Driving assistance device and computer program

Abstract

To provide a drive support device and a computer program, capable of generating a concrete travel track required for entering a parking space from a passage, and capable of performing drive support based on a more proper travel track relative to prior arts.SOLUTION: A drive support device is configured to: when a vehicle is parked in a parking area, acquire an inside-of-parking area network which is a network indicating a passage which the vehicle may select in the parking area; generate an entry locus for entering the parking space from the inside-of-parking area network on the basis of a length of a segment which connects a parking space being a vehicle parking target and the inside-of-parking area network, and an angle of the inside-of-parking area network to the parking space at a connection point to which the segment is connected: combine an entry locus to a travel track of the vehicle from an inlet of the parking area to a start point of the entry locus generated by using the inside-of-parking area network, to generate the travel track of the vehicle to the parking space from the inlet of the parking area; and perform drive support based on the travel track.SELECTED DRAWING: Figure 12

Description

[Technical Field]

[0001] The present invention relates to a driving assistance device and a computer program that assists a driver in driving a vehicle in a parking lot. [Background technology]

[0002] When a vehicle travels to a destination, it typically travels to a parking lot attached to the destination or a parking lot in the vicinity of the destination, parks the vehicle, and then travels from the parking space in the parking lot to the destination by walking, etc. However, when assisting travel to such a destination, particularly when traveling in a parking lot, although the distance traveled is shorter than when traveling on a road, there are many possible travel trajectories that the vehicle can take, and it has been difficult to select the optimal travel trajectory from among them.

[0003] For example, Japanese Patent Application Laid-Open No. 2010-117864 discloses a technology that creates a network of nodes and links representing routes that vehicles can travel within a parking lot, and uses the network to search for recommended routes from the entrance / exit of the parking lot to potential parking spaces within the parking lot. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2010-117864 A (paragraphs 0028-0029, Figure 3) Summary of the Invention [Problem to be solved by the invention]

[0005] In the network disclosed in Patent Document 1, a node is set in the center of a parking space where a vehicle can park, and the aisles in the parking lot and the nodes set in the parking spaces are connected at right angles by links consisting of straight lines with the shortest distance. The route searched by such a network only shows the route to the parking space (which aisle to take and which parking space to park in), but it cannot provide the specific driving trajectory required for the vehicle to actually park in the parking space.

[0006] Furthermore, since the above-mentioned Patent Document 1 does not take into consideration specific driving trajectories, for example, if there is a parking space that is close to the entrance of the parking lot but requires complex parking maneuvers such as multiple turns to enter the parking space, and another parking space that is far from the entrance of the parking lot but is easy to enter, it is not possible to properly compare the two, which makes it difficult to search for a recommended route within the parking lot.

[0007] The present invention has been made to solve the above-mentioned problems in the past, and aims to provide a driving assistance device and computer program that can generate a specific driving trajectory required to enter a parking space from an aisle based on the relationship of distance and angle between the parking lot network and the parking space, and that can provide driving assistance based on a more appropriate driving trajectory than in the past. [Means for solving the problem]

[0008] In order to achieve the above object, a first driving assistance device according to the present invention comprises: an intra-parking lot network acquisition means for acquiring an intra-parking lot network which is a network showing routes that a vehicle can select within a parking lot when parking in the parking lot; a parking space information acquisition means for acquiring layout information of parking spaces provided in the parking lot; an approach trajectory generation means for generating an approach trajectory for approaching the parking space from the intra-parking lot network to the parking space based on the length of a line segment connecting the parking space where the vehicle is to be parked and the intra-parking lot network and the angle of the intra-parking lot network with respect to the parking space at the connection point where the line segment connects; a driving trajectory generation means for generating a driving trajectory of the vehicle from the entrance of the parking lot to a starting point of the approach trajectory using the intra-parking lot network and for generating a driving trajectory of the vehicle from the entrance of the parking lot to the parking space by combining the approach trajectory; and a driving assistance means for providing driving assistance based on the driving trajectory. The approach trajectory generation means generates the approach trajectory including straight movement when the length of the line segment is sufficient for the distance required for the vehicle to exit the parking space at a predetermined turning radius and become parallel to the network within the parking lot, and generates the approach trajectory not including straight movement when the length of the line segment is insufficient for the distance required for the vehicle to exit the parking space at a predetermined turning radius and become parallel to the network within the parking lot. .

[0009] Furthermore, a second driving assistance device according to the present invention comprises: an intra-parking lot network acquisition means for acquiring an intra-parking lot network, which is a network that indicates routes that a vehicle can select within a parking lot when exiting the parking lot where the vehicle is parked; a parking space information acquisition means for acquiring layout information of a parking space in which the vehicle is parked in the parking lot; an exit trajectory generation means for generating an exit trajectory for exiting from the parking space to the intra-parking lot network based on the length of a line segment connecting the parking space in which the vehicle is parked and the intra-parking lot network and the angle of the intra-parking lot network with respect to the parking space at the connection point where the line segment connects; a driving trajectory generation means for generating a driving trajectory of the vehicle from an end point of the exit trajectory to an exit of the parking lot using the intra-parking lot network and for combining the exit trajectories to generate a driving trajectory of the vehicle from the parking space to the exit of the parking lot; and a driving assistance means for performing driving assistance based on the driving trajectory. The exit trajectory generation means generates the exit trajectory including straight movement when the length of the line segment is sufficient for the distance required for the vehicle to exit the parking space at a predetermined turning radius and become parallel to the network within the parking lot, and generates the exit trajectory not including straight movement when the length of the line segment is insufficient for the distance required for the vehicle to exit the parking space at a predetermined turning radius and become parallel to the network within the parking lot. .

[0010] Furthermore, the present invention 1stThe computer program is a program for generating assistance information used for driving assistance implemented in a vehicle. Specifically, the computer is made to function as: an intra-parking lot network acquisition means for acquiring an intra-parking lot network that indicates routes that a vehicle can choose within the parking lot when parking in the parking lot; a parking space information acquisition means for acquiring layout information of parking spaces provided in the parking lot; an approach trajectory generation means for generating an approach trajectory for approaching the parking space from the intra-parking lot network to the parking space based on the length of a line connecting the parking space where the vehicle is to be parked and the intra-parking lot network and the angle of the intra-parking lot network with respect to the parking space at the connection point where the line connects; a travel trajectory generation means for generating a travel trajectory of the vehicle from the entrance of the parking lot to the starting point of the approach trajectory using the intra-parking lot network and combining the approach trajectories to generate a travel trajectory of the vehicle from the entrance of the parking lot to the parking space; and a driving assistance means for performing driving assistance based on the travel trajectory. The approach trajectory generating means generates the approach trajectory including straight movement when the length of the line segment is sufficient for the distance required for the vehicle to exit the parking space at a predetermined turning radius and become parallel to the parking lot network, and generates the approach trajectory not including straight movement when the length of the line segment is insufficient for the distance required for the vehicle to exit the parking space at a predetermined turning radius and become parallel to the parking lot network. Furthermore, a second computer program according to the present invention is a computer program that functions as an intra-parking lot network acquisition means that acquires an intra-parking lot network, which is a network that shows routes that a vehicle can select within a parking lot when exiting the parking lot where the vehicle is parked; a parking space information acquisition means that acquires layout information of the parking space in which the vehicle is parked in the parking lot; an exit trajectory generation means that generates an exit trajectory for exiting from the parking space to the intra-parking lot network based on the length of the line connecting the parking space in which the vehicle is parked and the intra-parking lot network and the angle of the intra-parking lot network with respect to the parking space at the connection point where the line connects; a driving trajectory generation means that uses the intra-parking lot network to generate a driving trajectory for the vehicle from the end point of the exit trajectory to the exit of the parking lot and combines the exit trajectory to generate a driving trajectory for the vehicle from the parking space to the exit of the parking lot; and a driving assistance means that provides driving assistance based on the driving trajectory. In addition, the exit trajectory generation means generates the exit trajectory including straight-line movement when the length of the line segment is sufficient for the distance required for the vehicle to exit the parking space at a predetermined turning radius and become parallel to the parking lot network, and generates the exit trajectory not including straight-line movement when the length of the line segment is insufficient for the distance required for the vehicle to exit the parking space at a predetermined turning radius and become parallel to the parking lot network. [Effects of the Invention]

[0011] The first driving assistance device and computer program according to the present invention having the above configuration make it possible to generate a specific driving trajectory required to enter a parking space from an aisle based on the relationship of distance and angle between the parking lot network and the parking space. By combining this with the driving trajectory generated using the parking lot network, it becomes possible to provide driving assistance based on a more appropriate driving trajectory than before.

[0012] Also, the second driving assistance device and computer programs According to this technology, it is possible to generate a specific driving trajectory required to exit a parking space to an aisle based on the relationship of distance and angle between the parking lot network and the parking space. By combining this with the driving trajectory generated using the parking lot network, it is possible to provide driving assistance based on a more appropriate driving trajectory than before. [Brief explanation of the drawings]

[0013] [Figure 1] 1 is a schematic configuration diagram showing a driving assistance system according to an embodiment of the present invention; [Figure 2] 1 is a block diagram showing the configuration of a driving assistance system according to an embodiment of the present invention. [Figure 3] 1 is a block diagram showing a navigation device according to an embodiment of the present invention; [Figure 4] 4 is a flowchart of an autonomous driving assistance program according to the present embodiment. [Figure 5] FIG. 1 is a diagram showing an example of a parking lot network established in a parking lot where vehicles are parked. [Figure 6] FIG. 10 is a diagram showing connection points set for parking position candidates. [Figure 7] FIG. 10 is a diagram showing an example of a driving route to a connection point. [Figure 8] FIG. 2 is a diagram showing an area for which high-precision map information is acquired. [Figure 9] FIG. 10 is a diagram illustrating a method for calculating a dynamic traveling trajectory. [Figure 10] 10 is a flowchart of a sub-processing program of a static traveling trajectory generation process. [Figure 11] FIG. 10 is a diagram showing an example of excluding candidates for a driving trajectory in a parking lot. [Figure 12] FIG. 10 is a diagram showing an example of a travel trajectory to a connection point and an approach trajectory for entering a parking space. [Figure 13] FIG. 10 is a diagram showing an example of a candidate travel trajectory generated by combining with an approach trajectory. [Figure 14] FIG. 10 is a diagram showing an example of a travel trajectory to a connection point and an approach trajectory for entering a parking space. [Figure 15] FIG. 10 is a diagram showing an example of a candidate travel trajectory generated by combining with an approach trajectory. [Figure 16] FIG. 10 is a diagram showing an example of a travel path in a conditionally prohibited area. [Figure 17] FIG. 10 is a diagram showing an example of a driving route to a destination parking lot. [Figure 18] FIG. 18 is a diagram showing an example of a lane network constructed for the travel route shown in FIG. 17. [Figure 19] FIG. 10 is a diagram showing a recommended driving trajectory when entering a parking lot from an access road. [Figure 20] 10 is a flowchart of a sub-processing program of an approach trajectory generation process. [Figure 21] FIG. 10 is a diagram showing the relationship between distance and angle between a parking space and a network within the parking lot. [Figure 22] FIG. 10 is a diagram showing the relationship between distance and angle between a parking space and a network within the parking lot. [Figure 23] FIG. 10 is a diagram showing an approach trajectory (exit trajectory) generated based on Pattern 1. [Figure 24] FIG. 10 is a diagram showing an approach trajectory (exit trajectory) generated based on Pattern 2. [Figure 25] FIG. 10 is a diagram showing an approach trajectory (exit trajectory) generated based on Pattern 3. [Figure 26] FIG. 10 is a diagram showing an approach trajectory (exit trajectory) generated based on pattern 4. [Figure 27] FIG. 10 is a diagram showing an approach trajectory (exit trajectory) generated based on pattern 5. DETAILED DESCRIPTION OF THE INVENTION

[0014] An embodiment in which a driving assistance device according to the present invention is embodied in a navigation device 1 will be described in detail below with reference to the drawings. First, a schematic configuration of a driving assistance system 2 including a navigation device 1 according to this embodiment will be described with reference to Fig. 1 and Fig. 2. Fig. 1 is a schematic configuration diagram showing the driving assistance system 2 according to this embodiment. Fig. 2 is a block diagram showing the configuration of the driving assistance system 2 according to this embodiment.

