Travel state prediction device, travel state prediction method, travel state prediction program, and recording medium

Abstract

To provide a technique for improving prediction accuracy in prediction of a traveling state of an own vehicle.SOLUTION: A travel state prediction device (1) that predicts a travel state of a host vehicle includes an index acquisition unit (3) that acquires a traffic state index that is an index related to a traffic state on a scheduled travel route of the host vehicle, a change factor acquisition unit (4) that acquires a traffic change factor that is a factor by which the traffic state changes, and a deceleration position prediction unit (5) that predicts a deceleration position of the host vehicle on the scheduled travel route based on the traffic change factor and the traffic state index. The deceleration position predicting unit predicts the deceleration position based on a relationship between the traffic condition indexes of the specific traffic changing factor in two different situations or a relationship between the traffic condition index corresponding to the specific traffic changing factor and the traffic condition index in an average traffic condition.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to a driving state prediction device, a driving state prediction method, and a driving state prediction program that predict the driving state of a vehicle, and a computer-readable, non-transitive, tangible recording medium on which such a driving state prediction program is recorded. [Background technology]

[0002] Patent Document 1 discloses a vehicle energy consumption estimation device that estimates the energy consumed by a vehicle's drive source by taking into account the vehicle's driving behavior when passing through an intersection. Specifically, the vehicle energy consumption estimation device predicts the vehicle's behavior at an intersection included in the vehicle's planned driving route, i.e., whether the vehicle will continue straight, turn right, or turn left. The vehicle energy consumption estimation device also acquires attribute information about the intersection and the roads connecting to the intersection. The attribute information includes the presence or absence of traffic lights, the presence or absence of pedestrian crossings, the number of lanes, and the speed limit. The vehicle energy consumption estimation device then calculates a stopping probability based on the acquired various information and an energy consumption estimation table, and uses the stopping probability to estimate the energy consumed by the drive motor based on acceleration resistance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2009-67350 Summary of the Invention [Problem to be solved by the invention]

[0004] As described above, the technology described in Patent Document 1 estimates energy consumption based on the probability of stopping. However, even for the same route and location, the probability of stopping varies depending on traffic conditions, such as the more congested the road, the more likely it is that a vehicle will stop at a traffic light. Therefore, with the technology described in Patent Document 1, errors may occur in the energy consumption estimation results due to fluctuations in traffic conditions.

[0005] The present disclosure has been made in consideration of the circumstances exemplified above, etc. That is, the present disclosure provides a technique for, for example, improving the prediction accuracy in predicting the driving state of a vehicle compared to conventional techniques. [Means for solving the problem]

[0006] The driving state prediction device (1) is configured to predict the driving state of the vehicle. The driving state prediction device according to claim 1 is an index acquisition unit (3) that acquires a traffic condition index, which is an index related to a traffic condition on a planned travel route of the host vehicle; a fluctuation factor acquisition unit (4) that acquires traffic fluctuation factors that are factors that cause fluctuations in the traffic state; a deceleration position prediction unit (5) that predicts a deceleration position of the host vehicle on the planned travel route based on the traffic fluctuation factors and the traffic condition index; Equipped with The deceleration position prediction unit is configured to predict the deceleration position based on the relationship between the traffic condition index in two different situations of the specific traffic fluctuation factor, or the relationship between the traffic condition index corresponding to the specific traffic fluctuation factor and the traffic condition index in the average traffic condition. A driving state prediction method according to claim 8 is a method for predicting a driving state of a host vehicle, and includes the following steps or processes: acquiring a traffic condition index which is an index relating to a traffic condition on a planned travel route of the host vehicle; Acquire a traffic fluctuation factor, which is a factor that causes fluctuations in the traffic state; predicting a deceleration position of the host vehicle on the planned travel route based on the traffic fluctuation factors and the traffic condition index; In predicting the deceleration position, the deceleration position is predicted based on the relationship between the traffic condition index in two different situations for a specific traffic fluctuation factor, or the relationship between the traffic condition index corresponding to the specific traffic fluctuation factor and the traffic condition index in the average traffic condition. The driving state prediction program according to claim 9 is a computer program executed by a driving state prediction device (1) that predicts a driving state of a vehicle, The process executed by the driving state prediction device is A process of acquiring a traffic condition index which is an index related to a traffic condition on a planned travel route of the host vehicle; A process of acquiring traffic fluctuation factors that are factors that cause fluctuations in the traffic state; a process of predicting a deceleration position of the host vehicle on the planned travel route based on the traffic fluctuation factors and the traffic condition index; Including, In the process of predicting the deceleration position, the deceleration position is predicted based on the relationship between the traffic condition index in two different situations for a specific traffic fluctuation factor, or the relationship between the traffic condition index corresponding to the specific traffic fluctuation factor and the traffic condition index in the average traffic condition. The recording medium according to claim 10 is a computer-readable non-transitive physical recording medium that records a driving state prediction program executed by a driving state prediction device (1) that predicts the driving state of a vehicle, The process included in the running state prediction program is A process of acquiring a traffic condition index which is an index related to a traffic condition on a planned travel route of the host vehicle; A process of acquiring traffic fluctuation factors that are factors that cause fluctuations in the traffic state; a process of predicting a deceleration position of the host vehicle on the planned travel route based on the traffic fluctuation factors and the traffic condition index; Including, In the process of predicting the deceleration position, the deceleration position is predicted based on the relationship between the traffic condition index in two different situations for a specific traffic fluctuation factor, or the relationship between the traffic condition index corresponding to the specific traffic fluctuation factor and the traffic condition index in the average traffic condition.

