33 determines that there is a traffic light installed within a predetermined range from the specific surrounding vehicle based on the road condition detected by the surrounding area monitoring device 34, the specific surrounding vehicle will take deceleration behavior in the future It may be determined that there is a traffic signal as a factor.

If the prediction ECU 33 determines that there is a traffic signal as a factor that causes the specific surrounding vehicle to decelerate in the future, it makes an affirmative determination in the process of step S35, and continues the current of the specific surrounding vehicle as the process of subsequent step S36. It is determined whether the traveling position is in the vicinity of a traffic light and the signal information of the traffic light can be recognized by the surroundings monitoring device 34. The prediction ECU 33 determines that the current traveling position of the specific surrounding vehicle is near the traffic light when the distance from the specific surrounding vehicle to the traffic light is less than a predetermined threshold based on the road condition detected by the surrounding area monitoring device 34 Do. The signal information is information indicating whether the traffic light is lit in blue, yellow or red. The prediction ECU 33 acquires the signal information of the traffic signal by the periphery monitoring device 34.

If the current traveling position of the specific surrounding vehicle is in the vicinity of the traffic light and the signal information of the traffic light can be recognized by the vicinity monitoring device 34, the prediction ECU 33 makes an affirmative determination in the process of step S36. In this case, the prediction ECU 33 calculates the predicted value p _ftr of the deceleration behavior occurrence probability according to the switching timing of the traffic signal as the process of the subsequent step S37.

Specifically, when the host vehicle 10 is traveling, the prediction ECU 33 stores information on the switching timing of the signal of the traffic light based on the signal information of the traffic light detected by the surroundings monitoring device 34. The prediction ECU 33 according to the present embodiment stores green signal duration information as information on the switching timing of the signal of the traffic light. The green signal duration information means the time required for the signal of the traffic light to switch from red to green to switch to yellow.

For example, as shown in FIG. 17, the prediction ECU 33 switches the signal of the traffic light to a green light when the signal of the traffic light is a red light at time t40 when the traffic light is recognized by the periphery monitoring device 34 The time from time t41 to time t42 at which the signal of the traffic light switches to yellow light is stored in the storage device as green light duration time information.

On the other hand, when the signal of the traffic light is a red signal at time t50 when the traffic light is recognized by the surrounding area monitoring device 34, for example, as shown in FIG. The time until the time t51 at which the signal is switched to is stored in the storage device as green light duration time information.

In addition, in the case of a traffic signal in which the switching cycle of the traffic signal changes according to the traffic flow, the green signal duration information is learned according to the traffic flow information acquired by VICS (Vehicle Information and Communication System, registered trademark) or the like. May be
The prediction ECU 33 creates a map as shown in FIG. 19 based on the green signal duration information stored in the storage device. The map shown in FIG. 19 shows the relationship between the green signal duration γ as the horizontal axis and the probability p _sig as the switching from the green signal to the yellow signal as the vertical axis. This map is stored in the storage device of the prediction ECU 33.

In addition, while transmitting the green light continuation time information each acquired by the several vehicle to the server apparatus 41 from each vehicle, the server apparatus 41 learns the green light continuation time information transmitted from each vehicle, and the server apparatus 41 is obtained. However, the map as shown in FIG. 19 may be created. In this case, the prediction ECU 33 can use the map shown in FIG. 19 by acquiring this map from the server device 41 via the communication unit 36.

If the traffic signal is a red signal when the traffic signal is recognized by the periphery monitoring device 34 in the process of step S35 or the process of step S36 shown in FIG. 16, the prediction ECU 33 switches the red signal to a green signal thereafter. The duration of the green light from the time of Further, when the signal of the traffic light is a green light when the traffic light is recognized by the periphery monitoring device 34 in the processing of step S35 and the processing of step S36, the prediction ECU 33 measures the duration of the green light from that time. There is. The probability p _sig switched from the green light to the yellow light after δ seconds with respect to the green signal duration γ measured in this way is that when the value of the horizontal axis is “γ + δ” in the map as shown in FIG. It can be obtained as the value of the probability p _sig of

On the other hand, when the signal of the traffic light in the vicinity of the current traveling position of the specific surrounding vehicle is switched from the green light to the yellow light, it is assumed that the specific surrounding vehicle takes a decelerating behavior. That is, there is a correlation between the probability p _sig switching from the green light to the yellow light and the probability that the particular surrounding vehicle takes the decelerating behavior. Therefore, the prediction ECU 33 of the present embodiment uses the probability p _sig calculated based on the map shown in FIG. 19 as the predicted value p _ftr of the deceleration behavior occurrence probability.

