Route search method and route search device

Abstract

To provide a route search method and a route search device with which it is possible to conduct an appropriate search of a route in line with updating of specification of automatic driving or the development state of functional elements of a vehicle that automatically drives.SOLUTION: The route search method and route search device acquire specification of a vehicle when searching for a route for a vehicle to carry out automatic driving to travel, extracts the functional elements used for automatic driving that are associated with specification of the vehicle, identifies on the basis of the functional elements a travelable condition for the vehicle when carrying out automatic driving to travel, and extracts a route on the basis of the travelable condition.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a route search method and a route search device.

Background Art

[0002] There is known a route search method for automatic driving support that searches for a route including an automatic driving section of a vehicle using a navigation system, and extracts one or more routes in which the distance or required time from the departure point to the destination is less than or equal to a threshold value, and the distance or required time of the manual driving section is within a predetermined range having the shortest or shortest as a lower limit (Patent Document 1).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] According to the invention described in Patent Document 1, a route is searched by referring to road link data that identifies an automatic driving section in which automatic driving is possible and a manual driving section in which automatic driving is not executed. Here, the road link data is a fixed value. Therefore, even when the specifications of automatic driving are updated or when the development of functional elements of a vehicle that performs automatic driving progresses, the corresponding road link data cannot be immediately updated. Therefore, there is a problem that an appropriate route cannot be searched according to the update of the specifications of automatic driving or the development status of the functional elements of a vehicle that performs automatic driving.

[0005] The present invention has been made in view of the above problems. An object thereof is to provide a route search method and a route search device capable of searching for an appropriate route according to the update of the specifications of automatic driving or the development status of the functional elements of a vehicle that performs automatic driving. [Means for solving the problem]

[0006] A route search method and route search device according to one aspect of the present invention, when searching for a route for a vehicle to travel while performing autonomous driving, acquires the specifications of the vehicle, extracts functional elements used in autonomous driving that are associated with the vehicle specifications, identifies the conditions under which the vehicle can travel while performing autonomous driving based on the functional elements, and extracts a route based on the conditions under which the vehicle can travel. [Effects of the Invention]

[0007] According to the present invention, it is possible to search for an appropriate route in accordance with the update of the specifications for autonomous driving or the development status of the functional elements of the vehicle performing autonomous driving. [Brief explanation of the drawing]

[0008] [Figure 1] Figure 1 is a block diagram showing the configuration of a pathfinding device according to one embodiment of the present invention. [Figure 2] Figure 2 is a flowchart showing the processing of a pathfinding device according to one embodiment of the present invention. [Figure 3A] Figure 3A shows an example of the correspondence between specifications (hardware) and functional elements. [Figure 3B] Figure 3B shows an example of the correspondence between specifications (software) and functional elements. [Figure 4A] Figure 4A is a diagram illustrating the first example of a use case. [Figure 4B] Figure 4B is a diagram illustrating a second example of a use case. [Figure 5] Figure 5 shows an example of the correspondence between use cases and functional elements. [Figure 6] Figure 6 shows a first example of extracting use cases based on functional elements. [Figure 7] Figure 7 shows a second example of extracting use cases based on functional elements. [Figure 8]Figure 8 shows an example of identifying drivability conditions based on constraints that characterize a use case. [Figure 9] Figure 9 shows an example of weighting when searching for a path. [Figure 10] Figure 10 shows an example of a visualization of the drivable area based on drivable conditions. [Modes for carrying out the invention]

[0009] Embodiments of the present invention will be described below with reference to the drawings. In the drawings, identical parts are denoted by the same reference numerals and their descriptions are omitted.

[0010] [Configuration of the route search device] Figure 1 is a block diagram showing the configuration of the route search device according to this embodiment. As shown in Figure 1, the route search device according to this embodiment includes a controller 100. The controller 100 is connected to the operation unit 71, the storage unit 75, and the display device 300 by a wired or wireless communication path.

[0011] The operation unit 71 receives user operations corresponding to various instructions from the user to the route search device and transmits information identifying the various instructions to the controller 100, which will be described later. For example, the operation unit 71 may be an input interface with multiple buttons, or a touch panel with a touch interface.

[0012] More specifically, icons represented by pictures or symbols may be displayed on the screen so that the user can operate them, and the operation unit 71 may accept user input by touching, dragging, etc., the displayed icons.

