Patent Publication Number: US-2020276972-A1

Title: Vehicle control device

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     The present application is a continuation application of International Application No. PCT/JP2018/037434, filed on Oct. 5, 2018, which claims priority to Japanese Patent Application No. 2017-221734 filed on Nov. 17, 2017 and No. 2018-129289 filed on Jul. 6, 2018. The contents of these applications are incorporated herein by reference in their entirety. 
    
    
     BACKGROUND 
     Technical Field 
     The present disclosure relates to a vehicle control device. 
     Background Art 
     There is a conventional vehicle control device described below. The vehicle control device sets the minimum inter-vehicle distance according to the velocity of the own device, and stops motive power sources such as the engine and the motor to coast the own vehicle when the inter-vehicle distance between the own vehicle and a preceding vehicle traveling in front of the own vehicle becomes shorter than the minimum inter-vehicle distance. The vehicle control device sets the maximum inter-vehicle distance according to the velocity of the own device, and starts to drive the motive power sources when the inter-vehicle distance becomes longer than the maximum inter-vehicle distance during coasting. 
     SUMMARY 
     In the present disclosure, provided is a vehicle control device as the following. The vehicle control device includes: an environment prediction unit that predicts whether an adverse-effect change has occurred in a surrounding environment around an own vehicle, the adverse-effect change being likely to have an adverse effect on a fuel economy of the own vehicle; and an acceleration control unit configured to execute a prediction control that enables an acceleration of the own vehicle to be limited when the environment prediction unit predicts that the adverse-effect change has occurred in the surrounding environment. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram showing a schematic configuration of a vehicle in a first embodiment. 
         FIG. 2  is a graph showing an example of a method of vehicle control by an ACC ECU in the first embodiment. 
         FIG. 3  is a graph showing an example of a method of vehicle control by the ACC ECU in the first embodiment. 
         FIG. 4  is a flowchart showing a procedure of processing executed by the ACC ECU and a prediction ECU in the first embodiment. 
         FIG. 5  is a graph showing an example of a method for calculating a deviation amount of an own vehicle from an ideal traveling range predicted by the prediction ECU in the first embodiment. 
         FIG. 6  is a graph showing a relationship between vehicle velocity and probability used by the prediction ECU in the first embodiment. 
         FIGS. 7A to 7C  are timing charts showing the transitions of vehicle velocity, driving energy, and inter-vehicle distance of the vehicle in the first embodiment. 
         FIG. 8  is a block diagram showing a schematic configuration of a vehicle in a second embodiment. 
         FIG. 9  is a flowchart showing a procedure of processing executed by an ACC ECU and a prediction ECU in the second embodiment. 
         FIG. 10  is a map showing a relationship between acceleration and actual engine efficiency used by the prediction ECU in the second embodiment. 
         FIGS. 11A to 11C  are timing charts showing the transitions of vehicle velocity, driving energy, and engine rotation speed in the vehicle in the second embodiment. 
         FIG. 12  is a time chart showing a preceding vehicle switching procedure executed by a prediction ECU in another embodiment. 
         FIGS. 13A and 13B  are timing charts showing an example of temporal transitions of vehicle velocity and deceleration behavior occurrence probability. 
         FIG. 14  is a graph showing respective transitions of a calculated value of frequency of a deceleration behavior model, a calculated value of frequency of a passing behavior model and a value of deceleration behavior occurrence probability with respect to a difference between respective likelihoods of the deceleration behavior model and the passing behavior model in a third embodiment. 
         FIG. 15  is a flowchart showing a procedure of processing executed by an ACC ECU and a prediction ECU in the third embodiment. 
         FIG. 16  is a flowchart showing a procedure of a behavior occurrence probability calculating process executed by the prediction ECU in the third embodiment. 
         FIG. 17  is a graph showing an example of a method for measuring a green light duration time in the third embodiment. 
         FIG. 18  is a graph showing an example of a method for measuring the green light duration time in the third embodiment. 
         FIG. 19  is a map showing a relationship between a green light duration time γ and probability p sig  of a traffic light&#39;s turning from green to yellow in the third embodiment. 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     
         
