Vehicle control device

Abstract

Vehicle control device (50) performing a driving control that controls the driving of its own vehicle (10) in order to enable the own vehicle to follow a vehicle driving ahead of it, with an environmental prediction unit (33) that predicts whether a change in impairment has occurred in an environment around the own vehicle, wherein the change in impairment is likely to have an adverse effect on the fuel economy of the own vehicle, and an acceleration control unit (32) for performing a prediction control which makes it possible to limit the acceleration of the vehicle itself when the environment prediction unit predicts that the impairment change in the environment has occurred, wherein The driving control system is configured to control the acceleration and deceleration of the vehicle so that the vehicle follows the vehicle in front, and the driving control system is configured to control the deceleration of the vehicle at a first deceleration rate adjustable by the driving control system. The acceleration control unit is configured to predict that the impairment change has occurred in the environment around the vehicle when the environment prediction unit predicts that the impairment change is a deceleration requirement change in the environment, where the deceleration requirement change is necessary to decelerate the vehicle, and Executing a deceleration control as a predictive control that decelerates the own vehicle at a predetermined second deceleration rate when the environmental prediction unit predicts that the impairment change in the environment has occurred, where the second deceleration rate is lower than the first deceleration rate. The environmental prediction unit, based on a first index for the fuel efficiency of the vehicle and a second index for the vehicle's performance relative to the vehicle in front, predicts whether the change in environmental conditions has occurred. The first index for the fuel efficiency of one's own vehicle contains a predicted value for braking energy or a predicted value for fuel efficiency, The predicted value of the braking energy represents a value of the braking energy whose generation is predicted based on a deceleration of the vehicle by the driving control system during a first predetermined time period from a current time to a predetermined first future time, and the second index for the vehicle's subsequent performance represents a deviation amount of a position of the vehicle or a deviation amount of a speed of the vehicle or the sum of deviations in the position of the vehicle from ideal driving, based on the vehicle control system, during a second predetermined time period from the current time to a predetermined second future time or The sum of deviations in the speed of the vehicle from ideal driving, based on the driving control, during the second predetermined time period from the current time to the predetermined second future time.

Description

Cross-reference to related registration

[0001] This patent application is based on and claims priority from Japanese patent application no. 2017-221734, filed on November 17, 2017, and Japanese patent application no. 2018-129289, filed on July 6, 2018, with the Patent Office of Japan. The full disclosures of these patent applications are incorporated herein by reference. Technical field

[0002] The present disclosure relates to a vehicle control device. Technical background

[0003] A conventional vehicle control device is described in PTL 1 below. The vehicle control device sets the minimum inter-vehicle distance according to its own speed and stops propulsion sources, such as the internal combustion engine and electric motor, to allow the vehicle to idle when the inter-vehicle distance between the vehicle and a vehicle traveling ahead of it falls below the minimum inter-vehicle distance. The vehicle control device sets the maximum inter-vehicle distance according to its own speed and starts controlling the propulsion sources when the inter-vehicle distance exceeds the maximum inter-vehicle distance while idling. Citation list for patent literature

[0004] PTL1 JP 2007-291919A

[0005] DE 10 2014 118 636 A1 discloses a device for controlling vehicles which is capable of performing a follower control which has a follower mode based on a vehicle distance in which speed control is performed based on a vehicle distance target distance, and a follower mode based on the vehicle speed in which speed control is performed based on a vehicle target speed.The vehicle control device comprises the following: an image processing unit that monitors forward-directed information of a vehicle, and a hybrid control unit that attenuates the following action during follow-through control and executes a deceleration operation of the vehicle when at least one of the situations where a deceleration of a preceding vehicle is predicted and a braking operation of the preceding vehicle is executed is detected based on the forward-direction information.

[0006] US 2017 / 0226947A1 discloses a vehicle control device that effectively achieves improved fuel consumption and a reduction in exhaust emissions without negatively affecting the driver while driving by following a vehicle ahead.The vehicle control device comprises a following determination device which, while driving and following a leading vehicle, determines, based on the speed of the host vehicle, the speed of the leading vehicle and the distance to the leading vehicle, whether the host vehicle can follow the leading vehicle by driving in neutral; an idle stop determination device which, if the following determination device has determined that the host vehicle can follow the leading vehicle by driving in neutral and the driving / travel condition of the host vehicle meets other criteria for driving in neutral stops, determines that a driving in neutral stop should be performed; and a determination criterion update device for updating the determination criteria for the idle stop determination device with respect to criteria such as characteristics of the leading vehicle, road surface conditions and weather.If, based on the updated conditions regarding the characteristics of the leading vehicle, etc., it is determined that following at idle is possible, a control action is taken to shut down the internal combustion engine in the host vehicle. Brief summary of the invention

[0007] If the vehicle ahead suddenly slows down or another vehicle cuts into the lane from an adjacent lane, maintaining a safe distance from the vehicle ahead may necessitate deceleration control by braking or stopping the engine immediately after starting due to acceleration limitations. Therefore, deceleration control by braking results in energy loss. Stopping the engine immediately after starting reduces its efficiency. Consequently, deceleration control by braking or stopping the engine immediately after starting can negatively impact fuel economy.

[0008] This problem can, on the other hand, be countered by measures such as maintaining a greater distance between the vehicle in front and driving one's own vehicle with reduced acceleration. However, these countermeasures would impair the performance of the vehicle following the vehicle in front and cause the driver discomfort.

[0009] The PTL 1 does not mention any countermeasures against these problems regarding the vehicle control device.

[0010] One objective of the present disclosure is to provide a vehicle control device that achieves an improvement in fuel economy while ensuring the performance of following a vehicle ahead.

[0011] According to the invention, a vehicle control device as specified in the attached patent claims is provided.

[0012] A vehicle control device according to one embodiment performs a driving control function that controls the driving of the vehicle (10) to enable the vehicle to follow a vehicle driving ahead of it. This vehicle control device includes an environment prediction unit that predicts whether a change in environmental conditions has occurred in the environment surrounding the vehicle, wherein the change in environmental conditions is likely to have an adverse effect on the fuel efficiency of the vehicle, and an acceleration control unit for performing a predictive control function that allows a limitation of the acceleration of the vehicle when the environment prediction unit predicts that the change in environmental conditions has occurred.

[0013] If a change in the environment surrounding the vehicle occurs, and this change is likely to negatively impact the vehicle's fuel efficiency, this configuration will preemptively limit the vehicle's acceleration. This prevents situations where the vehicle's fuel efficiency is actually affected, resulting in improved fuel economy. Brief description of the drawings Fig. Figure 1 shows a block diagram of a schematic configuration of a vehicle according to a first embodiment. Fig. Figure 2 shows a graphic of an example of a method of vehicle control by an ACC-ECU in the first embodiment. Fig. Figure 3 shows a graphic of an example of a method of vehicle control by the ACC-ECU in the first embodiment. Fig. Figure 4 shows a flowchart of a processing procedure performed by the ACC-ECU and a prediction-ECU in the first embodiment. Fig. Figure 5 shows a graphic of an example of a method for calculating a deviation of one's own vehicle from an ideal driving range in the first embodiment, which is predicted by the prediction ECU. Fig. Figure 6 shows a graph of a relationship between vehicle speed and probability, which is used by the prediction ECU in the first embodiment. The Fig. Figures 7(A) to 7(C) show time sequence diagrams of the transitions of vehicle speed, drive energy and inter-vehicle distance of the vehicle in the first embodiment. Fig. Figure 8 shows a block diagram of a schematic configuration of a vehicle according to a second embodiment. Fig. Figure 9 shows a flowchart of a processing procedure performed by an ACC ECU and a prediction ECU in the second embodiment. Fig. Figure 10 shows a diagram of a relationship between acceleration and actual internal combustion engine efficiency, which is used by the prediction ECU in the second embodiment. The Fig. Figures 11(A) to 11(C) show time sequence diagrams of the transitions of vehicle speed, drive energy and internal combustion engine speed for the vehicle according to the second embodiment. Fig. Figure 12 shows a time sequence diagram of a leading vehicle shifting procedure performed by a prediction ECU according to another embodiment. The Fig. 13(A) and Fig. Figure 13(B) shows time sequence diagrams of an example of temporal transitions of vehicle speed and deceleration behavior - probability of occurrence. Fig. Figure 14 shows a graph of respective transitions of a calculated value of a frequency of a slowing behavior model, a calculated value of a frequency of a passing behavior model and a value of a slowing behavior occurrence probability with respect to a difference between respective probabilities of the slowing behavior model and the passing behavior model according to a third embodiment. Fig. Figure 15 shows a flowchart of a processing procedure performed by an ACC ECU and a prediction ECU in the third embodiment. Fig. Figure 16 shows a flowchart of a procedure for a behavior occurrence probability calculation process performed by the prediction ECU in the third embodiment. Fig. Figure 17 shows a graphic of an example of a method for measuring a green time duration in the third embodiment. Fig. Figure 18 shows a graphic of an example of a method for measuring the green time duration in the third embodiment. Fig. Figure 19 shows a diagram of a relationship between a green time duration γ and a probability p sig , that a traffic light switches from green to yellow, in the third embodiment. Description of the exemplary implementations

[0014] The following describes exemplary embodiments of a vehicle control device with reference to the drawings. To facilitate understanding, identical components shown in the drawings are designated with identical reference numerals wherever possible, and redundant descriptions are omitted. First embodiment

[0015] First, a schematic configuration of a vehicle equipped with a vehicle control device according to a first embodiment is described.

[0016] As in Fig. As shown in Figure 1, vehicle 10 is an electric car that drives under the power of an electric motor generator 20. Vehicle 10 includes the motor generator 20, an inverter unit 21, a battery 22, and a coupling 23.

[0017] The battery 22 is a secondary battery, such as a lithium-ion battery, which can be charged and discharged. The inverter 21 converts the DC power stored in the battery 22 into AC power and supplies this AC power to the motor-generator 20. The motor-generator 20 is driven by the AC power supplied by the inverter 21 and rotates a first drive shaft 24. The first drive shaft 24 is coupled to a second drive shaft 25 by means of the coupling 23.The coupling 23 can be switched between a connected state, in which the first drive shaft 24 and the second drive shaft 25 are coupled to allow the transmission of drive power between these two shafts, and a disconnected state, in which the first drive shaft 24 and the second drive shaft 25 are decoupled to prevent the transmission of drive power between these two shafts. When the coupling 23 is in the connected state, the drive power transmitted from the motor-generator 20 to the first drive shaft 24 is then transmitted via the second drive shaft 25, a differential 26, and a drive shaft 27 to a wheel 28 of the vehicle 10. The vehicle 10 then begins to move. In this respect, the motor-generator 20 in the present embodiment corresponds to a drive train.

[0018] When the vehicle 10 brakes, the motor-generator 20 performs regenerative power generation. This means that the braking force acting on the wheel 28 when the vehicle 10 brakes is transmitted to the motor-generator 20 via the drive shaft 27, the differential 26, the second drive shaft 25, the clutch 23, and the first drive shaft 24. The motor-generator 20 generates electrical power under the driving force applied by the wheel 28. The electrical power generated by the motor-generator 20 is converted from alternating current (AC) to direct current (DC) power by the inverter 21 and charged into the battery 22.

[0019] 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 predictive ECU 33, a perimeter monitoring device 34, and a vehicle state variable sensor 35. The ECUs 30 to 33 are mainly composed of a microprocessor unit with a CPU and memory devices, such as a ROM and a RAM, and perform various control functions by executing programs pre-stored in the memory devices.

[0020] The vehicle condition sensor 35 detects various condition variables of the vehicle 10. The various condition variables detected by the vehicle condition sensor 35 include information about the speed and acceleration of the vehicle 10, among other things.

