Driving condition prediction device, driving condition prediction method, and driving condition prediction program

Abstract

To provide a highly versatile technology for predicting an own vehicle's driving condition.SOLUTION: A driving condition prediction device (4) that predicts a driving condition of an own vehicle includes a moving speed acquisition unit (5) and a deceleration position prediction unit (6). The moving speed acquisition unit acquires speed result information, which is information related to the results of the moving speeds of one or more vehicles. The deceleration position prediction unit predicts the deceleration position of the own vehicle based on the acquired speed result information. A driving condition prediction method is a method for predicting the driving condition of the own vehicle, in which speed result information, which is information related to the results of the moving speeds of one or more vehicles, is acquired, and the deceleration position of the own vehicle is predicted based on the acquired speed result information. A driving condition prediction program is a computer program executed by the driving condition prediction device, including a moving speed acquisition process that acquires the speed result information and a deceleration position prediction process that predicts the deceleration position of the own vehicle based on the acquired speed result information.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 for predicting the driving state of a host vehicle.

Background Art

[0002] Patent Document 1 discloses a vehicle energy consumption estimation device, a vehicle energy consumption estimation method, and a computer program that estimate the energy consumed by a drive source of a vehicle in consideration of the driving mode when the vehicle travels through an intersection. Specifically, a vehicle energy consumption estimation device having a route specifying means and an energy consumption estimating means includes an intersection specifying means, a speed limit acquisition means, and an acceleration estimation means.

[0003] The route specifying means specifies the planned travel route of the vehicle. The energy consumption estimating means estimates the energy consumed by a drive source that generates the driving force of the vehicle when traveling on the planned travel route specified by the route specifying means. The intersection specifying means specifies an intersection on the planned travel route. The speed limit acquisition means acquires the speed limit of the road connecting to the intersection specified by the intersection specifying means. The acceleration estimation means estimates the acceleration time of the vehicle when the vehicle travels through the intersection based on the speed limit acquired by the speed limit acquisition means and a predetermined acceleration. Then, the energy consumption estimating means estimates the energy consumption based on the acceleration time of the vehicle estimated by the acceleration estimation means.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] According to the technology described in Patent Document 1, it is possible to estimate the energy consumption based on the acceleration resistance when the vehicle travels through an intersection. However, as traffic information widely provided in the world, there is no stop probability at intersections. For this reason, the technology described in Patent Document 1 can only be implemented in limited environments. This disclosure has been made in view of the circumstances exemplified above. That is, this disclosure provides a driving state prediction technology for a host vehicle that is excellent in versatility, for example.

Means for Solving the Problems

[0006] The driving state prediction device (4) is configured to predict the driving state of the host vehicle. The driving state prediction device according to claim 1, a moving speed acquisition unit (5) that acquires speed performance information which is information on the actual moving speeds of one or more vehicles, a deceleration position prediction unit (6) that predicts the deceleration position of the host vehicle based on the acquired speed performance information, is provided. The driving state prediction method according to claim 10 is a method for predicting the driving state of a host vehicle, acquiring speed performance information which is information on the actual moving speeds of one or more vehicles, predicting the deceleration position of the host vehicle based on the acquired speed performance information. The driving state prediction program according to claim 11 is a computer program executed by a driving state prediction device (4) that predicts the driving state of a host vehicle, a moving speed acquisition process for acquiring speed performance information which is information on the actual moving speeds of one or more vehicles, a deceleration position prediction process for predicting the deceleration position of the host vehicle based on the acquired speed performance information, is included.

[0007] Such a configuration and method acquire speed performance information, which is information regarding the actual moving speeds of one or more vehicles, and predict the deceleration position of the host vehicle based on the acquired speed performance information. Such speed performance information is traffic information widely provided in the world or information that can be easily calculated based on such traffic information. Therefore, in such a configuration and method, it is possible to accurately estimate the driving state of the host vehicle based on traffic information widely provided in the world. Accordingly, according to such a configuration and method, it is possible to provide a driving state prediction technique for the host vehicle with excellent versatility.

[0008] Note that in each column of the application documents, each element may be accompanied by a reference sign in parentheses. However, such reference signs merely indicate an example of the correspondence between the same element and the specific means described in the embodiments below. Therefore, the present disclosure is not limited by the description of the above reference signs at all.

Brief Description of the Drawings

[0009]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Figure 9

Figure 10

Figure 11

Figure 12

Figure 13

Figure 14

Figure 15

Figure 16

Figure 17

MODE FOR CARRYING OUT THE INVENTION

[0010] (Embodiment) Hereinafter, exemplary embodiments and specific examples of the present disclosure will be described with reference to the drawings as appropriate. First, with reference to FIGS. 1 to 3, the overall configuration of a system 1 applicable to an automobile as a vehicle traveling on a road will be described. A vehicle to which the system 1 is applied, that is, a vehicle equipped with all or part of the components of such a system 1 is hereinafter referred to as the "host vehicle". As shown in FIG. 1, the system 1 includes route coordinate information 2, route traffic information 3, and a traveling state prediction device 4.