[0007] In addition, in each section of the application documents, each element may be assigned a reference symbol in parentheses. However, such reference symbols merely indicate an example of the correspondence between the element and the specific means described in the embodiments below. Therefore, the present disclosure is not limited in any way by the above-mentioned reference symbols. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a block diagram illustrating a schematic configuration of a traveling state prediction device according to an embodiment of the present disclosure. [Figure 2] 2 is a graph for explaining an outline of the operation of the traveling state prediction device according to the first embodiment shown in FIG. 1. [Figure 3] 2 is a graph for explaining an outline of the operation of the traveling state prediction device according to the first embodiment shown in FIG. 1. [Figure 4] 2 is a graph for explaining an outline of the operation of the traveling state prediction device according to the first embodiment shown in FIG. 1. [Figure 5] 3 is a flowchart showing an outline of the operation of the traveling state prediction device shown in FIG. 1 according to the first embodiment. [Figure 6] 6 is a graph for explaining an outline of the operation of the traveling state prediction device shown in FIG. 1 according to the second embodiment. [Figure 7] 6 is a graph for explaining an outline of the operation of the traveling state prediction device shown in FIG. 1 according to the second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0009] (Embodiment) Hereinafter, exemplary embodiments and specific examples of the present disclosure will be described with reference to the drawings as appropriate. Note that the following embodiments and their modifications, as well as the descriptions in the drawings related thereto, are schematic or simplified for the purpose of concisely explaining the contents of the present disclosure, and are not intended to limit the contents of the present disclosure in any way. Therefore, it goes without saying that the descriptions in the drawings do not necessarily correspond to the specific device configurations that are actually manufactured and sold. In other words, unless expressly limited by the applicant in the prosecution history of this application, it goes without saying that the present disclosure should not be interpreted as being limited by the descriptions in the drawings and the device configurations, functions, or operations described below corresponding thereto.

[0010] 1, a driving state prediction device 1 according to this embodiment is configured to predict the driving state of a vehicle. A vehicle whose driving state is predicted by the driving state prediction device 1 will be referred to as a "host vehicle" hereinafter. The host vehicle is typically a vehicle equipped with the driving state prediction device 1.

[0011] In this embodiment, the driving condition prediction device 1 is configured as an on-board microcomputer, i.e., ECU, mounted on the vehicle. ECU is an abbreviation for Electronic Control Unit. That is, the driving condition prediction device 1 includes a processor consisting of a CPU or MPU, and a storage medium communicably connected to the processor, and is configured to implement predetermined functions by reading and executing a computer program from the storage medium. Hereinafter, the processor and storage medium provided in the driving condition prediction device 1 will be simply referred to as the "processor" and the "storage medium."

[0012] The storage medium includes at least a ROM or a nonvolatile rewritable memory among various non-transient physical storage media such as a ROM or a nonvolatile rewritable memory. The nonvolatile rewritable memory is a storage device that allows information to be rewritten while the power is on but retains information in an unrewritable manner while the power is off, such as a flash memory. The storage medium stores the computer program as well as various data required to execute the program, such as initial values, maps, and lookup tables. The storage medium is also configured to hold a predetermined amount of information, such as image data and driving data acquired while the vehicle is traveling.

[0013] 1, the driving state prediction device 1 includes, as functional components realized on an in-vehicle microcomputer by executing a computer program, a route information acquisition unit 2, an index acquisition unit 3, a fluctuation factor acquisition unit 4, and a deceleration position prediction unit 5. Each of these components will be described below in order.

[0014] The route information acquisition unit 2 acquires information about the planned driving route of the vehicle from a navigation device installed in the vehicle or an external server of the vehicle. The information about the planned driving route includes, for example, the destination, intermediate points, intersections to be passed from the current location to the intermediate points or destination, the planned direction of travel at such intersections, road width, number of lanes, legal speed limit, etc.

[0015] The index acquisition unit 3 is configured to acquire a traffic condition index, which is an index related to the traffic condition on the planned travel route of the host vehicle acquired by the route information acquisition unit 2. Specifically, the index acquisition unit 3 can acquire (i.e., read and store for a predetermined period) information corresponding to the traffic condition index, for example, from a predetermined storage area in the storage medium or an external server. Here, the "traffic condition" refers to the traffic condition of vehicles on a road. The "traffic condition" includes whether or not traffic is smooth. Therefore, the "traffic condition index" includes, for example, vehicle speed, vehicle density, traffic volume, or the degree of change in these over time (i.e., the amount of change per unit time). The traffic volume is, for example, the number of passing vehicles in a predetermined period of time or per unit time.

