Driving assistance device

Abstract

Provided is a driving assistance device that can generate a travel trajectory of a vehicle that takes into account the congestion state of each lane, and that can appropriately implement driving assistance based on the travel trajectory, in a case where driving assistance of the vehicle is performed. Specifically, the device is configured to acquire a travel scheduled path along which the vehicle travels, acquire a congestion state of each lane in the travel scheduled path, generate a travel trajectory in which the vehicle is recommended to travel, for a road included in the travel scheduled path, using map information that includes at least information related to a lane division line and the congestion state of each lane, and perform driving assistance of the vehicle based on the generated travel trajectory.

Description

Technical Field

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

[0002] In recent years, in addition to manual driving based on user input, a new type of vehicle driving assistance system has been proposed: automated driving assistance systems. These systems assist users in driving by executing some or all of the user's driving operations on the vehicle side. For example, automated driving assistance systems may automatically control the vehicle, including steering, drive, and braking, by constantly detecting the vehicle's current position, the lane it is traveling in, and the positions of other vehicles in the vicinity, and then driving along a pre-set path.

[0003] Furthermore, in the case of driving based on automated driving assistance, the following control is performed: based on the vehicle's predetermined driving path, map information, etc., a recommended driving trajectory is pre-generated on the road where the vehicle will travel, and the vehicle drives along the generated driving trajectory. For example, Japanese Patent Application Publication No. 2017-83446 proposes the following technology: based on the vehicle's future driving path and high-precision map information, action plans are generated, such as a deceleration driving plan to slow down the vehicle, an acceleration driving plan to accelerate the vehicle, a stopping driving plan to stop the vehicle, a lane-changing driving plan to change lanes, an overtaking driving plan to pass the vehicle in front, and a lane-keeping driving plan to keep the vehicle in its lane without leaving the driving lane. By controlling the vehicle according to the generated action plans, automated driving is achieved.

[0004] Existing technical documents

[0005] Patent documents

[0006] Patent Document 1: Japanese Patent Application Publication No. 2017-83446 (paragraphs 0051-0053) Summary of the Invention

[0007] The problem that the invention aims to solve

[0008] Here, the roads on which vehicles travel are not always roads where vehicles can freely choose their travel paths. In particular, when congestion occurs in some lanes, movement towards the congested lanes is restricted. For example, as... Figure 22As shown, if there is a facility 102 that is the destination of vehicle 101, and the leftmost lane is congested due to vehicles waiting to enter facility 102, if vehicle 101 wants to move to the leftmost lane before arriving at facility 102, it may obstruct the passage of other vehicles traveling in the vicinity. Therefore, it is necessary to consider the congestion situation of each lane to generate an action plan with a pre-determined lane change position.

[0009] This invention was made to solve the aforementioned problems and aims to provide a driving assistance device that, when assisting in driving a vehicle, can take into account the congestion status of each lane obtained from the outside, generate a driving trajectory of the vehicle that determines the lane change position for changing to the recommended driving lane, and can appropriately implement driving assistance based on the driving trajectory.

[0010] Technical means to solve the problem

[0011] To achieve the above objectives, the driving assistance device of the present invention comprises: a driving predetermined path acquisition unit for acquiring a driving predetermined path for a vehicle; a congestion status acquisition unit for acquiring congestion status of each lane in the driving predetermined path from an external source; a driving trajectory generation unit for generating a driving trajectory that determines a lane change position for a recommended lane change to a recommended vehicle for a given distance from the vehicle's current position to a range including map information containing at least information related to lane markings and the congestion status of each lane; and a driving assistance unit for providing driving assistance to the vehicle based on the driving trajectory generated by the driving trajectory generation unit.

[0012] In addition, "driving assistance" refers to at least one function that replaces or assists the driver in operating the vehicle, or provides display guidance or voice guidance for assisting driving.

[0013] In addition, "information related to lane markings" can be information that determines the type and configuration of lane markings themselves, information that determines whether lane changes are possible between adjacent lanes, or information that directly or indirectly determines the shape of the lanes.

[0014] In addition, "obtaining congestion information from external sources" refers to obtaining congestion information generated by a subject other than the vehicle (such as a server) through means such as communication.

[0015] Invention Effects

[0016] According to the driving assistance device of the present invention having the above-described structure, when providing driving assistance, it can take into account the congestion conditions of each lane obtained from the outside and generate a driving trajectory of the vehicle that determines the lane-changing position for changing to the recommended driving lane. As a result, undesirable driving trajectories, such as merging from other lanes into a congested lane in the middle of a congested section, are not generated, and a more appropriate driving trajectory of the vehicle can be generated compared to the prior art. Moreover, by providing driving assistance based on the generated driving trajectory, driving assistance can be implemented appropriately. Attached Figure Description

[0017] Figure 1 This is a schematic structural diagram illustrating the driving assistance system of this embodiment.

[0018] Figure 2 This is a block diagram showing the structure of the driving assistance system in this embodiment.

[0019] Figure 3 This is an example of a diagram representing congestion information.

[0020] Figure 4 This is a block diagram illustrating the navigation device of this embodiment.

[0021] Figure 5 This is a flowchart of the autonomous driving assistance program in this embodiment.

[0022] Figure 6 It is a map representing the area where high-precision map information is obtained.

[0023] Figure 7 This is a flowchart of the subprocessing procedure for static driving trajectory generation.

[0024] Figure 8 This is a diagram illustrating an example of a vehicle's predetermined travel path.

[0025] Figure 9 It means targeting Figure 8 The diagram shows an example of a lane network constructed for driving along a predetermined path.

[0026] Figure 10 It is a graph showing the state of congestion information matched in the lane network.

[0027] Figure 11 It is a diagram representing the lane network reconstructed based on congestion information.

[0028] Figure 12 This is a diagram illustrating an example of adding congestion costs to lane costs.

[0029] Figure 13 This is a diagram illustrating the search method that uses lane cost to recommend routes.

[0030] Figure 14 This is a diagram illustrating an example of a recommended route to avoid congested areas.

[0031] Figure 15 This is a diagram illustrating an example of a recommended route through a congested area.

[0032] Figure 16 This is a diagram illustrating an example of a recommended route to avoid congested areas.

[0033] Figure 17 This is a diagram illustrating an example of a recommended route through a congested area.

[0034] Figure 18 This is a flowchart of the subprocessing procedure for generating and processing dynamic driving trajectories.

[0035] Figure 19 This diagram illustrates an example of an avoidance trajectory, which is one type of dynamic driving path.

[0036] Figure 20 This is a diagram illustrating an example of a following trajectory, which is one type of dynamic driving trajectory.

[0037] Figure 21 This is a flowchart of the sub-processing procedure for reflecting driving trajectory.

[0038] Figure 22 This is a diagram illustrating the problems with existing technologies. Detailed Implementation

[0039] Hereinafter, an embodiment of the driving assistance device of the present invention as a navigation device 1 will be described in detail with reference to the accompanying drawings. First, using Figure 1 and Figure 2 The general structure of the driving assistance system 2, including the navigation device 1 of this embodiment, will be described. Figure 1 This is a schematic structural diagram showing the driving assistance system 2 of this embodiment. Figure 2 This is a block diagram showing the structure of the driving assistance system 2 in this embodiment.

[0040] like Figure 1 As shown, the driving assistance system 2 of this embodiment basically includes: a server device 4 provided by an information distribution center 3, and a navigation device 1 installed in the vehicle 5 to perform various assistance related to the autonomous driving of the vehicle 5. Furthermore, the server device 4 and the navigation device 1 are configured to exchange and receive electronic data via a communication network 6. Alternatively, other in-vehicle devices installed in the vehicle 5 or vehicle control devices that perform controls related to the vehicle 5 may be used instead of the navigation device 1.

[0041] Here, vehicle 5 is a vehicle that, in addition to manual driving based on the user's driving operation, can also drive with the assistance of autonomous driving assistance, which enables the vehicle to drive automatically along a pre-set path and road without relying on the user's driving operation.

[0042] Furthermore, autonomous driving assistance can be applied to all road sections, or it can be configured to operate only during the period when the vehicle is traveling on a specific road section (e.g., a highway with entrances and exits at the boundaries (regardless of whether there are people or not, or whether it is toll-free)). In the following explanation, the autonomous driving zone for which autonomous driving assistance is applied will include not only all road sections, including general roads and highways, but also parking lots, and will essentially operate from the start of the journey until the end (until the vehicle stops). However, autonomous driving assistance may not necessarily operate while the vehicle is traveling within the autonomous driving zone; it is preferable to operate only when the user selects to activate autonomous driving assistance (e.g., by setting the autonomous driving start button to "on") and it is determined that driving based on autonomous driving assistance is possible. On the other hand, vehicle 5 may also be a vehicle capable only of assisted driving based on autonomous driving assistance.

[0043] Furthermore, in the vehicle control of the automated driving assistance system, for example, the vehicle's current position, the driving lane, and the position of surrounding obstacles are detected at any time, and the vehicle control, such as steering, drive, and braking, is automatically performed to drive along the driving trajectory generated by the navigation device 1 as described later and at a speed that conforms to the generated speed plan. In addition, in the assisted driving based on automated driving assistance in this embodiment, lane changes, left and right turns, and parking operations can also be performed by driving manually without using automated driving assistance.

[0044] On the other hand, the navigation device 1 is mounted on the vehicle 5 and displays a map of the area surrounding the vehicle's location based on map data stored in the navigation device 1 or map data and congestion information obtained from external sources. It can also accept user destination input, display the vehicle's current location on the map, or provide movement guidance along a pre-set path. In this embodiment, especially when the vehicle is assisted in driving using autonomous driving assistance, various auxiliary information related to autonomous driving assistance is generated. Examples of this auxiliary information include, for example, a recommended driving trajectory (including a recommended lane movement method), a selection of a parking location at the destination, and a speed plan indicating the vehicle's speed. Further details regarding the navigation device 1 will be explained later.

[0045] Furthermore, the server device 4 appropriately collects and accumulates detection information (material information) including current time and driving information from various vehicles traveling throughout the country, and generates various road-related auxiliary information (such as road closure information, accident information, congestion information, travel time, etc.) based on the accumulated detection information. This auxiliary information is then distributed to the navigation device 1 or to an information management server that performs various processing using the auxiliary information. Specifically, in this embodiment, the server device 4 collects the current position coordinates, speed, and images of the surrounding area captured by the vehicle's onboard camera from each vehicle 5. It then generates data (congestion information) representing the congestion status of each lane by statistically analyzing or parsing the collected information and distributes it to the vehicles 5.

