Driving assistance device and computer program product

Abstract

The present application provides a kind of driving assistance device and computer program product, which can determine the recommended travel track to the parking lot as the parking object, and can properly implement driving assistance. Specifically, the parking lot where the vehicle is parked at the destination is obtained, high-precision map information (16) containing at least information related to the driving lane, facility information (17) containing information related to the entrance of the parking lot, and connection information (18) representing the connection relationship between the driving lane contained in the access road and the entrance of the parking lot are used to generate the travel track recommended for the vehicle to travel from the travel starting point to the entrance of the parking lot, and driving assistance for the vehicle is performed based on the generated travel track.

Description

Technical Field

[0001] This invention relates to a driving assistance device and computer program product for assisting the driving of a vehicle. Background Technology

[0002] When a vehicle is moving towards a destination, it is generally moved to a parking lot attached to the destination or a parking lot located near the destination to park the vehicle. The movement is completed by walking from the parking space where the vehicle is parked to the destination location within the parking lot. For example, Japanese Patent Application Publication No. 2008-241602 discloses the following technology: after setting a destination, the entrance to the parking lot attached to the destination is determined, and a path is searched up to the road segment connecting to the determined entrance of the parking lot.

[0003] Existing technical documents

[0004] Patent documents

[0005] Patent Document 1: Japanese Patent Application Publication No. 2008-241602 (pp. 13-17) Figure 6 ). Summary of the Invention

[0006] The problem the invention aims to solve

[0007] In the technology of Patent Document 1 mentioned above, although a path is found up to the road segment connecting to the entrance of the parking lot, a travel track is not generated up to the subsequent parking lot. In the technology of Patent Document 1 mentioned above, there is a problem that the method of entering the parking lot from the road segment connecting to the entrance is not considered.

[0008] The present invention was made to solve the aforementioned problems, and its object is to provide a driving assistance device and a computer program product that, by using connection information indicating the connection relationship between the driving lanes included in the entrance road to the parking lot entrance and the parking lot entrance, can determine a recommended driving trajectory from the starting point of driving to the parking lot to which the parking is to be parked, and can appropriately implement driving assistance.

[0009] means for solving problems

[0010] To achieve the above objectives, the driving assistance device of the present invention comprises: a parking lot acquisition unit that acquires a parking lot where the vehicle will park at its destination; a driving trajectory generation unit that uses road information including at least information related to driving lanes, facility information including information related to the entrance of the parking lot, and connection information indicating the connection relationship between driving lanes included in the road leading to the entrance of the parking lot and the entrance of the parking lot, to generate a recommended driving trajectory for the vehicle from the starting point of travel to the entrance of the parking lot; and a driving assistance unit that provides driving assistance for the vehicle based on the driving trajectory.

[0011] In addition, "trajectory" can be information that determines the specific track (set of coordinates or line) on which the vehicle travels, or it can be information that, while not specifying the specific track, determines the road and lane on which the vehicle travels (i.e., the way the vehicle moves within the lane).

[0012] Furthermore, the computer program product involved in this invention includes a program for generating assistance information for driving assistance implemented in a vehicle. Specifically, the computer functions as the following units: a parking lot acquisition unit that acquires parking lots where the vehicle will park at its destination; a driving trajectory generation unit that uses road information containing at least information related to driving lanes, facility information containing information related to the entrance of the parking lot, and connection information indicating the connection relationship between driving lanes included in the road leading to the entrance of the parking lot and the entrance of the parking lot, to generate a recommended driving trajectory for the vehicle to travel from the starting point of travel to the entrance of the parking lot; and a driving assistance unit that provides driving assistance to the vehicle based on the driving trajectory.

[0013] The effects of the invention

[0014] According to the driving assistance device and computer program product of the present invention having the above-described structure, a recommended driving trajectory from the starting point of driving to the parking lot to which the parking is to be parked can be determined by using connection information, wherein the connection information indicates the connection relationship between the driving lanes included in the entrance road facing the parking lot entrance and the entrance of the parking lot. Furthermore, by providing driving assistance based on the determined driving trajectory, driving assistance can be appropriately implemented. Attached Figure Description

[0015] Figure 1 This is a schematic structural diagram of the driving assistance system involved in this embodiment.

[0016] Figure 2 This is a block diagram illustrating the structure of the driving assistance system involved in this embodiment.

[0017] Figure 3 This is a block diagram illustrating the navigation device according to this embodiment.

[0018] Figure 4 This is a flowchart of the autonomous driving assistance program involved in this embodiment.

[0019] Figure 5 It represents a region where high-precision map information is obtained.

[0020] Figure 6 This diagram illustrates the calculation method for a dynamic travel track.

[0021] Figure 7 This is a flowchart of the subprocessing procedure for generating static driving tracks.

[0022] Figure 8 This is a diagram showing an example of a waiting route to the parking lot.

[0023] Figure 9 It means to Figure 8 The diagram shows an example of a lane network constructed using alternative routes.

[0024] Figure 10 This is an example diagram of a lane sign that shows the correspondence between the lanes of traffic on the road before the intersection and the lanes of traffic on the road after the intersection.

[0025] Figure 11 This is a diagram illustrating an example of a parking network constructed within a parking lot.

[0026] Figure 12 This is a diagram illustrating an example of a pedestrian network constructed between a parking lot and a destination facility.

[0027] Figure 13 This is a diagram representing an example of connection information.

[0028] Figure 14 This is a graph showing the relationship between the number of lane changes and lane cost.

[0029] Figure 15 It is a graph showing the relationship between driving lanes and lane costs.

[0030] Figure 16 This is a graph showing the relationship between the location of lane changes and lane costs.

[0031] Figure 17 This diagram illustrates the method for determining the recommended location when changing lanes.

[0032] Figure 18This is a diagram illustrating an example of a recommended driving route when crossing an intersection.

[0033] Figure 19 This is a diagram illustrating an example of a recommended driving path when entering a parking lot.

[0034] Figure 20 This is a diagram illustrating an example of a recommended driving path when parking in a parking space.

[0035] Figure 21 This is a flowchart of the subprocessing procedure for speed plan generation.

[0036] Figure 22 This is a diagram illustrating an example of a speed plan. Detailed Implementation

[0037] Hereinafter, with reference to the accompanying drawings, a specific embodiment of the driving assistance device according to the present invention, specifically a navigation device 1, will be described in detail. First, using... Figure 1 and Figure 2 The general structure of the driving assistance system 2, which includes the navigation device 1 according to this embodiment, will be described. Figure 1 This is a schematic structural diagram showing the driving assistance system 2 according to this embodiment. Figure 2 This is a block diagram illustrating the structure of the driving assistance system 2 according to this embodiment.

[0038] like Figure 1 As shown, the driving assistance system 2 according to this embodiment basically includes: a server device 4 provided by an information publishing center 3; and a navigation device 1 installed in a vehicle 5 to provide various assistance related to the autonomous driving of the vehicle 5. Furthermore, the server device 4 and the navigation device 1 are configured to exchange electronic data via a communication network 6. Alternatively, other in-vehicle devices installed in the vehicle 5 or vehicle control devices that perform controls related to the vehicle 5 may be used instead of the navigation device 1.

[0039] Among them, vehicle 5 is a vehicle that, in addition to manual driving based on the user's driving operation, can also drive with the assistance of autonomous driving assistance, which enables the vehicle to drive automatically along a pre-set path or road without relying on the user's driving operation.

[0040] Furthermore, autonomous driving assistance can be implemented on all road sections, or it can be configured to operate only when the vehicle is traveling on a specific road section (e.g., a highway with exits and exits at the boundaries (regardless of whether there are people or not, or whether it is toll-free)). In the following description, the autonomous driving zone for which autonomous driving assistance is implemented will include not only all road sections, including general roads and highways, but also parking lots, where autonomous driving assistance is essentially implemented from the start of driving until the vehicle stops. However, it is desirable that autonomous driving assistance is not necessarily implemented while the vehicle is traveling within the autonomous driving zone, but only when the user selects to implement autonomous driving assistance (e.g., by setting the autonomous driving start button to "on") and it is determined that driving based on autonomous driving assistance is feasible. On the other hand, vehicle 5 can also be a vehicle capable only of assisted driving based on autonomous driving assistance.

[0041] Furthermore, in the vehicle control of the automated driving assistance system, for example, the vehicle's current position, the driving lane, and the position of surrounding obstacles are detected at any time, and the vehicle control of the steering device, drive source, brakes, etc., is automatically performed to drive along the driving trajectory generated by the navigation device 1 as described later and at the speed of the similarly generated speed plan. In addition, in the assisted driving based on automated driving assistance in this embodiment, lane changing, left and right turns, and parking operations can also be performed by driving manually without using automated driving assistance.

[0042] On the other hand, the navigation device 1 is mounted on the vehicle 5 and is an in-vehicle unit that displays a map of the area surrounding the vehicle's location based on map data stored in the navigation device 1 or map data acquired from external sources, or allows the user to input a destination, displays the vehicle's current location on the map image, or provides movement guidance along a pre-set guide path. In this embodiment, especially when the vehicle is driving with automated driving assistance, various auxiliary information related to automated driving assistance is generated. Examples of auxiliary information include, for instance, a recommended driving trajectory (including a recommended lane movement method) and a speed plan indicating the vehicle's speed. Further details regarding the navigation device 1 will be described later.

[0043] Additionally, server device 4 can also perform route search based on a request from navigation device 1. Specifically, it sends the necessary route search information, such as the origin and destination, along with the route search request from navigation device 1 to server device 4 (however, in the case of a re-search, it is not necessarily necessary to send destination-related information). Then, server device 4, having received the route search request, uses the map information it possesses to perform a route search and determine a recommended route from the origin to the destination. It then sends the determined recommended route to navigation device 1, which is the request source. Navigation device 1 can then provide information related to the received recommended route to the user, or use the recommended route to generate various auxiliary information related to autonomous driving assistance, as described later.