[0021] 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 moving around its own vehicle 10 and calculates various state variables of the surrounding vehicles. The surrounding vehicles include a vehicle ahead of its own vehicle 10 in the lane in which its own vehicle 10 is traveling, and adjacent vehicles traveling in lanes adjacent to the lane in which its own vehicle 10 is traveling. The state variables detected by the perimeter monitoring device 34 include relative positions, relative distances, relative speeds, relative accelerations, and the like from the surrounding vehicles to its 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 two-axis coordinate system, using, for example, the transverse axis and the longitudinal axis of the own vehicle 10. In this embodiment, the perimeter monitoring device 34 corresponds to a perimeter monitoring unit.

[0022] The MG-ECU 30 controls the operation of the motor-generator 20 by controlling the inverter unit 21 under a command from the EV-ECU 31. For example, the EV-ECU 31 transmits a drive force command value, representing the drive force output of the motor-generator 20, to the MG-ECU 30. Upon receiving the drive force command value from the EV-ECU 31, the MG-ECU 30 controls the inverter unit 21 such that the motor-generator 20 outputs the drive force corresponding to the drive force command value. When the vehicle 10 is braked, the MG-ECU 30 controls the inverter unit 21 such that the electrical power generated by the regenerative power production of the motor-generator 20 is charged into the battery 22.

[0023] The EV-ECU 31 implements the driving of the vehicle 10 according to the driver's driving requirements by calculating a traction command value necessary for implementing the driving according to the driver's driving requirements and transmitting the calculated traction command value to the MG-ECU 30. The EV-ECU 31 exchanges information required for various control functions with the ACC-ECU 32 and calculates a traction command value according to the request from the ACC-ECU 32. For example, upon receiving an acceleration command value as a command value for accelerating the vehicle 10 from the ACC-ECU 32, the EV-ECU 31 calculates the corresponding traction command value and transmits the calculated traction command value to the MG-ECU 30 in order to accelerate the vehicle 10 according to the acceleration command value.The EV-ECU 31, for example, switches the clutch 23 to the engaged or disengaged state according to the request from the ACC-ECU 32. In this embodiment, the EV-ECU 31 corresponds to a vehicle control unit.

[0024] The ACC-ECU 32, for example, executes vehicle control functions when an occupant operates a control unit provided in the vehicle 10. The ACC-ECU 32 performs two types of vehicle control: distance control (CC) to control the vehicle 10's driving so that it maintains a constant speed, and adaptive cruise control (ACC) to control the vehicle 10's driving so that it follows the vehicle ahead. In this embodiment, the ACC control corresponds to a speed control system that regulates the acceleration and deceleration of the vehicle 10 so that it follows the vehicle ahead. In this embodiment, the ACC-ECU 32 corresponds to an acceleration control unit.

[0025] The ACC-ECU 32 calculates, in particular, a separation time THW, which represents the time until vehicle 10 reaches the vehicle ahead, based on the relative speed and relative distance of the vehicle ahead to vehicle 10. If the separation time THW is greater than or equal to a predetermined first time threshold Tth1, i.e., if there is a time margin for vehicle 10 to reach the vehicle ahead, the ACC-ECU 32, as described in Fig. Figure 2 shows the CC control. As CC control, the ACC-ECU 32 repeatedly accelerates and decelerates the vehicle 10. At this point, the ACC-ECU 32 controls the acceleration and deceleration of the vehicle 10 such that the average speed of the vehicle 10 reaches a speed Vset set by the occupant via the control unit.

[0026] The ACC-ECU 32 specifically sets a lower speed limit (VL) lower than the set speed (Vset) and an upper speed limit (VH) higher than the set speed, as shown in Fig. Figure 3 shows the process based on the occupant's set speed Vset. When the vehicle 10's speed Vc reaches the lower limit speed VL by decelerating the vehicle 10, the ACC-ECU 32 executes an acceleration control to accelerate the vehicle 10. As an acceleration control, the ACC-ECU 32 transmits a preset positive acceleration command value to the EV-ECU 31. The EV-ECU 31 then calculates a corresponding positive drive command value and transmits this drive command value to the MG-ECU 30, causing the vehicle 10 to accelerate with a predetermined rate.

[0027] When the vehicle speed Vc reaches the upper limit speed VH during acceleration of vehicle 10, the ACC-ECU 32 executes an idle control to drive vehicle 10 in neutral, thus decelerating the vehicle 10. As an idle control, the ACC-ECU 32 transmits an acceleration command value of zero to the EV-ECU 31 and a command to the EV-ECU 31 to disengage the clutch 23. The EV-ECU 31 then transmits a drive command value of zero to the MG-ECU 30 and disengages the clutch 23. As a result, the drive of the motor-generator 20 is stopped, and vehicle 10 begins to travel in neutral, thus decelerating naturally.If the vehicle speed Vc 10 then reaches the lower limit speed VL, the ACC-ECU 32 transmits a command to bring the clutch 23 into the connected state to the EV-ECU 31 and performs the acceleration control described above again.

[0028] If, on the other hand, the THW's separation time is as in Fig. 2 illustrates that if the second time threshold Tth2 is equal to or longer than the first time threshold Tth1, the ACC-ECU 32 executes ACC control. As ACC control, the ACC-ECU 32 performs burn-and-coast control, repeatedly accelerating and decelerating the vehicle 10 so that the vehicle 10 travels in pursuit of the vehicle ahead. Specifically, if the relative speed Vr of the vehicle ahead is less than the predetermined first speed threshold Vth1, i.e., if the vehicle 10 quickly catches up to the vehicle ahead, the ACC-ECU 32 performs regenerative control. As regenerative control, the ACC-ECU 32 transmits a negative acceleration command value to the EV-ECU 31.The EV-ECU 31 therefore calculates a negative traction command value corresponding to the acceleration command value and transmits this traction command value to the MG-ECU 30, causing the motor-generator 20 to perform regenerative power generation. When the motor-generator 20 performs regenerative power generation, a braking force is applied to the wheel 28 of vehicle 10 by its regenerative energy. Vehicle 10 can thus be decelerated more quickly than if it were allowed to coast. This increases the distance between vehicle 10 and the vehicle ahead.

[0029] The ACC-ECU 32 has a second speed threshold, Vth2, which is higher than the first speed threshold, Vth1. If the relative speed, Vr, of the vehicle ahead lies within the range between the first speed threshold, Vth1, and the second speed threshold, Vth2, the ACC-ECU 32 executes the idle control described above. The ACC-ECU 32 also has a third time threshold, Tth3, which is set between the first time threshold, Tth1, and the second time threshold, Tth2. Therefore, even if the relative speed, Vr, of the vehicle ahead is equal to or greater than the second speed threshold, Vth2, and the following distance, THW, is a value within the range between the second time threshold, Tth2, and the third time threshold, Tth3, the ACC-ECU 32 executes the idle control.This idle control makes it possible to increase the distance between vehicle 10 and the vehicle in front.

[0030] If the relative speed Vr of the vehicle ahead is equal to or higher than the second speed threshold Vth2 and the distance time THW is a value that lies within a range from the third time threshold Tth3 to the first time threshold Tth1, the ACC-ECU 32 performs the acceleration control described above.

[0031] In this way, the ACC-ECU 32 selectively executes regenerative control, idle control and acceleration control according to the distance time THW and the relative speed Vr of the vehicle ahead, causing the own vehicle 10 to follow the vehicle ahead.

[0032] If the vehicle ahead suddenly decelerates while the ACC-ECU 32 is performing acceleration control as either CC or ACC control, the following distance THW and the relative speed Vr can decrease significantly. Therefore, when the ACC-ECU 32 performs regenerative control to generate braking force at wheel 28, some of the vehicle's kinetic energy can be recovered as electrical energy in the battery 22 through regenerative control. However, the remaining kinetic energy is converted into heat energy during the generation of braking force at wheel 28 and is dissipated into the atmosphere, and thus cannot be recovered. This results in an unavoidable energy loss. An energy loss also occurs when the vehicle's kinetic energy is converted into electrical energy. This energy loss can negatively impact the vehicle's fuel efficiency.

[0033] In the vehicle 10 of this embodiment, the prediction ECU 33 therefore predicts whether a change in environmental conditions has occurred in the environment surrounding the vehicle, where the change in environmental conditions is likely to have a detrimental effect on the fuel efficiency of the vehicle ahead during sharp deceleration. In this embodiment, the prediction ECU 33 corresponds to an environmental prediction unit. If the prediction ECU 33 predicts that a change in environmental conditions has occurred in the environment surrounding the vehicle, where the change in environmental conditions is likely to have a detrimental effect on the fuel efficiency of the vehicle 10, the ACC ECU 32 performs a predictive control operation to pre-limit the acceleration of the vehicle 10 before the regenerative control is executed as ACC control.

[0034] As in Fig. As shown in Figure 1, the prediction ECU 33 can connect wirelessly to a network cable 40 via a communication unit 36 ​​installed in the vehicle 10. The prediction ECU 33 performs various types of communication with a server 41 over the network 40. The server 41 acquires various state variables from a large number of vehicles and creates a database of these state variables. The server 41 generates various driving models based on the large number of vehicle state variables in the database. The prediction ECU 33 can predict the driving paths of surrounding vehicles using the driving models generated by the server 41. In this embodiment, the ACC ECU 32, the prediction ECU 33, and the communication unit 36 ​​form a vehicle control device 50.

[0035] The prediction ECU 33 is arranged independently of the ECUs that control the components, as the prediction ECU 33 requires high-speed processing and connections with the multitude of ECUs.

[0036] Next, a procedure for a prediction control system executed by the ACC-ECU 32 and the prediction-ECU 33 will be described, in particular with reference to Fig. 4 described. The ACC-ECU 32 and the prediction-ECU 33 carry out the in Fig. The process shown in step 4 is repeated in predetermined cycles.

[0037] As in Fig. As shown in Figure 4, in step S10 the prediction ECU 33 first obtains the current state variables of the surrounding vehicles from the perimeter monitoring device 34. The information obtained by the prediction ECU 33 from the perimeter monitoring device 34 includes the relative distances, relative speeds, and relative accelerations of the surrounding vehicles.

[0038] After step S10, the ACC-ECU 32 provisionally sets an acceleration command value α to be transmitted to the EV-ECU 31 in step S11. The ACC-ECU 32 uses the information obtained from the perimeter monitoring device 34 in step S10, in particular the relative speed and relative distance of the vehicle ahead, to calculate a following distance and executes the following commands. Fig. The control unit shown in Figure 2 uses the calculated distance time and the relative speed to calculate a first setting value α1 of the acceleration command value α. The ACC-ECU 32 provisionally sets the acceleration command value α to the first setting value α1.

[0039] Following step S11, in step S12, the prediction ECU 33 forecasts the future state variables of the surrounding vehicles, including the vehicle ahead and the adjacent vehicles. These forecasted state variables contain time-series data on the future relative positions, relative distances, relative speeds, and relative accelerations of the surrounding vehicles. Specifically, the prediction ECU 33 forecasts these future state variables from the present time until a predetermined time thereafter using calculation equations and models based on the current and past values ​​of the surrounding vehicles' state variables. Therefore, the prediction ECU 33 can forecast the behavior of the surrounding vehicles from the present time until a predetermined time thereafter.

[0040] The prediction processing in step S12 is not limited to being performed based on the current and past values ​​of the state variables of the surrounding vehicles, and it can be performed based on other information about the state variables of the surrounding vehicles. This prediction can be performed in time series signal form, where the behavior of the surrounding vehicles is expressed in predetermined probability models based on past vehicle driving data, or by statistically processing the driving data of vehicles that have passed through a point in the past where the own vehicle is currently passing, in order to calculate the potential for slowdown and merging by vehicles at a given point.