[0046] Additionally, server device 4 performs a route search based on the request from navigation device 1. Specifically, it sends the necessary information for the route search, such as the origin and destination, along with the route search request from navigation device 1 to server device 4 (however, in the case of a re-search, it is not necessarily necessary to send destination-related information). Then, server device 4, having received the route search request, uses the map information it possesses to perform a route search and determine a recommended route from the origin to the destination. It then sends the determined recommended route to navigation device 1, which is the request source. Navigation device 1 can then provide information related to the received recommended route to the user, or use the recommended route to generate various auxiliary information related to autonomous driving assistance, as described later.

[0047] Furthermore, unlike the typical map information used in the aforementioned path search, server device 4 also possesses higher-precision map information, namely, high-precision map information. High-precision map information includes, for example, information related to road lane shapes (road shape, curvature, lane width, etc. in terms of driving lanes) and lane markings (lane center lines, lane dividers, lane outer lines, guide lines, etc.) drawn on the road. In addition, it also includes information related to intersections, parking lots, etc. Moreover, server device 4 distributes the high-precision map information along with the aforementioned congestion information based on requests from navigation device 1. Navigation device 1 uses the congestion information and high-precision map information distributed from server device 4 to generate various auxiliary information related to autonomous driving assistance, as described later. Furthermore, while high-precision map information is generally map information focusing only on roads (road segments) and their surroundings, it may also include map information covering areas beyond the road's perimeter.

[0048] However, the path search processing described above does not necessarily have to be performed by server device 4. If the navigation device 1 has map information, it can also perform the processing. Furthermore, the high-precision map information may not be distributed from server device 4, but may be pre-existing in the navigation device 1.

[0049] Furthermore, the communication network 6 includes multiple base stations deployed throughout the country and a communication company that manages and controls each base station. The base stations and the communication company are interconnected via wired (fiber optic, ISDN, etc.) or wireless means. Each base station has a transceiver and an antenna for communicating with the navigation device 1. In addition to communicating wirelessly with the communication company, the base station also serves as a relay station for communication between the communication network 6 and the server device 4, relaying communication between the navigation device 1 and the server device 4 within the range (cell) reached by the radio waves from the base station.

[0050] Next, use Figure 2 The structure of the server device 4 in the driver assistance system 2 will be described in more detail. For example... Figure 2 As shown, the server device 4 includes: a server control unit 11, a server-side map DB12 connected to the server control unit 11 as an information recording unit, a high-precision map DB13, a congestion information DB14, and a server-side communication device 15.

[0051] The server control unit 11 is a control unit (MCU, MPU, etc.) that performs overall control of the server device 4. It includes internal storage devices such as a CPU 21 serving as both an arithmetic and control unit, RAM 22 used as working memory during various arithmetic operations performed by the CPU 21, a ROM 23 storing control programs, and flash memory 24 storing programs read from the ROM 23. Furthermore, both the server control unit 11 and the ECU of the navigation device 1 (described later) have various units serving as processing algorithms.

[0052] On the other hand, the server-side map DB12 is a storage unit that stores server-side map information. This server-side map information is the latest version of the map information registered based on input data or input operations from external sources. This server-side map information consists of various information required for path search, path guidance, and map display, represented by road networks. For example, it includes network data representing nodes and road segments, road segment data related to roads (road segments), node data related to nodes, intersection data related to intersections, location data related to facilities and other locations, map display data for displaying the map, search data for searching paths, and retrieval data for retrieving locations.

[0053] In addition, the high-precision map DB13 is a storage unit that stores map information with higher precision than the aforementioned server-side map information, namely high-precision map information 16. High-precision map information 16 stores map information with more detailed information, particularly regarding roads, parking lots, etc., which are the objects of vehicle travel. In this embodiment, for example, regarding roads, it includes information related to lane shapes (road shape, curvature, lane width, etc. in units of driving lanes) and markings drawn on the road (lane center lines, lane dividers, lane outer lines, guide lines, etc.). Furthermore, it records data indicating road slope, inclination, embankments, merging sections, places where the number of driving lanes decreases, places where the width narrows, intersections, etc.; regarding curves, it records data indicating radius of curvature, intersections, T-junctions, curve entrances and exits, etc.; regarding road attributes, it records data indicating downhill roads, uphill roads, etc.; regarding road types, in addition to data indicating general roads such as national highways, county roads, and narrow streets, it also records data indicating toll roads such as high-speed national highways, urban expressways, dedicated motor vehicle roads, general toll roads, and toll bridges, etc. In this embodiment, in addition to the number of lanes, the system also stores information on traffic divisions determining the direction of travel for each lane, and road connections (specifically, the correspondence between lanes included in the road before an intersection and lanes included in the road after an intersection). Furthermore, it stores speed limits set for the roads. Additionally, the high-precision map information is generally map information focusing only on the road (section) and its surroundings, but it can also include map information covering areas beyond the road's perimeter. Furthermore, in Figure 2 In the example shown, the server-side map information stored in the server-side map DB12 and the high-precision map information 16 are different map information, but the high-precision map information 16 can also be part of the server-side map information.

[0054] On the other hand, the congestion information DB14 is a storage unit that cumulatively stores information (congestion information) generated by statistically analyzing the detection information collected from each vehicle, which determines the congestion status of each lane. Furthermore, in this embodiment, the detection information collected from the vehicles specifically includes: (a) images captured by the vehicle's onboard camera; (b) the vehicle's position coordinates and speed at the time of capture of each frame contained in the image; and (c) the road segment the vehicle is currently traveling on and the time it entered that road segment. Moreover, by statistically analyzing this detection information, in this embodiment, the congestion status is determined to be, for example, at two levels: "free flow" and "congested." However, it may also be determined to be at three or more levels: "free flow," "chaotic," and "congested." Additionally, the congestion status can also be determined based on average vehicle speed and travel time.

[0055] Figure 3 This is a diagram representing an example of congestion information stored in congestion information DB14, which identifies the congestion status of each lane. For example... Figure 3 As shown, the congestion information determining the congestion status of each lane includes: the date and time of the congestion assessment, and the start and end points (points where congestion begins and ends) of the congested section, organized by lane. Specifically, these correspond to "statistical date and time," "section start point," "section end point," "lane number," and "congestion status," respectively. Furthermore, the "section start point" and "section end point" are determined by the road segment ID and the distance from the start point of the road segment. For example, in... Figure 3 The example shown illustrates congestion from 20m from the start of segment ID "100001" to 40m from the start of segment ID "100004". However, instead of "interval end point", "congestion length" can be included, indicating the length of the congested interval starting from the "interval start point". Furthermore, "lane numbering" is used, for example, to designate multiple lanes within a segment, sequentially from left to right as 1, 2, 3... For example, in the case of segment ID "100001" and lane number "1", it represents the leftmost lane of segment ID "100001". Additionally, in... Figure 3 In the example shown, the information related to the congestion status of each lane only includes the information of the sections and lanes that are judged as "congested". For sections and lanes not included in the information, they are labeled as "unobstructed" or "unclear (too little information to determine the congestion status)". However, it may also include the information of sections and lanes that are judged as "unobstructed".

[0056] Furthermore, the server device 4 distributes congestion information, which determines the congestion status of each lane, stored in the congestion information DB14, to the navigation device 1 upon request from the navigation device 1. On the other hand, the navigation device 1, having received the congestion information that determines the congestion status of each lane, uses the distributed congestion information to generate various assistance information related to autonomous driving assistance, as described later.

[0057] On the other hand, the server-side communication device 15 is a communication device used to communicate with the navigation devices 1 of each vehicle 5 via the communication network 6. In addition to the navigation devices 1, it can also receive traffic information composed of various information such as congestion information, restriction information, and traffic accident information sent by the Internet, traffic information centers such as VICS (registered trademark: Vehicle Information and Communication System) centers.

[0058] Next, use Figure 4 The general structure of the navigation device 1 mounted on vehicle 5 will be described. Figure 4 This is a block diagram illustrating the navigation device 1 of this embodiment.

[0059] like Figure 4 As shown, the navigation device 1 of this embodiment includes: a current position detection unit 31 that detects the current position of the vehicle equipped with the navigation device 1; a data recording unit 32 that records various data; a navigation ECU 33 that performs various calculations based on input information; an operation unit 34 that receives operations from the user; a liquid crystal display 35 that displays a map of the vehicle's surroundings and information related to the guidance path (pre-determined driving path of the vehicle) set by the navigation device 1; a speaker 36 that outputs sound navigation related to the path guidance; a DVD drive 37 that reads DVDs as storage media; and a communication module 38 that communicates with information centers such as detection centers and VICS centers. Furthermore, the navigation device 1 is connected to an external camera 39 and various sensors installed on the vehicle equipped with the navigation device 1 via an in-vehicle network such as CAN. In addition, it is bidirectionally connected to a vehicle control ECU 40 that performs various controls on the vehicle equipped with the navigation device 1.

[0060] The following sections will describe the structural elements of the navigation device 1 in turn.

[0061] The current position detection unit 31 consists of a GPS 41, a vehicle speed sensor 42, a steering sensor 43, and a gyroscope sensor 44, and is capable of detecting the current vehicle position, orientation, vehicle speed, and current time. In particular, the vehicle speed sensor 42 is used to detect the vehicle's travel distance and speed. It generates pulses based on the rotation of the vehicle's drive wheels and outputs these pulse signals to the navigation ECU 33. The navigation ECU 33 then counts the generated pulses to calculate the rotational speed and travel distance of the drive wheels. Furthermore, the navigation device 1 does not necessarily need to possess all four types of sensors; it can be configured to have only one or more of these sensors.

[0062] Additionally, the data recording unit 32 includes: a hard disk (not shown), which serves as an external storage device and recording medium; and a recording head (not shown), which is a drive for reading map information DB45, cache 46, specified programs, etc., recorded on the hard disk and writing specified data to the hard disk. Alternatively, instead of a hard disk, the data recording unit 32 may also include a flash memory, memory card, CD, DVD, or other optical disc. Furthermore, in this embodiment, as described above, since the path to the destination is found in the server device 4, the map information DB45 can be omitted. Even when the map information DB45 is omitted, map information can still be obtained from the server device 4 as needed.