[0044] Furthermore, in addition to the usual map information used in the path search described above, server device 4 also possesses higher-precision map information, namely high-precision map information and facility information. High-precision map information includes, for example, information related to road lane shapes (road shape, curvature, lane width, etc. in terms of driving lanes) and lane markings (lane center lines, lane dividers, lane outer lines, guide lines, etc.) drawn on the road. It also includes other information related to intersections. On the other hand, facility information is more detailed information related to facilities, stored separately from the facility-related information contained in the map information. For example, it includes information related to parking lot entrances and connection information indicating the connection between parking lot entrances and driving lanes. Furthermore, server device 4 publishes high-precision map information and facility information according to requests from navigation device 1. Navigation device 1 uses the high-precision map information and facility information published from server device 4 to generate various auxiliary information related to autonomous driving assistance, as described later. Additionally, while high-precision map information is generally map information focusing only on roads (road segments) and their surroundings, it may also include map information covering areas beyond the road's perimeter.

[0045] However, the path search processing described above does not necessarily have to be performed by server device 4. If the navigation device 1 has map information, it can also perform the processing. Furthermore, the high-precision map information and facility information may not be published by server device 4, but rather be pre-existing in the navigation device 1.

[0046] Furthermore, the communication network 6 includes multiple base stations deployed throughout the country and a communication company that manages and controls each base station. The base stations and communication companies are interconnected via wired (fiber optic, ISDN, etc.) or wireless means. Each base station has a transceiver and an antenna for communicating with the navigation device 1. The base station communicates wirelessly with the communication company, and as a terminal of the communication network 6, it relays communication between itself and the server device 4 with the navigation device 1 located within the range (cell) of the base station's radio waves.

[0047] Next, use Figure 2 The structure of the server device 4 in the driver assistance system 2 will be described in more detail. For example... Figure 2 As shown, the server device 4 includes: a server control unit 11, a server-side map DB12 connected to the server control unit 11 as an information recording unit, a high-precision map DB13, a facility DB14, and a server-side communication device 15.

[0048] The server control unit 11 is a control unit (MCU, MPU, etc.) that performs overall control of the server device 4. It includes: a CPU 21 serving as both an arithmetic and control unit; and internal storage devices such as RAM 22 used as working memory during various arithmetic operations performed by the CPU 21, a ROM 23 storing control programs, and flash memory 24 storing programs read from the ROM 23. Furthermore, both the server control unit 11 and the ECU of the navigation device 1 (described later) have various units serving as processing algorithms.

[0049] On the other hand, the server-side map DB12 is a storage unit that stores server-side map information. This server-side map information is the latest version of the map information registered based on input data or input operations from external sources. This server-side map information consists of various information required for path search, path guidance, and map display, represented by road networks. For example, it includes network data representing nodes and road segments, road segment data related to roads (road segments), node data related to nodes, intersection data related to intersections, location data related to facilities and other locations, map display data for displaying the map, search data for searching paths, and retrieval data for retrieving locations.

[0050] In addition, the high-precision map DB13 is a storage unit that stores map information with higher precision than the aforementioned server-side map information, namely high-precision map information 16. High-precision map information 16 stores map information containing more detailed information, particularly regarding roads and facilities that are the objects of vehicle travel. In this embodiment, for example, regarding roads, it includes information related to lane shapes (road shape, curvature, lane width, etc. in units of driving lanes) and markings drawn on the road (lane center lines, lane dividers, lane outer lines, guide lines, etc.). Furthermore, it records the following data: data indicating road slope, inclination, embankments, merging sections, places where the number of driving lanes decreases, places where the width narrows, intersections, etc.; regarding curves, data indicating radius of curvature, intersections, T-junctions, curve entrances and exits, etc.; regarding road attributes, data indicating downhill roads, uphill roads, etc.; and regarding road types, in addition to representing general roads such as national highways, county roads, and narrow streets, it also represents toll roads such as high-speed national highways, urban expressways, dedicated motor vehicle roads, general toll roads, and toll bridges. In this embodiment, in addition to the number of lanes, information is also stored regarding the traffic division of each lane, the direction of travel, and the road connections (specifically, the correspondence between lanes included in the road before an intersection and lanes included in the road after an intersection). Furthermore, speed limits set for the roads are also stored.

[0051] On the other hand, Facility DB14 is a storage unit that stores facility-related information in more detail than the facility-related information stored in the aforementioned server-side map information. Specifically, as facility information 17, particularly for parking lots (including parking lots attached to facilities and independent parking lots) that are intended for vehicles, it includes information determining the location of the parking lot's entrance and exit, information determining the configuration of parking spaces within the parking lot, information related to the zoning lines for the parking spaces, and information related to the passageways that vehicles or pedestrians can pass through. For facilities other than parking lots, it includes information determining the entrance and exit of the facility or the passageways that users (pedestrians) within the facility can pass through. Facility information 17 can also be information specifically generated from a 3D model of a parking lot or facility. In addition, Facility DB14 also includes connection information 18 and road shape information 19. Connection information 18 indicates the connection relationship between the driveway included in the entrance road facing the parking lot entrance and the parking lot entrance, and road shape information 19 determines the area that vehicles can pass through between the entrance road and the parking lot entrance. Details of the information stored in Facility DB14 will be described later.

[0052] Furthermore, high-precision map information 16 is basically map information focusing only on roads (road segments) and their surroundings, but it can also include map information covering areas beyond the road's perimeter. Additionally, in Figure 2 In the example shown, the server-side map information stored in server-side map DB12 and the information stored in high-precision map DB13 or facility DB14 are different map information, but the information stored in high-precision map DB13 or facility DB14 can also be part of the server-side map information. Alternatively, high-precision map DB13 and facility DB14 can be treated as a single database without distinguishing between them.

[0053] On the other hand, the server-side communication device 15 is a communication device used to communicate with the navigation devices 1 of each vehicle 5 via the communication network 6. In addition to the navigation devices 1, it can also receive traffic information composed of various information such as congestion information, restriction information, and traffic accident information sent by the Internet, traffic information centers, such as VICS (registered trademark: Vehicle Information and Communication System) centers.

[0054] Next, use Figure 3 The general structure of the navigation device 1 mounted on vehicle 5 will be described. Figure 3 This is a block diagram illustrating the navigation device 1 according to this embodiment.

[0055] like Figure 3 As shown, the navigation device 1 according to this embodiment includes: a current position detection unit 31 that detects the current position of the vehicle equipped with the navigation device 1; a data recording unit 32 that records various data; a navigation ECU 33 that performs various calculations based on input information; an operation unit 34 that receives operations from the user; a liquid crystal display 35 that displays a map of the vehicle's surroundings and information related to the guidance path (pre-determined driving path of the vehicle) set by the navigation device 1 to the user; a speaker 36 that outputs sound navigation related to the path guidance; a DVD drive 37 that reads DVDs as storage media; and a communication module 38 that communicates with information centers such as detection centers and VICS centers. Furthermore, the navigation device 1 is connected to an external camera 39 and various sensors installed on the vehicle equipped with the navigation device 1 via an in-vehicle network such as CAN. In addition, it is bidirectionally connected to a vehicle control ECU 40 that performs various controls on the vehicle equipped with the navigation device 1.

[0056] The following sections will describe each component of the navigation device 1.

[0057] The current position detection unit 31 consists of a GPS 41, a vehicle speed sensor 42, a steering sensor 43, and a gyroscope sensor 44, and is capable of detecting the current vehicle position, orientation, vehicle speed, and current time. In particular, the vehicle speed sensor 42 is used to detect the vehicle's travel distance and speed. It generates pulses based on the rotation of the vehicle's drive wheels and outputs these pulse signals to the navigation ECU 33. The navigation ECU 33 then counts the generated pulses to calculate the rotational speed and travel distance of the drive wheels. Furthermore, the navigation device 1 does not necessarily need to possess all four types of sensors; it can be configured to have only one or more of these sensors.

[0058] In addition, the data recording unit 32 includes: a hard disk (not shown), which serves as an external storage device and recording medium; and a recording head (not shown), which serves as a drive for reading map information DB45, cache 46, prescribed programs, etc., recorded on the hard disk, and for writing prescribed data to the hard disk. Alternatively, instead of a hard disk, the data recording unit 32 may also include a flash memory, memory card, CD, DVD, or other optical disc. Furthermore, in this embodiment, as described above, since the path to the destination is found in the server device 4, the map information DB45 can be omitted. Even when the map information DB45 is omitted, map information can still be obtained from the server device 4 as needed.

[0059] Among them, map information DB45 is, for example, a storage unit that stores road segment data related to roads (road segments), node data related to nodes, search data for processing route search or changes, facility data related to facilities, map display data for displaying the map, intersection data related to each intersection, and retrieval data for retrieving locations.

[0060] On the other hand, the cache 46 is a storage unit that stores high-precision map information 16, facility information 17, connection information 18, and roadside shape information 19 previously published from the server device 4. The storage period can be appropriately set, for example, from a predetermined period starting from storage (e.g., one month), or until the vehicle's ACC (accessory power supply) is turned off. Alternatively, older data can be deleted sequentially after the amount of data stored in the cache 46 reaches its limit. Furthermore, the navigation ECU 33 uses the high-precision map information 16, facility information 17, connection information 18, and roadside shape information 19 stored in the cache 46 to generate various auxiliary information related to autonomous driving assistance. Details will be described later.

[0061] On the other hand, the navigation ECU (electronic control unit) 33 is an electronic control unit that performs overall control of the navigation device 1. This navigation ECU includes: a CPU 51 that serves as both a computing and control unit; and a RAM 52 that acts as working memory during various calculations performed by the CPU 51 and stores path data such as the path found. In addition to the control program, it also stores the automated driving assistance program (described later). Figure 4 The navigation ECU 33 includes internal storage devices such as ROM 53 and flash memory 54 for storing programs read from ROM 53. Additionally, the navigation ECU 33 has various units that function as processing algorithms. For example, a parking lot acquisition unit acquires the parking lot where the vehicle will park at its destination. A driving trajectory generation unit uses road information containing at least information related to the driving lanes, facility information containing information related to the entrance of the parking lot, and connection information indicating the connection relationship between the driving lanes included in the road leading to the entrance of the parking lot and the entrance of the parking lot, to generate a recommended driving trajectory for the vehicle to travel from the starting point of travel to the entrance of the parking lot. A driving assistance unit provides driving assistance to the vehicle based on the driving trajectory.