[0013] The storage unit 75 (database) stores various types of information used in the processing by the controller 100 described later. More specifically, the storage unit 75 stores map information indicating the structure of the road on which the vehicle travels. The map information may include the position information of traffic lights, the types of traffic lights, the position of the stop line corresponding to the traffic lights, and the like. The storage unit 75 may be a map database that stores the map information, or may be one that stores the map information acquired from an external map data server by cloud computing. Alternatively, the storage unit 75 may be one that stores the map information acquired using vehicle-to-vehicle communication and road-to-vehicle communication.

[0014] The map information may include information on the road structure such as the number of lanes, the absolute position of the lanes, the connection relationship between the lanes, and the relative position relationship. Further, the map information may also include facility information such as parking lots and gas stations.

[0015] Alternatively, the storage unit 75 may store the specifications of the vehicle. Further, the storage unit 75 may store the functional elements used in autonomous driving in association with the specifications of the vehicle. FIGS. 3A and 3B show examples of the correspondence between the specifications and the functional elements. FIG. 3A is a diagram showing an example of the correspondence between the specifications (hardware) and the functional elements. FIG. 3B is a diagram showing an example of the correspondence between the specifications (software) and the functional elements.

[0016] According to the correspondence between the vehicle specifications and functional elements shown in FIGS. 3A and 3B, it is specified whether the hardware configuration and software configuration installed in the vehicle are specifically utilized as functional elements for autonomous driving. The "hardware specifications" include, for example, the configuration of sensors, the mounting positions of sensors, any of the coverage ranges of sensors, or combinations thereof. Also, the "software specifications" include, for example, functions such as recognition, prediction, control, and trajectory generation, which are functions that the software installed in the vehicle has. The information on "hardware specifications" and "software specifications" may be extracted from a pre-prepared table. For example, according to FIG. 3A, when a lateral sensor B with a recognition distance of 150 m is attached based on the vehicle specifications, it can be specified that the corresponding functional element is "long-distance lateral recognition".

[0017] In addition, the storage unit 75 may store the drivable conditions regarding the roads on the map information in association with the map information. Here, the "drivable conditions" mean the conditions under which the vehicle is allowed to perform autonomous driving and travel, and the drivable conditions are set based on at least one of the vehicle's behavior and road attributes. The so-called "Operational Design Domain" of autonomous driving is defined by the drivable conditions. That is, the driving environment in which autonomous driving is performed is restricted by the drivable conditions.

[0018] Furthermore, the storage unit 75 may store a plurality of use cases to which the vehicle's autonomous driving can be applied. Here, the "use case" is a set of information in which a series of vehicle driving patterns are associated with the vehicle's behavior with respect to the road structure, various constraint conditions, and the vehicle's behavior under the constraint conditions. Here, the "constraint conditions" are set based on at least one of the vehicle's behavior, environmental attributes, road attributes, and object attributes.

[0019] Examples of vehicle behavior in relation to road structure include "turning right," "turning left," "maintaining a lane," "changing lanes," "crossing lanes," "behavior when merging or diverging lanes," "entering or exiting a road," and "parking." Examples of constraints include the number of lanes ("single lane" or "multiple lanes"), the presence or absence of traffic lights ("with traffic lights" or "without traffic lights"), the presence or absence of distance constraints ("distance constraint" or "no distance constraint"), and the presence or absence of obstacles ("no obstacles," "single obstacle," or "multiple obstacles"). A vehicle's driving pattern is the specific driving path required to perform the vehicle's actions under these constraints.

[0020] When setting constraints, "environmental attributes" include time of day and weather. "Road attributes" include the number of lanes, presence or absence of traffic lights, and presence or absence of distance constraints. "Object attributes" include the presence or absence of objects, the number of objects, the state of objects, and the predicted movement of objects.

[0021] Figures 4A and 4B show examples of use cases. Figure 4A is a diagram illustrating the first example of a use case. Figure 4B is a diagram illustrating the second example of a use case. The diagrams of the intersection at the top of Figure 4A and Figure 4B show an example where the vehicle (EgoVehicle) attempts to turn left. More specifically, the "vehicle action" in Figures 4A and 4B is "turning left". The constraints are "no traffic lights" (no stop line) and "other vehicles present" at the intersection. Figure 4A shows the case where the other vehicle (V1) goes straight, and Figure 4B shows the case where the other vehicle (V1) turns left towards the vehicle. The driving pattern shows the driving path when the vehicle turns left within the intersection.

[0022] Note that the document UD1 shown at the bottom of Figure 4A corresponds to use case U1 shown at the top of Figure 4A and is a use case description document written using predicate logic. Similarly, the document UD2 shown at the bottom of Figure 4B corresponds to use case U2 shown at the top of Figure 4B and is a use case description document written using predicate logic. The vehicle's behavior in relation to the road structure and various constraints included in the use case can be extracted by referring to the use case description document.

