JP2013193671A - Vehicle travelling support device - Google Patents

Abstract

PROBLEM TO BE SOLVED: To control vehicle speed according to preference of a driver.SOLUTION: A vehicle traveling support device 100 includes: a travelling pattern recording section 11 for recording to an HDD 2 a travelling pattern indicating the variation in speed and the distance between a preceding vehicle in a section set in advance on a map by classifying the travelling state of the vehicle including presence of the preceding vehicle and the presence of a following vehicle for every travel in the section; and a target travelling pattern generation section 12 for outputting a target travelling pattern to be a reference for vehicle speed control based on the travelling pattern classified in the travelling state of the same vehicle as the present travelling state of the vehicle among the recorded travelling patterns.

Description

  The present invention relates to a vehicle travel support apparatus that outputs a target travel pattern serving as a reference for vehicle speed control in accordance with the travel state of a vehicle.

  Conventionally, a technique for detecting at least one of the presence or absence of a preceding vehicle and the presence or absence of a following vehicle with a millimeter wave radar or the like and controlling the vehicle speed or the like according to the detection result is known.

  For example, when the following vehicle is not detected by the millimeter wave radar, the host vehicle is driven by the fuel consumption traveling such that the acceleration traveling by the engine drive and the deceleration traveling by the engine stop are repeated within the speed range, and the succeeding vehicle by the millimeter wave radar. A vehicle travel control device is disclosed in which the host vehicle travels in normal travel without acceleration / deceleration (see Patent Document 1).

  According to this vehicle travel control device, it is possible to improve fuel efficiency while suppressing inconvenience to the surrounding environment.

JP 2010-167994 A

  However, in the vehicle travel control device described in Patent Document 1, since the control reflecting the operation characteristics of the driver is not performed, the driver may feel uncomfortable. In other words, since driving control that suits the driver's preference may not be performed, for example, a driver who performs agile driving may feel irritated because the acceleration / deceleration is too slow. A driver who places importance on driving may feel anxiety because acceleration / deceleration is too early.

  The present invention has been made in view of the above problems, and an object of the present invention is to provide a vehicle travel support device capable of controlling the vehicle speed according to the driver's preference.

  In order to solve the above problems, a vehicle travel support apparatus according to the present invention is configured as follows.

  That is, the vehicle travel support device according to the present invention is a vehicle travel support device that outputs a target travel pattern that serves as a reference for controlling the vehicle speed according to the travel state of the vehicle. Each time the vehicle travels, the vehicle is classified based on the presence / absence of a preceding vehicle and the traveling state of the vehicle including the presence / absence of a following vehicle, and a traveling pattern indicating a change in vehicle speed or distance between the preceding vehicle in the section is stored. The target travel pattern is generated and output based on the travel patterns classified into the same vehicle travel state as the current vehicle travel state among the stored travel patterns.

  According to the vehicle travel support device having such a configuration, every time the vehicle travels in a preset section on the map, the vehicle is classified based on the presence / absence of the preceding vehicle and the traveling state of the vehicle including the presence / absence of the subsequent vehicle, A traveling pattern indicating a change in the vehicle speed or the inter-vehicle distance with the preceding vehicle in the section is stored. And since the said target driving | running | working pattern is produced | generated and output based on the said driving | running | working pattern classified into the driving | running state of the same vehicle among the driving | running states memorize | stored, a driver | operator's The vehicle speed can be controlled according to your preference.

  That is, each time the vehicle travels in a predetermined section on the map, the vehicle is classified based on the presence or absence of the preceding vehicle and the traveling state of the vehicle including the presence or absence of the following vehicle, and the vehicle speed in the section or the preceding vehicle Since the travel pattern indicating the change in the inter-vehicle distance is stored, the driver's preference is reflected in the stored travel pattern. In addition, since the target travel pattern is generated and output based on the travel pattern classified into the same vehicle travel state as the current vehicle travel state, the driver's preference for the output target travel pattern Can be reflected. Therefore, it is possible to control the vehicle speed according to the driver's preference.

  Further, the vehicle travel support device according to the present invention obtains an average travel pattern that is an average of travel patterns classified for each travel state of the vehicle for the stored travel patterns, It is preferable to generate the target travel pattern by performing a weighted average of the optimal travel pattern that is the best travel pattern with a preset weight.

  According to the vehicle travel support device having such a configuration, an average travel pattern that is an average of travel patterns classified for each travel state of the vehicle is obtained for the stored travel patterns, and the obtained average travel pattern, Since the optimal driving pattern that is the driving pattern with the best fuel consumption is weighted and averaged with a preset weight, and the target driving pattern is generated and output, by setting the weight to an appropriate value, An appropriate target travel pattern can be generated and output.

  In the vehicle travel support apparatus according to the present invention, it is preferable that the weight is set based on at least one of the number of stored travel patterns and variation.

  According to the vehicle travel support device having such a configuration, since the weight is set based on at least one of the number of stored travel patterns and variation, the weight can be set to an appropriate value.

  In the vehicle travel support device according to the present invention, it is preferable that the weight of the average travel pattern is set to be larger as the number of stored travel patterns is larger.

  According to the vehicle travel support apparatus having such a configuration, the weight of the average travel pattern is set to be larger as the number of stored travel patterns is larger, so that the weight can be set to a more appropriate value.

  In the vehicle travel support device according to the present invention, it is preferable that the weight of the average travel pattern is set to be larger as the variation in the stored travel pattern is smaller.

  According to the vehicle travel support apparatus having such a configuration, the weight of the average travel pattern is set to be larger as the variation in the stored travel pattern is smaller. Therefore, the weight can be set to a more appropriate value.

  Further, the vehicle travel support apparatus according to the present invention preferably performs correction to increase the weight of the average travel pattern when the driver performs an acceleration / deceleration operation contrary to the target travel pattern.

  According to the vehicle travel support device having such a configuration, when an acceleration / deceleration operation contrary to the target travel pattern is performed by the driver, correction for increasing the weight of the average travel pattern is performed. It can be corrected to an appropriate value.

  In the vehicle travel support device according to the present invention, when there is no preceding vehicle, the travel pattern is a vehicle speed pattern indicating a change in vehicle speed, and when there is a preceding vehicle, the travel pattern is It is preferable that the inter-vehicle distance pattern indicates a change in the inter-vehicle distance from the vehicle.

  According to the vehicle travel support device having such a configuration, when there is no preceding vehicle, the travel pattern is a vehicle speed pattern indicating a change in vehicle speed, and when there is a preceding vehicle, the travel pattern is Since the inter-vehicle distance pattern indicates a change in the inter-vehicle distance from the vehicle, the travel pattern can be stored appropriately.

  In the vehicle travel support device according to the present invention, when the travel pattern is a vehicle speed pattern, an average travel pattern that is an average of the travel patterns classified for each travel state of the vehicle and for each section. Among the average travel patterns, the average travel pattern classified into the same vehicle travel state as the current vehicle travel state, and the average travel related to the section corresponding to the current vehicle position It is preferable to generate and output the target travel pattern based on a pattern.

  According to the vehicle travel support device having such a configuration, when the travel pattern is a vehicle speed pattern, an average travel pattern that is an average of the travel patterns classified for each travel state of the vehicle and for each section. Is required. And among the obtained average travel patterns, the average travel pattern classified into the same vehicle travel state as the current vehicle travel state, and the average relating to the section corresponding to the current vehicle position Since the target travel pattern is generated and output based on the travel pattern, a more appropriate target travel pattern can be generated and output.

  Further, the vehicle travel support device according to the present invention is set regardless of the section instead of the average travel pattern when the average travel pattern is not present or when the reliability of the average travel pattern is low. Preferably, the target travel pattern is generated using a virtual average travel pattern that is an average travel pattern.

  According to the vehicle travel support device having such a configuration, when there is no average travel pattern or when the reliability of the average travel pattern is low, the average travel pattern is set regardless of the section. Since the target travel pattern is generated using the virtual average travel pattern that is the average travel pattern, it is appropriate even when there is no average travel pattern or when the reliability of the average travel pattern is low. The target travel pattern can be generated.

  Further, the vehicle travel support apparatus according to the present invention is based on the travel pattern in which the virtual average travel pattern is classified into the same travel state of the vehicle as the current travel state of the stored travel patterns. It is preferable that the average running pattern is obtained.

  According to the vehicle travel support device having such a configuration, the virtual average travel pattern is based on the travel patterns classified into the same vehicle travel state as the current vehicle travel state among the stored travel patterns. Since it is the obtained average running pattern, the appropriate virtual average running pattern can be generated using the stored running pattern.

  In the vehicle travel support device according to the present invention, the number of stored travel patterns is equal to or less than a preset threshold number, and the variation of the stored travel patterns is equal to or greater than a preset threshold value variation. In addition, it is preferable to determine that the reliability of the average running pattern is low.

  According to the vehicle travel support device having such a configuration, the number of stored travel patterns is equal to or less than a preset threshold number, and the variation in the stored travel patterns is equal to or greater than the preset threshold variation. In addition, since it is determined that the reliability of the average travel pattern is low, it is appropriately determined whether the reliability of the average travel pattern is low by appropriately setting the threshold number and the threshold variation. it can.

  In the vehicle travel support device according to the present invention, the vehicle speed pattern is preferably a vehicle speed pattern associated with a distance.

  According to the vehicle travel support device having such a configuration, since the vehicle speed pattern is a vehicle speed pattern associated with a distance, an appropriate vehicle speed pattern can be set.

  In addition, the vehicle travel support device according to the present invention converts each vehicle speed pattern, which is a vehicle speed pattern associated with a distance, into a predetermined acceleration for each vehicle speed category, and accelerates for each vehicle speed category. It is preferable to obtain the average travel pattern by obtaining an average acceleration that is an average value of the vehicle and obtaining an average vehicle speed pattern associated with a distance from the average acceleration obtained for each vehicle speed category.

  According to the vehicle travel support device having such a configuration, each vehicle speed pattern, which is a vehicle speed pattern associated with the distance, is converted into a predetermined acceleration for each vehicle speed category, and the acceleration for each vehicle speed category is converted. Average acceleration is obtained, and the average travel pattern is obtained by obtaining the average vehicle speed pattern associated with the distance from the average acceleration obtained for each of the vehicle speed categories. The average running pattern can be obtained appropriately.

  In the vehicle travel support device according to the present invention, when there is no preceding vehicle and there is a subsequent vehicle, a vehicle speed pattern set at a vehicle speed equal to or higher than a preset minimum vehicle speed pattern is used as the target travel pattern. It is preferable to obtain.

  According to the vehicle travel support device having such a configuration, when there is no preceding vehicle and there is a subsequent vehicle, a vehicle speed pattern set to a vehicle speed equal to or higher than a preset minimum vehicle speed pattern is used as the target travel pattern. Therefore, by setting the vehicle speed of the minimum vehicle speed pattern to an appropriate value, the driver of the succeeding vehicle feels frustrated because the acceleration of the vehicle is too slow (or the deceleration of the vehicle is too early). Can be suppressed.

  In addition, the vehicle travel support apparatus according to the present invention further travels when the vehicle is in a state where the vehicle travels further includes an acceleration scene that is an acceleration state, a deceleration scene that is a deceleration state, and a steady travel. It is preferable that the vehicle speed pattern is stored by classifying the vehicle based on the presence / absence of the following vehicle, the acceleration scene, the deceleration scene, and the steady traveling scene, including the steady traveling scene that is the situation.

  According to the vehicle travel support apparatus having such a configuration, when there is no preceding vehicle, the travel state of the vehicle further includes an acceleration scene that is a state of acceleration, a deceleration scene that is a state of deceleration, and a steady travel. The vehicle speed pattern is appropriately classified because the vehicle speed pattern is stored, including the steady running scene that is the situation, classified based on the presence or absence of the following vehicle, the acceleration scene, the deceleration scene, and the steady running scene. Patterns can be stored.

  In the vehicle travel support device according to the present invention, it is preferable that the inter-vehicle distance pattern is an inter-vehicle distance pattern associated with a vehicle speed.

  According to the vehicle travel support device having such a configuration, since the inter-vehicle distance pattern is a pattern of inter-vehicle distance associated with vehicle speed, the inter-vehicle distance pattern can be properly stored.

  Further, the vehicle travel support apparatus according to the present invention converts each inter-vehicle distance pattern, which is a pattern of inter-vehicle distance associated with the vehicle speed, into an inter-vehicle distance for each preset vehicle speed category, Calculating the average travel pattern by determining an average inter-vehicle distance, which is an average value of the inter-vehicle distances, and determining an average inter-vehicle distance pattern associated with the vehicle speed from the average inter-vehicle distance determined for each vehicle speed category. Is preferred.

  According to the vehicle travel support device having such a configuration, each inter-vehicle distance pattern, which is a pattern of inter-vehicle distance associated with the vehicle speed, is converted into the inter-vehicle distance for each preset vehicle speed classification, and the vehicle speed classification An average inter-vehicle distance, which is an average value of the inter-vehicle distance, is obtained every time, and the average travel pattern is obtained by obtaining an average inter-vehicle distance pattern associated with the vehicle speed from the average inter-vehicle distance obtained for each vehicle speed category. Therefore, the appropriate average running pattern can be obtained with a simple configuration.

  Further, the vehicle travel support device according to the present invention changes the target inter-vehicle distance pattern that is the target travel pattern to an inter-vehicle distance pattern that has a longer inter-vehicle distance than the average inter-vehicle distance pattern that is the average travel pattern when there is a preceding vehicle. It is preferable to set.

  According to the vehicle travel support device having such a configuration, when there is a preceding vehicle, the target inter-vehicle distance pattern that is the target travel pattern is changed to an inter-vehicle distance pattern that has a longer inter-vehicle distance than the average inter-vehicle distance pattern that is the average travel pattern. Since it is set, the rear-end collision with the preceding vehicle can be appropriately suppressed.

  According to the vehicle travel support device according to the present invention, every time the vehicle travels in a preset section on the map, the vehicle is classified based on the presence / absence of the preceding vehicle and the traveling state of the vehicle including the presence / absence of the subsequent vehicle, A traveling pattern indicating a change in the vehicle speed or the inter-vehicle distance with the preceding vehicle in the section is stored. And since the said target driving | running | working pattern is produced | generated and output based on the said driving | running | working pattern classified into the driving | running state of the same vehicle among the driving | running states memorize | stored, a driver | operator's The vehicle speed can be controlled according to your preference.

  That is, each time the vehicle travels in a predetermined section on the map, the vehicle is classified based on the presence or absence of the preceding vehicle and the traveling state of the vehicle including the presence or absence of the following vehicle, and the vehicle speed in the section or the preceding vehicle Since the travel pattern indicating the change in the inter-vehicle distance is stored, the driver's preference is reflected in the stored travel pattern. In addition, since the target travel pattern is generated and output based on the travel pattern classified into the same vehicle travel state as the current vehicle travel state, the driver's preference for the output target travel pattern Can be reflected. Therefore, it is possible to control the vehicle speed according to the driver's preference.