[0016] Specifically, the traffic condition index is, for example, the vehicle speed pattern of an individual vehicle or the average of the vehicle speed patterns of multiple vehicles. The vehicle speed pattern of the subject vehicle as an individual vehicle, i.e., the vehicle speed pattern of the subject vehicle when it was traveling in the past, can be read out, for example, from a storage medium. The vehicle speed pattern of a specific other vehicle as an individual vehicle can be obtained, for example, through vehicle-to-vehicle communication or an external server. The average of the vehicle speed patterns of multiple vehicles can be obtained from an external server. Alternatively, the traffic condition index is, for example, the vehicle speed for each section when the planned travel route is divided into multiple sections. Alternatively, the traffic condition index is, for example, the traffic volume at multiple points on the planned travel route. The traffic condition or traffic condition index may also be referred to as a "traffic condition quantity."

[0017] The fluctuation factor acquisition unit 4 acquires traffic fluctuation factors, which are factors that cause fluctuations in traffic conditions. Specifically, the fluctuation factor acquisition unit 4 can acquire information corresponding to the traffic fluctuation factors from an external server or the like, for example.

[0018] "Traffic fluctuation factors" include, for example, information about time and / or date. Information about time and / or date includes, for example, time zones, days of the week, weekdays / holidays, date ranges, months, seasons, etc. For example, events such as sporting events and entertainment events, traffic restrictions, demonstrations, and construction work that take place on specific dates, times, or within specific date ranges or time zones affect traffic conditions. Such traffic-impacting events can be understood as a combination of time information and date information, i.e., information about time and / or date. "Traffic fluctuation factors" also include, for example, traffic volume and weather. Note that congestion information is the traffic condition itself, but it can also be treated as both a traffic condition indicator and a traffic fluctuation factor. Here, a distinction is sometimes made between the type of traffic fluctuation factor (e.g., weather, time zone, etc.) and information corresponding to the content or results of a specific type of traffic fluctuation factor (e.g., whether it is rainy or sunny if the traffic fluctuation factor is weather). In this case, the former is sometimes referred to as a "factor type," and the latter is sometimes referred to as "factor information." The factor information may also be referred to as "factor information acquisition value," "factor acquisition result information," "factor value," or "factor level."

[0019] The deceleration position prediction unit 5 predicts a deceleration position of the host vehicle on the planned travel route based on the traffic condition index acquired by the index acquisition unit 3 and the traffic fluctuation factor acquired by the fluctuation factor acquisition unit 4. Specifically, in this embodiment, the deceleration position prediction unit 5 predicts the deceleration position based on the relationship between traffic condition indexes of a specific traffic fluctuation factor (e.g., weather) under two different conditions (e.g., rainy weather and sunny weather) or the relationship between a traffic condition index corresponding to a specific traffic fluctuation factor and a traffic condition index in average traffic conditions. In other words, the deceleration position prediction unit 5 predicts the deceleration position based on the relationship between traffic condition indexes corresponding to different factor information or the relationship between a traffic condition index corresponding to a specific factor information and a traffic condition index in average traffic conditions.

[0020] In this embodiment, the term "deceleration" in the "deceleration position" refers to both stopping and essentially stopping. A "substantial stop" refers to a temporary (e.g., within a few seconds) reduction in speed from a normal driving speed (e.g., typically over 10 km / h) to a slow speed or slower, such as when passing through an intersection without a mandatory stop. A slow speed is a speed of 10 km / h at which the vehicle can stop within 1 meter. "Slow" also refers to a "very slow speed" of a few km / h. In other words, the term "deceleration" in the "deceleration position" refers to a temporary reduction in speed to the extent that a departure acceleration or an equivalent start-up acceleration is required immediately thereafter. Therefore, the term "deceleration position" can also be referred to as a "stop position" or a "effective stop position." Therefore, the term "deceleration position" in this embodiment does not include the position at which the legal speed changes or the start of a congestion section in the expressway information.

[0021] Below, we will explain examples of predicting a deceleration position based on the difference (e.g., difference or ratio) between two traffic condition indexes classified by a certain traffic fluctuation factor, or the difference between a traffic condition index corresponding to a specific traffic fluctuation factor and a traffic condition index in average traffic conditions. That is, each of the following examples predicts a deceleration position based on the relationship between traffic condition indexes corresponding to different factor information, or the difference between a traffic condition index corresponding to a specific factor information and a traffic condition index in average traffic conditions.

