JP7552288B2 - Driving assistance device and computer program - Google Patents

Description

The present invention relates to a driving assistance device and a computer program that provide driving assistance for a vehicle.

In order to provide appropriate driving assistance when factors that may affect the driving of the vehicle, such as other vehicles, are detected around the vehicle while the vehicle is traveling on a road, it is important to identify the best driving trajectory for the vehicle to avoid those factors, i.e., the recommended driving trajectory.

For example, JP 2018-154200 A proposes a technology in which, when a vehicle ahead is detected in the lane in which the vehicle is traveling, links are set at regular intervals on the road on which the vehicle is traveling that connect nodes between the nodes, and the range in which the vehicle ahead is located is set as a no-go zone, and the series of links that allow the vehicle to avoid the no-go zone is identified as the vehicle's traveling trajectory.

JP 2018-154200 A (pages 10-16, FIG. 4)

In the above-mentioned Patent Document 1, when setting nodes on the road on which the vehicle is traveling, the nodes are set at regular intervals for all lanes on the road. In this case, there are usually many other vehicles traveling on the road in addition to the vehicle itself and the vehicle ahead. In such a situation, in order to calculate a travel trajectory for avoiding a vehicle ahead by changing lanes, for example, it is necessary to limit the timing of lane changes, i.e., to narrow the intervals at which nodes are set to a certain extent.

However, if the nodes are set too close together, the vehicle may have to make sudden turns or change lanes. In addition, the number of nodes and links becomes extremely large, which means that it takes a long time to calculate the vehicle's driving trajectory, making it difficult to provide appropriate driving assistance.

The present invention has been made to solve the above-mentioned problems in the conventional technology, and aims to provide a driving assistance device and computer program that, when it detects the presence of a factor that affects vehicle driving, reconstructs the lane network by adding a new avoidance node to the existing lane network to avoid the factor, and generates a vehicle driving trajectory using the reconstructed lane network, thereby preventing the number of nodes and links from becoming too large, and making it possible to generate an appropriate driving trajectory to avoid the factor.

In order to achieve the above object, a first driving assistance device according to the present invention includes a lane network acquisition means for acquiring a lane network indicated by nodes set at intersections and positions where lanes increase or decrease and links connecting the nodes for lane changes that a vehicle may select within a specified section , a road condition acquisition means for acquiring road conditions within a specified detection range around the vehicle, a prohibited section setting means for, when detecting the presence of a factor that may affect vehicle travel within the specified section using the road conditions, setting a lane change prohibited section within the specified section in which lane changes are prohibited due to the presence of the factor, and a lane change prohibited section setting means for setting a lane change prohibited section within the specified section in which lane changes are prohibited due to the presence of the factor, and the lane network is reconstructed by adding an avoidance node to the lane network as a limit point where a lane movement must be completed when a lane movement is performed to a lane where no lane exists ; a route searching means for searching for a route that avoids the factor in the lane movement prohibited section from a departure node located at the start point of the specified section to a destination node located at the end point of the specified section, using a cost added to the reconstructed lane network ; a traveling trajectory generating means for generating a traveling trajectory of the vehicle using the route; and a driving assistance means for providing driving assistance to the vehicle based on the traveling trajectory.
The second driving assistance device according to the present invention includes a lane network acquisition means for acquiring a lane network indicated by nodes set at intersections and positions where lanes increase or decrease and links connecting the nodes for lane movements that a vehicle may select within a specified section, a road condition acquisition means for acquiring road conditions within a specified detection range around the vehicle, a network reconstruction means for, when detecting the presence of a factor that may affect the traveling of the vehicle within the specified section using the road conditions, adding an overtaking start node and an overtaking completion node to the lane network in order to avoid the factor and reconstructing the lane network, and a network reconstruction means for reconstructing the lane network using a cost added to the reconstructed lane network. The network reconstruction means has a route searching means for searching for a route that avoids the factor to a target node, a traveling trajectory generating means for generating a traveling trajectory of the vehicle using the route, and a driving assistance means for assisting the vehicle in driving based on the traveling trajectory, and when it is assumed that the vehicle moves from the lane in which it is currently traveling to another lane to avoid the factor and overtakes, sets an overtaking section as a start point of the overtaking section, and sets the earliest point at which the vehicle can start moving from the other lane back to the original traveling lane after overtaking the factor as an end point of the overtaking section, sets the overtaking start node to the start point of the overtaking section which is a lane in which the factor does not exist, and sets the overtaking completion node to the end point of the overtaking section.
In addition, "driving assistance" refers to a function that performs or assists at least a portion of the driver's vehicle operation on behalf of the driver, or provides visual guidance or audio guidance to assist driving.

A first computer program according to the present invention is a program for generating assistance information used for driving assistance implemented in a vehicle. Specifically, the computer includes a lane network acquisition means for acquiring a lane network indicated by nodes set at intersections and positions where lanes increase or decrease and links connecting the nodes with respect to lane changes that a vehicle may select within a predetermined section, a road condition acquisition means for acquiring road conditions within a predetermined detection range around the vehicle, a prohibited section setting means for setting a lane change prohibited section within the predetermined section in which lane change is prohibited due to the presence of the factor when it is detected that a factor that may affect vehicle travel exists within the predetermined section using the road conditions, and a lane change prohibited section setting means for setting a lane change prohibited section within the predetermined section in which lane change is prohibited due to the presence of the factor at a start point of the lane change prohibited section in a lane in which the factor does not exist. the lane network is reconstructed by adding an avoidance node to the lane network as a limit point where lane movement must be completed when making a lane movement of a predetermined length, and reconstructing the lane network; a route searching means for searching, using a cost added to the reconstructed lane network, for a route that avoids the factor in the lane movement prohibited section from a departure node located at the start point of the predetermined section to a destination node located at the end point of the predetermined section without passing through the lane in which the factor is located in the lane movement prohibited section ; a driving trajectory generating means for generating a driving trajectory of the vehicle using the route; and a driving assistance means for providing driving assistance to the vehicle based on the driving trajectory.
A second computer program according to the present invention includes a lane network acquisition means for acquiring a lane network indicated by nodes set at intersections and positions where lanes increase or decrease and links connecting the nodes for lane movement that a vehicle within a predetermined section may select; a road condition acquisition means for acquiring road conditions within a predetermined detection range around the vehicle; a network reconstruction means for, when detecting the presence of a factor that may affect vehicle travel within the predetermined section using the road conditions, adding an overtaking start node and an overtaking completion node to the lane network in order to avoid the factor and reconstructing the lane network; and a network reconstruction means for reconstructing the lane network using a cost added to the reconstructed lane network, A computer program for causing a network to function as a route search means for searching for a route that avoids the factor up to a certain point, a driving trajectory generation means for generating a driving trajectory of the vehicle using the route, and a driving assistance means for providing driving assistance to the vehicle based on the driving trajectory, wherein the network reconstruction means sets an overtaking section as the start point of the overtaking section, assuming that the vehicle moves from the lane in which it is currently traveling to another lane to overtake in order to avoid the factor, and sets the overtaking section as the earliest point at which the vehicle can start moving from the other lane back to the original lane after overtaking the factor, sets the overtaking start node at the start point of the overtaking section, which is a lane in which the factor does not exist, and sets the overtaking completion node at the end point of the overtaking section.

According to the first driving assistance device and computer program of the present invention having the above-mentioned configuration, when the presence of a factor that affects vehicle driving is detected, an avoidance node for avoiding the factor is newly added to the existing lane network to reconstruct the lane network, and a driving trajectory of the vehicle is generated using the reconstructed lane network, so that it is possible to generate an appropriate driving trajectory for avoiding the factor without increasing the number of nodes and links more than necessary.
In addition, according to the second driving assistance device and computer program, when the presence of a factor that will affect the vehicle's driving is detected, an overtaking start node and an overtaking completion node are newly added to the existing lane network to avoid the factor, thereby reconstructing the lane network, and the vehicle's driving trajectory is generated using the reconstructed lane network, thereby making it possible to generate an appropriate driving trajectory to avoid the factor without increasing the number of nodes and links more than necessary.

1 is a schematic configuration diagram showing a driving assistance system according to a first embodiment. 1 is a block diagram showing a configuration of a driving assistance system according to a first embodiment. 1 is a block diagram showing a navigation device according to a first embodiment. 4 is a flowchart of an autonomous driving assistance program according to the first embodiment. FIG. 2 is a diagram showing an area for which high-precision map information is acquired. 13 is a flowchart of a sub-processing program of a static traveling trajectory generation process. FIG. 2 is a diagram showing an example of a planned driving route of a vehicle. FIG. 8 is a diagram showing an example of a lane network constructed for the planned travel route shown in FIG. 7 . 11 is a diagram showing an example of a lane flag indicating a correspondence relationship between lanes included in a road before passing through an intersection and lanes included in a road after passing through the intersection. FIG. FIG. 2 is a diagram showing a reference route and candidate routes. FIG. 11 is a diagram showing a reference route and candidate routes taking into account set lane change positions. FIG. 13 is a diagram showing the relationship between a travel lane and a lane cost. 10A and 10B are diagrams illustrating a method for calculating a static driving trajectory within an intersection. 5 is a flowchart of a sub-processing program of a dynamic traveling trajectory generation process according to the first embodiment. FIG. 2 is a diagram showing an example of a lane change prohibited section; FIG. 13 is a diagram showing a newly set avoidance node for a lane network. FIG. 13 is a diagram showing a newly set avoidance node for a lane network. FIG. 13 is a diagram showing an example of reconstructing a lane network based on avoided nodes. FIG. 13 is a diagram showing a driving trajectory calculated based on a reconstructed lane network. 13 is a flowchart of a sub-processing program of a travel trajectory reflection process. 13 is a flowchart of a sub-processing program of a dynamic traveling trajectory generation process according to the second embodiment. FIG. 2 is a diagram showing an example of an overtaking section. A figure showing an overtaking start node and an overtaking completion node that are newly set for a lane network. A figure showing an overtaking start node and an overtaking completion node that are newly set for a lane network. FIG. 13 is a diagram showing an example of a lane network reconstructed based on an overtaking start node and an overtaking completion node. FIG. 13 is a diagram showing a driving trajectory calculated based on a reconstructed lane network.

Below, a first embodiment and a second embodiment in which the driving assistance device according to the present invention is embodied in a navigation device 1 will be described in detail with reference to the drawings.

[First embodiment]
First, a schematic configuration of a driving assistance system 2 including a navigation device 1 according to the first embodiment will be described with reference to Figures 1 and 2. Figure 1 is a schematic configuration diagram showing the driving assistance system 2 according to the first embodiment. Figure 2 is a block diagram showing the configuration of the driving assistance system 2 according to the first embodiment.