It is a block diagram which shows an example of the vehicle travel assistance apparatus which concerns on this invention. It is a block diagram which shows an example of the driving | running | working pattern recording part of the vehicle driving assistance device shown in FIG. 3 is a graph showing an example of a vehicle speed pattern and an inter-vehicle pattern stored in a vehicle speed pattern storage unit and an inter-vehicle pattern storage unit shown in FIG. 2, respectively. It is a graph which shows an example of a process of the section average calculation part and average vehicle distance calculation part which are shown in FIG. 3 is a graph showing an example of an average vehicle speed pattern and an average inter-vehicle distance pattern stored in a vehicle speed pattern storage unit and an inter-vehicle pattern storage unit shown in FIG. 3 is a flowchart (first half) showing an example of an operation of a travel pattern recording unit shown in FIG. 2. 3 is a flowchart (second half) showing an example of an operation of a travel pattern recording unit shown in FIG. It is a detailed flowchart which shows an example of the scene determination process performed by step S107 of the flowchart shown in FIG. 6, and S115. It is a detailed flowchart which shows an example of the area average process performed by step S111 of the flowchart shown in FIG. 6, and S119. It is a detailed flowchart which shows an example of the whole average process performed by step S113 of the flowchart shown in FIG. 6, and S121. 8 is a detailed flowchart illustrating an example of an average inter-vehicle distance calculation process executed in steps S127 and S131 of the flowchart illustrated in FIG. 7. It is a block diagram which shows an example of the target driving | running | working pattern production | generation part of the vehicle driving assistance device shown in FIG. It is a graph which shows an example of a process of the target vehicle speed production | generation part and target vehicle interval production | generation part which are shown in FIG. 13 is a flowchart (first half) illustrating an example of an operation of a target travel pattern generation unit illustrated in FIG. 12. 13 is a flowchart (second half) illustrating an example of the operation of the target travel pattern generation unit illustrated in FIG. 12. 15 is a detailed flowchart (first half) showing an example of target vehicle speed generation processing executed in steps S409 and S413 of the flowchart shown in FIG. 15 is a detailed flowchart (second half) showing an example of target vehicle speed generation processing executed in steps S409 and S413 of the flowchart shown in FIG. 18 is a detailed flowchart illustrating an example of weighting factor correction processing executed in step S511 of the flowchart shown in FIG. 16 and step S521 of the flowchart shown in FIG. FIG. 17 is a detailed flowchart showing an example of a target vehicle speed correction process executed in step S527 of the flowchart shown in FIG. 16 is a detailed flowchart (first half) illustrating an example of target inter-vehicle generation processing executed in steps S421 and S423 of the flowchart shown in FIG. 16 is a detailed flowchart (second half) illustrating an example of target inter-vehicle generation processing executed in steps S421 and S423 of the flowchart shown in FIG. FIG. 22 is a detailed flowchart illustrating an example of weighting factor correction processing executed in step S709 of the flowchart shown in FIG. 20 and step S719 of the flowchart shown in FIG. FIG. 21 is a detailed flowchart showing an example of a target inter-vehicle correction process executed in step S725 of the flowchart shown in FIG. 20. It is a block diagram which shows an example of the driving | running | working pattern evaluation part of the vehicle driving assistance device shown in FIG. It is a graph explaining an example of operation | movement of the weight lower limit change part shown in FIG. It is a flowchart which shows an example of the running pattern evaluation part shown in FIG. 27 is a detailed flowchart illustrating an example of a lower limit value changing process executed in steps S905 and S909 of the flowchart shown in FIG.

  Embodiments of the present invention will be described below with reference to the drawings.

-Overall configuration of vehicle travel support device-
First, the overall configuration of the vehicle travel support apparatus 100 according to the present invention will be described with reference to FIG. FIG. 1 is a block diagram showing an example of a vehicle travel support apparatus 100 according to the present invention. The vehicle travel support device 100 outputs a target travel pattern serving as a reference for vehicle speed control according to the travel state of the vehicle. The vehicle travel support device 100 includes a travel support ECU 1, an HDD 2, a vehicle speed sensor 31, a front radar 32, and a rear radar 33. And a navigation device 34.

  A travel support ECU (Electronic Control Unit) 1 controls the overall operation of the vehicle travel support apparatus 100 and includes a CPU, a ROM, a RAM, and a backup RAM.

  A ROM (Read Only Memory) is a non-volatile memory that stores various control programs, a look-up table (LUT) that is referred to when executing the various control programs, a map, and the like. A CPU (Central Processing Unit) executes arithmetic processing based on various control programs stored in the ROM, a lookup table, a map, and the like. A RAM (Random Access Memory) is a memory that temporarily stores calculation results in the CPU, data input from each sensor, and the like. The backup RAM is a non-volatile memory that stores data to be saved when the engine is stopped or the ignition is turned off.

  The driving support ECU 1 is communicably connected to the vehicle speed sensor 31, the front radar 32, the rear radar 33, the navigation device 34, and the like. Further, the travel support ECU 1 is connected to the HDD 2 and the like so as to be communicable.

  An HDD (Hard Disk Drive) 2 is a non-volatile storage medium that stores various data such as driving patterns in a readable and writable manner, and records various information according to instructions from the driving support ECU 1. Is output to the driving support ECU 1.

  The vehicle speed sensor 31 is a sensor that detects the vehicle speed that is the traveling speed of the vehicle. The vehicle speed V detected by the vehicle speed sensor 31 is output to the travel support ECU 1.

  The front radar 32 is a radar that detects the presence or absence of a preceding vehicle that is a vehicle traveling ahead, and the detection result is output to the travel support ECU 1.

  The rear radar 33 is a radar that detects the presence or absence of a succeeding vehicle that is a vehicle traveling behind, and the detection result is output to the travel support ECU 1.

  The navigation device 34 includes a GPS (Global Positioning System), uses the map information stored in the HDD 2 to identify the position of the vehicle on the map and displays it on the monitor. In addition, the navigation device 34 outputs position information on the map of the vehicle to the travel support ECU 1.

  In addition, a node included in a map used in the navigation device 34 or the like has a node Nn set at a preset position (for example, a signal or an intersection). A road between the two nodes N (n−1) and Nn is set as a link (section) Ln. Here, the length of the link (section) Ln is set to about 10 m to 50 m, for example.

  In the present embodiment, the case where the front radar 32 detects the presence / absence of a preceding vehicle will be described. However, another sensor may detect the presence / absence of a preceding vehicle. For example, an image sensor composed of a CCD (Charge Coupled Device) camera or the like may detect the presence or absence of a preceding vehicle. Similarly, instead of (or in addition to) the rear radar 33, an image sensor including a CCD camera or the like may detect the presence or absence of a following vehicle.

-Overall functional configuration of the driving support ECU-
Next, the functional configuration of the travel support ECU 1 will be described with reference to FIG. As shown in FIG. 1, the driving support ECU 1 is functionally configured so that the CPU reads and executes a control program stored in a ROM or the like, so that the driving pattern recording unit 11, the target driving pattern generation unit 12, and the driving pattern evaluation are performed. It functions as a functional unit such as the unit 13. Here, the travel pattern recording unit 11, the target travel pattern generation unit 12, and the travel pattern evaluation unit 13 constitute a vehicle travel support device according to the present invention.

  The travel pattern recording unit 11 is a functional unit that records, on the HDD 2, a travel pattern that indicates a change in vehicle speed or distance between the vehicle and a preceding vehicle in the section every time the section travels on a map. Details of the function and operation of the traveling pattern recording unit 11 will be described later with reference to FIGS.

The target travel pattern generation unit 12 is a functional unit that generates and outputs a target travel pattern serving as a reference for vehicle speed control based on the travel pattern stored in the HDD 2. Here, “outputting the target travel pattern” includes the following three modes.
First aspect: The vehicle speed is controlled according to the target travel pattern.
Second mode: Assists in controlling the vehicle speed according to the target travel pattern.
Third aspect: A target travel pattern is output as guidance.
Details of functions and operations of the target travel pattern generation unit 12 will be described later with reference to FIGS.

  The travel pattern evaluation unit 13 is a functional unit that corrects the target travel pattern output from the target travel pattern generation unit 12 when an operation contrary to the target travel pattern output by the target travel pattern generation unit 12 is performed by the driver. is there. Details of functions and operations of the running pattern evaluation unit 13 will be described later with reference to FIGS.

-Detailed functional configuration of travel pattern recording unit-
Next, a detailed functional configuration of the traveling pattern recording unit 11 and the like shown in FIG. 1 will be described with reference to FIG. FIG. 2 is a block diagram illustrating an example of the travel pattern recording unit 11 of the vehicle travel support device 100 illustrated in FIG. As shown in FIG. 2, the driving support ECU 1 is functionally configured so that the CPU reads and executes a control program stored in a ROM or the like, so that the first classification unit 111, the second classification unit 112, and the vehicle speed pattern recording unit are functionally performed. 113, function as functional units such as a section average calculation unit 114, an overall average calculation unit 115, an inter-vehicle pattern recording unit 116, an average inter-vehicle calculation unit 117, and the like. The driving support ECU 1 functions as the vehicle speed pattern storage unit 21, the average vehicle speed storage unit 22, the inter-vehicle pattern storage unit 24, and the like when the CPU reads out and executes a control program stored in the ROM or the like. Let

The first classification unit 111 is a functional unit that classifies the traveling state of the vehicle based on the presence / absence of a preceding vehicle and the presence / absence of a subsequent vehicle every time the vehicle travels in a preset section on the map. Specifically, the first classification unit 111 determines the presence / absence of a preceding vehicle based on the detection result of the front radar 32. Further, the first classification unit 111 determines the presence / absence of the following vehicle based on the detection result of the rear radar 33. Then, the first classification unit 111 classifies the traveling state of the vehicle in the section in which the vehicle has traveled into the following four (A classification to D classification).
A classification: No preceding vehicle and no following vehicle.
B classification: No preceding vehicle and following vehicle.
C classification: There is a preceding vehicle and there is no following vehicle.
D classification: Presence vehicle existence and subsequent vehicle existence.

When there is no preceding vehicle each time the second classifying unit 112 travels in a preset section on the map (when the first classifying unit 111 determines that the class is A or B), navigation Based on road information and the like related to the section included in the map information of the device 34, the driving state of the vehicle is further accelerated in an acceleration scene, a deceleration scene in a deceleration situation, and a steady running situation. It is a functional unit that classifies a certain steady running scene. Specifically, when the first classification unit 111 determines that the classification is the A classification or the B classification, the second classification unit 112 sets the following three driving conditions (A-1 classification), respectively. -A-3 classification, B-1 classification-B-3 classification).
A-1 classification: A classification and acceleration scene.
A-2 classification: A classification and deceleration scene.
A-3 classification: A classification and a steady running scene.
B-1 classification: B classification and accelerated scene.
B-2 classification: B classification and deceleration scene.
B-3 classification: B classification and a steady running scene.

  Here, the “acceleration scene” is, for example, a traveling state of the vehicle in a section in which a transition is made from a temporary stop position that is a starting point to steady traveling. In addition, the “deceleration scene” is, for example, a traveling state of the vehicle in a section in which the vehicle travels from steady traveling to a temporary stop position that is an end point. The “steady traveling scene” is, for example, a traveling state of the vehicle in a section excluding the “acceleration scene” and the “deceleration scene”.

  The vehicle speed pattern recording unit 113 indicates a travel pattern indicating a change in the vehicle speed V in the section for each section when there is no preceding vehicle (when the first classification section 111 determines that the classification is A or B). Is a functional unit that records the vehicle speed pattern in the vehicle speed pattern storage unit 21. Specifically, the vehicle speed pattern recording unit 113 classifies the above six categories (A-1 classification to A-3 classification, B-1 classification to B-3 classification), and sets each section (for example, the section). The vehicle speed pattern, which is the pattern of the vehicle speed V associated with the distance L, is recorded in the vehicle speed pattern storage unit 21 in association with the identifiable section identification information.

  The vehicle speed pattern storage unit 21 classifies the above six categories (A-1 classification to A-3 classification, B-1 classification to B-3 classification), and identifies each section (for example, section identification that can identify the section). It is a functional unit that stores a vehicle speed pattern that is a pattern of the vehicle speed V associated with the distance L) in association with the information. The vehicle speed pattern stored in the vehicle speed pattern storage unit 21 is written by the vehicle speed pattern recording unit 113 and read by the section average calculation unit 114 and the like.

  Here, the vehicle speed pattern will be described with reference to FIG. FIG. 3 is a graph showing an example of the vehicle speed pattern and the inter-vehicle pattern stored in the vehicle speed pattern storage unit 21 and the inter-vehicle pattern storage unit 24 shown in FIG. In FIG. 3A, the horizontal axis is the distance L, the vertical axis is the vehicle speed V, and a graph G11 indicating a vehicle speed pattern that is a pattern of the vehicle speed V associated with the distance L is shown. Graph G11 is a vehicle speed pattern belonging to the A-2 classification (deceleration scene) from deceleration to stop.

  As described above, when there is no preceding vehicle, the second classification unit 112 classifies the vehicle based on the presence / absence of the following vehicle, the acceleration scene, the deceleration scene, and the steady running scene, and records the vehicle speed pattern. The unit 113 classifies the above six categories (A-1 classification to A-3 classification, B-1 classification to B-3 classification) and corresponds to the classification identification information that can identify the classification (for example, the classification). In addition, since the vehicle speed pattern is stored, an appropriately classified vehicle speed pattern can be stored.

  In this embodiment, every time the second classification unit 112 travels in a predetermined section on the map, when there is no preceding vehicle, the traveling state of the vehicle is changed to an acceleration scene, a deceleration scene, and a steady traveling scene. Although the case where it classifies is demonstrated, the form which the 2nd classification | category part 112 classifies a steady driving | running | working scene further finely may be sufficient. For example, roads such as a steady traveling scene, an uphill scene traveling on an uphill, a downhill scene traveling on a downhill, a curve traveling scene traveling on a curve, a multi-lane traveling scene traveling on a road with many lanes, etc. It may be in the form of classification according to the situation.

  When there is no preceding vehicle, the section average calculation unit 114 classifies the vehicle into the above six (A-1 classification to A-3 classification, B-1 classification to B-3 classification) for each traveling state of the vehicle. And an average vehicle speed pattern that is an average of the classified vehicle speed patterns for each section. Specifically, the section average calculation unit 114 classifies into the above six (A-1 classification to A-3 classification, B-1 classification to B-3 classification), and the distance for each section. Each vehicle speed pattern, which is a vehicle speed V pattern associated with L, is converted into a predetermined acceleration α for each vehicle speed V section, and an average acceleration that is an average value of the acceleration α for each vehicle speed V section. The average vehicle speed pattern is obtained by obtaining αA and obtaining the average vehicle speed VA pattern associated with the distance L from the obtained average acceleration αA for each section of the vehicle speed V.