         [PTL 1] JP 2007-291919 A 
       
    
     In PTL 1 listed above, when the preceding vehicle suddenly decelerates or another vehicle cuts into the lane from an adjacent lane, in order to secure a vehicle-to-vehicle distance from the preceding vehicle, a deceleration control by braking or the stop of the engine immediately after starting due to limitation of acceleration, may be unavoidable. Accordingly, energy loss occurs when the deceleration control is performed by braking. The stop of the engine immediately after starting leads to decrease of engine efficiency. Thus, the deceleration control by braking or the stop of the engine immediately after starting may cause impairment of fuel economy. 
     On the other hand, this problem may be handled by taking countermeasures such as keeping a longer inter-vehicle distance from the preceding vehicle and traveling the own vehicle with a limitation on acceleration. However, these countermeasures would deteriorate the performance of following the preceding vehicle and cause the driver a feeling of discomfort. 
     PTL 1 does not mention any countermeasures against these problems in relation to the vehicle control device. 
     An object of the present disclosure is to provide a vehicle control device that achieves improvement in fuel economy while ensuring the performance of following a preceding vehicle. 
     A vehicle control device in an aspect of the present disclosure executes a traveling control that controls traveling of an own vehicle to enable the own vehicle to follow a preceding vehicle traveling in front of the own vehicle. The vehicle control device includes: an environment prediction unit that predicts whether an adverse-effect change has occurred in a surrounding environment around the own vehicle, the adverse-effect change being likely to have an adverse effect on a fuel economy of the own vehicle; and an acceleration control unit configured to execute a prediction control that enables an acceleration of the own vehicle to be limited when the environment prediction unit predicts that the adverse-effect change has occurred in the surrounding environment. 
     According to this configuration, if an adverse-effect change has occurred in the surrounding environment around the own vehicle, the adverse-effect change being likely to have an adverse effect on a fuel economy of the own vehicle, the acceleration of the own vehicle is limited in advance. Thus, it is possible to avoid a situation in which the own vehicle actually becomes impaired in fuel economy. This leads to improvement in fuel economy of the own vehicle. 
     Hereinafter, embodiments of a vehicle control device will be described with reference to the drawings. For the ease of comprehension, identical constituent elements shown in the drawings are given identical reference signs as much as possible and duplicated description thereof will be omitted. 
     First Embodiment 
     First, a schematic configuration of a vehicle equipped with a vehicle control device in a first embodiment will be described. 
     As illustrated in  FIG. 1 , a vehicle  10  is an electric automobile that runs under motive power of a motor generator  20 . The vehicle  10  includes the motor generator  20 , an inverter device  21 , a battery  22 , and a clutch  23 . 
     The battery  22  is formed of a secondary battery such as a lithium-ion battery that is capable of charging and discharging. The inverter device  21  converts direct-current power charged in the battery  22  into alternating-current power, and supplies the converted alternating-current power to the motor generator  20 . The motor generator  20  is driven under the alternating-current power supplied from the inverter device  21  and rotates a first motive power transmission shaft  24 . The first motive power transmission shaft  24  is coupled to a second motive power transmission shaft  25  with the clutch  23 . The clutch  23  is switchable between a connection state in which the first motive power transmission shaft  24  and the second motive power transmission shaft  25  are coupled to allow transfer of motive power between these two shafts and a non-connection state in which the first motive power transmission shaft  24  and the second motive power transmission shaft  25  are decoupled to shut off the transfer of motive power between these two shafts. When the clutch  23  is in the connection state, the motive power transferred from the motor generator  20  to the first motive power transmission shaft  24  is then transferred to a wheel  28  of the vehicle  10  via the second motive power transmission shaft  25 , a differential gear  26 , and a drive shaft  27 . Accordingly, the vehicle  10  starts to run. In this manner, in the present embodiment, the motor generator  20  corresponds to a power train. 
     The motor generator  20  performs regenerative power generation at the time of braking of the vehicle  10 . That is, the braking force acting on the wheel  28  at the time of braking of the vehicle  10  is input into the motor generator  20  via the drive shaft  27 , the differential gear  26 , the second motive power transmission shaft  25 , the clutch  23 , and the first motive power transmission shaft  24 . The motor generator  20  generates electric power under the motive power input from the wheel  28 . The electric power generated by the motor generator  20  is converted by the inverter device  21  from alternating-current power into direct-current power and is charged into the battery  22 . 
     The vehicle  10  further includes a motor generator (MG) electronic control unit (ECU)  30 , an electric vehicle (EV) ECU  31 , an adaptive cruise control (ACC) ECU  32 , a prediction ECU  33 , a perimeter monitoring device  34 , and a vehicle state amount sensor  35 . The ECUs  30  to  33  are formed mainly of a microcalculater having a CPU and storage devices such as a ROM and a RAM, and perform various controls by executing programs stored in advance in the storage devices. 
     The vehicle state amount sensor  35  detects various state amounts of the vehicle  10 . The various state amounts detected by the vehicle state amount sensor  35  include information on the velocity and acceleration and the like of the vehicle  10 . 
     The perimeter monitoring device  34  includes a camera, a millimeter radar device, a laser radar device, or the like. The perimeter monitoring device  34  detects surrounding vehicles that are traveling around the own vehicle  10 , and calculates various state amounts of the surrounding vehicles. The surrounding vehicles include a preceding vehicle that is traveling in front of the own vehicle  10  in the lane in which the own vehicle  10  is traveling and adjacent vehicles that are traveling in lanes adjacent to the lane in which the own vehicle  10  is traveling. The state amounts detected by the perimeter monitoring device  34  include relative positions, relative distances, relative velocities, relative accelerations, and others of the surrounding vehicles to the own vehicle  10 . The relative distance of a surrounding vehicle corresponds to an inter-vehicle distance. The relative position of a surrounding vehicle to the own vehicle  10  is defined as a position in a biaxial coordinate system using the lateral axis of the own vehicle  10  and the longitudinal axis of the vehicle  10 , for example. In the present embodiment, the perimeter monitoring device  34  corresponds to a perimeter monitoring unit. 
     The MG ECU  30  controls operations of the motor generator  20  by driving the inverter device  21  under a command from the EV ECU  31 . For example, the EV ECU  31  transmits a motive power command value as a command value of output motive power of the motor generator  20  to the MG ECU  30 . Upon receipt of the motive power command value from the EV ECU  31 , the MG ECU  30  controls driving of the inverter device  21  such that the motor generator  20  outputs the motive power corresponding to the motive power command value. When the vehicle  10  is being braked, the MG ECU  30  drives the inverter device  21  such that electric power generated by regenerative power generation of the motor generator  20  is charged into the battery  22 . 
     The EV ECU  31  implements traveling of the vehicle  10  in accordance with the driver driving requests by calculating a motive power command value necessary for implementation of traveling in accordance with the driver driving requests and transmitting the calculated motive power command value to the MG ECU  30 . The EV ECU  31  exchanges necessary information for various controls with the ACC ECU  32  and calculates a motive power command value in accordance with the request from the ACC ECU  32 . For example, upon receipt of an acceleration command value as a command value of acceleration of the vehicle  10  from the ACC ECU  32 , the EV ECU  31  calculates the motive power command value corresponding to the acceleration command value and transmits the calculated motive power command value to the MG ECU  30 , thereby to accelerate the vehicle  10  with the acceleration in accordance with the acceleration command value. The EV ECU  31  turns the clutch  23  to the connection or non-connection state in accordance with the request from the ACC ECU  32 , for example. In the present embodiment, the EV ECU  31  corresponds to a traveling control unit. 
     When an occupant operates an operation unit provided in the vehicle  10 , for example, the ACC ECU  32  executes traveling controls of the vehicle. As the traveling controls, the ACC ECU  32  executes a cruise control (CC) to control the traveling of the vehicle  10  such that the vehicle  10  runs at a constant velocity and an adaptive cruise control (ACC) to control the traveling of the vehicle  10  such that the vehicle  10  follows the preceding vehicle traveling in front of the own vehicle  10 . In the present embodiment, the ACC control corresponds to a velocity control by which to control acceleration and deceleration of the own vehicle  10  such that the own vehicle  10  follows the preceding vehicle. In the present embodiment, the ACC ECU  32  corresponds to an acceleration control unit. 
     Specifically, the ACC ECU  32  calculates a time headway THW that is a time until the vehicle  10  reaches the preceding vehicle, based on the relative velocity and relative distance of the preceding vehicle to the vehicle  10 . As illustrated in  FIG. 2 , when the time headway THW is equal to or greater than a predetermined first time threshold Tth 1 , that is, when there is a temporal leeway for the vehicle  10  to reach the preceding vehicle, the ACC ECU  32  executes the CC control. As the CC control, the ACC ECU  32  repeatedly accelerates and decelerates the vehicle  10 . At that time, the ACC ECU  32  controls the acceleration and deceleration of the vehicle  10  such that the average velocity of the vehicle  10  reaches a velocity Vset set by the occupant through the operation unit. 
     Specifically, the ACC ECU  32  sets a lower limit velocity VL lower than the set velocity Vset and an upper limit velocity VH higher than the set velocity as shown in  FIG. 3 , based on the set speed Vset of the occupant. When the velocity Vc of the vehicle  10  reaches the lower limit velocity VL by decelerating the vehicle  10 , the ACC ECU  32  executes an acceleration control to accelerate the vehicle  10 . As the acceleration control, the ACC ECU  32  transmits a preset positive acceleration command value to the EV ECU  31 . Accordingly, the EV ECU  31  calculates a positive motive power command value corresponding to the acceleration command value and transmits this motive power command value to the MG ECU  30 , whereby the vehicle  10  accelerates at predetermined acceleration. 
     When the vehicle velocity Vc reaches the upper limit velocity VH during acceleration of the vehicle  10 , the ACC ECU  32  executes a coasting control to coast the vehicle  10  such that the vehicle  10  decelerates. As the coasting control, the ACC ECU  32  transmits an acceleration command value of zero to the EV ECU  31  and transmits a command for bringing the clutch  23  into the non-connection state to the EV ECU  31 . Accordingly, the EV ECU  31  transmits the motive power command value of zero to the MG ECU  30  and brings the clutch  23  into the non-connection state. As a result, the driving of the motor generator  20  is stopped and the vehicle  10  starts to coast and thus naturally decelerates. After that, when the velocity Vc of the vehicle  10  reaches the lower limit velocity VL, the ACC ECU  32  transmits a command for bringing the clutch  23  into the connection state to the EV ECU  31  and executes again the acceleration control described above. 
     On the other hand, as illustrated in  FIG. 2 , when the time headway THW is equal to or longer than a second time threshold Th 2  and is shorter than the first time threshold Tth 1 , the ACC ECU  32  executes the ACC control. As the ACC control, the ACC ECU  32  executes a burn-and-coast control to repeatedly accelerate and decelerate the vehicle  10  such that the own vehicle  10  runs following the preceding vehicle. 
     Specifically, when the relative velocity Vr of the preceding vehicle is lower than the predetermined first velocity threshold Vth 1 , that is, when the own vehicle  10  is rapidly approaching the preceding vehicle, the ACC ECU  32  performs regenerative control. As the regenerative control, the ACC ECU  32  transmits a negative acceleration command value to the EV ECU  31 . Accordingly, the EV ECU  31  calculates a negative motive power command value corresponding to the acceleration command value, and transmits this motive power command value to the MG ECU  30 , whereby the motor generator  20  performs regenerative power generation. When the motor generator  20  performs regenerative power generation, a braking force is applied to the wheel  28  of the vehicle  10  by regenerative energy thereof. Thus, the vehicle  10  can be decelerated more quickly than in the case where the vehicle  10  is caused to coast. This widens the inter-vehicle distance between the vehicle  10  and the preceding vehicle. 
     The ACC ECU  32  has a second velocity threshold Vth 2  greater than the first velocity threshold Vth 1 . When the relative velocity Vr of the preceding velocity is within a range of the first velocity threshold Vth 1  to the second velocity threshold Vth 2 , the ACC ECU  32  executes the coasting control described above. The ACC ECU  32  also has a third time threshold Tth 3  that is set between the first time threshold Tth 1  and the second time threshold Tth 2 . Accordingly, even when the relative velocity Vr of the preceding vehicle is equal to or higher than the second velocity threshold Vth 2  and the time headway THW is a value which is within a range of the second time threshold Tth 2  to the third time threshold Tth 3 , the ACC ECU  32  executes the coasting control. This coasting control makes it possible to widen the inter-vehicle distance between the vehicle  10  and the preceding vehicle. 
     When the relative velocity Vr of the preceding vehicle is equal to or higher than the second velocity threshold Vth 2  and the time headway THW is a value which is within a range of the third time threshold Tth 3  to the first time threshold Tth 1 , the ACC ECU  32  executes the acceleration control described above. 
     In this manner, the ACC ECU  32  selectively executes the regenerative control, the coasting control, and the acceleration control according to the time headway THW and the relative velocity Vr of the preceding vehicle, whereby the own vehicle  10  follows the preceding vehicle. 
     If the preceding vehicle suddenly decelerates while the ACC ECU  32  executes the acceleration control as the CC control or the ACC control, the time headway THW and the relative velocity Vr may sharply decrease. Accordingly, when the ACC ECU  32  executes the regenerative control to generate a braking force on the wheel  28 , part of the kinetic energy of the vehicle  10  can be recovered as electric energy in the battery  22  by the regenerative control. However, the remaining kinetic energy is converted into thermal energy during generation of the braking force on the wheel  28  and dissipated into the atmosphere, and thus cannot be recovered. This causes inevitable energy loss. Energy loss occurs also when the kinetic energy of the vehicle  10  is converted into electric energy. This energy loss may impair the fuel economy of the vehicle  10 . 
     Thus, in the vehicle  10  of the present embodiment, the prediction ECU  33  predicts whether an adverse-effect change has occurred in the surrounding environment around the own vehicle, the adverse-effect change being likely to have an adverse effect on sharp decelerating of the preceding vehicle, that is, on a fuel economy of the own vehicle. In the present embodiment, the prediction ECU  33  corresponds to an environment prediction unit. When the prediction ECU  33  predicts that an adverse-effect change has occurred in a surrounding environment around the own vehicle, the adverse-effect change being likely to have an adverse effect on a fuel economy of the own vehicle  10 , the ACC ECU  32  executes a prediction control to limit in advance the acceleration of the own vehicle  10  before the execution of the regenerative control as the ACC control. 
     As illustrated in  FIG. 1 , the prediction ECU  33  is capable of wirelessly connecting to a network line  40  via a communication unit  36  installed in the vehicle  10 . The prediction ECU  33  performs various kinds of communication with a server device  41  via the network line  40 . The server device  41  acquires various state amounts from a plurality of vehicles and makes a database of those state amounts. The server device  41  creates various traveling models based on the plurality of the state amounts of vehicles in the database. The prediction ECU  33  can predict the traveling paths of the surrounding vehicles by the use of the traveling models created by the server device  41 . In the present embodiment, the ACC ECU  32 , the prediction ECU  33 , and the communication unit  36  constitute a vehicle control device  50 . 
     The prediction ECU  33  is arranged independently of the ECUs controlling the components because the prediction ECU  33  requires high-speed processing and connections with the plurality of ECUs. 
     Next, a procedure of a prediction control executed by the ACC ECU  32  and the prediction ECU  33  will be specifically described with reference to  FIG. 4 . The ACC ECU  32  and the prediction ECU  33  repeatedly execute the process shown in  FIG. 4  in predetermined cycles. 
     As illustrated in  FIG. 4 , in step S 10 , the prediction ECU  33  first acquires the current state amounts of the surrounding vehicles from the perimeter monitoring device  34 . The information acquired by the prediction ECU  33  from the perimeter monitoring device  34  includes the relative distances, relative velocities, and relative accelerations of the surrounding vehicles. 
     Subsequent to step S 10 , in step S 11 , the ACC ECU  32  provisionally sets an acceleration command value α to be transmitted to the EV ECU  31 . Specifically, out of the information acquired from the perimeter monitoring device  34  in step S 10 , the ACC ECU  32  uses the relative velocity and relative distance of the preceding vehicle to calculate time headway, and executes the control shown in  FIG. 2  based on the calculated time headway and the relative velocity to calculate a first set value α 1  of the acceleration command value α. The ACC ECU  32  provisionally sets the acceleration command value α to the first set value α 1 . 
     Subsequent to step S 11 , in step S 12 , the prediction ECU  33  predicts the future state amounts of the surrounding vehicles including the preceding vehicle and the adjacent vehicles. The predicted state amounts of the surrounding vehicles include time-series data on the future relative positions, relative distances, relative velocities, and relative accelerations of the surrounding vehicles. Specifically, the prediction ECU  33  predicts the future state amounts from the present to a predetermined time later by the use of calculating equations and models from the current and past values of the state amounts of the surrounding vehicles. Accordingly, the prediction ECU  33  can predict behaviors of the surrounding vehicles from the present to a predetermined time later. 
     The prediction processing in step S 12  is not limited to be executed based on the current and past values of the state amounts of the surrounding vehicles, it may be executed based on other information on the state amounts of the surrounding vehicles. This prediction may be performed in time-series wave form in which the behaviors of the surrounding vehicles are expressed in predetermined probability models based on the past vehicle traveling data, or may be performed by statistically processing the traveling data of vehicles having run through a spot in the past where the own vehicle is currently traveling to calculate the capabilities of deceleration and cut-in by vehicles at a certain spot. 
     The prediction time is set to a time in which a vehicle can reach a full speed that is allowed as a traveling speed at an acceleration under normal traveling conditions. For example, the range of acceleration can be set to −1 G to 1 G, and the full speed can be set to be in the range from 0 km/h to a legal limit vehicle speed. 
     Subsequent to step S 12 , in step S 13 , the prediction ECU  33  determines whether the vehicle  10  needs deceleration based on the behaviors of the surrounding vehicles. This determination processing is specifically performed by the method described below. 
     Suppose that, when there exist N surrounding vehicles, the own vehicle  10  runs with a predetermined state amount b(t) with respect to the traveling of an i-th surrounding vehicle where the value i is defined as an integer within a range of 1≤i≤N. The state amount b(t) is a function of acceleration with time t as a variable, for example. When the own vehicle  10  runs with the state amount b(t), the braking energy generated in the own vehicle  10  can be expressed as E brk i (b(t)). The value E brk i (b(t)) is a predicted value of braking energy that will be generated when the own vehicle  10  is decelerated by execution of the ACC control during a period of time from the present to a predetermined time later. 
     The following performance of the own vehicle  10  to the i-th surrounding vehicle can be evaluated as shown in  FIG. 5  by a deviation amount y i  of the predicted position of the own vehicle  10  from an ideal traveling range A during a period of time from the present to a predetermined time later, where the ideal traveling range A is set to a range of ideal inter-vehicle distance in execution of the ACC control under the own vehicle  10  follows the preceding vehicle. The ideal traveling range A is set with respect to the predicted traveling position of the i-th surrounding vehicle shown by a dashed-dotted line and can be obtained by a calculating equation or the like from the predicted traveling position of the surrounding vehicle. A following performance evaluation value C i (b(t)) of the own vehicle  10  can be obtained using the deviation amount y i  of predicted position of the own vehicle  10  from the ideal traveling range A by the following formula f1 where T represents prediction time: 
       [Math. 1] 
         C   i ( b ( t ))=∫ 0   T   y   i ( b ( t )) dt   (f1)
 