[0063] Among them, map information DB45 is a storage unit that stores, for example, road segment data related to roads (road segments), node data related to nodes, search data for processing route search or change, facility data related to facilities, map display data for displaying the map, intersection data related to each intersection, and retrieval data for retrieving locations.

[0064] On the other hand, the cache 46 is a storage unit that stores the high-precision map information 16 and congestion information previously distributed from the server device 4. The storage period can be appropriately set, for example, it can be a specified period from the start of storage (e.g., 1 month), or it can be until the vehicle's ACC power (accessory power supply) is turned off. Alternatively, older data can be deleted sequentially after the amount of data stored in the cache 46 reaches its limit. Furthermore, the navigation ECU 33 uses the high-precision map information 16 and congestion information stored in the cache 46 to generate various auxiliary information related to autonomous driving assistance. Details will be described later.

[0065] On the other hand, the navigation ECU (electronic control unit) 33 is an electronic control unit that performs overall control of the navigation device 1. This navigation ECU includes: a CPU 51 serving as both a computing and control unit; and internal storage devices such as RAM 52, ROM 53, and flash memory 54. RAM 52 is used as working memory during various calculations performed by the CPU 51 and stores path data when a path is found. ROM 53, in addition to the control program, also records the autonomous driving assistance program (described later). Figure 5The ROM 53 stores programs read from the ROM 54, etc. Additionally, the navigation ECU 33 has various units that function as processing algorithms. For example, the driving route acquisition unit acquires the driving route of the vehicle. The congestion acquisition unit acquires the congestion status of each lane on the driving route from an external source. The driving trajectory generation unit uses map information containing at least information related to lane markings and the congestion status of each lane to generate a driving trajectory for roads included in the driving route, targeting the interval from the vehicle's current position to a predetermined distance ahead of the vehicle's current position. This trajectory determines the lane change position for the recommended lane change to the recommended vehicle. The driving assistance unit provides driving assistance to the vehicle based on the driving trajectory generated by the driving trajectory generation unit.

[0066] The operation unit 34 is operated when the starting point of the journey and the destination point are input, and it has various operation switches (not shown) such as buttons and switches. Furthermore, the navigation ECU 33 controls the execution of various actions based on the switch signals output by pressing the switches. Additionally, the operation unit 34 may also have a touch panel on the front surface of the LCD display 35. It may also include a microphone and a voice recognition device.

[0067] In addition, the LCD display 35 can display map images of roads, traffic information, operation guidance, operation menus, button guidance, guidance information along the guided route (driving the predetermined route), news, weather forecasts, timetables, emails, TV programs, etc. Alternatively, a HUD or HMD can be used instead of the LCD display 35.

[0068] In addition, the speaker 36 provides sound navigation and traffic information guidance based on the instruction output from the navigation ECU 33 to guide driving along the guide path (driving the predetermined path).

[0069] Additionally, the DVD drive 37 is a drive capable of reading data recorded on media such as DVDs or CDs. Furthermore, it can be used to play music or videos, update map information DB45, etc., based on the read data. Alternatively, a card slot for reading and writing memory cards can be provided instead of the DVD drive 37.

[0070] Additionally, the communication module 38 is a communication device, such as a mobile phone or DCM, used to receive traffic information, detection information, weather information, etc., sent from traffic information centers such as VICS centers and detection centers. It also includes vehicle-to-vehicle communication devices for communication between vehicles and each other, and road-to-vehicle communication devices for communication with roadside equipment. Furthermore, it is used to send and receive route information, high-precision map information 16, congestion information, etc., retrieved by the server device 4 between the server device 4 and the server device 4.

[0071] Furthermore, the exterior camera 39, for example, is a camera using a solid-state imaging element such as a CCD, mounted above the front bumper of the vehicle, with its optical axis set at a predetermined angle from horizontal to downward. Moreover, when the vehicle is operating in the autonomous driving zone, the exterior camera 39 captures images of the area in front of the vehicle's direction of travel. Additionally, the navigation ECU 33 processes the captured images to detect obstacles such as lane markings on the roadway and other surrounding vehicles, and generates various assistance information related to autonomous driving assistance based on the detection results. For example, when an obstacle is detected, a new driving trajectory is generated to avoid or follow the obstacle. Alternatively, the exterior camera 39 can be configured to be positioned behind or to the side of the vehicle, other than in front of it. Furthermore, sensors such as millimeter-wave radar and laser sensors, vehicle-to-vehicle communication, and road-to-vehicle communication can be used instead of cameras as the obstacle detection unit.

[0072] Furthermore, the vehicle control ECU 40 is an electronic control unit that controls the vehicle equipped with the navigation device 1. The vehicle control ECU 40 is connected to various drive components of the vehicle, such as the steering system, brakes, and accelerator. In this embodiment, especially after the vehicle's autonomous driving assistance is activated, autonomous driving assistance is implemented by controlling these drive components. Additionally, if the user overrides during autonomous driving assistance, the override situation is detected.

[0073] After driving begins, the navigation ECU 33 sends various assistance information related to autonomous driving assistance generated by the navigation device 1 to the vehicle control ECU 40 via CAN. The vehicle control ECU 40 then uses this received assistance information to implement autonomous driving assistance after driving begins. This assistance information includes, for example, a recommended driving trajectory and a speed plan indicating the vehicle's speed.

[0074] Next, based on Figure 5 The automatic driving assistance program executed by CPU 51 in the navigation device 1 of this embodiment having the above-described structure will be described. Figure 5 This is a flowchart of the autonomous driving assistance program of this embodiment. The autonomous driving assistance program is executed after the vehicle's ACC (accessory power supply) is turned on and the vehicle begins to move based on autonomous driving assistance. Furthermore, it implements an assisted driving program based on autonomous driving assistance according to the assistance information generated by the navigation device 1. Additionally, the following... Figure 5 , Figure 7 , Figure 18 ,as well as Figure 22The program, represented by a flowchart, is stored in the RAM52 and ROM53 of the navigation device 1 and is executed by the CPU51.

[0075] First, in the autonomous driving assistance program, in step (hereinafter referred to as S)1, CPU 51 obtains the predetermined path for the vehicle to travel in the future (hereinafter also referred to as the driving predetermined path). Furthermore, the vehicle's driving predetermined path may be, for example, a recommended path to the destination searched by server device 4 based on the user's set destination. Alternatively, if no destination is set, the path traveled from the vehicle's current position along the road may also be used as the driving predetermined path.

[0076] In addition, when searching for a recommended route, CPU 51 first sends a route search request to server device 4. The route search request includes the terminal ID of the navigation device 1 that sent the request, as well as information on the origin (e.g., the vehicle's current location) and destination. Furthermore, destination information is not necessarily required when re-searching. Then, CPU 51 receives search route information sent from server device 4 based on the route search request. The search route information is information (e.g., a list of road segments included in the recommended route) for reaching the destination from the origin, obtained by server device 4 using the latest version of map information based on the sent route search request. For example, the search may be performed using the well-known Dijkstra method.

[0077] Furthermore, in the search for the aforementioned recommended routes, it is preferable to select a recommended parking location (parking space) for parking the vehicle at the destination parking lot, and search for the recommended route up to the selected parking location. That is, in addition to the route to the parking lot, the searched recommended routes preferably also include the route indicating the movement of the vehicle within the parking lot. Moreover, regarding the selection of parking locations, in addition to the vehicle's movement to the parking location, walking distance after parking is also considered, and parking locations that reduce the burden on the user are preferably selected.

[0078] Next, in S2, CPU 51 acquires high-precision map information 16 targeting an area within a predetermined distance from the vehicle's current position along the predetermined driving path obtained in S1. For example, high-precision map information 16 can be acquired targeting the predetermined driving path contained in the two-dimensional grid where the vehicle is currently located. However, the area targeting the acquisition of high-precision map information 16 can be appropriately changed; for example, high-precision map information 16 can also be acquired targeting an area within 3 km of the vehicle's current position along the predetermined driving path. Alternatively, high-precision map information 16 can be acquired targeting the entire predetermined driving path.

[0079] Among them, such as Figure 6 As shown, the high-precision map information 16 is divided into rectangular shapes (e.g., 500m × 1km) and stored in the high-precision map DB13 of the server device 4. Therefore, for example, as... Figure 6 As shown, when a predetermined driving path 61 is obtained, high-precision map information 16 is obtained by taking regions 62-64 of the predetermined driving path 61, which includes the current position of the vehicle, as objects. The high-precision map information 16 includes information such as the shape of road lanes, lane widths, and lane markings (lane center lines, lane dividers, lane outer lines, guide lines, etc.) drawn on the road. Additionally, it also includes information related to intersections, parking lots, etc.

[0080] Furthermore, the high-precision map information 16 is primarily obtained from the server device 4, but in cases where high-precision map information 16 for areas already stored in the cache 46 exists, it is obtained from the cache 46. Additionally, the high-precision map information 16 obtained from the server device 4 is temporarily stored in the cache 46.

[0081] Then, in S3, CPU51 performs the static driving trajectory generation process described later. Figure 7 Here, the static driving trajectory generation process is based on the vehicle's predetermined driving path, the high-precision map information 16 obtained in S2 above, and the congestion status of each lane obtained from the external server device 4. For roads included in the predetermined driving path, a recommended driving trajectory, i.e., a static driving trajectory, is generated. In particular, the CPU 51 determines the recommended driving trajectory of the vehicle on a per-lane basis within the predetermined driving path as the static driving trajectory. That is, the static driving trajectory is the driving trajectory of the recommended driving lane of the recommended vehicle, and in the case of a lane change, the lane change position to the recommended driving lane is also determined. Furthermore, as described later, the static driving trajectory is generated for the interval from the vehicle's current position along the direction of travel to a predetermined distance ahead (e.g., within the two-dimensional grid where the vehicle is currently located, or the entire interval to the destination). In addition, the predetermined distance can be appropriately changed, but the static driving trajectory is generated for the area that at least includes the area outside the range (detection range) where the road conditions around the vehicle can be detected by the external camera 39 and other sensors.

[0082] Next, in S4, CPU51 generates a speed plan for the vehicle traveling on the static driving trajectory generated in S3, based on the high-precision map information 16 obtained in S2 above. For example, considering speed limit information and speed change locations on the predetermined driving path (such as intersections, curves, road crossings, pedestrian crossings, etc.), it calculates the recommended driving speed for the vehicle when traveling on the static driving trajectory.