[0022] (First embodiment) This embodiment is a specific example in which time periods are used as traffic fluctuation factors, and deceleration positions are extracted based on changes in vehicle speed over time. FIG. 2 shows the point-to-point average vehicle speed Va for each time period on a certain planned travel route. The point-to-point average vehicle speed Va is the average vehicle speed for each section when the planned travel route is divided into multiple sections (for example, 100-meter intervals). Specifically, for example, a graph of the point-to-point average vehicle speed Va at "7:00" shows the average vehicle speed for each section over a 20-minute period with 7:00 as the median, i.e., between 6:50 and 7:10, as a line graph, with the midpoint of each section as the representative position. The solid arrows along the horizontal axis of FIG. 2 indicate deceleration positions that occur regardless of the time period.

[0023] On the other hand, Fig. 3 shows the difference between the point-to-point average vehicle speed Va in each time period shown in Fig. 2 and the value in a specific reference time period. As shown in Fig. 3, it can be seen that deceleration occurs at the position of the minimum value of the difference value ΔV. That is, the dashed arrows along the horizontal axis of Fig. 3 indicate the positions corresponding to the minimum values ​​below the threshold indicated by the dashed-dotted line, and correspond to the positions of deceleration that occur in the specific time period.

[0024] Fig. 4 shows a result of predicting the running state of the host vehicle, i.e., a result of determining the expected vehicle speed Vt of the host vehicle, obtained by combining the deceleration positions independent of the time period shown in Fig. 2 and the deceleration positions dependent on the time period shown in Fig. 3. Fig. 5 shows an example of a deceleration position estimation process corresponding to this embodiment for obtaining the expected vehicle speed Vt. In the flowchart shown in Fig. 5, "S" is an abbreviation for "step."

[0025] 5 starts, the processor first executes the processes of steps 101 to 103. After the process of step 103, the processor advances the process to step 104 and subsequent steps. The processes of step 104 and subsequent steps correspond to the operation of the deceleration position prediction unit 5.

[0026] In step 101, the processor acquires information about a planned driving route of the host vehicle. The processing of step 101 corresponds to the operation of the route information acquisition unit 2. In step 102, the processor acquires information corresponding to traffic fluctuation factors. The processing of step 102 corresponds to the operation of the fluctuation factor acquisition unit 4.

[0027] In step 103, the processor acquires vehicle speed information. More specifically, the processor acquires two types of vehicle speed information linked to a certain traffic fluctuation factor, specifically, in this embodiment, for example, a vehicle speed pattern for a predetermined reference time period and a vehicle speed pattern for a time period including the current time. The processing of step 103 corresponds to the operation of the index acquisition unit 3.

[0028] In step 104, the processor calculates the vehicle speed information acquired in step 103. Specifically, in this embodiment, the difference between the two types of acquired vehicle speed information is calculated. In step 105, the processor performs a vehicle stop determination using the calculation result in step 104 and a threshold value. Specifically, in this embodiment, the processor determines whether the minimum value of the difference value ΔV exceeds the threshold value (i.e., whether it protrudes below the threshold value in FIG. 3) as shown in FIG. 3.

[0029] The processor sets the deceleration position by executing the process of step 106 according to the determination result of step 105. Specifically, in step 106, the processor sets the position where the minimum value of the difference value ΔV exceeds the threshold as the deceleration position. On the other hand, the processor does not set the position where the minimum value of the difference value ΔV does not exceed the threshold as the deceleration position.

[0030] In this way, in this embodiment, the deceleration position can be estimated with high accuracy by calculating the difference using the vehicle speed corresponding to the reference traffic state. That is, for example, by using the difference in vehicle speed and traffic volume under different driving conditions (e.g., rush hour and normal time) or different driving states (e.g., congestion and normal time), it is possible to estimate the deceleration position that correctly reflects traffic information. Therefore, according to this embodiment, it is possible to improve the prediction accuracy in predicting the driving state of the vehicle compared to conventional methods. Note that the reference value for calculating the difference may be that of a specific time period, as in the above specific example, or may be the average value for all time periods.

[0031] (Second embodiment) A second embodiment of the present disclosure will be described below. Note that in the following description of the second embodiment, differences from the first embodiment will be mainly described. In addition, identical or equivalent parts in the first and second embodiments are assigned the same reference numerals. Therefore, in the following description of the second embodiment, the description of the first embodiment can be appropriately applied to components having the same reference numerals as those in the first embodiment, unless there is a technical contradiction or special additional explanation.

[0032] This embodiment corresponds to the case where the traffic condition index is traffic volume. Fig. 6 shows the change in traffic volume between holidays and weekdays. In Fig. 6, the vertical axis of traffic volume indicates the number of passing vehicles per hour. In the plots of Fig. 6, white circles (i.e., ◯) indicate weekdays, and black circles (i.e., ●) indicate holidays. Note that the "traffic volume" in this case may be the average value for the entire day, or may be that for a specific time period.

[0033] 6, there are locations where there is a large difference in traffic volume between weekdays and holidays. Therefore, in this embodiment, the stopping location (i.e., the deceleration location or the substantial stopping location) is determined based on this difference.