     From the foregoing, an expected value E brk (b(t)) of braking energy of the own vehicle with respect to the N surrounding vehicles and an expected value C(b(t)) of the following performance evaluation value can be defined by the following formulas f2 and f3: 
     
       
         
           
             
               
                 
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     In the formulas f2 and f3, p i  represents the occurrence probability of behavior of the i-th surrounding vehicle. More specifically, in the present embodiment, the probability p i  is used as a parameter for certainty of appearance of the state amount of the i-th surrounding vehicle when the own vehicle  10  is traveling with the state amount b(t), considering that the prediction result of the behavior of the i-th surrounding vehicle includes predetermined uncertainty. For example, the velocity of the i-th surrounding vehicle at a predetermined time can be represented by probability as shown in  FIG. 6 . 
     With the use of the expected value E brk (b(t)) of braking energy and the expected value C(b(t)) of the following performance evaluation value of the own vehicle, an evaluation function F E1  can be formed as expressed by the following formula f4: 
     
       
         
           
             
               
                 
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     In the formula f4, k represents respective weighting coefficients of the braking energy and the following performance evaluation value. The coefficient k is set within a range of 0≤k≤1. In the present embodiment, the weighting coefficients take on predetermined values. 
     Determining the state amount b(t) of the own vehicle  10  such that the value of the evaluation function F E1  becomes minimum makes it possible to obtain the state amount b(t) of the own vehicle  10  with the braking energy suppressed while ensuring the following performance. In other words, it is possible to obtain the state amount b(t) of the own vehicle  10  that can improve the fuel economy while ensuring the following performance. 
     Based on the following method, the prediction ECU  33  executes the determination processing in step S 13 . Specifically, the prediction ECU  33  uses a calculating equation previously obtained by experiment or the like, for example, as the calculating equation of the braking energy E brk  i(b(t)). 
     The prediction ECU  33  also calculates the predicted traveling path of the i-th surrounding vehicle from based on the predicted state amount of the i-th surrounding vehicle out of the prediction information acquired in step S 12 . The prediction ECU  33  also determines the ideal traveling range A based on the calculated predicted traveling path of the i-th surrounding vehicle to set the calculating equation of the following performance evaluation value C i (b(t)) of the own vehicle  10 . 
     The prediction ECU  33  further acquires the traveling models from the server device  41  via the communication unit  36  and calculates the occurrence probability p i  of the state amount of the i-th surrounding vehicle based on the acquired traveling models and the state amount of the i-th surrounding vehicle. 
     In this manner, the prediction ECU  33  determines the calculating equation of the braking energy E brk  i(b(t)) in the foregoing formula f4, the calculating equation of the following performance evaluation value C i (b(t)), and the occurrence probability p i , and then determines the state amount b(t) of the own vehicle  10  such that the value of the evaluation function F E1  becomes minimum. The evaluation function F E1  may be minimized such that a plurality of patterns of behavior of the own vehicle  10  is figured out and the respective values of the evaluation function in these patterns are calculated, and then the state amount b(t) of the own vehicle  10  with the minimum value of the evaluation function F E1  is selected. Otherwise, the evaluation function F E1  may be set to be minimum by using the optimization method. Since the state amount b(t) is a function of acceleration of the vehicle  10 , the prediction ECU  33  can obtain from the foregoing calculation, a second set value α 2  of the acceleration command value α such that the evaluation function F E1  becomes minimum. 
     The prediction ECU  33  may obtain a second set value α 2  with which the vehicle  10  can be subjected to the coasting control by setting a lower limit of the second set value α 2  when calculating the second set value α 2  of the acceleration command value α. This makes it possible to avoid generation of braking energy in the vehicle  10  when the vehicle  10  is decelerated using the second set value α 2  as the acceleration command value α, thereby improving the fuel economy of the vehicle  10 . 
     In step S 13 , the prediction ECU  33  compares the first set value α 1  and the second set value α 2  to determine whether the vehicle  10  needs deceleration. Specifically, when the first set value α 1  is equal to or smaller than the second set value α 2 , the prediction ECU  33  determines that the vehicle  10  needs no deceleration. That is, the prediction ECU  33  makes a negative determination in step S 13 . In this case, the prediction ECU  33  determines that no adverse-effect change has occurred in a surrounding environment around the own vehicle, the adverse-effect change being likely to have an adverse effect on a fuel economy of the own vehicle  10 . When the prediction ECU  33  makes a negative determination in step S 13 , the ACC ECU  32  transmits the acceleration command value α set as the first set value α 1  to the EV ECU  31  in step S 15 . 
     When determining in step S 13  that the second set value α 2  is smaller than the first set value α 1 , the prediction ECU  33  determines that the vehicle  10  needs deceleration. That is, the prediction ECU  33  makes an affirmative determination in step S 13 . In this case, the prediction ECU  33  determines that an adverse-effect change has occurred in a surrounding environment around the own vehicle, the adverse-effect change being likely to have an adverse effect on a fuel economy of the own vehicle  10 . When the prediction ECU  33  makes an affirmative determination in step S 13 , the ACC ECU  32  changes the acceleration command value α from the first set value α 1  to the second set value α 2  in step S 14 . Then, in step S 15 , the ACC ECU  32  transmits the acceleration command value α set as the second set value α 2  to the EV ECU  31 . Accordingly, the second set value α 2  which is smaller than the first set value α 1  and set under the ACC control is transmitted to the EV ECU  31 , as the acceleration command value α. Thus, the ACC ECU  32  implements a deceleration control to decelerate the own vehicle  10  at a lower deceleration than the deceleration settable under the ACC control. 
     Next, an operation example of the vehicle control device  50  in the present embodiment will be described. 
     Suppose that a velocity Vp of the preceding vehicle suddenly drops from time t 11  as shown by a dashed-dotted line in  FIG. 7(A) . In such a situation, the time headway and relative velocities of the own vehicle  10  and the preceding vehicle sharply decrease. Thus, when only the ACC control is being executed, the regenerative control is executed after time t 11  so that driving energy Ec of the vehicle  10  decreases sharply as shown by a dashed-two dotted line in  FIG. 7(B) . Referring to the driving energy Ec shown in  FIG. 7(B) , the magnitude of the driving energy Ec for traveling the vehicle  10  generated by the motor generator  20  is represented by a positive value and the magnitude of the braking energy generated under the regenerative control is represented by a negative value. The execution of this regenerative control increases an inter-vehicle distance Lc between the own vehicle  10  and the preceding vehicle after time t 11  as shown by a dashed-two dotted line in  FIG. 7(C) , and decreases the velocity Vb of the own vehicle  10  as shown by a dashed-two dotted line in  FIG. 7(A) . In this manner, when the braking energy is generated, part of the braking energy is converted into thermal energy or the like, which results in energy loss. 
     In this respect, in the present embodiment, when predicting at time t 10  earlier than time t 11  that the braking energy will be generated after time t 11 , the prediction ECU  33  calculates the second set value α 2  of the acceleration command value α with which the braking energy can be suppressed by the foregoing formula f4, and sets the acceleration command value α to the second set value α 2 . After the transmission of the acceleration command value α from the ACC ECU  32  to the EV ECU  31 , when the EV ECU  31  sets the motive power command value to zero, for example, the driving energy Ec of the motor generator  20  becomes zero at time t 10  as shown by a solid line in  FIG. 7(B) . Accordingly, the velocity Va of the vehicle  10  decreases after time t 10  as shown by a solid line in  FIG. 7(A) , and the inter-vehicle distance Lc between the own vehicle  10  and the preceding vehicle increases as shown by a solid line in  FIG. 7(C) . In this manner, decelerating the vehicle  10  makes it possible to suppress the generation of the braking energy as shown in  FIG. 7(B) , thereby resulting in improvement of fuel economy of the vehicle  10 . 
     According to the vehicle control device  50  in the present embodiment described above, it is possible to obtain the following operations and advantageous effects (1) to (7): 
     (1) When the prediction ECU  33  predicts that an adverse-effect change has occurred in a surrounding environment around the own vehicle, the adverse-effect change being likely to have an adverse effect on a fuel economy of the own vehicle  10 , the ACC ECU  32  executes the prediction control under which the acceleration of the own vehicle  10  can be limited. Accordingly, the acceleration of the own vehicle  10  is limited in advance when an adverse-effect change has occurred in a surrounding environment around the own vehicle, the adverse-effect change being likely to have an adverse effect on a fuel economy of the own vehicle  10 . This makes it possible to avoid a situation where the own vehicle  10  actually becomes impaired in fuel economy, thereby improving the fuel economy of the own vehicle  10 . 
     (2) When predicting that a deceleration requirement change has occurred in a surrounding environment around the own vehicle, the deceleration requirement change being required for delectation of the own vehicle  10 , the ACC ECU  32  predicts that an adverse-effect change has occurred in a surrounding environment around the own vehicle, the adverse-effect change being likely to have an adverse effect on a fuel economy of the own vehicle  10 . When predicting that a deceleration requirement change has occurred in a surrounding environment around the own vehicle, the deceleration requirement change being required for delectation of the own vehicle  10 , the ACC ECU  32  executes the acceleration control to actually limit the acceleration of the vehicle  10  by the use of the second set value α 2  of the acceleration command value α which is smaller than the first set value α 1  and is set by the ACC control. Accordingly, as the prediction control, the ACC ECU  32  executes the deceleration control to decelerate the own vehicle at a deceleration smaller than the deceleration settable by the ACC control. According to this configuration, it is possible to reduce energy loss that could occur at the deceleration for keeping the inter-vehicle distance. 
     (3) The prediction ECU  33  predicts whether a deceleration requirement change has occurred in a surrounding environment around the own vehicle, the deceleration requirement change being required for delectation of the own vehicle  10 , based on an index for the fuel economy of the own vehicle  10  and an index for the following performance of the own vehicle to the preceding vehicle. Specifically, the prediction ECU  33  uses, as the index for the fuel economy of the own vehicle  10 , the predicted value of the braking energy which is predicted that the change in the surrounding environment occurs when the own vehicle  10  is decelerated by execution of the ACC control during a period of time from the present to a predetermined time later. The prediction ECU  33  also uses, as the index for the following performance of the own vehicle to the preceding vehicle, the deviation amount y i  of the position of the own vehicle, the sum of deviations in position of the own vehicle from an ideal traveling that is based on the ACC control during a period from the present time to a predetermined future time. Accordingly, the prediction ECU  33  can reliably determine on the deceleration of the vehicle  10  in order to obtain the effects of improving the target fuel economy and suppressing the decrease of the following performance. 
     (4) The prediction ECU  33  represents the index for the fuel economy of the own vehicle  10  and the index for the following performance of the own vehicle to the preceding vehicle as probability information as shown in the foregoing formulas f2 and f3. The prediction ECU  33  uses the function expressed by the formula f4 as the evaluation function including the expected value based on the index for the fuel economy of the own vehicle  10  and the expected value based on the index for the following performance of the own vehicle  10  to the preceding vehicle, and predicts, based on the calculated value in the formula f4, a deceleration requirement change has occurred in a surrounding environment around the own vehicle, the deceleration requirement change being required for delectation of the own vehicle  10 . Accordingly, the prediction ECU  33  can reliably determine on the deceleration of the vehicle  10  in order to obtain the effects of improving the fuel economy and suppressing the decrease of the following performance even if the prediction information on a change in the surrounding environment includes uncertainty. 
     (5) The prediction ECU  33  calculates the second set value α 2  of the acceleration command value with which the vehicle  10  can be subjected to the coasting control. Accordingly, the ACC ECU  32  executes the coasting control to coast the own vehicle  10  in a state where the output from the motor generator  20  does not transfer to the wheels of the vehicle  10 . According to this configuration, it is possible to decelerate the vehicle  10  with higher fuel economy when the vehicle  10  is decelerated by using the prediction information. 
     (6) The ACC ECU  32  repeatedly accelerates and decelerates the own vehicle  10  to execute a burn-and-coast control under which the own vehicle  10  follows the preceding vehicle. Accordingly, the vehicle  10  can generally run by a traveling method with high fuel efficiency. 
     (7) The prediction ECU  33  predicts the deceleration of the preceding vehicle as an adverse-effect change has occurred in a surrounding environment around the own vehicle, the adverse-effect change being likely to have an adverse effect on a fuel economy of the own vehicle. Accordingly, it is possible to improve the fuel economy against a change in the surrounding environment that greatly affects the fuel economy. 
     Modification Example 
     Next, a modification example of the vehicle control device  50  in the first embodiment will be described. 
     As shown by a broken line in  FIG. 1 , a vehicle control device  50  in the present modification example further has a human machine interface (HMI) ECU  37 . The HMI ECU  37  is a part that controls a notification device  38  installed in the vehicle  10  to make various notifications to the occupant of the vehicle  10 . The notification device  38  can be a speaker, a display, or the like. 
     In step S 15  shown in  FIG. 4 , the ACC ECU  32  transmits the acceleration command value α to the HMI ECU  37 . The HMI ECU  37  executes an instruction control to instruct the occupant of the own vehicle  10  on a driving method such that the acceleration of the own vehicle  10  is limited, based on the acceleration command value α transmitted from the ACC ECU  32 . For example, the HMI ECU  37  instructs the occupant on the driving method by causing the occupant to recognize the acceleration and velocity corresponding to the acceleration command value α by sound from the speaker or by displaying the acceleration and velocity corresponding to the acceleration command value α on the display. 
     The HMI ECU  37  may instruct the occupant on the driving method by adjusting the push-down amount of the accelerator pedal or adjusting the push-down amount of the brake pedal based on the acceleration command value α. 
     The vehicle  10  can be decelerated even by this method. 
     Second Embodiment 
     Next, a vehicle control device  50  in a second embodiment will be described. The description below focuses on the differences from the vehicle control device  50  in the first embodiment. First, a schematic configuration of the vehicle  10  equipped with the vehicle control device  50  in the second embodiment will be explained. 
     As illustrated in  FIG. 8 , the vehicle  10  in the present embodiment is a hybrid automobile that uses not only the motor generator  20  but also an engine  60  as motive power sources. The engine  60  is driven to rotate a first motive power transmission shaft  29   a . The first motive power transmission shaft  29   a  is coupled to a second motive power transmission shaft  29   b  via a clutch  23 . The clutch  23  is switchable between a connection state in which the first motive power transmission shaft  29   a  and the second motive power transmission shaft  29   b  are coupled to allow transfer of motive power between these shafts and a non-connection state in which the first motive power transmission shaft  29   a  and the second motive power transmission shaft  29   b  are decoupled to shut off transfer of motive power between these shafts. 
     The motor generator  20  applies motive power to the second motive power transmission shaft  29   b  based on the energization. Therefore, when the clutch  23  is in the connection state, the second motive power transmission shaft  29   b  is supplied with motive power from at least one of the engine  60  and the motor generator  20 . The motive power supplied to the second motive power transmission shaft  29   b  is input into a transmission  62 . 
     Specifically, the transmission  62  increases or decreases the total motive power of the engine  60  and the motor generator  20  input from the second motive power transmission shaft  29   b , and transfers the increased or decreased total motive power to a third motive power transmission shaft  29   c . Alternatively, the transmission  62  subtracts, from the motive power of the engine  60 , motive power converted by the motor-generator  20  into electric power to thereby obtain resultant motive power, and increases or decreases the resultant motive power. Then, the transmission shaft  62  transfers the increased or decreased resultant power to the third motive power transmission shaft  29   c.    
     The motive power transferred to the third motive power transmission shaft  29 C is then transferred to the wheel  28  of the vehicle  10  via the differential gear  26  and the drive shaft  27 . Accordingly, the vehicle  10  starts to run. In this manner, in the present embodiment, the motor generator  20  and the engine  60  correspond to a power train. 
     The vehicle  10  is equipped with an engine ECU  63  that comprehensively controls the driving of the engine  60 . The engine ECU  63  controls the driving of the clutch  23 . 
     The vehicle  10  is equipped with a hybrid vehicle (HV) ECU  39  instead of the EV ECU  31 . The HV ECU  39  exchanges information necessary for control with the MG ECU  30  and the engine ECU  63  to perform an integrated adjustment control of the engine  60 , the motor generator  20 , and the battery  22 . Specifically, the HV ECU  39  controls the driving of the motor generator  20  and the engine  60  based on the acceleration command value transmitted from the ACC ECU  32 . When the engine  60  is stopped and the acceleration command value α is equal to or greater than a predetermined acceleration threshold αth, the HV ECU  39  transmits a predetermined motive power command value to the engine ECU  63  to restart the engine  60 , whereby the vehicle  10  is accelerated. When the acceleration command value α is smaller than the acceleration threshold αth, the HV ECU  39  transmits a command for stopping the engine  60  to the engine ECU  63  and transmits a predetermined motive power command value to the MG ECU  30  to suppress fuel consumption, whereby the vehicle  10  performs EV traveling. In the present embodiment, the HV ECU  39  corresponds to a traveling control unit that controls the driving and stop of the engine  60  and the motor generator  20  based on the traveling state of the own vehicle  10 . 
     Next, a procedure for a prediction control process executed by the ACC ECU  32  and the prediction ECU  33  will be specifically described with reference to  FIG. 9 . The ACC ECU  32  and the prediction ECU  33  repeatedly execute the process shown in  FIG. 9  in predetermined cycles. 
     As shown in  FIG. 9 , subsequent to step S 12 , the prediction ECU  33  determines in step S 20  whether it is necessary to limit the acceleration of the vehicle  10  for suppressing short-time driving of the engine  60 . This determination is specifically executed by the method as described below. 
     The efficiency of deriving energy from the engine  60  may become deteriorated due to a delay in air intake by the engine  60 , increase in energy consumption for start of the engine  60 , increase in fuel consumption at the start of the engine  60 , and the like. Taking these factors into account, actual engine efficiency η eng  at engine traveling is expressed by the following formula f5: 
     