[0041] The prediction time is set to the time it takes for a vehicle to reach a legal maximum speed under normal driving conditions. The acceleration range can be set, for example, from -1G to 1G, and the maximum speed can be defined as lying within the range of 0 km / h up to a legal maximum speed.

[0042] After step S12, the prediction ECU 33 determines in step S13, based on the behavior of the surrounding vehicles, whether vehicle 10 needs to slow down. This determination process is carried out in particular by the procedure described below.

[0043] It is assumed that if there are N surrounding vehicles, the own vehicle 10 travels with a predetermined state variable b(t) with respect to driving an i-th surrounding vehicle, where the value i is defined as an integer in the range 1 ≤ i ≤ N. The state variable b(t) is, for example, an acceleration function with time t as the variable. If the own vehicle 10 travels with the state variable b(t), the braking energy generated by 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 vehicle 10 is slowed down by executing the ACC control during a period of time from the present time until a predetermined time thereafter.

[0044] The following performance of the own vehicle 10 relative to the i-th surrounding vehicle can be determined as in Fig. 5 shown with a deviation size y iThe predicted position of the own vehicle 10 is evaluated within an ideal driving range A during a time interval from the present time to a predetermined time thereafter, wherein the ideal driving range A is set to a range of an ideal inter-vehicle distance during the execution of the ACC control, within which the own vehicle 10 follows the vehicle ahead. The ideal driving range A is set with respect to the predicted driving position of the i-th surrounding vehicle, which is represented by a dashed line, and can be obtained from the predicted driving position of the surrounding vehicle by a calculation equation or the like. A following performance evaluation value C i (b(t)) of the own vehicle 10 can be determined using the deviation quantity y iThe predicted position of the vehicle 10 from the ideal driving range A can be obtained using the following formula f1, where T represents the prediction time: Mathematics 1 Ci(b(t))=∫0Tyi(b(t))dt

[0045] From the above, an expected value E can be calculated. brk (b(t)) of the braking energy of the own vehicle with respect to the N surrounding vehicles and an expected value C(b(t)) of the subsequent performance evaluation value are defined by the following formulas f2 and f3: Mathematics 2 Ebrk(b(t))=∑i=1NpiEbrk i(b(t)) C(b(t))=∑i=1NpiCi(b(t))

[0046] In formulas f2 and f3, p represents i This represents the probability of a behavior occurring in the i-th surrounding vehicle. More precisely, it represents the probability p. iIn this embodiment, the parameter is used for the certainty of the appearance of the state variable of the i-th surrounding vehicle when the own vehicle 10 is moving with the state variable b(t), taking into account that the prediction result of the behavior of the i-th surrounding vehicle includes a predetermined uncertainty. For example, the speed of the i-th surrounding vehicle at a predetermined time can be determined by a probability as in Fig. 6 will be shown.

[0047] Using the expected value E brk (b(t)) of the braking energy and the expected value C(b(t)) of the subsequent performance evaluation value of the own vehicle can be used to form an evaluation function F E1 as expressed by the following formula f4: Mathematics 3 FE1=min{kEbrk(b(t))+(1−k)C(b(t))}=min∑i=1npi{kEbrk i(b(t))+(1−k)∫0Tyi(b(t))dt}

[0048] In formula f4, k represents the respective weighting coefficients for the braking energy and the subsequent performance rating. The coefficient k is set within a range of 0 ≤ k ≤ 1. In this embodiment, the weighting coefficients assume predetermined values.

[0049] A determination of the state variable b(t) of one's own vehicle 10 such that the value of the valuation function F E1 Minimizing the braking energy makes it possible to maintain the state variable b(t) of the vehicle 10 with suppressed braking energy while ensuring continued performance. In other words, the state variable b(t) of the vehicle 10 can be maintained, improving fuel efficiency while ensuring continued performance.

[0050] The prediction ECU 33 performs the determination processing in step S13 based on the following procedure. In particular, the prediction ECU 33 uses a calculation equation, which is obtained beforehand, for example, through an experiment or the like, as the calculation equation for the braking energy E. brk i (b(t)).

[0051] Prediction ECU 33 also calculates the predicted route of the i-th surrounding vehicle based on the predicted state variable of the i-th surrounding vehicle from the prediction information obtained in step S12. Prediction ECU 33 also determines the ideal driving range A based on the calculated predicted route of the i-th surrounding vehicle for setting the calculation equation for the follow-up performance evaluation value C. i (b(t)) of own vehicle 10.

[0052] The prediction ECU 33 also obtains the driving models via the communication unit 36 ​​from the server facility 41 and calculates the probability of occurrence p. i the state variable of the i-th surrounding vehicle based on the procured driving models and the state variable of the i-th surrounding vehicle.

[0053] In this way, the prediction ECU 33 determines the calculation equation for the braking energy E. brk i (b(t)) in the above formula f4, the calculation equation for the subsequent performance assessment value C i (b(t)) and the probability of occurrence p i and then determines the state variable b(t) of its own vehicle 10 such that the value of the valuation function F E1 becomes minimal. The evaluation function F E1can be minimized in such a way that a multitude of behavioral patterns of the own vehicle 10 are found, and the respective values ​​of the evaluation function in these patterns are calculated, and then the state variable b(t) of the own vehicle 10 with the minimum value of the evaluation function F E1 is selected. On the other hand, the evaluation function F E1 using the optimization procedure, it can be adjusted such that it is minimal. Since the state variable b(t) is an acceleration function of the vehicle 10, the prediction ECU 33 can obtain a second setting value α2 of the acceleration command value α from the previous calculation such that the evaluation function F E1 becomes minimal.

[0054] The prediction ECU 33 can receive a second setting value α2, which allows the vehicle 10 to be subjected to idle control by setting a lower limit of the second setting value α2 when calculating the second setting value α2 of the acceleration command value α. This prevents the generation of braking energy in the vehicle 10 when the vehicle 10 is decelerated using the second setting value α2 as the acceleration command value α, thereby improving the fuel economy of the vehicle 10.

[0055] In step S13, the prediction ECU 33 compares the first setting value α1 and the second setting value α2 to determine whether the vehicle 10 needs to slow down. If the first setting value α1 is equal to or less than the second setting value α2, the prediction ECU 33 specifically determines that the vehicle 10 does not need to slow down. That is, in step S13, the prediction ECU 33 performs a negative determination. In this case, the prediction ECU 33 determines that no change in environmental conditions has occurred in the environment surrounding the vehicle, where the change in environmental conditions is capable of having an adverse effect on the fuel economy of the vehicle 10. If the prediction ECU 33 makes a negative determination in step S13, the ACC ECU 32 transmits the acceleration command value α, set as the first setting value α1, to the EV ECU 31 in step S15.

[0056] If, in step S13, it is determined that the second setting value α2 is smaller than the first setting value α1, the prediction ECU 33 determines that the vehicle 10 needs to decelerate. That is, the prediction ECU 33 performs a confirmation determination in step S13. In this case, the prediction ECU 33 determines that a change in environmental conditions has occurred in the vicinity of the vehicle, and this change in environmental conditions is likely to have an adverse effect on the fuel economy of the vehicle 10. If the prediction ECU 33 performs a confirmation determination in step S13, the ACC ECU 32 changes the acceleration command value α from the first setting value α1 to the second setting value α2 in step S14. Then, in step S15, the ACC-ECU 32 transmits the acceleration command value α, set as the second setting value α2, to the EV-ECU 31.Accordingly, the second setting value α2, which is smaller than the first setting value α1 and is set under the ACC control, is transmitted as the acceleration command value α to the EV-ECU 31. The ACC-ECU 32 thus implements a deceleration control to slow down the vehicle 10 with a lower deceleration than the deceleration adjustable under the ACC control.

[0057] Next, an operating example of the vehicle control device 50 in this embodiment will be described.

[0058] It is assumed that a velocity V P the speed of the vehicle ahead drops suddenly from a time t11, as indicated by a dashed line in Fig. Figure 7(A) shows that in such a situation, the distance time and relative speeds of the vehicle 10 and the vehicle ahead decrease sharply. If only the ACC control is executed, the regenerative control is therefore executed after time t11, so that the drive energy Ec of the vehicle 10 drops sharply, as shown by a dashed and double-dotted line in Figure 7(A). Fig. 7(B) is shown. Regarding the in Fig. In Figure 7(B), the drive energy Ec shown is the magnitude of the drive energy Ec for propelling vehicle 10, generated by the motor-generator 20, represented by a positive value, and the magnitude of the braking energy generated under regenerative control is represented by a negative value. The execution of this regenerative control increases the inter-vehicle distance Lc between the vehicle 10 and the vehicle ahead after time t11, as shown by a dashed and doubly dotted line in Figure 7(B). Fig. 7(C) is shown, and reduces the speed Vb of the own vehicle 10, as shown by a dashed and doubly dotted line in Fig. Figure 7(A) shows that when braking energy is generated, some of it is converted into heat energy or the like, resulting in an energy loss.

[0059] If, in this embodiment, it is predicted at time t10 before time t11 that the braking energy will be generated after time t11, the prediction ECU 33 calculates the second setting α2 of the acceleration command value α, with which the braking energy can be suppressed, using the formula f4 above, and sets the acceleration command value α to the second setting α2. After the transmission of the acceleration command value α from the ACC ECU 32 to the EV ECU 31, for example, when the EV ECU 31 sets the drive force command value to zero, the drive energy Ec of the motor generator 20 becomes zero at time t10, as indicated by a solid line in Fig. Figure 7(B) shows that the speed Va of vehicle 10 decreases after time t10, as indicated by a solid line in Figure 7(B). Fig. 7(A) is shown, and the inter-vehicle distance Lc between the own vehicle 10 and the vehicle ahead increases, as shown by a solid line in Fig. 7(C) is shown. In this way, the deceleration of the vehicle 10 enables the generation of braking energy as in Fig. 7(B) shown to suppress, resulting in an improvement in the fuel economy of vehicle 10.