[0034] Specifically, as shown in Fig. 6, it is possible to take the difference in traffic volume between weekdays and holidays, and make a stop determination (i.e., deceleration determination) if this difference exceeds a threshold. Alternatively, as shown in Fig. 7, it is possible to take the ratio of traffic volume between weekdays and holidays on the vertical axis, and make a stop determination (i.e., deceleration determination) if this ratio exceeds a threshold. The threshold is indicated by a dashed line in Fig. 7. This embodiment also provides the same effects as the first embodiment.

[0035] (Variation) The present disclosure is not limited to the above-described embodiments and specific examples. Therefore, the above-described embodiments and the like can be modified as appropriate. Representative modifications will be described below. In the following description of the modifications, differences from the above-described embodiments and the like will be mainly described. Furthermore, the same reference numerals are used for parts that are identical or equivalent to each other in the above-described embodiments and the following modifications. Therefore, in the following description of the modifications, the explanations in the above-described embodiments and the like can be used as appropriate for components that have the same reference numerals as the above-described embodiments and the like, unless there is a technical contradiction or special additional explanation.

[0036] The present disclosure is not limited to the specific applications and device configurations shown in the above embodiments. For example, the driving state prediction device 1 can be used for various applications in addition to predicting vehicle driving energy and remaining battery charge.

[0037] All or part of the driving condition prediction device 1 may be provided on an external server. Therefore, for example, the deceleration position prediction unit 5 may be provided on an external server. Furthermore, the traffic condition index and the traffic fluctuation factor may be acquired in combination with each other rather than individually. Specifically, for example, the predicted passage time through a congested section caused by a traffic impact event occurring within a specific date and time range may be understood to correspond to a combination of the traffic condition index and the traffic fluctuation factor. Therefore, there may be cases where the index acquisition unit 3 and the fluctuation factor acquisition unit 4 cannot be clearly distinguished in terms of function. However, even in this case, the traffic condition index and the traffic fluctuation factor are still acquired.

[0038] All or part of the driving condition prediction device 1 may be configured to include a digital circuit, such as an ASIC or FPGA, configured to be able to realize the above-described functions or operations. ASIC stands for Application Specific Integrated Circuit. FPGA stands for Field Programmable Gate Array. In other words, the driving condition prediction device 1 may include both an on-board microcomputer and a digital circuit.

[0039] A computer program according to the present disclosure that enables the execution of various operations, procedures, or processes described in the above embodiments can be downloaded or upgraded via V2X communication. V2X stands for Vehicle to X. Alternatively, such a computer program can be downloaded or upgraded via a terminal device installed in a vehicle manufacturing plant, a repair shop, a dealer, or the like. Such a computer program can be stored on a memory card, an optical disk, a magnetic disk, or the like.

[0040] In this way, each of the above functional configurations and processes may be realized by a special-purpose computer provided by configuring a processor and memory programmed to execute one or more functions embodied in a computer program. Alternatively, each of the above functional configurations and processes may be realized by a special-purpose computer provided by configuring a processor with one or more dedicated hardware logic circuits. Alternatively, each of the above functional configurations and processes may be realized by one or more special-purpose computers configured by combining a processor and memory programmed to execute one or more functions with a processor configured with one or more hardware logic circuits.

[0041] The computer program may be stored as instructions executed by a computer on a computer-readable non-transitory storage medium. That is, each of the above-described functional configurations and processes may be expressed as a computer program including procedures for realizing the same, or as a non-transitory storage medium storing the program.

[0042] The present disclosure is not limited to the specific operational modes shown in the above embodiment. For example, the time period may be set not only every 20 minutes as shown in FIG. 2, but may be every minute, every 30 minutes, or every hour. There is also no particular limitation on the interval. For example, by setting the interval to 10 meters or less, the vehicle speed pattern may become substantially continuous data.

[0043] In FIG. 4, the expected vehicle speed Vt at the deceleration position is set to a stopping speed, i.e., 0 km / h. However, the present disclosure is not limited to this. For example, there may be positions where the legal obligation to stop is not uniform but varies depending on the situation, such as a crosswalk without traffic lights. Taking such positions into consideration, the expected vehicle speed Vt at the deceleration position may be set to a predetermined value equal to or less than 10 km / h, which is a slow speed, specifically, a predetermined value within a range of 1 to 3 km / h, which corresponds to the slowest speed. Furthermore, the expected vehicle speed Vt at each deceleration position may not be uniform, but may be different values ​​depending on individual traffic conditions.

[0044] Similar expressions such as "obtain," "calculate," "estimate," "detect," "sensing," and "determine" may be substituted for each other as appropriate within the scope of technical compatibility. "Detect" or "detection" and "extract" may also be substituted for each other as appropriate within the scope of technical compatibility. Furthermore, "exceeding the threshold" and "above the threshold" may also be substituted for each other as appropriate within the scope of technical compatibility. The same applies to "below the threshold" and "below the threshold."