  Further, the section average calculation unit 114 performs the above-described six (A-1 classification to A-3 classification, B-1 classification to B-3 classification) for each traveling state of the vehicle when there is no preceding vehicle. And a functional unit for obtaining a variation in the classified traveling pattern for each section. Specifically, the section average calculation unit 114 classifies into the above six (A-1 classification to A-3 classification, B-1 classification to B-3 classification), and the distance for each section. Each vehicle speed pattern, which is a pattern of vehicle speed V associated with L, is converted into a predetermined acceleration α for each vehicle speed V section, and a standard deviation ασ of acceleration α is determined for each vehicle speed V section, The variation in the vehicle speed pattern is obtained by obtaining the standard deviation Vσ of the vehicle speed V associated with the distance L from the standard deviation ασ of the acceleration obtained for each of the obtained vehicle speed V categories.

  Further, the section average calculation unit 114 classifies the obtained average vehicle speed pattern and variations in the vehicle speed pattern into the above six (A-1 classification to A-3 classification, B-1 classification to B-3 classification). And it records in the vehicle speed pattern memory | storage part 21 for every said area.

  Here, the operation of the section average calculation unit 114 will be described with reference to FIGS. 4 and 5. FIG. 4 is a graph illustrating an example of processing of the section average calculation unit 114 and the average inter-vehicle calculation unit 117 illustrated in FIG. FIG. 4A shows a vehicle speed pattern which is a pattern of the vehicle speed V associated with the distance L shown in FIG. 3A, and a preset classification of the vehicle speed V (here, a classification is set every 10 km / hour). It is graph G21 which shows an example of the result converted into acceleration (alpha) for every. Here, the horizontal axis is a classification of the vehicle speed V set at an interval of 10 km / hour, and the vertical axis is the acceleration α.

  FIG. 5 is a graph showing an example of the average vehicle speed pattern and the average inter-vehicle distance pattern stored in the vehicle speed pattern storage unit 21 and the inter-vehicle pattern storage unit 24 shown in FIG. FIG. 5A is a graph showing the average vehicle speed pattern obtained by the section average calculation unit 114 and variations in the vehicle speed pattern. The horizontal axis is the distance L, and the vertical axis is the vehicle speed V. A graph G31 indicated by a bold line is an average vehicle speed pattern, and a graph G32 and a graph G33 indicated by a broken line and an alternate long and short dash line are graphs showing variations in the vehicle speed pattern, respectively. Here, the graph G32 is a graph showing variations in the vehicle speed pattern corresponding to ((average vehicle speed VA) + (standard deviation Vσ)), and the graph G33 is ((average vehicle speed VA) − (standard deviation Vσ)). 6 is a graph showing variations in the vehicle speed pattern corresponding to.

  In this way, each vehicle speed pattern, which is a pattern of the vehicle speed V associated with the distance L, is converted into a predetermined acceleration α for each section of the vehicle speed V, and the acceleration α for each section of the vehicle speed V is converted. Since the average acceleration αA, which is an average value, is obtained, and the average vehicle speed pattern is obtained by obtaining the pattern of the vehicle speed V associated with the distance L from the average acceleration αA obtained for the classification of the vehicle speed V, a simple configuration Thus, the average vehicle speed pattern can be obtained appropriately.

  In addition, each vehicle speed pattern, which is a pattern of the vehicle speed V associated with the distance L, is converted into an acceleration α for each segment of the vehicle speed V set in advance, and the standard deviation ασ of the acceleration α for each segment of the vehicle speed V Since the variation of the pattern of the vehicle speed V corresponding to the distance L is obtained from the standard deviation ασ of the acceleration α obtained for each section of the vehicle speed V, the variation of the vehicle speed pattern is obtained. Thus, variation in the vehicle speed pattern can be obtained appropriately.

  That is, since the average value of the acceleration α (average acceleration) αA and the standard deviation ασ of the acceleration α can be obtained for each category of the vehicle speed V, the variation in the average vehicle speed pattern and the vehicle speed pattern can be obtained. The average vehicle speed pattern and the variation in the vehicle speed pattern can be obtained appropriately.

  In this embodiment, the case where the classification of the vehicle speed V is set every 10 km / hour will be described. However, as the classification of the vehicle speed V is set more finely, the variation in the average vehicle speed pattern and the vehicle speed pattern is obtained more accurately. Can do. Conversely, the processing is simplified as the number of sections of the vehicle speed V is smaller.

  When there is no preceding vehicle, the overall average calculation unit 115 classifies the vehicle into the above six (A-1 classification to A-3 classification, B-1 classification to B-3 classification) for each traveling state of the vehicle. This is a functional unit that obtains an overall average vehicle speed pattern that is an average of the classified vehicle speed patterns regardless of the section. Specifically, the overall average calculation unit 115 classifies the above six categories (A-1 classification to A-3 classification, B-1 classification to B-3 classification), and regardless of the section, the distance Each vehicle speed pattern, which is a pattern of the vehicle speed V associated with L, is converted into a predetermined acceleration β for each section of the vehicle speed V, and an average value βA of the values of the acceleration β for each section of the vehicle speed V is obtained. The overall average vehicle speed pattern is obtained by obtaining a pattern of the average vehicle speed UA associated with the distance L from the average acceleration βA obtained for each category of the vehicle speed V.

  In addition, when there is no preceding vehicle, the overall average calculation unit 115 performs the above-described six (A-1 classification to A-3 classification, B-1 classification to B-3 classification) for each traveling state of the vehicle. This is a functional unit that determines the variation of the classified traveling pattern regardless of the section. Specifically, the overall average calculation unit 115 classifies the above six categories (A-1 classification to A-3 classification, B-1 classification to B-3 classification), and regardless of the section, the distance Each vehicle speed pattern, which is a vehicle speed V pattern associated with L, is converted into a predetermined acceleration β for each vehicle speed V section, and a standard deviation βσ of the value of acceleration β is determined for each vehicle speed V section. The variation in the vehicle speed pattern is obtained by obtaining the standard deviation Uσ of the vehicle speed V associated with the distance L from the acceleration standard deviation βσ obtained for each section of the vehicle speed V.

  Further, the overall average calculation unit 115 divides the obtained overall average vehicle speed pattern and the variation of the overall vehicle speed pattern into the above six (A-1 classification to A-3 classification, B-1 classification to B-3 classification). It is classified and recorded in the average vehicle speed storage unit 22 regardless of the section.

  The process of the overall average calculation unit 115 is different in that the process of the section average calculation unit 114 is executed for each section, but is executed regardless of the section, but the other processes are generally the same. Therefore, the description of the function with reference to FIGS. 4 and 5 is omitted.

  The average vehicle speed storage unit 22 is classified into the above six (A-1 classification to A-3 classification, B-1 classification to B-3 classification), and is associated with the distance L regardless of the section. This is a functional unit that stores the overall average vehicle speed pattern of the vehicle speed V and variations in the overall vehicle speed pattern. The overall average vehicle speed pattern stored in the average vehicle speed storage unit 22 is written by the overall average calculation unit 115 and read by a target vehicle speed generation unit 123 (see FIG. 12) described later.

  The inter-vehicle pattern recording unit 116 indicates an inter-vehicle distance pattern indicating a change in the inter-vehicle distance D in the section when there is a preceding vehicle (when the first classification unit 111 determines that it is the C classification or the D classification). This is a functional unit that records in the inter-vehicle pattern storage unit 24. Specifically, the inter-vehicle pattern recording unit 116 classifies the above-mentioned two (C classification and D classification), and the inter-vehicle distance is a pattern of the inter-vehicle distance D associated with the vehicle speed V regardless of the section. The pattern is recorded in the inter-vehicle pattern storage unit 24.

  Here, the inter-vehicle pattern will be described with reference to FIG. FIG. 3 is a graph showing an example of the vehicle speed pattern and the inter-vehicle pattern stored in the vehicle speed pattern storage unit 21 and the inter-vehicle pattern storage unit 24 shown in FIG. In FIG. 3 (b), a graph G12 showing an inter-vehicle distance pattern, which is a pattern of the inter-vehicle distance D associated with the vehicle speed V, is represented by the vehicle speed V on the horizontal axis and the inter-vehicle distance D on the vertical axis. Yes.

  As described above, when there is a preceding vehicle, the first classification unit 111 classifies the vehicle based on the presence / absence of the following vehicle, and the inter-vehicle pattern recording unit 116 determines the above two (C classification and D classification). Since the vehicle-to-vehicle pattern is classified and stored, the vehicle-to-vehicle pattern properly classified can be stored.

  When there is a preceding vehicle, the average inter-vehicle distance calculation unit 117 classifies the inter-vehicle distance, regardless of the section, for each traveling state of the vehicle (categorized into the above two (C classification and D classification)). It is a functional unit for obtaining an average inter-vehicle distance pattern that is an average of the distance patterns. Specifically, the average inter-vehicle distance calculation unit 117 categorizes the above two (C classification and D classification), and each vehicle speed that is a pattern of the inter-vehicle distance D associated with the vehicle speed V regardless of the section. Each pattern is converted into an inter-vehicle distance D for each vehicle speed V section set in advance, and an average value DA of the inter-vehicle distance D is determined for each vehicle speed V section. The average inter-vehicle distance pattern is obtained by obtaining the average inter-vehicle distance DA pattern associated with the vehicle speed V from the inter-vehicle distance DA.

  Further, when there is a preceding vehicle, the average inter-vehicle distance calculation unit 117 is classified for each traveling state of the vehicle (classified into the above two (C classification and D classification)) regardless of the section. This is a functional unit for obtaining variations in the inter-vehicle distance pattern. Specifically, the average inter-vehicle distance calculation unit 117 categorizes the above two (C classification and D classification), and each inter-vehicle distance is a pattern of the inter-vehicle distance D associated with the vehicle speed V regardless of the section. The distance pattern is converted into an inter-vehicle distance D for each vehicle speed V segment set in advance, and a standard deviation Dσ of the inter-vehicle distance D is determined for each vehicle speed V segment. The variation in the inter-vehicle distance pattern is obtained by obtaining the standard deviation Dσ of the inter-vehicle distance D associated with the vehicle speed V from the obtained standard deviation Dσ of the inter-vehicle distance D.

  Further, the average inter-vehicle distance calculation unit 117 classifies the obtained average inter-vehicle distance pattern and the variation in the inter-vehicle distance pattern into the above two (C classification and D classification), and stores the inter-vehicle pattern regardless of the section. Record in part 24.

  Here, the operation of the average inter-vehicle distance calculation unit 117 will be described with reference to FIGS. 4 and 5. FIG. 4 is a graph illustrating an example of processing of the section average calculation unit 114 and the average inter-vehicle calculation unit 117 illustrated in FIG. FIG. 4B shows an inter-vehicle distance pattern which is a pattern of the inter-vehicle distance D corresponding to the vehicle speed V shown in FIG. 3B, and is divided into preset vehicle speed V classifications (here, every 10 km / hour). It is graph G22 which shows an example of the result converted into the inter-vehicle distance D for every. Here, the horizontal axis is the classification of the vehicle speed V set at an interval of 10 km / hour, and the vertical axis is the inter-vehicle distance D.

  FIG. 5 is a graph showing an example of the average vehicle speed pattern and the average inter-vehicle distance pattern stored in the vehicle speed pattern storage unit 21 and the inter-vehicle pattern storage unit 24 shown in FIG. FIG. 5B is a graph showing variations in the average inter-vehicle distance pattern and the inter-vehicle distance pattern obtained by the average inter-vehicle distance calculation unit 117. The horizontal axis is the vehicle speed V, and the vertical axis is the inter-vehicle distance D. A graph G41 indicated by a bold line is an average inter-vehicle distance pattern, and a graph G42 and a graph G43 indicated by a broken line and an alternate long and short dash line are graphs showing variations in the inter-vehicle distance pattern. Here, the graph G42 is a graph showing variations in the inter-vehicle distance pattern corresponding to ((average inter-vehicle distance DA) + (standard deviation Dσ)), and the graph G43 is ((average inter-vehicle distance DA) − (standard deviation). It is a graph which shows the dispersion | variation in the inter-vehicle distance pattern corresponding to D (sigma)).

  In this way, each inter-vehicle distance pattern, which is a pattern of the inter-vehicle distance D associated with the vehicle speed V, is converted into the inter-vehicle distance D for each segment of the vehicle speed V set in advance, and for each segment of the vehicle speed V By obtaining an average inter-vehicle distance DA, which is an average value of the obtained inter-vehicle distance D, and obtaining a pattern of the inter-vehicle distance D associated with the vehicle speed V from the average inter-vehicle distance DA determined for each of the vehicle speeds V, Since the average inter-vehicle distance pattern is obtained, the average inter-vehicle distance pattern can be appropriately obtained with a simple configuration.

  In addition, each inter-vehicle distance pattern, which is a pattern of inter-vehicle distance D associated with the vehicle speed V, is converted into a predetermined inter-vehicle distance D for each vehicle speed V section, and the inter-vehicle distance D for each vehicle speed V section. The standard deviation Dσ is calculated, and the variation in the inter-vehicle distance pattern associated with the vehicle speed V is determined from the standard deviation Dσ of the inter-vehicle distance D determined for each of the vehicle speeds V. Therefore, the variation in the inter-vehicle distance pattern can be appropriately obtained with a simple configuration.

  That is, by calculating the average value (average inter-vehicle distance) DA of the inter-vehicle distance D and the standard deviation Dσ of the inter-vehicle distance D for each category of the vehicle speed V, the average inter-vehicle distance pattern and the variation in the inter-vehicle distance pattern can be obtained. Thus, the average inter-vehicle distance pattern and the variation in the inter-vehicle distance pattern can be appropriately obtained with a simple configuration.

  In this embodiment, the case where the classification of the vehicle speed V is set every 10 km / hour will be described. However, the finer the classification of the vehicle speed V, the more accurately the average inter-vehicle distance pattern and the inter-vehicle distance pattern vary. Can be sought. Conversely, the processing is simplified as the number of sections of the vehicle speed V is smaller.

  The inter-vehicle pattern storage unit 24 classifies into the above two (C classification and D classification), and the variation of the average inter-vehicle distance pattern and the inter-vehicle distance pattern of the inter-vehicle distance D associated with the vehicle speed V regardless of the section. Is a functional unit for storing. The average inter-vehicle distance pattern stored in the inter-vehicle pattern storage unit 24 is written by the average inter-vehicle distance calculation unit 117 and read by a target inter-vehicle distance generation unit 124 (see FIG. 12) described later.