[0015] As shown in Fig. 1, a driving assistance system 2 according to this embodiment basically includes a server device 4 provided in an information distribution center 3, and a navigation device 1 that is mounted on a vehicle 5 and provides various types of assistance related to the autonomous driving of the vehicle 5. The server device 4 and the navigation device 1 are configured to be able to send and receive electronic data to and from each other via a communication network 6. Note that instead of the navigation device 1, other on-board devices mounted on the vehicle 5 or a vehicle control device that controls the vehicle 5 may be used.

[0016] Here, vehicle 5 is a vehicle capable of manual driving, in which the vehicle drives based on the user's driving operations, as well as assisted driving using automatic driving assistance, in which the vehicle automatically drives along a pre-set route or road without the user's driving operations.

[0017] Furthermore, autonomous driving assistance may be provided for all road sections, or may be configured to be provided only while the vehicle is traveling on a specific road section (for example, a highway with a gate (manned or unmanned, toll or free) at the boundary). In the following explanation, the autonomous driving section in which autonomous driving assistance is provided includes all road sections, including general roads and highways, as well as parking lots, and the explanation will be given assuming that autonomous driving assistance is basically provided from the time the vehicle starts traveling until it ends traveling (until the vehicle is parked). However, autonomous driving assistance is not always provided when the vehicle is traveling on an autonomous driving section, but is preferably provided only when the user selects to provide autonomous driving assistance (for example, by turning on the autonomous driving start button) and it is determined that autonomous driving assistance is possible. On the other hand, vehicle 5 may be a vehicle capable of only assisted traveling using autonomous driving assistance.

[0018] In vehicle control in automated driving assistance, for example, the current position of the vehicle, the lane the vehicle is traveling on, and the positions of surrounding obstacles are detected at any time, and vehicle control of the steering, drive source, brakes, etc. is automatically performed so that the vehicle travels at a speed according to a speed plan generated along a travel trajectory generated by the navigation device 1, as will be described later. Note that in assisted driving with automated driving assistance in this embodiment, lane changes, right and left turns, and parking operations are also performed by performing vehicle control with the automated driving assistance described above, but special driving operations such as lane changes, right and left turns, and parking operations may be performed by manual driving without using automated driving assistance.

[0019] On the other hand, the navigation device 1 is an on-board device that is mounted on the vehicle 5 and displays a map of the area around the vehicle's position based on map data stored in the navigation device 1 or map data acquired from an external source, allows the user to input a destination, displays the vehicle's current position on a map image, and provides travel guidance along a set guide route. In this embodiment, various types of assistance information related to autonomous driving assistance are generated, particularly when the vehicle is performing assisted driving using autonomous driving assistance. Examples of assistance information include a recommended driving trajectory for the vehicle (including recommended lane movement patterns), selection of a parking position for parking the vehicle at the destination, and a speed plan indicating the vehicle speed when driving. Details of the navigation device 1 will be described later.

[0020] Furthermore, the server device 4 executes a route search in response to a request from the navigation device 1. Specifically, information necessary for the route search, such as the departure point and destination, is transmitted from the navigation device 1 to the server device 4 along with a route search request (however, in the case of a re-search, it is not necessary to transmit information about the destination). Then, upon receiving the route search request, the server device 4 performs a route search using map information held by the server device 4 and identifies a recommended route from the departure point to the destination. Thereafter, the identified recommended route is transmitted to the navigation device 1 that issued the request. The navigation device 1 can then provide the user with information about the received recommended route, or use the recommended route to generate various types of assistance information related to autonomous driving assistance, as will be described later.

[0021] Furthermore, the server device 4 stores high-precision map information and facility information, which are map information with higher accuracy, in addition to the normal map information used for the route search. The high-precision map information includes, for example, information about road lane shapes (such as the road shape and curvature for each lane, and lane width) and road markings (such as center lines, lane boundaries, outer road lines, and guide lines). It also includes information about intersections. Meanwhile, the facility information is more detailed information about facilities stored separately from the facility information included in the map information. For example, the facility information includes a floor map of the facility, information about parking lot entrances, layout information about aisles and parking spaces in the parking lot, information about the marking lines that divide the parking spaces, and connection information indicating the connection between parking lot entrances and lanes. The server device 4 distributes the high-precision map information and facility information in response to a request from the navigation device 1, and the navigation device 1 generates various types of assistance information related to autonomous driving assistance, as described below, using the high-precision map information and facility information distributed from the server device 4. While the high-precision map information basically covers only roads (links) and their surrounding areas, it may also include map information that covers areas other than the roads.

[0022] However, the above-described route search process does not necessarily have to be performed by the server device 4, and may be performed by the navigation device 1 as long as the navigation device 1 has map information. Also, the high-precision map information and facility information may not be distributed from the server device 4, but may be stored in the navigation device 1 in advance.

[0023] The communication network 6 includes numerous base stations located throughout the country and communication companies that manage and control each base station, and is configured by connecting the base stations and communication companies to each other via wire (optical fiber, ISDN, etc.) or wirelessly. Here, the base stations have transceivers (transmitters / receivers) and antennas that communicate with the navigation device 1. The base stations perform wireless communications between communication companies, and are also end points of the communication network 6, and have the role of relaying communications between the navigation device 1 within the range (cell) of the base station's radio waves and the server device 4.

[0024] Next, the configuration of the server device 4 in the driving assistance system 2 will be described in more detail with reference to Fig. 2. As shown in Fig. 2, the server device 4 includes a server control unit 11, a server-side map DB 12 as information recording means connected to the server control unit 11, a high-precision map DB 13, a facility DB 14, and a server-side communication device 15.

[0025] The server control unit 11 is a control unit (MCU, MPU, etc.) that controls the entire server device 4, and includes a CPU 21 as an arithmetic and control device, a RAM 22 used as a working memory when the CPU 21 performs various arithmetic processing, a ROM 23 in which control programs and the like are recorded, and a flash memory 24 for storing programs read from the ROM 23. The server control unit 11 has various means as processing algorithms together with the ECU of the navigation device 1, which will be described later.

[0026] On the other hand, the server-side map DB 12 is a storage means for storing server-side map information, which is the latest version of map information registered based on external input data and input operations. Here, the server-side map information is composed of various information necessary for route search, route guidance, and map display, including the road network. For example, it includes network data including nodes and links indicating the road network, link data related to roads (links), node data related to node points, intersection data related to each intersection, point data related to points such as facilities, map display data for displaying the map, search data for searching for routes, and search data for searching for points.

[0027] The high-precision map DB 13 is also a storage means for storing high-precision map information 16, which is map information with higher accuracy than the server-side map information. The high-precision map information 16 is map information that stores more detailed information, particularly about roads on which vehicles travel. In this embodiment, the high-precision map information 16 includes, for example, information about lane shapes (such as the road shape and curvature for each lane, and lane width) and road dividing lines (such as center lines, lane boundaries, outer lane lines, and guide lines) drawn on roads. Furthermore, the high-precision map information 16 stores data representing road gradients, cants, banks, merging sections, areas where the number of lanes decreases, areas where road width narrows, and railroad crossings; data representing corners such as the radius of curvature, intersections, T-junctions, and corner entrances and exits; data representing road attributes such as downhill roads and uphill roads; and data representing road types such as general roads (such as national highways, prefectural roads, and narrow streets) as well as toll roads (such as national expressways, urban expressways, motorways, general toll roads, and toll bridges). In particular, in this embodiment, in addition to the number of lanes on a road, information specifying the traffic divisions in the direction of travel for each lane and the road connections (specifically, the correspondence between the lanes included in the road before passing through the intersection and the lanes included in the road after passing through the intersection) is also stored. Furthermore, the speed limit set for the road is also stored.

[0028] On the other hand, the facility DB 14 is a storage means for storing more detailed facility information than the facility information stored in the server-side map information. Specifically, the facility information 17 includes, particularly for parking lots (including both parking lots attached to facilities and stand-alone parking lots) where vehicles are parked, information identifying the location of the parking lot entrances and exits, information identifying the layout of parking spaces within the parking lot, information about the dividing lines that separate the parking spaces, information about pathways through which vehicles and pedestrians can pass, crosswalks within the parking lot, and information about pedestrian passageways. For facilities other than parking lots, information identifying a floor map of the facility is included. The floor map includes, for example, information identifying the locations of entrances and exits, pathways, stairs, elevators, and escalators. Furthermore, in the case of a commercial complex with multiple tenants, information identifying the location of each tenant is included. The facility information 17 may be information generated using a 3D model of the parking lot or facility. Furthermore, the facility DB 14 also includes connection information 18 that indicates the connection relationship between the lanes included in the approach road facing the entrance to the parking lot and the entrance to the parking lot, and road outer shape information 19 that specifies the area between the approach road and the entrance to the parking lot that is passable by vehicles. The details of each piece of information stored in the facility DB 14 will be described later.

[0029] Although the high-precision map information 16 is basically map information that covers only roads (links) and their surrounding areas, it may also be map information that includes areas other than the surrounding areas of the roads. Furthermore, in the example shown in Fig. 2, the server-side map information stored in the server-side map DB 12 and the information stored in the high-precision map DB 13 and facility DB 14 are different map information, but the information stored in the high-precision map DB 13 and facility DB 14 may also be part of the server-side map information. Furthermore, the high-precision map DB 13 and facility DB 14 may not be separated but may be a single database.

[0030] On the other hand, the server-side communication device 15 is a communication device for communicating with the navigation device 1 of each vehicle 5 via the communication network 6. In addition to the navigation device 1, it is also possible to receive traffic information including congestion information, regulation information, traffic accident information, etc. transmitted from the Internet network or a traffic information center, such as a VICS (registered trademark: Vehicle Information and Communication System) center.

[0031] Next, the schematic configuration of the navigation device 1 mounted on the vehicle 5 will be described with reference to Fig. 3. Fig. 3 is a block diagram showing the navigation device 1 according to this embodiment.

[0032] 3, the navigation device 1 according to this embodiment includes a current position detection unit 31 that detects the current position of the vehicle in which the navigation device 1 is installed, 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 accepts user operations, a liquid crystal display 35 that displays to the user a map of the area around the vehicle and information about the guide route (planned route of the vehicle) set in the navigation device 1, a speaker 36 that outputs audio guidance regarding the route guidance, a DVD drive 37 that reads a DVD as a storage medium, and a communication module 38 that communicates with an information center such as a probe center or a VICS center. The navigation device 1 is also connected to an external camera 39 and various sensors installed in the vehicle in which the navigation device 1 is installed via an in-vehicle network such as a CAN. The navigation device 1 is also connected to a vehicle control ECU 40 that performs various controls on the vehicle in which the navigation device 1 is installed, allowing bidirectional communication.

[0033] Each of the components of the navigation device 1 will be explained below in order. The current position detection unit 31 is composed of a GPS 41, a vehicle speed sensor 42, a steering sensor 43, a gyro sensor 44, etc., and is capable of detecting the current vehicle position, direction, vehicle traveling speed, current time, etc. Here, the vehicle speed sensor 42 in particular is a sensor for detecting the vehicle's travel distance and speed, and generates pulses in response to the rotation of the vehicle's drive wheels and outputs the pulse signals to the navigation ECU 33. The navigation ECU 33 then calculates the rotation speed of the drive wheels and travel distance by counting the generated pulses. Note that the navigation device 1 does not need to be equipped with all four types of sensors described above, and the navigation device 1 may be configured to be equipped with only one or more of these types of sensors.

[0034] The data recording unit 32 also includes a hard disk (not shown) as an external storage device and recording medium, and a recording head (not shown) which is a driver for reading the map information DB 45, cache 46, predetermined programs, etc. recorded on the hard disk and writing predetermined data to the hard disk. Note that the data recording unit 32 may include a flash memory, a memory card, or an optical disk such as a CD or DVD instead of a hard disk. In addition, in this embodiment, as described above, the server device 4 searches for a route to the destination, so the map information DB 45 may be omitted. Even if the map information DB 45 is omitted, it is possible to obtain map information from the server device 4 as needed.

[0035] Here, the map information DB45 is a storage means that stores, for example, link data related to roads (links), node data related to node points, search data used for processing related to route search and change, facility data related to facilities, map display data for displaying maps, intersection data related to each intersection, search data for searching for points, etc.