[0062] The operation unit 34 is operated when the starting point of the journey and the destination point are input, and it has various operation switches (not shown) such as buttons and switches. Furthermore, the navigation ECU 33 controls the execution of various actions based on the switch signals output by pressing the switches. Additionally, the operation unit 34 may also have a touch panel on the front surface of the LCD display 35. A microphone and voice recognition device may also be included.

[0063] In addition, the LCD display 35 can display map images of roads, traffic information, operation guidance, operation menus, button guidance, guidance information along the guided route (driving the predetermined route), news, weather forecasts, timetables, emails, TV programs, etc. Alternatively, a HUD or HMD can be used instead of the LCD display 35.

[0064] In addition, the speaker 36 provides sound navigation and traffic information guidance based on the instruction output from the navigation ECU 33 to guide driving along the guide path (driving the predetermined path).

[0065] Additionally, the DVD drive 37 is a drive capable of reading data recorded on media such as DVDs or CDs. Furthermore, it can be used to play music or videos, update map information DB45, etc., based on the read data. Alternatively, a card slot for reading and writing memory cards can be provided instead of the DVD drive 37.

[0066] Additionally, the communication module 38 is a communication device, such as a mobile phone or DCM, used to receive traffic information, detection information, weather information, etc., sent from traffic information centers, such as VICS centers or detection centers. It also includes a vehicle-to-vehicle communication device for communication between vehicles and vehicles, and a road-to-vehicle communication device for communication with roadside equipment. Furthermore, it is used to send and receive route information, high-precision map information 16, facility information 17, connection information 18, and road external shape information 19 retrieved by the server device 4 between the server device 4 and the server device 4.

[0067] Furthermore, the exterior camera 39, for example, is a camera using a solid-state imaging element such as a CCD, mounted above the front bumper of the vehicle, with its optical axis set at a predetermined angle from horizontal to downward. Moreover, when the vehicle is traveling in the autonomous driving zone, the exterior camera 39 captures images of the area in front of the vehicle's direction of travel. Additionally, the navigation ECU 33 processes the captured images to detect obstacles such as lane markings on the roadway and other surrounding vehicles, and generates various assistance information related to autonomous driving assistance based on the detection results. For example, when an obstacle is detected, a new driving trajectory is generated to avoid or follow the obstacle. Alternatively, the exterior camera 39 can be configured to be positioned behind or to the side of the vehicle, other than in front of it. Furthermore, sensors such as millimeter-wave radar and laser sensors, vehicle-to-vehicle communication, and road-to-vehicle communication can be used instead of cameras as the obstacle detection unit.

[0068] Furthermore, the vehicle control ECU 40 is an electronic control unit that controls the vehicle equipped with the navigation device 1. The vehicle control ECU 40 is connected to various drive components of the vehicle, such as the steering system, brakes, and accelerator. In this embodiment, especially after the vehicle's automatic driving assistance is activated, automatic driving assistance is implemented by controlling these drive components. Additionally, if the user overtakes the vehicle during automatic driving assistance, the overtaking situation is detected.

[0069] After driving begins, the navigation ECU 33 sends various auxiliary information related to autonomous driving assistance generated by the navigation device 1 to the vehicle control ECU 40 via CAN. The vehicle control ECU 40 then uses this received auxiliary information to implement autonomous driving assistance after driving begins. This auxiliary information includes, for example, a recommended driving trajectory and a speed plan indicating the vehicle's speed.

[0070] Next, based on Figure 4 The automatic driving assistance program executed by CPU 51 in the navigation device 1 according to this embodiment having the above structure will be described. Figure 4This is a flowchart of the autonomous driving assistance program according to this embodiment. The autonomous driving assistance program is executed after the vehicle's ACC (accessory power supply) is turned on and autonomous driving based on driver assistance begins, and implements an assisted driving procedure based on autonomous driving assistance according to the assistance information generated by the navigation device 1. Furthermore, the following... Figure 4 , Figure 7 and Figure 21 The program, represented by a flowchart, is stored in the RAM52 and ROM53 of the navigation device 1 and is executed by the CPU51.

[0071] First, in the autonomous driving assistance program, in step (hereinafter referred to as S)1, the CPU 51 obtains the vehicle's destination. Basically, the destination is set through user input received on the navigation device 1. Furthermore, the destination can be a parking lot or a location other than a parking lot. However, if the destination is a location other than a parking lot, the parking lot where the user will park at that destination is also obtained. If there is a dedicated parking lot or a cooperative parking lot at the destination, that parking lot is used as the user's parking lot. On the other hand, if there is no dedicated parking lot or cooperative parking lot, parking lots around the destination are used as the user's parking lot. Additionally, if there are multiple alternative parking lots, the user can select a parking lot on the navigation device 1, or the user can make the selection.

[0072] Next, in S2, CPU51 obtains candidate paths (hereinafter referred to as candidate paths) for reaching the parking lot where the user parks from the vehicle's current location. It is preferable to obtain multiple candidate paths. It is particularly preferable to include candidate paths with different travel directions when reaching the parking lot. Furthermore, for large parking lots with multiple entrances, it is preferable to obtain multiple candidate paths to each entrance.

[0073] Furthermore, in this embodiment, the aforementioned candidate paths are specifically searched via server device 4. When searching for candidate paths, CPU 51 first sends a path search request to server device 4. The path search request includes the terminal ID of the navigation device 1 that sent the path search request, as well as information on the origin (e.g., the vehicle's current location) and the parking lot where the user is parking (the parking lot entrance if possible). Then, CPU 51 receives search path information sent from server device 4 based on the path search request. The search path information is information (e.g., road segment columns included in the candidate path) that server device 4 has searched using the latest version of map information based on the sent path search request to determine candidate paths from the origin to the parking lot where the user is parking. For example, the well-known Dijkstra method is used for the search. However, candidate paths may also be searched in navigation device 1 instead of server device 4.

[0074] Next, in S3, CPU51 takes the area containing the candidate paths obtained in S2 as the object and obtains high-precision map information 16.

[0075] Here, as Figure 5 As shown, the high-precision map information 16 is divided into rectangular shapes (e.g., 500m × 1km) and stored in the high-precision map DB13 of the server device 4. Therefore, for example, as... Figure 5 As shown, when paths 61 and 62 are available as candidate paths, regions 63-67 containing paths 61 and 62 are used as objects to obtain high-precision map information 16. However, when the distance to the parking lot where the user parks is particularly far, for example, only the two-dimensional grid where the vehicle is currently located can be used as an object to obtain high-precision map information 16, or only the area within a specified distance (e.g., within 3km) of the vehicle's current location can be used as an object to obtain high-precision map information 16.

[0076] The high-precision map information 16 includes, for example, information related to the shape of road lanes and the markings drawn on the road (lane center lines, lane dividers, lane outer lines, guide lines, etc.). It also includes information related to intersections, parking lots, etc. The high-precision map information 16 is generally obtained from the server device 4 in units of the aforementioned rectangular areas; however, if high-precision map information 16 for areas already stored in the cache 46 exists, it is obtained from the cache 46. Additionally, the high-precision map information 16 obtained from the server device 4 is temporarily stored in the cache 46.

[0077] Additionally, in S3 above, CPU 51 also acquires facility information 17, taking the destination and the parking lot where the user parks as objects. Furthermore, it similarly acquires connection information 18 and road shape information 19. Connection information 18 indicates the connection relationship between the driving lanes included in the entrance road to the parking lot and the parking lot entrance, while road shape information 19 determines the area where vehicles can pass between the entrance road and the parking lot entrance.

[0078] Facility information 17 includes, for example, information determining the location of the parking lot's entrances and exits, information determining the configuration of parking spaces within the parking lot, information related to the zoning lines for the parking spaces, and information related to pathways accessible to vehicles and pedestrians. For facilities other than parking lots, it includes information determining the facility's entrances and exits and pathways accessible to users (pedestrians) within the facility. Facility information 17 can also be information specifically generated from a 3D model of a parking lot or facility. Furthermore, facility information 17, connection information 18, and road exterior shape information 19 are generally obtained from server device 4, but if the corresponding information is already stored in cache 46, it is obtained from cache 46. Additionally, facility information 17, connection information 18, and road exterior shape information 19 obtained from server device 4 are temporarily stored in cache 46.

[0079] Then, in S4, CPU51 performs the static trajectory generation process described later. Figure 7 The static driving track generation process generates a recommended driving track for the vehicle, i.e., a static driving track, based on the vehicle's current position, the parking lot where the user is parking, the high-precision map information 16, facility information 17, connection information 18, and road shape information 19 obtained in S2 above. Furthermore, as described later, the static driving track includes: a first driving track recommending the vehicle to travel on the roadway from the starting point of travel to the entrance road facing the parking lot entrance; a second driving track recommending the vehicle to travel from the entrance road to the parking lot entrance; and a third driving track recommending the vehicle to travel from the parking lot entrance to the parking space where the vehicle is parked. However, if the distance to the parking lot where the user is parking is particularly far, a first driving track may be generated only covering the area from the vehicle's current position along the direction of travel to a predetermined distance ahead (e.g., within the two-dimensional grid where the vehicle is currently located). Additionally, the predetermined distance can be appropriately changed, but at least the area outside the detection range (the area where the road conditions around the vehicle can be detected by the external camera 39 or other sensors) will be included when generating the static driving track.

[0080] Next, in S5, CPU51 executes the speed plan generation process described later. Figure 21In the speed plan generation process, based on the high-precision map information 16, facility information 17, connection information 18, and road external shape information 19 obtained in S3 above, a speed plan for vehicles traveling on the static driving track generated in S4 above is generated. For example, considering speed limit information and speed change locations on the static driving track (e.g., parking lot entrances, intersections, curves, road crossings, pedestrian crossings, etc.), a recommended vehicle speed for traveling on the static driving track is calculated.

[0081] Furthermore, the static driving trajectory generated in S4 and the speed plan generated in S5 are stored as auxiliary information for autonomous driving assistance in flash memory 54, etc. Additionally, a plan representing the acceleration of the vehicle required to achieve the speed plan generated in S5 can also be generated as auxiliary information for autonomous driving assistance.