-Overall operation of the running pattern recording unit-
Next, the operation of the travel pattern recording unit 11 will be described with reference to FIGS. 6 is a flowchart (first half) showing an example of the operation of the running pattern recording unit 11 shown in FIG. 2, and FIG. 7 is a flowchart (second half) showing an example of the operation of the running pattern recording unit shown in FIG. ). First, as shown in FIG. 6, the first classification unit 111 determines whether or not the end point of the section has been reached (step S101). If NO in step S101, the process is in a standby state. If YES in step S101, the process proceeds to step S103.

  Then, the first classification unit 111 determines whether there is a preceding vehicle (step S103). If YES in step S103, the process proceeds to step S123 shown in FIG. If NO in step S103, the process proceeds to step S105. Next, the first classification unit 111 determines whether there is a following vehicle (step S105). If YES in step S105, the process proceeds to step S115. If NO in step S105, the process proceeds to step S107.

  Next, “scene determination” is a process for determining by the second classification unit 112 whether the vehicle state in the section that has reached the end point in step S101 corresponds to an acceleration scene, a deceleration scene, or a steady traveling scene. Processing "is executed (step S107). Then, the vehicle speed pattern recording unit 113 classifies the vehicle speed patterns in the section that has reached the end point in Step S101 into six (A-1 classification to A-3 classification, B-1 classification to B-3 classification). Thus, each section is recorded in the vehicle speed pattern storage unit 21 (step S109).

  Next, the section average calculation unit 114 classifies the above six categories (A-1 classification to A-3 classification, B-1 classification to B-3 classification), and averages vehicle speed patterns for each section. A “section averaging process”, which is a process for obtaining a certain average vehicle speed pattern, is executed (step S111). Then, an overall average vehicle speed pattern that is an average of the vehicle speed patterns classified into the A-1 classification to A-3 classification and the B-1 classification to B-3 classification by the overall average calculation unit 115 regardless of the section. “Overall averaging process” is executed (step S113), and the process returns to step S101.

  Also in the case of YES at step S105, the processes corresponding to steps S107 to S113 are executed at steps S115 to S121, respectively, and the process is returned to step S101.

  In the case of YES in step S103, as shown in FIG. 7, the first classification unit 111 determines whether there is a following vehicle (step S123). If YES in step S123, the process proceeds to step S129. If NO in step S123, the process proceeds to step S125. Then, the inter-vehicle pattern recording unit 116 records the inter-vehicle distance pattern in the section that has reached the end point in step S101 in the inter-vehicle pattern storage unit 24 (step S125). Next, the average inter-vehicle distance calculation unit 117 classifies the above two (C classification and D classification), and obtains an average inter-vehicle distance pattern that is an average of the classified inter-vehicle distance patterns regardless of the section. Is executed (step S127), and the process returns to step S101 in FIG.

  Also in the case of YES in step S123, the processes corresponding to steps S125 and S127 are executed in steps S129 and S131, and the process is returned to step S101.

  Thus, the average vehicle speed pattern which is classified into the above six (A-1 classification to A-3 classification, B-1 classification to B-3 classification) and is the average of the vehicle speed patterns for each section. Is recorded in the vehicle speed pattern storage unit 21. Moreover, it classify | categorizes into said 6 (A-1 classification-A-3 classification, B-1 classification-B-3 classification), and the whole average which is the average of the classified vehicle speed pattern irrespective of the area The inter-vehicle distance pattern is recorded in the average vehicle speed storage unit 22. Further, an average inter-vehicle distance pattern that is an average of the classified inter-vehicle distance patterns is recorded in the inter-vehicle pattern storage unit 24 regardless of the section by classifying into the above two (C classification and D classification).

-Detailed operation of scene determination processing-
Next, with reference to FIG. 8, a specific process of the “scene determination process” executed in steps S107 and S115 of FIG. 6 will be described. FIG. 8 is a detailed flowchart showing an example of the “scene determination process” executed in steps S107 and S115 of the flowchart shown in FIG. The following processes are all executed by the second classification unit 112.

  First, it is determined whether or not the state of the vehicle in the section that has reached the end point in step S101 in FIG. 6 is a deceleration scene that is a situation where the vehicle decelerates (step S201). If NO in step S201, the process proceeds to step S205. If YES in step S201, the process proceeds to step S203. Then, the state of the vehicle in the section that has reached the end point in step S101 in FIG. 6 is classified as a “deceleration scene” (step S203), and the process is returned.

  In the case of NO in step S201, it is determined whether or not the state of the vehicle in the section that has reached the end point in step S101 of FIG. 6 is an acceleration scene that is an accelerating situation (step S205). If NO in step S205, the process proceeds to step S209. If YES in step S205, the process proceeds to step S207. Then, the state of the vehicle in the section that has reached the end point in step S101 in FIG. 6 is classified as an “acceleration scene” (step S207), and the process is returned.

  In the case of NO in step S205, the state of the vehicle in the section that has reached the end point in step S101 of FIG. 6 is classified into a steady running scene that is a situation of steady running (step S209), and the process is returned.

  In this way, when there is no preceding vehicle (when it is determined that the vehicle is in the A class or the B class), the driving state of the vehicle is “acceleration scene”, “deceleration scene”, and “steady driving scene”. are categorized.

-Detailed operation of interval averaging process-
Next, with reference to FIG. 9, the specific operation of the “section averaging process” executed in steps S111 and S119 in FIG. 6 will be described. FIG. 9 is a detailed flowchart showing an example of the “section averaging process” executed in steps S111 and S119 of the flowchart shown in FIG. The following processing is all executed by the section average calculation unit 114.

  First, a vehicle speed pattern that is a pattern of the vehicle speed V associated with the distance L is converted into a preset acceleration α for each section of the vehicle speed V (step S301). Then, an average value (average acceleration) αA and a standard deviation ασ of the acceleration α are obtained for each category of the vehicle speed V (step S303). Next, the average value VA and the standard deviation Vσ of the vehicle speed V associated with the distance L are obtained from the average value (average acceleration) αA and the standard deviation ασ of the acceleration α obtained for the classification of the vehicle speed V in step S303 (step S303). S305). Next, the average value VA, standard deviation Vσ, and number of data N of the vehicle speed V obtained in step S305 are classified into six (A-1 classification to A-3 classification, B-1 classification to B-3 classification). Then, it is recorded in the vehicle speed pattern storage unit 21 in association with the section identification information that is the identification information of the section that has reached the end point in step S101 in FIG. 6 (step S307), and the process is returned.

  In this way, the average value VA and the standard deviation Vσ of the vehicle speed V associated with the distance L are obtained and classified into six (A-1 classification to A-3 classification, B-1 classification to B-3 classification). Then, it is recorded in the vehicle speed pattern storage unit 21 in association with the section identification information that is the identification information of the section that has reached the end point in step S101 of FIG.

-Detailed operation of overall average processing-
Next, with reference to FIG. 10, a specific process of the “overall average process” executed in step S113 and step S121 of FIG. 6 will be described. FIG. 10 is a detailed flowchart showing an example of the “overall averaging process” executed in steps S113 and S121 of the flowchart shown in FIG. The following processes are all executed by the overall average calculation unit 115.

  First, a vehicle speed pattern, which is a vehicle speed V pattern associated with the distance L, is converted into a preset acceleration β for each section of the vehicle speed V (step S311). Then, an average value (average acceleration) βA and a standard deviation βσ of the acceleration β are obtained for each category of the vehicle speed V (step S313). Next, the average value UA and the standard deviation Uσ of the vehicle speed V associated with the distance L are obtained from the average value (average acceleration) βA and the standard deviation βσ of the acceleration β obtained for each section of the vehicle speed V in step S313 ( Step S315). Next, the average value UA, standard deviation Uσ and number of data NA of the vehicle speed V obtained in step S315 are classified into six (A-1 classification to A-3 classification, B-1 classification to B-3 classification). Is recorded in the average vehicle speed storage unit 22 (step S317), and the process is returned.

  In this way, the average value UA and the standard deviation Uσ of the vehicle speed V associated with the distance L are obtained and classified into six (A-1 classification to A-3 classification, B-1 classification to B-3 classification). And recorded in the average vehicle speed storage unit 22.

-Detailed operation of average inter-vehicle distance calculation process-
Next, with reference to FIG. 11, a specific process of the “average inter-vehicle distance calculation process” executed in step S127 and step S131 of FIG. 7 will be described. FIG. 11 is a detailed flowchart showing an example of the average inter-vehicle distance calculation process executed in steps S127 and S131 of the flowchart shown in FIG. The following processes are all executed by the average inter-vehicle distance calculation unit 117.

  First, the inter-vehicle distance pattern, which is the pattern of the inter-vehicle distance D associated with the vehicle speed V, is converted into the inter-vehicle distance D for each segment of the preset vehicle speed V (step S321). Then, an average value (average vehicle distance) CA and standard deviation Cσ of the inter-vehicle distance D are obtained for each category of the vehicle speed V (step S323). Next, the average value DA and standard deviation Dσ of the inter-vehicle distance D associated with the vehicle speed V are obtained from the average value (average inter-vehicle distance) CA and the standard deviation Cσ of the inter-vehicle distance D obtained for each classification of the vehicle speed V in step S323. It is obtained (step S325). Next, the average value DA, the standard deviation Dσ, and the number of data NB of the inter-vehicle distance D obtained in step S325 are classified into two (C classification and D classification) and recorded in the inter-vehicle pattern storage unit 24 (step S327). ), The process is returned.

  In this way, the average value DA and the standard deviation Dσ of the inter-vehicle distance D associated with the vehicle speed V are obtained, classified into two (C classification and D classification), and recorded in the inter-vehicle pattern storage unit 24.

-Detailed functional configuration of target travel pattern generator-
Next, with reference to FIG. 12, a detailed functional configuration of the target travel pattern generation unit 12 and the like shown in FIG. 1 will be described. FIG. 12 is a block diagram illustrating an example of the target travel pattern generation unit 12 of the vehicle travel support device illustrated in FIG. As shown in FIG. 12, the driving support ECU 1 is functionally configured such that the CPU reads and executes a control program stored in a ROM or the like, so that the third classification unit 121, the fourth classification unit 122, and the target vehicle speed generation unit are functionally performed. 123 and the functional units such as the target inter-vehicle generation unit 124. In addition, the driving support ECU 1 reads out and executes a control program stored in the ROM or the like by the CPU, thereby causing the HDD 2 to operate as a vehicle speed pattern storage unit 21, an average vehicle speed storage unit 22, an optimum vehicle speed storage unit 23, and an inter-vehicle pattern storage unit. 24, function as the optimal inter-vehicle storage unit 25, the weight storage unit 26, and the like.

-Section classification function-
The third classification unit 121 is a functional unit that classifies a section in which a vehicle enters next based on the presence or absence of a preceding vehicle and the presence or absence of a subsequent vehicle. Specifically, the third classification unit 121 uses the detection result of the front radar 32 when the vehicle approaches the next entry section (for example, when the vehicle reaches 10 m before the start point of the next entry section). Based on this, the presence / absence of the preceding vehicle is determined, and the presence / absence of the following vehicle is determined based on the detection result of the rear radar 33. Then, the third classification unit 121 classifies the traveling state of the vehicle in the section in which the vehicle enters next into the following four (A classification to D classification).
A classification: No preceding vehicle and no following vehicle.
B classification: No preceding vehicle and following vehicle.
C classification: There is a preceding vehicle and there is no following vehicle.
D classification: Presence vehicle existence and subsequent vehicle existence.

When there is no preceding vehicle in the section in which the vehicle enters next, the fourth classification unit 122 has a map that the navigation device 34 has when the third classification unit 121 determines that the classification is the A class or the B classification. Based on the road information and the like related to the section included in the information, the driving state of the vehicle is further changed to an acceleration scene that is an acceleration situation, a deceleration scene that is a deceleration situation, and a steady running scene that is a steady running situation. It is a functional part to classify. Specifically, when the third classification unit 121 determines that the classification is the A classification or the B classification, the fourth classification unit 122 indicates the traveling state of the vehicle in the section in which the vehicle enters next, respectively. (A-1 classification to A-3 classification, B-1 classification to B-3 classification).
A-1 classification: A classification and acceleration scene.
A-2 classification: A classification and deceleration scene.
A-3 classification: A classification and a steady running scene.
B-1 classification: B classification and accelerated scene.
B-2 classification: B classification and deceleration scene.
B-3 classification: B classification and a steady running scene.

-Target vehicle speed pattern setting function (basic)-
The target vehicle speed generation unit 123 is a vehicle speed pattern that serves as a reference for vehicle speed control based on the vehicle speed patterns classified into the same vehicle traveling state as the current vehicle traveling state among the stored vehicle speed patterns. It is a functional unit that generates and outputs a target vehicle speed pattern. Specifically, the target vehicle speed generation unit 123 stores the vehicle speed pattern when the third classification unit 121 determines that the next section in which the vehicle enters is the A class or the B class (there is no preceding vehicle). Based on the vehicle speed patterns stored in the unit 21, the average vehicle speed storage unit 22, and the optimum vehicle speed storage unit 23, the target vehicle speed pattern is generated and output.

  Here, the optimum vehicle speed storage unit 23 is a functional unit that stores an optimum vehicle speed pattern, which is a vehicle speed pattern with the best fuel efficiency, in association with a section (in association with section identification information that is section identification information).

More specifically, the target vehicle speed generation unit 123 stores the average vehicle speed pattern corresponding to the section in which the vehicle enters next among the average vehicle speed patterns stored in the vehicle speed pattern storage unit 21 and the optimal vehicle speed storage unit 23. The target vehicle speed pattern is generated by performing a weighted average of the set optimal vehicle speed pattern with a preset weight ω (here, 0 ≦ ω ≦ 1). That is, the target vehicle speed VR associated with the distance L is obtained by the following equation (1).
VR = VI × (1−ω) + VA × ω (1)
Here, the optimal vehicle speed VI is the vehicle speed associated with the distance L corresponding to the optimal travel pattern stored in the optimal vehicle speed storage unit 23, and the average vehicle speed VA is the average travel stored in the vehicle speed pattern storage unit 21. Among the patterns, the vehicle speed is included in the average travel pattern corresponding to the section in which the vehicle enters next and is associated with the distance L.

  Here, the processing of the target vehicle speed generation unit 123 will be described with reference to FIG. FIG. 13 is a graph illustrating an example of processing of the target vehicle speed generation unit 123 and the target inter-vehicle generation unit 124 illustrated in FIG. FIG. 13A is a graph showing an example of processing of the target vehicle speed generation unit 123, where the horizontal axis is the distance L and the vertical axis is the vehicle speed V. FIG. 13A shows a graph G51 indicating the average vehicle speed pattern stored in the vehicle speed pattern storage unit 21, a graph G52 indicating the optimal vehicle speed pattern stored in the optimal vehicle speed storage unit 23, and a target vehicle speed generation unit. A graph G <b> 53 showing the target vehicle speed pattern generated by 123 is shown.