[0036] On the other hand, the cache 46 is a storage means for storing the high-precision map information 16, facility information 17, connection information 18, and road exterior shape information 19 that have been distributed from the server device 4 in the past. The storage period can be set as appropriate, for example, it may be a predetermined period (e.g., one month) from the time of storage, or it may be until the vehicle's ACC power supply (accessory power supply) is turned off. Furthermore, once the amount of data stored in the cache 46 reaches an upper limit, the oldest data may be deleted sequentially. Then, the navigation ECU 33 generates various types of assistance information related to autonomous driving assistance using the high-precision map information 16, facility information 17, connection information 18, and road exterior shape information 19 stored in the cache 46. Details will be described later.

[0037] Meanwhile, the navigation ECU (electronic control unit) 33 is an electronic control unit that controls the entire navigation device 1. It includes internal storage devices such as a CPU 51, which functions as a calculation device and a control device; a RAM 52, which is used as a working memory when the CPU 51 performs various calculation processes and stores route data and other information used when a route is searched; a ROM 53, which stores control programs as well as the autonomous driving assistance program (see FIG. 4 ), which will be described later; and a flash memory 54, which stores programs read from the ROM 53. The navigation ECU 33 also includes various processing algorithms. For example, the parking lot network acquisition means acquires the parking lot network, which indicates routes that a vehicle can choose within the parking lot when parking in the parking lot. The parking space information acquisition means acquires layout information for parking spaces provided in the parking lot. The approach trajectory generation means generates an approach trajectory for approaching a parking space from the parking lot network based on the length of a line segment connecting the parking space to the parking lot network and the angle of the parking lot network at the connection point where the line segment connects to the parking space. The driving trajectory generation means generates a driving trajectory for the vehicle from the entrance of the parking lot to the start point of the approach trajectory using the parking lot network, and generates a driving trajectory for the vehicle from the entrance of the parking lot to the parking space by combining the approach trajectories. The driving assistance means provides driving assistance based on the generated driving trajectory.

[0038] The operation unit 34 is operated when inputting a departure point as a starting point of a trip and a destination point as a destination of a trip, and has a plurality of operation switches (not shown) such as various keys and buttons. The navigation ECU 33 controls the execution of various corresponding operations based on switch signals output by pressing each switch. The operation unit 34 may have a touch panel provided on the front surface of the liquid crystal display 35. It may also have a microphone and a voice recognition device.

[0039] The liquid crystal display 35 also displays map images including roads, traffic information, operation guidance, operation menus, key guidance, guidance information along the guided route (planned driving route), news, weather forecasts, time, emails, television programs, etc. Note that a HUD or HMD may be used instead of the liquid crystal display 35.

[0040] Furthermore, the speaker 36 outputs voice guidance for guiding the vehicle along the guide route (planned travel route) based on instructions from the navigation ECU 33, and also outputs traffic information guidance.

[0041] The DVD drive 37 is a drive that can read data recorded on a recording medium such as a DVD or CD. Based on the read data, music and video are played, and the map information DB 45 is updated. Instead of the DVD drive 37, a card slot for reading and writing data to a memory card may be provided.

[0042] The communication module 38 is a communication device for receiving traffic information, probe information, weather information, etc. transmitted from a traffic information center, such as a VICS center or a probe center, and is, for example, a mobile phone or DCM. It also includes a vehicle-to-vehicle communication device for communicating between vehicles and a road-to-vehicle communication device for communicating with roadside devices. It is also used to transmit and receive route information, high-precision map information 16, facility information 17, connection information 18, and road outer shape information 19 searched by the server device 4 to and from the server device 4.

[0043] The exterior camera 39 is composed of a camera using a solid-state image sensor such as a CCD, and is mounted above the vehicle's front bumper with its optical axis oriented downward at a predetermined angle from the horizontal. The exterior camera 39 captures images of the area ahead of the vehicle when the vehicle is traveling in an autonomous driving zone. The navigation ECU 33 processes the captured images to detect obstacles, such as lane markings on the road the vehicle is traveling on and other vehicles in the vicinity, and generates various assistance information related to autonomous driving assistance based on the detection results. For example, if an obstacle is detected, the navigation ECU 33 generates a new driving trajectory to avoid or follow the obstacle. The exterior camera 39 may be positioned at the rear or side of the vehicle, in addition to the front. Instead of a camera, sensors such as millimeter-wave radar or laser sensors, or vehicle-to-vehicle communication or road-to-vehicle communication may be used to detect obstacles.

[0044] 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 each drive unit of the vehicle, such as the steering, brakes, and accelerator, and in this embodiment, after automatic driving assistance has started in the vehicle, the vehicle control ECU 40 controls each drive unit to implement automatic driving assistance for the vehicle. If an override is performed by the user during automatic driving assistance, the ECU 40 detects that an override has been performed.

[0045] Here, after starting to travel, the navigation ECU 33 transmits various types of assistance information related to the autonomous driving assistance generated by the navigation device 1 to the vehicle control ECU 40 via the CAN. Then, the vehicle control ECU 40 uses the received various types of assistance information to implement the autonomous driving assistance after starting to travel. Examples of the assistance information include a recommended traveling trajectory for the vehicle, a speed plan indicating the vehicle speed when traveling, etc.

[0046] Next, an automatic driving assistance program executed by the CPU 51 in the navigation device 1 according to this embodiment having the above configuration will be described with reference to Fig. 4. Fig. 4 is a flowchart of the automatic driving assistance program according to this embodiment. Here, the automatic driving assistance program is executed when the vehicle starts traveling with automatic driving assistance after the ACC power supply (accessory power supply) of the vehicle is turned on, and is a program that performs assisted traveling with automatic driving assistance in accordance with assistance information generated by the navigation device 1. The programs shown in the flowcharts in Figs. 4, 10 and 20 below are stored in the RAM 52 and ROM 53 provided in the navigation device 1, and are executed by the CPU 51.

[0047] First, in step (hereinafter abbreviated as S) 1 of the autonomous driving assistance program, the CPU 51 acquires a destination that the user sets as a travel target. Basically, the destination is set by a user operation accepted by the navigation device 1. The destination may 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 CPU 51 also acquires a parking lot where the user will park at the destination. If there is a dedicated parking lot or affiliated parking lot at the destination, that parking lot is set as the parking lot where the user will park. On the other hand, if there is no dedicated parking lot or affiliated parking lot, a parking lot near the destination is set as the parking lot where the user will park. Note that if there are multiple candidate parking lots, all of the candidate parking lots may be acquired as the parking lot where the user will park, or any parking lot selected by the user may be acquired as the parking lot where the user will park.

[0048] Next, in S2, the CPU 51 acquires candidate parking positions (parking spaces) recommended for the user to park in the parking lot where the user will park, acquired in S1. Specifically, the CPU 51 acquires from the server device 4 layout information of parking spaces provided in the parking lot where the user will park, and further acquires information on vacant parking spaces from the server that manages the parking lot, and determines, from among the vacant parking spaces in the parking lot, a parking space that is easy for the user to park in (for example, a parking space close to the entrance of the parking lot, a parking space close to the entrance of the destination, a parking space with no other vehicles parked on either side, etc.), as a candidate parking position recommended for the user to park. Note that all vacant parking spaces in the parking lot may be set as candidate parking positions.

[0049] Next, in S3, the CPU 51 uses the facility information 17 acquired from the server device 4 to construct links and nodes (construct a parking lot network) within the parking lot where the user will park, similar to roads. The facility information 17 includes information identifying the location of the parking lot entrance and exit, information identifying the layout of parking spaces within the parking lot, information on the dividing lines that separate the parking spaces, information on paths that vehicles and pedestrians can use, information on crosswalks and pedestrian passage spaces, etc. However, the facility information 17 may also be information generated using a 3D model of the parking lot. Using this information, routes that vehicles can take within the parking lot are identified, and the parking lot network is constructed. However, the server device 4 may construct the parking lot network for each parking lot nationwide in advance, and the CPU 51 may acquire the corresponding parking lot network from the server device 4 in S3.

[0050] An example of the parking lot network constructed for the parking lot in S3 is shown in Figure 5. As shown in Figure 5, parking lot nodes 58 are set at the entrances and exits of the parking lot, intersections where vehicle-passable passageways intersect, corners of vehicle-passable passageways (i.e., connection points between passageways), and the end points of the passageways. On the other hand, parking lot links 59 are set for passageways between parking lot nodes 58 where vehicles can pass. They are basically set at the center of the passageways. Note that parking lot links 59 are allowed to cross areas where vehicles are allowed to pass when there are no pedestrians, such as crosswalks and pedestrian passageways. Parking lot links 59 also contain information specifying the direction in which vehicles can pass through the passageways within the parking lot. For example, Figure 5 shows an example in which the passageways within the parking lot can only be passed clockwise.

[0051] Furthermore, when the current location of the vehicle is particularly within a parking lot and the destination is outside the parking lot, the CPU 51 similarly constructs a parking lot network as shown in FIG. 5 for the parking lot where the vehicle is currently located.

[0052] Thereafter, in S4, the CPU 51 acquires the link (hereinafter referred to as the nearest link) that is closest to the parking position candidate acquired in S2 from among the parking lot links 59 included in the intra-parking lot network constructed in S3. For example, if parking space 60 is the recommended parking position as shown in Fig. 6, the passage (link A) located directly adjacent to parking space 60 will be acquired as the nearest link.

[0053] Furthermore, in S5, the CPU 51 calculates a line segment that connects the parking space (particularly, the center of the parking space, but not necessarily the center) that is the parking position candidate to the nearest link acquired in S4 in the shortest distance, and calculates a connection point where the line segment and the nearest link connect. For example, if parking space 60 is the recommended parking position as shown in FIG. 6, the aisle (link A) located directly in front of and adjacent to parking space 60 becomes the nearest link, and calculates a line segment 61 that connects the center of parking space 60 to link A in the shortest distance, and further calculates a connection point 62 where line segment 61 connects to link A. The CPU 51 also calculates line segments and connection points for links adjacent to the nearest link (hereinafter referred to as adjacent links) in the same manner. For example, in the example shown in FIG. 6, a line segment 63 is calculated for link B adjacent to link A in the same manner, and a connection point 64 where line segment 63 connects to link B is calculated.

[0054] The connection point calculated in S5 is a reference point when generating candidate approach trajectories for approaching the parking space (candidate parking location) from the parking lot network (however, as described below, the approach trajectory does not necessarily start from the connection point). Therefore, in the example shown in FIG. 6, when approaching parking space 60, two candidate approach trajectories are calculated: one approach trajectory from near connection point 62 of link A and one approach trajectory from near connection point 64 of link B. The final approach trajectory is determined from among the calculated candidate approach trajectories (S8). Links C and D are also adjacent to link A. However, since links C and D are too far from the parking space (candidate parking location) and it is basically impossible to use a trajectory from these links to approach the parking space 60 without passing through link A, no connection points are set for these links. However, connection points may be set for all adjacent links regardless of the distance from the parking space 60. Furthermore, connection points may be set for all links within a predetermined distance from the candidate parking location, not just for links adjacent to the closest link. If there are multiple parking position candidates, the processes in S4 and S5 are performed for each parking position candidate.

[0055] Furthermore, when the current position of the vehicle is particularly one of the parking spaces in the parking lot, i.e., when the vehicle is leaving the parking lot and heading toward the destination, the CPU 51 acquires information specifying the layout of the parking space in the parking lot where the vehicle is currently located (the parking space where the vehicle will be parked), replaces the parking position candidate with the parking space where the vehicle is currently located, and performs the processes of S4 and S5 again, thereby calculating the line segment that connects the parking space where the vehicle is currently located with the nearest link and the adjacent link by the shortest distance, and calculates the connection points where the line segment connects with the nearest link and the adjacent link.

[0056] Next, in S6, the CPU 51 searches for a recommended driving route for the vehicle from the current location of the vehicle to the connection point set in S6. If there are multiple connection points, the search is performed for each connection point. Furthermore, if the current location of the vehicle is a parking space in a parking lot, the connection point set in S5 for that parking space is set as the starting point of the driving route.

[0057] In this embodiment, the search for a driving route in S6 is performed particularly by the server device 4. When searching for a driving route, the CPU 51 first transmits a route search request to the server device 4. The route search request includes a terminal ID that identifies the navigation device 1 that sent the route search request, and information that identifies the starting point (e.g., the current location of the vehicle) and the connection point set in S5. The CPU 51 then receives searched route information transmitted from the server device 4 in response to the route search request. The searched route information is information (e.g., a series of links included in the driving route) that identifies a recommended driving route from the starting point to a parking location candidate, which the server device 4 has searched for using the latest version of map information based on the transmitted route search request. The search is performed using, for example, the well-known Dijkstra algorithm.