As shown in FIG. 16, when the prediction ECU 33 makes a negative determination in the process of step S 36, that is, when the current traveling position of the specific surrounding vehicle is not in the vicinity of a traffic light, or If not recognized, in the subsequent step S38, the predicted value p _ftr of the deceleration behavior occurrence probability is calculated based on the statistical information.

Specifically, the server device 41 communicates with a plurality of vehicles to acquire information as to whether each vehicle adopts the behavior of deceleration or passing by a traffic signal, and also decelerates the vehicles based on the statistical information. Behavior occurrence probability is calculated. For example, when there are 100 vehicles targeted for statistics, the server device 41 decelerates when 50 vehicles of them are decelerated by the traffic light and the other 50 vehicles pass through the traffic light without decelerating. Calculate the deceleration behavior occurrence probability in “0.5”. Prediction ECU33 acquires the statistic information Psta deceleration behavior occurrence probability in the traffic from the server device 41, using the statistics Psta of the deceleration behavior probability as a predicted value p _ftr deceleration behavior probability.

As shown in FIG. 16, when the prediction ECU 33 makes a negative determination in the process of step S 35, that is, when it is determined that there is no traffic signal as a factor for the specific surrounding vehicle to take deceleration behavior in the future. Then, as the process of the subsequent step S39, it is determined whether the state quantities of other surrounding vehicles can be acquired. The state quantities of other nearby vehicles include their traveling positions, speeds, and the like. Specifically, the prediction ECU 33 makes an affirmative determination in the process of step S39 when the state quantity of another surrounding vehicle can be acquired by the surrounding area monitoring device 34. Further, when inter-vehicle communication is possible between the own vehicle 10 and another surrounding vehicle, the prediction ECU 33 performs the process of step S39 by acquiring the state quantity by communication with the other surrounding vehicle. You may make a positive decision on

When the prediction ECU 33 makes an affirmative determination in the process of step S39, it calculates the predicted value p _ftr of the deceleration behavior occurrence probability based on the state quantities of other surrounding vehicles as the process of the subsequent step S40. Specifically, the prediction ECU 33 calculates the future behavior of each vehicle based on the information such as the current traveling position and speed of the specific peripheral vehicle and the information on the current traveling position and speed of the other peripheral vehicles. Predict by simulation. By this simulation, the probability p _sur is calculated that the predetermined behavior causing the specific surrounding vehicle to decelerate is generated in another surrounding vehicle. The predetermined behavior of another surrounding vehicle that causes the specific surrounding vehicle to decelerate is, for example, a behavior in which the other surrounding vehicle changes lanes to the lane in which the specific surrounding vehicle is traveling. The prediction ECU 33 uses the calculated occurrence probability p _sur of the predetermined behavior of another surrounding vehicle as the predicted value p _ftr of the deceleration behavior occurrence probability.

When the prediction ECU 33 makes a negative determination in the process of step S39, it calculates the predicted value _pftr of the deceleration behavior occurrence probability based on the statistical information as the process of the subsequent step S41.
Specifically, the server device 41 communicates whether or not each vehicle has decelerated at a predetermined place or passed by communication with a plurality of vehicles, and the deceleration behavior occurrence probability of the vehicle is calculated based on the statistical information. It is calculated. For example, when the number of vehicles targeted for statistics is 100, the server device 41 decelerates 50 of the vehicles at a predetermined location and the other 50 vehicles pass through the predetermined location without deceleration. In this case, the deceleration behavior occurrence probability in the traffic light is calculated as “0.5”. The prediction ECU 33 acquires statistical information Psta of the deceleration behavior occurrence probability corresponding to the current location of the host vehicle from the server device 41, and uses the statistical information Psta of the deceleration behavior occurrence probability as the prediction value p _ftr of the deceleration behavior occurrence probability Use.