[0083] Then, the speed plan generated in S4 above is stored in flash memory 54 or the like as auxiliary information for use in autonomous driving assistance. Alternatively, a plan representing the acceleration and deceleration of the vehicle, necessary for implementing the speed plan generated in S4 above, can also be generated as auxiliary information for autonomous driving assistance.

[0084] Next, in S5, CPU51 performs image processing on the images captured by the external camera 39 to determine, in particular, whether there are any factors affecting the vehicle's driving in the vicinity of the surrounding road conditions. Here, the "factors affecting the vehicle's driving" that are the objects of determination in S5 are defined as dynamic factors that change in real time, excluding static factors based on road structure. For example, these could be other vehicles traveling or parked ahead of the vehicle, congested vehicles, pedestrians ahead of the vehicle's driving direction, or construction zones ahead of the vehicle's driving direction. On the other hand, intersections, curves, junctions, merging sections, and lane reduction sections are excluded. Furthermore, even if other vehicles, pedestrians, or construction zones are present, those whose future driving trajectory cannot overlap with the vehicle's (e.g., those located far from the vehicle's future driving trajectory) are also excluded from the "factors affecting the vehicle's driving". In addition, as a unit for detecting factors that may affect the driving of a vehicle, sensors such as millimeter-wave radar and laser sensors, vehicle-to-vehicle communication, and road-to-vehicle communication can be used instead of cameras.

[0085] In addition, for example, if an external server manages the real-time location of each vehicle traveling on roads across the country, the CPU51 can also obtain the location of other vehicles around its own vehicle from the external server to perform the aforementioned S5 decision-making process.

[0086] Then, if it is determined that there are factors in the vicinity of the vehicle that affect its operation (S5 is "Yes"), the process proceeds to S6. Conversely, if it is determined that there are no factors in the vicinity of the vehicle that affect its operation (S5 is "No"), the process proceeds to S9.

[0087] In S6, CPU51 performs the dynamic driving trajectory generation process described later. Figure 18Here, the dynamic driving trajectory generation process generates a new trajectory as a dynamic driving trajectory to avoid or follow the "factors affecting the driving of this vehicle" detected in S5 above from the vehicle's current position and return to the static driving trajectory. Furthermore, as described later, the dynamic driving trajectory is generated for the interval containing the "factors affecting the driving of this vehicle". Additionally, the length of the interval varies depending on the content of the factor. For example, if the "factor affecting the driving of this vehicle" is another vehicle (the vehicle in front) driving in front of the vehicle, as an example, a trajectory is generated that extends from changing lanes to the right to overtake the vehicle in front, then changing lanes to the left to return to the original lane, and this is the dynamic driving trajectory. Furthermore, since the dynamic driving trajectory is generated based on the road conditions around the vehicle obtained by the external camera 39 and other sensors, the area where the dynamic driving trajectory is generated is at least within the range (detection range) where the road conditions around the vehicle can be detected by the external camera 39 and other sensors.

[0088] Next, in S7, CPU51 performs the driving trajectory reflection processing described later. Figure 21 Here, the driving trajectory reflection processing involves reflecting the newly generated dynamic driving trajectory in S6 onto the static driving trajectory generated in S3. Specifically, from the vehicle's current position to the terminal of the interval containing "factors affecting the vehicle's driving," the cost of the static driving trajectory and at least one dynamic driving trajectory is calculated, and the driving trajectory with the lowest cost is selected. As a result, a portion of the static driving trajectory is replaced with a dynamic driving trajectory as needed. Furthermore, there are cases where the dynamic driving trajectory is not replaced depending on the situation; that is, there are cases where even if the dynamic driving trajectory is reflected, there will be no change from the static driving trajectory generated in S3. Moreover, there are also cases where, if the dynamic driving trajectory and the static driving trajectory are the same trajectory, even if replacement is performed, there will be no change from the static driving trajectory generated in S3.

[0089] Next, in S8, CPU51 corrects the vehicle speed plan generated in S4 based on the static driving trajectory after the dynamic driving trajectory has been reflected in S7. Furthermore, if the result of reflecting the dynamic driving trajectory is no change from the static driving trajectory generated in S3, the processing in S8 can be omitted.

[0090] Next, in S9, CPU 51 calculates control quantities to make the vehicle travel at the speed of the static driving trajectory generated in S3 (or the reflected trajectory if the dynamic driving trajectory is reflected in S7) according to the speed plan generated in S4 (or the revised plan if the speed plan is revised in S8). Specifically, control quantities for the accelerator, brakes, transmission, and steering are calculated respectively. Furthermore, the processing of S9 and S10 may be performed not by the navigation device 1, but by the vehicle control ECU 40 that controls the vehicle.

[0091] Then, in S10, CPU51 reflects the control quantity calculated in S9. Specifically, the calculated control quantity is sent to vehicle control ECU40 via CAN. In vehicle control ECU40, the accelerator, brakes, transmission, and steering are controlled based on the received control quantity. As a result, driving assistance control is possible to drive at a speed according to the speed plan generated in S4 (or the corrected plan if the speed plan is corrected in S8) on the static driving trajectory generated in S3 above.

[0092] Next, in S11, CPU51 determines whether the vehicle has traveled a certain distance since the static driving trajectory was generated in S3 above. For example, the certain distance is 1km.

[0093] Furthermore, if it is determined that the vehicle has traveled a certain distance since the static driving trajectory was generated in S3 (S11 is "Yes"), the process returns to S2. Then, the static driving trajectory is generated again (S2 to S4) targeting the interval within a predetermined distance from the vehicle's current position along the predetermined driving path. In addition, in the first embodiment, the static driving trajectory is repeatedly generated targeting the interval within a predetermined distance from the vehicle's current position along the predetermined driving path whenever the vehicle travels a certain distance (e.g., 1 km). However, if the distance to the destination is short, the static driving trajectory to the destination may be generated only once at the start of the journey.

[0094] 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 S3 (S11 is "No"), it is determined whether to terminate the assisted driving based on automatic driving assistance (S12). In addition to reaching the destination, the assisted driving based on automatic driving assistance can also terminate if the user operates the control panel set on the vehicle, or intentionally releases (overtakes control) the assisted driving based on automatic driving assistance by operating the steering wheel, brakes, etc.

[0095] Then, if it is determined that the assisted driving based on autonomous driving assistance should be terminated (S12 is "Yes"), the autonomous driving assistance program is terminated. Conversely, if it is determined that the assisted driving based on autonomous driving assistance should continue (S12 is "No"), the process returns to S5.

[0096] Next, based on Figure 7 The subprocesses of the static driving trajectory generation process executed in S3 above will be explained. Figure 7 This is a flowchart of the subprocessing procedure for static driving trajectory generation.

[0097] First, in S21, the CPU 51 acquires the vehicle's current position detected by the current position detection unit 31. Furthermore, the vehicle's current position is preferably determined in detail using, for example, high-precision GPS information or high-precision positioning technology. The high-precision positioning technology utilizes image recognition to detect white lines and road surface markings captured by a camera installed on the vehicle, and then matches the detected white lines and road surface markings with, for example, high-precision map information 16, thereby enabling the detection of the driving lane and the high-precision vehicle position. Moreover, when the vehicle is traveling on a road consisting of multiple lanes, the driving lane of the vehicle is also determined.

[0098] Next, in S22, CPU51, based on the high-precision map information 16 obtained in S2 above, takes the area of ​​the static driving trajectory ahead of the vehicle's direction of travel (e.g., within a two-dimensional grid containing the vehicle's current position) as the object, and obtains lane shape, lane marking information, and information related to intersections. Furthermore, the lane shape and lane marking information obtained in S22 includes: determining the number of lanes, lane width, the location and method of adding or removing lanes if the number of lanes increases or decreases, the traffic division of each lane's direction of travel, and road connections (specifically, the correspondence between lanes included in the road before the intersection and lanes included in the road after the intersection). Additionally, as information related to intersections, besides the shape of the intersection, it also includes information related to the position and shape of ground features located at the intersection. Furthermore, in the category of "ground features located at intersections", in addition to road surface markings such as guide lines (guide white lines) and diamond-shaped traffic dividers (diamond-shaped markers) placed in the center of the intersection, there are also structures such as utility poles.

[0099] Next, in S23, CPU51 constructs a lane network based on the lane shape and lane marking information obtained in S22 above, using the interval of the static driving trajectory ahead of the vehicle's direction of travel as the object. Here, the lane network represents the network that the vehicle can choose to move in the driving lane.

[0100] Here, as an example of constructing the lane network in S23, for example, with vehicles in Figure 8 The following example illustrates the situation of traveling on a predetermined route. Figure 8 The planned driving path shown is as follows: after proceeding straight from the vehicle's current position, turn right at the next intersection 71, then turn right again at the next intersection 72, and finally turn left at the next intersection 73. Figure 8 In the planned driving path shown, for example, when turning right at intersection 71, one can enter either the right-hand lane or the left-hand lane. However, since a right turn is required at the next intersection 72, one must move to the rightmost lane when entering intersection 72. Similarly, when turning right at intersection 72, one can enter either the right-hand lane or the left-hand lane. However, since a left turn is required at the next intersection 73, one must move to the leftmost lane when entering intersection 73. Figure 9 The diagram shows a lane network constructed for sections of road where such lane movements are possible.

[0101] like Figure 9 As shown, the lane network divides the area of ​​the static driving trajectory ahead of the vehicle's direction of travel into multiple zones (groups). Specifically, the entry position of an intersection, the exit position of an intersection, and the position of lane entry / exit are used as boundaries for the division. Furthermore, each lane located at the boundary of each divided zone is assigned a node (hereinafter referred to as a lane node) 75. Further, road segments (hereinafter referred to as road segments) 76 connecting the lane nodes 75 are defined.

[0102] Furthermore, the aforementioned lane network specifically includes information on the lanes that can be moved after passing through an intersection, determined by the connection between lane nodes and road segments at intersections. Specifically, this indicates the lanes that a vehicle can move between, relative to, the lane nodes set in the road segments before and after the intersection. In other words, it indicates the lanes that a vehicle can move between, and the lane nodes connected by road segments within, the lane nodes set in the road segments before and after the intersection. To generate such a lane network, in the high-precision map information 16, lane markings representing the lane correspondences are set and stored for each road connected to the intersection, according to combinations of roads entering and leaving the intersection. When constructing the lane network in S23, the CPU 51 refers to these lane markings to form the connection between lane nodes and road segments at the intersection.