[0058] Furthermore, when searching for a driving route in S6, costs and directions (directions that can pass through the parking lot nodes) are set for the parking lot nodes 58 and parking lot links 59 included in the parking lot network, just like for road links. For example, a cost is set for each parking lot node 58 corresponding to an intersection or a parking lot entrance / exit, according to the contents of the parking lot node 58, and a direction that can be passed when a vehicle passes through the parking lot node 58 is also set. Furthermore, a cost is set for the parking lot link 59 using the time required for travel or the length of the link as a reference value. In other words, the longer the time or distance required for travel through a parking lot link 59, the higher the calculated cost.

[0059] The server device 4 then uses Dijkstra's algorithm to calculate the total cost of traveling from the vehicle's current location to the connection point via the parking lot entrance (and via the parking lot exit if the vehicle's current location is within the parking lot and the destination is outside the parking lot), and designates the route with the smallest total value as the recommended vehicle driving route. However, the server device 4 does not specify a single recommended driving route; if there are multiple candidate driving routes within the parking lot, the server device 4 acquires all of these candidates as the recommended vehicle driving routes. For example, as shown in FIG. 7, if parking space 60 is the recommended parking location, there are two possible driving routes: driving route 65, which goes straight from the parking lot entrance and turns right near the center, and driving route 66, which goes straight from the parking lot entrance to the end, turns right, and then turns around. In such a case, the server device 4 determines which driving route is more appropriate by generating and comparing actual driving trajectories, as described below, so it is desirable to acquire both as recommended driving routes in step S6. Furthermore, if there are multiple parking location candidates and connection points, the server device 4 acquires a recommended vehicle driving route from the parking lot entrance to the connection point for each parking location candidate and connection point. 7, in addition to the driving route to the connection point 62, a driving route to the connection point 64 is also acquired separately. If there are multiple candidate routes for the recommended driving route from the entrance of the parking lot to the connection point (including the case where there are multiple candidate routes for the recommended driving route from the connection point to the exit of the parking lot when the current position of the vehicle is inside the parking lot), a specific driving trajectory is generated for each driving route in the static driving trajectory generation process (S8) described below (multiple driving trajectories may be generated for one driving route), and the generated driving trajectories are compared to determine the final driving route from among the multiple routes.

[0060] The server device 4 refers to connection information 18 that indicates the connection relationship between the lanes included in the road facing the entrance to the parking lot where the user will park (hereinafter referred to as the approach road) and the entrance to the parking lot, and if the possible directions of travel into the parking lot from the approach road are limited (for example, only left turns are allowed), the server device 4 also takes into consideration the approach direction when searching for the driving route. Note that a search method other than the Dijkstra algorithm may be used as a route search method. The search for the driving route in S6 may also be performed by the navigation device 1 rather than the server device 4.

[0061] Next, in S7, the CPU 51 acquires high-precision map information 16 for an area including the vehicle travel route acquired in S6.

[0062] Here, the high precision map information 16 is divided into rectangular shapes (for example, 500 m x 1 km) as shown in Fig. 8 and stored in the high precision map DB 13 of the server device 4. Therefore, for example, when a route 70 is acquired as the vehicle's driving route as shown in Fig. 8, high precision map information 16 is acquired for areas 71 to 74 including the route 70. However, if the distance to the parking lot where the user will park is particularly long, high precision map information 16 may be acquired for only the secondary mesh where the vehicle is currently located, or high precision map information 16 may be acquired for only the area within a predetermined distance (for example, within 3 km) from the vehicle's current position.

[0063] The high-precision map information 16 includes, for example, information about the lane shapes of roads and the dividing lines painted on the roads (roadway center lines, lane boundaries, roadway outer lines, guiding lines, etc.). It also includes information about intersections, parking lots, etc. The high-precision map information 16 is basically acquired from the server device 4 in units of the rectangular areas described above, but if high-precision map information 16 for an area is already stored in the cache 46, it is acquired from the cache 46. The high-precision map information 16 acquired from the server device 4 is temporarily stored in the cache 46.

[0064] In addition, in S4, the CPU 51 also acquires connection information 18 that indicates the connection relationship between the lanes included in the approach road facing the entrance to the parking lot where the user will park and the entrance to the parking lot, and road outer shape information 19 that specifies the area through which vehicles can pass between the approach road and the entrance to the parking lot where the user will park.

[0065] Thereafter, in S8, the CPU 51 executes a static driving trajectory generation process (FIG. 10) described below. Here, the static driving trajectory generation process generates a driving trajectory recommended for traveling along the recommended driving route for the vehicle from the current position of the vehicle to the connection point found in S6. Furthermore, an approach trajectory for approaching a parking space, which is a parking position candidate, from the parking lot network is also generated, and these are combined to generate a driving trajectory for the vehicle from the entrance of the parking lot to the parking space, which is a parking position candidate. Furthermore, if there are multiple driving route candidates, the driving trajectories for each driving route are compared and the most recommended driving trajectory is selected (i.e., the driving route is also determined). Furthermore, a parking position where the vehicle is to be parked is determined from the parking position candidates acquired in S2 based on the selected driving trajectory. The driving trajectory finally generated in S8 (hereinafter referred to as the static driving trajectory) includes a first driving trajectory recommended for the vehicle along the lane from the driving start point to the approach road facing the entrance of the destination parking lot, as described below. (If the vehicle's current location is within the parking lot, this includes a driving trajectory recommended for the vehicle from the vehicle's current location to the parking lot exit, and a driving trajectory recommended for the vehicle from the parking lot exit to the approach road facing the entrance of the destination parking lot.) A second driving trajectory recommended for the vehicle from the approach road to the parking lot entrance, and a third driving trajectory recommended for the vehicle from the parking lot entrance to the parking position (parking space) where the vehicle will be parked. In particular, the third driving trajectory is a trajectory that identifies at least the specific vehicle driving position within the parking lot. However, if the parking lot where the user will park is particularly far away, only the first driving trajectory may be generated, covering a section from the vehicle's current location to a predetermined distance ahead in the direction of travel (for example, within the secondary mesh where the vehicle is currently located). Although the specified distance can be changed as appropriate, the static driving trajectory is generated for an area that includes at least the area outside the range (detection range) where the road conditions around the vehicle can be detected by the exterior camera 39 or other sensors.

[0066] Next, in S9, the CPU 51 generates a speed plan for the vehicle when traveling along the static traveling track generated in S8, based on the high-precision map information 16 acquired in S7. For example, the CPU 51 calculates a recommended traveling speed for the vehicle when traveling along the static traveling track, taking into consideration speed limit information and speed change points (e.g., intersections, curves, railroad crossings, crosswalks, etc.) on the planned traveling route.

[0067] The speed plan generated in S9 is stored in the flash memory 54 or the like as support information to be used for the automatic driving support. In addition, an acceleration plan indicating the acceleration / deceleration of the vehicle required to realize the speed plan generated in S6 may also be generated as support information to be used for the automatic driving support.

[0068] Next, in S10, the CPU 51 performs image processing on the image captured by the exterior camera 39 to determine whether there are any factors, particularly factors around the vehicle, that may affect the vehicle's travel. The "factors that may affect the vehicle's travel" determined in S10 are dynamic factors that change in real time, excluding static factors based on road structure. For example, these factors include other vehicles traveling or parked ahead of the vehicle, pedestrians ahead of the vehicle, and construction zones ahead of the vehicle. On the other hand, intersections, curves, railroad crossings, merging sections, and lane-narrowing sections are excluded. Even if other vehicles, pedestrians, or construction zones exist, they are excluded from the "factors that may affect the vehicle's travel" 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). Instead of cameras, sensors such as millimeter-wave radar or laser sensors, vehicle-to-vehicle communication, or road-to-vehicle communication may be used to detect factors that may affect the vehicle's travel.

[0069] In addition, for example, the real-time positions of each vehicle traveling on roads across the country may be managed by an external server, and the CPU 51 may obtain the positions of other vehicles located around the vehicle from the external server and perform the determination process of S10.

[0070] If it is determined that there is a factor in the vicinity of the host vehicle that may affect the running of the host vehicle (S10: YES), the process proceeds to S11. On the other hand, if it is determined that there is no factor in the vicinity of the host vehicle that may affect the running of the host vehicle (S10: NO), the process proceeds to S14.

[0071] In S11, the CPU 51 generates a new trajectory as a dynamic driving trajectory for returning to the static driving trajectory from the current position of the vehicle by avoiding or following the "factors that affect the driving of the host vehicle" detected in S10. The dynamic driving trajectory is generated for a section including the "factors that affect the driving of the host vehicle." The length of the section varies depending on the content of the factor. For example, if the "factors that affect the driving of the host vehicle" is another vehicle (forward vehicle) traveling in front of the vehicle, an avoidance trajectory is generated as the dynamic driving trajectory 76, which is a trajectory that changes lanes to the right to overtake the forward vehicle 75, then changes lanes to the left to return to the original lane, as shown in FIG. 9. A following trajectory that follows the forward vehicle 75 a predetermined distance behind (or travels parallel to) the forward vehicle 75 without overtaking the forward vehicle 75 may also be generated as the dynamic driving trajectory.

[0072] 9, the calculation method of the dynamic driving trajectory 76 will be explained as an example. First, the CPU 51 calculates a first trajectory L1 required for the vehicle to start turning the steering wheel and move to the right lane, and then return the steering wheel position to a straight-ahead direction. The first trajectory L1 is calculated by calculating the lateral acceleration (lateral G) that occurs when changing lanes based on the vehicle's current speed, and then calculating a trajectory using a clothoid curve or a circular arc that is as smooth as possible and minimizes the distance required for the lane change, under the condition that the lateral G does not interfere with the automatic driving assistance and does not exceed an upper limit (e.g., 0.2 G) that does not cause discomfort to the vehicle occupants. Another condition is that an appropriate inter-vehicle distance N or more must be maintained between the vehicle and the preceding vehicle 75. Next, a second trajectory L2 is calculated, which is the trajectory for traveling in the right lane at the upper limit of the speed limit to overtake the preceding vehicle 75 and maintain an appropriate inter-vehicle distance N or more between the preceding vehicle 75. The second trajectory L2 is basically a straight trajectory, and the length of the trajectory is calculated based on the speed of the preceding vehicle 75 and the speed limit of the road. Next, a third trajectory L3 is calculated, which is required to start turning the steering wheel and return to the left lane, and to return the steering position to a straight-ahead direction. The third trajectory L3 is calculated by calculating the lateral acceleration (lateral G) that occurs when changing lanes based on the vehicle's current speed, and using a clothoid curve or a circular arc, calculates a trajectory that is as smooth as possible and minimizes the distance required for the lane change, on the condition that the lateral G does not interfere with the automated driving assistance and does not exceed an upper limit (e.g., 0.2 G) that does not cause discomfort to the vehicle occupants. Another condition is that an appropriate inter-vehicle distance N or more must be maintained between the vehicle and the preceding vehicle 75. Furthermore, since the dynamic driving trajectory is generated based on the road conditions around the vehicle acquired by the exterior camera 39 and other sensors, the area for which the dynamic driving trajectory is generated is at least within the range (detection range) in which the road conditions around the vehicle can be detected by the exterior camera 39 and other sensors.

[0073] Next, in S12, the CPU 51 reflects the dynamic driving trajectory newly generated in S11 on the static driving trajectory generated in S8. Specifically, the CPU 51 calculates the costs of the static driving trajectory and the dynamic driving trajectory from the current vehicle position to the end of the section including the "factors that affect the vehicle's driving," and selects the driving trajectory with the smallest cost. As a result, part of the static driving trajectory is replaced with the dynamic driving trajectory as needed. Note that, depending on the situation, the dynamic driving trajectory may not be replaced, i.e., the static driving trajectory generated in S8 may not change even if the dynamic driving trajectory is reflected. Furthermore, if the dynamic driving trajectory and the static driving trajectory are the same trajectory, the static driving trajectory generated in S8 may not change even if the dynamic driving trajectory is replaced.