[0082] Next, in S6, CPU51 performs image processing on the images captured by the external camera 39 to determine the surrounding road conditions, specifically whether there are factors affecting the vehicle's movement. The "factors affecting the vehicle's movement" identified in S6 are defined as dynamic factors that change in real time, excluding static factors based on road structure. Examples include other vehicles traveling or parked ahead of the vehicle, pedestrians ahead of the vehicle's direction of travel, and construction zones ahead of the vehicle's direction of travel. Intersections, curves, junctions, merging sections, and lane reduction zones are excluded. Furthermore, even if other vehicles, pedestrians, or construction zones are present, they are excluded from the "factors affecting the vehicle's movement" if they are unlikely to overlap with the vehicle's future travel path (e.g., if they are located far from the vehicle's future travel path). In addition, as a unit for detecting factors that may affect the driving of a vehicle, sensors such as millimeter-wave radar and laser sensors, vehicle-to-vehicle communication, and road-to-vehicle communication can be used instead of cameras.

[0083] Then, if it is determined that there are factors in the vicinity of the vehicle that affect its operation (S6: Yes), the process proceeds to S7. Conversely, if it is determined that there are no factors in the vicinity of the vehicle that affect its operation (S6: No), the process proceeds to S10.

[0084] In S7, CPU51 generates a new dynamic driving track to return to the static driving track from the vehicle's current position, avoiding or following the "factors affecting the vehicle's driving" detected in S6. Furthermore, the dynamic driving track is generated for the interval containing the "factors affecting the vehicle's driving". The length of the interval varies depending on the content of the factor. For example, if the "factor affecting the vehicle's driving" is another vehicle (the vehicle in front) driving in front of the vehicle, then... Figure 6 As shown, a avoidance track is generated as a dynamic driving track 70. This avoidance track is a track that changes lanes to the right to overtake the vehicle 69 in front, and then changes lanes to the left to return to the original driving lane. Alternatively, a following track can be generated as a dynamic driving track, which does not overtake the vehicle 69 in front but follows (or runs parallel to) the vehicle 69 in front at a specified distance.

[0085] When Figure 6 Taking the calculation method of the dynamic driving track 70 shown as an example, the CPU 51 first calculates the first track L1. This first track L1 is the track required for the vehicle to move to the right lane when the steering device starts rotating and for the steering device to return to the straight-line direction. Furthermore, the first track L1 is calculated based on the lateral acceleration (lateral G) generated during lane change at the vehicle's current speed. The calculation uses a spiral curve to find the track that is as smooth as possible and has the shortest possible distance for lane change, provided that the lateral G does not obstruct the automatic driving assistance and does not exceed an upper limit (e.g., 0.2G) that does not cause discomfort to the vehicle's occupants. Additionally, it is also designed to maintain an appropriate vehicle distance D or higher from the vehicle 69 in front.

[0086] Next, the second track L2 is calculated. This second track L2 is the track used when traveling in the right lane at the maximum speed limit to overtake the vehicle 69 ahead, and maintaining a proper vehicle-to-vehicle distance D or more between the two vehicles. Furthermore, the second track L2 is essentially a straight track, and its length is calculated based on the speed of the vehicle 69 ahead and the speed limit of the road.

[0087] Next, the third track L3 is calculated. This third track L3 is the track required for the steering mechanism to rotate and return to the left lane, and for the steering mechanism to return to the straight-ahead position. Furthermore, the third track L3 is calculated based on the lateral acceleration (lateral G) generated during lane change at the vehicle's current speed. The track is calculated using a spiral curve to ensure the smoothest possible lane change distance while maintaining a suitable distance of at least D from the vehicle ahead (69). This is done under the condition that the lateral G does not obstruct the autonomous driving assistance and does not exceed an upper limit (e.g., 0.2G) that does not cause discomfort to the vehicle occupants.

[0088] In addition, since the dynamic driving track is generated based on the road conditions around the vehicle obtained by the external camera 39 or other sensors, the area that is the object of generating the dynamic driving track is at least within the range (detection range) where the road conditions around the vehicle can be detected by the external camera 39 or other sensors.

[0089] Next, in S8, CPU51 reflects the newly generated dynamic driving track in S7 onto the static driving track generated in S4. Specifically, from the vehicle's current position to the end of the interval containing "factors affecting the vehicle's driving," the cost of the static and dynamic driving tracks is calculated, and the driving track with the lowest cost is selected. As a result, a portion of the static driving track is replaced with the dynamic driving track as needed. Furthermore, depending on the situation, sometimes the dynamic driving track replacement is not performed; that is, even if the dynamic driving track is reflected, the static driving track generated in S4 remains unchanged. Moreover, when the dynamic and static driving tracks are the same track, sometimes even if replacement is performed, the static driving track generated in S4 remains unchanged.

[0090] Next, in S9, CPU51 corrects the vehicle speed plan generated in S5 based on the static driving track after the dynamic driving track was reflected in S8. Alternatively, if reflecting the dynamic driving track does not change the static driving track generated in S4, the processing in S9 can be omitted.

[0091] Next, in S10, CPU51 calculates the control quantities used to make the vehicle travel at the speed of the static driving track generated in S4 (or the reflected track if the dynamic driving track is reflected in S8) according to the speed plan generated in S5 (or the revised plan if the speed plan is revised in S9). Specifically, the control quantities of the accelerator, brake, transmission, and steering are calculated respectively. Furthermore, the processing in S10 and S11 may not be performed by the navigation device 1, but by the vehicle control ECU 40 that controls the vehicle.

[0092] Then, in S11, the CPU 51 reflects the control quantity calculated in S10. Specifically, the calculated control quantity is sent to the vehicle control ECU 40 via CAN. In the vehicle control ECU 40, the accelerator, brakes, transmission, and steering are controlled based on the received control quantity. As a result, driving assistance control is possible, enabling the static driving track generated in S4 (or the reflected track in S8 if the dynamic driving track is reflected) to travel at the speed of the speed plan generated in S5 (or the revised plan in S9 if the speed plan is revised).

[0093] Next, in S12, CPU51 determines whether the vehicle has traveled a certain distance after the static driving track was generated in S4 above. For example, the certain distance is 1km.

[0094] Furthermore, if it is determined that the vehicle has traveled a certain distance after generating the static driving track in S4 (S12: Yes), the process returns to S1. Then, based on the vehicle's current position, the static driving track and speed plan are generated again (S1-S5). In this embodiment, the static driving track and speed plan are repeatedly generated based on the vehicle's current position whenever the vehicle travels a certain distance (e.g., 1 km), but it is also possible to generate the static driving track and speed plan only once at the start of travel.

[0095] On the other hand, if it is determined that the vehicle has not traveled a certain distance after the static driving track is generated in S4 (S12: No), it is determined whether to terminate the assisted driving based on automatic driving assistance (S13). In addition to completing the parking of the vehicle, the assisted driving based on automatic driving assistance can also be terminated by the user operating the control panel set on the vehicle, or by intentionally releasing (overtaking) the assisted driving based on automatic driving assistance through steering wheel operation, brake operation, etc.

[0096] Then, if it is determined that the assisted driving based on autonomous driving assistance should be terminated (S13: Yes), the autonomous driving assistance program is terminated. Conversely, if it is determined that the assisted driving based on autonomous driving assistance should continue (S13: No), the process returns to S6.

[0097] Next, based on Figure 7 The subprocesses of the static travel track generation process performed in S4 above will be explained. Figure 7 This is a flowchart of the subprocessing procedure for generating static driving tracks.

[0098] First, in S21, the CPU 51 acquires the vehicle's current position detected by the current position detection unit 31. Furthermore, the vehicle's current position is preferably determined in detail using, for example, high-precision GPS information or high-precision positioning technology. High-precision positioning technology refers to a technique that uses image recognition to detect white lines and road surface markings captured by a camera installed on the vehicle, and then matches the detected white lines and road surface markings with, for example, high-precision map information 16, thereby enabling the detection of the driving lane and a high-precision vehicle position. Moreover, when the vehicle is traveling on a road consisting of multiple lanes, the driving lane of the vehicle is also determined.

[0099] Next, in S22, CPU51 constructs a lane network based on the high-precision map information 16 obtained in S3, taking the candidate paths (obtained in S2) leading to the parking lot where the user will park as objects. The high-precision map information 16 includes lane shapes, lane markings, and information related to intersections. Furthermore, the lane shapes and lane markings include the number of lanes, the location and method of lane increases or decreases, the direction of travel for each lane, road connections (specifically, the correspondence between lanes on roads before and after intersections), and information on guide lines (white guide lines) within intersections. The lane network generated in S22 represents the lane movement that a vehicle can choose when traveling on a candidate path. If multiple candidate paths are obtained in S2, the above lane network is constructed for all candidate paths. In addition, the lane network is constructed with respect to the section from the vehicle's current position (the starting point of the journey) to the entrance road of the parking lot where the user parks.

[0100] As an example of constructing the lane network in S22, for instance, taking vehicles in... Figure 8 The following example illustrates the situation of traveling on the alternative route. Figure 8In the example shown, the alternative path is to go straight from the vehicle's current position, turn right at the next intersection 71, then turn right again at the next intersection 72, and finally turn left into the parking lot 73, which is the intended parking area. Figure 8 In the alternative routes shown, for example, when turning right at intersection 71, one can enter either the right-hand lane or the left-hand lane. However, since a right turn is required at the next intersection 72, one must move to the far right lane when entering intersection 72. Additionally, when turning right at intersection 72, one can enter either the right-hand lane or the left-hand lane. Figure 9 The diagram shows a lane network constructed by treating candidate paths capable of such lane movements as objects.

[0101] like Figure 9 As shown, the lane network is divided into multiple zones (groups) to serve as candidate paths for generating static driving tracks. Specifically, the lanes are divided using the entry point at an intersection, the exit point at an intersection, and the points where lanes are added or removed as boundaries. Then, nodes (hereinafter referred to as lane nodes) 75 are set for each lane located at the boundaries of each zone. Furthermore, road segments (hereinafter referred to as road segments) 76 connecting the lane nodes 75 are set. In addition, the starting position of the lane network (i.e., the starting node) is the current position of the vehicle (the starting point of travel), and the ending position of the lane network (i.e., the ending node) is the location on the entry road to the entrance of a parking lot for users, specifically the location of the parking lot entrance (hereinafter referred to as the entry point).