[0011] The route coordinate information 2 is information including the coordinate information, i.e., the latitude and longitude information, of each point RP on the planned travel route R of the host vehicle. The route coordinate information 2 can be obtained from a server outside the host vehicle or from a map data storage area in a non-transitory physical storage medium (such as a flash memory, etc.) mounted on the host vehicle. The planned travel route R can be obtained from a server outside the host vehicle or from a navigation device mounted on the host vehicle. In FIG. 2, the point RP as the starting point on the planned travel route R is shown as the starting point RPs, and the point RP as the end point is shown as the destination point RPg. Note that the number and intervals of the points RP on the map shown in FIG. 2 are simplified for the sake of brevity in the description of the present disclosure and do not limit the content of the present disclosure in any way.

[0012] The route traffic information 3 is information regarding the traffic conditions of each point RP on the planned travel route R, i.e., the performance of the driving states of one or more vehicles, and can be obtained from a server or the like outside the host vehicle. Specifically, the route traffic information 3 includes the travel time between predetermined points and can be obtained by means of a map information API service or the like. API is an abbreviation for Application Programming Interface. Note that the predetermined points for which the travel time can be obtained are not necessarily for each section of all the points RP shown in FIG. 2. Specifically, for example, it may be possible to obtain only the travel time between major points such as intersections, branches, crosswalks, and signal installation positions. In this case, the points RP shown in FIG. 2 can be set as the in-points between the major points in addition to such major points. In this case, the travel time between adjacent points RP can be calculated by multiplying the obtained travel time between the major points by the ratio of the distance between adjacent points RP to the distance between the major points.

[0013] The traveling state prediction device 4 is configured to predict the traveling state of the host vehicle based on at least the route coordinate information 2 and the route traffic information 3. In the present embodiment, the traveling state prediction device 4 has a configuration as an in-vehicle microcomputer, that is, an ECU mounted on the host vehicle. ECU is an abbreviation for Electronic Control Unit. That is, the traveling state prediction device 4 includes a processor composed of a CPU or an MPU, and a storage medium communicably connected to the processor, and is configured to realize a predetermined function by reading and executing a computer program from the storage medium. The storage medium includes at least a ROM or a non-volatile rewritable memory among various non-transitory physical storage media such as a ROM and a non-volatile rewritable memory. The non-volatile rewritable memory is a storage device that can rewrite information while the power is on and holds the information in a non-rewritable state while the power is off, and is, for example, a flash memory or the like. The storage medium stores various data such as initial values, maps, and look-up tables necessary for executing the above computer program together with the computer program.

[0014] As shown in FIG. 1, the driving state prediction device 4 has a moving speed calculation function 5 and a target speed determination function 6 as functional configurations realized on an in-vehicle microcomputer by executing a computer program. The moving speed calculation function 5 as the moving speed acquisition unit according to the present disclosure acquires speed performance information, which is information on the actual moving speed (i.e., vehicle speed) of one or more vehicles, based on the route coordinate information 2 and the route traffic information 3. The target speed determination function 6 as the deceleration position prediction unit according to the present disclosure predicts the deceleration position of the host vehicle based on the acquired speed performance information. Here, the "deceleration" in the "deceleration position" includes stopping and substantial stopping in the present embodiment. "Substantial stopping" includes a temporary speed reduction (e.g., within several seconds) from the vehicle speed during normal driving (e.g., typically over 10 km / h) to a crawling speed or less, such as when passing through an intersection in a situation where there is no obligation to stop temporarily. The crawling speed is a speed at which the vehicle can stop within 1 m at 10 km / h. "Crawling" includes the "slowest crawl" of about several km / h. In other words, the "deceleration" in the "deceleration position" here is a temporary speed reduction to such an extent that acceleration at start or equivalent rising acceleration is required immediately after that.

[0015] As shown in FIG. 3, in the present embodiment, the moving speed calculation function 5 has a route information acquisition function 51, a distance acquisition function 52, a moving time acquisition function 53, and a speed calculation function 54. The route information acquisition function 51 is a function for acquiring the route coordinate information 2 from an external server or the like of the host vehicle. The distance acquisition function 52 is configured to acquire the distance between adjacent points RP along the planned travel route R based on the acquired route coordinate information 2. The moving time acquisition function 53 is configured to acquire the moving time between adjacent points RP along the planned travel route R based on the acquired route traffic information 3. The speed calculation function 54 is configured to acquire, that is, calculate, the moving speed between points RP as speed performance information based on the distance between points RP acquired by the distance acquisition function 52 and the moving time between points RP acquired by the moving time acquisition function 53. In the present embodiment, the speed calculation function 54 is an average speed as the average value of the actual vehicle speeds of one or more vehicles as the moving speed between points RP.