If the prediction ECU 33 determines that there is no specific surrounding vehicle whose recognition accuracy is equal to or higher than a predetermined threshold in the process of step S31, the prediction ECU 33 makes a negative determination in the process of step S31. In this case, the prediction ECU 33 determines whether or not there is a specific surrounding vehicle corresponding to the distant vehicle as the process of the subsequent step S43. A distant vehicle is a vehicle whose recognition accuracy is less than a predetermined threshold. When there is a specific surrounding vehicle corresponding to the distant vehicle, the prediction ECU 33 makes an affirmative determination in the process of step S43, and predicts the deceleration behavior occurrence probability based on the information of the distant vehicle as the process of subsequent step S44. Calculate p _ftr .

For example, the prediction ECU 33 calculates the distance from the host vehicle 10 to an object recognized as a distant vehicle, and calculates the existence probability p _{far of the} object based on an arithmetic expression or the like based on the calculated distance. In this equation, for example, the value of the object presence probability p _far decreases as the distance to the object increases. The prediction ECU 33 uses the calculated existence probability p _far of the object as the prediction value p _ftr of the deceleration behavior occurrence probability.

Prediction ECU33, the step S37, S38, S40, S41, after calculating the predicted value p _ftr deceleration behavior occurrence probability in the process of S44, the processing in step S42, calculates the deceleration behavior probability p _i. Specifically, the prediction ECU 33 performs the calculation in the learning value p _lrn of the deceleration behavior occurrence probability calculated in one of the processes in steps S33 and S34 and the process in one of the steps S37, S38, S40, S41, and S44. It is the computed deceleration behavior probability p _i using the above equation 10 from the predicted value p _ftr deceleration behavior probability. In the present embodiment, “P _lrn2 ” used in calculating “z” can be calculated from an operation expression “P _lrn2 = 1−P _lrn ”.

On the other hand, if a negative determination is made in the process of step S43, that is, if there is no distant vehicle information, the prediction ECU 33 ends the series of processes shown in FIG. 4 without executing the process of step S42. In this case, there is no vehicle around the host vehicle 10 that causes the host vehicle 10 to generate deceleration behavior, so the prediction ECU 33 makes a negative determination in the process of step S13 shown in FIG. Therefore, as the process of step S15, the ACC ECU 32 transmits, to the EVECU 31, the acceleration command value α temporarily set to the first set value α1 in the process of step S11.

According to the vehicle control device 50 of the present embodiment described above, the actions and effects shown in the following (11) to (17) can be obtained.
(11) The prediction ECU 33 learns the behavior of the vehicle based on the traveling data of the vehicle, specifically, the deceleration behavior of the specific surrounding vehicle based on the learning model of the vehicle behavior such as the deceleration behavior model or the passing behavior model. The occurrence probability p _i is calculated. The prediction ECU 33 determines the computing equation of the equations f2 and f3 using the deceleration behavior occurrence probability p _i and then determines the state amount b of the vehicle 10 for which the value of the evaluation function FE1 of the equation f4 becomes minimum. By determining (t), the second set value α2 of the acceleration command value α is calculated. Then, as shown in FIG. 15, when the prediction ECU 33 determines that the host vehicle 10 needs to be decelerated in the process of step S13, the ACC ECU 32 performs the process of step S14 to execute the second acceleration command value α. The set value α2 is set. By executing the acceleration control of the vehicle 10 based on the acceleration command value α set in this way, it is possible to predict the deceleration behavior of the specific surrounding vehicle earlier and to decelerate the vehicle 10. .

(12) The prediction ECU 33 calculates the likelihood, which is an index indicating the similarity between the traveling data of the specific surrounding vehicle acquired by the surroundings monitoring device 34 and the learning model of the vehicle behavior such as the deceleration behavior model or the passing behavior model. Then, based on the likelihood, the deceleration behavior occurrence probability p _i of the specific surrounding vehicle is calculated. According to such a configuration, it is possible to calculate the deceleration behavior occurrence probability p _i of the specific surrounding vehicle with high accuracy.

(13) When the prediction ECU 33 can not calculate the deceleration behavior occurrence probability p _i using the learning model of the vehicle behavior such as the deceleration behavior model or the passage behavior model, the deceleration behavior is generated based on static information of the road. Calculate the probability p _i . According to such a configuration, even in a situation where it is impossible to use a learning model of vehicle behavior, it is possible to calculate the deceleration behavior probability p _i.