[0102] Furthermore, the aforementioned lane network, particularly through the connection between lane nodes and road segments at intersections, includes determining the correspondence between the driving lanes included in the road before the intersection and the driving lanes included in the road after the intersection; that is, information on the driving lanes that can be moved from the driving lanes before the intersection to the driving lanes after the intersection. Specifically, it indicates the driving lanes that a vehicle can move between the driving lanes corresponding to the lane nodes set as driving lanes before the intersection and the lane nodes set as driving lanes after the intersection, which are connected by driving lane segments.

[0103] To generate such a lane network, in the high-precision map information 16, for each road connecting to an intersection, lane markings representing the correspondence of driving lanes are set and stored according to the combination of roads entering and leaving the intersection. For example, in Figure 10The diagram shows lane markings for entering an intersection from the right-hand road and leaving onto the upper road, lane markings for entering an intersection from the right-hand road and leaving onto the left road, and lane markings for entering an intersection from the right-hand road and leaving onto the lower road. Furthermore, it shows lanes that can move before and after passing through the intersection, corresponding to lane markings set to "1" in the lanes included in the road before the intersection and lane markings set to "1" in the lanes included in the road after the intersection. When constructing the lane network in S22, CPU51 refers to the lane markings to form connections between lane nodes and road segments in the intersection.

[0104] Furthermore, if there are multiple candidate paths obtained in S2 above, the same applies to the construction of each of the multiple candidate paths. Figure 9 The lane network shown.

[0105] Next, in S23, CPU51 constructs a parking network based on the facility information 17 obtained in S3, taking the parking lot where the user parks as the target. The facility information 17 includes information determining the location of the parking lot entrance, information determining the configuration of parking spaces within the parking lot, information related to the zoning lines for the parking spaces, and pathways for vehicles or pedestrians. The parking network generated in S23 represents a network of paths that vehicles can choose when driving within the parking lot.

[0106] in, Figure 11 This represents an example of a parking network constructed as described in S23 above. For example... Figure 11 As shown, the parking network, like the lane network described above, is constructed using lane nodes 75 and lane segments 76. Lane nodes 75 are located at parking lot entrances / exits, intersections where vehicle-accessible pathways intersect, and corners of vehicle-accessible pathways. Lane segments 76 are defined relative to the vehicle-accessible pathways between lane nodes 75. Furthermore, lane segments 76 also contain information determining the direction of vehicle movement within the parking lot pathways, for example, in… Figure 11 The image shows an example where only clockwise traffic is permitted on the pathways within the parking lot.

[0107] In addition, Figure 11 In the example shown, lane node 75 is set at the corner of a passageway where vehicles can travel. However, it is also possible to set lane node 75 not at the corner, but only at locations where there are multiple directions of vehicle travel, such as intersections of passageways. Alternatively, it can be set at... Figure 9Under the same conditions of the lane network shown, lane nodes 75 and road segments 76 are set. For example, even in a parking lot, in the passage of multiple lanes, road segments 76 can be set for each lane, and parking lot-related attributes can be added to the road segments.

[0108] Next, in S24, CPU51 constructs a pedestrian network based on the facility information 17 obtained in S3, taking the walking distance from the parking lot where the user parks to their destination as the object. The facility information 17 includes, in addition to information related to the parking lot, information about facilities other than the parking lot, including information on the entrances and exits of the facilities and the paths accessible to users (pedestrians) within the facilities. The pedestrian network generated in S24 represents a network of walking paths from the parking lot to the destination. Furthermore, if the destination is a parking lot, no pedestrian network is constructed.

[0109] in, Figure 12 An example of a pedestrian network constructed as described in S24 above is shown. Figure 12 As shown, the parking network, like the aforementioned lane network and parking network, is constructed using lane nodes 75 and vehicle lane segments 76. Lane nodes 75 are located at intersections of pedestrian-accessible pathways (including both pedestrian-only pathways and vehicle-accessible pathways, hereinafter the same), at corners of pedestrian-accessible pathways, at the endpoints of parking space demarcation lines, and at entrances / exits of facilities serving as destinations. Vehicle lane segments 76 are established relative to pedestrian-accessible pathways between lane nodes 75. These pedestrian-accessible pathways include not only pathways accessible to vehicles but also pathways accessible only to pedestrians and at the boundaries (demarcation lines) of parking spaces. Furthermore, vehicle lane segments 76 also contain information determining the direction of pedestrian movement.

[0110] In addition, Figure 12 In the example shown, the parking lot where the user parks is located within the land of the facility that is the destination. However, if there is a distance between the parking lot and the facility that is the destination, and the user must walk on the road outside the parking lot after parking, a pedestrian network is also constructed for the road.

[0111] In addition, when the destination is a complex commercial facility consisting of multiple tenants, and any tenant is designated as the destination, a pedestrian network can also be constructed for the pathways within the facility.

[0112] Then, in S25, CPU51 generates a network that includes the movement of a vehicle from its current location to the parking lot, the movement of a vehicle within the parking lot, and the walking movement after getting off the vehicle in the parking lot, by connecting the lane network constructed in S22, the parking network constructed in S23, and the walking network constructed in S24.

[0113] In particular, the connection between the lane network and the parking network is performed using the connection information 18 obtained in S3 above. Here, the connection information 18 represents the connection relationship between the driving lanes included in the entrance road to the parking lot entrance and the parking lot entrance. More specifically, it is information that determines whether it is possible to enter the parking lot entrance from each driving lane included in the entrance road.

[0114] Figure 13 This is a diagram illustrating an example of connection information 18. Connection information 18 sets and stores entry signs indicating whether access to the parking lot is permitted for each lane included in the access road 78. For example, in... Figure 13 The diagram above shows the connection information 18 between the parking lot 73, where right turns are prohibited or impossible due to the central divider, and the access road 78 with two lanes on each side. For the lane closest to the parking lot 73 among the four lanes of the access road 78, an entry sign "1" indicating an entrance to the parking lot is set. On the other hand, in Figure 13 The diagram below shows the connection information 18 between the parking lot 73, which can be entered by turning right, and the access road 78 with one lane on one side. For either of the two lanes included in the access road 78, an entry sign "1" indicating that the entrance to the parking lot can be entered is set.

[0115] The result is, for example, when based on Figure 13 When connecting the lane network and parking network using the connection information 18 shown in the diagram above, lane networks for candidate paths leading to parking lot 73 from the left side of the diagram can be connected to the parking network, but lane networks for candidate paths leading to parking lot 73 from the right side of the diagram cannot be connected to the parking network. Furthermore, candidate paths that cannot be connected to the parking network are excluded from subsequent processing. On the other hand, when based on... Figure 13 When connecting the lane network and the parking network using the connection information 18 shown in the diagram below, both the lane network for the alternative path to parking lot 73 from the left side of the diagram and the lane network for the alternative path to parking lot 73 from the right side of the diagram can be connected to the parking network. If a connection is possible, the lane node 75 at the entrance of the parking lot is reconnected to the lane node 75 at the entry point on the accessible driving lane using the road segment 76.

[0116] On the other hand, the connection between the parking network and the pedestrian network is based on the parking space provided by the parking lot, connecting the parking network and the pedestrian network that are close to the parking space.

[0117] Next, in S26, CPU51 sets the starting point of the vehicle's movement and the destination point of the movement for the network constructed in S25. The starting point is the vehicle's current location, and the destination point is the parking space within the parking lot if the destination is a parking lot, and the entrance to the facility if the destination is not a parking lot. Furthermore, if the destination facility is a mixed-use commercial facility comprised of multiple tenants, and any tenant is designated as the destination, the location of the tenant within the facility can also be used as the destination point.

[0118] Then, in S27, CPU51, referring to the network constructed in S25 above, first derives multiple candidate routes that continuously connect the starting point of movement to the destination point of movement. It compares the total lane costs among these candidate routes and determines the candidate route with the minimum total cost as the recommended vehicle travel path (lane movement mode) and the recommended walking path after parking. Additionally, in S27, a parking space is also selected within the parking lot. Specifically, using parking lot vacancy information obtained in advance from an external server, the parking space with the minimum total lane cost is selected from the vacant parking spaces.

[0119] Here, the total lane cost includes both the cost of moving the vehicle to the parking space and the cost of walking from the parking space. That is, in addition to the burden of moving to the parking space, the burden of walking after parking is also considered when selecting a parking space.

[0120] Furthermore, lane costs are assigned to each lane segment 76, with the length of each lane segment 76 serving as the baseline value. The baseline value is then adjusted based on the travel time (the product of travel speed and segment length) required for each lane segment 76 to move. For example, compared to lane networks, parking networks and pedestrian networks have relatively slower travel speeds and longer travel times relative to length; therefore, for lane segments in parking networks and pedestrian networks, it is preferable to adjust the baseline value of lane costs to a larger value. Regarding the travel speed of lane segments 76, for example, it is determined based on the road type if it is a road, and is set to 10 km / h if it is in a parking lot. If it is in a pedestrian area, it is set to 5 km / h. Furthermore, particularly for lane costs in lane networks, the baseline value is adjusted under the following conditions (1) to (3). Additionally, lane segments 76 within substantially the same zone (group) are considered to have the same length. Furthermore, the length of lane segments 76 within intersections is considered to be 0 or a fixed value. However, search methods other than Dijkstra's method can also be used as long as it is possible to search for a continuous route from the starting lane to the target lane.

[0121] (1) Lane cost is calculated by adding a predetermined value to the baseline value, corresponding to the required number of lane changes. The more precise the added predetermined value, the better. Figure 14 The higher the value for lane segment 76, the more lane changes required. For example, lane segment 76 moving from lane 81 to lane 82 requires one lane change, so "2" is added to the base value. Similarly, lane segment 76 moving from lane 81 to lane 83 requires two consecutive lane changes, so "5" is added to the base value. On the other hand, lane segment 76 maintaining lane 81 does not require a lane change, so no addition is performed. As a result, routes with more lane changes result in a larger total lane cost, making them less likely to be selected as recommended lane movement methods. Furthermore, routes with multiple lane changes within the same section (i.e., consecutive lane changes) have a larger total lane cost than routes without consecutive lane changes, making them less likely to be selected as recommended lane movement methods.