[0016] (First Embodiment) FIG. 4 shows a schematic functional configuration of the target speed determination function 6 in the first embodiment of the travel state prediction device 4 shown in FIG. 1. Referring to FIG. 4, in the present embodiment, the target speed determination function 6 has a target speed acquisition function 611, a deceleration determination function 612, and a target speed setting function 613.

[0017] The target speed acquisition function 611 is configured to acquire a provisional target speed at each point RP on the planned travel route R. In the present embodiment, the provisional target speed is the speed limit at each point RP. The speed limit can be acquired, for example, from a map information API service or map data for navigation mounted on the host vehicle.

[0018] The deceleration determination function 612 determines, for each point RP, whether it is a deceleration position, that is, whether there is a significant deceleration, based on the speed performance information. "Significant deceleration" includes stopping and substantial stopping. Whether there is a significant deceleration can be determined, for example, based on the change in average speed. Specifically, whether there is a significant deceleration can be determined, for example, based on whether the average speed is lower than the threshold speed or whether the change amount of the average speed exceeds the threshold change amount.

[0019] The target speed setting function 613 sets the target speed of the host vehicle at each point RP based on the temporary target speed acquired by the target speed acquisition function 611 and the deceleration position determined, that is, predicted, by the deceleration determination function 612. Specifically, the target speed setting function 613 sets the target speed to a predetermined stop-equivalent speed V0 when the point RP is a deceleration position, and sets it to the temporary target speed when it is not a deceleration position. The stop-equivalent speed V0 is the stop speed of 0 km / h or a predetermined value less than the creep speed, specifically, a predetermined value within the range of 1 to 3 km / h corresponding to the slowest creep speed. In this embodiment, the target speed setting function 613 sets the target speeds at the starting point RPs and the destination point RPg to the stop speed, that is, 0 km / h.

[0020] Figures 5 to 7 show a specific example of the driving state prediction device 4 according to this embodiment, and the driving state prediction method and driving state prediction program executed thereby. Hereinafter, the driving state prediction device 4 according to this embodiment, the driving state prediction method and driving state prediction program executed thereby are collectively referred to as "this embodiment". In the flowchart shown in Figure 5, "S" is an abbreviation for "step". The same applies to the flowcharts shown in other figures. Also, the black dot plots in Figure 6 etc. show the average speed etc. at each point RP, but such plots are described in a simplified manner for the sake of brevity in the description of this disclosure, and do not limit the content of this disclosure in any way. Therefore, such plots are only schematic and are not described to be consistent with the arrangement of the points RP shown in Figure 2.

[0021] In step 101, the travel state prediction device 4 acquires the travel planned route information, that is, the coordinate information of the travel planned route R. Step 101 corresponds to the route information acquisition function 51. In step 102, the travel state prediction device 4 acquires the distance between adjacent points, that is, the distance between adjacent points RP on the travel planned route R. Step 102 corresponds to the distance acquisition function 52. In step 103, the travel state prediction device 4 acquires the travel time between adjacent points. Step 103 corresponds to the travel time acquisition function 53. In step 104, the travel state prediction device 4 calculates the average speed, which is the travel speed between adjacent points. Step 104 corresponds to the speed calculation function 54. FIG. 6 is a graph showing the average speed for each point RP in order on the travel planned route R. However, due to setting the target speed at the starting point RPs and the destination point RPg to the stop speed, that is, 0 km / h, in FIG. 6, the average speed at the starting point RPs and the destination point RPg is set to 0 km / h. The same applies to the average speed in the graphs in other figures.

[0022] In step 105, the travel state prediction device 4 sets the target speed of each point RP on the travel planned route R to 0 km / h for the starting point RPs and the destination point RPg, and to a provisional target speed, that is, the speed limit, for the others. Step 105 corresponds to the target speed acquisition function 611 and the target speed setting function 613. In step 106, the travel state prediction device 4 determines whether there is a large deceleration at each point RP. Step 106 corresponds to the deceleration determination function 612. The upper part in FIG. 7 shows an example of the determination method for whether there is a large deceleration at each point RP. As indicated by the arrows in FIG. 7, it is possible to determine that there is a large deceleration when the average speed is lower than the threshold speed or when the decrease amount of the average speed exceeds the threshold amount. The threshold speed and the threshold amount can be obtained by computer simulation, optimization experiments, etc. The threshold speed can be, for example, 10 to 15 km / h. The threshold amount can be, for example, 20 to 30 km / h.