       
         
           
             
               
                 
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     In the formula f5, δ delay  represents a coefficient of air intake delay, η e  represents ideal engine efficiency that is engine efficiency when the engine  60  is operated in a steady state, E out  represents ideal output energy of the engine  60 , E egon  represents start energy of the engine  60 , E in  represents input fuel energy of the engine  60 , E add  represents start-time additional energy, and T acc  represents time necessary for acceleration. 
     The actual engine efficiency η* eng  on the left side of the formula f5 is used as the index for the fuel economy of the own vehicle  10 . The value on the right side of the formula f5 indicates the ratio of the output energy of the engine to the input energy of the engine. 
     On the other hand, when the actual engine efficiency of the vehicle  10  in EV traveling state where the vehicle  10  runs only by motive power of the motor generator  20  is defined by system efficiency η sys  based on the traveling result up to the present, the actual engine efficiency η sys  in the EV traveling state can be expressed by the following formula f6: 
     
       
         
           
             
               
                 
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     In the formula f6, E sysout  represents the output energy of the power train, and E sysin  represents input fuel energy. 
     The actual engine efficiency η sys  in the EV traveling state indicates the ratio of the output energy of the power train to the input energy of the power train of the own vehicle  10  in the state where the engine  60  is stopped. 
     From the foregoing, the future actual engine efficiency η* eng  with respect to the acceleration command value α can be expressed as shown in  FIG. 10 . That is, when the acceleration command value α is smaller than the acceleration threshold αth, the vehicle  10  runs by the motive power of the motor generator  20 , and thus the future actual engine efficiency η *   eng  takes on the value on the right side of the formula f6. When the acceleration command value α is equal to or greater than the acceleration threshold αth and is smaller than an acceleration command value αbc that is used for acceleration in the burn-and-coast control during the ACC control, the future actual engine efficiency η* eng  can be determined by the right side of the formula f5. The thus determined future actual engine efficiency η* eng  indicates the ratio of the output energy of the power train to the input energy of the power train of the own vehicle  10 . As under the deceleration control for suppression of the braking energy described above in relation to the first embodiment, when the own vehicle  10  runs with the predetermined state amount b(t) with respect to the i-th surrounding vehicle, an expected value η* eng (b(t)) of the actual engine efficiency η* eng  of the own vehicle  10  at that time can be determined by the following formula f7: 
     
       
         
           
             
               
                 
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     The use of the expected value η* eng (b(t)) makes it possible to form an evaluation function F E2  as expressed by the following formula f8: 
     
       
         
           
             
               
                 
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     Determining the state amount b(t) of the own vehicle  10  such that the evaluation function F E2  becomes minimum makes it possible to obtain the state amount b(t) of the own vehicle  10  with which the short-time driving of the engine  60  is suppressed while ensuring the following performance. In other words, it is possible to obtain the state amount b(t) of the own vehicle  10  that improves the fuel economy while ensuring the following performance. 
     Based on the foregoing method, the prediction ECU  33  executes the determination processing in step S 20 . Specifically, the prediction ECU  33  has a map that indicates a relationship between the acceleration command value α and the actual engine efficiency η* eng  as shown in  FIG. 10 . The prediction ECU  33  accumulates data on the output energy and the input fuel energy of the power train up to the present, and sequentially calculates the actual engine efficiency η sys  in the EV traveling state from the formula f6 based on the accumulated data. The prediction ECU  33  uses the calculated actual engine efficiency η sys  as actual engine efficiency η* eng  where the acceleration command value α is smaller than the acceleration threshold αth. 
     The prediction ECU  33  determines the state amount b(t) of the own vehicle  10  such that the evaluation function F E2  becomes minimum. Since the state amount b(t) is a function of acceleration of the vehicle  10 , the prediction ECU  33  can obtain from the foregoing calculation a third set value α 3  of the acceleration command value α with which the value of the evaluation function F E2  becomes minimum. 
     In step S 20 , the prediction ECU  33  compares the first set value α 1  with the third set value α 3  to determine whether it is necessary to limit the acceleration of the vehicle  10  in order to suppress the short-time driving of the engine  60 . Specifically, when the first set value α 1  is equal to or smaller than the third set value α 3 , 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 step S 20 . In this case, the prediction ECU  33  determines that there has not occurred a change in the surrounding environment by which the own vehicle  10  will become impaired in fuel economy. Then, the ACC ECU  32  and the prediction ECU  33  execute step S 13  and the subsequent steps. 
     When the third set value α 3  is smaller than the first set value α 1 , the prediction ECU  33  determines that it is necessary to limit the acceleration of the vehicle  10 . That is, the prediction ECU  33  makes an affirmative determination in step S 20 . In this case, the prediction ECU  33  determines that an adverse-effect change has occurred in a surrounding environment around the own vehicle, the adverse-effect change being likely to have an adverse effect on a fuel economy of the own vehicle  10 . When the prediction ECU  33  makes an affirmative determination in step S 20 , the ACC ECU  32  changes the acceleration command value α from the first set value α 1  to the third set value α 3  in step S 21 . After that, the ACC ECU  32  and the prediction ECU  33  execute step S 13  and the subsequent steps. 
     In step S 13 , the prediction ECU  33  compares the first set value α 1 , the second set value α 2 , and the third set value α 3  to determine whether the vehicle  10  needs deceleration. Specifically, when the third set value α 3  is smaller than the first set value α 1  and the third set value α 3  is smaller than the second set value α 2 , the prediction ECU  33  makes an affirmative determination in step S 13 . On the other hand, when the first set value α 1  is equal to or smaller than the third set value α 3  or the second set value α 2  is equal to or smaller than the third set value α 3 , the prediction ECU  33  makes a negative determination in step S 13 . 
     Next, an operation example of the vehicle control device  50  in the present embodiment will be described. 
     Suppose that the velocity Vp of the preceding vehicle sharply increases and then sharply decreases as shown by a dashed-dotted line in  FIG. 11(A) . In such a situation, when only the ACC control is executed, the ACC ECU  32  starts the engine  60  at time t 20  to cause the own vehicle  10  to follow the preceding vehicle. When the ACC ECU  32  starts the engine  60  at time t 20 , the driving energy Ec of the vehicle  10  becomes larger than energy Es at the time of engine start as shown by a dashed-two dotted line in  FIG. 11(B) . In addition, the rotation speed Nc of the engine  60  increases after time t 20  as shown by a dashed-two dotted line in  FIG. 11(C) . 
     Then, when the preceding vehicle suddenly decelerates, the time headway and relative velocities between the own vehicle  10  and the preceding vehicle sharply decrease. Accordingly, when the regenerative control is executed at time t 21 , the driving energy Ec of the vehicle  10  sharply decreases as shown by a dashed-two dotted line in  FIG. 11(B) . Due to the execution of this regenerative control, the rotation speed Nc of the engine  60  sharply decreases after time t 21  and the engine  60  stops, as shown by a dashed-two dotted line in  FIG. 11(C) . In this manner, when the engine  60  is started and then stopped in a short time, the energy used for the start of the engine  60  is lost. 
     In this respect, the prediction ECU  33  of the present embodiment calculates, by the foregoing formula f8, the third set value α 3  of the acceleration command value with which the short-time driving of the engine  60  can be suppressed, and sets the acceleration command value α to the third set value α 3 . When the acceleration command value α is transmitted from the ACC ECU  32  to the HV ECU  39 , the actual acceleration of the vehicle  10  is hard to increase to the acceleration threshold αth with which the engine  60  is supposed to start, and thus the engine  60  will not start. Accordingly, the velocity Vc of the vehicle  10  decreases as shown in  FIG. 11(A)  and the driving energy Ec of the vehicle  10  does not increase to the energy Es at the time of engine start as shown in  FIG. 11(B) . Therefore, it is possible to suppress a waste of the energy Es at the time of engine start, thereby resulting in improvement of the fuel economy of the vehicle  10 . 
     According to the vehicle control device  50  in the present embodiment described above, it is possible to obtain not only the foregoing operations and advantageous effects (1) to (7) but also the following operations and advantageous effects (8) to (10): 
     (8) The ACC ECU  32  limits the acceleration of the own vehicle  10  such that the engine  60  is unlikely to restart by the engine ECU  63 . Accordingly, the short-time driving of the engine  60  can be suppressed to decrease energy loss. This improves the fuel economy of the vehicle  10 . 
     (9) The prediction ECU  33  determines whether to limit the acceleration of the own vehicle  10  based on the index for the fuel economy of the own vehicle  10  and the index for the following performance of the own vehicle  10  to the preceding vehicle. Specifically, as the index for the fuel economy of the own vehicle  10 , the prediction ECU  33  uses the predicted value of the ratio of the output energy of the power train to the input energy of the power train of the own vehicle  10  during a period from a present time to a predetermined future time as shown in the formula f7. In addition, as the index for the following performance of the own vehicle  10  to the preceding vehicle, the prediction ECU  33  uses the deviation amount y i  of the position of the own vehicle from the ideal value in the ACC control during a period of time from the present to a predetermined time later. Accordingly, it is possible to reliably determine on the deceleration of the vehicle  10  in order to obtain the effects of improving the target fuel economy and suppressing the decrease of the following performance. 
     (10) The predicted value of the ratio of the output energy of the power train to the input energy of the power train of the own vehicle  10  includes the predicted value shown by the formula f5 and the predicted value shown by the formula f6. The predicted value shown by the formula 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 the state where the engine  60  is driven. The predicted value shown by the formula f6 is a predicted value of the ratio of the output energy of the power train to the input energy of the power train of the own vehicle  10  in the state where the engine  60  is stopped. Accordingly, it is possible to run the vehicle  10  by the determined traveling method with high fuel efficiency even if the engine  60  is stopped. 
     Third Embodiment 
     Next, a vehicle control device  50  in a third embodiment will be described. The description below focuses on the differences from the vehicle control device  50  in the first embodiment. 
     In relation to the present embodiment, an example of a method for calculating the occurrence probability p i  of behavior of a surrounding vehicle used in the foregoing formulas f2 and f3 will be described. For ease of explanation, the following description is based on the assumption that the occurrence probability p i  of behavior of the surrounding vehicle is deceleration behavior occurrence probability as the probability that the surrounding vehicle will decelerate. 
     First, consider that the deceleration behavior of the vehicle is predicted at a spot where there are assumed two patterns of behavior in which the surrounding vehicle decelerates at a predetermined place and in which the surrounding vehicle passes through the predetermined place. When the perimeter monitoring device  34  acquires information as shown in  FIG. 13(A)  as vehicle velocity information of the surrounding vehicle, for example, it will be predicted whether the surrounding vehicle will take on deceleration behavior at the current time t 30 . This prediction is based on the vehicle velocity information of the surrounding vehicle at a time in the past earlier than the current time t 30 . As shown in  FIG. 13(A) , the velocity of the surrounding vehicle is constant before the time t 30 . Thus, before the time t 30 , the deceleration behavior occurrence probability that is the probability of the surrounding vehicle&#39;s undergoing deceleration behavior can be calculated as 0.5, that is, 50%, for example, as shown in  FIG. 13(B) . Therefore, before the time t 30 , the probability of the surrounding vehicle decelerating is 0.5, and the probability of the surrounding vehicle passing through is 0.5. After the time t 30 , when the velocity of the surrounding vehicle gradually decreases along with a lapse of time, the surrounding vehicle is considered to start deceleration behavior. Thus, the value of the deceleration behavior occurrence probability gradually increases from 0.5. 
     In this manner, in the case of predicting the deceleration behavior of the surrounding vehicle using the traveling data such as the past velocity information of the surrounding vehicle, the deceleration behavior occurrence probability can be calculated with higher accuracy by predicting the deceleration behavior of the surrounding vehicle with learning of the past traveling data. 
     On the other hand, it is possible to predict that the surrounding vehicle will take on deceleration behavior before the actual detection of deceleration of the surrounding vehicle in a situation in which the surrounding vehicle passes through a place where a plurality of vehicles will take on deceleration behavior according to statistics or in a situation in which a traffic light in front of the surrounding vehicle turns from green to yellow. If such a situation is detected before the time t 30 , correcting the deceleration behavior occurrence probability to a value greater than 0.5 at that time makes it possible to calculate the deceleration behavior occurrence probability that reflects not only the information of the past traveling data of the surrounding vehicle but also the information on the future predicted behavior of the surrounding vehicle. Executing the traveling control of the own vehicle  10  based on the thus calculated deceleration behavior occurrence probability makes it possible to achieve the more appropriate traveling control of the own vehicle  10  in accordance with the predicted behavior of the surrounding vehicle. 
     Thus, in the present embodiment, the server device  41  constructs a learning model of vehicle behavior based on the past traveling data transmitted from a predetermined vehicle. The predetermined vehicle is not limited to the own vehicle  10  but may include a vehicle different from the own vehicle  10 . One or more predetermined vehicles may be set. The learning model of vehicle behavior is formed from a likelihood function that is capable of calculating a likelihood which is composed of the value indicating plausibility that a vehicle will take on predetermined behavior with respect to an observation value in the traveling data of the vehicle. The likelihood corresponds to an index indicating the similarity between the vehicle traveling data and the learning information. Based on the constructed learning model of vehicle behavior, the server device  41  creates a calculating equation by which the deceleration behavior occurrence probability of the vehicle can be obtained. This calculating equation is created, for example, in a manner as described below. 
     When there are assumed two situations in which a vehicle decelerates at a predetermined place and in which a vehicle passes through the predetermined place, the server device  41  construct a deceleration behavior model and a passing behavior model based on the traveling data transmitted from the predetermined vehicle. The deceleration behavior model and the passing behavior model are learning models of vehicle behavior. The traveling data includes time-series information on vehicle velocity. 
     The server device  41  calculates the likelihood of the deceleration behavior model and the likelihood of the passing behavior model based on the traveling data of the predetermined vehicle, and obtains a likelihood difference that is a differential value between these likelihoods. The server device  41  performs this calculation on all the past traveling data to calculate the frequency with which the deceleration behavior occurred and the frequency with which the passing behavior occurred, at the time with each of the likelihood differences. Accordingly, the server device  41  can obtain, for example, a relationship between likelihood difference and deceleration occurrence frequency as shown by a dashed-dotted line in  FIG. 14  and a relationship between likelihood difference and passing occurrence frequency as shown by a dashed-two dotted line in  FIG. 14 . Based on the information shown in  FIG. 14 , the server device  41  creates a calculating equation of a learning value P lm  of the deceleration behavior occurrence probability as shown by the following formula f9: 
     