[0074] Next, in S13, the CPU 51 corrects the vehicle speed plan generated in S9 based on the content of the reflected dynamic trajectory for the static trajectory after the dynamic trajectory has been reflected in S12. Note that if the static trajectory generated in S8 remains unchanged as a result of reflecting the dynamic trajectory, the process of S13 may be omitted.

[0075] Next, in S14, the CPU 51 calculates control amounts for the vehicle to travel on the static traveling trajectory generated in S8 (or the trajectory after reflection if the dynamic traveling trajectory has been reflected in S12) at a speed in accordance with the speed plan generated in S9 (or the revised plan if the speed plan has been revised in S13). Specifically, control amounts for the accelerator, brake, gear, and steering are calculated. Note that the processing of S14 and S15 may be performed by the vehicle control ECU 40 that controls the vehicle, rather than the navigation device 1.

[0076] Thereafter, in S15, the CPU 51 reflects the control amount calculated in S14. Specifically, the calculated control amount is transmitted to the vehicle control ECU 40 via the CAN. The vehicle control ECU 40 performs vehicle control of the accelerator, brake, gear, and steering based on the received control amount. As a result, driving assistance control is possible in which the vehicle travels along the static driving trajectory generated in S8 (or the trajectory after reflection if the dynamic driving trajectory has been reflected in S12) at a speed in accordance with the speed plan generated in S9 (or the revised plan if the speed plan has been modified in S13).

[0077] Next, in S16, the CPU 51 determines whether the vehicle has traveled a certain distance since the static travel trajectory was generated in S8. For example, the certain distance is 1 km.

[0078] Then, if it is determined that the vehicle has traveled a certain distance since the static travel trajectory was generated in S8 (S16: YES), the process returns to S7. After that, the static travel trajectory is generated again for a section within a predetermined distance from the current position of the vehicle along the travel route (S8 to S9). Note that in this embodiment, the static travel trajectory is repeatedly generated for a section within a predetermined distance from the current position of the vehicle along the travel route every time the vehicle travels a certain distance (for example, 1 km), but if the distance to the destination is short, the static travel trajectory to the destination may be generated all at once at the start of travel.

[0079] On the other hand, if it is determined that the vehicle has not traveled a certain distance since the static driving trajectory was generated in S5 (S16: NO), it is determined whether or not to end the assisted driving by autonomous driving assistance (S17). Assisted driving by autonomous driving assistance can be ended not only when the vehicle has arrived at the destination, but also when the user intentionally cancels (overrides) the assisted driving by autonomous driving assistance by operating an operation panel provided in the vehicle, or by operating the steering wheel or brakes.

[0080] If it is determined that the assisted driving by the autonomous driving assistance should be ended (S17: YES), the autonomous driving assistance program is ended. On the other hand, if it is determined that the assisted driving by the autonomous driving assistance should be continued (S17: NO), the process returns to S10.

[0081] Next, the sub-processing of the static traveling trajectory generation processing executed in S8 will be described with reference to Fig. 10. Fig. 10 is a flowchart of the sub-processing program of the static traveling trajectory generation processing.

[0082] First, in S21, the CPU 51 acquires the current position of the vehicle detected by the current position detection unit 31. It is desirable to identify the current position of the vehicle in detail using, for example, high-precision GPS information or high-precision location technology. Here, high-precision location technology is a technology that detects white lines and road paint information captured by a camera installed in the vehicle using image recognition, and then compares the detected white lines and road paint information with, for example, high-precision map information 16, thereby enabling the detection of the driving lane and the vehicle's position with high precision. Furthermore, if the vehicle is traveling on a road with multiple lanes, the lane in which the vehicle is traveling is also identified. Furthermore, if the vehicle is located in a parking lot, the specific position within the parking lot (e.g., the parking space in which the vehicle is located) and the vehicle's attitude (e.g., the vehicle's direction of travel, or, if located in a parking space, the orientation of the vehicle relative to the parking space) are also identified.

[0083] Then, in the following S22 and subsequent steps, the CPU 51 calculates a recommended driving path for driving from the current position of the vehicle to the parking position candidate. Also, if multiple routes are searched as candidates for the recommended driving path within the parking lot in S6, a driving path is generated for each route, and the generated multiple driving paths are compared to determine the final driving path from among the multiple routes.

[0084] First, in S22, the CPU 51 reads the parking lot network constructed in S3, and uses the parking lot network and facility information 17 (including information on the layout of parking spaces in the parking lot) to calculate possible driving trajectories (driving trajectory candidates) for each driving route searched for in S6, from the parking lot entrance where the vehicle enters the destination parking lot to the connection point along that route. If the vehicle's current location is within a parking lot, the CPU 51 reads the parking lot network for the parking lot where the user is located, and similarly uses the parking lot network and facility information 17 (including information on the layout of parking spaces in the parking lot) to calculate possible driving trajectories for each driving route searched for in S6, from the connection point along that route to the parking lot exit. That is, in S22, possible driving trajectories are calculated for the portion of the driving route searched for in S6 that travels within the parking lot (but only up to the connection point).

[0085] As mentioned above, the parking lot network is a network that identifies routes that a vehicle can take in the parking lot, and is composed of parking lot nodes 58 and parking lot links 59 as shown in Figure 6. Furthermore, facility information 17 includes information identifying the location of the parking lot entrances and exits, information identifying the layout of parking spaces within the parking lot, information on the dividing lines that separate the parking spaces, information on pathways that vehicles and pedestrians can use, information on crosswalks and pedestrian passageways, and so on. When calculating the driving trajectory, the vehicle is assumed to drive at a slow speed (e.g., 10 km / h) within the parking lot, turn with the steering wheel stationary and with a minimum turning radius. The minimum turning radius is determined based on vehicle data. The driving trajectory during a turn is essentially a circular arc, and a trajectory that connects the arcs as smoothly as possible is calculated. Furthermore, when driving along an aisle, the driving trajectory is generally set to the center of the aisle (i.e., on parking lot link 59 of the parking lot network). The generated driving trajectory candidates are not limited to one driving trajectory for each driving route; if there are multiple driving trajectories that a vehicle can take when driving along the same route, multiple driving trajectories are generated. However, as shown in Figure 11, driving trajectories in which part of the vehicle body enters a parking space other than the parking space where the vehicle is parked or the parking space to be parked, or driving trajectories in which part of the vehicle body enters outside the parking lot area (for example, a public road), are excluded from the targets for generation. On the other hand, driving trajectories in which part of the vehicle body enters an area where the vehicle is allowed to pass if there are no obstacles within the area, such as a crosswalk in a parking lot or a passage space provided for pedestrians, are allowed.

[0086] Next, in S23, the CPU 51 executes an approach trajectory generation process (FIG. 20), which will be described later, using the intra-parking lot network and facility information 17 to generate an approach trajectory from the intra-parking lot network (more specifically, a link including the connection point) to the parking space for each combination of the parking space, which is the parking location candidate acquired in S2, and the connection point set in S5. An approach trajectory is generated for each combination of a connection point and a parking space. However, instead of generating only one trajectory for each combination, if there are multiple approach trajectories leading to the same parking space from links including the same connection point, multiple approach trajectories are generated. When multiple approach trajectories are generated, the approach trajectory to be adopted is determined by comparing the multiple ultimately generated driving trajectories (S26, S27). Furthermore, in the approach trajectory generation process, if the vehicle's current position is within a parking space, an exit trajectory for exiting the parking space to the intra-parking lot network (more specifically, a link including the connection point) is also generated. However, when calculating the exit trajectory, the attitude of the vehicle being parked (for example, forward parking, backward parking) is acquired, and the exit trajectory for exiting the parking space is calculated taking into account the attitude of the vehicle.

[0087] Thereafter, in S24, the CPU 51 combines the approach trajectory generated in S23 with the travel trajectory calculated in S22 (and also the exit trajectory if an exit trajectory has been generated). More specifically, for each travel trajectory calculated in S22, the CPU 51 combines the approach trajectory generated in S23 (and also the exit trajectory if an exit trajectory has been generated) with the connection point located at the end point of that travel trajectory. Note that combining trajectories means, more specifically, connecting trajectories to form a single trajectory. After the combination in S24, a candidate approach trajectory, which is a candidate for the vehicle's travel trajectory from the entrance of the parking lot to the parking position where the vehicle will be parked, and a candidate exit trajectory, which is a candidate for the vehicle's travel trajectory from the parking position where the vehicle will be parked to the exit of the parking lot, are generated.

[0088] Here, a specific example of the combination of the driving trajectory and the approach trajectory in S24 will be described. For example, Fig. 12 shows an example in which a driving trajectory 81 calculated for a driving route that travels straight from the entrance of the parking lot, turns right near the center, and arrives at a connection point 62 corresponding to a parking space 60 is combined with an approach trajectory 82 that approaches the parking space 60 from a link with the connection point 62. Note that a method for generating the approach trajectory 82 will be described later, but the approach trajectory 82 shown in Fig. 12 is an example in which the vehicle leaves the parking lot network by turning left forward, and then reverses and turns to approach the parking space 60. Note that the driving trajectory 81 and the approach trajectory 82 are linked on the parking lot network, and if there is a point where the driving trajectory 81 and the approach trajectory 82 overlap on the parking lot network, they are linked at that point. For example, in the example shown in FIG. 12, the start point Y of the approach trajectory 82 is on the parking lot network, and the end point X of the driving trajectory 81 is further in the direction of travel than the start point Y of the approach trajectory 82; in other words, the driving trajectory 81 and the approach trajectory 82 overlap on the parking lot network at the start point Y of the approach trajectory 82. Therefore, the driving trajectory 81 and the approach trajectory 82 are connected at the boundary of the start point Y of the approach trajectory 82. Note that the section after the point of overlap with the start point Y of the driving trajectory 81 is excluded from the driving trajectory. By combining the above-mentioned driving trajectory 81 and approach trajectory 82, the driving trajectory 83 shown in FIG. 13 is finally generated. The driving trajectory 83 is one of the candidates for the vehicle's driving trajectory from the entrance of the parking lot to parking space 60, which is the parking position where the vehicle is parked.

[0089] On the other hand, for example, FIG. 14 shows an example of a combination of a driving trajectory 84 calculated for a driving route that travels straight from the entrance of the parking lot, turns right near the center, and arrives at another connection point 64 corresponding to the parking space 60, and an approach trajectory 85 that approaches the parking space 60 from the link containing the connection point 64. While a method for generating the approach trajectory 85 will be described later, the approach trajectory 85 shown in FIG. 14 is an example of an approach trajectory that starts to retreat from the parking lot network, leaves the network, and then combines multiple turns to enter the parking space 60. The driving trajectory 84 and the approach trajectory 85 are connected on the parking lot network, and if there is a point where the driving trajectory 84 and the approach trajectory 85 overlap on the parking lot network, they are connected at that point. However, in the example shown in FIG. 14, the start point Y of the approach trajectory 85 is on the parking lot network, but is located on the traveling direction side of the end point X of the driving trajectory 84. In other words, the driving trajectory 84 and the approach trajectory 85 do not overlap on the parking lot network at the start point Y of the approach trajectory 85. Therefore, the end point X of the traveling trajectory 84 is extended to the start point Y of the approach trajectory 85 along the links of the network within the parking lot. Then, the extended traveling trajectory 84 and the approach trajectory 85 are connected with the start point Y of the approach trajectory 85 as a boundary. By combining the traveling trajectory 84 and the approach trajectory 85, the traveling trajectory 86 shown in FIG. 15 is finally generated. The traveling trajectory 86 becomes one of the candidates for the traveling trajectory of the vehicle from the entrance of the parking lot to the parking space 60, which is the parking position where the vehicle will be parked.

[0090] In the examples shown in Figures 12 to 15, only one approach trajectory is shown for the combination of the parking space and the connection point. However, if there are other approach trajectories that enter the parking space 60 from the link with the connection point 62 in the example shown in Figure 12, the travel trajectory that combines these approach trajectories with the travel trajectory 81 is also generated as one of the candidate travel trajectories for the vehicle.

[0091] 13 and 15 are both driving trajectories for entering the same parking lot entrance and parking in the same parking space 60, but their shapes are different. Furthermore, if there are multiple parking position candidates, a driving trajectory candidate such as that shown in FIG. 13 or 15 will be generated for each parking position candidate, making it difficult to determine which of the many driving trajectories is the recommended driving trajectory. Therefore, when multiple driving trajectory candidates are generated in S22 to S24 as described below, the cost is calculated for each driving trajectory and compared.