[0023] When there is a large deceleration (i.e., step 106 = YES), the driving state prediction device 4 executes the process of step 107. In step 107, the driving state prediction device 4 sets the target speed of the point RP, which is the deceleration position with a large deceleration, to a predetermined stop-equivalent speed V0, like the position indicated by the upward arrow in FIG. 7. Step 107 corresponds to the target speed setting function 613. On the other hand, when there is no large deceleration (i.e., step 106 = NO), the driving state prediction device 4 skips the process of step 107. In this case, the target speed is maintained at the restricted speed, which is the temporary target speed.

[0024] This embodiment acquires speed performance information, which is information regarding the moving speeds of one or more vehicles' achievements, and predicts the deceleration position of the host vehicle based on the acquired speed performance information. Such speed performance information is traffic information widely provided in the world or information that can be easily calculated based on such traffic information. Therefore, in this embodiment, based on the traffic information widely provided in the world, such as map information services, it is possible to accurately estimate the driving state of the host vehicle, specifically, the vehicle speed pattern and deceleration position of the host vehicle. Therefore, according to this embodiment, it is possible to provide a driving state prediction technology for the host vehicle with excellent versatility.

[0025] (Second Embodiment) FIG. 8 shows a schematic functional configuration of the target speed determination function 6 in the second embodiment of the driving state prediction device 4 shown in FIG. 1. Referring to FIG. 8, in this embodiment, the target speed determination function 6 includes a target speed acquisition function 621, a reference speed acquisition function 622, a speed difference determination function 623, and a target speed setting function 624. The target speed acquisition function 621 is configured to acquire the temporary target speed at each point RP. That is, the target speed acquisition function 621 is the same as the target speed acquisition function 611 in the first embodiment. Therefore, hereinafter, the functional configurations of the reference speed acquisition function 622, the speed difference determination function 623, and the target speed setting function 624 will be mainly described.

[0026] The reference speed acquisition function 622 is configured to acquire a reference speed, which is the moving speed of a vehicle when not stopped, at each point RP. The "moving speed of a vehicle when not stopped" refers to the assumed moving speed of a vehicle at each point RP in a normal traffic situation where there are no traffic hindrance situations such as traffic regulations or traffic jams, and typically, for example, it is the speed limit. The speed difference determination function 623 is configured to calculate the relationship between the reference speed and the average speed, specifically, a ratio or a difference. Further, the speed difference determination function 623 is configured to determine whether the calculated ratio or difference exceeds a threshold value. That is, the speed difference determination function 623 estimates a point RP where the calculated ratio or difference exceeds the threshold value as a deceleration position.

[0027] The target speed setting function 624 is configured to set the target speed of the host vehicle at each point RP based on the determination result by the speed difference determination function 623. Specifically, the target speed setting function 624 sets the target speed to a predetermined stop-equivalent speed V0 when the point RP is a deceleration position, and sets it to a provisional target speed when it is not a deceleration position.

[0028] Figs. 9 to 12 show a specific example of the traveling state prediction device 4 according to the present embodiment, and the traveling state prediction method and the traveling state prediction program executed thereby. The processing contents of steps 201 to 204 in the flowchart of Fig. 9 are the same as the processing contents of steps 101 to 104 in the flowchart of Fig. 5. Therefore, for the processing contents of steps 201 to 204, the description of the processing contents of steps 101 to 104 in the above first embodiment is incorporated, and hereinafter, the processing following step 204 will be described.

[0029] In step 205, the driving state prediction device 4 sets the target speed of each point RP on the planned driving route R to 0 km / h for the starting point RPs and the destination point RPg, and sets it to the temporary target speed, that is, the speed limit, for the rest. Step 205 corresponds to the target speed acquisition function 621 and the target speed setting function 624. In step 206, as shown in FIG. 10, the driving state prediction device 4 acquires the speed limit as the reference speed of each point RP. Step 206 corresponds to the reference speed acquisition function 622. In step 207, the driving state prediction device 4 calculates the speed difference ΔV, which is the difference between the reference speed and the average speed. The difference between the reference speed and the average speed is the difference between the solid-line reference speed and the dashed-line average speed in FIG. 11. In step 208, the driving state prediction device 4 determines whether the speed difference ΔV exceeds the threshold speed difference ΔVth. Steps 207 and 208 correspond to the speed difference determination function 623. The threshold speed difference ΔVth can be obtained by computer simulation, optimization experiments, etc., and can be, for example, 20 to 30 km / h.