(14) When the recognition accuracy of the specific surrounding vehicle recognized by the surrounding area monitoring device 34 is less than a predetermined threshold, the prediction ECU 33 uses the existence probability indicating that the object recognized as the specific surrounding vehicle may actually exist. Based on the above, the deceleration behavior occurrence probability p _i is corrected. According to such a configuration, it can be calculated in accordance with the recognition accuracy of the surroundings monitoring device 34, the accuracy high reduction behavior occurrence probability of p _i.

(15) prediction ECU33 corrects the deceleration behavior probability p _i based on the occurrence probability of the switching of the traffic signals. According to such a configuration, it can be calculated according to the situation of the switching of the traffic signal, a more accurate reduction behavior probability p _i.
(16) prediction ECU33 corrects the deceleration behavior probability p _i around the vehicle based on the deceleration occurrence probability of the statistical information of the vehicle. According to such a configuration, can be calculated according to the statistical information, the accuracy high reduction behavior occurrence probability of p _i.

(17) The prediction ECU 33 acquires traveling data of the surrounding vehicle by communication between the vehicle 10 and the surrounding vehicle. According to such a configuration, it is possible to acquire traveling data of nearby vehicles with higher accuracy.
Other Embodiments
In addition, each embodiment can also be implemented in the following modes.

The vehicle 10 according to the second embodiment may not include the motor generator 20, the inverter device 21, the battery 22, and the MGECU 30. That is, the vehicle 10 of the second embodiment may use only the engine 60 as the power for traveling.
The prediction ECU 33 according to the third embodiment uses the deceleration behavior model and the passage behavior model as a learning model of the behavior of a surrounding vehicle, but may use other learning models. For example, as the deceleration behavior model, a first deceleration behavior model that assumes a stop and a second deceleration behavior model that does not assume a stop may be used.

In the vehicle control device 50 of the third embodiment, the prediction ECU 33 may perform construction of a learning model of the vehicle behavior instead of the server device 41.
The prediction ECU 33 according to the third embodiment is not limited to one that predicts the deceleration behavior of the surrounding vehicle as the behavior of the surrounding vehicle, and may predict any behavior of the surrounding vehicle. Also, in accordance with this, the server device 41 or the prediction ECU 33 may learn any behavior of the vehicle.

The prediction ECU 33 may predict the interruption of the vehicle traveling on the adjacent lane as a change in the surrounding environment such that the fuel efficiency of the host vehicle 10 is deteriorated. Specifically, when the vehicle Cb cuts into between the vehicle Ca and the vehicle traveling in front of the own vehicle, the prediction ECU 33 determines the state of the vehicle Ca before the interruption as shown by the solid line in FIG. 12. The amount is used as the state amount of the leading vehicle, and when the vehicle Cb cuts in at time t30, the state amount of the vehicle Cb is used as the state amount of the leading vehicle after that.

A function including information such as the speed and the position of the vehicle 10 may be used as the state quantity b (t).
The ACC ECU 32 may transmit a speed command value for specifying the speed of the vehicle 10 to the EVECU 31 or the HVECU 39 instead of the acceleration command value α.

In calculating the follow-up performance evaluation value of the own vehicle 10, the prediction ECU 33 may use the speed information of each of the i-th preceding vehicle and the own vehicle 10 instead of the positional information of the same. For example, after defining the ideal traveling range in the range from the lowest speed V _min to the highest speed V _max , the amount of deviation z _i of the expected future speed of the vehicle 10 from this ideal traveling range is expressed by the following equation (11) Represented by

Then, a value obtained by integrating the deviation amount z _i in the range of the prediction time T from the present may be used as the follow-up performance evaluation value of the own vehicle 10.
The periphery monitoring device 34 may acquire information such as pedestrians walking around the road, traffic signals, road travel restrictions, speed limits, slopes, curves, intersections, and the like. In this case, the prediction ECU 33 may determine whether or not the vehicle 10 needs to be decelerated based on the information acquired by the surroundings monitoring device 34.

The prediction ECU 33 may use the predicted value of the fuel consumption as an index related to the fuel consumption of the host vehicle 10. Specifically, the prediction ECU 33 stores fuel consumption data and calculates a fuel consumption prediction value based on the stored past fuel consumption data.
The method of limiting the acceleration of the vehicle 10 is not limited to the method of changing the acceleration command value α, but may be a command method such that the acceleration changes as a result, such as a method of limiting the driving torque or power of the vehicle 10 It may be adopted. The limitation of the drive torque and the power of the vehicle 10, unlike the output limitation for protection of the motor generator 20 and the battery 22, limits the output in control regardless of the maximum output of the component.