[0092] In the above embodiment, backward parking is selected as the vehicle attitude when parking a vehicle into a parking space, and possible driving trajectories are generated as driving trajectories until the vehicle is parked in the parking space that is the parking position candidate for backward parking, but forward parking may be selected and possible driving trajectories may be generated until the vehicle is parked in the parking space that is the parking position candidate for forward parking. Alternatively, possible driving trajectories for backward parking and for forward parking may be generated separately, and costs may be calculated and compared for each driving trajectory as described below, to ultimately determine the vehicle attitude when parking.

[0093] First, in S25, before comparing costs, the CPU 51 smooths the driving trajectory generated in S24 if it is possible to do so. Specifically, the driving trajectory generated in S24 is generated assuming that the vehicle will turn with stationary steering and a minimum turning radius. However, if the driving trajectory can be smoothed by increasing the turning radius or by using a clothoid curve (changing the turning angle while moving without stationary steering) instead of an arc, the turning radius and turning trajectory are modified. The possible turning radii of the vehicle are identified based on vehicle data. Furthermore, the driving trajectory generated in S24 is generated assuming that the vehicle will travel along the center of the aisle (i.e., on the parking lot link 59 of the parking lot network) when traveling along the aisle. However, if the driving trajectory can be smoothed by shifting the driving position to the right or left of the center of the aisle (i.e., on the parking lot link 59 of the parking lot network), the driving position is shifted.

[0094] Next, in S26, the CPU 51 calculates the cost of vehicle travel in consideration of the vehicle behavior when traveling on the candidate travel trajectory generated in S24 (or the smoothed travel trajectory if smoothing was performed in S25). If multiple candidate travel trajectories have been generated, the CPU 51 calculates the cost for each of the multiple candidate travel trajectories. The cost calculation method in S26 will be described in detail below.

[0095] Specifically, the final cost for each candidate travel trajectory is calculated by adding up the costs calculated based on the following elements (1) to (6). (1) Travel distance (forward or backward) - Travel distance [m] x 1.0 (2) Backward movement distance...Movement distance [m] x 10.0 (3) Number of times to switch between forward and backward: Number of times x 10.0 (4) Turning angle: Turning angle x 0.1 (5) Number of times the steering direction is changed: Number of times x 5.0 (6) Distance traveled in the conditional no-travel area: Distance traveled [m] × 10.0

[0096] First, regarding (1), the cost is determined by the travel distance of the travel path. Specifically, the longer the total length of the travel path, the higher the calculated cost, meaning that it is less likely to be selected as a recommended travel path.

[0097] On the other hand, for (2), the cost is determined by the distance traveled along the track, particularly the distance traveled while reversing. Specifically, the longer the reversing distance, the higher the calculated cost. Note that the coefficient is 10 times higher than for (1), meaning that a track with a shorter overall length but a longer reversing distance may be more costly than a track with a longer overall length.

[0098] Regarding (3), the cost is determined according to the number of times the vehicle switches between forward and reverse within the driving trajectory. Specifically, the more times the vehicle switches between forward and reverse, the higher the calculated cost, meaning that the vehicle is less likely to be selected as a recommended driving trajectory.

[0099] Regarding (4), the cost is determined according to the amount of turning angle of the vehicle required to travel along the driving path. Specifically, the larger the turning angle, i.e., the greater the steering operation amount, the higher the cost calculated for the driving path, meaning that it is less likely to be selected as a recommended driving path.

[0100] Regarding (5), the cost is determined according to the number of times the steering direction is changed within the driving trajectory. Specifically, the more times the steering direction is changed, the higher the calculated cost, i.e., the less likely it is to be selected as a recommended driving trajectory.

[0101] Finally, for (6), the cost is determined by the distance traveled through the conditionally prohibited area within the driving trajectory, particularly the conditionally prohibited area. Specifically, the longer the distance traveled through the conditionally prohibited area, the higher the calculated cost. Note that, essentially, a vehicle is considered to be traveling through a conditionally prohibited area if even a portion of the vehicle enters the conditionally prohibited area. Here, a "conditionally prohibited area" is an area in which a vehicle is permitted to pass if there are no obstacles within the area, but is not permitted to pass if there are obstacles within the area. Note that obstacles include, for example, pedestrians and wheelchairs. In other words, a "conditionally prohibited area" specifically refers to a crosswalk in a parking lot or a passage space designated for pedestrians (excluding pedestrian-only passages where vehicular entry is prohibited). Information identifying the conditionally prohibited area is included in the facility information 17. For example, if part of the driving trajectory passes through a pedestrian passage space 88 designated for pedestrians, as shown in FIG. 16, a cost is added according to the distance L traveled through the passage space 88. Furthermore, the coefficient is 10 times higher than in (1), and a driving trajectory that travels through a conditionally prohibited area, even if it is shorter in length, may be more costly than a driving trajectory with a longer length.

[0102] When calculating the cost of a candidate travel path in S26, the cost may be calculated by considering only some of the elements (1) to (6) above, rather than all of the elements (1) to (6). For example, the total cost of elements (1), (2), (3), and (5) may be calculated.

[0103] Then, in S27, the CPU 51 compares the costs of each of the candidate driving paths generated in S24 and selects the driving path with the smallest calculated cost from among the candidate driving paths from the entrance of the destination parking lot to the parking location where the vehicle will be parked as the recommended vehicle driving path from the entrance of the destination parking lot to the parking location where the vehicle will be parked. As a result, the parking location where the vehicle will be parked is also determined from among the candidate parking locations acquired in S2. Specifically, the parking space located at the end point of the selected driving path becomes the parking location where the vehicle will be parked. Similarly, if multiple candidate driving paths were acquired in S6, the driving path is determined together with the driving path. In addition, if the vehicle's current location is within the parking lot, the driving path with the smallest calculated cost from among the candidate driving paths from the vehicle's current location in the parking lot to the parking location exit is selected as the recommended vehicle driving path from the vehicle's current location in the parking lot to the parking location exit.

[0104] In the above embodiment, the parking location and driving path for parking are selected taking into consideration the burden of driving the vehicle to the destination. However, the parking location and driving path for parking may also be selected taking into consideration the burden of driving the vehicle when returning home from the destination. For example, in S22 to S24, in addition to the driving path from the parking lot entrance to the candidate parking location, the driving path for returning from the candidate parking location to the parking lot exit is also acquired. Then, in S26, costs may be calculated for the driving path from the parking lot entrance to the candidate parking location and the driving path for returning from the candidate parking location to the parking lot exit, and the driving path that minimizes the total may be selected in S27. As a result, it is possible to select the parking location and driving path for parking taking into consideration the burden of driving the vehicle when returning home from the destination.

[0105] Next, in S28, the CPU 51 constructs a lane network for the portion of the road along which the vehicle will travel, based on the high-precision map information 16 acquired in S7, of the recommended driving route for the vehicle from the vehicle's current position to the connection point, as found in S6. The high-precision map information 16 includes information on lane shapes, dividing line information, and intersections. The lane shapes and dividing line information further include the number of lanes, where and how the number of lanes will be increased or decreased if any, traffic divisions in the direction of travel for each lane, road connections (specifically, the correspondence between the lanes on the road before passing through the intersection and the lanes on the road after passing through the intersection), and information identifying guide lines (white guide lines) within the intersection. The lane network generated in S28 is a network that indicates lane movements that the vehicle can select when traveling along the candidate driving route found in S6. If multiple candidate driving routes are found in S3, the lane network is constructed for each of the candidate routes. In addition, the lane network is constructed for the section from the vehicle's current location (however, if the vehicle's current location is a parking lot, the exit road facing the parking lot exit) to the entry road facing the entrance to the parking lot where the user will park at their destination.

[0106] Here, as an example of constructing a lane network in S28, a case where a vehicle travels along the travel route shown in FIG. 17 will be described. In the following description, it is assumed that the current position of the vehicle is on a public road and not in a parking lot. In the example shown in FIG. 17, the travel route is a route in which the vehicle travels straight from the current position, turns right at the next intersection 91, turns right again at the next intersection 92, and turns left to enter the parking lot 93 where the vehicle is to be parked. In the candidate route shown in FIG. 17, for example, when turning right at intersection 91, it is possible to enter either the right lane or the left lane. However, since it is necessary to turn right at the next intersection 92, it is necessary to move to the rightmost lane when entering the intersection 92. Furthermore, when turning right at intersection 92, it is also possible to enter either the right lane or the left lane. A lane network constructed for such a candidate route in which lane movement is possible is shown in FIG. 18.

[0107] As shown in Figure 18, the lane network divides candidate routes for which static driving trajectories are to be generated into multiple sections (groups). Specifically, the division is made using the intersection entry position, intersection exit position, and positions where lanes increase or decrease as boundaries. Node points (hereinafter referred to as lane nodes) 95 are set for each lane located on the boundary of each divided section. Furthermore, links (hereinafter referred to as lane links) 96 connecting the lane nodes 95 are set. The starting position (i.e., start node) of the lane network is the current position of the vehicle (start point of travel), and the ending position (i.e., end node) of the lane network is a node (hereinafter referred to as entry point) near the entrance to the parking lot where the vehicle will be parked, which is newly generated based on the node position of the parking lot entrance set in the parking lot network.

[0108] The lane network also includes information that identifies the correspondence between lanes included in the road before the intersection and lanes included in the road after the intersection, particularly by connecting lane nodes and lane links at the intersection. This information identifies the lanes that a vehicle can move between after passing the intersection and the lanes included in the road before the intersection. Specifically, this information indicates that a vehicle can move between lanes that correspond to lane nodes connected by lane links between lane nodes included in the road before the intersection and lane nodes included in the road after the intersection. To generate such a lane network, the high-precision map information 16 stores lane flags that indicate the correspondence between lanes for each combination of roads entering and leaving the intersection for each road connecting to the intersection. When constructing the lane network at S28, the CPU 51 references the lane flags to form connections between lane nodes and lane links at the intersection.

[0109] Next, in S29, the CPU 51 connects the lane network constructed in S28 with the intra-parking lot network constructed in S3. Specifically, the CPU 51 sets a new node near the parking lot entrance on the approach road facing the entrance to the parking lot where the vehicle will be parked, using the node position of the parking lot entrance set in the intra-parking lot network as a reference, and connects the newly set node with the node of the parking lot entrance with a link.

[0110] Then, in S30, the CPU 51 sets a movement start point, where the vehicle starts moving, to the lane node located at the start point of the constructed lane network, and sets a movement destination point, to which the vehicle will move, to the end point of the lane network, i.e., the lane node connected to the parking lot entrance (the lane node provided corresponding to the entry point). The CPU 51 then references the constructed lane network and searches for a route that connects the movement start point to the movement destination point. For example, using the Dijkstra algorithm, the CPU 51 identifies the route that minimizes the total lane cost as the recommended lane movement mode for the vehicle. The lane cost is set, for example, using the length of the lane link 96 or the required time for movement as a reference value, taking into account the presence or absence of lane changes and the number of lane changes. However, search methods other than the Dijkstra algorithm may be used as long as they can search for a route that connects the movement start point to the movement destination point.

[0111] Then, in S31, the CPU 51 uses the high-precision map information 16, facility information 17, connection information 18, and road exterior shape information 19 acquired in S7 to generate a specific driving trajectory for driving along the route identified by the lane network. For a driving trajectory in a section requiring lane changes, the CPU 51 sets the lane change location so that lane changes are minimized and are performed at a recommended location a predetermined distance away from the intersection. When generating a driving trajectory for turning right or left or changing lanes at an intersection, the CPU 51 calculates the lateral acceleration (lateral G) acting on the vehicle and calculates a trajectory that connects the lanes as smoothly as possible using a clothoid curve or a circular arc, provided that the lateral G does not exceed an upper limit (e.g., 0.2 G) that does not interfere with the automated driving assistance or cause discomfort to vehicle occupants. Meanwhile, for a section that is neither a lane change section nor an intersection section, the recommended driving trajectory for the vehicle is a trajectory that passes through the center of the lanes. However, for corners that are approximately right-angle turns, it is desirable to provide a radius at the corner of the trajectory. By performing the above process, a recommended travel path for the vehicle from the current position of the vehicle to the entry point is generated.