[0023] Use cases are characterized by vehicle behavior, constraints, and vehicle driving patterns. Therefore, by pre-setting the vehicle behavior, constraints, and driving patterns expected in autonomous driving, multiple use cases necessary for verifying autonomous vehicles can be comprehensively generated. The storage unit 75 may store the comprehensively generated multiple use cases. Alternatively, the storage unit 75 may acquire and store the necessary multiple use cases from an external server or the like based on signals from the controller 100.

[0024] In addition, the memory unit 75 may store functional elements required for autonomous driving applicable to each use case. Figure 5 is a diagram showing an example of the correspondence between use cases and functional elements. In Figure 5, functional elements E1 to E4 belong to the "recognition" group, functional elements F1 and F2 belong to the "understanding" group, and functional elements G1 to G4 belong to the "decision (path generation)" group are shown. Figure 5 shows which specific functional elements are required for use cases U1 to U4.

[0025] For example, use case U1 requires functional elements E3, F2, and G4 to perform autonomous driving. Use case U2 requires functional elements E3 and F2. Use case U3 requires functional elements E2, E3, E4, and F2. Use case U4 requires functional elements E2, E3, and F2.

[0026] In Figure 5, the areas marked with a "○" indicate locations where functional elements were found to be available based on the vehicle specifications. Conversely, the areas marked with an "×" indicate locations where functional elements were found to be unavailable based on the vehicle specifications. Therefore, in the example shown in Figure 5, it can be seen that autonomous driving cannot be performed in all of use cases U1 to U4.

[0027] On the other hand, Figures 6 and 7, which show the relationship between use cases and functional elements similar to Figure 5, indicate that all functional elements necessary for autonomous driving are available in use cases U1 and U2. Therefore, in the examples shown in Figures 6 and 7, it can be seen that autonomous driving can be implemented in use cases U1 and U2.

[0028] The display device 300 presents various types of information received from the controller 100 (described later) to the user of the route search device. For example, the display device 300 is a display that displays shapes and characters using a combination of multiple display pixels. The display device 300 is not limited to presenting various types of information visually. The display device 300 may also present information to the user through auditory information, or it may present information to the user through vibration stimulation.

[0029] The controller 100 (an example of a control unit or processing unit) is a general-purpose microcomputer equipped with a CPU (central processing unit), memory, and an input / output unit. The controller 100 has a computer program (route search program) installed on it to function as part of the route search device. By executing the computer program, the controller 100 functions as one of the multiple information processing circuits (110, 120, 130, 170, 180, 190) of the route search device.

[0030] Here, we show an example of implementing multiple information processing circuits (110, 120, 130, 170, 180, 190) in a route search device using software. However, it is also possible to configure the information processing circuits (110, 120, 130, 170, 180, 190) by preparing dedicated hardware to perform each of the information processing operations shown below. Furthermore, multiple information processing circuits (110, 120, 130, 170, 180, 190) may be configured using separate hardware. In addition, the information processing circuits (110, 120, 130, 170, 180, 190) may also be used in conjunction with an electronic control unit (ECU) used for other vehicle-related control.

[0031] The controller 100 includes multiple information processing circuits (110, 120, 130, 170, 180, 190), namely a specification acquisition unit 110, a functional element extraction unit 120, a use case extraction unit 130, a drivability condition identification unit 170, a route extraction unit 180, and an output unit 190. The use case extraction unit 130 may be omitted.

[0032] The specification acquisition unit 110 acquires the vehicle specifications. For example, the specification acquisition unit 110 acquires the vehicle specifications stored in the storage unit 75. Alternatively, the specification acquisition unit 110 may acquire the vehicle specifications associated with the vehicle identification number and model number from an external server or the like, based on the vehicle identification number and model number.

[0033] The functional element extraction unit 120 extracts functional elements used in autonomous driving that are associated with the vehicle specifications. For example, the specification acquisition unit 110 extracts functional elements based on the correspondence between specifications and functional elements stored in the storage unit 75. Alternatively, the functional element extraction unit 120 may extract functional elements associated with the vehicle specifications from an external server or the like.

[0034] The functional element extraction unit 120 may also extract functional elements that are newly extracted and associated with the updated specifications when the specifications are updated, as additional functional elements. An example of the extraction of additional functional elements will be explained using Figures 5 and 6. Compared to the example shown in Figure 5, functional elements E3 and G4 are available in the example shown in Figure 6. This means that as a result of the change in the vehicle specifications, functional elements E3 and G4 have been added to the functional elements associated with the vehicle specifications. The functional element extraction unit 120 compares the functional elements associated with the specifications before the change with the functional elements associated with the specifications after the change. Then, the functional element extraction unit 120 extracts the functional elements added by the changed specifications as additional functional elements. In the example shown in Figure 6, functional elements E3 and G4 are extracted as additional functional elements. In the series of processes in which the development of autonomous driving vehicles progresses and the vehicle specifications are updated, the functional element extraction unit 120 can be said to have the function of extracting newly available functional elements.