[0045] It goes without saying that the elements constituting the above-described embodiments are not necessarily essential unless expressly stated as essential or clearly considered essential in principle. Furthermore, when numerical values ​​such as the number, value, amount, and range of components are mentioned, the present disclosure is not limited to those specific numbers unless expressly stated as essential or clearly limited to a specific number in principle. Similarly, when the shape, direction, positional relationship, etc. of components are mentioned, the present disclosure is not limited to those shapes, directions, positional relationships, etc. unless expressly stated as essential or clearly limited to a specific shape, direction, positional relationship, etc. in principle.

[0046] The modified examples are not limited to the above examples. For example, all or part of one of the multiple specific examples may be combined with all or part of another of the multiple specific examples, provided that there is no technical inconsistency. There is no particular limit to the number of combinations. Similarly, all or part of one of the multiple modified examples may be combined with all or part of another of the multiple modified examples, provided that there is no technical inconsistency. Furthermore, all or part of the above specific example and all or part of the above modified examples may be combined with each other, provided that there is no technical inconsistency.

[0047] (Disclosure perspective) As is clear from the above description of the embodiments and modifications, this specification discloses at least the following matters.

[0048] [Point 1-1] A driving state prediction device (1) that predicts the driving state of a vehicle, an index acquisition unit (3) that acquires a traffic condition index, which is an index related to a traffic condition on a planned travel route of the host vehicle; a fluctuation factor acquisition unit (4) that acquires traffic fluctuation factors that are factors that cause fluctuations in the traffic state; a deceleration position prediction unit (5) that predicts a deceleration position of the host vehicle on the planned travel route based on the traffic fluctuation factors and the traffic condition index; Equipped with the deceleration position prediction unit predicts the deceleration position based on a relationship between the traffic condition index in two different situations of the specific traffic fluctuation factor, or a relationship between the traffic condition index corresponding to the specific traffic fluctuation factor and the traffic condition index in the average traffic condition. Driving condition prediction device. [Point 1-2] the index acquisition unit acquires, as the traffic condition index, information corresponding to a vehicle speed pattern of an individual vehicle or an average of vehicle speed patterns of a plurality of vehicles; The driving state prediction device according to Aspect 1-1. [Points 1-3] the index acquisition unit acquires, as the traffic condition index, information corresponding to a vehicle speed for each of a plurality of sections when the planned travel route is divided into the plurality of sections; The driving state prediction device according to aspect 1-1 or 1-2. [Points 1-4] the index acquisition unit acquires, as the traffic condition index, information corresponding to traffic volumes at a plurality of points on the planned travel route; The driving state prediction device according to Aspect 1-1. [Points 1-5] The fluctuation factor acquisition unit acquires at least one of information related to time and / or date, information related to traffic volume, and information related to weather. The traveling state prediction device according to any one of Aspects 1-1 to 1-4. [Points 1-6] the deceleration position prediction unit predicts the deceleration position based on a difference between two of the traffic condition indexes classified by a certain traffic fluctuation factor, or a difference between the traffic condition index corresponding to a specific traffic fluctuation factor and the traffic condition index in the average traffic condition. The driving state prediction device according to any one of Aspects 1-1 to 1-5. [Points 1-7] the deceleration position prediction unit predicts the deceleration position based on a ratio between two of the traffic condition indexes classified by a certain traffic fluctuation factor, or a ratio between the traffic condition index corresponding to a specific traffic fluctuation factor and the traffic condition index in the average traffic condition. The driving state prediction device according to any one of Aspects 1-1 to 1-5.

[0049] [Point 2-1] A driving state prediction method for predicting a driving state of a vehicle, comprising: acquiring a traffic condition index which is an index relating to a traffic condition on a planned travel route of the host vehicle; Acquire a traffic fluctuation factor, which is a factor that causes fluctuations in the traffic state; predicting a deceleration position of the host vehicle on the planned travel route based on the traffic fluctuation factors and the traffic condition index; In predicting the deceleration position, the deceleration position is predicted based on a relationship between the traffic condition index in two different situations of a specific traffic fluctuation factor, or a relationship between the traffic condition index corresponding to the specific traffic fluctuation factor and the traffic condition index in an average traffic condition. Driving state prediction method. [Point 2-2] The traffic condition index is acquired by acquiring information corresponding to the speed pattern of an individual vehicle or the average speed pattern of a plurality of vehicles. The driving state prediction method according to Aspect 2-1. [Point 2-3] In acquiring the traffic condition index, when the planned travel route is divided into a plurality of sections, information corresponding to the vehicle speed for each of the sections is acquired. The driving state prediction method according to aspect 2-1 or 2-2. [Point 2-4] The traffic condition index is acquired by acquiring information corresponding to traffic volume at a plurality of points on the planned travel route. The driving state prediction method according to Aspect 2-1. [Point 2-5] The acquisition of the traffic fluctuation factors includes acquiring at least one of information on time and / or date, information on traffic volume, and information on weather. The driving state prediction method according to any one of Aspects 2-1 to 2-4. [Point 2-6] In predicting the deceleration position, the deceleration position is predicted based on a difference between two of the traffic condition indexes classified by a certain traffic fluctuation factor, or a difference between the traffic condition index corresponding to a specific traffic fluctuation factor and the traffic condition index in the average traffic condition. The driving state prediction method according to any one of Aspects 2-1 to 2-5. [Point 2-7] In predicting the deceleration position, the deceleration position is predicted based on a ratio between two of the traffic condition indexes classified by a certain traffic fluctuation factor, or a ratio between the traffic condition index corresponding to a specific traffic fluctuation factor and the traffic condition index in the average traffic condition. The driving state prediction method according to any one of Aspects 2-1 to 2-5.