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

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

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

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

Meanwhile, the navigation device 1 is an in-vehicle device that is mounted on the vehicle 5 and displays a map of the area around the vehicle's position based on map data held by the navigation device 1 or map data acquired from an external source, allows the user to input a destination, displays the vehicle's current position on a map image, and provides travel guidance along a set guide route. In the first embodiment, various types of support information related to automatic driving support are generated, particularly when the vehicle is performing assisted driving using automatic driving support. Examples of the support information include a recommended driving trajectory for the vehicle (including a recommended lane movement pattern), a parking space for parking the vehicle at the destination, and a speed plan indicating the vehicle speed when driving. Details of the navigation device 1 will be described later.

The server device 4 can also perform route searches in response to requests from the navigation device 1. Specifically, information required for route searches, such as the starting point and destination, is sent from the navigation device 1 to the server device 4 along with a route search request (however, in the case of a re-search, information about the destination does not necessarily need to be sent). The server device 4 that receives the route search request then performs a route search using map information held by the server device 4, and identifies a recommended route from the starting point to the destination. It then transmits the identified recommended route to the navigation device 1 that made the request. The navigation device 1 can then provide the user with information about the received recommended route, or use the recommended route to generate various types of support information related to autonomous driving support, as described below.

Furthermore, the server device 4 has high-precision map information, which is map information with higher accuracy, in addition to the normal map information used for the above route search. The high-precision map information includes, for example, information on the lane shape of the road (road shape and curvature for each lane, lane width, etc.) and the dividing lines drawn on the road (center line, lane boundary line, outer line of the road, guiding line, etc.). It also includes information on intersections, parking lots, etc. The server device 4 distributes the high-precision map information in response to a request from the navigation device 1, and the navigation device 1 uses the high-precision map information distributed from the server device 4 to generate various support information related to automatic driving support, as described below. Note that the high-precision map information is basically map information that only covers roads (links) and their surroundings, but it may also be map information that includes areas other than the surroundings of the roads.

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

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

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

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

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

The high-precision map DB 13 is a storage means for storing high-precision map information 15, which is map information with higher accuracy than the server-side map information. The high-precision map information 15 is map information that stores more detailed information, particularly on roads and parking lots on which the vehicle is traveling, and in the first embodiment, includes information on the lane shape of the road (road shape and curvature for each lane, lane width, etc.) and dividing lines drawn on the road (center line of the road, lane boundary line, outer side line of the road, guiding line, etc.). Further, the high-precision map DB 13 stores data representing the width, gradient, cant, bank, road surface condition, shape complement point data for specifying the link shape between nodes (for example, the shape of the curve in the case of a curved road) for each link constituting the road, merging sections, road structure, number of lanes on the road, points where the number of lanes decreases, points where the width narrows, railroad crossings, etc. for corners, data representing the radius of curvature, intersections, T-junctions, corner entrances and exits, etc. for road attributes, data representing downhill roads, uphill roads, etc. for road types, and data representing general roads such as national highways, prefectural roads, and narrow streets, as well as toll roads such as national expressways, urban expressways, motorways, general toll roads, and toll bridges, etc. In particular, in the first embodiment, in addition to the number of lanes on the road, information is also stored that specifies the traffic divisions in the travel direction for each lane and the connection of the roads (specifically, the correspondence between the lanes on the road before passing through the intersection and the lanes on the road after passing through the intersection). Furthermore, the speed limit set for the road is also stored. Furthermore, although the high-precision map information is basically map information that only covers roads (links) and their surroundings, it may also be map information that includes areas other than the surroundings of roads. Also, in the example shown in FIG. 2, the server-side map information stored in the server-side map DB 12 and the high-precision map information 15 are different map information, but the high-precision map information 15 may be a part of the server-side map information.

On the other hand, the server-side communication device 14 is a communication device for communicating with the navigation device 1 of each vehicle 5 via the communication network 6. It is also possible to receive traffic information, such as congestion information, regulation information, and traffic accident information, sent from sources other than the navigation device 1, such as the Internet network and traffic information centers, for example, VICS (registered trademark: Vehicle Information and Communication System) centers.

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

As shown in FIG. 3, the navigation device 1 according to the first embodiment includes a current position detection unit 31 that detects the current position of the vehicle in which the navigation device 1 is mounted, a data recording unit 32 in which various data is recorded, a navigation ECU 33 that performs various calculation processes based on input information, an operation unit 34 that accepts operations from the user, a liquid crystal display 35 that displays to the user a map of the area around the vehicle and information on the guide route (the planned route of the vehicle) set in the navigation device 1, a speaker 36 that outputs voice guidance regarding the route guide, a DVD drive 37 that reads a DVD, which is a storage medium, and a communication module 38 that communicates with an information center such as a probe center or a VICS center. In addition, the navigation device 1 is connected to an external camera 39 and various sensors installed in the vehicle in which the navigation device 1 is mounted via an in-vehicle network such as a CAN. Furthermore, the navigation device 1 is also connected to a vehicle control ECU 40 that performs various controls on the vehicle in which the navigation device 1 is mounted, so that bidirectional communication is possible.

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

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

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

On the other hand, the cache 46 is a storage means for storing high-precision map information 15 previously distributed from the server device 4. The storage period can be set as appropriate, but may be, for example, a predetermined period from storage (e.g., one month), or until the vehicle's ACC power source (accessory power supply) is turned off. In addition, once the amount of data stored in the cache 46 reaches an upper limit, the oldest data may be deleted in order. The navigation ECU 33 then uses the high-precision map information 15 stored in the cache 46 to generate various types of assistance information related to autonomous driving assistance. Details will be described later.

On the other hand, the navigation ECU (electronic control unit) 33 is an electronic control unit that controls the entire navigation device 1, and includes a CPU 51 as a calculation device and a control device, a RAM 52 that is used as a working memory when the CPU 51 performs various calculation processes and stores route data when a route is searched, a ROM 53 that stores a control program as well as an automatic driving assistance program (see FIG. 4) described later, and an internal storage device such as a flash memory 54 that stores a program read from the ROM 53. The navigation ECU 33 has various means as processing algorithms. For example, the lane network acquisition means acquires a lane network that is shown by links connecting nodes and nodes for lane movements that a vehicle can select within a specified section. The road condition acquisition means acquires road conditions within a specified detection range around the vehicle. When the network reconstruction means detects the presence of a factor that affects the vehicle's driving within a specified section using the road conditions, it adds an avoidance node to the lane network to avoid the factor and reconstructs the lane network. The route search means uses the cost added to the reconstructed lane network to search for a route that avoids factors from a departure node located at the start point of a specified section to a destination node located at the end point of the specified section. The travel trajectory generation means generates a travel trajectory of the vehicle using the route. The driving assistance means provides driving assistance for the vehicle based on the travel trajectory.

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

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

The speaker 36 also outputs voice guidance to guide the vehicle along the guided route (planned driving route) based on instructions from the navigation ECU 33, as well as traffic information.

The DVD drive 37 is a drive capable of reading data recorded on recording media such as DVDs and CDs. Based on the read data, music and video are played, and the map information DB 45 is updated. Instead of the DVD drive 37, a card slot for reading and writing to a memory card may be provided.

The communication module 38 is a communication device for receiving traffic information, probe information, weather information, etc. transmitted from a traffic information center, such as a VICS center or a probe center, and is, for example, a mobile phone or DCM. It also includes a vehicle-to-vehicle communication device for communicating between vehicles, and a road-to-vehicle communication device for communicating with roadside devices. It is also used to transmit and receive route information and high-precision map information 15 searched by the server device 4 to and from the server device 4.

The exterior camera 39 is composed of a camera using a solid-state image sensor such as a CCD, and is attached to the top of the front bumper of the vehicle with the optical axis oriented downward at a predetermined angle from the horizontal. When the vehicle is traveling in an automatic driving section, the exterior camera 39 captures the area ahead of the vehicle in the direction of travel. The navigation ECU 33 performs image processing on the captured image to detect obstacles such as dividing lines drawn on the road on which the vehicle is traveling and other vehicles in the vicinity, and generates various support information related to automatic driving support based on the detection results. For example, when an obstacle is detected, a new driving trajectory is generated to avoid or follow the obstacle. The exterior camera 39 may be configured to be placed behind or to the side of the vehicle in addition to the front. Instead of a camera, a sensor such as a millimeter wave radar or a laser sensor, or vehicle-to-vehicle communication or road-to-vehicle communication may be used as a means for detecting obstacles.

The vehicle control ECU 40 is an electronic control unit that controls the vehicle in which the navigation device 1 is mounted. The vehicle control ECU 40 is also connected to each driving part of the vehicle, such as the steering, brakes, and accelerator, and in the first embodiment, after automatic driving assistance is started in the vehicle, the vehicle control ECU 40 controls each driving part to implement automatic driving assistance for the vehicle. If an override is performed by the user during automatic driving assistance, the ECU 40 detects that an override has been performed.

Here, after starting to drive, the navigation ECU 33 transmits various types of support information related to the automatic driving support generated by the navigation device 1 to the vehicle control ECU 40 via the CAN. The vehicle control ECU 40 then uses the various types of support information received to implement the automatic driving support after starting to drive. Examples of support information include a recommended driving trajectory for the vehicle, a speed plan indicating the vehicle speed when driving, etc.

Next, the automatic driving assistance program executed by the CPU 51 in the navigation device 1 according to the first embodiment having the above configuration will be described with reference to FIG. 4. FIG. 4 is a flowchart of the automatic driving assistance program according to the first embodiment. Here, the automatic driving assistance program is executed when the vehicle's ACC power supply (accessory power supply) is turned on and the vehicle starts traveling with automatic driving assistance, and is a program that performs assisted traveling with automatic driving assistance according to assistance information generated by the navigation device 1. The programs shown in the flowcharts in the following FIGS. 4, 6, 14, and 20 are stored in the RAM 52 and ROM 53 of the navigation device 1, and are executed by the CPU 51.

First, in step (hereinafter abbreviated as S) 1 of the automated driving assistance program, the CPU 51 acquires the route along which the vehicle is scheduled to travel in the future (hereinafter referred to as the planned travel route). The planned travel route of the vehicle is, for example, a recommended route to a destination searched for by the server device 4 when the destination is set by the user. If a destination is not set, the route along which the vehicle travels from the current position of the vehicle may be set as the planned travel route.

When searching for a recommended route, the CPU 51 first sends a route search request to the server device 4. The route search request includes a terminal ID that identifies the navigation device 1 that sent the route search request, and information that identifies the starting point (e.g., the current position of the vehicle) and the destination. When re-searching, information that identifies the destination is not necessarily required. The CPU 51 then receives searched route information sent from the server device 4 in response to the route search request. The searched route information is information (e.g., a series of links included in the recommended route) that identifies a recommended route (center route) from the starting point to the destination that the server device 4 has searched for using the latest version of map information based on the sent route search request. For example, the search is performed using the well-known Dijkstra algorithm.