[0035] The use case extraction unit 130 extracts use cases to which autonomous driving using functional elements is applied. For example, the use case extraction unit 130 extracts use cases that correspond to functional elements extracted by the functional element extraction unit 120, based on the correspondence between use cases and functional elements stored in the memory unit 75.

[0036] The extraction of use cases based on functional elements will be explained using Figures 6 and 7. Figure 6 shows a first example of extracting use cases based on functional elements. Figure 7 shows a second example of extracting use cases based on functional elements.

[0037] For example, suppose the functional element extraction unit 120 extracts functional elements E3, F2, and G4 based on the vehicle specifications. In this case, the use case extraction unit 130 extracts use cases U1 and U2 to which autonomous driving, utilizing functional elements E3, F2, and G4, is applicable. On the other hand, since functional elements other than functional elements E3, F2, and G4 are not available, the use case extraction unit 130 does not extract use cases U3 and U4.

[0038] The use case extraction unit 130 may also identify use cases to which automated driving using the additional functional elements extracted by the functional element extraction unit 120 is applied as additional use cases. An example of identifying additional use cases will be explained using Figure 6. In the example shown in Figure 6, functional elements E3 and G4 are extracted as additional functional elements. Therefore, the use case extraction unit 130 extracts use cases U1 and U2 to which automated driving using at least one of functional elements E3 and G4 is applied.

[0039] The use case extraction unit 130 does not need to extract use cases based on functional elements F2 that are not additional functional elements, thus reducing the computational load when extracting use cases. Furthermore, it can improve the extraction speed when extracting from multiple use cases stored in a database such as the storage unit 75.

[0040] The use case extraction unit 130 may also identify a representative use case from among the additional use cases to which autonomous driving using all of the additional functional elements is applied. An example of identifying a representative use case will be explained using Figure 7. In the example shown in Figure 7, functional elements E3 and G4 are extracted as additional functional elements, similar to Figure 6. Therefore, the use case extraction unit 130 extracts use case U1 to which autonomous driving using all of functional elements E3 and G4 is applied.

[0041] The use case extraction unit 130 extracts use cases that include both functional elements E3 and G4 to identify a representative use case. Therefore, when using functional elements as search keys during extraction, the number of search keys increases. As a result, the extraction speed can be improved when extracting from multiple use cases stored in a database such as the storage unit 75.

[0042] The drivability condition identification unit 170 identifies the conditions under which the vehicle can operate when performing autonomous driving, based on the extracted functional elements. Here, the drivability conditions are set based on at least one of the following: the vehicle's behavior and the road attributes.

[0043] More specifically, the drivability condition identification unit 170 identifies drivability conditions based on constraints that characterize the use case. For example, the constraints that characterize each use case extracted by the use case extraction unit 130 can be extracted by referring to the use case description document corresponding to the use case.

[0044] An example of constraint extraction will be explained using Figure 8. Figure 8 shows an example of identifying drivability conditions based on constraints that characterize a use case. According to Figure 8, the drivability condition identification unit 170 extracts the descriptions WP related to vehicle behavior and road attributes from the use case description document UD1. Whether or not a part of the use case description document describes vehicle behavior and road attributes can be determined by the type of function in the predicate logic.

[0045] The drivability condition identification unit 170 then identifies the description WPs that are common to all extracted use case description documents from the extracted description WPs as drivability condition ODDs.

[0046] Furthermore, the drivability condition identification unit 170 may update the drivability conditions based on constraints that characterize the additional use case. That is, it may identify descriptions of vehicle behavior and road attributes that are common in the use case description document corresponding to the additional use case as drivability conditions. The drivability condition identification unit 170 may then update the drivability conditions by adding the identified drivability conditions to the already registered drivability conditions. This reduces the computational load when identifying drivability conditions.

[0047] Furthermore, the drivability condition identification unit 170 may update the drivability conditions based on constraints that characterize a representative use case. That is, it may identify descriptions of vehicle behavior and road attributes that are common in the use case description document corresponding to the representative use case as drivability conditions. The drivability condition identification unit 170 may then update the drivability conditions by adding the identified drivability conditions to the already registered drivability conditions. This reduces the computational load when identifying drivability conditions.