The ACC ECU 32 uses speed control for controlling the speed of the vehicle 10, instead of using acceleration control for controlling the acceleration of the vehicle 10, as a method for controlling the traveling of the vehicle 10 by ACC control, CC control, etc. A method may be adopted. The ACC ECU 32 can also use instruction control for instructing the occupant of the host vehicle 10 of the driving method as in the modification of the first embodiment.

The means and / or functions provided by the vehicle control device 50 can be provided by software stored in the tangible storage device and a computer that executes the software, only software, only hardware, or a combination thereof. For example, if the vehicle control device 50 is provided by an electronic circuit that is hardware, it can be provided by a digital circuit or analog circuit that includes a number of logic circuits.

The present disclosure is not limited to the above specific example. Those skilled in the art may appropriately modify the above-described specific example as long as the features of the present disclosure are included. The elements included in the specific examples described above, and the arrangement, conditions, shape, and the like of the elements are not limited to those illustrated, and can be changed as appropriate. The elements included in the above-described specific examples can be appropriately changed in combination as long as no technical contradiction arises.

Claims

A vehicle control device (50) that executes traveling control capable of controlling traveling of the host vehicle in order to cause the host vehicle to follow a preceding vehicle traveling in front of the host vehicle (10),
An environment prediction unit (33) for predicting whether or not there is a change in the surrounding environment such that the fuel efficiency of the host vehicle is deteriorated;
Acceleration control that executes prediction control that can limit the acceleration of the host vehicle when it is predicted by the environment prediction unit that a change in the surrounding environment is occurring such that the fuel efficiency of the host vehicle is degraded A vehicle control device comprising: a part (32).
The travel control is speed control that controls acceleration and deceleration of the host vehicle to make the host vehicle follow the preceding vehicle.
The acceleration control unit causes a change in the surrounding environment such that the fuel efficiency of the own vehicle is deteriorated based on the environment prediction unit predicting that there is a change in the surrounding environment requiring deceleration of the own vehicle. When it is predicted that there is a change in the surrounding environment that requires the vehicle to decelerate, a deceleration smaller than the deceleration that can be set by the speed control is used as the prediction control. The vehicle control device according to claim 1, wherein deceleration control is performed to decelerate the host vehicle.
The environment prediction unit predicts the presence or absence of a change in the surrounding environment in which the host vehicle needs to be decelerated, based on the indicator related to the fuel efficiency of the host vehicle and the indicator related to the follow-up performance of the host vehicle with respect to the preceding vehicle. The vehicle control apparatus of claim 2.
The index related to the fuel efficiency of the host vehicle is a predicted value of braking energy predicted to be generated when the host vehicle is decelerated by the execution of the travel control during a period from the present to the end of a predetermined time, or Yes,
The index related to the follow-up performance of the vehicle is an amount of deviation of the position of the vehicle from the ideal value of the travel control in the period from the current time to the predetermined time, or the vehicle in the time period from the current time to the predetermined time The vehicle control device according to claim 3, wherein the deviation amount is a speed deviation amount.
The acceleration control unit executes coasting control for causing the host vehicle to coast while the output from the power train (20, 60) is not transmitted to the wheels (28) of the host vehicle as the deceleration control. The vehicle control device according to 2.
The driving and stopping of the engine (60) of the vehicle are controlled based on the traveling state of the vehicle, and the engine is re-generated based on the acceleration of the vehicle when the engine of the vehicle is stopped. The vehicle further comprises a traveling control unit for starting
The vehicle control device according to claim 1, wherein the acceleration control unit limits an acceleration of the host vehicle such that restart of the engine by the travel control unit is less likely to be performed as the prediction control.
The environment prediction unit determines whether to limit the acceleration of the host vehicle based on the indicator related to the fuel efficiency of the host vehicle and the indicator related to the follow-up performance of the host vehicle with respect to the leading vehicle. Vehicle control device as described.
The index related to the fuel efficiency of the host vehicle is a predicted value of the ratio of the output energy of the power train to the input energy of the power train (20, 60) of the host vehicle in the period from the current time to a predetermined time It is a value,