In addition, in the search for the recommended route, it is desirable to select a parking space recommended for parking the vehicle in a parking lot at the destination, and to search for a recommended route to the selected parking space. In other words, it is desirable for the recommended route to include a route showing the movement of the vehicle within the parking lot in addition to the route to the parking lot. In addition, when selecting a parking space, it is desirable to select a parking space that reduces the burden on the user by taking into consideration not only the movement of the vehicle to the parking space, but also the movement on foot after parking the vehicle.

Next, in S2, the CPU 51 acquires high-precision map information 15 for a section within a predetermined distance from the current position of the vehicle along the planned driving route acquired in S1. For example, high-precision map information 15 is acquired for the planned driving route included in the secondary mesh in which the vehicle is currently located. However, the area for which high-precision map information 15 is acquired can be changed as appropriate, and for example, high-precision map information 15 for an area within 3 km from the current position of the vehicle along the planned driving route may be acquired. Also, high-precision map information 15 may be acquired for the entire planned driving route.

Here, the high-precision map information 15 is divided into rectangular shapes (e.g., 500 m x 1 km) as shown in FIG. 5 and stored in the high-precision map DB 13 of the server device 4. Therefore, for example, when a planned driving route 61 is acquired as shown in FIG. 5, high-precision map information 15 is acquired for areas 62 to 64 including the planned driving route 61 within a secondary mesh that includes the current position of the vehicle. The high-precision map information 15 includes, for example, information on the lane shape and lane width of the road and dividing lines drawn on the road (center line, lane boundary line, outer side line of the road, guiding line, etc.). It also includes information on intersections, parking lots, etc.

In addition, the high-precision map information 15 is basically obtained from the server device 4, but if high-precision map information 15 for an area is already stored in the cache 46, it is obtained from the cache 46. In addition, the high-precision map information 15 obtained from the server device 4 is temporarily stored in the cache 46.

Then, in S3, the CPU 51 executes a static driving trajectory generation process (FIG. 6) described later. Here, the static driving trajectory generation process is a process for generating a static driving trajectory, which is a driving trajectory recommended for the vehicle on the roads included in the planned driving route, based on the planned driving route of the vehicle and the high-precision map information 15 acquired in S2. In particular, the CPU 51 specifies the driving trajectory recommended for the vehicle on a lane-by-lane basis included in the planned driving route as the static driving trajectory. Note that the static driving trajectory is generated for a section from the current position of the vehicle along the traveling direction to a predetermined distance ahead (for example, within the secondary mesh where the vehicle is currently located, or the entire section to the destination), as described later. Note that the predetermined distance can be changed as appropriate, but the static driving trajectory is generated for an area that includes at least the outside of the range (detection range) where the road conditions around the vehicle can be detected by the exterior camera 39 or other sensors.

Next, in S4, the CPU 51 generates a speed plan for the vehicle when traveling along the static travel path generated in S3, based on the high-precision map information 15 acquired in S2. For example, the CPU 51 calculates the recommended travel speed for the vehicle when traveling along the static travel path, taking into account speed limit information and speed change points (e.g., intersections, curves, railroad crossings, crosswalks, etc.) on the planned travel route.

The speed plan generated in S4 is stored in the flash memory 54 or the like as support information to be used for autonomous driving support. In addition, an acceleration plan indicating the acceleration/deceleration of the vehicle required to realize the speed plan generated in S4 may also be generated as support information to be used for autonomous driving support.

Next, in S5, the CPU 51 performs image processing on the image captured by the external camera 39 to determine whether there are any factors that may affect the running of the vehicle, particularly around the vehicle, as the surrounding road conditions. Here, the "factors that may affect the running of the vehicle" to be determined in S5 are dynamic factors that change in real time, and static factors based on the road structure are excluded. For example, other vehicles running or parked ahead of the vehicle, pedestrians ahead of the vehicle, and construction zones ahead of the vehicle. On the other hand, intersections, curves, railroad crossings, merging sections, lane narrowing sections, etc. are excluded. Even if other vehicles, pedestrians, or construction zones exist, they are excluded from the "factors that may affect the running of the vehicle" if they are not likely to overlap with the future running trajectory of the vehicle (for example, if they are located away from the future running trajectory of the vehicle). In addition, as a means for detecting factors that may affect the running of the vehicle, sensors such as millimeter wave radar and laser sensors, vehicle-to-vehicle communication, and road-to-vehicle communication may be used instead of cameras.

In addition, for example, the real-time positions of each vehicle traveling on roads across the country may be managed by an external server, and the CPU 51 may obtain the positions of other vehicles located around the vehicle from the external server and perform the determination process of S5.

If it is determined that there is a factor around the vehicle that may affect the running of the vehicle (S5: YES), the process proceeds to S6. On the other hand, if it is determined that there is no factor around the vehicle that may affect the running of the vehicle (S5: NO), the process proceeds to S9.

In S6, the CPU 51 executes a dynamic driving trajectory generation process (FIG. 14) to be described later. Here, the dynamic driving trajectory generation process generates a new trajectory as a dynamic driving trajectory to avoid or follow the "factors that affect the driving of the vehicle" detected in S5 from the current position of the vehicle and return to the static driving trajectory. The dynamic driving trajectory is generated for a section including the "factors that affect the driving of the vehicle" as described later. The length of the section varies depending on the content of the factor. For example, if the "factors that affect the driving of the vehicle" are other vehicles (forward vehicles) traveling in front of the vehicle, a trajectory that changes lanes to the right to overtake the forward vehicle, and then changes lanes to the left to return to the original lane is generated as the dynamic driving trajectory. The dynamic driving trajectory is generated based on the road conditions around the vehicle acquired by the exterior camera 39 and other sensors, so the area in which the dynamic driving trajectory is generated is at least within a range (detection range) in which the road conditions around the vehicle can be detected by the exterior camera 39 and other sensors.

Next, in S7, the CPU 51 executes the driving trajectory reflection process (FIG. 20) described later. Here, the driving trajectory reflection process is a process of reflecting the dynamic driving trajectory newly generated in S6 on the static driving trajectory generated in S3. Specifically, the costs of the static driving trajectory and at least one dynamic driving trajectory from the current position of the vehicle to the end of the section including the "factors that affect the driving of the vehicle" are calculated, and the driving trajectory with the smallest cost is selected. As a result, a part of the static driving trajectory is replaced with the dynamic driving trajectory as necessary. Note that, depending on the situation, there are cases where the dynamic driving trajectory is not replaced, that is, even if the dynamic driving trajectory is reflected, the static driving trajectory generated in S3 does not change. Furthermore, if the dynamic driving trajectory and the static driving trajectory are the same trajectory, there are cases where the static driving trajectory generated in S3 does not change even if the replacement is performed.

Next, in S8, the CPU 51 modifies the vehicle speed plan generated in S4 for the static driving trajectory after the dynamic driving trajectory is reflected in S7 based on the contents of the reflected dynamic driving trajectory. Note that if the static driving trajectory generated in S3 does not change as a result of reflecting the dynamic driving trajectory, the process of S8 may be omitted.

Next, in S9, the CPU 51 calculates the control amounts for the vehicle to travel on the static driving trajectory generated in S3 (the reflected trajectory if the dynamic driving trajectory has been reflected in S7) at a speed according to the speed plan generated in S4 (the revised plan if the speed plan has been modified in S8). Specifically, the control amounts for the accelerator, brake, gear, and steering are each calculated. Note that the processing of S9 and S10 may be performed by the vehicle control ECU 40 that controls the vehicle, rather than the navigation device 1.

Then, in S10, the CPU 51 reflects the control amount calculated in S9. Specifically, the calculated control amount is transmitted to the vehicle control ECU 40 via the CAN. The vehicle control ECU 40 controls the accelerator, brakes, gears, and steering of the vehicle based on the received control amount. As a result, driving assistance control is possible for driving the static driving trajectory generated in S3 (the trajectory after reflection if the dynamic driving trajectory has been reflected in S7) at a speed according to the speed plan generated in S4 (the revised plan if the speed plan has been modified in S8).

Next, in S11, the CPU 51 determines whether the vehicle has traveled a certain distance since the static travel trajectory was generated in S3. For example, the certain distance is 1 km.

If it is determined that the vehicle has traveled a certain distance since the static driving trajectory was generated in S3 (S11: YES), the process returns to S2. After that, the static driving trajectory is generated again for a section within a certain distance from the vehicle's current position along the planned driving route (S2 to S4). Note that in the first embodiment, the static driving trajectory is repeatedly generated for a section within a certain distance from the vehicle's current position along the planned driving route each time the vehicle travels a certain distance (for example, 1 km), but if the distance to the destination is short, the static driving trajectory to the destination may be generated all at once at the start of driving.

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: NO), it is determined whether or not to end the assisted driving by autonomous driving assistance (S12). Assisted driving by autonomous driving assistance can be ended not only when the vehicle has arrived at the destination, but also when the user intentionally cancels (overrides) the assisted driving by autonomous driving assistance by operating an operation panel provided in the vehicle, or by operating the steering wheel or brakes.

If it is determined that the assisted driving by the automated driving assistance should be terminated (S12: YES), the automated driving assistance program is terminated. On the other hand, if it is determined that the assisted driving by the automated driving assistance should be continued (S12: NO), the process returns to S5.

Next, the sub-processing of the static driving trajectory generation process executed in S3 will be described with reference to FIG. 6. FIG. 6 is a flowchart of the sub-processing program of the static driving trajectory generation process.

First, in S21, the CPU 51 acquires the current position of the vehicle detected by the current position detection unit 31. It is desirable to specify the current position of the vehicle in detail using, for example, highly accurate GPS information or high-precision location technology. Here, high-precision location technology is a technology that detects white lines and road paint information captured by a camera installed in the vehicle by image recognition, and furthermore, compares the detected white lines and road paint information with, for example, high-precision map information 15, thereby making it possible to detect the travel lane and the vehicle position with high precision. Furthermore, when the vehicle is traveling on a road consisting of multiple lanes, the lane in which the vehicle is traveling is also specified.

Next, in S22, the CPU 51 acquires lane shape, division line information, intersection information, etc. for the section (e.g., within the secondary mesh including the current position of the vehicle) for which a static driving trajectory is to be generated ahead in the traveling direction of the vehicle based on the high-precision map information 15 acquired in S2. The lane shape and division line information acquired in S22 include information specifying the number of lanes, lane width, where and how the number of lanes increases or decreases if there is an increase or decrease, traffic divisions in the traveling direction for each lane, and road connections (specifically, the correspondence between the lanes included in the road before passing the intersection and the lanes included in the road after passing the intersection). In addition to the shape of the intersection, the information about the intersection includes information about the position and shape of features placed on the intersection. Furthermore, the "feature placed on the intersection" includes road markings painted on the road surface such as guiding lines (white guide lines) and diamond-shaped guiding strips (diamond marks) placed in the center of the intersection, as well as structures such as poles.