[0048] The route extraction unit 180 searches for a route for the vehicle to travel using autonomous driving. More specifically, the route extraction unit 180 extracts a route for the vehicle to travel using autonomous driving based on the identified driving conditions. For example, the route extraction unit 180 may obtain the departure and destination points of the vehicle's journey set by the user, calculate multiple route candidates with the departure point as the starting point and the destination point as the ending point, and then extract the route candidates that satisfy the driving conditions.

[0049] Furthermore, the route extraction unit 180 may set a cost value for each route based on the drivability conditions and prioritize extracting routes with smaller cost values, considering them as recommended routes suitable for automated driving. In addition, the route extraction unit 180 may set a cost value for each route based on the drivability conditions, calculate the driving distance for each route, and prioritize extracting routes with smaller product values ​​of cost value and driving distance, considering them as recommended routes suitable for automated driving.

[0050] Various methods can be used to calculate the cost value. For example, the route extraction unit 180 may set a higher cost value the more manual driving sections there are on the route where the driving conditions are not met. In other words, the more sections there are on the route for which the cost value is to be calculated where automatic driving is not possible and manual driving is required, the higher the cost value of that route may be set by the route extraction unit 180.

[0051] Furthermore, the route extraction unit 180 may set the cost value of routes that have manual driving sections where the driving conditions are not met to a predetermined value or higher, and the cost value of routes that do not have manual driving sections to a predetermined value or lower. In other words, the route extraction unit 180 may set the cost value of routes that include sections where automatic driving is not possible and manual driving is required to be performed to be higher than the cost value of routes where automatic driving is possible throughout the entire section.

[0052] Furthermore, the route extraction unit 180 may set an evaluation value indicating the difficulty of traveling the route based on the route, and the larger the evaluation value, the smaller the cost value may be set.

[0053] Various methods can be used to calculate the difficulty of driving a route. For example, the route extraction unit 180 may set a higher evaluation value the more right and left turns the vehicle makes along the route. Alternatively, the route extraction unit 180 may set a higher evaluation value the more congested the area around the vehicle along the route is.

[0054] The weighting used to calculate cost values ​​and driving difficulty will be explained using Figure 9. Figure 9 is a diagram showing an example of weighting when searching for a route. As shown in Figure 9, the route extraction unit 180 may set high cost values ​​and driving difficulty by assigning a large weight to roads where autonomous driving is impossible (roads where autonomous driving cannot be continued).

[0055] Furthermore, the route extraction unit 180 may assign different weights to roads where autonomous driving is possible (roads where autonomous driving can be continued) depending on the type of road. In particular, roads that include intersections or merges may be given a greater weight than other roads, and the cost value and driving difficulty may be set higher.

[0056] The output unit 190 outputs the route extracted by the route extraction unit 180. The output route is transmitted to a vehicle control unit (not shown) or the like, and is used for automatic vehicle operation or to provide driving assistance to the user.

[0057] In addition, the output unit 190 may output the percentage of roads where autonomous driving is possible based on the identified drivability conditions, linked to map information. Furthermore, the output unit 190 may output the drivability area based on the drivability conditions. Figure 10 shows an example of visualizing the drivability area based on the drivability conditions.

[0058] For example, as shown in Figure 10, the squares on the map may be displayed according to the percentage of roads that are capable of autonomous driving within a given square. Methods for displaying the squares include changing the display color and transparency. For example, squares may be displayed in red if the percentage of roads capable of autonomous driving is less than 30%, yellow if the percentage is between 30% and 90%, and green if the percentage is 90% or more.

[0059] Furthermore, the priority of route extraction in the route extraction unit 180 may be changed according to the proportion of roads in each square that are capable of automated driving.

[0060] In addition, if the vehicle is driving an automated driving section on the route output by the output unit 190 where the conditions for driving are met, and the automated driving is interrupted more than a predetermined number of times, the storage unit 75 may store the automated driving section as a section where automated driving is not possible.

[0061] [Processing procedure of the route search device] Next, the processing procedure of the route search device according to this embodiment will be described with reference to the flowchart in Figure 2. The processing of the route search device shown in Figure 2 may be started based on user instructions, or it may be executed repeatedly at predetermined intervals.

[0062] First, in step S101, the specification acquisition unit 110 acquires the vehicle specifications.

[0063] In step S103, the functional element extraction unit 120 determines whether the vehicle specifications have been changed. If the vehicle specifications have not been changed (NO in step S103), the process proceeds to step S115. On the other hand, if the vehicle specifications have been changed (YES in step S103), the process proceeds to step S105.