[0030] When the speed difference ΔV exceeds the threshold speed difference ΔVth (i.e., step 208 = YES), the driving state prediction device 4 executes the process of step 209. In step 209, the driving state prediction device 4 sets the target speed of the point RP that is the deceleration position to the speed V0 equivalent to stopping, with the point RP where the speed difference ΔV exceeds the threshold speed difference ΔVth as the deceleration position. Step 209 corresponds to the target speed setting function 624. On the contrary, when the speed difference ΔV is less than or equal to the threshold speed difference ΔVth (i.e., step 208 = NO), the driving state prediction device 4 skips the process of step 209. In this case, the target speed is maintained at the speed limit, which is the temporary target speed. In this way, based on the processing result of step 205 and the processing result of step 209 based on the determination result of step 208, the driving state prediction device 4 sets the final target speed at each point RP, as shown in FIG. 12.

[0031] This embodiment predicts a deceleration position based on the relationship between a reference speed, which is the moving speed during non-stop driving, and the average speed. As a result, the deceleration position such as the stop position can be predicted accurately. Therefore, according to this embodiment, it is possible to provide a driving state prediction technology for the host vehicle that is excellent in versatility and prediction accuracy.

[0032] (Third Embodiment) FIG. 13 shows a schematic functional configuration of the target speed determination function 6 in the third embodiment of the driving state prediction device 4 shown in FIG. 1. Referring to FIG. 13, in this embodiment, the target speed determination function 6 includes a target speed acquisition function 631, a deceleration probability acquisition function 632, and a target speed setting function 633. The target speed acquisition function 631 is configured to acquire a provisional target speed at each point RP. That is, the target speed acquisition function 621 is the same as the target speed acquisition function 611 in the first embodiment. Therefore, hereinafter, the functional configurations of the deceleration probability acquisition function 632 and the target speed setting function 633 will be mainly described.

[0033] The deceleration probability acquisition function 632 is configured to acquire the deceleration probability of the vehicle at each point RP. The "deceleration probability" is the occurrence probability of a vehicle stopping or substantially stopping at each point RP in a normal traffic situation where no traffic hindrance such as traffic regulations or traffic jams occurs. The deceleration probability can be obtained based on the result of recording the vehicle running state at each point RP. That is, the deceleration probability can be obtained based on the historical information of the actual moving speed of one or more vehicles at each point RP. Specifically, the change in the average speed, that is, the decrease amount, or the relationship (for example, the difference) between the reference speed, which is the moving speed when not stopping, and the average speed is defined as the speed fluctuation parameter. By statistically processing the correlation between such a speed fluctuation parameter and the occurrence situation of the vehicle stopping or substantially stopping, a map or formula indicating the relationship between the speed fluctuation parameter and the deceleration probability can be obtained. Such a map or formula can be calculated, for example, by a server outside the host vehicle, and can be used by the host vehicle by appropriately reading it from such a server or the like. Then, the deceleration probability acquisition function 632 can acquire the deceleration probability at each point RP based on such a map or formula and the speed fluctuation parameter at each point RP.

[0034] Also, the deceleration probability acquisition function 632 is configured to determine whether the deceleration probability of the vehicle at each point RP is lower than the threshold probability. That is, the deceleration probability acquisition function 632 determines whether each point RP is a deceleration position. Then, the target speed setting function 633 is configured to set the target speed of the host vehicle at each point RP based on the determination result by the deceleration probability acquisition function 632. Specifically, the target speed setting function 633 sets the target speed to the speed equivalent to stopping V0 when the point RP is a deceleration position, and sets it to the provisional target speed when it is not a deceleration position.

[0035] Figs. 14 to 17 show a specific example of the traveling state prediction device 4 according to the present embodiment, and a traveling state prediction method and a traveling state prediction program executed thereby. The processing contents of steps 301 to 304 in the flowchart of Fig. 14 are the same as the processing contents of steps 101 to 104 in the flowchart of Fig. 5. Therefore, for the processing contents of steps 301 to 304, the description of the processing contents of steps 101 to 104 in the above-described first embodiment is incorporated, and hereinafter, the processing following step 304 will be described.

[0036] In step 305, the traveling state prediction device 4 sets the target speed of each point RP on the planned traveling route R to 0 km / h for the starting point RPs and the destination point RPg, and sets it to a temporary target speed, that is, the speed limit, for the others. Step 305 corresponds to the target speed acquisition function 631 and the target speed setting function 633. In step 306, the traveling state prediction device 4 acquires the deceleration probability at each point RP. In this specific example, as shown in Fig. 15, the deceleration probability is calculated using the amount of decrease in the average speed. In step 307, the traveling state prediction device 4 determines whether there is a large deceleration at each point RP. That is, the traveling state prediction device 4 determines whether the deceleration probability at each point RP exceeds a threshold probability based on the amount of decrease in the average speed at each point RP and a map showing the correspondence between the deceleration probability and the amount of decrease in the average speed shown in Fig. 16. Steps 306 and 307 correspond to the deceleration probability acquisition function 632.