[0050] [Point 3-1] A driving state prediction program executed by a driving state prediction device (1) that predicts the driving state of a vehicle, The process executed by the driving state prediction device is A process of acquiring a traffic condition index which is an index related to a traffic condition on a planned travel route of the host vehicle; A process of acquiring traffic fluctuation factors that are factors that cause fluctuations in the traffic state; a process of predicting a deceleration position of the host vehicle on the planned travel route based on the traffic fluctuation factors and the traffic condition index; Including, In the process of predicting the deceleration position, the deceleration position is predicted based on a relationship between the traffic condition index in two different situations of the specific traffic fluctuation factor, or a relationship between the traffic condition index corresponding to the specific traffic fluctuation factor and the traffic condition index in the average traffic condition. Driving condition prediction program. [Point 3-2] The process of acquiring the traffic condition index includes acquiring information corresponding to a vehicle speed pattern of an individual vehicle or an average of vehicle speed patterns of a plurality of vehicles. The driving state prediction program according to Aspect 3-1. [Point 3-3] The process of acquiring the traffic condition index acquires information corresponding to a vehicle speed for each of a plurality of sections when the planned travel route is divided into the plurality of sections. A driving state prediction program according to aspect 3-1 or 3-2. [Point 3-4] The process of acquiring the traffic condition index acquires information corresponding to traffic volumes at a plurality of points on the planned travel route. The driving state prediction program according to Aspect 3-1. [Point 3-5] The process of acquiring the traffic fluctuation factor acquires at least one of information on time and / or date, information on traffic volume, and information on weather. The driving state prediction program according to any one of Aspects 3-1 to 3-4. [Point 3-6] In the process of predicting the deceleration position, the deceleration position is predicted based on a difference between two of the traffic condition indexes classified by a certain traffic fluctuation factor, or a difference between the traffic condition index corresponding to a specific traffic fluctuation factor and the traffic condition index in the average traffic condition. The driving state prediction program according to any one of Aspects 3-1 to 3-5. [Point 3-7] predicting the deceleration position based on a ratio between two of the traffic condition indexes classified by a certain traffic fluctuation factor, or a ratio between the traffic condition index corresponding to a specific traffic fluctuation factor and the traffic condition index in the average traffic condition; The driving state prediction program according to any one of Aspects 3-1 to 3-5.

[0051] [Point 4-1] A computer-readable non-transient tangible recording medium that records a driving state prediction program executed by a driving state prediction device (1) that predicts the driving state of a vehicle, The process included in the running state prediction program is A process of acquiring a traffic condition index which is an index related to a traffic condition on a planned travel route of the host vehicle; A process of acquiring traffic fluctuation factors that are factors that cause fluctuations in the traffic state; a process of predicting a deceleration position of the host vehicle on the planned travel route based on the traffic fluctuation factors and the traffic condition index; Including, In the process of predicting the deceleration position, the deceleration position is predicted based on a relationship between the traffic condition index in two different situations of the specific traffic fluctuation factor, or a relationship between the traffic condition index corresponding to the specific traffic fluctuation factor and the traffic condition index in the average traffic condition. Recording medium. [Point 4-2] The process of acquiring the traffic condition index includes acquiring information corresponding to a vehicle speed pattern of an individual vehicle or an average of vehicle speed patterns of a plurality of vehicles. A recording medium according to aspect 4-1. [Point 4-3] The process of acquiring the traffic condition index acquires information corresponding to a vehicle speed for each of a plurality of sections when the planned travel route is divided into the plurality of sections. A recording medium according to aspect 4-1 or 4-2. [Point 4-4] The process of acquiring the traffic condition index acquires information corresponding to traffic volumes at a plurality of points on the planned travel route. A recording medium according to aspect 4-1. [Points 4-5] The process of acquiring the traffic fluctuation factor acquires at least one of information on time and / or date, information on traffic volume, and information on weather. A recording medium according to any one of Aspects 4-1 to 4-4. [Points 4-6] In the process of predicting the deceleration position, the deceleration position is predicted based on a difference between two of the traffic condition indexes classified by a certain traffic fluctuation factor, or a difference between the traffic condition index corresponding to a specific traffic fluctuation factor and the traffic condition index in the average traffic condition. A recording medium according to any one of Aspects 4-1 to 4-5. [Points 4-7] predicting the deceleration position based on a ratio between two of the traffic condition indexes classified by a certain traffic fluctuation factor, or a ratio between the traffic condition index corresponding to a specific traffic fluctuation factor and the traffic condition index in the average traffic condition; A recording medium according to any one of Aspects 4-1 to 4-5. [Explanation of symbols]