[0060] With the vehicle control device 50 described above in this embodiment, the following operating modes and advantageous effects (1) to (7) can be obtained: (1) If the prediction ECU 33 predicts that a change in environmental conditions has occurred in the vicinity of the vehicle, and that this change is likely to adversely affect the fuel efficiency of the vehicle 10, the ACC ECU 32 executes the prediction control, which may limit the acceleration of the vehicle 10. The acceleration of the vehicle 10 is thus limited in advance if a change in environmental conditions has occurred in the vicinity of the vehicle, and that change is likely to adversely affect the fuel efficiency of the vehicle 10. This prevents a situation in which the vehicle 10 is actually impaired with regard to fuel efficiency, thereby improving the fuel efficiency of the vehicle 10. (2) If it is predicted that a change in the deceleration requirement has occurred in an environment around the own vehicle, wherein the change in the deceleration requirement is necessary for the own vehicle 10 to decelerate, the ACC-ECU 32 predicts that an impairment change has occurred in an environment around the own vehicle, wherein the impairment change is likely to have an adverse effect on the fuel economy of the own vehicle 10.If a change in deceleration requirements is predicted to have occurred in the vicinity of the vehicle, and this change necessitates a deceleration of the vehicle 10, the ACC-ECU 32 executes the acceleration control to actually limit the acceleration of the vehicle 10 by using the second setting value α2 of the acceleration command value α, which is smaller than the first setting value α1 and is set by the ACC controller. The ACC-ECU 32 thus executes the deceleration control as a predictive control to decelerate the vehicle by a deceleration that is less than the deceleration adjustable by the ACC controller. This configuration can reduce energy loss that might occur during deceleration to maintain the distance to the other vehicle. (3) Based on an index for the fuel efficiency of the own vehicle 10 and an index for the following performance of the own vehicle with respect to the vehicle ahead, the prediction ECU 33 predicts whether a change in the deceleration requirement has occurred in the environment around the own vehicle, wherein the change in the deceleration requirement is necessary for the own vehicle 10 to decelerate. The prediction ECU 33 uses, in particular, the predicted value of the braking energy as the index for the fuel efficiency of the own vehicle 10. This value is predicted such that the change in the environment occurs when the own vehicle 10 decelerates from its current speed to a predetermined time thereafter by executing the ACC control during a time interval. The prediction ECU 33 also uses the deviation quantity y as the index for the following performance of the own vehicle with respect to the vehicle ahead. ithe position of the vehicle, the sum of deviations in the vehicle's position from ideal driving based on ACC control, during a time period from the present time to a predetermined future time. The prediction ECU 33 can therefore appropriately determine the deceleration of the vehicle 10 in order to maintain the effects of improving target fuel economy and suppressing the reduction in subsequent performance. (4) The prediction ECU 33 presents the own vehicle's fuel efficiency index 10 and the own vehicle's following performance index with respect to the vehicle ahead as probability information as in the forms f2 and f3 above. The prediction ECU 33 uses the function expressed by formula f4 as an evaluation function, which contains the expected value based on the own vehicle's fuel efficiency index 10 and the expected value based on the own vehicle's following performance index 10 with respect to the vehicle ahead, and predicts, based on the calculated value in formula f4, that a change in the deceleration requirement has occurred in the environment around the own vehicle, wherein the change in the deceleration requirement necessitates a deceleration of the own vehicle 10.The prediction ECU 33 can therefore reliably make a determination regarding the deceleration of the vehicle 10 in order to preserve the effects of the improvement in fuel economy and to suppress the reduction in subsequent performance even if the prediction information about a change in the environment contains an uncertainty. (5) The prediction ECU 33 calculates the second setting value α2 of the acceleration command value, which allows the vehicle 10 to be subjected to idle control. The ACC ECU 32 therefore performs idle control to drive the vehicle 10 in neutral, in a state where the output from the motor generator 20 is not transmitted to the wheels of the vehicle 10. With this configuration, the vehicle 10 can be slowed down with greater fuel economy when the vehicle 10 is slowed down using the prediction information. (6) The ACC-ECU 32 repeatedly accelerates and decelerates the vehicle 10 to perform a burn-and-coast control, under which the vehicle 10 follows the vehicle ahead. The vehicle 10 can therefore generally drive in a highly fuel-efficient manner. (7) The prediction ECU 33 predicts the deceleration of the vehicle ahead as a change in the environment surrounding the vehicle, where the change in the environment is likely to adversely affect the fuel economy of the vehicle. Therefore, fuel economy can be improved compared to a change in the environment that significantly impairs fuel economy. Modification example

[0061] Next, a modification example of the vehicle control device 50 in the first embodiment is described.

[0062] As indicated by a dotted line in Fig. As shown in Figure 1, a vehicle control device 50 according to this modification example further comprises a human-machine interface (HMI) ECU 37. The HMI ECU 37 is a section that controls a communication device 38 installed in the vehicle 10 to deliver various messages to the occupant of the vehicle 10. The communication device 38 can be a loudspeaker, a display, or the like.

[0063] In a Fig. In step S15, shown in Figure 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 vehicle 10 on a driving method such that the acceleration of the vehicle 10 is limited, based on the acceleration command value α transmitted by the ACC-ECU 32. The HMI-ECU 37 instructs the occupant on the driving method, for example, by prompting the occupant to recognize the acceleration and speed corresponding to the acceleration command value α, by emitting a tone from the speaker, or by displaying the acceleration and speed corresponding to the acceleration command value α on the display.

[0064] The HMI-ECU 37 can instruct the occupant on the driving method by adjusting the amount of depressor pedal depressor or the amount of brake pedal depressor based on the acceleration command value α.

[0065] Vehicle 10 can be slowed down even using this method. Second embodiment

[0066] Next, a vehicle control device 50 according to a second embodiment is described. The description focuses on the differences compared to the vehicle control device 50 according to the first embodiment. First, a schematic configuration of the vehicle 10 equipped with the vehicle control device 50 according to the second embodiment is described.

[0067] As it is in Fig. As shown in Figure 8, the vehicle 10 in this embodiment is a hybrid car that uses not only the motor-generator 20 but also an internal combustion engine 60 as a source of motive power. The internal combustion engine 60 is driven to rotate a first power transmission shaft 29a. The first power transmission shaft 29a is coupled to a second power transmission shaft 29b via a clutch 23. The clutch 23 is switchable between a connected state in which the first power transmission shaft 29a and the second power transmission shaft 29b are coupled to allow the transmission of motive power between these shafts, and a disconnected state in which the first power transmission shaft 29a and the second power transmission shaft 29b are decoupled to prevent the transmission of motive power between these shafts.

[0068] The motor-generator 20 supplies the second drive shaft 29b with motive power based on the current supply. When the clutch 23 is in the engaged state, motive power is thus supplied to the second drive shaft 29b by the internal combustion engine 60 and / or the motor-generator 20. The motive power supplied to the second drive shaft 29b is fed into a gearbox 62.

[0069] The gearbox 62 increases or decreases the total tractive force of the internal combustion engine 60 and the motor-generator 20, which is input via the second tractive force transmission shaft 29b, and transmits the increased or decreased total tractive force to a third tractive force transmission shaft 29c. Alternatively, the gearbox 62 subtracts the tractive force converted into electrical power by the motor-generator 20 from the tractive force of the internal combustion engine 60 to obtain a resultant tractive force, and then increases or decreases the resultant tractive force. The gearbox 62 then transmits the increased or decreased resultant tractive force to the third tractive force transmission shaft 29c.

[0070] The driving force transmitted to the third drive shaft 29c is then transmitted via the differential 26 and the drive shaft 27 to the wheel 28 of the vehicle 10. The vehicle 10 thus begins to move. In this embodiment, the motor-generator 20 and the internal combustion engine 60 constitute a drive train.

[0071] Vehicle 10 is equipped with a machine ECU 63, which comprehensively controls the operation of the internal combustion engine 60. The machine ECU 63 controls the operation of the clutch 23.

[0072] Vehicle 10 is equipped with a hybrid vehicle (HV) ECU 39 instead of the EV ECU 31. The HV ECU 39 exchanges information with the MG ECU 30 and the machine ECU 63 necessary for integrated adaptive control of the internal combustion engine 60, the motor-generator 20, and the battery 22. Specifically, the HV ECU 39 controls the operation of the motor-generator 20 and the internal combustion engine 60 based on the acceleration command value transmitted by the ACC ECU 32. If the internal combustion 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 drive command value to the machine ECU 63 to restart the internal combustion engine 60, thereby accelerating the vehicle 10.If the acceleration command value is less than the acceleration threshold value αth, the HV-ECU 39 transmits a command to stop the internal combustion engine 60 to the machine ECU 63 and transmits a predetermined drive force command value to the MG-ECU 30 to suppress fuel consumption, thereby enabling the vehicle 10 to perform EV driving. In this embodiment, the HV-ECU 39 corresponds to a vehicle control unit that controls the driving and stopping of the internal combustion engine 60 and the motor-generator 20 based on the driving state of the vehicle 10 itself.

[0073] Next, a procedure for a prediction control process executed by the ACC-ECU 32 and the prediction-ECU 33 is described with reference to Fig. 9 described in more detail. The ACC-ECU 32 and the prediction-ECU 33 carry out the in Fig. The process shown in 9 is repeated in predetermined cycles.

[0074] As in Fig. As shown in Figure 9, the prediction ECU 33 determines, after step S12 in step S20, whether limiting the acceleration of the vehicle 10 is necessary to suppress a short-term control of the internal combustion engine 60. This determination is carried out in particular by the procedure described below.

[0075] The efficiency of energy extraction from the internal combustion engine 60 can deteriorate due to a delay in air intake by the internal combustion engine 60, an increase in energy consumption for starting the internal combustion engine 60, an increase in fuel consumption during starting the internal combustion engine 60, and the like. Taking these factors into account, the actual internal combustion engine efficiency η eng When driving an internal combustion engine, this is expressed by the following formula f5: Mathematics 4 ηeng=Eout×δdelayEin+(1ηeEegon+Eadd)1Tacc

[0076] In the formula f5, δ representsdelay a coefficient of an aerial photograph delay, η e an ideal internal combustion engine efficiency, which is an internal combustion engine efficiency when the internal combustion engine 60 is operated in a steady state, E out an ideal output energy of the internal combustion engine 60, E egon a starting energy of the internal combustion engine 60, E in a fuel energy input of the internal combustion engine 60, E add an additional start-up energy and T acc represents the time required for acceleration.

[0077] The actual internal combustion engine efficiency η* eng On the left side of formula f5, 10 is used as an index for the fuel efficiency of the vehicle. The value on the right side of formula f5 indicates the ratio of the engine's output energy to its input energy.

[0078] If the actual combustion engine efficiency of the vehicle 10 in the EV driving condition, in which the vehicle 10 drives solely by means of the drive force of the motor generator 20, is replaced by a system efficiency η sys Based on the driving result as defined so far, the actual internal combustion engine efficiency η can be sys In EV driving mode, on the other hand, it can be expressed by the following formula f6: Mathematics 5 ηsys=EsysoutEsysin

[0079] In formula f6, E represents sysout represents the output energy of the drivetrain, and E sysin represents an input fuel energy.

[0080] The actual internal combustion engine efficiency η sys In EV driving mode, the ratio of the powertrain output energy to the vehicle's own powertrain input energy is given by 10 in the state when the internal combustion engine is stopped.

[0081] Based on the above, the future actual internal combustion engine efficiency η* can be determined. eng regarding the acceleration command value α as in Fig. 10 can be expressed as shown. That is, if the acceleration command value α is less than the acceleration threshold value ath, the vehicle 10 is driven by the tractive force of the motor-generator 20, and thus the future actual internal combustion engine efficiency η* eng the value on the right-hand side of formula f6. If the acceleration command value α is equal to or greater than the acceleration threshold value ath and less than an acceleration command value abc used for acceleration in burn-and-coast control during ACC control, the future actual internal combustion engine efficiency η* eng can be determined by the right-hand side of formula f5. The future actual internal combustion engine efficiency η* thus determined engindicates the ratio of the output energy of the powertrain to the input energy of the powertrain of the vehicle's own vehicle 10.

[0082] If the vehicle 10, as described above in the first embodiment, drives with the predetermined state variable b(t) with respect to the i-th surrounding vehicle to suppress braking energy, an expected value n* can be obtained. ens (b(t)) of the actual internal combustion engine efficiency n* eng of one's own vehicle 10 at this time can be determined by the following formula f7: Mathematics 6 ηeng∗(b(t))=∑i=1Npiηeng i∗(b(t))

[0083] The use of the expected value η* eng (b(t) enables the generation of an evaluation function F E2 as expressed by the following formula f8: Mathematics 7 FE2=min{k / ηeng∗(b(t))+(1−k)C(b(t))}=min∑i=1npi{k / (piηeng i∗(b(t)))+(1−k)∫0Tyi(b(t))dt}

[0084] Determining the state variable b(t) of one's own vehicle 10 such that the valuation function F E2 Minimizing the current allows the preservation of the state variable b(t) of the vehicle 10, which suppresses the short-term control of the internal combustion engine 60 while ensuring consistent power output. In other words, the state variable b(t) of the vehicle 10 can be preserved, improving fuel efficiency while ensuring consistent power output.