[0037] When the deceleration probability exceeds the threshold probability (i.e., step 307 = YES), the driving state prediction device 4 executes the process of step 308. In step 308, the driving state prediction device 4 sets the target speed of the point RP, which is the deceleration position, to a predetermined stop-equivalent speed V0 with the point RP where the deceleration probability exceeds the threshold probability as the deceleration position. Step 308 corresponds to the target speed setting function 633. On the other hand, when the deceleration probability does not exceed the threshold probability (i.e., step 307 = NO), the driving state prediction device 4 skips the process of step 308. In this case, the target speed is maintained at the restricted speed, which is the temporary target speed. In this way, the driving state prediction device 4 sets the final target speed at each point RP as shown in FIG. 17 based on the processing result of step 305 and the processing result of step 398 based on the determination result of step 307.

[0038] (Modification example) The present disclosure is not limited to the above-described embodiments and specific examples. Therefore, the above-described embodiments and the like can be appropriately modified. Hereinafter, representative modification examples will be described. In the following description of the modification examples, the differences from the above-described embodiments and the like will be mainly described. Also, in the above-described embodiments and the following modification examples, parts that are identical or equivalent to each other are given the same reference numerals. Therefore, in the following description of the modification examples, with respect to the components having the same reference numerals as those in the above-described embodiments and the like, the descriptions in the above-described embodiments and the like can be appropriately incorporated unless there is a technical contradiction or specific additional explanation.

[0039] The present disclosure is not limited to the specific applications and device configurations shown in the above embodiments. That is, for example, the driving state prediction device 4 can be used in various applications in addition to predicting vehicle driving energy and battery remaining amount. Also, all or part of the driving state prediction device 4 may be provided on the server side outside the host vehicle. Specifically, for example, the moving speed calculation function 5 may be provided in such a server.

[0040] All or part of the traveling state prediction device 4 may be configured to include a digital circuit, such as an ASIC or an FPGA, that can realize the above functions or operations. ASIC is an abbreviation for Application Specific Integrated Circuit. FPGA is an abbreviation for Field Programmable Gate Array. That is, in the traveling state prediction device 4, the in-vehicle microcomputer part and the digital circuit part can coexist.

[0041] The program according to the present disclosure that enables execution of various operations, procedures, or processes described in the above embodiment can be downloaded or upgraded via V2X communication. V2X is an abbreviation for Vehicle to X. Alternatively, such a program can be downloaded or upgraded via terminal devices provided at the manufacturing factory, maintenance factory, dealership, etc. of the host vehicle. The storage destination of such a program may be a memory card, an optical disk, a magnetic disk, etc.

[0042] As described above, each of the above functional configurations and processes may be realized by a dedicated computer provided by configuring a processor and a memory programmed to execute one or more functions embodied by a computer program. Alternatively, each of the above functional configurations and processes may be realized by a dedicated 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 dedicated computers configured by a combination of a processor and a memory programmed to execute one or more functions and a processor configured by one or more hardware logic circuits. Further, the computer program may be stored in a non-transitory tangible storage medium readable by a computer as instructions to be executed by the computer. That is, each of the above functional configurations and processes can also be represented as a computer program including procedures for realizing the same, or as a non-transitory tangible storage medium storing the program.

[0043] The present disclosure is not limited to the specific operation modes shown in the above embodiments. That is, for example, all the points RP may be such that the travel time between adjacent points RP can be obtained as traffic information. Also, the processing of step 102 and the processing of step 103 in FIG. 5 may be in reverse order or may be simultaneous. The various graphs shown in FIG. 6 and the like are also described in a simplified manner for the sake of brevity in the description of the present disclosure, and do not limit the content of the present disclosure in any way. Also, the correspondence between each functional configuration and each step in the flowchart can be appropriately changed. That is, for example, step 208 may correspond to the target speed setting function 624. Further, the stop-equivalent speed V0 may be different values for each type of stop position. Specifically, for example, for a stop position where there is an obligation to stop temporarily, V0 = 0 km / h, and for a stop position where there is no obligation to stop temporarily, V0>0 km / h. For a stop position where an obligation to stop temporarily may occur depending on the situation, such as a crosswalk without a traffic signal, V0>0 km / h may also be used. For stop positions where V0≠0 km / h, they may be different values according to individual traffic conditions.

[0044] Similar expressions such as "acquire", "calculate", "estimate", "detect", "sense", "determine", etc. can be appropriately replaced with each other within a technically non-contradictory range. "Detect" or "sense" and "extract" can also be appropriately replaced within a technically non-contradictory range. Also, "exceeding the threshold value" and "equal to or greater than the threshold value" can be appropriately replaced with each other within a technically non-contradictory range. The same applies to "less than the threshold value" and "equal to or less than the threshold value".