[0122] (2) For lane costs of road segments where vehicles travel in the overtaking lane (e.g., the rightmost lane in left-hand traffic) without changing lanes, the baseline value is multiplied by a prescribed coefficient. For example... Figure 15 As shown, the road segment 76 traveling in lane 83, which serves as the overtaking lane, has its baseline value multiplied by "1.5". As a result, the longer the route travels in the overtaking lane, the greater the total lane cost is calculated, making it difficult to select as the recommended lane movement mode.

[0123] (3) For candidate routes that include sections (groups) involving lane changes, multiple modes are used to generate candidate locations for lane changes, and the total lane cost is calculated for each of the multiple modes. Specifically, referring to the location for lane changes in each mode, for the mode corresponding to any of the following cases (A), (B), and (C): (A) the travel distance in the overtaking lane before or after a lane change is longer than a threshold; (B) the interval between lane changes is shorter than a threshold when making multiple lane changes (i.e., continuous lane changes); and (C) the lane change is made within a specified distance (e.g., 700m for general roads, 2km for highways) near the intersection, a specified value is added to the base value for the lane cost of the road segment where the lane change occurs. For example, if... Figure 16 As shown, for the pattern of moving from lane 81 to lane 82 between a predetermined distance from the intersection and the intersection, "5" is added to the base value. Furthermore, in S27 above, the total lane cost of each route (and for routes containing multiple lane movement patterns, each pattern is also compared) is compared, and the route and pattern with the smallest total lane cost is determined as the recommended lane movement mode for the vehicle. Thus, in addition to the recommended lane change interval, the recommended position for lane change within that interval is also determined. Additionally, for lane movement patterns involving continuous lane changes, lane movement patterns involving long distances traveled in the overtaking lane before or after a lane change, and lane movement patterns involving lane changes between a predetermined distance from the intersection and the intersection, the total lane cost is calculated to be larger than that of patterns without such lane changes, making it difficult to select these as recommended lane movement modes.

[0124] Furthermore, under the conditions described in (3) above, the same procedure applies, especially when the alternative route includes sections (groups) involving multiple lane changes. For example, in Figure 17 The following example illustrates a candidate route that includes two lane changes from the leftmost lane. Figure 17In the example shown, considering the location for lane changes, and taking into account that the vehicle should continue traveling in its current lane as much as possible, the following options are considered: a first mode where two lane changes are made near the intersection; a second mode where two lane changes are made at approximately equal intervals during the journey to the intersection; and a third mode where lane changes are made as early as possible. The first mode, due to its short intervals and proximity to the intersection, is calculated to have a high cost. Similarly, the third mode, due to its longer travel distance in the right-hand overtaking lane, is also calculated to have a high cost. In contrast, the second mode, which does not involve short intervals or near the intersection, and has a relatively short travel distance in the right-hand overtaking lane, has a lower cost compared to the other modes. Therefore, in Figure 17 In the example shown, the second mode has the lowest cost and is easily selected as the recommended lane movement method for vehicles, as it is the location for lane changing.

[0125] Next, in S28, if the vehicle moves along the route selected in S27, the CPU51 calculates a recommended travel path, specifically targeting lane change zones (groups). Alternatively, if the route selected in S27 does not involve any lane changes, the processing in S28 can be omitted.

[0126] Specifically, CPU51 calculates the driving trajectory using map information such as the location of the lane change determined in S27 above. For example, based on the vehicle's speed (set as the speed limit for the road) and the lane width, it calculates the lateral acceleration (lateral G) generated when the vehicle changes lanes, and uses a spiral curve to calculate a trajectory that is as smooth as possible and requires the shortest possible distance for lane changes, provided that the lateral G does not obstruct the automatic driving assistance and does not exceed an upper limit (e.g., 0.2G) that does not cause discomfort to the vehicle's occupants. Furthermore, a spiral curve is a curve depicted on the vehicle's trajectory when the vehicle is traveling at a certain speed and the steering mechanism is rotated at a certain angular velocity.

[0127] Next, in S29, if the vehicle moves along the route selected in S27 above, the CPU51 calculates a recommended travel path, specifically targeting the zones (groups) within the intersection. Furthermore, if the route selected in S27 is a route that does not pass through any intersections, the processing in S29 can be omitted.

[0128] For example, in Figure 18The following example illustrates the calculation of a driving trajectory using a lane division (group) within an intersection where a vehicle moves from the rightmost lane into the intersection and then exits into the leftmost lane. First, the CPU51 marks the positions where the vehicle should pass through the intersection. Specifically, it marks the entry lane towards the intersection, the entry position towards the intersection, the guide line within the intersection (only if guide lines are present), the exit position from the intersection, and the exit lane from the intersection. Then, it calculates the driving trajectory using the curves of all the marked lanes. More specifically, it connects the marked lanes with spline curves and calculates the driving trajectory using a spiral curve that approximates the connected curves. Furthermore, a spiral curve is a curve depicting the vehicle's trajectory when the vehicle travels at a certain speed and the steering mechanism rotates at a certain angular velocity.

[0129] Next, in S30, if the vehicle moves along the route selected in S27 above, CPU51 calculates a recommended driving trajectory, especially when entering the parking lot from the road.

[0130] For example, in Figure 19 The following example illustrates the calculation of a driving trajectory when a route is set from the leftmost lane of the entry road 78 to the entrance of the parking lot 73. First, the CPU 51 determines the area (hereinafter referred to as the passage area) between the entry road 78 and the parking lot 73, based on the road shape information obtained in S3 above. For example, in... Figure 19 In the example shown, the rectangular area defined by the horizontal (x) and vertical (y) axes becomes the passageway through which vehicles can pass between the entry road 78 and the parking lot 73. Then, taking the entrance from the entry road 78 through the passageway to the parking lot 73 as a condition, a spiral curve is used to calculate the track that is as smooth as possible and as short as possible in terms of the required entry distance.

[0131] Then, in S31, when the vehicle moves along the route selected in S27 above, CPU51 calculates the recommended travel trajectory, especially when parking in the parking space selected in S27 above.

[0132] For example, in Figure 20Here, we will explain an example of calculating the driving trajectory when parking in parking space 85 within a selected parking lot. First, based on the facility information obtained in S3 above, CPU 51 obtains the shape of the parking space 85 and the width of the passageway facing the parking space 85. Then, it sets a switching point z for switching from forward to reverse and calculates the trajectory for entering the parking space 85 from a state of parallel travel via the switching point z, in a way that is as smooth as possible and minimizes the distance required for parking. Furthermore, for parking lots requiring forward parking, the switching point z is not set, but a driving trajectory for forward parking is calculated instead.

[0133] Then, in S32, CPU51 generates a recommended driving track, i.e., a static driving track, by connecting the driving tracks calculated in S28 to S31. Furthermore, for sections that are neither lane-changing zones, intersection zones, zones for entering parking lots, nor zones for parking operations, the track passing through the center of the driving lane (or the track passing through the center of the passageway in the parking lot) is used as the recommended driving track. However, for corners that curve to approximately a right angle, it is preferable to set R for the portion of the corner that becomes the track.

[0134] The static driving tracks generated in S32 above include: a first driving track for recommending vehicles to travel on the roadway from the starting point of travel to the entrance road facing the parking lot; a second driving track for recommending vehicles to travel from the entrance road to the entrance of the parking lot; and a third driving track for recommending vehicles to travel from the entrance of the parking lot to the parking space where the vehicle is parked. The pedestrian movement path from the parking space to the entrance of the facility is not included in the static driving track.

[0135] Then, the static driving track generated in S32 above is stored in flash memory 54, etc., as auxiliary information for autonomous driving assistance.

[0136] Next, based on Figure 21 The subprocesses of the speed plan generation process executed in S5 above will be explained. Figure 21 This is a flowchart of the subprocessing procedure for speed plan generation.

[0137] First, in step S41, CPU51 uses map information to obtain speed limit information for each road included in the static driving track generated in step S4. Furthermore, for roads where speed limit information is unavailable, the speed limit is determined based on the road type. For example, 30 km / h for narrow streets, 40 km / h for general roads other than trunk roads, 60 km / h for trunk roads such as national highways, and 100 km / h for expressways. The speed limit information can be obtained from either high-precision map information 16 or regular map information used for path searching. Additionally, the speed limit set for driving in parking lots where the user has parked is also obtained. For parking lots without specified speed limits, for example, 10 km / h is used as the speed limit.

[0138] Next, in S42, CPU51 determines the locations on the static travel track where the vehicle's speed changes, i.e., speed change locations. Examples of speed change locations include parking lot entrances, intersections, curves, road crossings, pedestrian crossings, and temporary parking areas. If multiple speed change locations exist on the static travel track, all such locations are determined. Specifically, parking lot entrances are determined using facility information 17 and road exterior shape information 19, and the presence of a pedestrian crossing between the parking lot entrance and the access road is also determined. Furthermore, pedestrian crossings and temporary parking areas located within the parking lot are determined using facility information 17.

[0139] Next, in S43, CPU51 sets a recommended speed for each speed change location determined in S42 above. For example, for the entrance to a parking lot where there is a pedestrian crossing between the vehicle and the road, the recommended speed is to first stop (0 km / h) and then proceed at a slow speed (e.g., 10 km / h). On the other hand, for the entrance to a parking lot where there is no pedestrian crossing between the vehicle and the road, the recommended speed is to proceed at a slow speed (e.g., 10 km / h). Additionally, at intersections with lane markings or temporary parking lines, the recommended speed is to first stop (0 km / h) and then proceed at a slow speed (e.g., 10 km / h). Furthermore, a recommended speed is set at a speed where the lateral acceleration (lateral G) generated by the vehicle at curves or intersections where left or right turns are possible does not impede the autonomous driving assistance and does not exceed an upper limit (e.g., 0.2G) that does not cause discomfort to the vehicle occupants. This is calculated, for example, based on the curvature of the curve, the shape of the intersection, etc.