[0064] Note that the determination of whether or not the specifications have changed in step S103 may be omitted. In that case, regardless of whether or not the specifications have changed, the process proceeds to step S105.

[0065] In step S105, the functional element extraction unit 120 extracts functional elements used in autonomous driving that correspond to the vehicle specifications.

[0066] In step S107, the use case extraction unit 130 extracts use cases to which automated driving using functional elements is applicable.

[0067] In step S109, the drivability condition identification unit 170 extracts constraints that characterize the use case.

[0068] In step S111, the drivability condition identification unit 170 identifies the conditions under which the vehicle can drive when it is performing autonomous driving.

[0069] In step S113, the memory unit 75 stores the extracted drivability conditions.

[0070] In step S115, the route extraction unit 180 extracts a route for the vehicle to travel using autonomous driving based on the identified drivability conditions.

[0071] In step S117, the output unit 190 outputs the route extracted by the route extraction unit 180.

[0072] [Effects of the Embodiment] As described in detail above, the route search method and route search device according to this embodiment, when searching for a route for a vehicle to travel while performing autonomous driving, acquire the vehicle specifications, extract functional elements used in autonomous driving that are associated with the vehicle specifications, identify the conditions under which the vehicle can travel while performing autonomous driving based on the functional elements, and extract a route based on the conditions under which the vehicle can travel.

[0073] This allows for the search for appropriate routes in line with updates to the autonomous driving specifications or the development status of the functional elements of the autonomous vehicle. Furthermore, it enables the search for routes that match the autonomous driving specifications without requiring a driving history.

[0074] Furthermore, the route search method and route search device according to this embodiment may extract use cases to which automated driving using functional elements is applied, and identify drivable conditions based on constraints that characterize the use cases. This makes it possible to verify automated driving based on a comprehensively generated set of use cases, and to accurately verify changes in drivable conditions due to specification changes.

[0075] Furthermore, the route search method and route search device according to this embodiment may, when specifications are updated, extract newly extracted functional elements associated with the updated specifications as additional functional elements, identify use cases to which automated driving using the additional functional elements is applied as additional use cases, and update the drivability conditions based on the constraints characterizing the additional use cases. This reduces the computational load when extracting use cases. Moreover, it is possible to accurately verify changes in drivability conditions due to specification changes while suppressing the computational load. In addition, the time required to identify drivability conditions can be shortened.

[0076] Furthermore, the route search method and route search device according to this embodiment may identify a use case to which automated driving using all additional functional elements is applied as a representative use case from among the additional use cases, and update the drivability conditions based on the constraints that characterize the representative use case. This reduces the computational load when extracting use cases. Moreover, it is possible to accurately verify changes in drivability conditions due to specification changes while keeping the computational load down. In addition, the time required to identify drivability conditions can be shortened.

[0077] Furthermore, in the route search method and route search device according to this embodiment, the constraints may be set based on at least one of the following: vehicle behavior, environmental attributes, road attributes, and object attributes. This makes it possible to comprehensively verify the vehicle conditions expected in autonomous driving. In particular, it is possible to comprehensively generate multiple use cases required for verifying autonomous vehicles. As a result, it is possible to accurately verify changes in drivability conditions due to specification changes.

[0078] Furthermore, in the route search method and route search device according to this embodiment, the vehicle's behavior in relation to the road structure, the constraints, and the vehicle's driving pattern representing the vehicle's behavior under those constraints may be associated with the use case. This allows for a comprehensive verification of the vehicle's situation as expected in autonomous driving. In addition, by clearly indicating the relationship between the vehicle's behavior in relation to the road structure, the constraints, and the vehicle's driving pattern representing the vehicle's behavior under those constraints, use cases that affect the conditions under which driving is possible can be easily recognized. Moreover, developers can recognize functional elements that need to be prioritized for development in order to increase the proportion of roads where autonomous driving is possible.

[0079] Furthermore, in the route search method and route search device according to this embodiment, the drivability conditions may be set based on at least one of the vehicle's behavior and road attributes. This clearly indicates that the drivability conditions are conditions under which the vehicle is permitted to operate autonomously. In particular, the drivability conditions define the so-called "operational design domain" of autonomous driving, and can restrict the driving environment in which autonomous driving is performed.

[0080] Furthermore, the route search method and route search device according to this embodiment may extract functional elements that correspond to the specifications by referring to a database that stores specifications and functional elements that correspond to the specifications. This makes it possible to speed up the extraction of functional elements that correspond to the specifications. Moreover, it is possible to shorten the time required to change the drivability conditions when the specifications are changed.