[0045] It goes without saying that the elements constituting the above-described embodiments are not necessarily essential, except when explicitly stated as being particularly essential or when considered to be clearly essential in principle. Also, when numerical values such as the number of components, numerical values, quantities, ranges, etc. are mentioned, the present disclosure is not limited to that specific number, except when explicitly stated as being particularly essential or when clearly limited to a specific number in principle. Similarly, when the shape, direction, positional relationship, etc. of components, etc. are mentioned, the present disclosure is not limited to that shape, direction, positional relationship, etc., except when explicitly stated as being particularly essential or when clearly limited to a specific shape, direction, positional relationship, etc. in principle.

[0046] The modification examples are not limited to the above examples. For example, all or part of one of the plurality of specific examples and all or part of another can be combined with each other as long as they do not technically conflict. There is no particular limitation on the number of combinations. Similarly, all or part of one of the plurality of modification examples and all or part of another can be combined with each other as long as they do not technically conflict. Furthermore, all or part of the above specific examples and all or part of the above modification examples can be combined with each other as long as they do not technically conflict.

[0047] (Disclosure perspective) As is clear from the descriptions of the embodiments and modification examples as described above, at least the following disclosure matters are disclosed in this specification.

[0048] [Viewpoint 1-1] A driving state prediction device (4) for predicting the driving state of the host vehicle, a moving speed acquisition unit (5) that acquires speed performance information, which is information on the actual moving speeds of one or more vehicles, a deceleration position prediction unit (6) that predicts the deceleration position of the host vehicle based on the acquired speed performance information, and a driving state prediction device comprising the same. [Viewpoint 1-2] The speed performance information is the average speed. The traveling state prediction device according to viewpoint 1-1. [Viewpoint 1-3] The deceleration position prediction unit predicts the deceleration position based on the change in the average speed. The traveling state prediction device according to viewpoint 1-2. [Viewpoint 1-4] The deceleration position prediction unit predicts the deceleration position based on whether the average speed is lower than a threshold speed or whether the amount of change in the average speed is greater than a threshold amount of change. The traveling state prediction device according to viewpoint 1-3. [Viewpoint 1-5] Comprising a reference speed acquisition unit (622) that acquires a reference speed which is the moving speed during non-stop driving. The deceleration position prediction unit predicts the deceleration position based on the relationship between the reference speed and the average speed. The traveling state prediction device according to viewpoint 1-2. [Viewpoint 1-6] The relationship is a ratio or a difference. The traveling state prediction device according to viewpoint 1-5. [Viewpoint 1-7] Comprising a deceleration probability acquisition unit (632) that acquires a deceleration probability calculated based on the change in the average speed or the relationship between the reference speed which is the moving speed during non-stop driving and the average speed. The deceleration position prediction unit predicts the deceleration position based on the deceleration probability. The traveling state prediction device according to viewpoint 1-2. [Viewpoint 1-8] The deceleration position prediction unit predicts the deceleration position based on whether the deceleration probability is lower than a threshold probability. The traveling state prediction device according to viewpoint 1-7. [Viewpoint 1-9] The deceleration probability is calculated based on the historical information of the actual moving speed at a predetermined position of one or more vehicles. The traveling state prediction device according to viewpoint 1-7 or 1-8.

[0049] [Viewpoint 2-1] A driving state prediction method for predicting the driving state of a host vehicle, acquire speed performance information which is information on the actual moving speeds of one or more vehicles, predict the deceleration position of the host vehicle based on the acquired speed performance information, Driving state prediction method. [Aspect 2-2] The speed performance information is an average speed, The driving state prediction method according to Aspect 2-1. [Aspect 2-3] predict the deceleration position based on the change in the average speed, The driving state prediction method according to Aspect 2-2. [Aspect 2-4] predict the deceleration position based on whether the average speed is lower than a threshold speed or whether the change amount of the average speed is higher than a threshold change amount, The driving state prediction method according to Aspect 2-3. [Aspect 2-5] acquire a reference speed which is the moving speed during non-stop driving, predict the deceleration position based on the relationship between the reference speed and the average speed, The driving state prediction method according to Aspect 2-2. [Aspect 2-6] The relationship is a ratio or a difference, The driving state prediction method according to Aspect 2-5. [Aspect 2-7] acquire a deceleration probability calculated based on the change in the average speed or the relationship between the reference speed which is the moving speed during non-stop driving and the average speed, predict the deceleration position based on the deceleration probability, The driving state prediction method according to Aspect 2-2. [Aspect 2-8] predict the deceleration position based on whether the deceleration probability is lower than a threshold probability, The driving state prediction method according to Aspect 2-7. [Aspect 2-9] The deceleration probability is calculated based on historical information of the actual moving speed at a predetermined position of one or more vehicles. The driving state prediction method according to Viewpoint 2-7 or 2-8.