       
         
           
             
               
                 
                   
                       
                   
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     In the formula f9, t represents time, t=0 represents the start time in each behavior model, t=T stop  represents the end time in each behavior model, μ dec  represents the average value of the deceleration behavior models, σ dec   2  represents the dispersion of the deceleration behavior models, μ pass  represents the average value of the passing behavior models, σ pass   2  represents the dispersion of the passing behavior models, N dec  represents the function in which the frequency of occurrence of the deceleration behavior model shown in  FIG. 14  is normally distributed, N pass  represents the function in which the frequency of occurrence of the passing behavior model shown in  FIG. 14  is normally distributed, and each of variables in the functions N dec  and N pass  represents a value on the lateral axis shown in  FIG. 14 , that is, the likelihood difference between the behavior models. Thus, the formula f9 is a calculating equation by which the learning value p lm  of the deceleration behavior occurrence probability can be obtained from the likelihood difference between the models. 
     The vehicle control device  50  acquires the deceleration behavior model, the passing behavior model, and the formula 9 from the server device  41 . The vehicle control device  50  calculates the likelihood of the deceleration behavior model and the likelihood of the passing behavior model from the past traveling data of the surrounding vehicles detected by the perimeter monitoring device  34  during a period from the present to a time prior to a predetermined time. The vehicle control device  50  calculates a likelihood difference between the calculated likelihoods of the models, and substitutes the calculated likelihood difference between the models into the formula f9 to calculate the learning value p lm  of the deceleration behavior occurrence probability. 
     On the other hand, the vehicle control device  50  in the present embodiment predicts the future deceleration behavior of the surrounding vehicles according to statistics or based on the information detected by the perimeter monitoring device  34 , and calculates the occurrence probability of the predicted deceleration behavior of the surrounding vehicles. The vehicle control device  50  uses the calculated value as a predicted value p ftr  of the deceleration occurrence probability. 
     The vehicle control device  50  corrects the learning value p lm  of the deceleration behavior occurrence probability by the predicted value p ftr  of the deceleration behavior occurrence probability to obtain final deceleration behavior occurrence probability p i . Specifically, the vehicle control device  50  calculates the deceleration behavior occurrence probability p i  by the following formula f10: 
     
       
         
           
             
               
                 