  As shown in FIG. 13A, the target vehicle speed pattern is located between the average vehicle speed pattern and the optimal vehicle speed pattern, and the target vehicle speed pattern becomes the average vehicle speed pattern as the weight ω approaches “1”. The closer the weight ω is to “0”, the closer the target vehicle speed pattern is to the optimum vehicle speed pattern.

  In this way, the average vehicle speed pattern and the optimal vehicle speed pattern that is the vehicle speed pattern with the best fuel efficiency are weighted and averaged with the preset weight ω, and the target vehicle speed pattern is generated and output. By setting to an appropriate value, an appropriate target vehicle speed pattern can be generated and output.

Further, the weight ω is obtained by the following equation (2), for example.
ω = f (Vσ / VA, N) (2)
Here, Vσ is the standard deviation of the vehicle speed V of the average vehicle speed pattern corresponding to the section where the vehicle enters next, and VA is the average value of the vehicle speed V of the average vehicle speed pattern corresponding to the section where the vehicle enters next. It is. N is the number of vehicle speed patterns corresponding to the section in which the vehicle enters next (that is, the actual number of travels).

  The function f is a function that is stored in the vehicle speed pattern storage unit 21 and sets the weight ω of the average vehicle speed pattern to be larger as the number N of vehicle speed patterns corresponding to the section in which the vehicle enters next is larger. Further, the function f is stored in the vehicle speed pattern storage unit 21, and the weight ω of the average vehicle speed pattern is set to be larger as the variation in the vehicle speed pattern (here, Vσ / VA) corresponding to the section in which the vehicle enters next is smaller. Function.

  As described above, the weight ω of the average vehicle speed pattern is set to be larger as the number N of vehicle speed patterns corresponding to the section in which the vehicle enters next (that is, the actual number of times of travel) is larger. Therefore, the weight ω can be set to an appropriate value. That is, the preference of the driver is more clearly reflected in the average vehicle speed pattern as the number N of vehicle speed patterns (that is, the actual driving number) N corresponding to the section in which the vehicle enters next is stored. Therefore, it is preferable to set the weight ω of the average vehicle speed pattern to be large.

  In addition, the weight ω of the average vehicle speed pattern is set to be larger as the variation (here, Vσ / VA) of the vehicle speed pattern that is stored in the vehicle speed pattern storage unit 21 and corresponds to the section in which the vehicle enters next is smaller. The weight ω can be set to a more appropriate value. That is, the preference of the driver is more clearly reflected in the average vehicle speed pattern as the variation in the vehicle speed pattern (here, Vσ / VA) stored in the vehicle speed pattern storage unit 21 and corresponding to the section in which the vehicle enters next is smaller. Therefore, it is preferable to set the weight ω of the average vehicle speed pattern to be large.

  In this embodiment, the weight ω of the average vehicle speed pattern is set to be larger as the number N of vehicle speed patterns stored in the vehicle speed pattern storage unit 21 and corresponding to the section in which the vehicle enters next is increased, and the vehicle speed pattern storage unit 21 is set. In this example, the weight ω of the average vehicle speed pattern is set to be larger as the variation in the vehicle speed pattern (here, Vσ / VA) corresponding to the section in which the vehicle enters next is smaller. The weight ω may be configured to be set based on at least one of the number N and the variation (here, Vσ / VA).

Further, in the present embodiment, as a variation in the vehicle speed pattern corresponding to the section in which the vehicle enters next, the quotient (Vσ /) is obtained by dividing the standard deviation Vσ of the vehicle speed pattern corresponding to the section in which the vehicle enters next by the average vehicle speed VA. VA) will be described. However, as a variation in the vehicle speed pattern corresponding to the section in which the vehicle enters next, a standard deviation Vσ of the vehicle speed pattern corresponding to the section in which the vehicle enters next may be used. May be used (for example, variance Vσ ² ).

-Target vehicle speed pattern setting function (exception)-
Further, the target vehicle speed generation unit 123, when the vehicle speed pattern storage unit 21 does not have an average vehicle speed pattern corresponding to the section where the vehicle enters next, or among the average vehicle speed patterns stored in the vehicle speed pattern storage unit 21, When the reliability of the average vehicle speed pattern corresponding to the section in which the vehicle enters next is low, instead of the average vehicle speed pattern, the vehicle is the next in the overall average vehicle speed pattern stored in the average vehicle speed storage unit 22. The said target vehicle speed pattern is produced | generated using the whole average vehicle speed pattern of the classification | category (A-1 classification-A-3 classification, any of B-1 classification-B-3 classification) of the area to approach.

In this case, the weight ω is obtained by the following equation (3) instead of the above equation (2).
ω = g (Uσ / UA, NA) (3)
Here, the average vehicle speed UA and the standard deviation Uσ are respectively the overall average of the classification of the section in which the vehicle enters next (any of A-1 classification to A-3 classification and B-1 classification to B-3 classification). The average value UA and the standard deviation Uσ of the vehicle speed in the vehicle speed pattern. Further, the data number NA is the number of vehicle speed patterns included in the classification of the section in which the vehicle enters next (any one of A-1 classification to A-3 classification and B-1 classification to B-3 classification).

  The function g is stored in the average vehicle speed storage unit 22 and included in the category of the section in which the vehicle enters next (any one of the A-1 classification to A-3 classification and the B-1 classification to B-3 classification). This is a function that sets the weight ω of the average vehicle speed pattern to a larger value as the number NA of vehicle speed patterns to be generated increases. The function g is stored in the average vehicle speed storage unit 22 and included in the category of the section in which the vehicle enters next (any one of the A-1 classification to A-3 classification and the B-1 classification to B-3 classification). This is a function that sets the weight ω of the average vehicle speed pattern to be larger as the variation in the vehicle speed pattern (in this case, Uσ / UA) is smaller.

  Note that the target vehicle speed generation unit 123 includes the number of vehicle speed patterns corresponding to the section in which the vehicle next enters among the vehicle speed patterns stored in the vehicle speed pattern storage unit 21 (that is, the actual travel of the section in which the vehicle enters next). The number of times N is equal to or less than a preset threshold number N0, and the variation in the vehicle speed pattern corresponding to the section in which the vehicle enters next (here, the maximum value of the standard deviation Vσ) is a predetermined threshold variation ( Here, when it is equal to or greater than the standard deviation threshold value Vσ0), it is determined that the reliability of the average vehicle speed pattern stored in the vehicle speed pattern storage unit 21 is low.

  As described above, when the vehicle speed pattern storage unit 21 does not have an average vehicle speed pattern corresponding to the next section in which the vehicle enters, or the vehicle is the next of the average vehicle speed patterns stored in the vehicle speed pattern storage unit 21. When the reliability of the average vehicle speed pattern corresponding to the section to be entered is low, the target vehicle speed pattern is generated using the overall average vehicle speed pattern stored in the average vehicle speed storage unit 22 instead of the average vehicle speed pattern. Therefore, when the vehicle speed pattern storage unit 21 does not have an average vehicle speed pattern corresponding to the section in which the vehicle enters next, or the vehicle enters the next vehicle among the average vehicle speed patterns stored in the vehicle speed pattern storage unit 21. Even when the reliability of the average vehicle speed pattern corresponding to the section is low, the appropriate target vehicle speed pattern can be generated.

  In addition, among the vehicle speed patterns stored in the vehicle speed pattern storage unit 21, the number N of vehicle speed patterns corresponding to the section in which the vehicle enters next (that is, the actual number of travels in the section in which the vehicle enters next) N is set in advance. The threshold number variation (here, the standard deviation threshold value Vσ0) in which the variation in the vehicle speed pattern (here, the maximum value of the standard deviation Vσ) corresponding to the section in which the vehicle enters next is preset. ) If it is above, it is determined that the reliability of the average vehicle speed pattern stored in the vehicle speed pattern storage unit 21 is low. Therefore, by setting the threshold number N0 and the standard deviation threshold Vσ0 to appropriate values, the average It is possible to appropriately determine whether or not the reliability of the vehicle speed pattern is low.

  In the present embodiment, when the vehicle speed pattern storage unit 21 does not have an average vehicle speed pattern corresponding to the section in which the vehicle enters next, or the vehicle is the next of the average vehicle speed patterns stored in the vehicle speed pattern storage unit 21. In the case where the reliability of the average vehicle speed pattern corresponding to the section to be entered is low, a case where the overall average vehicle speed pattern stored in the average vehicle speed storage unit 22 is used instead of the average vehicle speed pattern will be described. When the unit 21 does not have an average vehicle speed pattern corresponding to the section in which the vehicle enters next, or, among the average vehicle speed patterns stored in the vehicle speed pattern storage unit 21, the average vehicle speed corresponding to the section in which the vehicle enters next If the pattern reliability is low, the virtual average vehicle speed pattern set regardless of the section may be used. Further, as the virtual average vehicle speed pattern, a vehicle speed pattern other than the overall average vehicle speed pattern stored in the average vehicle speed storage unit 22 may be used. For example, a standard vehicle speed pattern may be set when a vehicle is manufactured. Good.

  In the present embodiment, the number of vehicle speed patterns corresponding to the section in which the vehicle enters next (that is, the actual number of times of traveling in the section in which the vehicle enters next) N and the vehicle speed pattern corresponding to the section in which the vehicle enters next. Will be described on the basis of the variation of the vehicle speed (here, the maximum value of the standard deviation Vσ). However, at least one of the number N of vehicle speed patterns and the variation of the vehicle speed pattern will be described. Based on the above, it is sufficient to determine whether or not the reliability of the average vehicle speed pattern is low.

-Target vehicle speed pattern correction function 1 (weight ω correction)-
In addition, the target vehicle speed generation unit 123 determines that the weight ω obtained by the equation (2) (or the equation (3)) is less than the weight threshold ω0 (for example, 0.2) stored in the weight storage unit 26. If it is, the weight threshold value ω0 is corrected to be used as the weight ω in place of the weight ω obtained by the above equation (2) (or the above equation (3)).

  In this way, when the weight ω obtained by the above equation (2) (or the above equation (3)) is less than the weight threshold value ω0 (for example, 0.2) stored in the weight storage unit 26. Since the weight threshold ω0 is used as the weight ω, the weight ω can be prevented from becoming less than the weight threshold ω0 (for example, 0.2). Therefore, by setting the weight threshold value ω0 to an appropriate value, the driving characteristics of the driver can be appropriately reflected in the target vehicle speed pattern.

  As the weight threshold ω0 is larger, the driving characteristics of the driver can be appropriately reflected in the target vehicle speed pattern. On the other hand, as the weight threshold value ω0 is smaller, the target vehicle speed pattern closer to the optimum traveling pattern that is the traveling pattern with the best fuel efficiency can be output. The weight threshold ω0 is changed in a “lower limit change process” to be described later with reference to FIG.

-Target vehicle speed pattern correction function 2 (correction for the following vehicle)-
In addition, the target vehicle speed generation unit 123, when the section in which the vehicle enters next has no preceding vehicle and there is a subsequent vehicle (that is, when the third classification unit 121 determines that it is the B classification) As the target vehicle speed pattern, a vehicle speed pattern set to a vehicle speed V equal to or higher than a preset minimum vehicle speed pattern is obtained.

  Specifically, in the target vehicle speed generation unit 123, the target vehicle speed VR corresponding to the distance L of the target vehicle speed pattern set in the section in which the vehicle enters next corresponds to the distance L of the preset minimum vehicle speed pattern. When the vehicle speed is lower than the minimum vehicle speed VL, the minimum vehicle speed VL is set as the target vehicle speed VR.

  In this way, when there is a succeeding vehicle, a vehicle speed pattern set to a vehicle speed V equal to or higher than a preset minimum vehicle speed pattern is obtained as the target travel pattern. Therefore, the vehicle speed VL of the minimum vehicle speed pattern is set appropriately. By setting the value, it is possible to suppress the driver of the following vehicle from feeling frustrated due to the slow acceleration of the vehicle (or the quick deceleration of the vehicle).

  The minimum vehicle speed pattern is preferably set for each section. The road conditions (whether paved roads, road widths, lane numbers, slopes, etc.) differ from section to section. Therefore, by setting the minimum vehicle speed pattern for each section, the appropriate minimum vehicle speed for each section This is because a pattern can be set.

-Target inter-vehicle pattern setting function (basic)-
The target inter-vehicle distance generating unit 124, among the inter-vehicle distance patterns stored in the inter-vehicle pattern storage unit 24, the inter-vehicle distance classified into the same vehicle traveling state as the vehicle traveling state (C classification or D classification) in the next section. It is a functional unit that generates and outputs the target inter-vehicle distance pattern based on a pattern. Specifically, the target inter-vehicle generation unit 124 stores the inter-vehicle pattern when the third classification unit 121 determines that the next section in which the vehicle enters is the C classification or the D classification (there is a preceding vehicle). Based on the inter-vehicle distance pattern stored in the unit 24 and the optimal inter-vehicle storage unit 25, the target inter-vehicle distance pattern is generated and output.

  Here, the optimal inter-vehicle storage unit 25 is a functional unit that stores an optimal inter-vehicle distance pattern that is the inter-vehicle distance pattern with the best fuel efficiency.

More specifically, the target inter-vehicle distance generating unit 124 includes, in the average inter-vehicle distance pattern stored in the inter-vehicle pattern storage unit 24, an average inter-vehicle distance of a classification (C classification or D classification) corresponding to a section in which the vehicle enters next. The target inter-vehicle distance pattern is generated by weighted averaging the distance pattern and the optimal inter-vehicle distance pattern stored in the optimal inter-vehicle storage unit 25 with a preset weight W (here, 0 ≦ W ≦ 1). . That is, the target inter-vehicle distance DR associated with the vehicle speed V is obtained by the following equation (4).
DR = DI × (1-W) + DA × W (4)
Here, the optimal inter-vehicle distance DI is the inter-vehicle distance corresponding to the optimal inter-vehicle distance pattern stored in the optimal inter-vehicle storage unit 25, and the average inter-vehicle distance DA is the average inter-vehicle distance pattern stored in the inter-vehicle pattern storage unit 24. The inter-vehicle distance corresponding to the average inter-vehicle distance pattern of the classification (C classification or D classification) corresponding to the section in which the vehicle enters next.