Next, in S23, the CPU 51 constructs a lane network for the section in front of the vehicle's traveling direction for which a static driving trajectory is to be generated, based on the lane shape and the dividing line information acquired in S22. Here, the lane network is a network that indicates the lane movements that the vehicle can select.

Here, as an example of constructing a lane network in S23, a case where a vehicle travels on a planned travel route shown in FIG. 7 will be described. The planned travel route shown in FIG. 7 is a route in which the vehicle travels straight from the current position of the vehicle, turns right at the next intersection 71, turns right at the next intersection 72, and turns left at the next intersection 73. In the planned travel route shown in FIG. 7, for example, when turning right at intersection 71, it is possible to enter the right lane or the left lane. However, since it is necessary to turn right at the next intersection 72, it is necessary to move to the rightmost lane at the time of entering the intersection 72. Also, when turning right at intersection 72, it is possible to enter the right lane or the left lane. However, since it is necessary to turn left at the next intersection 73, it is necessary to move to the leftmost lane at the time of entering the intersection 73. A lane network constructed for a section where such lane movement is possible is shown in FIG. 8.

As shown in FIG. 8, the lane network divides the section that generates the static driving trajectory ahead of the vehicle's direction of travel into multiple sections (groups). Specifically, the division is made based on the entrance position of the intersection, the exit position of the intersection, and the positions where the number of lanes increases or decreases. Then, node points (hereinafter referred to as lane nodes) 75 are set for each lane located at the boundary of each divided section. Furthermore, links (hereinafter referred to as lane links) 76 that connect the lane nodes 75 are set.

The lane network also includes information that specifies the correspondence between the lanes on the road before the intersection and the lanes on the road after the intersection, in other words, the lanes that can be moved after the intersection relative to the lanes before the intersection, by connecting the lane nodes and lane links at the intersection in particular. Specifically, it indicates that a vehicle can move between the lanes that correspond to the lane nodes that are connected by lane links between the lane nodes set on the road before the intersection and the lane nodes set on the road after the intersection.

In order to generate such a lane network, the high-precision map information 15 stores lane flags indicating the correspondence between lanes for each combination of roads entering and leaving the intersection for each road connected to the intersection. For example, FIG. 9 shows lane flags when entering the intersection from the right road and exiting to the upper road, lane flags when entering the intersection from the right road and exiting to the left road, and lane flags when entering the intersection from the right road and exiting to the lower road. This indicates that the lanes included in the road before passing the intersection and the lanes included in the road after passing the intersection and the lane flags set to "1" correspond to each other, that is, the lanes are movable before and after passing the intersection. When constructing the lane network in S23, the CPU 51 refers to the lane flags to form connections between lane nodes and lane links at the intersection.

Next, in S24, the CPU 51 sets a start lane, from which the vehicle will start moving, for the lane node located at the start point of the lane network constructed in S23, and sets a target lane, to which the vehicle will move, for the lane node located at the end point of the lane network. If the start point of the lane network is a road with multiple lanes in each direction, the lane node corresponding to the lane in which the vehicle is currently located becomes the start lane. On the other hand, if the end point of the lane network is a road with multiple lanes in each direction, the lane node corresponding to the leftmost lane (in the case of driving on the left side) becomes the target lane.

Then, in S25, the CPU 51 refers to the lane network constructed in S23, searches for a route that continuously connects the start lane to the target lane, and derives one route (hereinafter referred to as the reference route). For example, the route is searched from the target lane side using the Dijkstra algorithm. However, a search method other than the Dijkstra algorithm may be used as long as it is possible to search for a route that continuously connects the start lane to the target lane. Note that the reference route derived in S25 does not necessarily have to be the optimal route, and it will suffice as long as it is a route that continuously connects the start lane to the target lane.

Next, in S26, the CPU 51 derives another route (hereinafter, called a candidate route) that connects the start lane to the target lane based on the reference route identified in S25. For example, when the reference route is traced from the start lane and an intersection where a lane change is possible or a right/left turn is reached, a new route different from the reference route is generated by branching off. As a result, one reference route and one or more candidate routes are generated, as shown in FIG. 10.

Then, in S27, the CPU 51 sets a specific lane change position (hereinafter referred to as lane change position) for the reference route and the candidate routes generated in S25 and S26 that include a lane change. Note that if the reference route and the candidate routes generated in S25 and S26 are routes that do not include a single lane change, the process of S27 may be omitted.

The lane change position is set based on, for example, the following conditions (A) to (C).
(A) A lane change range in which lane changes are possible is set for each lane, and for lane changes to the left (when driving on the left side of the road), the lane change position is set to the position closest to the departure point within the lane change range.
(B) Regarding lane changes to the right (when driving on the left side of the road), a recommended lane-change position, which is a position recommended for making lane changes, is set based on the road type, and if the recommended lane-change position is within the possible lane-change range, the lane-change position is set to the recommended lane-change position.
(C) If there is no recommended lane change position within the possible lane change range, a lane change position is set closest to the departure point within the possible lane change range.

Next, in S28, the CPU 51 adds lane nodes 75 to the lane change positions set in S27 for the reference route and candidate routes generated in S25 and S26. Here, the lane change positions include the lane change start point where the lane change starts and the lane change end point where the lane change ends. In S28, lane nodes 75 are added to the lane change start point and lane change end point, respectively. In addition, lane links 76 connecting the lane nodes 75 are also added in conjunction with the addition of lane nodes. In addition, lane links other than the lane change positions are basically straight lines (shapes along the lanes). As a result, as shown in FIG. 11, the reference route and candidate routes indicate more detailed lane movement patterns that specify the lane change positions set in S27. Note that if the reference route and candidate routes generated in S25 and S26 are routes that do not include even one lane change, the processing of S28 may be omitted.

Next, in S29, the CPU 51 calculates the total lane cost for each route, taking into account the lane change positions, for the reference route and the candidate routes generated in S25 and S26, for which lane change positions were set in S27, and for which lane nodes and lane links were added in S28. The CPU 51 then compares the total lane cost for each route and identifies the route with the smallest total lane cost as the lane movement mode recommended for the vehicle when the vehicle moves.

Here, lane costs are assigned to each lane link 76, and in S29, the total lane costs of all lane links 76 included in each route are calculated and compared. The lane costs assigned to each lane link 76 are based on the length of each lane link 76 or the time required for movement. The reference value is then corrected under the following condition (1). Furthermore, for routes that include lane changes, the cost calculated under condition (2) (hereinafter referred to as lane change cost) is added to the total lane cost depending on the location and number of lane changes included. Note that lane links 76 in the same section (group) are basically considered to be of the same length (i.e., the increase in distance due to lane changes is ignored). The length of lane links 76 within an intersection is considered to be 0 or a fixed value.

(1) For the lane cost of a lane link on a road consisting of multiple lanes, a coefficient is multiplied by the reference value depending on the position of the lane on which the vehicle is traveling. Specifically, the lane cost of a lane link traveling on an overtaking lane is corrected to be higher than that of a lane link traveling on a driving lane. As a result, the longer the distance of the overtaking lane is traveled on a route, the larger the total lane cost is calculated, so that the lane is less likely to be selected as a recommended lane movement mode, and a route with a shorter distance of the overtaking lane is preferentially selected as a recommended lane movement mode. For roads without a division between a driving lane and an overtaking lane, the lane cost is corrected to be higher for lane links traveling on lanes located on the right side in countries where the vehicle drives on the left side. For example, as shown in FIG. 12, for a road with three lanes on each side, the lane cost of a lane link traveling on the leftmost lane 81 is multiplied by 1.0. For a lane link traveling on the center lane 82, the lane cost is multiplied by 1.1. For lane links traveling in the rightmost lane 83, the lane cost is multiplied by 1.2. On the other hand, in countries where people drive on the right side of the road, the lane cost is corrected so that the lane cost is higher for lane links traveling in lanes located on the left side.

(2) When calculating the lane change cost, first, the position recommended for changing lanes is obtained as the recommended lane change position. For example, the recommended lane change position is determined by the road type of the road entering the branch point (i.e., the road type of the road where the lane change is to be performed), and for example, on intercity expressways, the position is set to 2000 m before the branch point. On urban expressways, the position is set to 1000 m before the branch point. On other general roads and narrow streets, the position is set to 700 m before the branch point. Then, the farther the lane change position set in S27 is from the recommended lane change position, the higher the lane change cost is added to the total lane cost.

Next, in S30, the CPU 51 calculates a recommended driving trajectory for the vehicle to move between lanes according to the route selected in S29, particularly for the section (group) for which the lane change position was set in S27. Note that if the route selected in S29 is a route that does not include any lane changes, the process of S30 may be omitted.

Specifically, the CPU 51 calculates the driving trajectory using map information of the lane change position set in S27. For example, the CPU 51 calculates the lateral acceleration (lateral G) that occurs when the vehicle changes lanes from the vehicle speed (set to the speed limit of the road) and the lane width, and calculates a trajectory that connects the lane change start point to the lane change end point as smoothly as possible using a clothoid curve, provided that the lateral G does not interfere with the automatic driving assistance and does not exceed an upper limit (e.g., 0.2 G) that does not cause discomfort to the vehicle occupants. Note that a clothoid curve is a curve that is traced by the vehicle's trajectory when the vehicle travels at a constant driving speed and the steering wheel is turned at a constant angular velocity.

Next, in S31, the CPU 51 calculates a recommended driving trajectory for the vehicle to move between lanes according to the route selected in S29, particularly for a section (group) within the intersection. Note that if the route selected in S29 does not pass through any intersections, the process of S31 may be omitted.

For example, in FIG. 13, a case where a travel trajectory is calculated for a section (group) in an intersection in which a lane movement route is set in which the vehicle enters the intersection from the rightmost lane and then exits to the leftmost lane is described as an example. First, the CPU 51 marks the positions in the intersection where the vehicle should pass. Specifically, marking is performed in the entrance lane to the intersection, the entrance position to the intersection, the guide line in the intersection (only if there is a guide line), the exit position from the intersection, and the exit lane from the intersection. If there is a diamond-shaped guide strip (diamond mark) in the center of the intersection, marking is performed taking the diamond mark into consideration. Then, a curve that passes through all the marked marks is calculated as the travel trajectory. More specifically, after connecting each mark with a spline curve, a clothoid curve that approximates the connecting curve is calculated as the travel trajectory. The clothoid curve is a curve that is traced by the trajectory of the vehicle when the vehicle travels at a constant speed and the steering wheel is turned at a constant angular velocity.