[0052] 1. Driving condition prediction device 2. Route information acquisition unit 3 Indicator acquisition part 4. Fluctuation factor acquisition section 5 Deceleration position prediction section

Claims

1. A driving state prediction device (1) that predicts a driving state of a vehicle, an index acquisition unit (3) that acquires a traffic condition index that is an index related to a traffic condition on a planned travel route of the vehicle; a fluctuation factor acquisition unit (4) that acquires traffic fluctuation factors that are factors that cause fluctuations in the traffic state; a deceleration position prediction unit (5) that predicts a deceleration position of the vehicle on the planned travel route based on the traffic fluctuation factors and the traffic condition index; Equipped with the deceleration position prediction unit predicts the deceleration position based on a relationship between the traffic condition index in two different situations of the specific traffic fluctuation factor, or a relationship between the traffic condition index corresponding to the specific traffic fluctuation factor and the traffic condition index in the average traffic condition. Driving condition prediction device.

2. the index acquisition unit acquires, as the traffic condition index, information corresponding to a vehicle speed pattern of an individual vehicle or an average of vehicle speed patterns of a plurality of vehicles; The driving state prediction device according to claim 1 .

3. the index acquisition unit acquires, as the traffic condition index, information corresponding to a vehicle speed for each of a plurality of sections when the planned travel route is divided into the plurality of sections; The driving state prediction device according to claim 1 .

4. the index acquisition unit acquires, as the traffic condition index, information corresponding to traffic volumes at a plurality of points on the planned travel route; The driving state prediction device according to claim 1 .

5. the fluctuation factor acquisition unit acquires at least one of information related to time and / or date, information related to traffic volume, and information related to weather; The driving state prediction device according to claim 1 .

6. the deceleration position prediction unit predicts the deceleration position based on a difference between two of the traffic condition indexes classified by a certain traffic fluctuation factor, or a difference between the traffic condition index corresponding to a specific traffic fluctuation factor and the traffic condition index in the average traffic condition. The driving state prediction device according to claim 1 .

7. the deceleration position prediction unit predicts the deceleration position based on a ratio between two of the traffic condition indexes classified by a certain traffic fluctuation factor, or a ratio between the traffic condition index corresponding to a specific traffic fluctuation factor and the traffic condition index in the average traffic condition. The driving state prediction device according to claim 1 .

8. A driving state prediction method for predicting a driving state of a vehicle, comprising: acquiring a traffic condition index which is an index relating to a traffic condition on a planned travel route of the host vehicle; Acquire a traffic fluctuation factor, which is a factor that causes fluctuations in the traffic state; predicting a deceleration position of the host vehicle on the planned travel route based on the traffic fluctuation factors and the traffic condition index; In predicting the deceleration position, the deceleration position is predicted based on a relationship between the traffic condition index in two different situations of a specific traffic fluctuation factor, or a relationship between the traffic condition index corresponding to the specific traffic fluctuation factor and the traffic condition index in an average traffic condition. Driving state prediction method.

9. A driving state prediction program executed by a driving state prediction device (1) that predicts the driving state of a vehicle, The process executed by the driving state prediction device is A process of acquiring a traffic condition index which is an index related to a traffic condition on a planned travel route of the host vehicle; A process of acquiring traffic fluctuation factors that are factors that cause fluctuations in the traffic state; a process of predicting a deceleration position of the host vehicle on the planned travel route based on the traffic fluctuation factors and the traffic condition index; Including, In the process of predicting the deceleration position, the deceleration position is predicted based on a relationship between the traffic condition index in two different situations of the specific traffic fluctuation factor, or a relationship between the traffic condition index corresponding to the specific traffic fluctuation factor and the traffic condition index in the average traffic condition. Driving condition prediction program.

10. A computer-readable non-transitional tangible recording medium that records a driving state prediction program executed by a driving state prediction device (1) that predicts the driving state of a vehicle, The process included in the running state prediction program is A process of acquiring a traffic condition index which is an index related to a traffic condition on a planned travel route of the host vehicle; A process of acquiring traffic fluctuation factors that are factors that cause fluctuations in the traffic state; a process of predicting a deceleration position of the host vehicle on the planned travel route based on the traffic fluctuation factors and the traffic condition index; Including, In the process of predicting the deceleration position, the deceleration position is predicted based on a relationship between the traffic condition index in two different situations of the specific traffic fluctuation factor, or a relationship between the traffic condition index corresponding to the specific traffic fluctuation factor and the traffic condition index in the average traffic condition. Recording medium.

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