[0081] Furthermore, the route search method and route search device according to this embodiment may set a cost value for each route based on the drivability conditions, and prioritize extracting routes with smaller cost values, considering them as recommended routes suitable for automated driving. This allows for the priority extraction of routes with a high probability of being able to perform automated driving, thereby improving user convenience.

[0082] Furthermore, the route search method and route search device according to this embodiment may set a cost value for each route based on the drivability conditions, calculate the travel distance for each route, and prioritize extracting routes that are suitable for autonomous driving, starting with the route with the smallest product of the cost value and travel distance. This makes it possible to prioritize the extraction of routes that have a high probability of being able to perform autonomous driving while keeping the travel distance down, thereby improving user convenience.

[0083] Furthermore, the route search method and route search device according to this embodiment may set a higher cost value as the number of manually driven sections on the route where the driving conditions are not met increases. This allows for the priority extraction of routes that have a high probability of being able to perform automated driving. Moreover, it reduces the possibility of extracting routes that require manual driving. As a result, the opportunities for the user to intervene in driving the vehicle can be reduced, improving user convenience.

[0084] Furthermore, the route search method and route search device according to this embodiment may set the cost value of routes with manual driving sections where the driving conditions are not met to a predetermined value or higher, and the cost value of routes without manual driving sections to a predetermined value or lower. This makes it possible to prioritize the extraction of routes that have a high probability of being able to perform automated driving. Moreover, it is possible to reduce the possibility of extracting routes that require manual driving. As a result, the opportunities for the user to intervene in the driving of the vehicle can be reduced, and user convenience can be improved.

[0085] Furthermore, the route search method and route search device according to this embodiment may set an evaluation value indicating the difficulty of driving a route based on the route, and the higher the evaluation value, the higher the cost value may be set. This makes it possible to prioritize the extraction of routes that can be easily implemented with automated driving. As a result, the possibility of automated driving being interrupted due to the occurrence of unexpected events can be reduced.

[0086] Furthermore, the route search method and route search device according to this embodiment may set a higher evaluation value the more times the vehicle makes right or left turns along the route. This allows for the priority extraction of routes that facilitate autonomous driving. In addition, since the number of right or left turns by the vehicle can be reduced, the risk of the autonomously driving vehicle obstructing traffic flow can be reduced.

[0087] Furthermore, the route search method and route search device according to this embodiment may set a higher evaluation value the higher the degree of congestion around the vehicle along the route. This makes it possible to prioritize the extraction of routes that can be easily implemented with autonomous driving. In addition, it is possible to reduce the risk of the autonomously driving vehicle obstructing traffic flow.

[0088] Furthermore, the route search method and route search device according to this embodiment may output the extracted route. This allows for automated vehicle operation or driving assistance for the user based on the extracted route.

[0089] Furthermore, the route search method and route search device according to this embodiment may store an automated driving section as a section where automated driving is not possible if the automated driving is interrupted a predetermined number of times or more while the vehicle is driving an automated driving section on the route where the conditions for driving are met. This reduces the possibility that a route including a section where it is not possible to determine whether automated driving is possible based on the conditions for driving may be extracted as a route where automated driving can be continuously performed. As a result, user convenience can be improved.

[0090] Each of the functions described in the embodiments above may be implemented by one or more processing circuits. These processing circuits may include programmed processors, electrical circuits, and even devices such as application-specific integrated circuits (ASICs) and circuit components arranged to perform the described functions.

[0091] Although the present invention has been described above in accordance with the embodiments, it will be obvious to those skilled in the art that the present invention is not limited to these descriptions and that various modifications and improvements are possible. The discussion and drawings that constitute part of this disclosure should not be understood as limiting the present invention. Various alternative embodiments, examples, and operational techniques will become apparent to those skilled in the art from this disclosure.

[0092] Of course, the present invention includes various embodiments and the like that are not described herein. Therefore, the technical scope of the present invention is defined solely by the inventive features of the claims that are reasonable based on the above description. [Explanation of symbols]

[0093] 71 Operation section 75 Memory section 100 controllers 110 Specification Acquisition Section 120 Functional element extraction unit 130 Use Case Extraction Unit 170 Conditions for determining drivability 180 Path extraction unit 190 Output section 300 display device

Claims

1. A path search method for controlling a controller that searches for a path for a vehicle to travel while performing autonomous driving, The aforementioned controller Obtain the specifications of the aforementioned vehicle, Extract the functional elements used in the autonomous driving that correspond to the above specifications, Use cases to which the autonomous driving using the aforementioned functional elements is applied are extracted. Based on the constraints that characterize the use case, the conditions under which the vehicle can operate while performing the autonomous driving are identified. When the aforementioned specifications are updated, the functional elements that are newly extracted in association with the updated specifications are extracted as additional functional elements. The use case to which the autonomous driving using the aforementioned additional functional elements is applied is identified as an additional use case. The drivability conditions are updated based on the constraints that characterize the additional use cases. Extracting the route based on the aforementioned drivability conditions. A pathfinding method characterized by the following.