  Here, with reference to FIG. 13, the process of the target inter-vehicle distance generating unit 124 will be described. FIG. 13 is a graph illustrating an example of processing of the target vehicle speed generation unit 123 and the target inter-vehicle generation unit 124 illustrated in FIG. FIG. 13B is a graph showing an example of processing of the target inter-vehicle distance generating unit 124, where the horizontal axis is the vehicle speed V and the vertical axis is the inter-vehicle distance D. FIG. 13B shows a graph G61 indicating an average inter-vehicle distance pattern stored in the inter-vehicle pattern storage unit 24, a graph G62 indicating an optimal inter-vehicle distance pattern stored in the optimal inter-vehicle storage unit 25, and a target inter-vehicle distance. A graph G63 indicating the target inter-vehicle distance pattern generated by the generation unit 124 is shown.

  As shown in FIG. 13 (b), the target inter-vehicle distance pattern is located between the average inter-vehicle distance pattern and the optimal inter-vehicle distance pattern, and as the weight W approaches “1”, the target inter-vehicle distance pattern is The closer to the average inter-vehicle distance pattern and the closer the weight W is to “0”, the closer the target inter-vehicle distance pattern is to the optimum inter-vehicle distance pattern.

  In this manner, the average inter-vehicle distance pattern and the optimal inter-vehicle distance pattern that is the best inter-vehicle distance pattern are weighted and averaged with the preset weight W, and the target inter-vehicle distance pattern is generated and output. Therefore, an appropriate target inter-vehicle distance pattern can be generated and output by setting the weight W to an appropriate value.

Moreover, the weight W is calculated | required by following (5) Formula, for example.
W = F (Dσ / DA, NB) (5)
Here, Dσ is a standard deviation of the inter-vehicle distance D of the average inter-vehicle distance pattern of the classification (C classification or D classification) corresponding to the section in which the vehicle enters next, and DA is the section in which the vehicle enters next. It is the average value of the inter-vehicle distance D of the average inter-vehicle distance pattern of the corresponding classification (C classification or D classification). Further, NB is the number of inter-vehicle distance patterns (that is, the actual number of running times) of the classification (C classification or D classification) corresponding to the section in which the vehicle enters next.

  Further, the function F is stored in the inter-vehicle pattern storage unit 24, and the weight of the average inter-vehicle distance pattern increases as the number NB of inter-vehicle distance patterns of the classification (C classification or D classification) corresponding to the section in which the vehicle enters next is larger. This is a function for setting W large. Further, the function F is stored in the inter-vehicle pattern storage unit 24, and the variation in the inter-vehicle distance pattern (here, Dσ / DA) of the classification (C classification or D classification) corresponding to the section in which the vehicle enters next is smaller. This is a function for setting a large weight W of the average inter-vehicle distance pattern.

  As described above, the larger the number NB (that is, the actual driving frequency) of the inter-vehicle distance patterns of the classification (C classification or D classification) stored in the inter-vehicle pattern storage unit 24 and corresponding to the section in which the vehicle enters next, Since the weight W of the average inter-vehicle distance pattern is set large, the weight W can be set to an appropriate value. That is, the average inter-vehicle distance increases as the number of inter-vehicle distance patterns (that is, the actual number of running times) NB of the classification (C classification or D classification) stored in the inter-vehicle pattern storage unit 24 and corresponding to the section in which the vehicle enters next is larger. Since it is estimated that the driver's preference is reflected more clearly in the pattern, it is preferable to set the weight W of the average inter-vehicle distance pattern large.

  Further, the smaller the variation (in this case, Dσ / DA) of the inter-vehicle distance pattern of the classification (C classification or D classification) stored in the inter-vehicle pattern storage unit 24 and corresponding to the section in which the vehicle enters next, the average inter-vehicle distance Since the pattern weight W is set large, the weight W can be set to a more appropriate value. That is, the average inter-vehicle distance is smaller as the variation (in this case, Dσ / DA) of the inter-vehicle distance pattern of the classification (C classification or D classification) stored in the inter-vehicle pattern storage unit 24 and corresponding to the section in which the vehicle enters next is smaller. Since it is estimated that the driver's preference is reflected more clearly in the pattern, it is preferable to set the weight W of the average inter-vehicle distance pattern large.

  In this embodiment, the weight W of the average inter-vehicle distance pattern increases as the number NB of inter-vehicle distance patterns of the classification (C classification or D classification) stored in the inter-vehicle pattern storage unit 24 and corresponding to the section in which the vehicle enters next is larger. Is set larger, and the variation in the inter-vehicle distance pattern (here, Dσ / DA) of the classification (C classification or D classification) corresponding to the section in which the vehicle enters next is smaller. The case where the weight W of the average inter-vehicle distance pattern is set to be large will be described. The mode in which the weight W of the average inter-vehicle distance pattern is set based on at least one of the number NB and the variation (here, Dσ / DA). If it is.

Further, in this embodiment, as the variation in the inter-vehicle distance pattern of the classification (C classification or D classification) corresponding to the section in which the vehicle enters next, the classification (C classification or D classification) corresponding to the section in which the vehicle enters next. ), The quotient (Dσ / DA) obtained by dividing the standard deviation Dσ of the inter-vehicle distance pattern by the average inter-vehicle distance DA will be described. The classification (C classification or D classification) corresponding to the section in which the vehicle enters next is described. As the variation in the inter-vehicle distance pattern, the standard deviation Dσ of the inter-vehicle distance pattern of the classification (C classification or D classification) corresponding to the section in which the vehicle enters next may be used, or other parameters (for example, variance Dσ ² ). A form using may be used.

-Target inter-vehicle distance pattern setting function (exception)-
Further, the target inter-vehicle distance generating unit 124, when the inter-vehicle pattern storage unit 24 does not have the average inter-vehicle distance pattern of the classification (C classification or D classification) corresponding to the section in which the vehicle enters next, or the inter-vehicle pattern storage unit 24. If the reliability of the average inter-vehicle distance pattern of the classification (C classification or D classification) corresponding to the section in which the vehicle enters next is low, the average inter-vehicle distance pattern stored in is replaced with the average inter-vehicle distance pattern. The target inter-vehicle distance pattern is generated using a virtual average inter-vehicle distance pattern that is an average inter-vehicle distance pattern set regardless of the classification (C classification or D classification).

  Here, the virtual average inter-vehicle distance pattern is similar to the average inter-vehicle distance pattern stored in the inter-vehicle pattern storage unit 24, the average value EA of inter-vehicle distances of the virtual average inter-vehicle distance pattern, the standard deviation Eσ of inter-vehicle distance, and the data It is assumed that the number NC is set. In this case, by substituting the average value EA, the standard deviation Eσ, and the data number NC in the above equation (5) in place of the average value DA, the standard deviation Dσ, and the data number NB, respectively. Since the weight W can be obtained, the processing is simplified. Here, for the sake of convenience, it is assumed that the virtual average inter-vehicle distance pattern is also stored in the inter-vehicle pattern storage unit 24.

  Note that the target inter-vehicle distance generating unit 124 includes the number of inter-vehicle distance patterns of the classification (C classification or D classification) corresponding to the section in which the vehicle enters next among the inter-vehicle distance patterns stored in the inter-vehicle pattern storage section 24 (that is, The class corresponding to the section in which the vehicle enters next (the actual number of times of driving in the classification (C classification or D classification)) NB is equal to or less than a preset threshold number NB0, and the classification corresponding to the section in which the vehicle enters next When the variation in the inter-vehicle distance pattern (here, the C classification or the D classification) (here, the maximum value of the standard deviation Dσ) is greater than or equal to a preset threshold variation (here, the standard deviation threshold Dσ0), the inter-vehicle pattern storage unit It is determined that the reliability of the average inter-vehicle distance pattern stored in 24 is low.

  As described above, when there is no average inter-vehicle distance pattern of the classification (C classification or D classification) corresponding to the section in which the vehicle enters next in the inter-vehicle pattern storage unit 24 or stored in the inter-vehicle pattern storage unit 24 Of the average inter-vehicle distance patterns, when the reliability of the average inter-vehicle distance pattern of the classification (C classification or D classification) corresponding to the section in which the vehicle enters next is low, the classification is performed instead of the average inter-vehicle distance pattern. Since the target inter-vehicle distance pattern is generated using the virtual average inter-vehicle distance pattern which is an average inter-vehicle distance pattern set regardless of (C classification and D classification), the vehicle is stored in the inter-vehicle pattern storage unit 24 next. When there is no average inter-vehicle distance pattern of the classification (C classification or D classification) corresponding to the section to enter, or among the average inter-vehicle distance patterns stored in the inter-vehicle pattern storage unit 24 Even when the reliability of the average distance between vehicles pattern classification vehicle next corresponding to the section to enter (C Classification or D classification) is low, it is possible to generate a proper the target inter-vehicle distance pattern.

  In addition, among the inter-vehicle distance patterns stored in the inter-vehicle pattern storage unit 24, the number of inter-vehicle distance patterns of the classification (C classification or D classification) corresponding to the section in which the vehicle enters next (that is, the vehicle enters next). Class (C class or D class) corresponding to the section (actual driving count) NB is equal to or less than a preset threshold number NB0, and the class corresponding to the section in which the vehicle enters next (C class or D class) When the variation in the inter-vehicle distance pattern (here, the maximum value of the standard deviation Dσ) is greater than or equal to a preset threshold variation (here, the standard deviation threshold Dσ0), the average inter-vehicle distance stored in the inter-vehicle pattern storage unit 24 Since it is determined that the reliability of the distance pattern is low, the reliability of the average inter-vehicle distance pattern is low by setting the threshold number NB0 and the standard deviation threshold Dσ0 to appropriate values. Whether it is possible to judge properly.

  In the present embodiment, when there is no average inter-vehicle distance pattern of the classification (C classification or D classification) corresponding to the section in which the vehicle enters next in the inter-vehicle pattern storage unit 24 or stored in the inter-vehicle pattern storage unit 24. Of the average inter-vehicle distance patterns, when the reliability of the average inter-vehicle distance pattern of the classification (C classification or D classification) corresponding to the section in which the vehicle enters next is low, the classification is performed instead of the average inter-vehicle distance pattern. Although the case where the virtual average vehicle speed pattern set regardless of (C classification and D classification) is used has been described, as a virtual average vehicle speed pattern, for example, a standard inter-vehicle distance pattern may be set when a vehicle is manufactured. Good.

  In the present embodiment, the number of inter-vehicle distance patterns of the classification (C classification or D classification) corresponding to the section in which the vehicle enters next (that is, the classification corresponding to the section in which the vehicle enters next (C classification or D). (Classification) actual driving frequency) NB and the average distance between vehicles based on the variation of the inter-vehicle distance pattern (here, the maximum value of the standard deviation Dσ) of the classification (C classification or D classification) corresponding to the section in which the vehicle enters next The case where it is determined whether or not the reliability of the distance pattern is low will be described. Whether or not the reliability of the average inter-vehicle distance pattern is low based on at least one of the number NB of the inter-vehicle distance patterns and the variation in the inter-vehicle distance pattern. Any form may be used.

-Correction function 1 for target inter-vehicle distance pattern (correction of weight W)-
Further, when the weight W obtained by the equation (5) is less than the weight threshold value W0 (for example, 0.2) stored in the weight storage unit 26, the target inter-vehicle generation unit 124 (5) The weight threshold W0 is corrected to be used as the weight W in place of the weight W obtained by the formula (1).

  In this way, when the weight W obtained by the above equation (5) is less than the weight threshold W0 (for example, 0.2) stored in the weight storage unit 26, the weight threshold W0 is set as the weight W. Since it is used, the weight W can be prevented from becoming less than the weight threshold value W0 (for example, 0.2). Therefore, by setting the weight threshold value W0 to an appropriate value, the driving characteristics of the driver can be appropriately reflected in the target vehicle speed pattern.

  As the weight threshold W0 is larger, the driving characteristics of the driver can be appropriately reflected in the target inter-vehicle distance pattern. On the other hand, as the weight threshold W0 is smaller, the target inter-vehicle distance pattern that is closer to the optimal inter-vehicle distance pattern that is the travel pattern with the best fuel efficiency can be output. The weight threshold value W0 is changed in a “lower limit value changing process” to be described later with reference to FIG.

-Target inter-vehicle distance pattern correction function 2 (correction for the following vehicle)-
In addition, the target inter-vehicle generation unit 124, when the section in which the vehicle enters next has a preceding vehicle and has a subsequent vehicle (that is, when the third classification unit 121 determines that it is a D classification) As the target inter-vehicle distance pattern, an inter-vehicle distance pattern set to an inter-vehicle distance D that is equal to or less than a preset maximum inter-vehicle distance pattern is obtained.

  Specifically, the target inter-vehicle distance generating unit 124 sets the target inter-vehicle distance DR corresponding to the vehicle speed V of the target inter-vehicle distance pattern set in the section where the vehicle enters next to the vehicle speed V of the preset maximum inter-vehicle distance pattern. Is greater than the maximum inter-vehicle distance DH corresponding to, the maximum inter-vehicle distance DH is set as the target inter-vehicle distance VR.

  In this way, when there is a succeeding vehicle, the inter-vehicle distance pattern set to the inter-vehicle distance D equal to or smaller than the preset maximum inter-vehicle distance pattern is obtained as the target inter-vehicle distance pattern. By setting the inter-vehicle distance DH to an appropriate value, it is possible to suppress the driver of the following vehicle from feeling irritated due to the long inter-vehicle distance between the vehicle and the preceding vehicle of the vehicle. .

-Operation of the target vehicle speed pattern generator-
Next, the operation of the target travel pattern generation unit 12 will be described with reference to FIGS. FIG. 14 is a flowchart (first half) illustrating an example of the operation of the target travel pattern generation unit 12 illustrated in FIG. 12, and FIG. 15 is a flowchart illustrating an example of the operation of the target travel pattern generation unit 12 illustrated in FIG. (Second half). First, as shown in FIG. 14, the third classification unit 121 determines whether or not the vehicle has approached the starting point of the next entry section (step S <b> 401). If NO in step S401, the process is in a standby state. If YES in step S401, the process proceeds to step S403.

  Then, the third classification unit 121 determines whether there is a preceding vehicle (step S403). If YES in step S403, the process proceeds to step S419 shown in FIG. If NO in step S403, the process proceeds to step S405. Next, the third classification unit 121 determines whether there is a following vehicle (step S405). If YES in step S405, the process proceeds to step S411. If NO in step S405, the process proceeds to step S407.

  Next, “scene determination” is a process by which the fourth classification unit 122 determines whether the vehicle state in the section approaching the start point in step S401 corresponds to an acceleration scene, a deceleration scene, or a steady traveling scene. Processing "is executed (step S407). Then, the target vehicle speed generation unit 123 classifies the vehicle state in the section approaching the start point in step S401 into six (A-1 classification to A-3 classification, B-1 classification to B-3 classification). Then, a “target vehicle speed generation process” that is a process of generating the target vehicle speed pattern is executed (step S409), and the process proceeds to step S415.