[0085] The prediction ECU 33 performs the determination processing in step S20 based on the aforementioned procedure. In particular, the prediction ECU 33 has a mapping that establishes a relationship between the acceleration command value α and the actual internal combustion engine efficiency η*. eng as in Fig. Figure 10 indicates this. The prediction ECU 33 accumulates data on the powertrain's output energy and input fuel energy up to the present and, based on this accumulated data, sequentially calculates the actual internal combustion engine efficiency η. sys In EV driving mode, the calculation is based on formula f6. The prediction ECU 33 uses the calculated actual internal combustion engine efficiency η. sys as actual internal combustion engine efficiency η* eng , where the acceleration command value α is less than the acceleration threshold value αth.

[0086] The prediction ECU 33 determines the state variable b(t) of its own vehicle 10 such that the evaluation function F E2becomes minimal. Since the state variable b(t) is a function of the acceleration of the vehicle 10, the prediction ECU 33 can obtain a third setting value α3 of the acceleration command value α from the above calculation, with which the value of the evaluation function F E2 becomes minimal.

[0087] In step S20, the predictive ECU 33 compares the first setting value α1 with the third setting value α3 to determine whether limiting the acceleration of the vehicle 10 is necessary to suppress the short-term control of the internal combustion engine 60. That is, if the first setting value α1 is equal to or less than the third setting value α3, the predictive ECU 33 determines that limiting the acceleration of the vehicle 10 is not necessary. In other words, the predictive ECU 33 performs a negative determination in step S20. In this case, the predictive ECU 33 determines that no change in the environment has occurred that will affect the fuel efficiency of the vehicle 10. Then, the ACC ECU 32 and the predictive ECU 33 execute step S13 and the subsequent steps.

[0088] If the third setting value α3 is less than the first setting value α1, the Predictive ECU 33 determines that limiting the acceleration of the vehicle 10 is necessary. That is, the Predictive ECU 33 performs a confirmation determination in step S20. In this case, the Predictive ECU 33 determines that an adverse change has occurred in the environment surrounding the vehicle, and this adverse change is likely to negatively affect the fuel economy of the vehicle 10. If the Predictive ECU 33 performs a confirmation determination in step S20, the ACC ECU 32 changes the acceleration command value α from the first setting value α1 to the third setting value α3 in step S21. Afterward, the ACC ECU 32 and the Predictive ECU 33 execute step S13 and the following steps.

[0089] In step S13, the prediction ECU 33 compares the first setting value α1, the second setting value α2, and the third setting value α3 to determine whether the vehicle 10 needs to be slowed down. If the third setting value α3 is less than the first setting value α1 and the third setting value α3 is less than the second setting value α2, the prediction ECU 33 performs a confirmation determination in step S13. Conversely, if the first setting value α1 is equal to or less than the third setting value α3, or the second setting value α2 is equal to or less than the third setting value α3, the prediction ECU 33 performs a negative determination in step S13.

[0090] Next, an operating example of the vehicle control device 50 according to this embodiment will be described.

[0091] It is assumed that the speed Vp of the vehicle ahead rises sharply and then drops sharply, as indicated by a dashed line in Fig. Figure 11(A) shows that if only the ACC control is executed in such a situation, the ACC-ECU 32 starts the internal combustion engine 60 at time t20 to cause the vehicle 10 to follow the vehicle ahead. When the ACC-ECU 32 starts the internal combustion engine 60 at time t20, the drive energy Ec of the vehicle 10 becomes greater than the energy Es at the time of an internal combustion engine start, as shown by the dashed and doubly dotted line in Figure 11(A). Fig. Figure 11(B) shows that the rotational speed Nc of the internal combustion engine 60 increases after time t20, as shown by a dashed and doubly dotted line in Figure 11(B). Fig. 11(C) is shown.

[0092] If the vehicle ahead suddenly slows down, the distance time and relative speeds between the vehicle 10 and the vehicle ahead decrease sharply. If regenerative control is executed at time t21, the drive energy Ec of vehicle 10 decreases sharply, as indicated by a dashed and double-dotted line in Fig. Figure 11(B) shows that, due to the implementation of this regenerative control, the rotational speed Nc of the internal combustion engine 60 decreases sharply after time t21 and the internal combustion engine 60 stops, as indicated by a dashed and doubly dotted line in Figure 11(B). Fig. Figure 11(C) shows that if the internal combustion engine 60 is started in this way and then stopped within a short time, the energy used to start the internal combustion engine 60 is lost.

[0093] In this respect, the prediction ECU 33 of this embodiment calculates the third setting value α3 of the acceleration command value using the formula f8 above, which suppresses the momentary control of the internal combustion engine 60, and sets the acceleration command value α to the third setting value α3. When the acceleration command value α is transmitted from the ACC ECU 32 to the HV ECU 39, it is difficult to increase the actual acceleration of the vehicle 10 to the acceleration threshold value αth at which the internal combustion engine 60 is supposed to start, and thus the internal combustion engine 60 will not start. Consequently, the speed Vc of the vehicle 10 decreases as described in Fig. 11(A) shown, and the motive power Ec of vehicle 10 does not increase to the energy Es at the time of the internal combustion engine start, as shown in Fig. Figure 11(B) shows that the waste of energy Es at the time of the internal combustion engine start-up can be suppressed, resulting in an improvement in the fuel economy of the vehicle.

[0094] According to the vehicle control device 50 described above in this embodiment, not only the above operating methods and advantageous effects (1) to (7) but also the following operating methods and advantageous effects (8) to (10) can be obtained: (8) The ACC-ECU 32 limits the acceleration of the vehicle 10 in such a way that it is unlikely the internal combustion engine 60 will be restarted by the engine ECU 63. Therefore, the momentary control of the internal combustion engine 60 can be suppressed to reduce energy loss. This improves the fuel efficiency of the vehicle 10. (9) Based on the fuel efficiency index of its own vehicle 10 and the following performance index of its own vehicle 10 with respect to the vehicle ahead, the predictive ECU 33 determines whether the acceleration of its own vehicle 10 should be limited. As the fuel efficiency index of its own vehicle 10, the predictive ECU 33 uses, in particular, the predicted value of the ratio of the powertrain output energy to the powertrain input energy of its own vehicle 10 during a time interval from a present time to a predetermined future time, as shown in formula f7. As the following performance index of its own vehicle 10 with respect to the vehicle ahead, the predictive ECU 33 also uses the deviation y. iThe position of the vehicle relative to the ideal value during ACC control is determined over a period of time from the current time until a predetermined time thereafter. Therefore, a reliable determination of the vehicle's deceleration can be made to achieve the desired effects of improved fuel economy and suppression of reduced performance. (10) The predicted value of the ratio of the powertrain output energy to the powertrain input energy of the vehicle 10 includes the predicted value shown by formula f5 and the predicted value shown by formula f6. The predicted value shown by formula f5 is a predicted value of the ratio of the internal combustion engine 60 output energy to the input energy of the

[0095] Internal combustion engine 60 in the state in which it is being controlled. The predicted value shown by formula f6 is a predicted value of the ratio of the output energy of the drivetrain to the input energy of the drivetrain of the vehicle 10 in the state in which the internal combustion engine 60 is stopped. Therefore, the vehicle 10 can be driven with high fuel efficiency using the specific driving method even when the internal combustion engine 60 is stopped. Third example

[0096] Next, a vehicle control device 50 according to a third embodiment is described. The following description focuses on the differences compared to the vehicle control device 50 in the first embodiment.

[0097] Regarding this embodiment, an example of a method for calculating the probability of occurrence p is presented. ithe behavior of a surrounding vehicle, which is used in the preceding formulas f2 and f3. To simplify the description, the following description is based on the assumption that the probability of occurrence p i The behavior of the surrounding vehicle is a deceleration behavior occurrence probability, which is the probability that the surrounding vehicle will decelerate.

[0098] First, it is considered that the deceleration behavior of the vehicle is predicted at a point where two behavior patterns are assumed: one in which the surrounding vehicle decelerates at a predetermined point, and another in which the surrounding vehicle passes the predetermined point. If the perimeter monitoring device has 34 pieces of information as shown in Fig. As shown in Figure 13(A), for example, vehicle speed information from the surrounding vehicle is obtained, it will be predicted whether the surrounding vehicle will exhibit deceleration behavior at the current time t30. This prediction is based on the vehicle speed information of the surrounding vehicle at a time in the past earlier than the current time t30. As shown in Fig. As shown in Figure 13(A), the speed of the surrounding vehicle before time t30 is constant. Therefore, the deceleration behavior occurrence probability, which is the probability that the surrounding vehicle undergoes deceleration behavior, can be calculated, for example, as shown in Figure 13(A). Fig. As shown in Figure 13(B), the probability of the surrounding vehicle slowing down is calculated to be 0.5, i.e., 50%. Before time t30, the probability of the surrounding vehicle slowing down is therefore 0.5, and the probability of the surrounding vehicle passing is also 0.5. If the speed of the surrounding vehicle gradually decreases after time t30, it is assumed that the surrounding vehicle is beginning to slow down. Thus, the probability of the slowing down gradually increases from 0.5.

[0099] In the case of predicting the deceleration behavior of the surrounding vehicle using driving data, such as past speed information of the surrounding vehicle, the probability of the deceleration behavior occurring can be calculated with greater accuracy by predicting the deceleration behavior of the surrounding vehicle by learning from past driving data.

[0100] On the other hand, it is possible to predict that the surrounding vehicle will slow down before the actual deceleration of the surrounding vehicle is detected. This can occur in situations where the surrounding vehicle passes a point where a large number of vehicles statistically exhibit deceleration, or when a traffic light in front of the surrounding vehicle changes from green to yellow. If such a situation is detected before time t30, correcting the probability of the deceleration to a value greater than 0.5 at that time makes it possible to calculate a probability that reflects not only the information from the past driving data of the surrounding vehicle, but also information about the future predicted behavior of the surrounding vehicle.The execution of the driving control of the own vehicle 10 based on the calculated probability of occurrence of the deceleration behavior makes it possible to achieve the more suitable driving control of the own vehicle 10 according to the predicted behavior of the surrounding vehicle.

[0101] In this embodiment, the server device 41 constructs a learning model of vehicle behavior based on previous driving data transmitted by a predetermined vehicle. The predetermined vehicle is not limited to the server's own vehicle 10, but can include a different vehicle. One or more predetermined vehicles can be configured. The learning model of vehicle behavior is formed from a likelihood function that calculates a probability derived from the value indicating the plausibility that a vehicle will exhibit predetermined behavior with respect to an observed value in the vehicle's driving data. This probability corresponds to an index indicating the similarity between the vehicle's driving data and the learning information.Based on the constructed learning model of vehicle behavior, the server unit 41 generates a calculation equation from which the probability of the vehicle's deceleration behavior occurring can be obtained. This calculation equation is generated, for example, in the manner described below.

[0102] Given two scenarios, one in which a vehicle decelerates at a predetermined location and the other in which a vehicle passes the predetermined location, the server facility 41 constructs a deceleration behavior model and a passing behavior model based on the driving data transmitted by the predetermined vehicle. The deceleration behavior model and the passing behavior model are training models of vehicle behavior. The driving data includes time-series information about the vehicle speed.