[0103] Next, in S24, for the lane network constructed in S23 above, CPU51 sets the starting lane (departure node) for the lane node located at the starting point of the lane network as the starting lane for the vehicle to begin moving, and sets the target lane (destination node) for the lane node located at the ending point of the lane network as the target lane for the vehicle to move. Specifically, when the ending point of the lane network is the destination, the target lane is set at the entry point to the destination (a location on the road near the entrance to the destination). Furthermore, when the starting point of the lane network is a road with multiple lanes on one side, the lane node corresponding to the lane the vehicle is currently in becomes the starting lane. On the other hand, when the ending point of the lane network is a road with multiple lanes on one side, especially when the ending point of the lane network is the destination, the lane node corresponding to the lane in the direction of the destination (the leftmost lane if the destination is on the left relative to the direction of travel, and the rightmost lane if the destination is on the right relative to the direction of travel) becomes the target lane. Additionally, the lane node corresponding to the leftmost lane (in the case of left-hand traffic) also becomes the target lane. In addition, especially when the endpoint of the lane network is the destination, the lane with the target lane is also equivalent to the recommended lane that needs to be driven in order to reach the destination.

[0104] Next, in S25, CPU51 obtains congestion information representing the congestion status of each lane from server device 4, targeting the sections where the lane network was constructed in S23. The congestion information is information that determines the start and end points (or congestion length, instead of the end point) of the congested section, on a lane-by-lane basis. Furthermore, as described above, server device 4 periodically collects the current position coordinates, speed, and images captured by the vehicle's onboard camera from each vehicle as detection information. By statistically analyzing or interpreting the collected information, it generates congestion information representing the congestion status of each lane and stores it cumulatively in congestion information DB14. Figure 3 In S25 above, the navigation device 1 acquires congestion information indicating the congestion status of the road at the current moment, which was recently generated.

[0105] Next, in S26, based on the congestion information obtained in S25, CPU51 sets new lane nodes for the lane network constructed in S23 at positions corresponding to the start (the point where congestion begins along the direction of travel) and end (the point where congestion ends along the direction of travel) of the congested interval. Furthermore, when the road consists of multiple lanes, new lane nodes are set not only for lanes that are substantially congested, but also for all lanes, including those that are not congested. However, it is also possible to set new lane nodes only for lanes that are currently congested.

[0106] For example, such as Figure 10 As shown, within a section with a lane network, if congestion occurs in the section from point X in the leftmost lane to point Y, as follows: Figure 11 As shown, lane nodes 75 are newly set at the locations corresponding to locations X and Y, respectively. In addition, lane nodes 75 are set not only for the leftmost lane where congestion occurred, but also for the rightmost lane where no congestion occurred.

[0107] Furthermore, if multiple congested sections exist within an area with a lane network, the lane node settings described in S26 are performed separately for each of the congested sections. Conversely, if no congested sections exist within an area with a lane network, the subsequent processes S26 to S28 are omitted, and the process is transferred to S29.

[0108] Then, in S27, CPU51 reconstructs the lane network established in S23 above. Specifically, as follows... Figure 11 As shown, a new lane segment 76 is created by connecting the newly created lane node 75 in S26 above with an adjacent existing lane node 75. Thus, a lane network including the newly created lane node 75 is constructed.

[0109] Next, in S28, CPU51 adds congestion costs to the lane costs of the lane segments corresponding to congested sections within each lane segment in the reconstructed lane network. Furthermore, when the road consists of multiple lanes, in addition to the lane segments corresponding to the congested lanes, lane segments moving towards the congested lanes are also added. For example, in... Figure 12 In the example shown, the four lane segments 76 (A-D) corresponding to the congested lane and the four lane segments 76 (E-H) moving towards the congested lane are considered as objects, and congestion costs are added to the lane costs. However, as... Figure 12 As shown, even for road segments moving towards congested lanes, road segments J connecting from other lanes to nodes corresponding to the start (end) of a congestion interval are excluded from the sum of congestion costs. That is, compared to road segments 76 within the lane nodes 75 included in the road segment column corresponding to the start of a congestion interval, road segments 76 connecting from other lanes to lane nodes 75 corresponding to points other than the start of a congestion interval incur higher costs.

[0110] Furthermore, as a method for adding congestion costs in S28 above, the costs can be added by adding a predetermined value to the lane costs, or by multiplying the lane costs by a predetermined coefficient. In particular, in this embodiment, the congestion costs are added by multiplying by a coefficient α calculated by the following equation (1).

[0111] α = Recommended speed of the road where congestion occurs (e.g., speed limit or legal speed limit) / congestion speed (e.g., assumed to be 5 km / h) ... (1)

[0112] Furthermore, a lane cost is assigned to each lane segment 76, which is the sum of the aforementioned congestion costs. The lane cost assigned to each lane segment 76 is based on the length of each lane segment 76 or the time required for movement. In particular, in this embodiment, the length of the lane segment (in meters) is used as the base value for the lane cost. However, for lane segments 76 that connect from other lanes to lane nodes 75 corresponding to points other than the starting point of the congestion zone (… Figure 12 The length from the start of the congested section (the point where congestion begins along the direction of travel) to the connecting point is used as the baseline value for lane cost. Furthermore, for lane segments involving lane changes, the lane change cost (e.g., 50) is added to the baseline value. In addition, the lane change cost can be adjusted based on the number of lane changes and the location of the lane changes. For example, in cases of lane changes near intersections or lane changes involving two lanes, the added lane change cost can be further increased.

[0113] For example, such as Figure 13 As shown, assuming the length of each lane segment 76 from A to H (if they are the same length, they are considered the same regardless of whether there is a lane change), the lane cost for each lane segment 76 from A to H is calculated as follows, adding congestion cost and lane change cost. Furthermore, the coefficient α is set to "10", and the lane change cost is fixed at 50.

[0114] (Road section A) 7m × 10 = 70

[0115] (Road section B) 10m × 10 = 100

[0116] (Road section C) 5m × 10 = 50

[0117] (Road section D) 12m × 10 = 120

[0118] (Road section E) (7m + 50) × 10 = 570

[0119] (Road section F) (10m + 7m + 50) × 10 = 670

[0120] (Road section G) (5m + 10m + 7m + 50) × 10 = 720

[0121] (H road section) (12m + 5m + 10m + 7m + 50) × 10 = 840

[0122] Furthermore, as a comparison, lane costs for lane segments 76 of routes I and J without congestion costs are calculated as follows.

[0123] (Road section I) 8m = 8

[0124] (Road section J) 8m + 50 = 58

[0125] That is, in this embodiment, for lane segments 76 corresponding to congested sections of congested lanes, the lane cost is calculated to be extremely high (e.g., about 10 times) compared to lane segments 76 not corresponding to congested sections. Furthermore, for lane segments 76 accompanied by lane changes, the lane cost is calculated to be high compared to lane segments 76 not accompanied by lane changes, but for lane segments 76 moving from other lanes into congested sections, the cost is calculated to be particularly high, except for lane segments 76 connected to the starting point of the congested section. Moreover, compared to lane segments 76 located near the starting point of the congested section (e.g., E, F), the cost of lane segments 76 connected to the ending point of the congested section (e.g., G, H) is calculated to be even higher.

[0126] Then, in S29, CPU51, referring to the lane network reconstructed in S27 and the lane costs finally calculated in S28, derives the route with the minimum lane cost (hereinafter referred to as the recommended route) within the route that continuously connects the starting lane to the target lane. For example, the Dijkstra method is used to search for the route from the target lane side. However, if a route that continuously connects the starting lane to the target lane can be found, a search method other than the Dijkstra method can also be used. The derived recommended route is a recommended lane movement pattern for vehicles when they move, and also includes information on determining lane change positions on a segment-by-segment basis.

[0127] As described above, for lane segments 76 A through D corresponding to the congested intervals of congested lanes, congestion costs are added, resulting in lane costs being calculated to be extremely high (e.g., approximately 10 times) compared to lane segments 76 that do not correspond to congested intervals. Furthermore, congestion costs are greater than the lane-changing costs incurred when changing lanes. Therefore, for example, as... Figure 14As shown, when a target lane is set ahead of the congested section, the following recommended routes are used: avoid road segments A to D corresponding to the congested section 76, that is, change lanes before the congested section and move to a lane other than the left lane, and return to the original lane after passing through the congested section.

[0128] On the other hand, for example, such as Figure 15 As shown, when congestion is caused by other vehicles waiting to enter the destination, i.e., when a target lane is set at the end of the congestion section, vehicles eventually need to be guided into the congestion section. In this case, for example, for a route that guides vehicles into the congestion section at the end of the congestion section (i.e., a route that changes lanes before the congestion section, travels in the right lane, and finally changes lanes again at road segment 76 of H to guide vehicles into the congestion section), the total lane cost is calculated by the following equation (2).

[0129] 8m+50+7m+10m+5m+(12m+5m+10m+7m+50)×10=920····(2)

[0130] In addition, for routes that are guided to the congested section in the middle of the congested section (i.e., routes that change lanes in front of the congested section and travel in the right lane, and then change lanes again in the middle of the F road segment 76 to be guided to the congested section), the total lane cost is calculated by the following formula (3).

[0131] 8m+50+7m+(10m+7m+50)×10+5m×10+12m×10=905····(3)

[0132] In addition, for routes that lead from the beginning of a congested section into the congested section (i.e., routes that continue to travel in the leftmost lane), the total lane cost is calculated by the following equation (4).

[0133] 8m+7m×10+10m×10+5m×10+12m×10=348· · · · (4)

[0134] As described above, for situations where vehicles ultimately need to be guided into congested sections, the lane cost of the route guiding vehicles into congested sections via lane segment 76 in G is lower than the route guiding vehicles into congested sections via lane segment 76 in H after avoiding the congested section. The lane cost of the route guiding vehicles into congested sections via lane segment 76 in F is lower than the route guiding vehicles into congested sections via lane segment 76 in F. The lane cost of the route guiding vehicles into congested sections via lane segment 76 in E is lower than the route guiding vehicles into congested sections via lane segment 76 in E. Furthermore, the lane cost of the route traveling from the beginning of the congested section into the congested section via lane segment 76 in I or J is lower than the route guiding vehicles into the congested section via lane segment 76 in E. That is, regarding routes entering a congested section midway through the congestion zone, the lane cost decreases for routes guided closer to the start of the congestion zone, and the lane cost decreases further for routes entering from the start of the congestion zone. Therefore, routes entering from the start of the congestion zone (in...) Figure 15 In the example shown, routes that travel from the start of the congested section through road segment 76 (either I or J) are searched as recommended routes. Furthermore, Figure 15 The example shown illustrates a scenario where a target lane is set at the end of a congested section. However, setting a target lane midway through a congested section yields the same result. That is, as... Figure 15 As shown, when a static driving trajectory to the destination is generated, if the end of the congested section coincides with or is located on the direction of travel of the road compared to the entry point to the destination, and the congested lane is the recommended lane that must be used to reach the destination, it is easy to find a recommended route to move from the start of the congested section to the recommended lane.