In addition, in S30 and S31, instead of calculating a driving trajectory, a driving trajectory of a predetermined shape may be assigned to the route selected in S29. In other words, it is possible to prepare a driving trajectory for lane changes and a driving trajectory for passing through intersections for each vehicle in advance, and to assign the driving trajectory for lane changes to the section of the route selected in S29 where lane changes are made, and to assign the driving trajectory for passing through the intersection to the section where the intersection is passed.

Then, in S32, the CPU 51 generates a static driving trajectory, which is a driving trajectory that is recommended for the vehicle to travel on roads included in the planned travel route, by connecting the driving trajectories calculated in S30 and S31. Note that for sections that are neither sections where a lane change is required nor sections within an intersection, the trajectory that passes through the center of the lane is set as the driving trajectory that is recommended for the vehicle to travel on.

The static driving trajectory generated in S32 is then stored in the flash memory 54 or the like as assistance information to be used for autonomous driving assistance.

Next, the sub-processing of the dynamic driving trajectory generation process executed in S6 will be described with reference to FIG. 14. FIG. 14 is a flowchart of the sub-processing program of the dynamic driving trajectory generation process.

First, in S41, the CPU 51 acquires the current position of the vehicle detected by the current position detection unit 31. It is desirable to specify the current position of the vehicle in detail using, for example, highly accurate GPS information or high-precision location technology. Here, high-precision location technology is a technology that detects white lines and road paint information captured by a camera installed in the vehicle by image recognition, and furthermore, compares the detected white lines and road paint information with, for example, high-precision map information 15, thereby making it possible to detect the travel lane and the vehicle position with high precision. Furthermore, when the vehicle is traveling on a road consisting of multiple lanes, the lane in which the vehicle is traveling is also specified.

Next, in S42, the CPU 51 acquires the static driving trajectory generated in S3 (i.e., the trajectory along which the vehicle is planned to travel in the future) and the speed plan generated in S4 (i.e., the vehicle's planned future speed).

Next, in S43, the CPU 51 acquires lane shape, dividing line information, etc., based on the high-precision map information 15 acquired in S2, targeting the area ahead in the vehicle's direction of travel, particularly around the "factors that affect the vehicle's travel (hereinafter, "affecting factors")" detected in S5. The lane shape and dividing line information acquired in S63 includes information specifying the number of lanes, and if there is an increase or decrease in the number of lanes, where and how the increase or decrease will occur.

Next, in S44, the CPU 51 acquires the current position of the influencing factor detected in S5, and if the influencing factor is moving, the movement status (movement direction, movement speed). The position and movement status of the influencing factor are acquired, for example, by performing image processing on an image captured by the exterior camera 39 within a predetermined detection range around the vehicle.

In addition, for example, the real-time position, direction of movement, speed, etc. of each vehicle traveling on roads across the country may be managed by an external server, and if another vehicle located near the vehicle is an influencing factor, the CPU 51 may obtain the position, direction of movement, and speed of the relevant other vehicle from the external server in S44.

After that, in S45, the CPU 51 first predicts the future movement trajectory of the influencing factor based on the current position and movement status of the influencing factor acquired in S44. If the influencing factor is another vehicle, the prediction may be made taking into consideration the lighting state of the blinker or brake lamp of the other vehicle. Furthermore, if the future running trajectory and speed plan of the other vehicle can be acquired by vehicle-to-vehicle communication or the like, the prediction may be made taking them into consideration. Then, based on the predicted future movement trajectory of the influencing factor and the static running trajectory and speed plan of the host vehicle acquired in S42, it is more accurately determined whether the influencing factor has an effect on the running of the host vehicle. Specifically, when the host vehicle and the influencing factor are currently or in the future located on the same lane and the distance between them is predicted to be within an appropriate vehicle distance D, it is determined that the influencing factor has an effect on the running of the host vehicle. The appropriate vehicle distance D is calculated, for example, by the following formula (1).
D = vehicle speed x 2 sec + braking distance of the vehicle - braking distance of the influencing factor (only when the influencing factor is a moving object) (1)

If it is determined that the influencing factor has an effect on the running of the vehicle (S45: YES), the process proceeds to S46. On the other hand, if it is determined that the influencing factor has no effect on the running of the vehicle (S45: NO), the process proceeds to S9 (S7 and S8 are omitted) without generating a dynamic running trajectory.

In S46, the CPU 51 calculates the range in which lane movement is prohibited due to the presence of the influencing factor (hereinafter referred to as the lane movement prohibited section) based on the current position and movement status of the influencing factor acquired in S44. An example of a method for calculating the lane movement prohibited section will be described below with reference to FIG. 15. FIG. 15 particularly illustrates the case where the influencing factor is a preceding vehicle 91 traveling ahead in the same lane as the host vehicle 90.

As shown in FIG. 15, first, assuming that the vehicle moves to another lane (hereinafter, referred to as the avoidance lane) to avoid the vehicle ahead 91, which is an influencing factor, and overtakes it, the limit point at which the lane change to the avoidance lane must be completed is set as the start point of the lane movement prohibited section. The position of the influencing factor when the influencing factor is detected may also be set as the start point of the lane movement prohibited section. After that, the earliest point at which the vehicle can start changing lanes from the avoidance lane to the original lane by overtaking the vehicle ahead 91 is set as the end point of the lane movement prohibited section. The condition for lane movement is that an appropriate distance is maintained between the vehicle ahead 91. The avoidance lane is basically set as the lane located to the right of the lane in which the vehicle is currently traveling (in the case of driving on the left side), but it may exceptionally be set as the lane located to the left when the vehicle is currently traveling in the rightmost lane.

Next, in S47, the CPU 51 newly sets a node (hereinafter, referred to as an avoidance node) for avoiding the influencing factor on a lane on the lane network constructed in S23 where there is no influencing factor. Specifically, as shown in FIG. 16, an avoidance node 93 is set on the avoidance lane, which is the start point of the lane movement prohibition section calculated in S46. As described above, the avoidance lane is basically a lane located on the right side (or the left side exceptionally) of the lane on which the vehicle 90 is currently traveling. However, for example, as shown in FIG. 17, in a situation where the vehicle 90 is traveling on three or more lanes and can change lanes to the right or left to avoid the vehicle ahead 91, each lane located on the left and right sides of the lane on which the vehicle is currently traveling may be set as an avoidance lane, and multiple avoidance nodes 93 may be set. In addition, the avoidance node 93 may be set on all lanes on which there is no influencing factor, not limited to lanes adjacent to the lane on which the vehicle 90 is currently traveling.

Furthermore, the avoidance node 93 may be set on the lane where the influencing factor exists, i.e., on the lane on which the vehicle is currently traveling. However, as described below, for a route in a no-lane-movement section where it is determined that it is necessary to pass through a lane where an influencing factor exists, the cost becomes infinite, so in the subsequent S49, a route that passes through the avoidance node 93 set on the lane where the influencing factor exists will not generally be selected. However, there is a possibility that it may be selected in a situation where it is not possible to change lanes or overtake.

In addition, if there are multiple influencing factors, the lane movement prohibition section in S46 and the avoidance node in S47 are set for each of the multiple influencing factors.

Then, in S48, the CPU 51 reconstructs the lane network, particularly targeting the section including the influencing factor, of the lane network constructed in S23. Specifically, first, a section (hereinafter, referred to as a reconstruction section) in which the lane network is to be reconstructed is set. The start point of the reconstruction section is set to any point within a range where lane movement can be started to overtake the influencing factor, and may be, for example, the current position of the vehicle, or may be a point a distance before the start point of the lane movement prohibited section that is the distance required for lane movement. On the other hand, the end point of the reconstruction section is set to any point within a range where lane movement back to the original lane can be completed after overtaking the influencing factor, and may be, for example, the next intersection or lane increase/decrease point, or may be a point a distance before the end point of the lane movement prohibited section that is the distance required for lane movement in the direction of travel. Then, as shown in FIG. 18, a start node 95 is set for each lane located at the start point of the reconstruction section, and a destination node 96 is set for each lane located at the end point of the reconstruction section. However, the start node 95 may be set only to the lane in which the vehicle 90 is currently located.

Next, as shown in FIG. 18, the CPU 51 sets a new lane link 76 for the reconstruction section so that it connects from the departure node 95 to the destination node 96 via the avoidance node 93 newly set in S47. This results in the construction of a lane network route that connects from the departure node 95 to the destination node 96 while avoiding influencing factors.

Next, in S49, the CPU 51 uses the costs added to the reconstructed lane network to search for a route with the smallest total cost among the routes from the starting node 95 of the lane in which the vehicle is located to any of the destination nodes 96.

When calculating the total cost of a route, the calculation takes into consideration at least one of the following: (a) driving time, (b) number of lane changes, (c) lane change locations, and (d) driving lanes. Specifically, the calculation is based on the following conditions:

(a) Regarding "travel time", the longer the travel time of a route, the higher the calculated total cost. The travel speed of the vehicle is determined based on the speed plan generated in S4.
(b) Regarding the "number of lane changes," the total cost is calculated to be higher for a route with a greater number of lane changes.
(c) Regarding the "location of lane changes", if multiple lane changes are made, the shorter the interval between lane changes is, the higher the calculated total cost will be. In addition, for routes where lane changes are made within a certain distance (e.g., 700 m on a general road, 2 km on a highway) before an intersection, the cost is added.
(d) Regarding "driving lane", the total cost is calculated to be higher for a route that has a longer driving distance in the overtaking lane.

However, regardless of the above conditions (a) to (d), the cost is set to infinity for routes where it is determined that the vehicle needs to pass through a lane in which an influencing factor exists in the lane movement prohibition section calculated in S46.

Next, in S50, the CPU 51 calculates a recommended driving trajectory for the reconstruction section when the vehicle moves along the lane according to the route selected in S49.

Specifically, the CPU 51 calculates the lateral acceleration (lateral G) that occurs when the vehicle changes lanes from the vehicle's current speed and lane width, and calculates a trajectory that connects the departure node 95, the avoidance node 93, and the destination node 96 as smoothly as possible using a clothoid curve, provided that the lateral G does not interfere with the automated driving assistance and does not exceed an upper limit (e.g., 0.2 G) that does not cause discomfort to the vehicle occupants. In doing so, it is desirable to calculate the travel trajectory so as not to pass through a lane in which an influencing factor exists in the lane movement prohibition section calculated in S46, and to shorten the distance traveled in the avoidance lane as much as possible. Another condition is that an appropriate inter-vehicle distance must be maintained between the vehicle and the influencing factor.