[0112] Next, in S32, the CPU 51 calculates a recommended driving trajectory, particularly when the vehicle moves along the recommended driving route from the current position of the vehicle found in S6 to the candidate parking position, when entering the parking lot from the access road.

[0113] For example, Fig. 19 will explain an example of calculating a travel trajectory when a route is set for entering the entrance to the parking lot 93 from the leftmost lane of the entrance road 98. First, the CPU 51 identifies an area between the entrance road 98 and the parking lot 93 where the vehicle can pass (hereinafter referred to as the passing area) based on the road exterior shape information acquired in S7. For example, in the example shown in Fig. 19, a rectangular area consisting of x width and y height is the passing area between the entrance road 98 and the parking lot 93 where the vehicle can pass. Then, on the condition that the vehicle passes through the passing area from the entrance road 98 and enters the entrance to the parking lot 93, a trajectory is calculated using a clothoid curve or a circular arc to be as smooth as possible and to shorten the distance required for entry as much as possible.

[0114] Thereafter, in S33, the CPU 51 generates a static driving trajectory, which is a driving trajectory that is recommended for the vehicle, by connecting the driving trajectories calculated in S27, S31, and S32. The static driving trajectory generated in S33 includes a first driving trajectory that is recommended for the vehicle to travel on a lane from the driving start point to the approach road facing the entrance to the parking lot, a second driving trajectory that is recommended for the vehicle to travel from the approach road to the entrance of the parking lot, and a third driving trajectory that is recommended for the vehicle to travel from the entrance to the parking lot to a parking position (parking space) where the vehicle will be parked.

[0115] The static driving trajectory generated in S33 is stored in the flash memory 54 or the like as assistance information to be used for automatic driving assistance. Then, the process proceeds to S9.

[0116] In addition, in the above embodiment, the driving trajectory when driving within the parking lot, the driving trajectory when driving on the road, and the driving trajectory when entering the parking lot from the road are each generated separately (S27, S31, S32), but it is also possible to connect the parking lot network and the lane network and generate a driving trajectory that includes all of the vehicle's movements from the vehicle's current position to candidate parking positions in the parking lot all at once.

[0117] Next, the sub-processing of the approach trajectory generation process executed in S23 will be described with reference to Fig. 20. Fig. 20 is a flowchart of the sub-processing program of the approach trajectory generation process.

[0118] The following steps S41 to S49 are performed for each combination of the parking space, which is the parking position candidate acquired in S2, and the connection point set in S5. Furthermore, if the current position of the vehicle is within a parking space, the approach trajectory generation process also targets the combination of the parking space where the vehicle is currently located and the connection point set for that parking space in S5. After steps S41 to S49 have been performed for all combinations of parking spaces and connection points, the process proceeds to S24.

[0119] First, in S41, the CPU 51 acquires the length L of the line segment connecting, at the shortest distance, the parking space (particularly, the center of the parking space, but not necessarily the center) that is the combination to be processed and the parking lot link 59 that includes the connection point. For example, when the combination of parking space 100 and connection point 101 shown in FIG. 21 is the combination to be processed, the length L of the line segment connecting, at the shortest distance, the center of parking space 100 and parking lot link 59 is calculated. Also, when the combination of parking space 102 and connection point 103 shown in FIG. 22 is the combination to be processed, the length L of the line segment connecting, at the shortest distance, the center of parking space 102 and parking lot link 59 is calculated. Note that in the case of connection point 101 in FIG. 21, the length L also corresponds to the distance from the parking space to the connection point.

[0120] Next, in S42, the CPU 51 calculates the angle (relative angle) θ of the network in the parking lot with respect to the parking space at the connection point where the line segments connect. Specifically, as shown in Figures 21 and 22, this corresponds to the angle θ formed by the arrangement direction of the parking space (the length direction of the parking space) and the parking lot link 59 including the connection point.

[0121] Next, in S43, the CPU 51 calculates the distance D required for the vehicle to exit the parking space that is the combination to be processed with the minimum turning radius and become parallel to the intra-parking lot network (particularly the parking lot link 59 including the connection point). For example, the distance D is calculated using the following formula (1). The minimum turning radius is determined based on the vehicle data. D=2R×sin 2 (θ / 2) (1) (R: minimum turning radius, θ: angle of the network in the parking lot relative to the parking space calculated in S42) In the above formula (1), for example, when the parking space is arranged perpendicular to the parking link 59 (θ=90 degrees) as shown in Figure 21, D=R. On the other hand, when the parking space is arranged parallel to the parking link 59 (θ=180 degrees) as shown in Figure 6 at connection point 64 for parking space 60, D=2R.

[0122] Thereafter, in S44, the CPU 51 determines whether the length L of the line segment calculated in S41 is sufficient for the distance D required for the vehicle to exit the parking space at the minimum turning radius and become parallel to the parking lot network. If it is determined that the length L of the line segment calculated in S41 is sufficient for the distance D required for the vehicle to exit the parking space at the minimum turning radius and become parallel to the parking lot network (S44: YES), the process proceeds to S45. On the other hand, if it is determined that the length L of the line segment calculated in S41 is insufficient for the distance D required for the vehicle to exit the parking space at the minimum turning radius and become parallel to the parking lot network (S44: NO), the process proceeds to S46.

[0123] In S45, the CPU 51 generates three patterns, Patterns 1 to 3, for the exit trajectory that exits from the parking space that is the combination to be processed to the parking lot link 59 that includes the connection point. Note that not all patterns of exit trajectories are necessarily generated, and some patterns may not be generated depending on the angle θ. The exit trajectories of each pattern will be described in more detail below.

[0124] (Pattern 1) As shown in Figure 23, the driving trajectory is one in which the vehicle moves straight from the parking space in the direction of the parking space arrangement, then turns with the minimum turning radius R and gets on the parking lot link 59 that includes the connection point. When turning, the exit trajectory is generated assuming that the vehicle turns stationary and turns with the minimum turning radius R (the same applies to patterns 2 and below). Pattern 1 is basically generated regardless of the angle of θ. However, cases in which θ is 180 degrees, such as the connection point 64 for parking space 60 shown in Figure 6, are excluded from the generation target. (Pattern 2) As shown in Figure 24, the travel trajectory is one in which the vehicle turns from the parking space with a minimum turning radius R, moves in a straight line after becoming perpendicular to the parking lot link 59 including the connection point, turns again with a minimum turning radius R, and gets onto the parking lot link 59 including the connection point. Note that pattern 2 is basically generated only when θ>90 degrees. (Pattern 3) As shown in Figure 25, the vehicle turns from the parking space with the minimum turning radius R, and after it becomes parallel to the parking lot link 59 including the connection point, it changes the turning direction and turns twice more to move closer to the parking lot link 59, thereby setting up a driving trajectory on the parking lot link 59 including the connection point. Note that pattern 3 is basically generated regardless of the angle θ.

[0125] Meanwhile, in S46, the CPU 51 determines whether the aisle width of the aisle in which the parking lot link 59 including the connection point is set is equal to or greater than the threshold at which turning is possible. For example, the threshold is 4 m. If it is determined that the aisle width of the aisle in which the parking lot link 59 including the connection point is set is equal to or greater than the threshold at which turning is possible (S46: YES), the process proceeds to S47. On the other hand, if it is determined that the aisle width of the aisle in which the parking lot link 59 including the connection point is set is not equal to or greater than the threshold at which turning is possible (S46: NO), the process proceeds to S48.

[0126] In S47, the CPU 51 generates two patterns, Patterns 4 and 5, for the exit trajectory that exits from the parking space that is the combination to be processed to the parking lot link 59 that includes the connection point. Note that the exit trajectories of Patterns 4 and 5 do not include any straight-line movement and are only made up of turning movements. The exit trajectories of each pattern will be explained in more detail below.

[0127] (Pattern 4) As shown in FIG. 26, the vehicle turns from the parking space with a minimum turning radius R, turns back after passing the parking lot link 59, and then turns again with a minimum turning radius R while retreating, forming a travel trajectory that places the vehicle on the parking lot link 59 that includes the connection point. (Pattern 5) As shown in Figure 27, the vehicle turns from the parking space with the minimum turning radius R, becomes parallel to the parking lot link 59 including the connection point, and then changes direction twice to move closer to the parking lot link 59, resulting in a travel trajectory on the parking lot link 59 including the connection point. Note that when θ is 180 degrees as in the case of the connection point 64 for the parking space 60 shown in Figure 6, if an exit trajectory is generated using pattern 5, the travel trajectory will be as shown in Figure 14.

[0128] On the other hand, in S48, the CPU 51 generates only the above-mentioned pattern 5 for the exit trajectory that exits from the parking space that is the combination to be processed to the parking lot link 59 that includes the connection point.

[0129] Then, in S49, the CPU 51 generates an approach trajectory for approaching the parking space from the parking lot network (more specifically, the link including the connection point) by reversing the traveling direction of the exit trajectory generated in S45, S47, and S48. Then, the process proceeds to S24. As described above, from S24 onwards, a cost indicating the suitability of each candidate trajectory as a trajectory is calculated, and the candidate trajectory with the lowest cost is selected as the recommended trajectory (S26, S27).

[0130] As described above in detail, the navigation device 1 and the computer program executed by the navigation device 1 according to this embodiment acquire a parking lot network, which is a network showing possible routes a vehicle can take within the parking lot when parking in a parking lot (S3), and generate an approach trajectory for entering the parking space from the parking lot network based on the length of the line connecting the parking space where the vehicle is to be parked and the parking lot network and the angle of the parking lot network relative to the parking space at the connection point of the line segment (S41-S49). The approach trajectory is then combined with the vehicle's travel trajectory from the parking lot entrance to the starting point of the approach trajectory, generated using the parking lot network, to generate a vehicle's travel trajectory from the parking lot entrance to the parking space (S24). Driving assistance is then provided based on the trajectory, making it possible to generate a specific trajectory required for entering the parking space from the aisle based on the relationship between the distance and angle between the parking lot network and the parking space. By combining the trajectory generated using the parking lot network, driving assistance based on a more appropriate trajectory than in the past is possible. Furthermore, if the length of the line segment is sufficient for the distance required for the vehicle to exit the parking space at the minimum turning radius and become parallel to the parking lot network, an approach trajectory that includes straight-line movement is generated; on the other hand, if the length of the line segment is insufficient for the distance required for the vehicle to exit the parking space at the minimum turning radius and become parallel to the parking lot network, an approach trajectory that does not include straight-line movement is generated. This makes it possible to generate a specific driving trajectory required to enter a parking space from an aisle based on the relationship of distance and angle between the parking lot network and the parking space. In addition, the parking lot network is a network in which links are set at the aisles within the parking lot where vehicles can travel, nodes are set at the connection points between the aisles, and the links are connected by the nodes.Since the approach trajectory is generated based on the line segment that connects the parking space where the vehicle is to be parked and the link included in the parking lot network in the shortest distance, it is possible to generate an approach trajectory for entering a parking space from each aisle within the parking lot where vehicles can travel. Furthermore, the links to which the line segments are connected are the links included in the parking lot network that are closest to the parking space and the links adjacent to that link, so it is possible to generate an approach trajectory for entering the parking space from each aisle that a vehicle can travel through within the parking lot, particularly from each aisle that is closest to the parking space. Furthermore, when a vehicle exits a parking lot where it is parked, an intra-parking lot network is acquired, which is a network that shows routes the vehicle can take within the parking lot (S3), and an exit trajectory for exiting the parking space to the intra-parking lot network is generated based on the length of the line segment connecting the parking space where the vehicle is parked to the intra-parking lot network and the angle of the intra-parking lot network relative to the parking space at the connection point of the line segment (S41-S49). The vehicle's travel trajectory from the parking space to the parking lot exit is generated by combining the exit trajectory generated using the intra-parking lot network with the vehicle's travel trajectory from the end point of the exit trajectory to the parking lot exit (S24), and driving assistance is provided based on the travel trajectory. Therefore, it is possible to generate a specific travel trajectory required to exit the parking space to the aisle based on the relationship between the distance and angle between the intra-parking lot network and the parking space. By combining this with the travel trajectory generated using the intra-parking lot network, driving assistance based on a more appropriate travel trajectory than before is possible.