The index related to the follow-up performance of the vehicle is an amount of deviation of the position of the vehicle from the ideal value of the travel control in the period from the current time to the predetermined time, or the vehicle in the time period from the current time to the predetermined time The vehicle control device according to claim 7, wherein the deviation amount is a speed deviation amount.
In the predicted value of the ratio of the output energy of the powertrain to the input energy of the powertrain of the host vehicle, the predicted value of the ratio of the output energy of the engine to the input energy of the engine and the state where the engine is stopped The vehicle control device according to claim 8, further comprising a predicted value of a ratio of output energy of the powertrain to input energy of the powertrain of the host vehicle at.
The environment prediction unit
The expected value of the indicator related to the fuel efficiency of the own vehicle is calculated based on the occurrence probability of the behavior of the surrounding vehicle and the value of the indicator related to the fuel efficiency of the own vehicle relative to the behavior of the surrounding vehicle;
The expected value of the indicator related to the follow-up performance of the own vehicle is calculated based on the occurrence probability of the behavior of the surrounding vehicle and the value of the indicator related to the follow-up performance of the own vehicle with respect to the behavior of the surrounding vehicle.
The environment prediction unit calculates a value of an evaluation function including an expected value of an index related to fuel consumption of the host vehicle and an expected value of an index related to tracking performance of the host vehicle, and the host vehicle based on the value of the evaluation function. The vehicle control device according to any one of claims 3, 4, 7 to 9, which predicts a change in the surrounding environment that requires deceleration of the vehicle.
The vehicle control device according to claim 10, wherein the environment prediction unit calculates an occurrence probability of the behavior of the surrounding vehicle based on learning information in which the behavior of the vehicle is learned based on travel data of the vehicle.
The vehicle further includes a periphery monitoring unit that acquires traveling data of a surrounding vehicle traveling around the host vehicle.
The environment prediction unit calculates the likelihood, which is an index indicating the similarity between the traveling data of the surrounding vehicle acquired by the surrounding area monitoring unit and the learning information, and the behavior of the surrounding vehicle is calculated based on the likelihood. The vehicle control device according to claim 11, wherein the occurrence probability of is calculated.
The periphery monitoring unit further acquires static information of the road,
The vehicle control device according to claim 12, wherein the environment prediction unit calculates the occurrence probability of the behavior of the surrounding vehicle based on static information of the road when the learning information can not be used.
When the recognition accuracy of the surrounding vehicle by the surrounding area monitoring unit is less than a predetermined threshold, the environment predicting unit performs the surrounding based on the presence probability indicating that the object recognized as the surrounding vehicle may actually exist. The vehicle control device according to claim 12, wherein the occurrence probability of the behavior of the vehicle is corrected.
The periphery monitoring unit further acquires information on switching timing of signals of traffic lights installed on the road,
The vehicle control device according to claim 12, wherein the environment prediction unit corrects the occurrence probability of the behavior of the surrounding vehicle based on the occurrence probability of switching of the signal of the traffic light.
The vehicle control device according to claim 12, wherein the environment prediction unit corrects the occurrence probability of the behavior of the surrounding vehicle based on statistical information of the occurrence probability of the behavior of the vehicle.
The vehicle control device according to any one of claims 12 to 16, wherein the environment prediction unit acquires travel data of the surrounding vehicle by communication between the host vehicle and the surrounding vehicle.
The vehicle according to any one of claims 1 to 17, wherein the traveling control is burn-and-coast control that causes the host vehicle to follow the leading vehicle by repeatedly executing acceleration and deceleration of the host vehicle. Control device.
The environment prediction unit predicts deceleration of the preceding vehicle or interruption of a vehicle traveling in an adjacent lane as a change in the surrounding environment such that the fuel efficiency of the host vehicle is deteriorated. The vehicle control device according to claim 1.
The traveling control is any one of speed control for controlling the speed of the host vehicle, acceleration control for controlling an acceleration of the host vehicle, and instruction control for instructing an occupant of the host vehicle of a driving method. 19. The vehicle control device according to any one of 19.
The acceleration control unit performs, as the prediction control, acceleration control for actually limiting the acceleration of the vehicle or instruction control for instructing the driver of the vehicle such that the acceleration of the vehicle is restricted. The vehicle control device according to any one of claims 1 to 20.