[0135] In addition, Figure 14 and Figure 15 The text explains the recommended routes searched when there are congested lanes near the next intersection the vehicle will pass through, and when the vehicle has a destination ahead of the congested intersection, and when there is no destination ahead. However, it also considers the congestion status of each lane when searching for recommended routes when there are congested lanes near the second or subsequent intersections ahead.

[0136] For example, in Figure 16 The example shown illustrates congestion in the left lane ahead of the second intersection 72. Figure 16In the example shown, since the destination is ahead of the straight-ahead lane at intersection 73, no target lane is designated in the middle or at the end of the congested section. Therefore, the left-hand lane where congestion occurs is not a recommended lane that must be used to reach the destination. In this case, as described above, by adding congestion costs to the road segment 76 corresponding to the congested lane's congested section, the lane cost is calculated to be extremely high compared to road segments 76 that do not correspond to the congested section. Therefore, as... Figure 16 As shown, the algorithm prioritizes searching for recommended routes that prioritize entering the right lane when turning right at intersection 72 and then moving to the left after the congestion zone ends. The result is the generation of driving trajectories that avoid congestion zones and reach the destination in the shortest possible time.

[0137] Similarly, in Figure 17 The example shown illustrates congestion in the left lane ahead of the second intersection 72. Figure 17 In the example shown, the destination is located before the left turn at intersection 73. A target lane is set at the middle or end of the congestion zone, and the left-hand lane where congestion occurs becomes the recommended lane for reaching the destination. In this case, as described above, for routes entering the congestion zone in the middle, the lane cost of routes leading to the congestion zone closer to its start point decreases, and the lane cost of routes entering the congestion zone from its start point further decreases. Therefore, the recommended route that enters the left-hand lane when turning right at intersection 72 and moves towards the start of the congestion zone (the end of the congestion) is prioritized. As a result, it is possible to prevent situations where a left turn at intersection 73 is not possible, forcing a detour to the destination.

[0138] Then, in S30, CPU51 generates a specific driving trajectory for traveling along the recommended route derived from S29 above. Furthermore, regarding the driving trajectory for sections with lane changes, the lane change positions are set in a manner that minimizes discontinuity and occurs far from intersections. Additionally, particularly when generating driving trajectories for left / right turns or lane changes at intersections, the lateral acceleration (lateral G) generated in the vehicle is calculated. The trajectory is calculated using a turning curve to ensure a smooth connection, provided that the lateral G does not impede autonomous driving assistance and does not exceed an upper limit (e.g., 0.2G) that does not cause discomfort to the vehicle occupants. Through the above processing, a recommended driving trajectory, i.e., a static driving trajectory, is generated for roads included in the predetermined driving path. Furthermore, for zones that are neither lane-changing zones nor zones within intersections, the trajectory passing through the center of the lane is used as the recommended driving trajectory.

[0139] Then, the static driving trajectory generated in S30 above is the driving trajectory of the recommended vehicle traveling in the recommended driving lane. In the event of a lane change, the lane change position to change to the recommended driving lane is also determined. The static driving trajectory is stored in the flash memory 54, etc., as auxiliary information for autonomous driving assistance.

[0140] Next, based on Figure 18 The subprocesses of the dynamic driving trajectory generation process executed in S6 above will be explained. Figure 18 This is a flowchart of the subprocessing procedure for generating and processing dynamic driving trajectories.

[0141] First, in S41, CPU51 acquires the current position of the vehicle detected by the current position detection unit 31. Furthermore, the vehicle's current position is preferably determined in detail using, for example, high-precision GPS information or high-precision positioning technology. Here, high-precision positioning technology is a technique that uses image recognition to detect white lines and road surface markings captured by a camera installed on the vehicle, and then matches the detected white lines and road surface markings with, for example, high-precision map information 16, thereby enabling the detection of the driving lane and the high-precision vehicle position. Furthermore, when the vehicle is traveling on a road consisting of multiple lanes, the driving lane of the vehicle is also determined.

[0142] Next, in S42, CPU51 acquires the static driving trajectory (i.e., the predetermined trajectory for the vehicle to travel in the future) generated in S3 above, and the speed plan (i.e., the predetermined speed for the vehicle in the future) generated in S4 above.

[0143] Next, in S43, CPU51, based on the high-precision map information 16 obtained in S2, acquires lane shape and lane marking information, focusing on the area in front of the vehicle's direction of travel, particularly around the "factors affecting the vehicle's driving (hereinafter referred to as influencing factors)" detected in S5. Furthermore, the lane shape and lane marking information acquired in S43 includes information such as determining the number of lanes and, in cases of lane increases or decreases, at what location and how.

[0144] Next, in S44, CPU51 retrieves congestion information representing the congestion status of each lane from server device 4, focusing on the direction ahead of the vehicle's travel. The congestion information defines the start and end points (or congestion length) of the congested section, using each lane as a unit. Furthermore, as described above, server device 4 periodically collects the vehicle's current position coordinates, speed, and images captured by the vehicle's onboard cameras from each vehicle as detection information. By statistically analyzing or interpreting the collected information, congestion information representing the congestion status of each lane is generated and cumulatively stored in congestion information DB14. Figure 3 In S44 above, the navigation device 1 acquires congestion information indicating the congestion status of the road at the current moment, which was recently generated.

[0145] Next, in S45, the CPU51 acquires the current position of the influencing factor detected in S5, and its movement status (direction of movement, speed of movement) if the influencing factor is moving. Furthermore, the position and movement status of the influencing factor are acquired, for example, by image processing of images captured by the external camera 39 within a predetermined detection range around the vehicle.

[0146] In addition, for example, if an external server manages the real-time location, direction of movement, and speed of vehicles traveling on roads throughout the country, and other vehicles around the vehicle are influencing factors, the CPU51 can also obtain the location, direction of movement, and speed of the corresponding other vehicles from the external server in the above S44.

[0147] Then, in S46, CPU51 first predicts the future trajectory of the influencing factor based on the current position and movement status of the influencing factor obtained from S45 above. Furthermore, if the influencing factor is another vehicle, the illumination status of the other vehicle's turn signals and brake lights can also be considered for prediction. Furthermore, if the future driving trajectory and speed plan of other vehicles can be obtained through vehicle-to-vehicle communication, these can also be considered for prediction. Additionally, if the influencing factor is a congested vehicle, it is assumed that the congested vehicle will not move from its position. Then, based on the predicted future trajectory of the influencing factor and the static driving trajectory and speed plan of the vehicle obtained in S42 above, it more accurately determines whether the influencing factor affects the vehicle's driving. Specifically, if it is predicted that the vehicle and the influencing factor are currently or in the future in the same lane, and the distance between them is close to an appropriate inter-vehicle distance D, it is determined that the influencing factor affects the vehicle's driving. Furthermore, the appropriate inter-vehicle distance D is calculated, for example, by the following equation (5).

[0148] D = Vehicle speed × 2sec + Vehicle braking distance - Braking distance of influencing factors (wherein, influencing factors are limited to the case of moving bodies)...(5)

[0149] Then, if it is determined that the influencing factor has an impact on the driving of the vehicle (S46 is "yes"), the process moves to S47. On the other hand, if it is determined that the influencing factor does not have an impact on the driving of the vehicle (S46 is "no"), no dynamic driving trajectory is generated and the process moves to S9 (S7 and S8 are omitted).

[0150] In S47, the CPU51 compares the congestion information obtained in S44 with the positions of other vehicles detected as influencing factors for the vehicle itself, and determines whether the influencing factor determined in S46 to affect the vehicle's driving is a congested vehicle. Furthermore, cases where a congested vehicle is detected as an influencing factor affecting the vehicle's driving include situations where a congestion zone exists ahead of the vehicle's direction of travel and the vehicle at the end of that congestion is detected by the external camera 39, etc.

[0151] Then, if the factor deemed to affect the vehicle's driving is congested traffic (S47 is "Yes"), no dynamic driving trajectory is generated, and the process moves to S9 (S7 and S8 omitted). Here, as described above, when generating a static driving trajectory, if a congested section exists, and especially if a target lane is set at the end or in the middle of the congested section, such as... Figure 15 As shown, a static driving trajectory is set that intentionally passes through the congested section from its starting point without avoiding it. Therefore, even if factors affecting the vehicle's driving are detected by external cameras such as the external camera 39, if the influencing factor is a congested vehicle, a new trajectory for avoidance is not generated, and the current static driving trajectory is maintained.

[0152] On the other hand, if it is determined that the influencing factor affecting the driving of this vehicle is not a congested vehicle (S47 is "No"), the process is transferred to S48.

[0153] In S48, CPU51 determines whether a new trajectory can be generated for the vehicle to avoid the influencing factor and return to a static driving trajectory (i.e., overtaking). Specifically, if the influencing factor and the vehicle are currently in the same lane, and the vehicle changes lanes to the right to overtake the influencing factor within the speed limit, then changes lanes to the left and returns to its original lane, it is determined that a new trajectory can be generated if it can draw a trajectory that maintains an appropriate distance D or more from the influencing factor. Furthermore, if the vehicle is currently in a different lane from the influencing factor and then moves into the same lane, and the vehicle overtakes the influencing factor within the speed limit, then changes lanes into the same lane as the influencing factor, it is determined that a new trajectory can be generated if it can draw a trajectory that maintains an appropriate distance D or more from the influencing factor. The determination process in S48 is based on the lane shape and lane marking information ahead of the vehicle's direction of travel, the vehicle's current position, the future movement trajectory of influencing factors, and the road's speed limit information obtained in S43.