[0140] Next, in S44, for the interval (the interval between speed change locations) that does not match the speed change location determined in S42 above, the CPU51 sets the speed limit set for the road or passage in that interval as the recommended speed for vehicles traveling in that interval, based on the speed limit information obtained in S41 above. However, for roads with narrow widths, poor visibility, high traffic volume, or high accident rates, a speed lower than the speed limit can also be used as the recommended speed.

[0141] Then, in S45, CPU51 combines the recommended speeds at the speed change locations set in S43 with the recommended speeds outside the speed change locations set in S44 to generate data representing the shift of recommended speeds in the vehicle's direction of travel as a vehicle speed plan. Furthermore, when generating the speed plan, the speed plan is appropriately modified to ensure that the speed changes between speed change locations meet specified conditions, more specifically, to ensure that the acceleration and deceleration of the vehicle traveling along the static travel track are both below a threshold.

[0142] in, Figure 22 This is a diagram illustrating an example of the vehicle speed plan generated in S45 above. For example... Figure 22 As shown, in the speed plan, the recommended speed outside of speed change locations essentially becomes the speed limit set for the road. On the other hand, for speed change locations such as parking lot entrances or intersections, speeds lower than the speed limit are set as recommended speeds. Furthermore, the recommended speed is adjusted to meet the condition that the acceleration and deceleration of a vehicle traveling along a static driving track are both below a threshold. However, the recommended speed is basically only adjusted in the decreasing direction, and is adjusted as much as possible within the range of meeting the conditions without reducing the recommended speed. In addition, the acceleration and deceleration thresholds are set as upper limits for acceleration and deceleration that do not hinder vehicle driving or autonomous driving assistance, and do not cause discomfort to vehicle occupants. It is also possible to set different values ​​for the acceleration and deceleration thresholds. As a result, as Figure 22 As shown, the recommended speed is adjusted, and a speed plan is generated.

[0143] Furthermore, the speed plan generated in S45 above will be stored as auxiliary information for autonomous driving assistance in flash memory 54 or the like. Additionally, a plan representing the acceleration and deceleration of the vehicle, required to implement the speed plan generated in S45 above, can also be generated as auxiliary information for autonomous driving assistance.

[0144] As detailed above, in the navigation device 1 and the computer program executed by the navigation device 1 according to this embodiment, the parking lot where the vehicle will park at its destination is obtained (S1). Using high-precision map information 16, which includes at least information related to the driving lane, and facility information 17, which includes information related to the entrance of the parking lot, a recommended driving track for the vehicle from the starting point of travel to the entrance of the parking lot is generated (S21 to S32). Based on the generated driving track, driving assistance for the vehicle is provided (S10, S11). Therefore, a recommended driving track from the starting point of travel to the parking lot is determined. Furthermore, the determined driving track includes not only the driving track to the entry road, but also a recommended driving track for entering the parking lot after reaching the entry road, thus providing appropriate assistance for the vehicle's entry into the parking lot.

[0145] Additionally, the connection information 18 includes information determining whether it is possible to enter the parking lot entrance from each lane included in the access road, thus enabling the generation of a travel track on the lanes in the access road that are accessible to the parking lot entrance.

[0146] In addition, by acquiring road shape information 19 to determine the area where vehicles can pass between the access road and the entrance to the parking lot, and using connection information 18 and road shape information 19, a second travel track (a travel track corresponding to the movement from the access road to the entrance to the parking lot) is generated (S21 to S32). Therefore, based on the location and shape of the entrance to the parking lot, an appropriate travel track recommended for entering from the access road to the entrance to the parking lot can be generated.

[0147] Furthermore, a lane network is constructed using high-precision map information 16. This lane network represents the selectable lane movements of a vehicle relative to the road it is traveling on. For the lane network set at the entry point on the entry road leading to the parking lot, a route connecting the starting point and the entry point is searched using costs attached to the lane network. A first travel trajectory (corresponding to the movement from the starting point to the entry road) is generated based on the searched route. Therefore, the most recommended lane movement mode within the travel interval to the entry road can be appropriately determined by using costs attached to the lane network. Furthermore, driving assistance can be appropriately implemented by using the determined lane movement mode.

[0148] Furthermore, along with access information related to the pathways accessible to vehicles within the parking lot, parking space information related to the available parking space is obtained. Using the access information and parking space information, a recommended driving trajectory is generated from the parking lot entrance to the parking space where the vehicle will be parked. Therefore, a recommended third driving trajectory (parking lot driving trajectory) can be determined from entering the parking lot until parking. Moreover, by providing driving assistance based on the determined driving trajectory, appropriate driving assistance can be implemented for driving within the parking lot.

[0149] Furthermore, a parking network is constructed using access information. This network represents the paths that vehicles can traverse within the parking lot. Costs added to the parking network are used to search for routes connecting the parking lot entrance and the parking spaces where vehicles are parked. Driving trajectories are generated based on the searched routes. Therefore, the most recommended vehicle movement path within the parking lot can be appropriately determined using the costs added to the parking network. And, by implementing driving assistance based on the determined vehicle movement paths, appropriate driving assistance can be implemented.

[0150] In addition, the parking lot is an attached parking lot of the facility. It obtains pedestrian access information related to the passageways that pedestrians can pass through in the parking lot, and obtains facility entrance information related to the entrance of the facility. Using the pedestrian access information and facility entrance information, it generates a recommended walking path for users to move from the parking space where the vehicle is parked to the entrance of the facility. It takes the walking path into account when selecting the parking space where the vehicle is parked. Therefore, it can also take into account the user's walking burden after parking the vehicle in the parking lot when selecting the parking space where the vehicle is parked.

[0151] Furthermore, using pedestrian access information, a network representing the paths a user can walk along is constructed, focusing on the area from the parking lot to the facility. This network uses the cost added to the pedestrian network to search for paths connecting parking spaces and facility entrances, and generates walking routes based on the found paths. Therefore, by using the cost added to the pedestrian network, the most recommended walking route after parking can be appropriately determined. Moreover, by considering the determined walking routes when selecting parking spaces, the user's walking burden after parking can also be taken into account when choosing a parking space.

[0152] Furthermore, by generating speed plans for vehicles traveling on the driving track and providing driving assistance based on these speed plans, the recommended speed for vehicles traveling on the driving track can be accurately determined in advance. Moreover, long-term vehicle speed plans can be generated based on the determined recommended speeds, enabling appropriate implementation of speed-plan-based driving assistance.

[0153] Furthermore, using high-precision map information 16 and facility information 17, the locations of speed changes that cause vehicle speed variations on the travel track are identified. A recommended speed for passing through each speed change location is generated, and a speed plan is generated in a manner that ensures speed variations between speed change locations meet specified conditions. Therefore, even when factors on the travel track affect the vehicle's speed, these factors can be considered to accurately determine the recommended speed in advance. Moreover, a long-term vehicle speed plan can be generated based on the determined recommended speed, enabling appropriate implementation of speed-plan-based driving assistance.

[0154] Furthermore, the present invention is not limited to the above-described embodiments, and various improvements and modifications can be made without departing from the spirit of the present invention.

[0155] For example, in this embodiment, when using a network to search for recommended travel routes, multiple candidate routes are generated, and the cost of each route is compared to determine the route with the lowest cost (S27). However, it is also possible to use, for example, Dijkstra's method to directly search the network for the route with the lowest cost to determine it.

[0156] Furthermore, this embodiment assumes the vehicle's starting point is on a road, but it can also be applied to situations where the starting point is within a parking lot. In this case, the recommended travel trajectory from the starting point to the parking lot exit and the recommended travel trajectory from the parking lot exit to the road facing the parking lot exit are also calculated. The recommended travel trajectory from the starting point to the parking lot exit is calculated using the parking network of the parking lot with the starting point. The recommended travel trajectory from the parking lot exit to the road facing the parking lot exit is compared with the trajectory upon entering the parking lot (S30). Figure 19 Similarly, connection information 18 and road exterior shape information 19 are used for calculation. In addition, in this case, connection information 18 includes information indicating the connection relationship between the driving lanes included in the road facing the parking lot exit and the parking lot exit.

[0157] In this embodiment, a static driving track is generated, which includes: a first driving track that recommends the vehicle to travel on the roadway from the starting point of travel to the entrance road facing the parking lot; a second driving track that recommends the vehicle to travel from the entrance road to the entrance of the parking lot; and a third driving track that recommends the vehicle to travel from the entrance of the parking lot to the parking space where the vehicle is parked. However, the static driving track may also include only the first and second driving tracks. That is, only the first and second driving tracks may be generated.

[0158] In addition, in this embodiment, the parking space for parking the vehicle is selected by considering both the vehicle's travel path until it stops in the parking space and the walking path after parking. However, it is also possible to select the parking space by considering only the vehicle's travel path until it stops in the parking space without considering the walking path.

[0159] Furthermore, in this embodiment, the final generated static driving track is information that determines the specific track (set of coordinates or line) on which the vehicle travels. However, it can also be information that, while not specifying a specific track, can determine the road and lane that the vehicle is traveling on. That is, the route of the network with the lowest lane cost determined in S27 (showing the lane movement method of how the vehicle moves on the lane) can also be used as the static driving track, without performing the processing after S28.

[0160] In addition, in this embodiment, high-precision map information 16 and facility information 17 are used to generate lane network, parking network and pedestrian network (S22-S24) when generating static driving track. However, each network targeting roads and parking lots across the country can also be pre-stored in DB and read from DB as needed.

[0161] Furthermore, in this embodiment, the high-precision map information possessed by the server device 4 includes both information related to the road lane shape (road shape, curvature, lane width, etc. in terms of driving lanes) and information related to the lane markings (lane center lines, lane dividers, lane outer lines, guide lines, etc.) drawn on the road. However, it may include only information related to lane markings or only information related to the road lane shape. For example, even when only information related to lane markings is included, information equivalent to information related to the road lane shape can be inferred based on the information related to lane markings. Furthermore, even when only information related to the road lane shape is included, information equivalent to information related to lane markings can be inferred based on the information related to the road lane shape. Additionally, "information related to lane markings" can be information determining the type and configuration of the lane markings themselves, information determining whether lane changes are possible between adjacent lanes, or information directly or indirectly determining the shape of the driving lanes.