[0103] Server 41 calculates the probability of the slowing behavior model and the probability of the passing behavior model based on the driving data of the predetermined vehicle and obtains a probability difference, which is the difference between these probabilities. Server 41 performs this calculation on all previous driving data to determine the frequency with which the slowing behavior occurred and the frequency with which the passing behavior occurred at that time, using each probability difference. Thus, server 41 can, for example, represent a relationship between a probability difference and a slowing occurrence frequency as shown by a dashed line in Fig. Figure 14 shows a relationship between the probability difference and the frequency of occurrence as represented by a dashed and doubly dotted line in Fig. 14 shown. The server setup 41 generates based on the in Fig. The information shown in section 14 contains a calculation equation for a learning value P. lrn the probability of occurrence of the slowdown behavior as shown by the following formula f9: Mathematics 8 plrn=Ndec(μdectTstop,σdec2tTstop)Ndec(μdectTstop,σdec2tTstop)+Npass(μpasstTstop,σpass2tTstop)

[0104] In formula f9, t represents time, t=0 represents the start time in the respective behavioral model, t=T stop represents the end time in the respective behavioral model, µ dec represents the mean of the deceleration behavior models, σ dec 2 represents the dispersion of the deceleration behavior models, µ pass represents the mean value of the passing behavior models, σ pass 2 represents the dispersion of the passing behavior models, N dec represents the function with which the in Fig. The frequency of occurrence of the slowing-down behavior model shown in Figure 14 is normally distributed, N pass represents the function with which the in Fig. The frequency of occurrence of the passing behavior model shown in Figure 14 is normally distributed, and variables in the functions N dec and N pass Each represents a value on the in Fig. The transverse axis shown in Figure 14 represents the probability difference between the behavioral models. Formula f9 is thus a calculation equation used to determine the learning value p. lrn The probability of the slowdown behavior occurring can be obtained from the probability difference between the models.

[0105] The vehicle control device 50 obtains the deceleration behavior model, the passing behavior model, and formula f9 from the server device 41. The vehicle control device 50 calculates the probability of the deceleration behavior model and the probability of the passing behavior model from the previous driving data of the surrounding vehicles, which are acquired by the perimeter monitoring device 34, during a time interval from the present to a time prior to a predetermined time. The vehicle control device 50 calculates a probability difference between the calculated probabilities of the models and inserts the calculated probability difference between the models into formula f9 to calculate the learned value p. lrn the probability of occurrence of slowdown behavior.

[0106] On the other hand, in this embodiment, the vehicle control device 50 predicts the future deceleration behavior of the surrounding vehicles according to statistics or based on information acquired by the perimeter monitoring device 34 and calculates the probability of the predicted deceleration behavior of the surrounding vehicles occurring. The vehicle control device 50 uses the calculated value as the predicted value p. ftr the probability of occurrence of slowdown behavior.

[0107] The vehicle control unit 50 corrects the learning value p lrn the probability of occurrence of the slowdown behavior with the predicted value p ftr the probability of occurrence of slowdown behavior to obtain a final probability of occurrence of slowdown behavior p iThe vehicle control device 50 calculates the probability of occurrence of the deceleration behavior p. i in particular by the following formula f10: Mathematics 9 pi=plrn−zzplrn+2z−plrnzpftr with z = (p lrn + p lrn2 ) / 2. In the formula, P represents lrn2 This represents the probability that the surrounding vehicle will pass a predetermined point. For example, the total value of P lrn and P lrn2 1 in a situation in which two behavior patterns are assumed, in which the surrounding vehicle slows down at a predetermined point, and in which the surrounding vehicle passes the predetermined point.

[0108] If the learning value p lrn When the probability of the slowdown behavior occurring is close to 0.5, the predicted value p is ftrthe probability of occurrence of the slowdown behavior according to the above formula f10 in the probability of occurrence of the behavior p i dominant. If the learning value p lrn If the probability of the slowdown behavior occurring is close to 0 or 1, the learning value p is lrn the probability of slowing down behavior occurring in the probability of behavior occurring p i dominant.

[0109] Next, a specific procedure for calculating the probability of occurrence of the slowdown behavior p will be described. i described. For the sake of simplicity, the surrounding vehicle, whose deceleration behavior-probability p is given, is described. iThe vehicle to be calculated is hereinafter referred to as the specific surrounding vehicle, and the surrounding vehicles other than the specific surrounding vehicle are referred to as other surrounding vehicles. The surrounding vehicles include the vehicle ahead of the own vehicle 10 and the vehicles surrounding the own vehicle 10, other than the vehicle ahead.

[0110] As in Fig. As shown in Figure 15, in this embodiment, the prediction ECU 33 performs a deceleration behavior occurrence probability calculation in step S30 after step S12. The specific procedure for the deceleration behavior occurrence probability calculation is described in Figure 15. Fig. 16 shown.

[0111] As in Fig. As shown in Figure 16, the prediction ECU 33 first determines in step S31 whether the specific surrounding vehicle is present.

[0112] This means that when any object is detected around the vehicle 10, the perimeter monitoring device 34 recognizes whether the detected object is a surrounding vehicle. At this point, the accuracy of the detection 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 detecting an object is lower when the distance between the vehicle 10 and the detected object is increased. Therefore, it is difficult for the perimeter monitoring device 34 to accurately determine whether an object far away from the vehicle 10 is the specific surrounding vehicle. In this embodiment, the perimeter monitoring device 34 therefore also calculates the detection accuracy when detecting the specific surrounding vehicle.When an object corresponding to the specific surrounding vehicle is detected, the perimeter monitoring device 34 calculates the detection accuracy, for example, using a diagram or a calculation equation based on the relative distance of the vehicle 10 to the object. The diagram and the calculation equation are configured such that the detection accuracy decreases with increasing distance between the vehicle 10 and the object. The perimeter monitoring device 34 transmits the calculated detection accuracy to the prediction ECU 33. The prediction ECU 33 determines that the specific surrounding vehicle is present, based on the condition that the specific surrounding vehicle has been detected by the perimeter monitoring device 34 and that the detection accuracy of the detected specific surrounding vehicle is equal to or greater than a predetermined threshold.

[0113] Upon determining that the specific surrounding vehicle is present, the prediction ECU 33 performs a confirmation determination in step S31, and then determines in step S32 whether there is learning information from driving data of the specific surrounding vehicle.

[0114] To calculate the learning value p lrnTo calculate the probability of the deceleration behavior occurring using the formula f9 above, it is necessary that the server facility 41 has generated the deceleration behavior model and the passing behavior model at the driving point of the own vehicle 10. Furthermore, the use of formula f9 requires the respective probabilities of the models, and therefore the driving data of the specific surrounding vehicle must be accumulated to a degree that allows the respective probabilities of the models to be calculated. Therefore, in step S32, the prediction ECU 33 performs a confirmation check based on the condition that the deceleration behavior model and the passing behavior model at the driving point of the own vehicle 10 have been obtained from the server facility 41 and that the driving data of the specific surrounding vehicle have been accumulated to a degree that allows the respective probabilities of the models to be calculated.

[0115] Regarding the accumulation of previous driving data from surrounding vehicles, the previous driving data of the surrounding vehicles can be accumulated on the server unit 41 by transmitting the driving data, such as vehicle speed information, from the communication unit 36 ​​to the server unit 41. Alternatively, the previous driving data of the surrounding vehicles can be accumulated by collecting the driving histories of the surrounding vehicles in the vehicle 10 itself.

[0116] When performing a confirmation determination in step S32, the prediction ECU 33 calculates the learning value p in step S33. lrnThe probability of the deceleration behavior occurring is based on the previous driving data of the specific surrounding vehicle. The prediction ECU 33 specifically calculates the respective probabilities of the deceleration behavior model and the passing behavior model based on the previous driving data of the specific surrounding vehicle and calculates the learned value p. lrn The probability of the slowdown behavior occurring is calculated using the above formula f9 from the difference between the calculated probabilities of the models.

[0117] If, on the other hand, the deceleration behavior model and the passing behavior model at the driving point of the own vehicle 10 were not procured by the server facility 41, or if the driving data of the specific surrounding vehicle were not accumulated to a degree that allows the respective probabilities of the models to be calculated, the prediction ECU 33 performs the following in the Fig. In step S32, shown in step 14, a negative determination is performed. In this case, the prediction ECU 33 calculates the learned value p in step S34. lrnThe probability of slowing down is based on static road information acquired by the perimeter monitoring device 34. This static road information includes the presence or absence of traffic lights, road signs, speed limits, gradients, curved roads, intersections, etc. For example, if a camera is used as the perimeter monitoring device 34, traffic signs, road conditions, etc., can be acquired based on the image data of the vehicle's surroundings captured by the camera. The prediction ECU 33 obtains the static road information based on the traffic signs, road conditions, etc., acquired by the perimeter monitoring device 34. The prediction ECU 33 has a diagram in which the probability of slowing down is defined for each element of the static road information.The prediction ECU 33 calculates the probability of occurrence of the deceleration behavior for each element of the acquired static road information and calculates the learning value p. lrn the probability of occurrence of the slowdown behavior using a calculation equation from the calculated probability of occurrence of the slowdown behavior for each element.

[0118] After step S33 or S34 has been executed, the prediction ECU 33 determines in step S35 whether the presence of a traffic signal is a cause for the specific surrounding vehicle to exhibit deceleration behavior in the future. For example, the prediction ECU 33 can determine, based on the previous driving history recorded by the proximity monitoring device 34, whether the presence of a traffic signal is a cause for the specific surrounding vehicle to exhibit deceleration behavior in the future.If, based on the road situation detected by the perimeter monitoring device 34, it is determined that a traffic light is present which is installed within a predetermined area of ​​the specific surrounding vehicle, the prediction ECU 33 can alternatively determine that the presence of a traffic signal is a cause that the specific surrounding vehicle will exhibit deceleration behavior in the future.

[0119] If it is determined that the presence of a traffic signal is a cause for the specific surrounding vehicle to exhibit deceleration behavior in the future, the prediction ECU 33 performs a confirmation determination in step S35 and then determines in step S36 whether a traffic light is present near the current driving position of the specific surrounding vehicle and whether the proximity monitoring device 34 has detected signal information about the traffic light. If, based on the road situation detected by the proximity monitoring device 34, it is determined that the distance from the specific surrounding vehicle to a traffic light is less than a predetermined threshold, the prediction ECU 33 determines that a traffic light is present near the current driving position of the specific surrounding vehicle. The signal information is information indicating whether the traffic light is blue, yellow, or red.The prediction ECU 33 obtains the signal information from the traffic light via the perimeter monitoring device 34.

[0120] If there is a traffic light near the current driving position of the specific surrounding vehicle, and the proximity monitoring device 34 has detected the signal information about the traffic light, the prediction ECU 33 performs a confirmation determination in step S36. In this case, the prediction ECU 33 then calculates the predicted value p in step S37. ftr the probability of the slowdown behavior occurring according to the change time specification of the traffic light.

[0121] When the vehicle is traveling at speed 10, the prediction ECU 33 accumulates information about the signal change time of the traffic light based on the signal information from the traffic light detected by the perimeter monitoring device 34. In this embodiment, the prediction ECU 33 accumulates green light duration information as information about the signal change time of the traffic light. The green light duration information refers to the time the traffic light needs to change from green to yellow from the point at which it changed from red to green.

[0122] If the traffic light is like in Fig. As shown in Figure 17, for example, if the traffic light is red at time t40 when it is detected by the perimeter monitoring device 34, the prediction ECU 33 stores a time period from time t41, when the traffic light switches to green afterwards, to time t42, when the traffic light switches further to yellow, in the storage device as green duration information.

[0123] If, on the other hand, the traffic light is like in Fig. If, for example, the traffic light is green at time t50 when the traffic light is detected by the perimeter monitoring device 34, the prediction ECU 33 stores a time period up to time t51 in the storage device as green duration information, when the traffic sign then switches to yellow.