[0131] The present invention is not limited to the above-described embodiment, and it goes without saying that various improvements and modifications are possible within the scope of the present invention. For example, in this embodiment, multiple candidates for parking positions are obtained, and the parking position is finally determined from among the multiple candidates when generating the static driving trajectory in S8. However, it is also possible to first determine a single parking position where the vehicle will be parked, and then generate a driving trajectory to the determined parking position.

[0132] In addition, although this embodiment assumes that the vehicle's travel start point is on a road, it can also be applied to a case where the travel start point is in a parking lot. In that case, the recommended travel trajectory for the vehicle from the travel start point to the parking lot exit is also derived in S27.

[0133] In this embodiment, the length L of the line segment connecting the parking space and the parking lot link including the connection point at the shortest distance is obtained in S41, but in particular when the parking space is inclined with respect to the parking lot link including the connection point as shown in Figure 22, a line segment may be set along the arrangement direction of the parking space (length direction of the parking space), and the length L of the line segment connecting the parking space at the shortest distance along the arrangement direction of the parking space may be obtained. In that case, the distance D calculated in S43 is calculated by the following formula (2). D = R × tan(θ / 2) (2) (R: minimum turning radius, θ: angle of the network in the parking lot relative to the parking space calculated in S42)

[0134] In addition, in this embodiment, the approach trajectory (exit trajectory) is generated using the above patterns 1 to 5, but the approach trajectory (exit trajectory) may be generated using content other than patterns 1 to 5 as long as it is possible to connect the parking space to the parking lot link including the connection point.

[0135] Furthermore, in this embodiment, the distance D is calculated in S43 based on the minimum turning radius R, but the distance D may be calculated based on a turning radius Rt (Rt is the turning radius that the vehicle can take) that is larger than the minimum turning radius R. In that case, in S44, it is determined whether or not the length L of the line segment calculated in S41 is sufficient for the distance D required for the vehicle to exit the parking space at the turning radius Rt and become parallel to the network within the parking lot.

[0136] In addition, in this embodiment, approach trajectories (exit trajectories) are generated using multiple patterns that the vehicle can take, and the final driving trajectory is determined by comparing the costs of driving trajectories that combine each of the generated approach trajectories (exit trajectories).However, it is also possible to generate an approach trajectory (exit trajectory) using only one most recommended pattern, taking into consideration in advance the layout of parking spaces, etc.

[0137] In this embodiment, the cost of the travel trajectory to the parking position is calculated by considering only the travel of the vehicle (S26), but the cost may also be calculated by taking into account the walking to the destination after getting off the vehicle. In other words, even if the travel of the vehicle to the parking position is easy, the cost may be calculated to be high for a travel trajectory to the parking position where the burden of walking to the destination is heavy.

[0138] In addition, in this embodiment, after generating a travel trajectory to a parking position, vehicle control is performed to drive the vehicle according to the generated travel trajectory (S14, S15), but the processes related to vehicle control from S14 onwards may be omitted. For example, the navigation device 1 may be a device that does not control the vehicle based on the travel trajectory, but instead provides guidance to the user on recommended parking positions and the travel trajectory.

[0139] In addition, in this embodiment, a lane network and a parking lot network are generated using high-precision map information 16 and facility information 17 (S3), but each network targeting roads and parking lots across the country may be stored in a database in advance and read out from the database as needed.

[0140] Furthermore, in this embodiment, the high-precision map information held by the server device 4 includes both information about the lane shape of the road (such as the road shape, curvature, and lane width for each lane) and information about the dividing lines drawn on the road (such as the center line, lane boundary line, outer lane line, and guide line). However, the high-precision map information may include only information about the dividing lines, or may include only information about the lane shape of the road. For example, even if the high-precision map information includes only information about the dividing lines, it is possible to estimate information equivalent to the information about the lane shape of the road based on the information about the dividing lines. Furthermore, even if the high-precision map information includes only information about the lane shape of the road, it is possible to estimate information equivalent to the information about the dividing lines based on the information about the lane shape of the road. Furthermore, the "information about the dividing lines" may be information that identifies the type and arrangement of the dividing lines that divide the lanes, information that identifies whether lane changes are possible between adjacent lanes, or information that directly or indirectly identifies the shape of the lanes.

[0141] In addition, in this embodiment, as a means of reflecting the dynamic driving trajectory in the static driving trajectory, part of the static driving trajectory is replaced with the dynamic driving trajectory (S12), but instead of replacing it, the trajectory may be corrected so that the static driving trajectory approaches the dynamic driving trajectory.

[0142] In addition, in this embodiment, the vehicle control ECU 40 has been described as controlling all of the accelerator operation, brake operation, and steering operation, which are operations related to the vehicle's behavior, as autonomous driving assistance for automatically driving the vehicle without the user's driving operation. However, the autonomous driving assistance may also be defined as the vehicle control ECU 40 controlling at least one of the accelerator operation, brake operation, and steering operation, which are operations related to the vehicle's behavior, as part of the vehicle operations. On the other hand, manual driving performed by the user's driving operation will be described as the user performing all of the accelerator operation, brake operation, and steering operation, which are operations related to the vehicle's behavior, as part of the vehicle operations.

[0143] Furthermore, the driving assistance of the present invention is not limited to automatic driving assistance related to automatic driving of a vehicle. For example, it is possible to display the static driving trajectory identified in S8 or the dynamic driving trajectory generated in S11 on a navigation screen, and to provide guidance using voice, a screen, or the like (for example, guidance on lane changes, guidance on recommended vehicle speeds, etc.). Furthermore, the static driving trajectory or the dynamic driving trajectory may be displayed on the navigation screen to assist the user's driving operation.

[0144] Furthermore, in this embodiment, the automatic driving assistance program (FIG. 4) is configured to be executed by the navigation device 1, but it may also be configured to be executed by an in-vehicle device other than the navigation device 1 or the vehicle control ECU 40. In that case, the in-vehicle device or the vehicle control ECU 40 is configured to acquire the current position of the vehicle, map information, etc. from the navigation device 1 or the server device 4. Furthermore, the server device 4 may execute some or all of the steps of the automatic driving assistance program (FIG. 4). In that case, the server device 4 corresponds to the driving assistance device of the present application.

[0145] Furthermore, the present invention can be applied to devices other than navigation devices, such as mobile phones, smartphones, tablet devices, and personal computers (hereinafter referred to as mobile devices, etc.). It can also be applied to a system consisting of a server and a mobile device, etc. In that case, each step of the above-described automatic driving assistance program (see FIG. 4) may be implemented by either the server or the mobile device, etc. However, when applying the present invention to a mobile device, etc., a vehicle capable of executing automatic driving assistance and the mobile device, etc., must be connected to each other so that they can communicate (whether wired or wireless). [Explanation of symbols]

[0146] 1...navigation device (driving assistance device), 2...driving assistance system, 3...information distribution 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, 58...parking lot node, 59...parking lot link, 60...parking space, 61, 63...line segment, 62, 64...connection point, 81, 84...driving trajectory, 82, 85...approach trajectory, 83, 86...driving trajectory after combining approach trajectories

Claims

1. a parking lot network acquisition means for acquiring a parking lot network that indicates routes that a vehicle can select within the parking lot when parking the vehicle in the parking lot; a parking space information acquisition means for acquiring layout information of parking spaces provided in the parking lot; an approach trajectory generation means for generating an approach trajectory for approaching the parking space from the intra-parking-lot network based on the length of a line segment connecting the parking space where the vehicle is to be parked and the intra-parking-lot network, and the angle of the intra-parking-lot network with respect to the parking space at the connection point where the line segment connects; a travel trajectory generation means for generating a travel trajectory of a vehicle from an entrance of the parking lot to a starting point of the approach trajectory using the network within the parking lot, and for generating a travel trajectory of a vehicle from the entrance of the parking lot to the parking space by combining the approach trajectories; a driving assistance means for performing driving assistance based on the travel trajectory, The approach trajectory generation means If the length of the line segment is sufficient for the distance required for the vehicle to exit the parking space at a predetermined turning radius and become parallel to the parking lot network, the approach trajectory including straight movement is generated; A driving assistance device that generates an approach trajectory that does not include straight-line movement when the length of the line segment is insufficient for the distance required for the vehicle to exit the parking space at a predetermined turning radius and become parallel to the parking lot network.

2. The parking lot network is a network in which links are set to paths in the parking lot where vehicles can travel, nodes are set to connection points between the paths, and the links are connected by the nodes, The driving assistance device according to claim 1 , wherein the line segment is a line segment that connects the parking space where the vehicle is to be parked and a link included in the parking lot network over the shortest distance.

3. The driving assistance device according to claim 2, wherein the link to which the line segment is connected is the link included in the parking lot network that is closest to the parking space and a link adjacent to that link.

4. a parking lot network acquisition means for acquiring a parking lot network that indicates routes that a vehicle can select within the parking lot when the vehicle leaves the parking lot; a parking space information acquisition means for acquiring layout information of parking spaces in which vehicles are parked in a parking lot; an exit trajectory generation means for generating an exit trajectory for exiting from the parking space to the intra-parking-parking-network based on the length of a line segment connecting the parking space where the vehicle is parked and the intra-parking-parking-network and the angle of the intra-parking-parking-network with respect to the parking space at the connection point where the line segment connects; a travel trajectory generation means for generating a travel trajectory of the vehicle from the end point of the exit trajectory to the exit of the parking lot using the network within the parking lot, and for generating a travel trajectory of the vehicle from the parking space to the exit of the parking lot by combining the exit trajectories; a driving assistance means for performing driving assistance based on the travel trajectory, The exit trajectory generation means If the length of the line segment is sufficient for the distance required for the vehicle to exit the parking space at a predetermined turning radius and become parallel to the parking lot network, generating the exit trajectory including a straight movement; A driving assistance device that generates an exit trajectory that does not include straight-line movement when the length of the line segment is insufficient for the distance required for the vehicle to exit the parking space at a predetermined turning radius and become parallel to the parking lot network.

5. Computer, a parking lot network acquisition means for acquiring a parking lot network that indicates routes that a vehicle can select within the parking lot when parking the vehicle in the parking lot; a parking space information acquisition means for acquiring layout information of parking spaces provided in the parking lot; an approach trajectory generation means for generating an approach trajectory for approaching the parking space from the intra-parking-lot network based on the length of a line segment connecting the parking space where the vehicle is to be parked and the intra-parking-lot network, and the angle of the intra-parking-lot network with respect to the parking space at the connection point where the line segment connects; a travel trajectory generation means for generating a travel trajectory of a vehicle from an entrance of the parking lot to a starting point of the approach trajectory using the network within the parking lot, and for generating a travel trajectory of a vehicle from the entrance of the parking lot to the parking space by combining the approach trajectories; a driving assistance means for performing driving assistance based on the travel trajectory; A computer program for causing a computer to function as follows: The approach trajectory generation means If the length of the line segment is sufficient for the distance required for the vehicle to exit the parking space at a predetermined turning radius and become parallel to the parking lot network, the approach trajectory including straight movement is generated; A computer program that generates the approach trajectory that does not include straight-line movement when the length of the line segment is insufficient for the distance required for the vehicle to exit the parking space at a predetermined turning radius and become parallel to the parking lot network.

6. A computer, a parking lot network acquisition means for acquiring a parking lot network that indicates routes that a vehicle can select within the parking lot when the vehicle leaves the parking lot; a parking space information acquisition means for acquiring layout information of parking spaces in which vehicles are parked in a parking lot; an exit trajectory generation means for generating an exit trajectory for exiting from the parking space to the intra-parking-parking-network based on the length of a line segment connecting the parking space where the vehicle is parked and the intra-parking-parking-network and the angle of the intra-parking-parking-network with respect to the parking space at the connection point where the line segment connects; a travel trajectory generation means for generating a travel trajectory of the vehicle from the end point of the exit trajectory to the exit of the parking lot using the network within the parking lot, and for generating a travel trajectory of the vehicle from the parking space to the exit of the parking lot by combining the exit trajectories; a driving assistance means for performing driving assistance based on the travel trajectory; A computer program for causing a computer to function as follows: The exit trajectory generation means If the length of the line segment is sufficient for the distance required for the vehicle to exit the parking space at a predetermined turning radius and become parallel to the parking lot network, generating the exit trajectory including a straight movement; A computer program that generates the exit trajectory that does not include straight-line movement when the length of the line segment is insufficient for the distance required for the vehicle to exit the parking space at a predetermined turning radius and become parallel to the parking lot network.

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