[0162] In addition, in this embodiment, as a method to reflect the dynamic travel track on the static travel track, a portion of the static travel track is replaced with the dynamic travel track (S8). However, the track can also be corrected in a way that makes the static travel track close to the dynamic travel track without replacement.

[0163] Furthermore, in this embodiment, as an automated driving assistance system that enables automatic driving without relying on user driving operations, all vehicle behavior-related operations—namely, accelerator operation, brake operation, and steering wheel operation—of the vehicle control ECU 40's control of the vehicle are described. However, automated driving assistance can also be at least one of the vehicle behavior-related operations—namely, accelerator operation, brake operation, and steering wheel operation—of the vehicle control ECU 40's control of the vehicle. On the other hand, manual driving based on user driving operations refers to all vehicle behavior-related operations—namely, accelerator operation, brake operation, and steering wheel operation—performed by the user.

[0164] Furthermore, the driving assistance of the present invention is not limited to autonomous driving assistance involved in the autonomous driving of vehicles. For example, driving assistance can also be provided by displaying the static driving trajectory determined in S4 and the dynamic driving trajectory generated in S7 on the navigation screen, and by providing guidance using sound, images, etc. (e.g., lane change guidance, recommended speed guidance, etc.). Additionally, the user's driving operation can be assisted by displaying the static driving trajectory and the dynamic driving trajectory on the navigation screen.

[0165] Furthermore, in this embodiment, although the autonomous driving assistance program ( Figure 4 The operation is performed by the navigation device 1, but it can also be performed by an in-vehicle unit or vehicle control ECU 40 other than the navigation device 1. In this case, the in-vehicle unit or vehicle control ECU 40 obtains the vehicle's current location, map information, etc., from the navigation device 1 or server device 4. Furthermore, the server device 4 can also execute automated driving assistance programs ( Figure 4 This refers to one or all of the steps involved. In this case, server device 4 is equivalent to the driving assistance device of this application.

[0166] In addition to navigation devices, this invention can also be applied to mobile phones, smartphones, tablets, personal computers, etc. (hereinafter referred to as mobile terminals, etc.). Furthermore, it can also be applied to systems composed of servers and mobile terminals, etc. In this case, the aforementioned automatic driving assistance program (see...) Figure 4 Each step of the process can be implemented by either a server or a mobile terminal. However, when applying this invention to a mobile terminal, it is necessary to connect the vehicle capable of performing autonomous driving assistance to the mobile terminal in a communicable manner (whether wired or wireless).

[0167] Explanation of reference numerals in the attached figures

[0168] 1…Navigation device, 2…Driving assistance system, 3…Information release center, 4…Server device, 5…Vehicle, 16…High-precision map information, 17…Facility information, 18…Connection information, 19…Road exterior shape information, 33…Navigation ECU, 40…Vehicle control ECU, 51…CPU, 75…Lane node, 76…Road segment, 73…Parking lot, 78…Entry road, 85…Parking space.

Claims

1. A driving assistance device, wherein, have: Parking lot acquisition unit, acquires parking lots where vehicles will park at their destination; The driving trajectory generation unit uses road information containing at least information related to the driving lanes, facility information containing information related to the entrance of the parking lot, connection information indicating the connection relationship between the driving lanes included in the road leading to the entrance of the parking lot and the entrance of the parking lot, and roadside shape information determining the area where vehicles can pass between the entrance road and the entrance of the parking lot, to generate a recommended driving trajectory for the vehicle to travel from the starting point of the journey to the entrance of the parking lot. The access information acquisition unit acquires access information related to the access routes that vehicles can pass through in the parking lot; The parking space information acquisition unit acquires parking space information related to the parking space available in the parking lot; The parking lot driving track generation unit uses the access information and the parking space information to generate a driving track within the parking lot. The driving track within the parking lot is a recommended driving track for a vehicle to travel from the entrance of the parking lot to the parking space where the vehicle is parked. as well as The driving assistance unit provides driving assistance to the vehicle based on the driving trajectory and the driving trajectory within the parking lot. The travel trajectory generation unit: The road information is used to construct a lane network, which represents a network of lanes that vehicles can choose to move in relative to the road they are traveling on. The lane network is set at the entry point on the entry road leading to the parking lot. Using the cost attached to the lane network, a route connecting the starting point and the entry point is searched, and based on the searched route, a travel track is generated that corresponds to the movement from the starting point to the entrance of the parking lot, and a travel track that corresponds to the movement from the starting point to the entry road. Regarding the travel trajectory corresponding to the movement from the starting point of travel to the entrance of the parking lot, and the travel trajectory corresponding to the movement from the access road to the entrance of the parking lot, the connection information is used to determine the driving lanes from the driving lanes included in the access road that can enter the entrance of the parking lot. Using the road exterior shape information, a travel trajectory for entering the entrance of the parking lot from the determined driving lanes through the area accessible to the vehicle is generated. The parking lot driving track generation unit: The aforementioned access information is used to construct a parking network, which represents a network of paths that vehicles can travel within the parking lot. The cost attached to the parking network is used to search for routes connecting the entrance of the parking lot and the parking spaces where vehicles are parked, and driving tracks within the parking lot are generated based on the searched routes. The connection information is used to connect the lane network and the parking network. The driving trajectory generation unit and the parking lot driving trajectory generation unit connect the constructed lane network and parking network to each other using the connection information to generate a network from the starting point of driving to the parking space where the vehicle is parked. Using the generated network and the costs attached to the lane network and the parking network, they search for a route connecting the starting point of driving through the entrance of the parking lot to the parking space where the vehicle is parked, and generate a driving trajectory based on the searched route.

2. The driving assistance device according to claim 1, wherein, The connection information includes information determining whether it is possible to enter the parking lot from each lane of the access road.

3. The driving assistance device according to claim 1, wherein, The parking lot mentioned is an attached parking lot to the facility. The driving assistance device has: The pedestrian access information acquisition unit acquires pedestrian access information related to the access routes that pedestrians can pass through in the parking lot; Facility entrance information acquisition unit acquires facility entrance information related to the entrance of the facility; as well as The pedestrian path generation unit uses the pedestrian access information and the facility entrance information to generate a recommended walking path for the user from the parking space where the vehicle is parked to the entrance of the facility. The parking lot driving track generation unit takes into account the walking path to select the parking space for the vehicle and generates the driving track in the parking lot.

4. The driving assistance device according to claim 3, wherein, The walking path generation unit: Using the pedestrian access information, a pedestrian network is constructed, representing the routes that users can walk from the parking lot to the facility. The cost attached to the pedestrian network is used to search for routes connecting parking spaces for vehicles to the entrance of the facility, and the pedestrian path is generated based on the searched routes.

5. The driving assistance device according to any one of claims 1 to 4, wherein, It includes a speed plan generation unit that generates speed plans for vehicles traveling on the stated travel track. The driving assistance unit provides driving assistance to the vehicle based on the speed plan.

6. The driving assistance device according to claim 5, wherein, The system includes a location determination unit that uses the road information and the facility information to determine the locations along the travel track where the vehicle's speed changes. The speed plan generation unit generates a recommended speed for each speed change location, and generates a speed plan in a manner that ensures the speed changes between the speed change locations meet specified conditions.

7. A computer program product comprising a computer program, wherein, The computer program is used to enable the computer to function as the following unit: Parking lot acquisition unit, acquires parking lots where vehicles will park at their destination; The driving trajectory generation unit uses road information containing at least information related to the driving lane, facility information containing information related to the entrance of the parking lot, connection information indicating the connection relationship between the driving lane contained in the road leading to the entrance of the parking lot and the entrance of the parking lot, and roadside shape information determining the area where vehicles can pass between the entrance road and the entrance of the parking lot, to generate a recommended driving trajectory for a vehicle to travel from the starting point of driving to the entrance of the parking lot. The access information acquisition unit acquires access information related to the access routes that vehicles can pass through in the parking lot; The parking space information acquisition unit acquires parking space information related to the parking space available in the parking lot; The parking lot driving track generation unit uses the access information and the parking space information to generate a driving track within the parking lot. The driving track within the parking lot is a recommended driving track for a vehicle to travel from the entrance of the parking lot to the parking space where the vehicle is parked. as well as The driving assistance unit provides driving assistance to the vehicle based on the driving trajectory and the driving trajectory within the parking lot. The travel trajectory generation unit: The road information is used to construct a lane network, which represents a network of lanes that vehicles can choose to move in relative to the road they are traveling on. The lane network is set at the entry point on the entry road leading to the parking lot. Using the cost attached to the lane network, a route connecting the starting point and the entry point is searched, and based on the searched route, a travel track is generated that corresponds to the movement from the starting point to the entrance of the parking lot, and a travel track that corresponds to the movement from the starting point to the entry road. Regarding the travel trajectory corresponding to the movement from the starting point of travel to the entrance of the parking lot, and the travel trajectory corresponding to the movement from the access road to the entrance of the parking lot, the connection information is used to determine the driving lanes from the driving lanes included in the access road that can enter the entrance of the parking lot. Using the road exterior shape information, a travel trajectory for entering the entrance of the parking lot from the determined driving lanes through the area accessible to the vehicle is generated. The parking lot driving track generation unit: The aforementioned access information is used to construct a parking network, which represents a network of paths that vehicles can travel within the parking lot. The cost attached to the parking network is used to search for routes connecting the entrance of the parking lot and the parking spaces where vehicles are parked, and driving tracks within the parking lot are generated based on the searched routes. The connection information is used to connect the lane network and the parking network. The driving trajectory generation unit and the parking lot driving trajectory generation unit connect the constructed lane network and parking network to each other using the connection information to generate a network from the starting point of driving to the parking space where the vehicle is parked. Using the generated network and the costs attached to the lane network and the parking network, they search for a route connecting the starting point of driving through the entrance of the parking lot to the parking space where the vehicle is parked, and generate a driving trajectory based on the searched route.

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