[0050] [Viewpoint 3-1] A driving state prediction program executed by a driving state prediction device (4) that predicts the driving state of the host vehicle, a moving speed acquisition process for acquiring speed performance information, which is information on the actual moving speed of one or more vehicles, a deceleration position prediction process for predicting the deceleration position of the host vehicle based on the acquired speed performance information, A driving state prediction program including the above. [Viewpoint 3-2] The speed performance information is the average speed. The driving state prediction program according to Viewpoint 3-1. [Viewpoint 3-3] In the deceleration position prediction process, the deceleration position is predicted based on the change in the average speed. The driving state prediction program according to Viewpoint 3-2. [Viewpoint 3-4] In the deceleration position prediction process, the deceleration position is predicted based on whether the average speed is lower than a threshold speed or whether the change amount of the average speed is higher than a threshold change amount. The driving state prediction program according to Viewpoint 3-3. [Viewpoint 3-5] including a reference speed acquisition process for acquiring a reference speed, which is the moving speed when not stopped, In the deceleration position prediction process, the deceleration position is predicted based on the relationship between the reference speed and the average speed. The driving state prediction program according to Viewpoint 3-2. [Viewpoint 3-6] The relationship is a ratio or a difference. The driving state prediction program according to Viewpoint 3-5. [Viewpoint 3-7] Obtain a deceleration probability calculated based on the relationship between the change in the average speed or the reference speed, which is the moving speed when not stopped, and the average speed, and include a deceleration probability acquisition process. In the deceleration position prediction process, predict the deceleration position based on the deceleration probability. The driving state prediction program according to Viewpoint 3-2. [Viewpoint 3-8] In the deceleration position prediction process, predict the deceleration position based on whether the deceleration probability is lower than a threshold probability. The driving state prediction program according to Viewpoint 3-7. [Viewpoint 3-9] The deceleration probability is calculated based on the historical information of the actual moving speed at a predetermined position of one or more vehicles. The driving state prediction program according to Viewpoint 3-7 or 3-8.

Explanation of Signs

[0051] 4 Driving state prediction device 5 Moving speed calculation function 51 Route information acquisition function 52 Distance acquisition function 53 Moving time acquisition function 54 Speed calculation function 6 Target speed determination function 612 Deceleration determination function 622 Reference speed acquisition function 632 Deceleration probability acquisition function

Claims

1. A driving state prediction device (4) for predicting the driving state of a host vehicle, comprising: a moving speed acquisition unit (5) that acquires speed achievement information which is information on the achieved moving speeds of one or more vehicles; a deceleration position prediction unit (6) that predicts the deceleration position of the host vehicle based on the acquired speed achievement information; A driving state prediction device comprising the above.

2. The speed achievement information is an average speed. The driving state prediction device according to Claim 1.

3. The deceleration position prediction unit predicts the deceleration position based on a change in the average speed. The driving state prediction device according to Claim 2.

4. The deceleration position prediction unit predicts the deceleration position based on whether the average speed is lower than a threshold speed or whether a change amount of the average speed is greater than a threshold change amount. The driving state prediction device according to Claim 3.

5. Comprising a reference speed acquisition unit (622) that acquires a reference speed which is a moving speed during non-stop driving, The deceleration position prediction unit predicts the deceleration position based on a relationship between the reference speed and the average speed. The driving state prediction device according to Claim 2.

6. The relationship is a ratio or a difference. The driving state prediction device according to Claim 5.

7. Comprising a deceleration probability acquisition unit (632) that acquires a deceleration probability calculated based on a change in the average speed or a relationship between a reference speed which is a moving speed during non-stop driving and the average speed, The deceleration position prediction unit predicts the deceleration position based on the deceleration probability. The driving state prediction device according to Claim 2.

8. The deceleration position prediction unit predicts the deceleration position based on whether the deceleration probability is lower than a threshold probability. The driving state prediction device according to Claim 7.

9. The deceleration probability is calculated based on history information of the achieved moving speeds at a predetermined position of one or more vehicles. The driving state prediction device according to Claim 7 or 8.

10. A driving state prediction method for predicting the driving state of a host vehicle, comprising: acquiring speed achievement information which is information on the achieved moving speeds of one or more vehicles; predicting the deceleration position of the host vehicle based on the acquired speed achievement information. A driving state prediction method.

11. A driving state prediction program executed by a driving state prediction device (4) for predicting the driving state of a host vehicle, comprising: a moving speed acquisition process that acquires speed achievement information which is information on the achieved moving speeds of one or more vehicles; A deceleration position prediction process that predicts a deceleration position of the host vehicle based on the obtained speed achievement information, A driving state prediction program including the above.

Images