Here, Figure 19 shows an example of a driving trajectory generated in S50 when a route is selected in which the vehicle 90 changes lanes to the right to overtake the influencing vehicle 91 ahead, and then changes lanes to the left to return to the original lane.
First, in the example shown in Fig. 19, a first trajectory L1 required for starting a steering wheel turn and moving to the right lane and returning the steering wheel position to the straight direction is calculated for the area between the departure node 95 and the avoidance node 93. The first trajectory L1 is calculated by calculating the lateral acceleration (lateral G) generated when changing lanes based on the current vehicle speed of the vehicle, and calculating a trajectory that is as smooth as possible and that shortens the distance required for lane change as much as possible using a clothoid curve, under the condition that the lateral G does not interfere with the automatic driving assistance and does not exceed an upper limit value (e.g., 0.2 G) that does not cause discomfort to the vehicle occupants. In addition, the conditions are that the timing of the lane change is delayed as much as possible to shorten the distance traveled in the avoidance lane, and an appropriate inter-vehicle distance or more is maintained between the vehicle and the vehicle ahead 91.
Next, a second trajectory L2 is calculated for the section from the avoidance node 93 to the destination node 96, in which the vehicle travels in the right lane at the speed limit as an upper limit, overtakes the vehicle ahead 91, and maintains an appropriate inter-vehicle distance from the vehicle ahead 91. The second trajectory L2 is basically a straight trajectory, and the length of the trajectory is calculated based on the vehicle speed of the vehicle ahead 91 and the speed limit of the road.
Next, a third trajectory L3 is calculated for the area between the avoidance node 93 and the target node 96, which is required to start turning the steering wheel and return to the left lane, and for the steering wheel position to return to the straight-ahead direction. The third trajectory L3 is calculated by calculating the lateral acceleration (lateral G) that occurs when changing lanes based on the current vehicle speed of the vehicle, and using a clothoid curve, a trajectory that is as smooth as possible and requires as short a distance as possible to change lanes, under the condition that the lateral G does not interfere with the automatic driving assistance and does not exceed an upper limit (e.g., 0.2 G) that does not cause discomfort to the vehicle occupants. In addition, in order to shorten the distance traveled in the avoidance lane, the timing of the lane change is made as early as possible, and an appropriate inter-vehicle distance or more is maintained between the vehicle and the vehicle ahead 91.

In addition, in S50, the recommended speed of the vehicle when traveling along the above-mentioned travel path is also calculated. The recommended speed of the vehicle is set to a maximum speed not exceeding the upper limit (e.g., 0.2 G) at which the lateral acceleration (lateral G) generated in the vehicle when changing lanes does not interfere with the automated driving assistance and does not cause discomfort to the vehicle occupants, with the speed limit as the upper limit. For example, the recommended speed is calculated based on the curvature of the travel path, the speed limit, etc.

In addition, in S50, instead of calculating a driving trajectory, a driving trajectory of a predetermined shape may be assigned to the route selected in S49. In other words, a driving trajectory for lane changing is prepared in advance for each vehicle, and the driving trajectory for lane changing is assigned to a position on the route selected in S29, for example, with the avoidance node 93 as the end point, and a trajectory for driving while maintaining the lane is set for other sections, thereby generating a driving trajectory.

In the example shown in FIG. 19, the vehicle 90 changes lanes to the right to overtake the influencing vehicle 91 ahead, and then changes lanes to the left to return to the original lane. However, depending on the route selected in S49, the vehicle 90 may continue to travel in the avoidance lane without returning to the original lane after overtaking the vehicle 91 ahead. Furthermore, if it is determined that the vehicle 90 cannot overtake the vehicle 91 ahead because the vehicle 91 ahead is moving too fast or the road is congested with surrounding vehicles, the vehicle 90 may continue to travel in the lane it is currently traveling in without changing lanes, regardless of the reconstructed lane network, i.e., the vehicle 90 may continue to travel in the lane it is currently traveling in by following the vehicle 91 ahead.

In the example shown in FIG. 19, the driving trajectory of the lane change for the host vehicle 90 to return to the original lane after changing lanes to the avoidance lane is also calculated, but the driving trajectory for the lane change to return to the original lane may not be calculated (i.e., the timing of returning to the original lane may not be specified in advance). In that case, the host vehicle that has changed lanes to the avoidance lane detects the surroundings at any time, and when it determines that it is possible to return to the original lane, it performs a lane change to return to the original lane.

Then, in S51, the CPU 51 generates the driving trajectory calculated in S50 as a dynamic driving trajectory, which is a driving trajectory that is recommended for the vehicle to drive on roads included in the planned driving route, taking into account the surrounding road conditions.

The dynamic driving trajectory generated in S51 is then stored in flash memory 54 or the like as assistance information to be used for autonomous driving assistance.

Next, the sub-processing of the running track reflection process executed in S7 will be described with reference to FIG. 20. FIG. 20 is a flowchart of the sub-processing program of the running track reflection process.

First, in S71, the CPU 51 reads the static driving trajectory generated in S3 and the dynamic driving trajectory generated in S6 from a storage medium such as the flash memory 54.

Next, in S72, the CPU 51 calculates a path cost indicating the suitability of each driving trajectory read out in S71 as a driving trajectory for the vehicle for each driving trajectory. Here, the path cost is calculated taking into consideration at least one of (a) driving time (average vehicle speed), (b) number of lane changes, (c) lane change positions, and (d) driving lanes. Specifically, the calculation is based on the following conditions:

(a) Regarding "travel time (average vehicle speed)", the longer the travel time (i.e., the slower the average vehicle speed) of a travel trajectory, the higher the path cost is calculated. The average vehicle speed of the static travel trajectory is determined based on the speed plan generated in S4. On the other hand, the average vehicle speed of the dynamic travel trajectory is determined based on the recommended speed calculated in S50.
(b) Regarding the "number of lane changes," the path cost is calculated to be higher for a driving trajectory with a greater number of lane changes.
(c) Regarding the "location of lane change", if multiple lane changes are made, the shorter the interval between lane changes, the higher the path cost is calculated. In addition, for a driving trajectory in which a lane change is made within a certain distance (e.g., 700 m on a general road, 2 km on a highway) before an intersection, the path cost is added.
(d) Regarding the "driving lane", the longer the driving distance in the overtaking lane, the higher the path cost is calculated to be.

However, regardless of the above conditions (a) to (d), the cost is set to infinity for a driving trajectory where the vehicle is determined to come into contact with an influencing factor detected in S5.

Then, in S73, the CPU 51 compares the path costs of the driving trajectories calculated in S72, and selects the driving trajectory with the smaller path cost value from among the static driving trajectory and the dynamic driving trajectory as the driving trajectory on which the vehicle is recommended to travel.

Next, in S74, the CPU 51 determines whether or not a dynamic driving trajectory was selected in S73.

If it is determined in S73 that a dynamic driving trajectory has been selected (S74: YES), the process proceeds to S75.

In S75, the CPU 51 replaces the static driving trajectory with the dynamic driving trajectory for the reconstructed section in which the selected dynamic driving trajectory was generated. When the static driving trajectory of the reconstructed section is replaced with the dynamic driving trajectory, the start point and end point of the dynamic driving trajectory will basically be connected to the static driving trajectory, but depending on the route selected in S49, the end point of the dynamic driving trajectory may not be connected to the static driving trajectory. In such a case, a new static driving trajectory may be generated with the end point of the dynamic driving trajectory as the start point, or the dynamic driving trajectory may be repeatedly generated at regular intervals until it is connected to the static driving trajectory.

Then, assisted driving is performed based on the static driving trajectory in which the reconstructed section has been replaced with the dynamic driving trajectory (S9, S10).

On the other hand, if it is determined in S73 that a static driving trajectory has been selected (S74: NO), the process proceeds to S8 without replacing the static driving trajectory with a dynamic driving trajectory.

If a static driving trajectory is selected as the driving trajectory with the small path cost value, the vehicle will continue to drive in the current lane without changing lanes, and the trajectory will follow the influencing factors. Therefore, the vehicle will not be replaced with a dynamic driving trajectory, but since the following trajectory requires an appropriate distance to be maintained between the vehicle and the influencing factors, the speed plan will be revised (S8).

As described above in detail, the navigation device 1 according to the first embodiment and the computer program executed by the navigation device 1 use high-precision map information 15 including at least information about lanes to acquire a lane network, which is a network indicated by links connecting nodes with each other with respect to lane movement that the vehicle may select for a predetermined section of the road on which the vehicle is traveling (S23), acquire road conditions within a predetermined detection range around the vehicle (S44), and when it is detected that there is an influencing factor that may affect the traveling of the vehicle on any lane in the predetermined section using the surrounding road conditions acquired by the vehicle, add an avoidance node 93 to the lane network as a node for avoiding the influencing factor. A new avoidance node 93 is added onto the work (S47), and the lane network is reconstructed with the addition of the avoidance node 93 (S48). Using the cost added to the reconstructed lane network, a route of the lane network that connects from the departure node located at the start point of the specified section to the destination node located at the end point of the specified section while avoiding the factors is searched for (S49). Based on the route search results, a recommended vehicle driving trajectory is generated for traveling while avoiding the influencing factors in the specified section (S51). Vehicle driving assistance is provided based on the driving trajectory (S9, S10). This makes it possible to generate an appropriate driving trajectory for avoiding the influencing factors without increasing the number of nodes and links more than necessary.
In addition, a range within a specified section where lane movement is prohibited based on the location of the influencing factor is set as a lane movement prohibited section (S46), and a route in the lane network connecting the start node to the destination node without passing through the lane in which the influencing factor is located in the lane movement prohibited section is searched for (S49), making it possible to generate an appropriate driving trajectory for avoiding the influencing factor.

[Second embodiment]
Next, a navigation device according to a second embodiment will be described with reference to Figures 21 to 26. In the following description, the same reference numerals as those in the configuration of the navigation device 1 according to the first embodiment shown in Figures 1 to 20 indicate the same or corresponding parts as those in the configuration of the navigation device 1 according to the first embodiment.

The schematic configuration of the navigation device according to the second embodiment is almost the same as that of the navigation device 1 according to the first embodiment. In addition, various control processes are also almost the same as those of the navigation device 1 according to the first embodiment.
However, the navigation device 1 according to the first embodiment adds a new node only at the start point of the lane movement prohibited section as shown in Figures 16 and 17, whereas the navigation device 1 according to the second embodiment adds a new node also at the point corresponding to the end point of the lane movement prohibited section.

Here, the automatic driving assistance program executed by the CPU 51 in the navigation device according to the second embodiment will be described with reference to FIG. 21, particularly the sub-processing of the dynamic driving trajectory generation process in S6. FIG. 21 is a flowchart of the sub-processing program of the dynamic driving trajectory generation process.