[0124] If there is a traffic light that has a switching cycle that varies depending on the traffic flow, the prediction ECU 33 can learn the green time duration information according to the traffic flow information obtained by the Vehicle Information and Communication System (VICS, registered trademark) or the like.

[0125] The prediction ECU 33 generates a map as shown in Fig. Figure 19 shows the green duration information accumulated in the storage device. The in Fig. Figure 19 shows a relationship between a green time duration γ and a probability p. sig , that the traffic light switches from green to yellow, such that the green duration is specified on the horizontal axis, and the probability p sig is shown on the vertical axis. This figure is stored in the memory of the prediction ECU 33.

[0126] A large number of vehicles obtain their respective green light duration information and transmit it to server facility 41, and server facility 41 learns the respective green light duration information transmitted by the vehicles, thereby enabling server facility 41 to... Fig. The prediction ECU 33 can generate the image shown in 19. In this case, the prediction ECU 33 obtains the image from the server unit 41 via the communication unit 36 ​​and can thereby generate the image shown in 19. Fig. Use the figure shown in 19.

[0127] Is the traffic light red at a time when the perimeter monitoring device 34 detects the traffic light in the area in Fig. If the predictive ECU 33 detects the traffic light in step S35 or S36 as shown in step 16, it measures the green light duration from the moment the traffic light changes from red to green. If the traffic light is green at a time when the perimeter monitoring device 34 detects the traffic light in step S35 or S36, the predictive ECU 33 measures the green light duration from that time. The probability p of the green light duration γ measured in this way can be determined. sig , that the traffic light switches from green to yellow δ seconds later, as the value of the probability p sig are obtained, with the value on the horizontal axis in the Fig. Figure 19 shows γ + δ.

[0128] If, on the other hand, the traffic light near the current driving position of the specific surrounding vehicle changes from green to yellow, the specific surrounding vehicle is expected to decelerate. That is, there is a correlation relationship between the probability p sig The probability that the traffic light will change from green to yellow, and the probability that the specific surrounding vehicle will exhibit the deceleration behavior. The prediction ECU 33 of this embodiment thus uses the probability p sig , which are based on the in Fig. Figure 19 shows a calculation of the predicted value p. ftr the probability of occurrence of slowdown behavior.

[0129] If, as in Fig. As shown in Figure 16, in step S36 a negative determination is carried out, i.e., if it is not determined that the traffic light is close to the current driving position of the specific surrounding vehicle, or if it is determined that the signal information of the traffic light was not detected by the perimeter monitoring device 34, the prediction ECU 33 calculates the predicted value p. ftr the probability of occurrence of the slowdown behavior in step S38 based on statistical information.

[0130] Server 41 communicates with a large number of vehicles to obtain information indicating the behavior pattern—slowing down or passing—that the vehicles exhibited at the traffic light. Based on this statistical information, it calculates the probability of the vehicles slowing down. For example, if 50 out of 100 vehicles included in the statistics slowed down at the traffic light and the other 50 passed through without slowing down, server 41 calculates the probability of the vehicles slowing down at the traffic light to be 0.5. Prediction ECU 33 obtains statistical information (Psta) regarding the probability of the vehicles slowing down at the traffic light from server 41 and uses this statistical information (Psta) as the predicted value (p). ftr the probability of occurrence of slowdown behavior.

[0131] If, as in Fig. As shown in Figure 16, in step S35 a negative determination is performed, i.e., if it is not determined that a signal exists as the cause of the specific surrounding vehicle exhibiting deceleration behavior in the future, the prediction ECU 33 determines in step S39 whether the state variables of the other surrounding vehicles have been acquired. The state variables of the other surrounding vehicles include the driving positions, speeds, etc. of the other surrounding vehicles. If the state variables of the other surrounding vehicles have been acquired by the perimeter monitoring device 34, the prediction ECU 33 performs a confirmation determination in step S39.If inter-vehicle communication between the own vehicle 10 and the other surrounding vehicles is possible, the prediction ECU 33 can perform a confirmation determination in step S39 under the condition that the state variables were obtained via communication with the other surrounding vehicles.

[0132] If it performs a confirmation determination in step S39, the prediction ECU 33 calculates the predicted value p in step S40. ftrThe probability of the deceleration behavior occurring is based on the state variables of the other surrounding vehicles. The prediction ECU 33 forecasts the respective future behavior of the vehicles through simulation based on information from the specific surrounding vehicle, such as its current driving position and speed, and information from the other surrounding vehicles, such as their current driving positions and speeds. Based on this simulation, the prediction ECU 33 calculates a probability P surthat the other surrounding vehicles will exhibit a predetermined behavior that could trigger the slowing down of the specific surrounding vehicle. The predetermined behavior of the other surrounding vehicles that could trigger the slowing down of the specific surrounding vehicle is, for example, the behavior of performing a lane change into the lane in which the specific surrounding vehicle is traveling. The prediction ECU 33 uses the calculated probability of occurrence p. sur of the predetermined behavior of the other surrounding vehicles as a predicted value p ftr the probability of occurrence of slowdown behavior.

[0133] If it performs a negative determination in step S39, the prediction ECU 33 then calculates the predicted value p in step S41. ftr The probability of occurrence of slowdown behavior is based on statistical information.

[0134] Server 41 communicates with a large number of vehicles to compile statistics on whether the vehicles slowed down at a predetermined location or passed through it. Based on this statistical information, it calculates the probability of each vehicle slowing down. For example, if 50 out of 100 vehicles included in the statistics slowed down at the predetermined location, and the other 50 passed through without slowing down, server 41 calculates the probability of each vehicle slowing down at the traffic light to be 0.5.The prediction ECU 33 obtains statistical information Psta of the deceleration behavior occurrence probability, corresponding to the current position of its own vehicle, from the server facility 41, and uses the statistical information Psta of the deceleration behavior occurrence probability as the predicted value P. ftr the probability of occurrence of slowdown behavior.

[0135] If step S31 does not determine that a specific surrounding vehicle is present whose detection accuracy is equal to or higher than a predetermined threshold, the prediction ECU 33 performs a negative determination in step S31. In this case, the prediction ECU 33 then determines in step S34 whether a specific surrounding vehicle is present that corresponds to a distant vehicle. The distant vehicle refers to a vehicle for which the detection accuracy is less than a predetermined threshold. If it determines that a specific surrounding vehicle corresponding to the distant vehicle is present, the prediction ECU 33 performs a confirmation determination in step S43 and then calculates the predicted value p. ftr the probability of the slowdown behavior occurring in step S44 based on the information from the remote vehicle.

[0136] For example, the prediction ECU 33 calculates the distance from the vehicle 10 to the object recognized as a distant vehicle and calculates an existence probability p. far of the object by a calculation equation or the like based on the calculated distance. The calculation equation or the like is set such that the value of the probability of existence p increases with increasing distance to an object. far the object becomes smaller. The prediction ECU 33 uses the calculated probability of existence p. far of the object as a predicted value p ftr the probability of occurrence of slowdown behavior.

[0137] After calculating the predicted value p ftr Based on the probability of occurrence of the slowdown behavior in steps S37, S38, S40, S41 and S44, the prediction ECU 33 then calculates the probability of occurrence of the slowdown behavior p in step S42.i That is, the prediction ECU 33 calculates the probability of occurrence of the slowdown behavior p. i using the above formula f10 based on the learning value p lrn the probability of occurrence of the slowdown behavior calculated in either step S33 or S34 and the predicted value p ftr the probability of occurrence of the deceleration behavior calculated in one of steps S37, S38, S40, S41 and S44. In this embodiment, the P used to calculate z lrn 2 from the calculation equation P lrn 2 =1 - P lrn will be calculated.

[0138] On the other hand, if it performs a negative determination in step S43, i.e., if no remote vehicle information is available, the prediction ECU 33 terminates the process in Fig. The sequence of steps shown is without execution of step S42. Since in this case there is no vehicle around the vehicle 10 that could cause the vehicle 10 to exhibit deceleration behavior, the prediction ECU 33 executes the following steps in the sequence shown. Fig. In step S13, shown in section 15, a negative determination is carried out. The ACC-ECU 32 therefore transmits the acceleration command value α to the EV-ECU 31, which was provisionally set to the first setting value α1 in step S11.

[0139] The vehicle control device 50 described above in this embodiment can achieve the following operating modes and advantageous effects (11) to (17): (11) The prediction ECU 33 calculates the probability of occurrence of the slowdown behavior p iof the specific surrounding vehicle based on the learning information about the learning of behaviors of a test vehicle according to the vehicle's driving data, in particular 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 calculation equations of the preceding formulas f2 and f3, and calculates the second setting value α2 of the acceleration command value α by determining the state variable b(t) of the own vehicle 10, with which the value of the evaluation function FE1 in formula f4 is minimized. If the prediction ECU 33 determines in step S13 that the own vehicle 10 needs deceleration, as described in Fig. As shown in Figure 15, the ACC-ECU 32 sets the acceleration command value α to the second setting value α2 in step S14. The execution of the acceleration control of the vehicle 10 based on this preset acceleration command value α makes it easier to predict the deceleration behavior of the specific surrounding vehicle and to decelerate the vehicle 10 accordingly. (12) The prediction ECU 33 calculates the probability as an index for the similarity between the driving data of the specific surrounding vehicle obtained 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 probability. With this configuration, the probability of the deceleration behavior occurring p can be determined. iof the specific surrounding vehicle can be calculated with high accuracy. (13) If the probability of the slowdown behavior occurring is p i Since the slowdown behavior cannot be calculated using learning models such as the slowdown behavior model and the passing behavior model, the prediction ECU 33 calculates the slowdown behavior occurrence probability p. i based on static road information. With this configuration, the probability of occurrence of the slowdown behavior p can be calculated. i even in a situation where the vehicle behavior learning models cannot be used. (14) If the detection accuracy of the specific surrounding vehicle detected by the perimeter monitoring device 34 is less than a predetermined threshold, the prediction ECU 33 corrects the probability of the deceleration behavior occurring p ibased on the probability of existence, which indicates the possibility that an object recognized as a specific surrounding vehicle is actually present. With this configuration, the probability of occurrence of the deceleration behavior p can be determined. i calculated with greater accuracy according to the detection accuracy of the perimeter monitoring device 34. (15) The prediction ECU 33 corrects the slowdown behavior occurrence probability p i based on the probability of the traffic light changing. With this configuration, the probability of the slowdown behavior occurring, p, can be calculated. i The calculation can be performed with greater accuracy depending on the situation of a change in the traffic light. (16) The prediction ECU 33 corrects the slowdown behavior occurrence probability p iof the surrounding vehicles based on the statistical information of the slowdown probability of test vehicles. With this configuration, the slowdown behavior probability p can be determined. i calculated with greater accuracy according to the statistical information. (17) The prediction ECU 33 obtains the driving data of the surrounding vehicles via communication between the own vehicle 10 and the surrounding vehicles. With this configuration, the driving data of the surrounding vehicles can be obtained with greater accuracy. Further examples of implementation