  Also in the case of YES in step S405, the processes corresponding to steps S407 and S409 are executed in steps S411 and S413, respectively, and the process proceeds to step S415.

  When the “target vehicle speed generation process” in step S409 or step S413 ends, the target vehicle speed generation unit 123 determines whether or not the vehicle has reached the start point of the next section (step S415). If NO in step S415, the process is set to a standby state. If YES in step S415, the process proceeds to step S417. Then, the target vehicle speed generation unit 123 outputs the target vehicle speed pattern generated in the “target vehicle speed generation process” in step S409 or step S413 (step S417), and the process returns to step S401.

  If NO in step S403, as shown in FIG. 15, the third classification unit 121 determines whether or not there is a subsequent vehicle (step S419). If YES in step S419, the process proceeds to step S423. If NO in step S419, the process proceeds to step S421. Then, the “target inter-vehicle generation process” that is a process of generating the target inter-vehicle distance pattern is executed by the target inter-vehicle generation unit 124 (step S421), and the process proceeds to step S425.

  Also in the case of YES in step S419, the process corresponding to step S421 is executed in step S423, and the process proceeds to step S425.

  When the “target inter-vehicle generation process” in step S421 or step S423 ends, the target inter-vehicle generation unit 124 determines whether or not the vehicle has reached the start point of the next section (step S425). If NO in step S425, the process is placed in a standby state. If YES in step S425, the process proceeds to step S427. Then, the target inter-vehicle generation unit 124 outputs the target inter-vehicle distance pattern generated in the “target inter-vehicle generation process” in step S421 or step S423 (step S427), and the process returns to step S401 in FIG.

  In this way, when the vehicle reaches the start point of the next section, the target vehicle speed pattern that is a vehicle speed pattern that is a reference for the control of the vehicle speed generated by the target vehicle speed generation unit 123 or the target inter-vehicle generation unit 124 The target inter-vehicle distance pattern, which is the inter-vehicle distance pattern that serves as a reference for controlling the vehicle speed, is output.

  The detailed flowchart of the “scene determination process” executed in steps S407 and S411 of the flowchart shown in FIG. 14 is the same as the flowchart shown in FIG.

-Detailed operation of target vehicle speed generation process-
Next, a specific operation of the “target vehicle speed generation process” executed in steps S409 and S413 in FIG. 14 will be described with reference to FIGS. The following processing is all executed by the target vehicle speed generation unit 123. First, it is determined whether or not there is a travel record (travel history) for the next section (step S501). If NO in step S501, the process proceeds to step S517 in FIG. If YES in step S501, the process proceeds to step S503.

  Then, the average vehicle speed VA of the vehicle speed pattern corresponding to the next section, the standard deviation Vσ of the vehicle speed, and the number of data (running times) N are read from the vehicle speed pattern storage unit 21 (step S503). Next, it is determined whether or not the number N of data read in step S503 is equal to or less than the threshold number N0 (step S505). If NO in step S505, the process proceeds to step S509. If YES in step S505, the process proceeds to step S507. Then, it is determined whether or not the maximum value of the standard deviation Vσ of the vehicle speed is greater than or equal to the standard deviation threshold Vσ0 (step S507). If YES in step S507, the process proceeds to step S517 in FIG. If NO in step S507, the process proceeds to step S509.

  If NO in step S505 or NO in step S507, the weight ω is obtained using the above equation (2) (step S509). Then, a “weight coefficient correction process” that is a process for correcting the weight ω is executed (step S511). The “weighting coefficient correction process” will be described later with reference to FIG. Next, the optimum vehicle speed VI is read from the optimum vehicle speed storage unit 23 (step S513). Next, using the average vehicle speed VA read in step S503, the weight ω obtained in step S509, and the optimum vehicle speed VI read in step S513, the target vehicle speed VR is obtained by the above equation (1). (Step S515). Then, a “target vehicle speed correction process” that is a process of correcting the target vehicle speed VR obtained in step S515 is executed (step S527), and the process is returned. The “target vehicle speed correction process” will be described later with reference to FIG.

  If NO in step S501 or YES in step S507, as shown in FIG. 17, the classification corresponding to the next section (A-1 classification to A-3 classification, B--) from the average vehicle speed storage unit 22 is performed. The average vehicle speed UA, the standard deviation Uσ of the vehicle speed, and the total number of data (the number of times traveled) NA are read out (from any one of the first to B-3 classifications) (step S517). Next, the weight ω is obtained using the above equation (3) (step S519). Then, a “weight coefficient correction process” that is a process for correcting the weight is executed (step S521). The “weighting coefficient correction process” will be described later with reference to FIG. Next, using the average vehicle speed UA read in step S517, the weight ω obtained in step S519, and the optimum vehicle speed VI read in step S523, the target vehicle speed VR is obtained by the above equation (1). (Step S525). However, here, the average vehicle speed UA is used in place of the average vehicle speed VA in the equation (1). Returning to FIG. 16, the “target vehicle speed correction process”, which is a process for correcting the target vehicle speed VR obtained in step S525, is executed (step S527), and the process is returned. The “target vehicle speed correction process” will be described later with reference to FIG.

  In this way, the average vehicle speed pattern corresponding to the next section stored in the vehicle speed pattern storage unit 21 (or the classification corresponding to the next section stored in the average vehicle speed storage unit 22 (A-1 classification to A-3 classification). , B-1 classification to B-3 classification) overall average vehicle speed pattern) and the optimal vehicle speed pattern stored in the optimal vehicle speed storage unit 23, the target vehicle speed pattern is output.

-Detailed operation of weighting factor correction processing-
Next, with reference to FIG. 18, the detailed operation of the “weight coefficient correction process” executed in step S511 in FIG. 16 and step S521 in FIG. 17 will be described. FIG. 18 is a detailed flowchart illustrating an example of the “weight coefficient correction process” executed in step S511 of the flowchart shown in FIG. 16 and step S521 of the flowchart shown in FIG. The following processing is all executed by the target vehicle speed generation unit 123.

  First, it is determined whether or not the weight ω obtained in step S509 of FIG. 16 or step S519 of FIG. 17 is less than the weight threshold ω0 (step S601). If NO in step S601, the process is returned. If YES in step S601, the weight threshold ω0 is set for the weight ω (step S603), and the process is returned.

  In this way, when the weight ω obtained by the above equation (2) or the above equation (3) is less than the weight threshold ω0, the weight ω is corrected to the weight threshold ω0.

-Detailed operation of target vehicle speed correction process-
Next, the detailed operation of the “target vehicle speed correction process” executed in step S527 of FIG. 16 will be described with reference to FIG. FIG. 19 is a detailed flowchart showing an example of the “target vehicle speed correction process” executed in step S527 of the flowchart shown in FIG. This is executed by the target vehicle speed generation unit 123.

  First, the third classification unit 121 determines whether or not there is a subsequent vehicle in a section in which the vehicle enters next (step S611). If NO in step S611, the process is returned. If YES in step S611, the process proceeds to step S613. Then, it is determined whether or not the target vehicle speed VR obtained in step S515 of FIG. 16 or step S525 of FIG. 17 is less than the minimum vehicle speed VL (step S613). If NO in step S613, the process is returned. If YES in step S613, the process proceeds to step S615. Next, the target vehicle speed VR is set to the minimum vehicle speed VL (step S615), and the process is returned.

  Thus, when the target vehicle speed VR obtained by the above equation (1) is less than the minimum vehicle speed VL, the target vehicle speed VR is corrected to the minimum vehicle speed VL.

-Detailed operation of target inter-vehicle generation process-
Next, with reference to FIG. 20 and FIG. 21, the specific operation of the “target inter-vehicle generation process” executed in steps S421 and S423 in FIG. 15 will be described. The following processing is all executed by the target inter-vehicle generating unit 124. First, the average inter-vehicle distance DA, the standard deviation DVσ of the inter-vehicle distance, and the data, which are classified from the inter-vehicle pattern storage unit 24 into the same vehicle traveling state as the traveling state (C classification or D classification) of the vehicle in the next section. The number (travel count) NB is read (step S701).

  Next, it is determined whether or not the number of data NB read in step S701 is equal to or less than the threshold number NB0 (step S703). If NO in step S703, the process proceeds to step S707. If YES in step S703, the process proceeds to step S705. Then, it is determined whether or not the maximum value of the standard deviation Dσ of the inter-vehicle distance is greater than or equal to the standard deviation threshold Dσ0 (step S705). If YES in step S705, the process proceeds to step S715 in FIG. If NO in step S705, the process proceeds to step S707.

  If NO in step S703 or NO in step S705, the weight W is obtained using the above equation (5) (step S707). Then, a “weight coefficient correction process” that is a process for correcting the weight W is executed (step S709). The “weighting coefficient correction process” will be described later with reference to FIG. Next, the optimal inter-vehicle distance DI is read from the optimal inter-vehicle storage unit 25 (step S711). Next, using the average inter-vehicle distance DA read in step S701, the weight W obtained in step S707, and the optimal inter-vehicle distance DI read in step S711, the target inter-vehicle distance according to the above equation (4). DR is obtained (step S713). Then, a “target inter-vehicle correction process” that is a process of correcting the target inter-vehicle distance DR obtained in step S713 is executed (step S725), and the process is returned. The “target inter-vehicle correction process” will be described later with reference to FIG.

  In the case of YES in step S705, as shown in FIG. 21, the average inter-vehicle distance EA, the standard deviation Eσ of the inter-vehicle distance, and the data number NC are read from the inter-vehicle pattern storage unit 24 (step S715). . Next, the weight W is obtained using the above equation (5) (step S717). Then, a “weight coefficient correction process” that is a process for correcting the weight W is executed (step S719). The “weighting coefficient correction process” will be described later with reference to FIG. Then, the optimal inter-vehicle distance DI is read from the optimal inter-vehicle storage unit 25 (step S721). Next, using the average inter-vehicle distance EA read out in step S715, the weight W obtained in step S717, and the optimal inter-vehicle distance DI read out in step S721, the target inter-vehicle distance according to the above equation (4). DR is obtained (step S723). However, here, the average inter-vehicle distance EA is used instead of the average inter-vehicle distance DA in the above equation (4). Then, returning to FIG. 20, the “target vehicle speed correction process” that is a process of correcting the target inter-vehicle distance DR obtained in step S723 is executed (step S725), and the process is returned. The “target vehicle speed correction process” will be described later with reference to FIG.

  Thus, the average inter-vehicle distance pattern (or virtual average inter-vehicle distance pattern) of the classification (C classification or D classification) of the next section stored in the inter-vehicle pattern storage unit 24 and the optimal inter-vehicle storage unit 25 are stored. A target inter-vehicle distance pattern is output based on the optimal inter-vehicle distance pattern.

-Detailed operation of weighting factor correction processing-
Next, with reference to FIG. 22, the detailed operation of the “weight coefficient correction process” executed in step S709 in FIG. 20 and step S719 in FIG. 21 will be described. FIG. 22 is a detailed flowchart showing an example of the weighting factor correction process executed in step S709 of the flowchart shown in FIG. 20 and step S719 of the flowchart shown in FIG. The following processing is all executed by the target inter-vehicle generating unit 124.

  First, it is determined whether or not the weight W obtained in step S707 of FIG. 20 or step S717 of FIG. 21 is less than the weight threshold W0 (step S801). If NO in step S801, the process is returned. If YES in step S801, the weight threshold W0 is set for the weight W (step S803), and the process returns.

  In this way, when the weight W obtained by the above equation (5) is less than the weight threshold W0, the weight W is corrected to the weight threshold W0.

-Detailed operation of target inter-vehicle correction processing-
Next, with reference to FIG. 23, the detailed operation of the “target inter-vehicle correction process” executed in step S725 of FIG. 20 will be described. FIG. 23 is a detailed flowchart showing an example of the “target inter-vehicle correction process” executed in step S725 of the flowchart shown in FIG. The following processing is all executed by the target inter-vehicle generating unit 124.

  First, the third classification unit 121 determines whether or not there is a subsequent vehicle in a section in which the vehicle enters next (step S811). If NO in step S811, the process is returned. If YES in step S811, the process proceeds to step S813. Then, it is determined whether or not the target inter-vehicle distance DR obtained in step S713 of FIG. 20 or step S723 of FIG. 21 is greater than the maximum inter-vehicle distance DH (step S813). If NO in step S813, the process is returned. If YES in step S813, the process proceeds to step S815. Next, the target inter-vehicle distance DR is set to the maximum inter-vehicle distance DH (step S815), and the process is returned.

  In this manner, when the target inter-vehicle distance DR obtained by the above equation (4) is larger than the maximum inter-vehicle distance DH, the target inter-vehicle distance DR is corrected to the maximum inter-vehicle distance DH.

-Detailed functional configuration of travel pattern evaluation unit-
Next, a detailed functional configuration of the traveling pattern evaluation unit 13 and the like shown in FIG. 1 will be described with reference to FIG. FIG. 24 is a block diagram illustrating an example of the travel pattern evaluation unit 13 of the vehicle travel support device 100 illustrated in FIG. 1. As shown in FIG. 24, the driving support ECU 1 is functionally functional units such as an operation determination unit 131 and a weight lower limit changing unit 132 when the CPU reads and executes a control program stored in a ROM or the like. Function as. In addition, the driving support ECU 1 causes the HDD 2 to function as the weight storage unit 26 and the like when the CPU reads and executes a control program stored in the ROM or the like.

  The weight storage unit 26 stores the weight threshold value ω0 used for the “weight coefficient correction process” described with reference to FIG. 18 and the weight threshold value W0 used for the “weight coefficient correction process” described with reference to FIG. It is. Here, the weight threshold ω0 corresponds to the lower limit value of the weight ω multiplied by the average vehicle speed pattern when the target vehicle speed pattern is generated using the above equation (1). The weight threshold value W0 corresponds to the lower limit value of the weight W multiplied by the average inter-vehicle distance pattern when the target inter-vehicle distance pattern is generated using the above equation (4). In addition, the weight threshold value ω0 and the weight threshold value W0 are rewritten by the weight lower limit changing unit 132.

  The operation determination unit 131 determines whether or not the driver has performed an acceleration / deceleration operation contrary to the target vehicle speed pattern generated by the target vehicle speed generation unit 123 (or the target inter-vehicle distance pattern generated by the target inter-vehicle generation unit 124). It is a functional part to do. Specifically, the operation determination unit 131 determines whether or not an acceleration / deceleration operation contrary to the output target vehicle speed pattern (or target inter-vehicle distance pattern) has been performed during section travel, and the target vehicle speed pattern ( Alternatively, it is a functional unit that counts the number NP of acceleration / deceleration operations that are contrary to the target inter-vehicle distance pattern (this number is referred to as “moderation frequency”) NP.