Note that the processing from S101 to S105 is similar to S41 to S45 of the dynamic driving trajectory generation processing (Figure 14) according to the first embodiment, so a description thereof will be omitted.

First, in S106, the CPU 51 calculates the range required for the host vehicle to overtake the influencing factor (hereinafter, referred to as the overtaking section). An example of a method for calculating the overtaking section will be described below with reference to FIG. 22. FIG. 22 particularly illustrates the case where the influencing factor is a preceding vehicle 91 traveling ahead in the same lane as the host vehicle 90.

As shown in FIG. 22, first, in order to avoid the influencing factor, the vehicle in question moves to another lane (hereinafter, referred to as the avoidance lane) and passes the vehicle in front 91, the limit point at which the lane change to the avoidance lane must be completed is set as the start point of the overtaking section. The position of the influencing factor when the influencing factor is detected may be set as the start point of the overtaking section. After that, the earliest point at which the vehicle can start changing lanes from the avoidance lane to the original lane after overtaking the vehicle in front 91 is set as the end point of the overtaking section. The condition for lane change is that an appropriate distance between the vehicle in front 91 is maintained. The avoidance lane is basically set as the lane located to the right of the lane in which the vehicle is currently traveling (in the case of driving on the left side), but it may exceptionally be set as the lane located to the left when the vehicle is currently traveling in the rightmost lane. The "overtaking section" in the second embodiment is substantially the same section as the "lane change prohibited section" in the first embodiment.

Next, in S107, the CPU 51 newly sets a node for avoiding the influencing factor on the lane where the influencing factor does not exist in the lane network constructed in S23. Specifically, as shown in FIG. 23, an overtaking start node 103 is set on the avoidance lane, which is the start point of the overtaking section calculated in S106, and an overtaking completion node 104 is set on the avoidance lane, which is the end point of the overtaking section. The "overtaking start node" in the second embodiment is substantially the same node as the "avoidance node" in the first embodiment. As described above, the avoidance lane is basically a lane located on the right side (or exceptionally on the left side) of the lane in which the vehicle 90 is currently traveling. However, for example, in a situation in which the vehicle 90 is traveling on a lane with three or more lanes and can change lanes to the right or left to avoid the vehicle ahead 91 as shown in FIG. 24, each lane located on the left and right sides of the lane in which the vehicle is currently traveling may be set as an avoidance lane, and a plurality of overtaking start nodes 103 and overtaking completion nodes 104 may be set. In addition, the overtaking start node 103 and the overtaking completion node 104 may be set not only for lanes adjacent to the lane in which the vehicle 90 is currently traveling, but also for all lanes where there are no influencing factors.

Furthermore, the overtaking start node 103 and the overtaking completion node 104 may be set on the lane where the influencing factor exists, i.e., on the lane on which the vehicle is currently traveling. However, as described below, for a route that is determined to need to pass through a lane where an influencing factor exists in an overtaking section, the cost becomes infinite, so in the subsequent S109, a route that passes through the overtaking start node 103 or the overtaking completion node 104 set on the lane on which the influencing factor exists will not generally be selected. However, there is a possibility that it may be selected in a situation where it is not possible to change lanes or to overtake.

In addition, if there are multiple influencing factors, the overtaking section in S106 and the overtaking start node and overtaking completion node in S107 are set for each of the multiple influencing factors.

Then, in S108, the CPU 51 reconstructs the lane network, particularly for the section including the influencing factor, of the lane network constructed in S23. Specifically, first, a section in which the lane network is to be reconstructed (hereinafter, referred to as the reconstruction section) is set. The start point of the reconstruction section is set to any point within a range where lane movement can be started to overtake the influencing factor, and may be, for example, the current position of the vehicle, or may be a point a distance before the start point of the overtaking section that is the distance required for lane movement. On the other hand, the end point of the reconstruction section is set to any point within a range where lane movement back to the original lane can be completed after overtaking the influencing factor, and may be, for example, the next intersection or lane increase/decrease point, or may be a point a distance before the end point of the overtaking section that is the distance required for lane movement in the direction of travel. Then, as shown in FIG. 25, a start node 95 is set for each lane located at the start point of the reconstruction section, and a destination node 96 is set for each lane located at the end point of the reconstruction section. However, the start node 95 may be set only to the lane in which the vehicle 90 is currently located.

Next, as shown in FIG. 25, the CPU 51 sets a new lane link 76 for the reconstruction section so that it connects from the departure node 95 to the destination node 96 via the overtaking start node 103 and the overtaking completion node 104 newly set in S107. This creates a lane network route that connects the departure node 95 to the destination node 96 while avoiding influencing factors.

Next, in S109, the CPU 51 uses the costs added to the reconstructed lane network to search for a route with the smallest total cost among the routes from the starting node 95 of the lane in which the vehicle is located to any of the destination nodes 96.

When calculating the total cost of a route, the calculation takes into consideration at least one of the following: (a) driving time, (b) number of lane changes, (c) lane change locations, and (d) driving lanes. Specifically, the calculation is based on the following conditions:

(a) Regarding "travel time", the longer the travel time of a route, the higher the calculated total cost. The travel speed of the vehicle is determined based on the speed plan generated in S4.
(b) Regarding the "number of lane changes," the total cost is calculated to be higher for a route with a greater number of lane changes.
(c) Regarding the "location of lane changes", if multiple lane changes are made, the shorter the interval between lane changes is, the higher the calculated total cost will be. In addition, for routes where lane changes are made within a certain distance (e.g., 700 m on a general road, 2 km on a highway) before an intersection, the cost is added.
(d) Regarding "driving lane", the total cost is calculated to be higher for a route that has a longer driving distance in the overtaking lane.

However, regardless of the above conditions (a) to (d), the cost is set to infinity for routes where it is determined that the vehicle needs to pass through a lane in which an influencing factor exists in the overtaking section calculated in S106.

Next, in S110, the CPU 51 calculates a recommended driving trajectory for the reconstruction section when the vehicle moves along the lane according to the route selected in S109.

Specifically, the CPU 51 calculates the lateral acceleration (lateral G) that occurs when the vehicle changes lanes from the vehicle's current speed and lane width, and calculates a trajectory that connects the departure node 95, the overtaking start node 103, the overtaking completion node 104, and the destination node 96 as smoothly as possible using a clothoid curve, provided that the lateral G does not interfere with the automated driving assistance and does not exceed an upper limit (e.g., 0.2 G) that does not cause discomfort to the vehicle occupants. In this case, it is desirable to calculate the driving trajectory so that the vehicle does not pass through a lane in which an influencing factor exists in the overtaking section calculated in S106, and the distance traveled in the avoidance lane is as short as possible. Another condition is that an appropriate inter-vehicle distance must be maintained between the vehicle and the influencing factor.

Here, Figure 26 shows an example of a driving trajectory generated in S110 when a route is selected in which the vehicle 90 changes lanes to the right to overtake the influencing vehicle 91 ahead, and then changes lanes to the left to return to the original lane.
First, in the example shown in Fig. 26, a first trajectory L1 required for starting a steering wheel turn and moving to the right lane and returning the steering wheel position to the straight direction is calculated for the area between the departure node 95 and the overtaking start node 103. The first trajectory L1 is calculated by calculating the lateral acceleration (lateral G) generated when changing lanes based on the current vehicle speed of the vehicle, and calculating a trajectory that is as smooth as possible and that shortens the distance required for lane change as much as possible using a clothoid curve, under the condition that the lateral G does not interfere with the automatic driving assistance and does not exceed an upper limit value (e.g., 0.2 G) that does not cause discomfort to the vehicle occupants. In addition, the conditions are that the timing of the lane change is delayed as much as possible to shorten the distance traveled in the avoidance lane, and an appropriate inter-vehicle distance or more is maintained between the vehicle and the preceding vehicle 91.
Next, a second trajectory L2 is calculated for the section from the overtaking start node 103 to the overtaking completion node 104, in which the vehicle travels in the right lane at the speed limit as an upper limit to overtake the preceding vehicle 91 and maintain an appropriate inter-vehicle distance from the preceding vehicle 91. The second trajectory L2 is basically a straight trajectory, and the length of the trajectory is calculated based on the vehicle speed of the preceding vehicle 91 and the speed limit of the road.
Next, a third trajectory L3 required for starting steering and returning to the left lane and returning the steering position to the straight direction is calculated for the area between the overtaking completion node 104 and the destination node 96. The third trajectory L3 is calculated by calculating the lateral acceleration (lateral G) generated when changing lanes based on the current vehicle speed of the vehicle, and using a clothoid curve, a trajectory that is as smooth as possible and requires as short a distance as possible for lane change, under the condition that the lateral G does not interfere with the automatic driving assistance and does not exceed an upper limit (e.g., 0.2 G) that does not cause discomfort to the vehicle occupants. In addition, the timing of lane change is made as early as possible to shorten the distance traveled in the avoidance lane, and an appropriate inter-vehicle distance or more is maintained between the vehicle and the preceding vehicle 91.

In addition, in S110, the recommended speed of the vehicle when traveling along the above-mentioned travel path is also calculated. The recommended speed of the vehicle is set to a maximum speed not exceeding the upper limit (e.g., 0.2 G) at which the lateral acceleration (lateral G) generated in the vehicle when changing lanes does not interfere with the automated driving assistance and does not cause discomfort to the vehicle occupants, with the speed limit as the upper limit. For example, the recommended speed is calculated based on the curvature of the travel path, the speed limit, etc.

In addition, in S110, instead of calculating a driving trajectory, a driving trajectory of a predetermined shape may be assigned to the route selected in S109. That is, a driving trajectory for lane changing is prepared in advance for each vehicle, and within the route selected in S109, for example, a driving trajectory for lane changing is assigned to a position ending at the overtaking start node 103 and a position starting at the overtaking completion node 104, and for other sections, a trajectory for driving while maintaining the lane is set, thereby generating a driving trajectory.

In the example shown in FIG. 26, the vehicle 90 changes lanes to the right to overtake the influencing vehicle 91 ahead, and then changes lanes to the left to return to the original lane. However, depending on the route selected in S109, the vehicle 90 may continue to travel in the avoidance lane without returning to the original lane after overtaking the vehicle 91 ahead. Furthermore, if it is determined that the vehicle 90 cannot overtake the vehicle 91 ahead because the vehicle 91 ahead is moving too fast or the road is congested with surrounding vehicles, the vehicle 90 may continue to travel in the lane it is currently traveling in without changing lanes, regardless of the reconstructed lane network, i.e., the vehicle 90 may continue to travel in the lane it is currently traveling in by following the vehicle 91 ahead.

Then, in S111, the CPU 51 generates the driving trajectory calculated in S110 as a dynamic driving trajectory, which is a driving trajectory that is recommended for the vehicle to drive on roads included in the planned driving route, taking into account the surrounding road conditions.