[0140] The above examples of embodiment can also be carried out in the manner described below. - The vehicle 10 of the second embodiment does not need to include the motor generator 20, the inverter unit 21, the battery 22, and the MG-ECU 30. That is, the vehicle 10 of the second embodiment can use only the internal combustion engine 60 as propulsion for driving. - The prediction ECU 33 of the third embodiment uses the deceleration behavior model and the passing behavior model as behavioral learning models of the surrounding vehicles. Alternatively, the prediction ECU 33 can use other learning models. As deceleration behavior models, the prediction ECU 33 can 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 can be constructed by the prediction ECU 33 instead of the server device 41. - The prediction ECU 33 of the third embodiment can predict not only the deceleration behavior of the surrounding vehicles, but also any other behavior of the surrounding vehicles. In conjunction with this, the server unit 41 or the prediction ECU 33 can learn any behavior of the vehicles. - The prediction ECU 33 can predict a vehicle cutting in from an adjacent lane as an impairment change that has occurred in an environment around the own vehicle, where the impairment change is capable of having an adverse effect on the fuel economy of the own vehicle 10. If a vehicle Cb cuts in between the own vehicle 10 and a vehicle Ca traveling in front of the own vehicle, the prediction ECU 33 uses the state variable of vehicle Ca as the state variable of the vehicle in front before the cutting in, as defined by a solid line in Fig. 12 is shown, and if the vehicle Cb has forced its way into the lane at time t30, the prediction ECU 33 then uses the state variable of vehicle Cb as the state variable of the vehicle ahead. - The state variable b(t) can be a function that contains information such as the speed and position of the vehicle 10. - Instead of the acceleration command value α, the ACC-ECU 32 can transmit a speed command value specifying the speed of the vehicle 10 to the EV-ECU 31 and the HV-ECU 39. - To calculate the following performance rating of the own vehicle 10, the prediction ECU 33 can use the respective speed information of the i-th preceding vehicle and the own vehicle 10 instead of the respective position information of these vehicles. The prediction ECU 33 defines, for example, the ideal driving range between a minimum speed V min and a maximum speed V max and expresses a future deviation amount z ithe predicted speed of the own vehicle 10 from the ideal driving range by the following formula (11): Mathematics 10 zi={Vmin−V(V<Vmin)V−Vmax(V> Vmax)

[0141] Then the prediction ECU 33 uses a value that is calculated by integrating the deviation amount z. i is obtained over a range from the current to a prediction time T, as a subsequent performance evaluation value of the own vehicle 10. - The perimeter monitoring device 34 can obtain information about pedestrians walking on and near roads, traffic lights, traffic rules, speed limits, gradients, curves, intersections, etc. In this case, the prediction ECU 33 can determine, based on the information obtained by the perimeter monitoring device 34, whether the vehicle 10 needs to slow down. - The Predictive ECU 33 can use a predicted fuel economy value as an index for the fuel economy of the vehicle itself 10. Specifically, the Predictive ECU 33 accumulates fuel economy data and calculates the predicted fuel economy value based on this accumulated historical fuel economy data. The acceleration of vehicle 10 can be limited not only by a method that changes the acceleration command value α, but also by a method for issuing a command that is intended to result in a change in acceleration, for example, a method that limits the drive torque or the power of vehicle 10. Limiting the drive torque or the power of vehicle 10 does not refer to an output limitation to protect the motor generator 20 and the battery 22, but rather to a limitation of the output during control regardless of the maximum output of the components. - To control the driving of vehicle 10 by the ACC control or the CC control, the ACC-ECU 32 can employ a method in which a speed control is used to control the speed of the vehicle 10 itself, instead of a method in which an acceleration control is used to control the acceleration of the vehicle 10 itself. The ACC-ECU 32 can use an instruction control to instruct the occupant of the vehicle 10 itself about the driving procedure, 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 physical storage device and a computer executing the software, by software only, by hardware only, or by a combination of software and hardware. For example, if 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 with many logic circuits.

[0142] The present disclosure is not limited to the specific examples described above. Specific examples where skilled persons may make design modifications are also included within the scope of this disclosure, provided they contain the features of this disclosure. The components and their arrangements, states, and forms of the specific examples are not limited to those listed above as examples, but may be modified as applicable. The components included in the specific examples described above may be modified in combination accordingly without causing a technical conflict.

Claims

[1] Vehicle control device (50) performing a driving control that controls the driving of its own vehicle (10) in order to enable its own vehicle to follow a vehicle driving ahead of it, with an environmental prediction unit (33) that predicts whether a change in impairment has occurred in an environment around the own vehicle, wherein the change in impairment is likely to have an adverse effect on the fuel economy of the own vehicle, and an acceleration control unit (32) for performing a prediction control which makes it possible to limit the acceleration of the vehicle itself when the environment prediction unit predicts that the impairment change in the environment has occurred, wherein The driving control system is configured to control the acceleration and deceleration of the vehicle so that the vehicle follows the vehicle in front, and the driving control system is configured to control the deceleration of the vehicle at a first deceleration rate adjustable by the driving control system. The acceleration control unit is configured to predict that the impairment change has occurred in the environment around the vehicle when the environment prediction unit predicts that the impairment change is a deceleration requirement change in the environment, where the deceleration requirement change is necessary to decelerate the vehicle, and Executing a deceleration control as a predictive control that decelerates the own vehicle at a predetermined second deceleration rate when the environmental prediction unit predicts that the impairment change in the environment has occurred, where the second deceleration rate is lower than the first deceleration rate. The environmental prediction unit, based on a first index for the fuel efficiency of the vehicle and a second index for the vehicle's performance relative to the vehicle in front, predicts whether the change in environmental conditions has occurred. The first index for the fuel efficiency of one's own vehicle contains a predicted value for braking energy or a predicted value for fuel efficiency, The predicted value of the braking energy represents a value of the braking energy whose generation is predicted based on a deceleration of the vehicle by the driving control system during a first predetermined time period from a current time to a predetermined first future time, and the second index for the vehicle's subsequent performance represents a deviation amount of a position of the vehicle or a deviation amount of a speed of the vehicle or the sum of deviations in the position of the vehicle from ideal driving, based on the vehicle control system, during a second predetermined time period from the current time to a predetermined second future time or The sum of deviations in the speed of the vehicle from ideal driving, based on the driving control, during the second predetermined time period from the current time to the predetermined second future time. [2] Vehicle control device according to claim 1, wherein the acceleration control unit performs an idle control as a deceleration control to cause the own vehicle to drive in neutral without transmitting an output from a drive train (20, 60) to a wheel (28) of the own vehicle. [3] Vehicle control device (50) performing a driving control that controls the driving of its own vehicle (10) in order to enable its own vehicle to follow a vehicle driving ahead of it, with an environmental prediction unit (33) that predicts whether a change in impairment has occurred in an environment around the own vehicle, wherein the change in impairment is likely to have an adverse effect on the fuel economy of the own vehicle, an acceleration control unit (32) for performing a prediction control which makes it possible to limit the acceleration of the vehicle itself when the environment prediction unit predicts that the impairment change in the environment has occurred, and a vehicle control unit that Controlling and stopping an internal combustion engine (60) of the own vehicle based on a driving condition of the own vehicle, and the internal combustion engine restarts based on the acceleration of the vehicle itself when the vehicle's internal combustion engine has stopped, whereby The acceleration control unit limits the acceleration of the vehicle itself as a predictive control system, in order to make it less likely that the vehicle control unit will restart the internal combustion engine. [4] Vehicle control device according to claim 3, wherein the environment prediction unit determines, based on a first index for the fuel economy of the own vehicle and a second index for the following performance of the own vehicle relative to the vehicle ahead, whether the acceleration of the own vehicle is to be limited. [5] Vehicle control device according to claim 4, wherein the first index for the fuel economy of the own vehicle contains a predicted value of a ratio of an output energy of a powertrain (20, 60) to the input energy of the powertrain of the own vehicle during a predetermined first time period from a current time to a predetermined first future time or a predicted value of fuel economy, and the second index for the vehicle's subsequent performance represents a deviation amount of a position of the vehicle or a deviation amount of a speed of the vehicle or the sum of deviations in the position of the vehicle from ideal driving, based on the vehicle control system, during a second predetermined time period from the current time to a predetermined second future time or The sum of deviations in the speed of the vehicle from ideal driving, based on the driving control, during the second predetermined time period from the current time to the predetermined second future time. [6] Vehicle control device according to claim 5, wherein the predicted value of the ratio of the output energy of the drive train to the input energy of the drive train of the own vehicle includes a predicted value of a ratio of an output energy of the internal combustion engine to the input energy of the internal combustion engine and a predicted value of the ratio of the output energy of the drive train to the input energy of the drive train of the own vehicle in a state when the internal combustion engine is stopped. [7] Vehicle control device according to one of claims 1 and 4 to 6, wherein The environmental prediction unit calculates an expected value of the first index for the fuel economy of the own vehicle based on a probability of occurrence of a behavior of a surrounding vehicle and a value of the first index for the fuel economy of the own vehicle in relation to the behavior of the surrounding vehicle. The environmental prediction unit calculates an expected value of the second index for the following performance of the own vehicle based on the probability of occurrence of the behavior of the surrounding vehicle and a value of the second index for the following performance of the own vehicle in relation to the behavior of the surrounding vehicle, and The environmental prediction unit calculates a value of an evaluation function that includes the expected value of the first index for the fuel economy of the vehicle and the expected value of the second index for the subsequent performance of the vehicle, and, based on the value of the evaluation function, predicts that the adverse change in the environment has occurred. [8] Vehicle control device according to claim 7, wherein the environment prediction unit calculates the probability of occurrence of the behavior of the surrounding vehicle based on learning information about learning behavior of test vehicles based on driving data of the test vehicles. [9] Vehicle control device according to claim 8, further comprising a perimeter monitoring unit which obtains driving data of the surrounding vehicle which drives around the own vehicle, wherein the perimeter prediction unit calculates a probability which is an index which indicates a similarity between the driving data of the surrounding vehicle obtained by the perimeter monitoring unit and the learning information, and calculates the probability of occurrence of the behavior of the surrounding vehicle based on the probability. [10] Vehicle control device according to claim 9, wherein The perimeter monitoring unit also obtains static road information, and If the learning information is unavailable, the environment prediction unit calculates the probability of the surrounding vehicle's behavior based on the static road information. [11] Vehicle control device according to claim 9 or 10, wherein, if a value of a detection accuracy of the surrounding vehicle by the perimeter monitoring unit is less than a predetermined threshold, the perimeter prediction unit corrects the probability of occurrence of the behavior of the surrounding vehicle based on an existence probability that indicates a possibility of the actual presence of an object recognized as the surrounding vehicle. [12] Vehicle control device according to claim 9 or 10, wherein The perimeter monitoring unit also obtains information about a change time specification of a traffic light installed on a road, and The environmental prediction unit corrects the probability of the surrounding vehicle's behavior based on the probability of a change in the traffic light. [13] Vehicle control device according to claim 9 or 10, wherein the environment prediction unit corrects the probability of occurrence of the behavior of the surrounding vehicle based on statistical information about the probability of occurrence of behavior of respective test vehicles. [14] Vehicle control device according to one of claims 9 to 13, wherein the environment prediction unit obtains the driving data of the surrounding vehicle via communication between the own vehicle and the surrounding vehicle. [15] Vehicle control device according to any one of claims 1 to 14, wherein the driving control is a burn-and-coast control for causing the own vehicle to follow the vehicle ahead by repeatedly accelerating and decelerating the own vehicle. [16] Vehicle control device according to one of claims 1 to 15, wherein the environment prediction unit is configured to predict a slowdown of the vehicle ahead and / or a vehicle encroaching from an adjacent lane as a change in impairment. [17] Vehicle control device according to one of claims 1 to 16, wherein the driving control is a speed control for controlling a speed of the own vehicle, an acceleration control for controlling the acceleration of the own vehicle or an instruction control for instructing an occupant of the own vehicle about a driving procedure. [18] Vehicle control device according to one of claims 1 to 17, wherein the acceleration control unit performs as a prediction control an acceleration control for actually limiting the acceleration of its own vehicle or an instruction control for instructing an occupant of its own vehicle about a driving procedure such that the acceleration of its own vehicle is limited.

Images