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     where z=(p lm +p lm2 )/2. In the formula, P lm2  represents the probability that the surrounding vehicle will pass through a predetermined place. For example, the total value of P lm  and P lm2  is 1 in a situation in which there are assumed two patterns of behavior in which the surrounding vehicle decelerates at a predetermined place and in which the surrounding vehicle passes through the predetermined place. 
     According to the foregoing formula f10, when the learning value p lm  of the deceleration behavior occurrence probability is close to 0.5, the predicted value p ftr  of the deceleration behavior occurrence probability is dominant in the behavior occurrence probability p i . When the learning value p lm  of the deceleration behavior occurrence probability is close to 0 or 1, the learning value p lm  of the deceleration behavior occurrence probability is dominant in the behavior occurrence probability p i . 
     Next, a specific method for calculating the deceleration behavior occurrence probability p i  will be described. Hereinafter, for the sake of convenience, the surrounding vehicle of which the deceleration behavior occurrence probability p i  is to be calculated will be called specific surrounding vehicle, and the surrounding vehicles excluding the specific surrounding vehicle will be called other surrounding vehicles. The surrounding vehicles include the preceding vehicle traveling in front of the own vehicle  10  and the vehicles surrounding the own vehicle  10  excluding the preceding vehicle. 
     As shown in  FIG. 15 , the prediction ECU  33  in the present embodiment executes deceleration behavior occurrence probability calculation processing in step S 30  subsequent to step S 12 . The specific procedure for the deceleration behavior occurrence probability calculation processing is as shown in  FIG. 16 . 
     As shown in  FIG. 16 , the prediction ECU  33  first determines in step S 31  whether there exists the specific surrounding vehicle. 
     Specifically, when any object is detected around the own vehicle  10 , the perimeter monitoring device  34  recognizes whether the detected object is a surrounding vehicle. At that time, the accuracy of recognition of the specific surrounding vehicle by the perimeter monitoring device  34  varies depending on the situation. For example, the accuracy of the perimeter monitoring device  34  for recognizing an object is lower with an increase in the distance from the own vehicle  10  to the detected object. Thus, it is hard for the perimeter monitoring device  34  to detect accurately whether an object existing far from the own vehicle  10  is the specific surrounding vehicle. Accordingly, when detecting the specific surrounding vehicle, the perimeter monitoring device  34  in the present embodiment calculates the recognition accuracy as well. For example, when detecting an object corresponding to the specific surrounding vehicle, the perimeter monitoring device  34  calculates the recognition accuracy by a map or a calculating equation based on the relative distance of the own vehicle  10  to the object. The map and the calculating equation are set such that, as the distance from the own vehicle  10  to the object is longer, the value of the recognition accuracy becomes smaller. The perimeter monitoring device  34  transmits the calculated recognition accuracy to the prediction ECU  33 . The prediction ECU  33  determines that there exists the specific surrounding vehicle based on the condition that the specific surrounding vehicle has been detected by the perimeter monitoring device  34  and the recognition accuracy of the detected specific surrounding vehicle is equal to or higher than a predetermined threshold. 
     When determining that there exists the specific surrounding vehicle, the prediction ECU  33  makes an affirmative determination in step S 31 , and then determines in step S 32  whether there exists learning information of traveling data of the specific surrounding vehicle. 
     Specifically, in order to calculate the learning value p lm  of the deceleration behavior occurrence probability using the foregoing formula f9, it is necessary that the server device  41  has constructed the deceleration behavior model and the passing behavior model at the traveling spot of the own vehicle  10 . In addition, the use of the formula f9 requires the respective likelihoods of the models, and thus the traveling data of the specific surrounding vehicle needs to be accumulated to a degree that the respective likelihoods of the models can be calculated. Thus, the prediction ECU  33  makes an affirmative determination in step S 32  based on the condition that the deceleration behavior model and the passing behavior model at the traveling spot of the own vehicle  10  have been acquired from the server device  41  and the traveling data of the specific surrounding vehicle has been accumulated to a degree that the respective likelihoods of the models can be calculated. 
     About the accumulation of the past traveling data of the surrounding vehicles, the past traveling data of the surrounding vehicles may be accumulated on the server device  41  by transmitting the traveling data such as the vehicle velocity information from the communication unit  36  to the server device  41 . Alternatively, the past traveling data of the surrounding vehicles may be accumulated by collecting the traveling histories of the surrounding vehicles in the own vehicle  10 . 
     When making an affirmative determination in step S 32 , the prediction ECU  33  calculates the learning value pin, of the deceleration behavior occurrence probability based on the past traveling data of the specific surrounding vehicles in step S 33 . Specifically, the prediction ECU  33  calculates the respective likelihoods of the deceleration behavior model and the passing behavior model based on the past traveling data of the specific surrounding vehicle, and calculates the learning value pin, of the deceleration behavior occurrence probability by the foregoing formula f9 from the difference between the calculated likelihoods of the models. 
     On the other hand, when the deceleration behavior model and the passing behavior model at the traveling spot of the own vehicle  10  have not been acquired from the server device  41 , or when the traveling data of the specific surrounding vehicle has not been accumulated to a degree that the respective likelihoods of the models can be calculated, the prediction ECU  33  makes a negative determination in step S 32  shown in  FIG. 14 . In this case, in step S 34 , the prediction ECU  33  calculates the learning value p lm  of the deceleration behavior occurrence probability based on road static information detected by the perimeter monitoring device  34 . The road static information includes the presences or absences of traffic lights, road traveling rules, speed limits, slopes, curved roads, the presences or absences of intersections, and others. For example, when a camera is used as the perimeter monitoring device  34 , it is possible to detect road signs, road conditions, and others based on the image data on the surroundings of the vehicle captured by the camera. The prediction ECU  33  acquires the road static information based on the road signs, the road conditions, and others detected by the perimeter monitoring device  34 . The prediction ECU  33  has a map in which the deceleration behavior occurrence probability is defined for each item of the road static information. The prediction ECU  33  calculates the deceleration behavior occurrence probability for each item of the acquired road static information, and calculates the learning value p lm  of the deceleration behavior occurrence probability using a calculating equation from the calculated deceleration behavior occurrence probability for each item. 
     After execution of step S 33  or S 34 , the prediction ECU  33  determines in step S 35  whether the presence of a traffic signal is a cause for the specific surrounding vehicle&#39;s taking a deceleration behavior in the future. For example, the prediction ECU  33  may determine whether the presence of a traffic signal is a cause for the specific surrounding vehicle to take on deceleration behavior in the future based on the past traveling history detected by the perimeter monitoring device  34 . Alternatively, when determining that there exists a traffic light installed within a predetermined range from the specific surrounding vehicle based on the road situation detected by the perimeter monitoring device  34 , the prediction ECU  33  may determine that the presence of a traffic signal is a cause for the specific surrounding vehicle to take on deceleration behavior in the future. 
     When determining that the presence of a traffic signal is a cause for the specific surrounding vehicle to take on deceleration behavior in the future, the prediction ECU  33  makes an affirmative determination in step S 35 , and then determines in step S 36  whether there is a traffic light near the current traveling position of the specific surrounding vehicle and the perimeter monitoring device  34  has recognized signal information on the traffic light. When determining that the distance from the specific surrounding vehicle to a traffic light is shorter than a predetermined threshold based on the road situation detected by the perimeter monitoring device  34 , the prediction ECU  33  determines that there is a traffic light near the current traveling position of the specific surrounding vehicle. 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 light by the perimeter monitoring device  34 . 
     When there is a traffic light near the current traveling position of the specific surrounding vehicle and the perimeter monitoring device  34  has recognized the signal information on the traffic light, the prediction ECU  33  makes an affirmative determination in step S 36 . In this case, the prediction ECU  33  then calculates the predicted value p ftr  of the deceleration behavior occurrence probability in accordance with the change timing of the traffic light in step S 37 . Specifically, when the own vehicle  10  is traveling, the prediction ECU  33  accumulates the information on the signal change timing of the traffic light based on the signal information of the traffic light detected by the perimeter monitoring device  34 . The prediction ECU  33  of the present embodiment accumulates green light duration time information as the information on the signal change timing of the traffic light. The green light duration time information refers to a time necessary for the traffic light to turn from green to yellow since a point in time when the traffic light has turned from red to green, until yellow. 
     For example, as shown in  FIG. 17 , when the traffic light is at red at time t 40  when the traffic light is recognized by the perimeter monitoring device  34 , the prediction ECU  33  stores, as green light duration time information in the storage device, as a period of time from time t 41  when the traffic light subsequently turns to green until time t 42  when the traffic light further turns to yellow in the storage device. 
     On the other hand, as illustrated in  FIG. 18 , for example, when the traffic light is at red at time t 50  when the traffic light is recognized by the perimeter monitoring device  34 , the prediction ECU  33  stores, as the green light duration time information, a period of time until time t 51  when the traffic light subsequently turns to yellow in the storage device. 
     If there is a traffic light that has a change cycle varying depending on traffic flow, the prediction ECU  33  may learn the green light duration time information in accordance with the information on traffic flow acquired by Vehicle Information and Communication System (VICS, registered trademark) or the like. 
     The prediction ECU  33  creates a map as shown in  FIG. 19  based on the green light duration time information accumulated in the storage device. The map shown in  FIG. 19  indicates a relationship between green light duration time γ and probability p sig  of the traffic light turning from green to yellow such that the green light duration time is placed on the lateral axis and the probability p sig  on the vertical axis. This map is stored in the storage device of the prediction ECU  33 . 
     A plurality of vehicles acquires respective green light duration time information and transmits the same to the server device  41  and the server device  41  learns the respective green light duration time information transmitted from the vehicles, thereby the server device  41  may create the map as shown in  FIG. 19 . In this case, the prediction ECU  33  acquires the map from the server device  41  via the communication unit  36 , and thereby may use the map shown in  FIG. 19 . 
     If the traffic light is at red at a point in time when the perimeter monitoring device  34  recognizes the traffic light in step S 35  or S 36  shown in  FIG. 16 , the prediction ECU  33  measures the green light duration time from a point in time when the traffic light turns from red to green. If the traffic light is at green at a point in time when the perimeter monitoring device  34  recognizes the traffic light in step S 35  or S 36 , the prediction ECU  33  measures the green light duration time from that point in time. With respect to the thus measured green light duration time γ, the probability p sig  of the traffic light&#39;s turning from green to yellow δ seconds later can be obtained as a value of the probability p sig  where the value on the lateral axis is γ+δ in the map as shown in  FIG. 19 . 
     On the other hand, when the traffic light near the current traveling position of the specific surrounding vehicle turns from green to yellow, it is presumed that the specific surrounding vehicle will take on deceleration behavior. That is, there is a correlative relationship between the probability p sig  of the traffic light&#39;s turning from green to yellow and the probability of the specific surrounding vehicle&#39;s taking deceleration behavior. Thus, 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 making a negative determination in step S 36 , that is, when not determining that the traffic light is near the current traveling position of the specific surrounding vehicle, or when determining that the signal information of the traffic light has not been recognized by the perimeter monitoring device  34 , the prediction ECU  33  then calculates the predicted value p ftr  of the deceleration behavior occurrence probability based on statistic information in step S 38 . 