2. A pathfinding method according to claim 1, The aforementioned controller From among the aforementioned additional use cases, the use case to which the autonomous driving using all of the aforementioned additional functional elements is applied is identified as a representative use case. Updating the drivability conditions based on the constraints that characterize the representative use case. A pathfinding method characterized by the following.

3. A pathfinding method according to claim 1 or 2, The aforementioned constraints are set based on at least one of the following: the vehicle's behavior, environmental attributes, road attributes, and object attributes. A pathfinding method characterized by the following.

4. A pathfinding method according to any one of claims 1 to 3, The aforementioned use case associates the vehicle's behavior with respect to the road structure, the constraints, and the vehicle's driving pattern representing the vehicle's behavior under those constraints. A pathfinding method characterized by the following.

5. A pathfinding method according to any one of claims 1 to 4, The aforementioned driving conditions shall be set based on at least one of the following: the vehicle's behavior and the road attributes. A pathfinding method characterized by the following.

6. A pathfinding method according to any one of claims 1 to 5, The controller refers to a database storing the specifications and the functional elements associated with the specifications, and extracts the functional elements associated with the specifications. A pathfinding method characterized by the following.

7. A pathfinding method according to any one of claims 1 to 6, The aforementioned controller Based on the aforementioned conditions for drivability, a cost value is set for each of the aforementioned routes. The routes with the lowest cost values ​​are selected first, and then prioritized as recommended routes suitable for autonomous driving. A pathfinding method characterized by the following.

8. A pathfinding method according to any one of claims 1 to 7, The aforementioned controller Based on the aforementioned conditions for drivability, a cost value is set for each of the aforementioned routes. The distance traveled for each of the aforementioned routes is calculated, The routes with the smallest product of the cost value and the driving distance are selected in order, and are prioritized as recommended routes suitable for autonomous driving. A pathfinding method characterized by the following.

9. A pathfinding method according to claim 7 or 8, The controller sets the cost value higher the more manual driving sections there are on the route where the driving conditions are not met. A pathfinding method characterized by the following.

10. A pathfinding method according to claim 7 or 8, The aforementioned controller The cost value of the route having a manual driving section where the aforementioned driving conditions are not met is set to a predetermined value or higher. The cost value of the route that does not have the manual driving section is set to less than the predetermined value. A pathfinding method characterized by the following.

11. A pathfinding method according to any one of claims 7 to 10, The aforementioned controller Based on the aforementioned route, an evaluation value indicating the difficulty of driving the aforementioned route is set. The larger the aforementioned evaluation value, the larger the aforementioned cost value should be set. A pathfinding method characterized by the following.

12. A pathfinding method according to claim 11, The controller sets the evaluation value to be larger the more times the vehicle makes right or left turns along the route. A pathfinding method characterized by the following.

13. A pathfinding method according to claim 11 or 12, The controller sets the evaluation value higher the higher the degree of congestion around the vehicle on the route. A pathfinding method characterized by the following.

14. A pathfinding method according to any one of claims 1 to 13, The controller outputs the extracted path. A pathfinding method characterized by the following.

15. A pathfinding method according to any one of claims 1 to 14, The controller stores the automated driving section as a section where automated driving is not possible if the automated driving is interrupted more than a predetermined number of times while the vehicle is driving in the automated driving section on the route where the driving conditions are met. A pathfinding method characterized by the following.

16. A route search device equipped with a controller that searches for a route for a vehicle to travel while performing autonomous driving, The aforementioned controller Obtain the specifications of the aforementioned vehicle, Extract the functional elements used in the autonomous driving that correspond to the above specifications, Use cases to which the autonomous driving using the aforementioned functional elements is applied are extracted. Based on the constraints that characterize the use case, the conditions under which the vehicle can operate while performing the autonomous driving are identified. When the aforementioned specifications are updated, the functional elements that are newly extracted in association with the updated specifications are extracted as additional functional elements. The use case to which the autonomous driving using the aforementioned additional functional elements is applied is identified as an additional use case. The drivability conditions are updated based on the constraints that characterize the additional use cases. Extracting the route based on the aforementioned drivability conditions. A pathfinding device characterized by the following.

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