  Here, the acceleration / deceleration operation contrary to the target vehicle speed pattern is, for example, a case where the driver performs an operation of depressing the brake in spite of outputting the instruction information indicating that the vehicle is accelerated by the target vehicle speed pattern. Further, the acceleration / deceleration operation contrary to the target inter-vehicle distance pattern is an operation in which the driver shortens the inter-vehicle distance D even though the instruction information indicating that the inter-vehicle distance D is increased according to the target inter-vehicle distance pattern is output. This is the case.

  The weight lower limit changing unit 132 determines the weight threshold value ω0 (or weight threshold value W0) when the operation determining unit 131 determines that an acceleration / deceleration operation contrary to the target vehicle speed pattern (or target inter-vehicle distance pattern) has been performed. This is a functional unit that performs correction to increase. Specifically, when the operation determination unit 131 reaches the end point of the section, the operation determination unit 131 obtains the “moderate frequency” in the section (that is, the target vehicle speed pattern (or the target vehicle speed in the section). Based on the number of acceleration / deceleration operations contrary to the inter-vehicle distance pattern) NP, the weight threshold value ω0 (or the weight threshold value W0) is corrected to be increased.

  Here, with reference to FIG. 25, a method of obtaining the change amount Δω of the weight threshold ω0 (or the weight threshold W0) will be described. FIG. 25 is a graph for explaining an example of the operation of the weight lower limit changing unit 132 shown in FIG. In FIG. 25, the horizontal axis represents the “moderate frequency” NP obtained by the operation determination unit 131, and the vertical axis represents the change amount Δω of the weight threshold value ω0 (or the weight threshold value W0). The graph G7 is a graph showing the relationship between the change amount Δω and the “moderate frequency” NP. As shown in the graph G7, the change amount Δω increases as the “moderation frequency” NP increases. Then, the weight lower limit changing unit 132 obtains a sum obtained by adding the change amount Δω to the weight threshold ω0 (or weight threshold W0), and obtains the sum (ω0 + Δω) obtained from the weight threshold ω0 (or weight threshold W0), or W + Δω) is corrected.

  In this way, the weight threshold value ω0 (or weight threshold value W0) is corrected to be increased based on the number of times NP at which the acceleration / deceleration operation is performed against the target vehicle speed pattern (or target inter-vehicle distance pattern). Weight threshold value ω0 (or weight threshold value W0), which is a lower limit value of the weight ω (or weight W0) of the average vehicle speed pattern (or average inter-vehicle distance pattern) used when determining the target vehicle speed pattern (or target inter-vehicle distance pattern) Can be corrected appropriately.

  In this embodiment, the case where the weight threshold value ω0 (or the weight threshold value W0) is corrected when an acceleration / deceleration operation contrary to the target vehicle speed pattern (or the target inter-vehicle distance pattern) is performed will be described. Alternatively, the weight ω (or the weight W) may be corrected when an acceleration / deceleration operation that is contrary to the target inter-vehicle distance pattern is performed. In this case, the correction effect can be exhibited more remarkably.

  In the present embodiment, the case where the weight threshold value ω0 (or the weight threshold value W0) is corrected based on the number of times of acceleration / deceleration operations that are contrary to the target vehicle speed pattern (or target inter-vehicle distance pattern) has been described. The weight threshold value ω0 (or the weight threshold value W0) may be corrected in accordance with the degree that is contrary to the target vehicle speed pattern (or target inter-vehicle distance pattern) of the acceleration / deceleration operation performed by the driver.

-Operation of the running pattern evaluation unit-
Next, the operation of the travel pattern evaluation unit 13 will be described with reference to FIGS. FIG. 26 is a flowchart illustrating an example of the traveling pattern evaluation unit 13 illustrated in FIG. Here, while the vehicle is traveling in the section, the operation determination unit 131 determines whether an acceleration / deceleration operation contrary to the target vehicle speed pattern (or target inter-vehicle distance pattern) has been performed, and the target vehicle speed pattern (or It is assumed that the number of times of accelerating / decelerating operation contrary to the target inter-vehicle distance pattern (this number is referred to as “moderate frequency”) NP is counted.

  First, the weight lower limit changing unit 132 determines whether or not the end point of the section has been reached (step S901). If NO in step S901, the process is in a standby state. If YES in step S901, the process proceeds to step S903. Then, the weight lower limit changing unit 132 determines whether or not an operation contrary to the target vehicle speed pattern (target vehicle speed VR) has been performed (step S903). If YES in step S903, the process proceeds to step S905. If NO in step S903, the process proceeds to step S907.

  Next, the weight lower limit changing unit 132 determines whether or not an operation contrary to the target inter-vehicle distance pattern (target inter-vehicle distance DR) has been performed (step S907). If YES in step S907, the process proceeds to step S909. If NO in step S907, the process is returned.

  In the case of YES in step S903 or in the case of YES in step S907, the “lower limit value” is a process in which the weight lower limit changing unit 132 corrects the weight threshold value ω0 (or the weight threshold value W0) based on the passage frequency NP. "Change process" is executed (steps S905 and S909), and the process returns.

-Detailed operation of lower limit change processing-
Here, with reference to FIG. 27, the processing content of the “lower limit value changing process” executed in steps S905 and S909 in FIG. 26 will be described. FIG. 27 is a detailed flowchart showing an example of the “lower limit value changing process” executed in steps S905 and S909 of the flowchart shown in FIG. The following processing is all executed by the weight lower limit changing unit 132.

  First, the node frequency NP obtained by the operation determination unit 131 is acquired (step S911). Next, based on the graph G7 shown in FIG. 25, the change amount Δω (or change amount ΔW) is obtained from the node frequency NP acquired in step S911 (step S913). Then, the process of setting the sum obtained by adding the change amount Δω (or change amount ΔW) obtained in step S913 to the weight threshold value ω0 (or weight threshold value W0) as the weight threshold value ω0 (or weight threshold value W0) is performed. (Step S915), the process is returned.

  In this way, the weight threshold value ω0 (or the weight threshold value W0) is changed based on the node frequency NP obtained by the operation determination unit 131.

-Other embodiments-
In this embodiment, although the case where the traveling pattern recording unit 11, the target traveling pattern generation unit 12, and the traveling pattern evaluation unit 13 are configured as functional units has been described, the traveling pattern recording unit 11, target traveling pattern generation At least one of the unit 12 and the traveling pattern evaluation unit 13 may be configured by hardware such as an electronic circuit.

  Similarly, in the present embodiment, the first classification unit 111, the second classification unit 112, the vehicle speed pattern recording unit 113, the section average calculation unit 114, the overall average calculation unit 115, the inter-vehicle pattern recording unit 116, and the average inter-vehicle distance calculation unit Although the case where 117 is configured as a functional unit has been described, the first classification unit 111, the second classification unit 112, the vehicle speed pattern recording unit 113, the section average calculation unit 114, the overall average calculation unit 115, the inter-vehicle pattern recording unit 116 and at least one of the average inter-vehicle distance calculation unit 117 may be configured by hardware such as an electronic circuit.

  Similarly, in the present embodiment, the case where the third classification unit 121, the fourth classification unit 122, the target vehicle speed generation unit 123, and the target inter-vehicle generation unit 124 are configured as functional units has been described. At least one of the third classification unit 121, the fourth classification unit 122, the target vehicle speed generation unit 123, and the target inter-vehicle generation unit 124 may be configured by hardware such as an electronic circuit.

  Similarly, in the present embodiment, the case where the operation determination unit 131 and the weight lower limit changing unit 132 are configured as functional units has been described. However, the operation determination unit 131 and the weight lower limit changing unit 132 may At least one of them may be configured by hardware such as an electronic circuit.

  In the present embodiment, the vehicle speed pattern storage unit 21, the average vehicle speed storage unit 22, the optimal vehicle speed storage unit 23, the inter-vehicle pattern storage unit 24, the optimal inter-vehicle storage unit 25, and the weight storage unit 26 are configured as functional units in the HDD 2. However, at least one of the vehicle speed pattern storage unit 21, the average vehicle speed storage unit 22, the optimal vehicle speed storage unit 23, the inter-vehicle pattern storage unit 24, the optimal inter-vehicle storage unit 25, and the weight storage unit 26 is provided. Further, the configuration may be configured as a functional unit in other types of nonvolatile storage media (for example, EPROM (Erasable Programmable Read Only Memory) or the like).

  INDUSTRIAL APPLICABILITY The present invention can be used for a vehicle travel support device that outputs a target travel pattern that is a reference for vehicle speed control according to the travel state of the vehicle.

100 vehicle travel support device 1 travel support ECU
DESCRIPTION OF SYMBOLS 11 Travel pattern recording part 111 1st classification | category part 112 2nd classification | category part 113 Vehicle speed pattern recording part 114 Section average calculation part 115 Overall average calculation part 116 Inter-vehicle pattern recording part 117 Average inter-vehicle distance calculation part 12 Target travel pattern generation part 121 3rd classification Unit 122 Fourth classification unit 123 Target vehicle speed generation unit 124 Target inter-vehicle generation unit 13 Travel pattern evaluation unit 131 Operation determination unit 132 Lower limit change unit 2 HDD
DESCRIPTION OF SYMBOLS 21 Vehicle speed pattern memory | storage part 22 Average vehicle speed memory | storage part 23 Optimal vehicle speed memory | storage part 24 Vehicle distance pattern memory | storage part 25 Optimal vehicle distance memory | storage part 26 Weight memory | storage part 31 Vehicle speed sensor 32 Front radar 33 Rear radar 34 Navigation apparatus

Claims (18)

A vehicle travel support device that outputs a target travel pattern serving as a reference for controlling the vehicle speed according to the travel state of the vehicle,
Each time the vehicle travels in a section set in advance on the map, it is classified based on the traveling state of the vehicle including the presence or absence of the preceding vehicle and the presence or absence of the following vehicle, and the vehicle speed in the section or the inter-vehicle distance with the preceding vehicle Memorize the driving pattern showing the change of
The vehicle travel support characterized in that the target travel pattern is generated and output based on the travel pattern classified into the same vehicle travel state as the current vehicle travel state among the stored travel patterns. apparatus. The vehicle travel support apparatus according to claim 1,
For the stored driving pattern, obtain an average driving pattern that is an average of the driving patterns classified for each driving state of the vehicle,
A vehicle travel support apparatus that generates the target travel pattern by performing a weighted average of the obtained average travel pattern and an optimal travel pattern that is the travel pattern having the best fuel efficiency with a preset weight. The vehicle travel support device according to claim 2,
The vehicle travel support apparatus according to claim 1, wherein the weight is set based on at least one of the number of stored travel patterns and variation. In the vehicle travel support device according to claim 3,
The vehicle travel support apparatus, wherein the weight of the average travel pattern is set larger as the number of stored travel patterns is larger. In the vehicle travel support device according to claim 3 or 4,
The vehicle travel support apparatus, wherein the weight of the average travel pattern is set larger as the variation in the stored travel pattern is smaller. In the vehicle travel support device according to any one of claims 2 to 5,
When the driver performs an acceleration / deceleration operation contrary to the target travel pattern, the vehicle travel support device performs correction to increase the weight of the average travel pattern. In the vehicle travel support device according to any one of claims 2 to 6,
When there is no preceding vehicle, the traveling pattern is a vehicle speed pattern indicating a change in vehicle speed,
When there is a preceding vehicle, the traveling pattern is an inter-vehicle distance pattern indicating a change in the inter-vehicle distance from the preceding vehicle. The vehicle travel support device according to claim 7,
When the traveling pattern is a vehicle speed pattern, an average traveling pattern that is an average of the classified traveling patterns is obtained for each traveling state of the vehicle and for each section,
Based on the average travel pattern relating to the section corresponding to the current vehicle position, the average travel pattern being classified into the same vehicle travel state as the current vehicle travel state among the average travel patterns. And generating and outputting the target travel pattern. The vehicle travel support device according to claim 8,
When there is no average running pattern or when the reliability of the average running pattern is low, a virtual average running pattern which is an average running pattern set regardless of the section is used instead of the average running pattern. And generating the target travel pattern. In the vehicle travel support device according to claim 9,
The virtual average travel pattern is an average travel pattern obtained based on the travel patterns classified into the same vehicle travel state as the current vehicle travel state among the stored travel patterns. A vehicle travel support device. In the vehicle travel assistance device according to claim 9 or 10,
When the stored number of travel patterns is less than or equal to a preset threshold number and the variation in the stored travel patterns is greater than or equal to the preset threshold variation, the reliability of the average travel pattern is low A vehicle travel support apparatus characterized by determining. In the vehicle travel support device according to any one of claims 7 to 11,
The vehicle speed support device is characterized in that the vehicle speed pattern is a vehicle speed pattern associated with a distance. The vehicle travel support device according to claim 12,
Each vehicle speed pattern, which is a vehicle speed pattern associated with the distance, is converted into an acceleration for each vehicle speed category set in advance, and an average acceleration, which is an average value of acceleration for each vehicle speed category, is obtained. A vehicle travel support apparatus, wherein the average travel pattern is obtained by obtaining a pattern of an average vehicle speed associated with a distance from an average acceleration obtained for each of the categories. In the vehicle travel support device according to any one of claims 7 to 13,
When there is no preceding vehicle and there is a subsequent vehicle, a vehicle travel support device that obtains a vehicle speed pattern set to a vehicle speed that is equal to or higher than a preset minimum vehicle speed pattern as the target travel pattern. In the vehicle travel support device according to any one of claims 7 to 14,
When there is no preceding vehicle, the running state of the vehicle further includes an acceleration scene that is a situation of acceleration, a deceleration scene that is a situation of deceleration, and a steady running scene that is a situation of steady running,
A vehicle travel support apparatus that stores vehicle speed patterns by classifying them based on the presence or absence of a following vehicle, the acceleration scene, the deceleration scene, and the steady travel scene. In the vehicle travel support device according to any one of claims 7 to 15,
The inter-vehicle distance pattern is a inter-vehicle distance pattern associated with a vehicle speed. The vehicle travel support device according to claim 16,
Each inter-vehicle distance pattern, which is a pattern of inter-vehicle distance associated with the vehicle speed, is converted into an inter-vehicle distance for each preset vehicle speed category, and an average inter-vehicle distance that is an average value of the inter-vehicle distance for each vehicle speed category And determining the average travel pattern by determining a pattern of the average inter-vehicle distance associated with the vehicle speed from the average inter-vehicle distance determined for each vehicle speed category. In the vehicle travel assistance device according to claim 16 or 17,
When there is a preceding vehicle, the vehicle travel support device is characterized in that the target inter-vehicle distance pattern that is the target travel pattern is set to an inter-vehicle distance pattern that has a longer inter-vehicle distance than the average inter-vehicle distance pattern that is the average travel pattern.

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