[0154] Then, if it is determined that a new trajectory can be generated for the vehicle to avoid influencing factors and return to a static driving trajectory (i.e., overtake) (S48 is "Yes"), proceed to S49. Conversely, if it is determined that a new trajectory cannot be generated for the vehicle to avoid influencing factors and return to a static driving trajectory (i.e., overtake) (S48 is "No"), proceed to S50.

[0155] In S49, CPU51 calculates the trajectory (hereinafter referred to as the avoidance trajectory) for the vehicle to avoid the influencing factor and return to its static driving trajectory. For example, if the vehicle and the influencing factor are currently in the same lane, such as... Figure 19 As shown, the trajectory of this vehicle changing lanes to the right to overtake the influencing factor, and then changing lanes to the left to return to the original lane, is equivalent to avoiding the influencing factor.

[0156] Here, Figure 19 This refers to an example of the avoidance trajectory generated in S49 above when the vehicle 85 is traveling in the left lane on a road with two lanes on one side, and the influencing factor is the vehicle 86 traveling in front of it in the left lane.

[0157] First of all, Figure 19In the example shown, the calculation calculates the first trajectory L1 required for the steering mechanism to rotate and move into the right lane, and for the steering mechanism to return to the straight-ahead direction. Furthermore, for the first trajectory L1, the lateral acceleration (lateral G) generated during the lane change is calculated based on the vehicle's current speed. The lateral G is calculated using a turning curve to ensure it does not impede autonomous driving assistance and does not exceed an upper limit (e.g., 0.2G) that does not cause discomfort to the vehicle's occupants. This trajectory is designed to be as smooth as possible and minimize the distance required for the lane change. Additionally, maintaining a suitable vehicle distance D or more from the vehicle ahead (86) is also considered.

[0158] Next, a second trajectory L2 is calculated, which involves overtaking vehicle 86 while traveling at the maximum speed limit in the right lane, and a suitable vehicle-to-vehicle distance D is set between the two vehicles. Furthermore, the second trajectory L2 is essentially a straight line, and its length is calculated based on the speed of vehicle 86 and the road speed limit.

[0159] Next, the third trajectory L3 required for the steering mechanism to rotate back to the left lane and for the steering mechanism to return to the straight-ahead direction is calculated. Furthermore, for the third trajectory L3, the lateral acceleration (lateral G) generated during lane change is calculated based on the vehicle's current speed. Under the condition that the lateral G does not impede autonomous driving assistance and does not exceed an upper limit (e.g., 0.2G) that does not cause discomfort to the vehicle's occupants, a spiral curve is used to calculate a trajectory that is as smooth as possible and minimizes the distance required for lane change. Additionally, maintaining a suitable vehicle distance D or more from the vehicle ahead 86 is also considered.

[0160] Additionally, in S49 above, a recommended speed for the vehicle while traveling on the aforementioned lane-avoidance trajectory is calculated. Regarding the recommended speed, the speed limit is used as the upper limit. The recommended speed is set at a speed where the lateral acceleration (lateral G) generated by the vehicle during lane changes does not impede autonomous driving assistance and does not exceed an upper limit (e.g., 0.2G) that does not cause discomfort to the vehicle's occupants. For example, it is calculated based on the curvature of the lane-avoidance trajectory, the speed limit, etc.

[0161] On the other hand, in S50, CPU51 calculates the trajectory (hereinafter referred to as the following trajectory) for the vehicle to travel in parallel with the influencing factor (or follow the influencing factor). Specifically, when the vehicle and the influencing factor are in the same lane at the current moment, such as Figure 20As shown, vehicle 85 continues to travel in its current lane without changing lanes, following the trajectory of an influencing factor (e.g., vehicle 86 ahead), which is equivalent to following a trajectory. Furthermore, the following trajectory essentially becomes the same as the static driving trajectory. However, in order to properly maintain the inter-vehicle distance from the influencing factor, adjustments to the speed plan are required as described later (S8).

[0162] In addition, in S50 above, a recommended speed for the vehicle while traveling on the aforementioned following trajectory is also calculated. Regarding the following speed of the vehicle, the recommended speed is set at a speed that maintains a suitable distance D or higher between the vehicle and the influencing factors ahead, with the speed limit as the upper limit. Furthermore, the suitable distance D is calculated based on the above equation (5).

[0163] Then, in S51, taking into account the surrounding road conditions, for the roads included in the predetermined driving path, the CPU51 will use the avoidance trajectory calculated in S49 (only the avoidance trajectory is calculated) and the following trajectory calculated in S50 as the recommended driving trajectory of the vehicle, i.e., the dynamic driving trajectory.

[0164] Then, the dynamic driving trajectory generated in S51 above is stored in flash memory 54, etc., as auxiliary information for autonomous driving assistance.

[0165] Next, based on Figure 21 The subprocesses for the driving trajectory reflection processing performed in S7 above will be explained. Figure 21 This is a flowchart of the sub-processing procedure for reflecting driving trajectory.

[0166] First, in S61, CPU51 reads the static driving trajectory generated in S3 and the dynamic driving trajectory generated in S6 from the storage medium such as flash memory 54.

[0167] Next, in S62, for each driving trajectory read in S61 above, CPU51 calculates a path cost for each driving trajectory, representing the suitability of the vehicle's driving trajectory. Here, the path cost is calculated considering (a) travel time (average vehicle speed), (b) number of lane changes, (c) the location of the lane change, and (d) at least one of the driving lanes. Specifically, it is calculated based on the following conditions.

[0168] Regarding (a) "travel time (average speed)", the longer the travel time (i.e., the slower the average speed), the higher the calculated path cost. Furthermore, the average speed of the static travel trajectory is determined based on the speed plan generated in S4 above. On the other hand, the average speed of the dynamic travel trajectory is determined based on the recommended speed calculated in S49 or S50 above.

[0169] Regarding (b) "Number of lane changes", the more lane changes a driving trajectory has, the higher the calculated path cost.

[0170] Regarding (c) "Location of lane change", in the case of multiple lane changes, the shorter the interval between lane changes, the higher the calculated path cost. Additionally, for lane changes made within a specified distance ahead of an intersection (e.g., 700m for general roads, 2km for highways), the path cost is added.

[0171] (d) Regarding “driving lane”, the longer the driving distance in the overtaking lane, the higher the calculated path cost.

[0172] However, regardless of the conditions (a) to (d) above, the cost is set to infinity for the driving trajectory that is determined to be in contact with the influencing factors detected in S5 above.

[0173] Then, in S63, CPU51 compares the path cost of each driving trajectory calculated in S62 above, and the driving trajectory with the smaller path cost value among the static driving trajectory and the dynamic driving trajectory is selected as the recommended driving trajectory for the vehicle.

[0174] Next, in S64, CPU51 determines whether a dynamic driving trajectory was selected in the above-mentioned S63.

[0175] Then, if it is determined that a dynamic driving trajectory was selected in S63 above (S64 is "yes"), the process moves to S65.

[0176] In S65, CPU51 replaces the static driving trajectory with a dynamic driving trajectory, targeting the reconstructed section that generates the selected dynamic driving trajectory. Furthermore, when replacing the static driving trajectory of the reconstructed section with a dynamic driving trajectory, the start and end points of the dynamic driving trajectory are generally connected to the static driving trajectory. However, depending on the route selected in S49 or S50 above, sometimes the end point of the dynamic driving trajectory is not connected to the static driving trajectory. In this case, either a new static driving trajectory can be generated starting from the end point of the dynamic driving trajectory, or the dynamic driving trajectory can be generated repeatedly at certain intervals until it is connected to the static driving trajectory.

[0177] Then, based on the static driving trajectory where the reconstructed interval is replaced by a dynamic driving trajectory, assisted driving based on autonomous driving assistance is carried out (S9, S10).

[0178] On the other hand, if it is determined that a static driving trajectory was selected in S63 (S64 is "No"), the transition to a dynamic driving trajectory is not performed and the process moves to S8.

[0179] Here, if a static driving trajectory is selected as the one with the lowest path cost, the vehicle continues to travel in the current lane without changing lanes, thus becoming a trajectory that follows the influencing factor. Therefore, although no replacement with a dynamic driving trajectory is performed, it is necessary to maintain an appropriate inter-vehicle distance from the influencing factor in the followed trajectory, and therefore the speed plan is modified (S8).

[0180] As detailed above, in the navigation device 1 and the computer program executed by the navigation device 1 of this embodiment, a predetermined driving path for the vehicle is obtained (S1), the congestion status of each lane in the predetermined driving path is obtained from the outside (S25), and using map information containing at least information related to the zoning lines and the congestion status of each lane, a driving trajectory is generated for the roads included in the predetermined driving path, taking the interval from the current position of the vehicle to a predetermined distance ahead of the current position of the vehicle as the target, and determining the lane change position for the recommended driving lane to be driven in (S29, S30). Based on the generated driving trajectory, driving assistance for the vehicle is performed (S9, S10). Therefore, when driving assistance for the vehicle is performed, the congestion status of each lane obtained from the outside can be taken into account to generate a driving trajectory for the vehicle that determines the lane change position for the recommended driving lane. As a result, for example, by avoiding undesirable driving trajectories such as merging from other lanes into congested lanes midway through a congested section, more appropriate vehicle driving trajectories can be generated compared to existing technologies. Furthermore, by implementing driving assistance based on the generated driving trajectory, appropriate driving assistance can be implemented.

[0181] Furthermore, the road conditions within a defined detection range around the vehicle are acquired (S5). A static driving trajectory (S3) is generated, encompassing the area outside the detection range up to a defined distance ahead of the vehicle's current position. Unlike the static trajectory, a dynamic driving trajectory (S6) is generated, using map information, lane congestion information, and the road conditions around the vehicle, within the detection range, and for roads included in the predetermined driving path. Driving assistance is then provided based on at least one of the static and dynamic driving trajectories (S9, S10). Therefore, an appropriate driving plan using information available at each distance from the vehicle can be generated. In particular, driving trajectories are generated for both narrow and wider areas where the road conditions around the vehicle can be acquired, allowing the selection of an appropriate driving trajectory based on the vehicle's condition.

[0182] Furthermore, for vehicles traveling along a predetermined route, a lane network represented by road segments connecting nodes is obtained (S23). Based on the congestion status of each lane, nodes are added to the lane network at positions corresponding to the start and end points of the congestion interval, and the lane network is reconstructed (S26, S27). After adding a higher cost to the road segments corresponding to the congested lanes and the congestion interval than other road segments, the cost added to the reconstructed lane network is used to search for a route from the starting node to the destination node, and a driving trajectory is generated using the searched route (S29, S30). Therefore, by using a lane network composed of nodes and road segments, an appropriate driving trajectory for avoiding or passing through congestion intervals can be generated.