     Specifically, the server device  41  communicates with a plurality of vehicles to acquire information indicating which pattern of behavior, decelerating or passing, the vehicles showed at the traffic light, and calculates the deceleration behavior occurrence probability of the vehicles based on the statistic information. For example, if 50 of 100 vehicles targeted for statistics decelerated at the traffic light and the other 50 passed through the traffic light without deceleration, the server device  41  calculates the deceleration behavior occurrence probability at the traffic light as 0.5. The prediction ECU  33  acquires statistical information Psta of the deceleration behavior occurrence probability at the traffic light from the server device  41 , and uses the statistical information Psta of the deceleration behavior occurrence probability as the predicted value p ftr  of the deceleration behavior occurrence probability. 
     As shown  FIG. 16 , when making a negative determination in step S 35 , that is, when not determining that there exists signal as a cause for the specific surrounding vehicle to take on deceleration behavior in the future, the prediction ECU  33  then determines in step S 39  whether the state amounts of the other surrounding vehicles have been acquired. The state amounts of the other surrounding vehicles include the traveling positions, velocities, and others of the other surrounding vehicles. Specifically, when the state amounts of the other surrounding vehicle have been acquired by the perimeter monitoring device  34 , the prediction ECU  33  makes an affirmative determination in step S 39 . When inter-vehicle communication is allowed between the own vehicle  10  and the other surrounding vehicles, the prediction ECU  33  may make an affirmative determination in step S 39  on the condition that the state amounts have been acquired through the communication with the other surrounding vehicles. 
     When making an affirmative determination in step S 39 , the prediction ECU  33  calculates the predicted value p ftr  of the deceleration behavior occurrence probability based on the state amounts of the other surrounding vehicles in step S 40 . Specifically, the prediction ECU  33  predicts the respective future behavior of the vehicles by simulation, based on the information of the specific surrounding vehicle such as the current traveling position and velocity and the information of the other surrounding vehicles such as the current traveling positions and velocities. According to this simulation, the prediction ECU  33  calculates a probability p sur  that the other surrounding vehicles will take on predetermined behavior that could trigger the deceleration of the specific surrounding vehicle. The predetermined behavior of the other surrounding vehicles that could trigger the deceleration of the specific surrounding vehicle is, for example, the behavior of making a lane change 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 the other surrounding vehicles as the predicted value p ftr  of the deceleration behavior occurrence probability. 
     When making a negative determination in step S 39 , the prediction ECU  33  then calculates the predicted value p ftr  of the deceleration behavior occurrence probability based on statistical information in step S 41 . 
     Specifically, the server device  41  communicates with a plurality of vehicles to take statistics on whether the vehicles decelerated at a predetermined place or passed through the predetermined place, and calculates the deceleration behavior occurrence probability of the vehicles based on the statistical information. For example, if 50 of 100 vehicles targeted for statistics decelerated at the predetermined place and the other 50 passed through the predetermined place without deceleration, the server device  41  calculates the deceleration behavior occurrence probability at the traffic light as 0.5. The prediction ECU  33  acquires statistical information Psta of the deceleration behavior occurrence probability corresponding to the current position of the own vehicle from the server device  41 , and uses the statistical information Psta of the deceleration behavior occurrence probability as the predicted value p ftr  of the deceleration behavior occurrence probability. 
     When not determining in step S 31  that there exists any specific surrounding vehicle of which the recognition accuracy is equal to or higher than a predetermined threshold, the prediction ECU  33  makes a negative determination in step S 31 . In this case, the prediction ECU  33  then determines in step S 43  whether there exists a specific surrounding vehicle corresponding to a distant vehicle. The distant vehicle refers to a vehicle of which the recognition accuracy is lower than a predetermined threshold. When determining that there exists a specific surrounding vehicle corresponding to the distant vehicle, the prediction ECU  33  makes an affirmative determination in step S 43 , and then calculates the predicted value p ftr  of the deceleration behavior occurrence probability based on the information of the distant vehicle in step S 44 . 
     For example, the prediction ECU  33  calculates the distance from the own vehicle  10  to the object recognized as the distant vehicle, and calculates an existence probability p far  of the object by a calculating equation or the like based on the calculated distance. The calculating equation or the like is set such that, as the distance to an object is longer, the value of the existence probability p far  of the object becomes smaller. The prediction ECU  33  uses the calculated existence probability p far  of the object as the predicted value p ftr  of the deceleration behavior occurrence probability. 
     After the calculation of the predicted value p ftr  of the deceleration behavior occurrence probability in steps S 37 , S 38 , S 40 , S 41 , and S 44 , the prediction ECU  33  then calculates the deceleration behavior occurrence probability p i  in step S 42 . Specifically, the prediction ECU  33  calculates the deceleration behavior occurrence probability p i  using the foregoing formula f10 from the learning value p lm  of the deceleration behavior occurrence probability calculated in either step S 33  or S 34  and the predicted value p ftr  of the deceleration behavior occurrence probability calculated in any of steps S 37 , S 38 , S 40 , S 41 , and S 44 . In the present embodiment, P lm   2  to be used for calculating z can be calculated from the calculating equation P lm   2 =1−P lm . 
     On the other hand, when making a negative determination in step S 43 , that is, when there exists no distant vehicle information, the prediction ECU  33  terminates the series of steps shown in  FIG. 4  without executing step S 42 . In this case, since there is no vehicle around the own vehicle  10  that could cause the own vehicle  10  to take on deceleration behavior, the prediction ECU  33  makes a negative determination in step S 13  shown in  FIG. 15 . Therefore, the ACC ECU  32  transmits to the EV ECU  31  the acceleration command value α that was provisionally set to the first set value α 1  in step S 11 . 
     According to the vehicle control device  50  of the present embodiment described above, it is possible to obtain the following operations and advantageous effects (11) to (17): 
     (11) The prediction ECU  33  calculates the deceleration behavior occurrence probability p i  of the specific surrounding vehicle based on the learning information on the learning of behaviors of sampled vehicle in accordance with the traveling data of the vehicle, specifically, based on the vehicle behavior learning models such as the deceleration behavior model and the passing behavior model. The prediction ECU  33  uses the deceleration behavior occurrence probability p i  to determine the calculating equations of the foregoing formulas f2 and f3, and calculates the second set value α 2  of the acceleration command value α by determining the state amount b(t) of the own vehicle  10  with which the value of the evaluation function F E1  in the formula f4 becomes minimum. When the prediction ECU  33  determines in step S 13  that the own vehicle  10  needs deceleration as shown in  FIG. 15 , the ACC ECU  32  sets the acceleration command value α to the second set value α 2  in step S 14 . Executing the acceleration control of the own vehicle  10  based on the thus preset acceleration command value α makes it earlier to predict the deceleration behavior of the specific surrounding vehicle and decelerate the own vehicle  10 . 
     (12) The prediction ECU  33  calculates the likelihood as an index for the similarity between the traveling data of the specific surrounding vehicle acquired by the perimeter monitoring device  34  and the vehicle behavior learning models such as the deceleration behavior model and the passing behavior model, and calculates the deceleration behavior occurrence probability p i  of the specific surrounding vehicle based on the likelihood. According to this configuration, it is possible to calculate the deceleration behavior occurrence probability p i  of the specific surrounding vehicle with high accuracy. 
     (13) When the deceleration behavior occurrence probability p i  cannot be calculated using the learning models such as the deceleration behavior model and the passing behavior model, the prediction ECU  33  calculates the deceleration behavior occurrence probability p i  based on road static information. According to this configuration, it is possible to calculate the deceleration behavior occurrence probability p i  even in a situation where the vehicle behavior learning models cannot be used. 
     (14) When the recognition accuracy of the specific surrounding vehicle recognized by the perimeter monitoring device  34  is lower than a predetermined threshold, the prediction ECU  33  corrects the deceleration behavior occurrence probability p i  based on the existence probability indicating the possibility that there actually exists an object recognized as a specific surrounding vehicle. According to this configuration, it is possible to calculate the deceleration behavior occurrence probability p i  with higher accuracy in accordance with the recognition accuracy of the perimeter monitoring device  34 . 
     (15) The prediction ECU  33  corrects the deceleration behavior occurrence probability p i  based on the occurrence probability of the traffic light&#39;s changing. According to this configuration, it is possible to calculate the deceleration behavior occurrence probability p i  with higher accuracy in accordance with the situation of change of the traffic light. 
     (16) The prediction ECU  33  corrects the deceleration behavior occurrence probability p i  of the surrounding vehicles based on the statistical information of the deceleration occurrence probability of sampled vehicles. According to this configuration, it is possible to calculate the deceleration behavior occurrence probability p i  with higher accuracy in accordance with the statistical information. 
     (17) The prediction ECU  33  acquires the traveling data of the surrounding vehicles through communication between the own vehicle  10  and the surrounding vehicles. According to this configuration, it is possible to acquire the traveling data of the surrounding vehicles with higher accuracy. 
     Other Embodiments 
     The foregoing embodiments can also be carried out in the modes described below.
         The vehicle  10  of the second embodiment may not have the motor generator  20 , the inverter device  21 , the battery  22 , and the MG ECU  30 . That is, the vehicle  10  of the second embodiment may use only the engine  60  as motive power for traveling.   The prediction ECU  33  of the third embodiment uses the deceleration behavior model and the passing behavior model as the behavior learning models of the surrounding vehicles. Alternatively, the prediction ECU  33  may use other learning models. For example, as the deceleration behavior models, the prediction ECU  33  may use a first deceleration behavior model based on the premise that the vehicle will stop and a second deceleration behavior model not based on the premise that the vehicle will stop.   In the vehicle control device  50  of the third embodiment, the vehicle behavior learning models may be constructed by the prediction ECU  33  instead of the server device  41 .   The prediction ECU  33  of the third embodiment may predict, as behavior of the surrounding vehicles, not only the deceleration behavior of the surrounding vehicles but also arbitrary behavior of the surrounding vehicles. Along with this, the server device  41  or the prediction ECU  33  may learn the arbitrary behavior of the vehicles.   The prediction ECU  33  may predict cut-in by a vehicle from an adjacent lane as an adverse-effect change has occurred in a surrounding environment around the own vehicle, the adverse-effect change being likely to have an adverse effect on a fuel economy of the own vehicle  10 . Specifically, when a vehicle Cb cuts into between the own vehicle  10  and a vehicle Ca traveling in front of the own vehicle, the prediction ECU  33  uses the state amount of the vehicle Ca as the state amount of the preceding vehicle before the cut-in as shown by a solid line in  FIG. 12 , and when the vehicle Cb has cut into the lane at time t 30 , the prediction ECU  33  uses the state amount of the vehicle Cb as the state amount of the preceding vehicle since then.   As the state amount b(t), a function including information such as the velocity and position of the vehicle  10  may be used.   The ACC ECU  32  may transmit a velocity command value specifying the velocity of the vehicle  10 , instead of the acceleration command value α, to the EV ECU  31  and the HV ECU  39 .   To calculate the following performance evaluation value of the own vehicle  10 , the prediction ECU  33  may use respective velocity information of the i-th preceding vehicle and the own vehicle  10 , instead of the respective positional information of these vehicles. For example, the prediction ECU  33  defines the ideal traveling range within a minimum velocity V min  to a maximum velocity V max , and expresses a future deviation amount z i  of the predicted velocity of the own vehicle  10  from the ideal traveling range by the following formula (11):       