The dynamic driving trajectory generated in S111 is then stored in the flash memory 54 or the like as assistance information to be used for autonomous driving assistance.

As described above in detail, the navigation device 1 and the computer program executed by the navigation device 1 according to the second embodiment use high-precision map information 15 including at least information regarding lanes to acquire a lane network, which is a network showing the lane movements that the vehicle may select for a specific section of the road on which the vehicle is traveling, by using links connecting nodes (S23), acquire road conditions within a specific detection range around the vehicle (S104), and when it is detected that there is an influencing factor that may affect the traveling of the vehicle on any lane within the specific section using the surrounding road conditions acquired by the vehicle, an overtaking start node 103 and an overtaking completion node 104 are added to the lane network as nodes for avoiding the influencing factor. A new node 103 is added onto the lane network (S107), and the lane network is reconstructed with the addition of the overtaking start node 103 and the overtaking completion node 104 (S108). Using the cost added to the reconstructed lane network, a route for the lane network is searched for that connects the departure node located at the start point of the specified section to the destination node located at the end point of the specified section while avoiding the factors (S109). Based on the route search results, a recommended vehicle travel trajectory is generated for traveling while avoiding the influencing factors in the specified section (S111). Driving assistance is provided to the vehicle based on the travel trajectory (S9, S10). This makes it possible to generate an appropriate travel trajectory for avoiding the influencing factors without increasing the number of nodes and links more than necessary.

Incidentally, the present invention is not limited to the above-described embodiment, and it is needless to say that various improvements and modifications are possible without departing from the spirit and scope of the present invention.
For example, in the first embodiment, a lane movement prohibited section is set, which is a range in which lane movement is prohibited due to the presence of an influencing factor, but since an avoidance node can be set as long as the position of the start point of the lane movement prohibited section can be specified, it is not necessarily required to set the lane movement prohibited section. Note that, if a lane movement prohibited section is not set, the timing for the host vehicle to return from the avoidance lane to the original lane after overtaking the influencing factor is not specified in advance, and the host vehicle that has changed lanes to the avoidance lane detects the surroundings as needed, and when it is determined that it is possible to return to the original lane, it performs a lane change to return to the original lane.

In the first and second embodiments, the high-precision map information held by the server device 4 includes both information on the lane shape of the road (road shape and curvature for each lane, lane width, etc.) and information on the dividing lines drawn on the road (center line, lane boundary, outer lane line, guiding line, etc.), but may include only information on the dividing lines, or may include only information on the lane shape of the road. For example, even if only information on the dividing lines is included, it is possible to estimate information equivalent to information on the lane shape of the road based on the information on the dividing lines. Even if only information on the lane shape of the road is included, it is possible to estimate information equivalent to information on the dividing lines based on the information on the lane shape of the road. Furthermore, the "information on the dividing lines" may be information that specifies the type and arrangement of the dividing lines themselves that divide the lanes, information that specifies whether lane changes are possible between adjacent lanes, or information that directly or indirectly specifies the shape of the lanes.

In addition, in the first and second embodiments, when an influencing factor that affects the vehicle driving is 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 (S72, S73), and the static driving trajectory is replaced with the dynamic driving trajectory only if it is determined that the dynamic driving trajectory is recommended (S75). However, it is also possible to always replace the static driving trajectory with the dynamic driving trajectory when a dynamic driving trajectory is generated.

In addition, in the first and second embodiments, a part of the static driving trajectory is replaced with the dynamic driving trajectory (S75) as a means of reflecting the dynamic driving trajectory in the static driving trajectory, but instead of replacing it, the trajectory may be corrected so that the static driving trajectory approaches the dynamic driving trajectory.

In the first and second embodiments, the vehicle control ECU 40 has been described as controlling all of the vehicle operations related to the vehicle's behavior, including accelerator operation, brake operation, and steering operation, as automatic driving assistance for automatically driving the vehicle without the user's driving operation. However, automatic driving assistance may also be defined as the vehicle control ECU 40 controlling at least one of the vehicle operations related to the vehicle's behavior, including accelerator operation, brake operation, and steering operation. On the other hand, manual driving by the user's driving operation is described as the user performing all of the vehicle operations related to the vehicle's behavior, including accelerator operation, brake operation, and steering operation.

The driving assistance of the present invention is not limited to automatic driving assistance related to automatic driving of a vehicle. For example, it is possible to provide driving assistance by displaying a static driving trajectory or a dynamic driving trajectory on a navigation screen and providing guidance using voice or a screen (e.g., guidance on lane changes, guidance on recommended vehicle speeds, etc.). In addition, the static driving trajectory or the dynamic driving trajectory may be displayed on the navigation screen to assist the user's driving operation.

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

In addition to navigation devices, the present invention can also be applied to mobile phones, smartphones, tablet terminals, personal computers, etc. (hereinafter referred to as mobile terminals, etc.). It can also be applied to a system consisting of a server and a mobile terminal, etc. In that case, each step of the above-mentioned automatic driving assistance program (see FIG. 4) may be implemented by either the server or the mobile terminal, etc. However, when applying the present invention to a mobile terminal, etc., a vehicle capable of executing automatic driving assistance and the mobile terminal, etc. must be connected so as to be able to communicate (either wired or wireless).

1...navigation device, 2...driving assistance system, 3...information distribution center, 4...server device, 5...vehicle, 15...high-precision map information, 33...navigation ECU, 39...exterior camera, 40...vehicle control ECU, 51...CPU, 52...RAM, 53...ROM, 54...flash memory, 61...planned driving route, 75...lane node, 76...lane link, 93...avoidance node, 95...start node, 96...destination node, 103...overtaking start node, 104...overtaking completion node

Claims (4)

A lane network acquisition means for acquiring a lane network indicated by nodes set at intersections and positions where lanes are increased or decreased and links connecting the nodes with respect to lane movement that may be selected by a vehicle within a predetermined section;
a road condition acquisition means for acquiring road conditions within a predetermined detection range around the vehicle;
a prohibited section setting means for setting, when it is detected that there is a factor that may affect vehicle travel within the specified section using the road conditions , a lane change prohibited section within the specified section in which lane change is prohibited due to the presence of the factor;
a network reconstruction means for adding an avoidance node to the lane network at a start point of the lane movement prohibited section in a lane where the factor does not exist, as a limit point where lane movement must be completed when moving to a lane where the factor does not exist, and reconstructing the lane network;
a route search means for searching for a route from a start node located at the start point of the specified section to a destination node located at the end point of the specified section , the route avoiding the factor without passing through the lane in which the factor is located in the lane movement prohibited section, by using a cost added to the reconstructed lane network ;
a travel trajectory generating means for generating a travel trajectory of a vehicle using the route;
and a driving assistance means for providing driving assistance for the vehicle based on the travel trajectory. A lane network acquisition means for acquiring a lane network indicated by nodes set at intersections and positions where lanes are increased or decreased and links connecting the nodes with respect to lane movement that may be selected by a vehicle within a predetermined section;
a road condition acquisition means for acquiring road conditions within a predetermined detection range around the vehicle;
a network reconstruction means for, when detecting the presence of a factor that may affect vehicle travel within the predetermined section using the road conditions, adding an overtaking start node and an overtaking completion node to the lane network in order to avoid the factor, and reconstructing the lane network;
a route search means for searching for a route from a departure node located at the start point of the predetermined section to a destination node located at the end point of the predetermined section, avoiding the factors, by using the cost added to the reconstructed lane network;
a travel trajectory generating means for generating a travel trajectory of a vehicle using the route;
a driving assistance means for providing driving assistance for a vehicle based on the travel trajectory ,
The network reconstruction means includes:
Assuming that the vehicle moves from the lane in which the vehicle is currently traveling to another lane to overtake in order to avoid the cause, the overtaking section is set to a limit point at which the movement to the other lane needs to be completed as a start point of the overtaking section, and the overtaking section is set to an end point of the overtaking section as an earliest point at which the vehicle can start moving from the other lane back to the original lane after overtaking the cause,
A driving assistance device that sets the overtaking start node at a start point of the overtaking section in a lane where the factor does not exist, and sets the overtaking completion node at an end point of the overtaking section . Computer,
A lane network acquisition means for acquiring a lane network indicated by nodes set at intersections and positions where lanes are increased or decreased and links connecting the nodes with respect to lane movement that may be selected by a vehicle within a predetermined section;
a road condition acquisition means for acquiring road conditions within a predetermined detection range around the vehicle;
a prohibited section setting means for setting, when it is detected that there is a factor that may affect vehicle travel within the specified section using the road conditions , a lane change prohibited section within the specified section in which lane change is prohibited due to the presence of the factor;
a network reconstruction means for adding an avoidance node to the lane network at a start point of the lane change prohibited section in a lane where the factor does not exist, as a limit point where lane change must be completed when changing to a lane where the factor does not exist, and reconstructing the lane network;
a route search means for searching for a route from a start node located at the start point of the specified section to a destination node located at the end point of the specified section , the route avoiding the factor without passing through the lane in which the factor is located in the lane movement prohibited section, by using a cost added to the reconstructed lane network ;
a travel trajectory generating means for generating a travel trajectory of a vehicle using the route;
A driving assistance means for providing driving assistance for a vehicle based on the travel trajectory;
A computer program that makes something function. Computer,
A lane network acquisition means for acquiring a lane network indicated by nodes set at intersections and positions where lanes are increased or decreased and links connecting the nodes with respect to lane movement that may be selected by a vehicle within a predetermined section;
a road condition acquisition means for acquiring road conditions within a predetermined detection range around the vehicle;
a network reconstruction means for, when detecting the presence of a factor that may affect vehicle travel within the predetermined section using the road conditions, adding an overtaking start node and an overtaking completion node to the lane network in order to avoid the factor, and reconstructing the lane network;
a route search means for searching for a route from a departure node located at the start point of the predetermined section to a destination node located at the end point of the predetermined section, avoiding the factors, by using the cost added to the reconstructed lane network;
a travel trajectory generating means for generating a travel trajectory of a vehicle using the route;
A driving assistance means for providing driving assistance for a vehicle based on the travel trajectory;
A computer program for causing a computer to function as described above,
The network reconstructing means comprises:
Assuming that the vehicle moves from the lane in which the vehicle is currently traveling to another lane to overtake in order to avoid the cause, the overtaking section is set to a limit point at which the movement to the other lane needs to be completed as a start point of the overtaking section, and the overtaking section is set to an end point of the overtaking section as an earliest point at which the vehicle can start moving from the other lane back to the original lane after overtaking the cause,
A computer program that sets the overtaking start node at a start point of the overtaking section in a lane where the factor does not exist, and sets the overtaking completion node at an end point of the overtaking section.