[0183] Furthermore, compared to a road segment connecting from other lanes to a node corresponding to the starting point of a congested section or a node within a road segment list that corresponds to the starting point of a congested section, a road segment connecting from other lanes to a node corresponding to a point other than the starting point of a congested section incurs a higher cost (S28). Therefore, it is possible to prevent the generation of undesirable driving trajectories such as merging from other lanes into a congested lane midway through a congested section.

[0184] Furthermore, if there is a congested section from the vehicle's current position to a predetermined distance ahead of the vehicle's current position, and the end of the congested section coincides with or is located on the direction of travel of the road compared to the entry point to the destination, and the congested lane is a recommended lane that needs to be used to reach the destination, a lane change position is determined to move from the start of the congested section to the recommended lane. Therefore, when it is necessary to travel in a congested lane, a travel trajectory that guides the vehicle to the end of the congestion can be generated.

[0185] Furthermore, the present invention is not limited to the above-described embodiments, and various improvements and modifications can be made without departing from the spirit of the present invention.

[0186] For example, in this embodiment, the server device 4 generates congestion information that determines the congestion status of each lane based on the detection information collected from the vehicle. Figure 3 However, it may not be generated by server device 4, but may be obtained from an external source, for example, as VICS information. In addition, the congestion information may not be information that determines the real-time congestion situation, but rather statistical information generated based on past congestion information (i.e., information that determines the predicted congestion situation).

[0187] In addition, in this embodiment, the final generated static driving trajectory is information that determines the specific trajectory (set of coordinates or line) of the vehicle's travel, but it can also be information that determines the road and lane as the object of the vehicle's travel, even if the specific trajectory is uncertain.

[0188] Furthermore, in this embodiment, if the factor deemed to affect the vehicle's driving is congested traffic (S47 is "Yes"), the current static driving trajectory may be maintained without generating a dynamic driving trajectory, but a dynamic driving trajectory may also be generated. Moreover, the lane costs of the static and dynamic driving trajectories can be compared, and the driving trajectory with the lower cost can be selected.

[0189] Additionally, in this embodiment, for lane segments 76 (connecting to lane nodes 75 that are other than the starting point of the congestion zone) that are connected to lane nodes 75 that are other than the starting point of the congestion zone, Figure 12 The length (E~H) of the lane segment is used as the baseline value. The length from the starting point of the congested section (the point where congestion begins along the direction of travel) to the connecting point is used as the baseline value for lane cost, but it can also be a sufficiently large fixed value. Even in such cases, it is possible to prevent the generation of driving trajectories that merge into the middle of the congested section.

[0190] In addition, in this embodiment, a lane network is generated using high-precision map information 16 (S23), but a lane network targeting roads nationwide can also be pre-stored in DB and read from DB as needed.

[0191] Furthermore, in this embodiment, the high-precision map information possessed by the server device 4 includes both information related to the road lane shape (road shape, curvature, lane width, etc. in terms of driving lanes) and information related to the lane markings (lane center lines, lane dividers, lane outer lines, guide lines, etc.) drawn on the road. However, it may only include information related to lane markings or only information related to the road lane shape. For example, even when only information related to lane markings is included, information equivalent to information related to the road lane shape can be inferred based on the information related to lane markings. Furthermore, even when only information related to the road lane shape is included, information equivalent to information related to lane markings can be inferred based on the information related to the road lane shape. Additionally, "information related to lane markings" can be information determining the type and configuration of the lane markings themselves, information determining whether lane changes are possible between adjacent lanes, or information that directly or indirectly determines the shape of the driving lanes.

[0192] In addition, in this embodiment, when factors affecting the vehicle's driving are detected, a dynamic driving trajectory is generated, and the path costs of the existing static driving trajectory and the newly generated dynamic driving trajectory are compared (S62, S63). Only when the dynamic driving trajectory is determined to be recommended is the static driving trajectory replaced with the dynamic driving trajectory (S65). However, if a dynamic driving trajectory is generated, the static driving trajectory can also be replaced with the dynamic driving trajectory.

[0193] In addition, in this embodiment, as a method to reflect the dynamic driving trajectory on the static driving trajectory, a portion of the static driving trajectory is replaced with the dynamic driving trajectory (S9). However, the trajectory can also be corrected in a way that makes the static driving trajectory close to the dynamic driving trajectory without replacement.

[0194] Furthermore, in this embodiment, as an automated driving assistance system that automatically drives without relying on the user's driving operations, all operations related to the vehicle's behavior, namely accelerator operation, brake operation, and steering wheel operation, are described in the operation of the vehicle control ECU 40 controlling the vehicle. However, automated driving assistance can also be at least one of the operations related to the vehicle's behavior, namely accelerator operation, brake operation, and steering wheel operation, in the operation of the vehicle control ECU 40 controlling the vehicle. On the other hand, manual driving based on the user's driving operations refers to the user performing all operations related to the vehicle's behavior, namely accelerator operation, brake operation, and steering wheel operation.

[0195] Furthermore, the driving assistance of the present invention is not limited to autonomous driving assistance involved in autonomous driving of vehicles. For example, driving assistance can also be provided by displaying the static driving trajectory determined in S5 and the dynamic driving trajectory generated in S8 on the navigation screen, and by providing guidance using sound, images, etc. (e.g., guidance for lane changes, guidance for recommended speeds, etc.). Additionally, the user's driving operation can be assisted by displaying the static driving trajectory and the dynamic driving trajectory on the navigation screen.

[0196] Furthermore, in this embodiment, although the autonomous driving assistance program ( Figure 4 The operation is performed by the navigation device 1, but it can also be performed by an in-vehicle unit or vehicle control ECU 40 other than the navigation device 1. In this case, the in-vehicle unit or vehicle control ECU 40 obtains the vehicle's current location, map information, etc., from the navigation device 1 or server device 4. Furthermore, the server device 4 can also execute automated driving assistance programs ( Figure 4 This refers to one or all of the steps involved. In this case, server device 4 is equivalent to the driving assistance device of this application.

[0197] In addition to navigation devices, this invention can also be applied to mobile phones, smartphones, tablets, personal computers, etc. (hereinafter referred to as mobile terminals, etc.). Furthermore, it can also be applied to systems composed of servers and mobile terminals, etc. In this case, the aforementioned autonomous driving assistance program (see...) Figure 4 Each step of the process can be implemented by either a server or a mobile terminal. However, when applying this invention to a mobile terminal, it is necessary to connect the vehicle capable of performing autonomous driving assistance to the mobile terminal in a communicable manner (whether wired or wireless).

[0198] Explanation of reference numerals in the attached figures

[0199] 1…Navigation device, 2…Driver assistance system, 3…Information distribution center, 4…Server device, 5…Vehicle, 14…Congestion information database, 16…High-precision map information, 33…Navigation ECU, 40…Vehicle control ECU, 51…CPU, 75…Lane nodes, 76…Road segments

Claims

1. A driving assistance device, wherein, have: The driving route acquisition unit acquires the driving route of the vehicle. The lane network acquisition unit acquires a lane network that shows the lane movement that a vehicle traveling on the predetermined path can select. The congestion acquisition unit acquires the congestion status of each lane in the predetermined driving route from an external source. The driving trajectory generation unit uses map information containing at least information related to the lane markings and the cost added to the lane network considering the congestion of each lane to search the lane network for a recommended lane movement route starting from the vehicle's current position. Using the searched route, the unit generates a recommended driving trajectory for the vehicle, taking the interval from the vehicle's current position to a predetermined distance ahead of the vehicle's current position as the target, for the roads included in the predetermined driving path. as well as The driving assistance unit provides driving assistance based on the driving trajectory generated by the driving trajectory generation unit. In the driving trajectory generation unit, when the lane causing congestion in a congested section is a recommended lane for the vehicle to travel in, the additional cost to the lane network is added in the following manner: Regarding routes that enter the recommended lane midway through a congested section, the closer the route is to the start of the congested section, the lower the cost; the cost of routes entering the recommended lane from the start of the congested section further decreases. The route search is performed using additional costs to generate the driving trajectory that determines the entry point to the recommended driving lane.

2. The driving assistance device according to claim 1, wherein, The driving assistance device includes a road condition acquisition unit that acquires road conditions within a specified detection range around the vehicle. The driving trajectory generation unit generates a static driving trajectory, which is a range extending from the vehicle's current position to a predetermined distance in front of the vehicle's current position, including the range outside the detection range. Unlike the static driving trajectory, the driving trajectory generation unit only targets the detection range, using the map information, the congestion status of each driving lane, and the surrounding road conditions obtained by the vehicle to generate a recommended driving trajectory, i.e., a dynamic driving trajectory, for the roads included in the predetermined driving path. The driving assistance unit provides driving assistance to the vehicle based on at least one of the static driving trajectory and the dynamic driving trajectory.

3. The driving assistance device according to claim 1, wherein, The lane network acquisition unit represents the lane movement that vehicles traveling on a predetermined path can choose from, using road segments connecting nodes. The driving assistance device includes: a network reconstruction unit that, based on the congestion status of each lane, adds nodes to the lane network at positions corresponding to the start and end points of the congestion interval, and reconstructs the lane network. The driving trajectory generation unit adds a higher cost to the road segments corresponding to congested lanes and congested intervals than to other road segments, and then uses the cost added to the reconstructed lane network to search for a route from the starting node to the destination node. The driving trajectory generation unit generates the driving trajectory using the searched route.

4. The driving assistance device according to claim 3, wherein, Compared to connecting from other lanes to a road segment or a node within a road segment list that corresponds to the starting point of a congested section, the driving trajectory generation unit incurs higher costs for connecting from other lanes to a road segment or a node within a road segment list that corresponds to a point other than the starting point of a congested section.

5. The driving assistance device according to any one of claims 1 to 4, wherein, If a congested section exists between the vehicle's current position and a predetermined distance ahead of the vehicle's current position, and the end of the congested section coincides with or is located on the direction of travel of the road relative to the entry point to the destination, and the congested lane is the recommended lane that needs to be used to enter the destination, the driving trajectory generation unit generates the driving trajectory from the start of the congested section to the recommended lane.

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