     
       
         
           
             
               
                 
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     Then, the prediction ECU  33  may use a value obtained by integrating the deviation amount z i  by a range from the present to a prediction time T as the following performance evaluation value of the own vehicle  10 .
         The perimeter monitoring device  34  may acquire information on pedestrians walking in and around roads, traffic lights, road traveling rules, speed limits, slopes, curves, intersections, and others. In this case, the prediction ECU  33  may determine whether the vehicle  10  needs deceleration based on the information acquired by the perimeter monitoring device  34 .   The prediction ECU  33  may use a predicted value of fuel economy as an index for fuel economy of the own vehicle  10 . Specifically, the prediction ECU  33  accumulates fuel economy data and calculates the predicted value of the fuel economy based on the accumulated past fuel economy data.   The acceleration of the vehicle  10  may be limited by not only a method by which to change the acceleration command value α but also a method for issuing a command by which to result in a change in the acceleration, for example, a method by which to limit the drive torque or power of the vehicle  10 . The limitation of the drive torque or power of the vehicle  10  refers to not the output limitation for protection of the motor generator  20  and the battery  22  but the limitation of the output in the control regardless of the maximum output of the components.   To control the traveling of the vehicle  10  by the ACC control or the CC control, the ACC ECU  32  may adopt a method by which to use a velocity control to control the velocity of the own vehicle  10 , instead of a method by which to use an acceleration control to control the acceleration of the own vehicle  10 . The ACC ECU  32  can use an instruction control to instruct the occupant of the own vehicle  10  for the driving method as in the modification example of the first embodiment.   The means and/or functions performed by the vehicle control device  50  can be provided by software stored in a tangible storage device and a calculator executing the software, software alone, hardware alone, or a combination of them. For example, when the vehicle control device  50  is provided as an electronic circuit that is hardware, the vehicle control device  50  can be a digital circuit or an analog circuit including many logic circuits.       

     The present disclosure is not limited to the specific examples described above. The specific examples to which persons skilled in the art make design changes as appropriate are also included in the scope of the present disclosure as far as they include the features of the present disclosure. The components and their arrangements, conditions, and shapes of the specific examples are not limited to the ones exemplified above but can be changed as appropriate. The components included in the specific examples described above can be changed in combination as appropriate without causing any technical conflict.